{"data":[{"id":"10.5061/dryad.k98sf7mqc","type":"dois","attributes":{"doi":"10.5061/dryad.k98sf7mqc","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["State University of New York"],"name":"Ulapane, Prashani","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6682-610X"}]},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Rogers, Andrea Deanne","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Parise, John B.","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Ehm, Lars","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5485-0316"}]}],"titles":[{"title":"Thermal and phase behavior of chloride–perchlorate brines on Mars: Insights from the MgCl₂–Mg(ClO₄)₂–H₂O system"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Physical sciences","subjectScheme":"fos"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Earth and related environmental sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Mars","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Phase diagrams","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Differential scanning calorimetry","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"X-ray diffraction","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Low temperature phenomena","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[{"nameType":"Personal","affiliation":["State University of New York"],"name":"Ulapane, Prashani","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6682-610X"}],"contributorType":"ProjectLeader"},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Ulapane, Prashani","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6682-610X"}],"contributorType":"DataCurator"},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Ulapane, Prashani","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6682-610X"}],"contributorType":"DataCollector"},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Ulapane, Prashani","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6682-610X"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Ulapane, Prashani","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6682-610X"}],"contributorType":"Editor"},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Ulapane, Prashani","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6682-610X"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Ulapane, Prashani","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6682-610X"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Rogers, Andrea Deanne","contributorType":"DataCollector","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Rogers, Andrea Deanne","contributorType":"ProjectManager","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Rogers, Andrea Deanne","contributorType":"Supervisor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Rogers, Andrea Deanne","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Parise, John B.","contributorType":"ProjectManager","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Parise, John B.","contributorType":"Supervisor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Parise, John B.","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Ehm, Lars","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5485-0316"}],"contributorType":"ProjectManager"},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Ehm, Lars","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5485-0316"}],"contributorType":"Supervisor"},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Ehm, Lars","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5485-0316"}],"contributorType":"Editor"},{"nameType":"Personal","affiliation":["State University of New York"],"name":"Ulapane, Prashani","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6682-610X"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2026-09-30T13:23:03Z","dateType":"Created"},{"date":"2026-09-30T13:23:09Z","dateType":"Submitted"},{"date":"2026-10-07T00:00:00Z","dateType":"Issued"},{"date":"2026-10-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["7228820 bytes"],"formats":[],"version":"3","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Cl-bearing brines (Cl⁻, ClO₄⁻, ClO₃⁻) are critical to the Martian water\n cycle because they can extend the liquid water stability under present-day\n conditions. Natural Martian brines are likely multicomponent mixtures, yet\n their low-temperature behavior remains poorly constrained. Here, we\n investigate the binary MgCl₂–H₂O and Mg(ClO₄)₂–H₂O and ternary\n MgCl₂–Mg(ClO₄)₂–H₂O systems to constrain cryogenic phase equilibria\n relevant to Mars. Differential Scanning Calorimetry (DSC) and X-ray\n Diffraction (XRD) experiments are used to characterize phase behavior\n during controlled cooling–heating cycles and establish a low-temperature\n phase framework for the ternary system. All systems exhibit pronounced\n supercooling and metastability. In the ternary system, liquids persist\n metastably to ~201 K prior to ice nucleation, while complete\n crystallization, encompassing ice formation and subsequent crystallization\n of the freeze-concentrated solution is delayed to ~173 K or avoided\n entirely through vitrification, depending on the Mg(ClO₄)₂ composition.\n Cold crystallization (crystallization of previously amorphous or partially\n crystalline phases upon reheating above the glass transition temperature)\n is commonly observed. The ternary eutectic temperature is 212 ± 0.8 K. XRD\n data show two final crystalline assemblages: (i) a stable assemblage of\n hexagonal ice+MgCl₂·12H₂O+Mg(ClO₄)₂·6H₂O and (ii) a metastable assemblage\n comprising MgCl₂·8H₂O+MgCl₂·6H₂O+Mg(ClO₄)₂·4H₂O. These results show that\n the ternary brines often produce non-equilibrium features during cooling,\n where kinetics factors strongly dictate phase selection and stabilization.\n This study implies that without laboratory constraints, the true\n complexity of Martian brines, including phase assemblages and\n transformation pathways may be underestimated, as equilibrium models\n cannot capture kinetically controlled brine evolution."},{"descriptionType":"TechnicalInfo","description":"# Raw DSC and XRD Data for Thermal and Phase Behavior of\n Chloride–Perchlorate Brines on Mars: Insights from the MgCl2–Mg(ClO4)2–H2O\n System ## General Information 1. **Title of Dataset:**     Raw DSC and XRD\n Data for Thermal and Phase Behavior of Chloride–Perchlorate Brines on Mars\n 2. **Author Information:**    - **Corresponding Author:** Prashani Ulapane\n        - Department: Department of Geosciences, Stony Brook University  \n    - **Co-Authors:** A. Deanne Rogers, John B. Parise, Lars Ehm        -\n Department / Institution: Department of Geosciences, Stony Brook\n University ## Data \u0026amp; File Inventory This dataset consists of raw data\n files organized into two primary subdirectories and a sample index: ### 1.\n File Structure Overview - `Sample_Index.txt` — Plain text cross-reference\n index file - `DSC_Data/` — Folder containing ASCII `.txt` raw thermal data\n files - `XRD_Data/` — Folder containing ASCII `.xy` raw diffraction\n pattern files --- ### 2. File Naming Conventions \u0026amp; Conditions #### A.\n Differential Scanning Calorimetry Files (`DSC_Data/*.txt`) Because the raw\n `.txt` files in `DSC_Data/` have had header metadata removed for\n programmatic reading, the experimental parameters for each run are encoded\n in the filename and structured as follows: - **Format:**\n `[SampleID]_[RunType].txt` - **Run Types \u0026amp; Experimental Conditions:**\n   - `*_cooling.txt` — Sample cooled from ~25 °C down to sub-eutectic\n temperatures (~ -150 °C / 123 K) at a controlled rate of **3 °C/min (3\n K/min)** under N2 purge gas (35 mL/min)   - `*_heating.txt` — Sample\n heated from sub-eutectic temperatures back to ~15 °C at a controlled rate\n of **1 °C/min (1 K/min)** under N2 purge gas (35 mL/min) #### B. X-ray\n Powder Diffraction Files (`XRD_Data/*.xy`) The `.xy` files in `XRD_Data/`\n contain two-column diffraction data collected at specific isothermal\n temperature steps: - **Format:** `[SampleID]_[Temperature].xy` -\n **Experimental Conditions:**   - Collected using Cu Kα radiation ($lambda\n = 1.5418text{ AA}$)   - Samples were cooled/heated to the specified\n temperature step (e.g., `200K`, `180K`), allowed to thermally equilibrate,\n and scanned isothermally --- ## Experimental Protocol \u0026amp; Instrument\n Parameters ### 1. Differential Scanning Calorimetry (DSC) Setup -\n **Instrument:** NETZSCH DSC 200 F3 Maia - **Sample Containment:** ~30-40\n mg solution encapsulated in hermetically sealed Concavus Aluminum pans -\n **Purge Gas:** High-purity Nitrogen ($text{N}_2$) at 35 mL/min - **Cooling\n Agent:** Liquid Nitrogen ($text{LN}_2$) ### 2. Low-Temperature X-ray\n Powder Diffraction (XRD) Setup - **Instrument:** Rigaku Ultima IV\n diffractometer with R300 low-temperature attachment - **Radiation:** Cu Kα\n ($lambda = 1.5418text{ AA}$) - **Atmosphere:** Vacuum environment to reach\n sub-zero temperatures --- ## Column Definitions \u0026amp; Data Formats Since\n the data files contain numerical values without header rows, use the\n column definitions below to parse the data: ### A. DSC Data Files\n (`*.txt`) - **Delimiter:** Semicolon (`;`) - **Data Columns:**   1.\n **Column 1:** Temperature (°C) — Stage temperature in degrees Celsius   2.\n **Column 2:** Time (min) — Elapsed time in minutes   3. **Column 3:** Heat\n Flow / DSC Signal (mW/mg) — Normalized heat flow *(Exothermic direction:\n DOWN)*   4. **Column 4:** Sensitivity ($mutext{V/mW}$) — Instrument\n sensitivity factor ### B. XRD Data Files (`*.xy`) - **Delimiter:** Space\n or Tab - **Data Columns:**   1. **Column 1:** $2theta$ (degrees) —\n Diffraction angle   2. **Column 2:** Intensity (Counts) — Measured\n diffraction intensity --- ## Software Compatibility - **DSC Data:** Load\n using Python (`pandas.read_csv('filename.txt',\n sep=';')`), MATLAB (`readtable`), R, or OriginPro - **XRD\n Data:** Compatible with GSAS-II, FullProf, TOPAS, HighScore Plus, and\n standard plotting software"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"NASA Planetary Science","funderIdentifier":"https://ror.org/04cr1bk24","awardTitle":"NASA Solar System Workings","awardNumber":"80NSSC18K0535"},{"funderName":"Joint Photon Science Institute"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.k98sf7mqc","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-10-07T00:40:10Z","registered":"2026-10-07T00:40:11Z","published":null,"updated":"2026-10-07T00:40:11Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.xgxd254zd","type":"dois","attributes":{"doi":"10.5061/dryad.xgxd254zd","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Central Florida"],"name":"Yasnov, Dmitry","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7090-2638"}]},{"nameType":"Personal","affiliation":["City College of New York"],"name":"Komissarenko, Filipp","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Australian National University"],"name":"Smirnova, Daria","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8033-3427"}]},{"nameType":"Personal","affiliation":["City College of New York"],"name":"Kawaguchi, Yuma","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7758-9691"}]},{"nameType":"Personal","affiliation":["City College of New York"],"name":"Kafeeva, Daria","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0000-8235-4328"}]},{"nameType":"Personal","affiliation":["City College of New York"],"name":"Kiriushechkina, Svetlana","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Central Florida"],"name":"Vakulenko, Anton","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["City College of New York"],"name":"Alù, Andrea","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4297-5274"}]},{"nameType":"Personal","affiliation":["United States Air Force Research Laboratory"],"name":"Allen, Jeffery","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["United States Air Force Research Laboratory"],"name":"Allen, Monica","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Central Florida"],"name":"Khanikaev, Alexander","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7689-216X"}]}],"titles":[{"title":"Geometric-phase resonators: models and data"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Metamaterials","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Photonic crystals","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Nanoscale photonics","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Nano-technology","subjectScheme":"fos"}],"contributors":[{"nameType":"Personal","affiliation":["University of Central Florida"],"name":"Yasnov, Dmitry","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7090-2638"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":["City College of New York"],"name":"Komissarenko, Filipp","contributorType":"ContactPerson","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Central Florida"],"name":"Khanikaev, Alexander","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7689-216X"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2026-09-25T04:03:35Z","dateType":"Created"},{"date":"2026-09-29T14:25:26Z","dateType":"Submitted"},{"date":"2026-10-06T00:00:00Z","dateType":"Issued"},{"date":"2026-10-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["41995968 bytes"],"formats":[],"version":"4","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Geometric phases govern wave and quantum dynamics in systems ranging from\n optical waveguides and metasurfaces to topological materials. Unlike\n conventional propagation phases, they arise from the path traced by a\n multimodal state during adiabatic evolution and can reshape interference\n and resonance. Here we introduce a geometric-phase resonator (GPR), whose\n resonance is set solely by geometric phase. Implemented in a photonic\n topological metasurface, the GPR guides modes through an evolution that\n accumulates a 2π geometric phase, producing resonance without dynamical\n propagation phase. As a result, the resonant frequency is independent of\n the resonator’s shape and length. Experiments on resonators with distinct\n geometries reveal a stable geometric resonance pinned to the frequency\n dictated by geometric phase alone. This mechanism offers a route to\n spectrally robust photonic and wave-based devices."},{"descriptionType":"TechnicalInfo","description":"# Geometric-phase resonators: models and data This dataset contains\n numerical data, fitted curves and fit results, experimental images, and\n simulated image data underlying selected figures in the *Science*\n manuscript and its Supplementary Materials. The\n archive GPR_Dryad_Dataset.zip has one top-level folder, `Dryad Dataset/`,\n with one subfolder per figure and a separate `Fitting matlab code/`\n folder. The paths in the sections below are relative to `Dryad Dataset/`.\n ## File conventions * All CSV files are comma-delimited UTF-8 text with a\n header row. A value in a CSV column is interpreted using that\n column's header. `um` means micrometers, `m` means meters, and `a.u.`\n means arbitrary units. * Files ending in `_fit_points.csv` contain points\n of a plotted fitted curve; files ending in `_fit_results.csv` contain the\n corresponding fitted peak parameters. * The BMP mode images have a\n resolution of 384 x 288 pixels. The `.BMP` files in Figs. 3, S6,S7 and S8\n are 8-bit indexed images with embedded color palettes; ## CSV columns and\n data layout | File group | Columns and interpretation | |\n --------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | Single-column simulation panels in Figs. 2, 3, 4 and S12; peak lists in Fig. 4C | `Wavelength, um`: one wavelength per row. These files list mode positions | | Experimental spectra and fitted curves in Figs. 3, 4 and S7; experimental spectrum in Fig. S8 | Two columns: `Wavelength, um` (or `Wavelengths, um` in Fig. S7) and `Intensity, a.u.`. Each row is one wavelength/intensity pair. The fitted-curve files contain a denser set of sampled points than the measured spectra. | | Fit results in Figs. 3, 4 and S7 | `spectrum_name` identifies the source spectrum; `peak_id` numbers fitted peaks; `center_um` is the center wavelength in µm; `amplitude` is the fitted Lorentzian amplitude in the intensity units of its spectrum; `FWHM_um` is the full width at half maximum in µm; `Q_factor` is `center_um / FWHM_um`. One row corresponds to one fitted peak. | | Fig. S2 and Fig. S4 curve data | Columns occur in labeled `X`, `Y` pairs, one pair per color or mode type relevant to the figure. | | Fig. S6 simulated far-field images | Three columns: `x, m`, `y, m`, and `Ifar` (simulated intensity). Each row gives one grid point. The k-space files contain complete 128 × 128 grids at `z = 100 um`; the real-space files contain complete 100 × 100 grids at `z = 50 um`. | | Fig. S10 and Fig. S11 simulation comparisons | Two independently listed sets of wavelengths in µm. Fig. S10 labels them `Top panel wavelengths, um` and `Bottom panel wavelengths, um`; Fig. S11 labels them `W=1 resonator modes wavelength, um` and `W=2 resonator modes wavelength, um`. | ## `Fig 2` ### Figure 2C: simulation panels | File | Description | | ------------------------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `Fig2C_1_hex_L25_w0_simulation_panel.csv` | Simulated data points for the top hexagonal resonator panel in Fig. 2C (`L_n = 25`, `w = 0`). | | `Fig2C_2_hex_L25_w1_simulation_panel.csv` | Simulated data points for the second panel from the top in Fig. 2C: hexagonal resonator (`L_n = 25`, `w = 1`). | | `Fig2C_3_hex_L22_w1_simulation_panel.csv` | Simulated mode wavelengths for the second panel from the bottom in Fig. 2C: hexagonal resonator (`L_n = 22`, `w = 1`). The stored values use a different numerical scale from their `um` header; see the CSV layout note above. | | `Fig2C_4_trig_L22_w1_simulation_panel.csv` | Simulated data points for the bottom panel in Fig. 2C: triangular resonator (`L_n = 22`, `w = 1`). | ## `Fig 3` ### Figure 3A: measured spectra and fits | File | Description | | -------------------------------------------- | ---------------------------------------------------------------- | | `Fig3a_hex_L28_ring_modes.csv` | Experimental points for the top-left panel: hexagonal ring. | | `Fig3a_hex_L28_ring_modes_fit_points.csv` | Points of the dashed fitted curve in the top-left panel. | | `Fig3a_hex_L28_ring_modes_fit_results.csv` | Model-fit results for the top-left panel. | | `Fig3a_hex_L28_vortex_modes.csv` | Experimental points for the bottom-left panel: hexagonal ring. | | `Fig3a_hex_L28_vortex_modes_fit_points.csv` | Points of the dashed fitted curve in the bottom-left panel. | | `Fig3a_hex_L28_vortex_modes_fit_results.csv` | Model-fit results for the bottom-left panel. | | `Fig3a_tri_L25_ring_modes.csv` | Experimental points for the top-right panel: triangular ring. | | `Fig3a_tri_L25_ring_modes_fit_points.csv` | Points of the dashed fitted curve in the top-right panel. | | `Fig3a_tri_L25_ring_modes_fit_results.csv` | Model-fit results for the top-right panel. | | `Fig3a_tri_L25_vortex_modes.csv` | Experimental points for the bottom-right panel: triangular ring. | | `Fig3a_tri_L25_vortex_modes_fit_points.csv` | Points of the dashed fitted curve in the bottom-right panel. | | `Fig3a_tri_L25_vortex_modes_fit_results.csv` | Model-fit results for the bottom-right panel. | ### Figure 3A: simulation panels | File | Description | | ------------------------------------ | -------------------------------------------------- | | `Fig3a_hex_L28_simulation_panel.csv` | Data points for the left-middle simulation panel. | | `Fig3a_tri_L25_simulation_panel.csv` | Data points for the right-middle simulation panel. | ### Figure 3B: real-space mode images | Files | Description | | --------------------------------------------------------------------------------------- | ----------------------------------------------------------------------------------- | | `Mode_I.BMP`, `Mode_II.BMP`, `Mode_III.BMP`, `Mode_IV.BMP`, `Mode_V.BMP`, `Mode_VI.BMP` | Bitmap real-space images of modes I-VI, respectively, without clipping or rotation. | ## `Fig 4` ### Figure 4A: measured spectra, fits, and simulation | File | Description | | -------------------------------------------- | ----------------------------------------------------------------------------------------------- | | `Fig4a_hollow_hex_L22_modes.csv` | Experimental points for the top panel: hollow hexagonal ring (`L_n = 22`). | | `Fig4a_hollow_hex_L22_modes_fit_points.csv` | Points of the dashed fitted curve in the top panel. | | `Fig4a_hollow_hex_L22_modes_fit_results.csv` | Model-fit results for the top panel. | | `Fig4a_hollow_hex_L25_modes.csv` | Experimental points for the middle panel: hollow hexagonal ring (`L_n = 25`). | | `Fig4a_hollow_hex_L25_modes_fit_points.csv` | Points of the dashed fitted curve in the middle panel. | | `Fig4a_hollow_hex_L25_modes_fit_results.csv` | Model-fit results for the middle panel. | | `Fig4a_hollow_hex_L28_modes.csv` | Experimental points for the bottom panel: hollow hexagonal ring (`L_n = 28`). | | `Fig4a_hollow_hex_L28_modes_fit_points.csv` | Points of the dashed fitted curve in the bottom panel. | | `Fig4a_hollow_hex_L28_modes_fit_results.csv` | Model-fit results for the bottom panel. | | `Fig4a_hollow_hex_L28_simulation_panel.csv` | Simulated data points for the bottom simulation panel: hollow hexagonal resonator (`L_n = 28`). | ### Figure 4B: microscope images | File | Description | | --------------------------------- | ------------------------------------------------------------------------------------------- | | `Fig4B_VIS_Sample_Image.png` | Uncropped visible-light microscope image of the hollow hexagonal and triangular resonators. | | `Fig4B_SEM_Hollow_Trig_Image.tif` | Full SEM image of the hollow region shown in the Fig. 4B inset. | ### Figure 4C: mode wavelengths | File | Description | | --------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------ | | `Fig4C_hollow_hex_L25_peaks.csv` | Mode wavelengths plotted in the left column for the hexagonal resonator (`L_n = 25`); derived from `Fig4a_hollow_hex_L25_modes_fit_results.csv`. | | `Fig4C_hollow_trig_L23_peaks.csv` | Mode wavelengths plotted in the right column for the triangular resonator (`L_n = 23`). | ## `Fig S2` | File | Description | | ------------------------ | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `FigS2A_data.csv` | Data underlying Fig. S2A. Red, green and purple curves have separate `X`, `Y` column pairs. | | `FigS2B_bot_data.csv` | Data underlying the bottom panel of Fig. S2B: one red-line `X`, `Y` pair, labeled \\|u\\_a\\|. | | `FigS2B_middle_data.csv` | Data underlying the middle panel of Fig. S2B: blue \\|u\\_b\\|, red \\|u\\_a\\| and dashed-blue \\|nu\\_b\\| `X`, `Y` pairs. | | `FigS2B_top_data.csv` | Data underlying the top panel of Fig. S2B: blue \\|u\\_b\\|, red \\|u\\_a\\| and gray dashed-line `X`, `Y` pairs. The three curves have different numbers of points, with blanks in unused cells. | ## `Fig S4` | File | Description | | ----------------- | ------------------------------------------------------------------------------------------- | | `FigS4A_data.csv` | Data underlying Fig. S4A. Red, green and purple curves have separate `X`, `Y` column pairs. | | `FigS4B_data.csv` | Data underlying Fig. S4B: one green-curve `X`, `Y` pair. | ## `Fig S6` ### Experimental images | Files | Description | | ---------------------------------------------------------------------------- | ---------------------------------------------------------------------------------------------------------- | | `real_space_Mode_I.BMP`, `real_space_Mode_II.BMP`, `real_space_Mode_III.BMP` | Experimental real-space images of modes I-III, respectively, without clipping or rotation. | | `k_space_Mode_I.BMP`, `k_space_Mode_II.BMP`, `k_space_Mode_III.BMP` | Experimental k-space (back-focal-plane) images of modes I-III, respectively, without clipping or rotation. | ### Simulated far-field images | Files | Description | | ---------------------------------------------------------------------------------------------------------------------------------------------------------- | --------------------------------------------------------------------------------------------- | | `k_space_far_field_simulation_Mode_I_data.csv`, `k_space_far_field_simulation_Mode_II_data.csv`, `k_space_far_field_simulation_Mode_III_data.csv` | Simulated far-field k-space image data for modes I-III in the corresponding Fig. S6 panel. | | `real_space_far_field_simulation_Mode_I_data.csv`, `real_space_far_field_simulation_Mode_II_data.csv`, `real_space_far_field_simulation_Mode_III_data.csv` | Simulated far-field real-space image data for modes I-III in the corresponding Fig. S6 panel. | ## `Fig S7` | File or files | Description | | ------------------------------------------- | ------------------------------------------------------------------------------------ | | `Fig_S7_hex_w0_modes_data.csv` | Experimental points for the top panel: hollow hexagonal ring without winding. | | `Fig_S7_hex_w0_modes_data_fit_points.csv` | Points of the dashed fitted curve in the top panel. | | `Fig_S7_hex_w0_modes_data_fit_results.csv` | Model-fit results for the top panel. | | `Mode_I.BMP`, `Mode_II.BMP`, `Mode_III.BMP` | Bitmap real-space images of modes I-III, respectively, without clipping or rotation. | ## `Fig S8` | File or files | Description | | ---------------------------------------------- | ---------------------------------------------------------------------------------------------------------- | | `Fig_S8_VIS_Image_Hollow_Trig_With_Defect.png` | Visible-light microscope image of the hollow triangular resonator with a defect on the domain wall. | | `Fig_S8_Hollow_Trig_With_Defect_data.csv` | Experimental points for the right panel: hollow triangular ring with a defect. | | `Mode_I.BMP`, `Mode_II.BMP`, `Mode_III.BMP` | Bitmap real-space images of modes I-III, respectively, without clipping, rotation, or an applied colormap. | ## `Fig S10` | File | Description | | ----------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------- | | `Fig_S10_simulation_panel_data.csv` | Data points for the simulation panel comparing resonators with and without an extended Valley-Hall domain. The two columns are independent lists. | ## `Fig S11` | File | Description | | ----------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ | | `Fig_S11_simulation_panel_data.csv` | Mode wavelengths for simulation panels comparing resonators with `w = 1` (first column, top panel) and `w = 2` (second column, bottom panel). The two columns are independent lists. | ## `Fig S12` | File | Description | | ----------------------------------- | -------------------------------------------------------------------------------------- | | `Fig_S12_simulation_panel_data.csv` | Data points for the simulation panel showing the near-infrared (NIR) resonator design. | ## `Fitting matlab code` | File | Description | | ----------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `multilorenz_plotter.m` | MATLAB R2024b script for fitting measured spectra with a sum of Lorentzian lineshapes and a constant offset. Set the analysis and plotting parameters in its `USER SETTINGS` section, run the script, and select one or more two-column spectrum CSV files in the file-selection dialog. Set `convertInvcmToUm` according to whether the first input column is wavenumber in cm\\^-1 or wavelength in µm. The script saves `summary_stacked_plot.fig` and `summary_stacked_plot.png` in the folder from which the input files were selected, and a `_fit_results.csv` table beside each input CSV. It uses `findpeaks` from Signal Processing Toolbox and `lsqcurvefit` from Optimization Toolbox. |"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"U.S. National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62","awardNumber":"2328993"},{"funderIdentifierType":"ROR","funderName":"Office of Naval Research","funderIdentifier":"https://ror.org/00rk2pe57","awardNumber":"N00014-24-1-2483"},{"funderIdentifierType":"ROR","funderName":"Simons Foundation","funderIdentifier":"https://ror.org/01cmst727"},{"funderIdentifierType":"ROR","funderName":"Australian Research Council","funderIdentifier":"https://ror.org/05mmh0f86","awardNumber":"FT230100058"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.xgxd254zd","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-10-06T20:28:05Z","registered":"2026-10-06T20:28:06Z","published":null,"updated":"2026-10-06T20:28:06Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.34tmpg51p","type":"dois","attributes":{"doi":"10.5061/dryad.34tmpg51p","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of California, Irvine"],"name":"Tom, Emily","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1687-9948"}]},{"nameType":"Personal","affiliation":["University of California, Irvine"],"name":"Skowronska-Krawczyk, Dorota","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-5758-4225"}]},{"nameType":"Personal","affiliation":["University of California, Irvine"],"name":"Gao, Fangyuan","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Irvine"],"name":"Franco, Carolina","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California San Diego"],"name":"Wong, Adrian","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California San Diego"],"name":"Kemmerer, Nathan","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California San Diego"],"name":"Wang, Zichen","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Irvine"],"name":"Xu, Qianlan","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nanyang Technological University"],"name":"Zhuang, Yinyin","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Irvine"],"name":"Du, Samuel","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Irvine"],"name":"Palczewska, Grazyna","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Irvine"],"name":"Palczewski, Krzysztof","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Irvine"],"name":"Budin, Itay","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Irvine"],"name":"Shi, Xiaoyu","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9634-2659"}]},{"nameType":"Personal","affiliation":["Case Western Reserve University"],"name":"Bonilha, Vera","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California San Diego"],"name":"Schöneberg, Johannes","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California San Diego"],"name":"Wahlin, Karl","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Irvine"],"name":"Albrecht, Lauren","nameIdentifiers":[]}],"titles":[{"title":"Data from: Lipid-driven membrane remodeling engages a lysosome-dependent adaptive repair program during retinal aging"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Health sciences","subjectScheme":"fos"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Basic medicine","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Aging","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Lipids","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Retina","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Fatty acids","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[{"nameType":"Personal","affiliation":["University of California, Irvine"],"name":"Tom, Emily","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1687-9948"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2026-08-11T04:38:20Z","dateType":"Created"},{"date":"2026-09-09T21:13:51Z","dateType":"Submitted"},{"date":"2026-10-06T00:00:00Z","dateType":"Issued"},{"date":"2026-10-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.21203/rs.3.rs-8607320/v1","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1186/s13024-026-00990-w","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["358448 bytes"],"formats":[],"version":"4","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Age-associated remodeling of membrane lipid composition has been\n implicated in cellular dysfunction, yet the mechanisms linking lipid\n changes to membrane integrity and disease remain poorly defined. In the\n retinal pigment epithelium (RPE), lipid dysregulation is strongly\n associated with aging and age-related macular degeneration (AMD), a\n neurodegenerative disease of the central nervous system, but the causal\n pathways remain unclear. Here, we identify reduced activity of the lipid\n elongase ELOVL2 as a central driver of age-dependent membrane remodeling.\n Loss of ELOVL2-dependent polyunsaturated fatty acid (PUFA) elongation\n shifts plasma membrane lipid composition, leading to altered membrane\n biophysical properties and compromised membrane integrity. In response to\n this stress, RPE cells do not undergo apoptosis but instead activate a\n lysosome-dependent plasma membrane repair program that preserves barrier\n function under metabolic challenge. However, this adaptive response drives\n spatially polarized lysosomal exocytosis, promoting extracellular\n remodeling and accumulation of sub-RPE deposits associated with aging and\n AMD. Restoration of ELOVL2-derived lipid products reverses membrane\n abnormalities and suppresses lysosome-mediated remodeling phenotypes,\n demonstrating direct metabolic control of membrane homeostasis. Together,\n these findings define an ELOVL2-dependent lipid–lysosome axis that links\n PUFA elongation to plasma membrane integrity and reveals how compensatory\n repair mechanisms can contribute to tissue remodeling and disease\n progression in aging epithelia."},{"descriptionType":"Methods","description":"\u003cstrong\u003eLipidomic analysis\u003c/strong\u003e\n \u003cem\u003eLipid extraction\u003c/em\u003e Lipid\n extractions were performed according to the methodology of Bligh and\n Dyer\u003csup\u003e74\u003c/sup\u003e. In brief, the tissue was homogenized in 200\n μL water, transferred to a glass vial, and 750 μL 1:2 (v/v)\n CHCl\u003csub\u003e3\u003c/sub\u003e: MeOH was added and vortexed. Then 250 μL\n CHCl\u003csub\u003e3\u003c/sub\u003e was added and vortexed. Finally, 250 μL\n ddH\u003csub\u003e2\u003c/sub\u003eO was added and vortexed. The samples were\n centrifuged at 3000 rpm for 5 min at 4 °C. The lower phase was transferred\n to a new glass vial, dried under nitrogen, and stored at -20 °C until\n subsequent lipid analysis.\n \u003cem\u003eLC-MS/MS\u003c/em\u003e Separation of\n lipids was performed on an Accucore C30 column (2.6 μm, 2.1 mm × 150 mm,\n Thermo Scientific). The Q Exactive MS was operated in a full MS scan mode\n (resolution 70,000 at m/z 200) followed by ddMS2 (17,500 resolution) in\n both positive and negative modes. The AGC target value was set at 1E6 and\n 1E5 for the MS and MS/MS scans, respectively. The maximum injection time\n was 200 ms for MS and 50 ms for MS/MS. HCD was performed with a stepped\n collision energy of 30 ± 10% for negative and 25% and 30% for positive ion\n mode with an isolation window of 1.5 Da. \u003cem\u003eData\n analysis and post-processing\u003c/em\u003e Data were\n analyzed with LipidSearch 4.2.21 software. Only peaks with molecular\n identification grade: A or B were accepted (A: lipid class and fatty acids\n completely identified or B: lipid class and some fatty acids identified).\n The relative abundance of each lipid species was obtained by normalization\n to the total lipid intensity. Significantly changed lipid species (FC\n \u0026gt; 1.5. \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05) were submitted to\n Lipid Ontology (LION) for lipid ontology analysis. Data visualization was\n performed using Prism 7 software (GraphPad Software, Inc.).\n \u003cem\u003eFA analysis\u003c/em\u003e Separation of\n PUFAs was achieved on an Acquity UPLC® BEH C18 column (1.7 μm, 2.1 mm ×\n 100 mm, Waters Corporation). The Q Exactive MS was operated in a full MS\n scan mode (resolution 70,000 at m/z 200) in negative mode. For the\n compounds of interest, a scan range of m/z 250–800 was chosen. The\n identification of fatty acids was based on retention time and\n formula."},{"descriptionType":"TechnicalInfo","description":"# Data from: Lipid-driven membrane remodeling engages a lysosome-dependent\n adaptive repair program during retinal aging Dataset DOI:\n [10.5061/dryad.34tmpg51p](https://doi.org/10.5061/dryad.34tmpg51p) ###\n Files and variables #### File: 16mo_3x_suppl_lipidomics.csv\n **Description:** Lipidomics analysis from 16-month-old mouse eyecups that\n received intravitreal 24:5n-3 supplementation and contralateral vehicle\n control every month for 3 months ##### Variables * LipidMolec: Names of\n the lipid molecules detected in the analysis. * Class: Lipid class each\n molecule belongs to. * Formula: Molecular formula of each lipid. * BaseRt:\n Retention time at which the lipid elutes during chromatographic\n separation. * MainGrade[c]: Quality and confidence classification assigned\n to lipid identification based on how completely the lipid class and\n constituent fatty acyl chains are resolved, ie. **A:** The **lipid class**\n and **all constituent fatty acid chains/positions** are completely and\n unambiguously identified (high structural confidence), **B:** The **lipid\n class** and **some (but not all) fatty acid chains** are\n identified, **Grade C:** Only the **lipid class** or a partial/ambiguous\n fatty acid component is identified ##### Sample Columns * VEH-1:\n vehicle-injected eye, replicate 1 * VEH-2: vehicle-injected eye, replicate\n 2 * VEH-3: vehicle-injected eye, replicate 3 * VEH-4: vehicle-injected\n eye, replicate 4 * SUPPL-1: 24:5n-3-supplemented eye, replicate 1 *\n SUPPL-2: 24:5n-3-supplemented eye, replicate 2 *\n SUPPL-3: 24:5n-3-supplemented eye, replicate 3 *\n SUPPL-4: 24:5n-3-supplemented eye, replicate 4 #### File:\n iPSC_RPE_phagocytosis.csv **Description:** Total fatty acids extracted\n from iPSC-RPE CTRL vs. ELOVL2 KO incubated with bovine photoreceptor outer\n segments (POS) at various concentrations (0, 10, 20 POS/cell) for 3 hours\n ##### Variables * FAs: Names of the fatty acids detected in the analysis.\n * Chemical formula: Molecular formula of each fatty acid. * m/z:\n Mass-to-charge ratio of the detected fatty acid ion. * tR/min: Retention\n time in minutes during chromatographic separation. ##### Sample Columns *\n CTRL_0-1: CTRL, 0 POS/cell, replicate 1 * CTRL_0-2: CTRL, 0 POS/cell,\n replicate 2 * CTRL_10-1: CTRL, 10 POS/cell, replicate 1 * CTRL_10-2: CTRL,\n 10 POS/cell, replicate 2 * CTRL_10-3: CTRL, 10 POS/cell, replicate 3 *\n CTRL_20-1: CTRL, 20 POS/cell, replicate 1 * CTRL_20-2: CTRL, 20 POS/cell,\n replicate 2 * CTRL_20-3: CTRL, 20 POS/cell, replicate 3 * KO_0-1: ELOVL2\n KO, 0 POS/cell, replicate 1 * KO_0-2: ELOVL2 KO, 0 POS/cell, replicate 2 \n * KO_10-1: ELOVL2 KO, 10 POS/cell, replicate 1 * KO_10-2: ELOVL2 KO, 10\n POS/cell, replicate 2 * KO_10-3: ELOVL2 KO, 10 POS/cell, replicate 3 *\n KO_20-1: ELOVL2 KO, 20 POS/cell, replicate 1 * KO_20-2: ELOVL2 KO, 20\n POS/cell, replicate 2 * KO_20-3: ELOVL2 KO, 20 POS/cell, replicate 3 ####\n File: iPSC_RPE_tFA.csv **Description:** Total fatty acids extracted\n from iPSC-RPE CTRL vs. ELOVL2 KO ##### Variables * FAs: Names of the fatty\n acids detected in the analysis. * Chemical formula: Molecular formula of\n each fatty acid. * m/z: Mass-to-charge ratio of the detected fatty acid\n ion. * tR/min: Retention time in minutes during chromatographic\n separation. ##### Sample Columns * C-1: CTRL, replicate 1 * C-2: CTRL,\n replicate 2 * KO_D-1: ELOVL2 KO, replicate 1 * KO_D-2: ELOVL2 KO,\n replicate 2 #### File: ELOVL2_KD_PM_lipidomics_exp1.csv\n **Description:** Lipidomics analysis from plasma membrane (PM) fractions\n from CTRL vs. ELOVL2 KD ARPE-19 cells, experiment 1 ##### Variables *\n LipidMolec: Names of the lipid molecules detected in the analysis. *\n Class: Lipid class each molecule belongs to. * Formula: Molecular formula\n of each lipid. * BaseRt: Retention time at which the lipid elutes during\n chromatographic separation. * MainGrade[c]: Quality and confidence\n classification assigned to lipid identification based on how completely\n the lipid class and constituent fatty acyl chains are resolved, ie. **A:**\n The **lipid class** and **all constituent fatty acid chains/positions**\n are completely and unambiguously identified (high structural\n confidence), **B:** The **lipid class** and **some (but not all) fatty\n acid chains** are identified, **Grade C:** Only the **lipid class** or a\n partial/ambiguous fatty acid component is identified ##### Sample Columns\n * C-1: CTRL PM, replicate 1, experiment 1 * C-2: CTRL PM, replicate 2,\n experiment 1 * C-3: CTRL PM, replicate 3, experiment 1 * KD-1: ELOVL2 KD\n PM, replicate 1, experiment 1 * KD-2: ELOVL2 KD PM, replicate 2,\n experiment 1 * KD-3: ELOVL2 KD PM, replicate 3, experiment 1 #### File:\n ELOVL2_KD_PM_lipidomics_exp2.csv **Description:** Lipidomics analysis from\n plasma membrane (PM) fractions from CTRL vs. ELOVL2 KD ARPE-19 cells,\n experiment 2 ##### Variables * LipidMolec: Names of the lipid molecules\n detected in the analysis. * Class: Lipid class each molecule belongs to. *\n Formula: Molecular formula of each lipid. * BaseRt: Retention time at\n which the lipid elutes during chromatographic separation. * MainGrade[c]:\n Quality and confidence classification assigned to lipid identification\n based on how completely the lipid class and constituent fatty acyl chains\n are resolved, ie. **A:** The **lipid class** and **all constituent fatty\n acid chains/positions** are completely and unambiguously identified (high\n structural confidence), **B:** The **lipid class** and **some (but not\n all) fatty acid chains** are identified, **Grade C:** Only the **lipid\n class** or a partial/ambiguous fatty acid component is identified #####\n Sample Columns * C-1: CTRL PM, replicate 1, experiment 2 * C-2: CTRL PM,\n replicate 2, experiment 2 * C-3: CTRL PM, replicate 3, experiment 2 *\n KD-1: ELOVL2 KD PM, replicate 1, experiment 2 * KD-2: ELOVL2 KD PM,\n replicate 2, experiment 2 * KD-3: ELOVL2 KD PM, replicate 3, experiment 2\n #### File: ELOVL2_KD_lipidomics.csv **Description:** Lipidomics analysis\n from CTRL vs. ELOVL2 KD differentiated ARPE-19 cells ##### Variables *\n LipidMolec: Names of the lipid molecules detected in the analysis. *\n Class: Lipid class each molecule belongs to. * Formula: Molecular formula\n of each lipid. * BaseRt: Retention time at which the lipid elutes during\n chromatographic separation. * MainGrade[c]: Quality and confidence\n classification assigned to lipid identification based on how completely\n the lipid class and constituent fatty acyl chains are resolved, ie. **A:**\n The **lipid class** and **all constituent fatty acid chains/positions**\n are completely and unambiguously identified (high structural\n confidence), **B:** The **lipid class** and **some (but not all) fatty\n acid chains** are identified, **Grade C:** Only the **lipid class** or a\n partial/ambiguous fatty acid component is identified ##### Sample Columns\n * C1: CTRL, replicate 1 * C2: CTRL, replicate 2 * C3: CTRL, replicate 3 *\n C4: CTRL, replicate 4 * KD1: ELOVL2 KD, replicate 1 * KD2: ELOVL2 KD,\n replicate 2 * KD3: ELOVL2 KD, replicate 3 * KD4: ELOVL2 KD, replicate 4\n #### File: ELOVL2_KD_tFA.csv **Description:** Total fatty acids extracted\n from from CTRL vs. ELOVL2 KD differentiated ARPE-19 cells ##### Variables\n * FAs: Names of the fatty acids detected in the analysis. * Chemical\n formula: Molecular formula of each fatty acid. * m/z: Mass-to-charge ratio\n of the detected fatty acid ion. * tR/min: Retention time in minutes during\n chromatographic separation. ##### Sample Columns * C1: CTRL, replicate 1 *\n C2: CTRL, replicate 2 * C3: CTRL, replicate 3 * C4: CTRL, replicate 4 *\n KD1: ELOVL2 KD, replicate 1 * KD2: ELOVL2 KD, replicate 2 * KD3: ELOVL2\n KD, replicate 3 * KD4: ELOVL2 KD, replicate 4 #### File:\n 15mo_Elovl2_vs_18mo_WT_eyecup.csv **Description:** Lipidomics analysis\n from 15-month-old Elovl2 mutant and 18-month-old WT mouse eyecups #####\n Variables * LipidMolec: Names of the lipid molecules detected in the\n analysis. * Class: Lipid class each molecule belongs to. * Formula:\n Molecular formula of each lipid. * BaseRt: Retention time at which the\n lipid elutes during chromatographic separation. * MainGrade[c]: Quality\n and confidence classification assigned to lipid identification based on\n how completely the lipid class and constituent fatty acyl chains are\n resolved, ie. **A:** The **lipid class** and **all constituent fatty acid\n chains/positions** are completely and unambiguously identified (high\n structural confidence), **B:** The **lipid class** and **some (but not\n all) fatty acid chains** are identified, **Grade C:** Only the **lipid\n class** or a partial/ambiguous fatty acid component is identified #####\n Sample Columns * 18moWT-1: 18-month-old, wildtype, replicate 1 * 18moWT-2:\n 18-month-old, wildtype, replicate 2 * 18moWT-3: 18-month-old, wildtype,\n replicate 3 * 15moELOVL2-1: 15-month-old, Elovl2 mutant, replicate 1 *\n 15moELOVL2-2: 15-month-old, Elovl2 mutant, replicate 2 * 15moELOVL2-3:\n 15-month-old, Elovl2 mutant, replicate 3 #### File:\n 18mo_WT_Elovl2_mouse_eyecup_tFA.csv **Description:** Total fatty acids\n extracted from 18-month-old wildtype and Elovl2 mutant mouse eyecups #####\n Variables * FAs: Names of the fatty acids detected in the analysis. *\n Chemical formula: Molecular formula of each fatty acid. * m/z:\n Mass-to-charge ratio of the detected fatty acid ion. * tR/min: Retention\n time in minutes during chromatographic separation. ##### Sample Columns *\n WT-1: 18-month-old, wildtype, replicate 1 * WT-2: 18-month-old, wildtype,\n replicate 2 * WT-3: 18-month-old, wildtype, replicate 3 *\n C234W1: 18-month-old, Elovl2 mutant, replicate 1 * C234W-2: 18-month-old,\n Elovl2 mutant, replicate 2 * C234W-3: 18-month-old, Elovl2 mutant,\n replicate 3 #### File: 3mo_18mo_mouse_eyecup_total_FA.csv\n **Description:** Total fatty acids extracted from 3-month-old and\n 18-month-old mouse eyeupcs ##### Variables * FAs: Names of the fatty acids\n detected in the analysis. * Chemical formula: Molecular formula of each\n fatty acid. * m/z: Mass-to-charge ratio of the detected fatty acid ion. *\n tR/min: Retention time in minutes during chromatographic separation. #####\n Sample Columns * 3mo-1: 3-month-old, replicate 1 - 3mo-2: 3-month-old,\n replicate 2 - 3mo-3: 3-month-old, replicate 3 - 3mo-4: 3-month-old,\n replicate 4 - 3mo-5: 3-month-old, replicate 5 - 18mo-1: 18-month-old,\n replicate 1 - 18mo-2: 18-month-old, replicate 2 - 18mo-3: 18-month-old,\n replicate 3 - 18mo-4: 18-month-old, replicate 4 - 18mo-5: 18-month-old,\n replicate 5 #### File: 18mo_Elovl2_mouse_eyecup_lip.csv\n **Description:** Lipidomics analysis of 18-month-old wildtype and Elovl2\n mutant mouse eyecups ##### Variables * LipidMolec: Names of the lipid\n molecules detected in the analysis. * Class: Lipid class each molecule\n belongs to. * Formula: Molecular formula of each lipid. * BaseRt:\n Retention time at which the lipid elutes during chromatographic\n separation. * MainGrade[c]: Quality and confidence classification assigned\n to lipid identification based on how completely the lipid class and\n constituent fatty acyl chains are resolved, ie. **A:** The **lipid class**\n and **all constituent fatty acid chains/positions** are completely and\n unambiguously identified (high structural confidence), **B:** The **lipid\n class** and **some (but not all) fatty acid chains** are\n identified, **Grade C:** Only the **lipid class** or a partial/ambiguous\n fatty acid component is identified ##### Sample Columns *\n WT-1: 18-month-old, wildtype, replicate 1 * WT-2: 18-month-old, wildtype,\n replicate 2 * C234W-1: 18-month-old, Elovl2 mutant, replicate 1 *\n C234W-2: 18-month-old, Elovl2 mutant, replicate 2 ## Code/software *Data\n analysis and post-processing* Data were analyzed with LipidSearch 4.2.21\n software. Only peaks with molecular identification grade: A or B were\n accepted (A: lipid class and fatty acids completely identified or B: lipid\n class and some fatty acids identified). The relative abundance of each\n lipid species was obtained by normalization to the total lipid intensity.\n Significantly changed lipid species (FC \u0026gt; 1.5. *P* \u0026lt; 0.05) were\n submitted to Lipid Ontology (LION) for lipid ontology analysis. Data\n visualization was performed using Prism 7 software (GraphPad Software,\n Inc.). *FA analysis* Separation of PUFAs was achieved on an Acquity UPLC®\n BEH C18 column (1.7 μm, 2.1 mm × 100 mm, Waters Corporation). The Q\n Exactive MS was operated in a full MS scan mode (resolution 70,000 at m/z\n 200) in negative mode. For the compounds of interest, a scan range of m/z\n 250–800 was chosen. The identification of fatty acids was based on\n retention time and formula. Blank cells mean that the lipid or fatty acid\n species was not detected/below the limit of detection."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"National Eye Institute","funderIdentifier":"https://ror.org/03wkg3b53"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.34tmpg51p","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":1,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-10-06T14:34:13Z","registered":"2026-10-06T14:34:14Z","published":null,"updated":"2026-10-06T14:34:14Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.v9s4mw79j","type":"dois","attributes":{"doi":"10.5061/dryad.v9s4mw79j","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Canterbury"],"name":"Steel, Michael","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["New Jersey Institute of Technology"],"name":"Wicke, Kristina","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Simon Fraser University"],"name":"Mooers, Arne","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0383-8856"}]}],"titles":[{"title":"Data and code from: Properties of biodiversity indices that incorporate future extinction risk"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"subject":"Phylogenetic trees"},{"subject":"networks"},{"subject":"Biodiversity Indices"},{"subject":"extinction"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"}],"contributors":[{"nameType":"Personal","affiliation":["Simon Fraser University"],"name":"Mooers, Arne","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0383-8856"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2026-02-28T21:57:39Z","dateType":"Created"},{"date":"2026-09-25T05:15:12Z","dateType":"Submitted"},{"date":"2026-10-06T00:00:00Z","dateType":"Issued"},{"date":"2026-10-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.48550/arxiv.2602.16059","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1093/sysbio/syag075","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["30517 bytes"],"formats":[],"version":"5","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"The loss of biodiversity due to the likely widespread extinction of\n species in the near future is a focus of current concern in conservation\n biology. One approach to measure the impact of this extinction is based on\n the predicted loss of phylogenetic diversity. These predictions have\n become a focus of the Zoological Society of London’s ‘EDGE2’ program for\n quantifying biodiversity loss and involves considering the HED (heightened\n evolutionary distinctiveness) and HEDGE (heightened evolutionary\n distinctiveness and globally endangered) indices which are based on\n phylogenetic diversity on a tree. Here, we show how to generalise the\n HED(GE) indices by expanding their application to more general settings\n (to phylogenetic networks, to feature diversity on discrete traits, and to\n arbitrary biodiversity measures). We provide a simple and explicit\n description of the mean and, importantly, the variance of such measures,\n and illustrate our results by an application to the phylogeny and a small\n set of features for all 27 extant Crocodilians. We also provide an example\n to illustrate how the approach extends to feature diversity."},{"descriptionType":"TechnicalInfo","description":"# Data and code from: Properties of biodiversity indices that incorporate\n future extinction risk Dataset DOI:\n [10.5061/dryad.v9s4mw79j](https://doi.org/10.5061/dryad.v9s4mw79j) ##\n Description of the data and file structure Arne Mooers The Crocodylian\n tree was created from the first 100 Archosauromorphs trees (the PASTIS\n set) from Coulson et al. 2020 (1), using mean branch lengths (topology was\n invariant). The IUCN categories are also from Coulson et al. (2020) (1),\n converted to p(ext) using values in Gumbs et al., 2023 (2). The R-scripts\n for producing HEDGE and FD values and their variances should be\n self-explanatory. The Features dataset draws primarily from Pyron et\n al.'s Tetrapod dataset (Pyron et al., 2026) (3), with nesting ecology\n drawn from Murray et al. (2019) (4), and includes some taxonomic\n imputation. References from Pyron et al. are included, and blank cells are\n missing values from Pyron et al. that could not easily be imputed from the\n data at hand. We note that this presence-absence feature dataset is\n illustrative only. References: (1)\n [https://doi.org/10.1186/s12862-020-01642-3](https://doi.org/10.1186/s12862-020-01642-3) (2) [https://doi.org/10.1371/journal.pbio.3001991](https://doi.org/10.1371/journal.pbio.3001991) (3) [https://www.researchsquare.com/article/rs-7556378/v1](https://www.researchsquare.com/article/rs-7556378/v1) (4) [https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.5859](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.5859) ### Files and variables #### File: dated_crocodile_tree.tre **Description:** newick format dated phylogenetic tree used #### File: crocodile_p_ext.csv **Description:** probability of extinction values per species #### File: Crocodiles_Features.csv **Description:** The traits as scored by Pyron et al. (3) and Murray et al. (4), and the conversion to \"Features\", such that the minority state was scored as \"presence\" of the Feature (1) and the majority state was scored as \"absence\" of the named Feature (0). #### File: Phylogenetic_Diversity_script_for_crocodiles.R **Description:** Short script using ape and phylotools to calculate HEDGE values (irreplaceability and conservation gain) and their variances.  #### File: Feature_Diversity_script_for_crocodiles.R **Description:** Short script to calculate Feature Diversity values (irreplaceability and conservation gain) and their variances.  ## Code/software Script is written in R 4.4.2 GUI 1.81 Big Sur ARM build (8462) ## Access information Data repurposed from Coulson et al. 2020: [https://doi.org/10.1186/s12862-020-01642-3](https://doi.org/10.1186/s12862-020-01642-3) and deposited on Dryad at doi:10.5061/dryad.h19t7b2 Other data from Pyron et al. 2026  [https://www.researchsquare.com/article/rs-7556378/v1](https://www.researchsquare.com/article/rs-7556378/v1) and Murray et al. (2019) [https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.5859](https://onlinelibrary.wiley.com/doi/full/10.1002/ece3.5859), not deposited on Dryad."}],"geoLocations":[],"fundingReferences":[{"funderName":"New Zealand Marsden Fund"},{"funderIdentifierType":"ROR","funderName":"Natural Sciences and Engineering Research Council of Canada","funderIdentifier":"https://ror.org/01h531d29"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.v9s4mw79j","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-10-06T13:57:03Z","registered":"2026-10-06T13:57:04Z","published":null,"updated":"2026-10-06T13:57:04Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.vx0k6dk6h","type":"dois","attributes":{"doi":"10.5061/dryad.vx0k6dk6h","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Washington"],"name":"Zelnick, Leila","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8461-5111"}]},{"nameType":"Personal","affiliation":["University of Washington"],"name":"de Boer, Ian","nameIdentifiers":[]}],"titles":[{"title":"Blood sugar sensing on maintenance dialysis (BLOSSOM) study"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Medical and health sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Medical dialysis","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Hyperglycemia","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Renal failure","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Diabetes mellitus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[{"nameType":"Personal","affiliation":["University of Washington"],"name":"Zelnick, Leila","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8461-5111"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2026-02-17T19:06:04Z","dateType":"Created"},{"date":"2026-02-17T19:06:08Z","dateType":"Submitted"},{"date":"2026-10-06T00:00:00Z","dateType":"Issued"},{"date":"2026-10-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1177/15209156251368934","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1681/asn.0000000693","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["490619 bytes"],"formats":[],"version":"4","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"The Blood Sugar Sensing On Maintenance dialysis (BLOSSOM) study is a\n prospective cohort study designed to rigorously assess glycemia using\n standardized CGM in a broad population of adults treated for kidney\n failure with maintenance dialysis. Participants were recruited from the\n Northwest Kidney Centers, a not-for-profit dialysis organization that\n provides care to the majority of maintenance dialysis patients in Seattle\n and King County, Washington. Inclusion criteria were kidney failure\n treated with dialysis (hemodialysis or PD), age 18 years or older, and\n primary language English, Spanish, Chinese, or Vietnamese (the most common\n languages in the region, into which study materials were translated). The\n only exclusion criterion was inability to provide informed consent.\n Patients with or without diabetes were eligible. Ninety-six non-dialysis\n controls were also recruited to the study for comparison.  The\n data have two data files, which can be linked by a unique participant ID\n (blossom_id_deid). Baseline data contains one row per participant, while\n the longitudinal data contains one row per participant per visit,\n containing data (CGM metrics, medications, glycemic biomarkers) that\n varied by study visit."},{"descriptionType":"TechnicalInfo","description":"# Blood sugar sensing on maintenance dialysis (BLOSSOM) study ###\n Description of the data and file structure This folder contains data from\n the Blood Sugar Sensing On Dialysis (BLOSSOM) cohort study. The data is\n broken into two data files that can be merged via a unique participant\n identifier (blossom_id_deid). The description of each dataset and the\n variables in each dataset can be found in the BLOSSOM data dictionary\n (blossom_data_dictionary_deid_2026_09_30.pdf) Please contact Leila\n Zelnick, PhD ([lzelnick@uw.edu](mailto:lzelnick@uw.edu)) with any\n questions. Deidentified data * blossom_dat_deid.csv *\n blossom_dat_long_deid.csv * README.Rmd The BLOSSOM data is split into the\n following data files, described below: 1. Baseline data 2. Longitudinal\n data ### 1. Baseline data (N = 516 rows, one row per participant) | Name |\n Label/Description | Format | Notes | | :------------------------------ |\n :---------------------------------------------------------------------------------------------------------------- | :--------------------------------------------------------------------------------------------------------------------------- | :------------------------ | | blossom\\_id\\_deid | De-identified BLOSSOM ID number | Numeric | \n \n | | case | Indicator that ppt was treated with dialysis | 0 = no, 1 = yes\n | \n \n | | age\\_cat | Categorical age at baseline | ≤40, 41-50, 51-60, 61-70,\n \u0026gt;70 | \n \n | | male | Male sex? | 0 = no, 1 = yes | \n \n | | pd | Peritoneal dialysis? | 0 = no, 1 = yes | \n \n | | vintage\\_cat | Dialysis vintage (categorical) | ≤3 months, 3-6\n months, \u0026gt;6 months | \n \n | | bmi\\_cat | BMI (kg/m2) | ≤25, 26-30, 31-35, \u0026gt;35 | \n \n | | dm\\_status | Diabetes status | No diabetes, untreated diabetes, or\n treated diabetes | \n \n | | q\\_mi | Have you ever been told you had a myocardial infarction\n (heart attack)? | 0 = no or don’t know, 1 = yes | \n \n | | q\\_chf | Have you ever been told you had CHF (congestive heart\n failure)? | 0 = no or don’t know, 1 = yes | \n \n | | q\\_stroke | Have you ever been told you had a stroke or TIA\n (transient ischemic attach)? | 0 = no or don’t know, 1 = yes | \n \n | | q\\_pvd | Have you ever been told you had a peripheral vascular\n disease, including aortic aneurysm or claudication of legs? | 0 = no or\n don’t know, 1 = yes | \n \n | | q\\_kx | Have you ever received a kidney transplant? | 0 = no; 1 =\n yes; 2 = don’t know | \n \n | | q\\_cancer | Have you ever been told that you have any type of cancer\n (other than skin cancer)? | 0 = no; 1 = yes; 2 = don’t know | \n \n | | q\\_urine | Do you urinate? | 0 = no, 1 = yes | \n \n | | q\\_urine\\_amnt | How much each day? | 1 = \u0026lt;100 mL; 2 = 100-\u0026lt;200\n mL; 3 = 200-\u0026lt;500 mL; 4 = 500-\u0026lt;1000 mL; 5 = \u0026gt;1000 mL | \n \n | | epo | Use of ESAs | 0 = no, 1 = yes | \n \n | | iron\\_iv | Use of IV iron | 0 = no, 1 = yes | \n \n | | iron\\_po | Use of PO iron | 0 = no, 1 = yes | \n \n | | q\\_smoke | Do you smoke, or have you ever? | 1 = I have never smoked\n cigarettes, 2 = I smoked in the past, but not now, 3 = I currently smoke,\n 4 = I prefer not to answer | \n \n | | sga\\_all | SGA overall score | 0-32 | \n \n | | pam\\_score | Summative PAM-13 score | Numeric (0-100, lower is less\n activated) | \n \n | | pam\\_level | PAM-13 activation level | Levels 1-4; see\n https\\://perspectivesonre ading.com/know-yourpam/  | \n \n | | habs\\_avoidance\\_subscore | HABS avoidance subscore |\n https\\://behavioraldiabet es.org/wpcontent/uploads/2025/0\n 3/1.-HABS-ENGLISH.pdf | \n \n | | habs\\_confidence\\_subscore | HABS confidence subscore | \n \n | \n \n | | habs\\_anxiety\\_subscore | HABS anxiety subscore | \n \n | \n \n | | habs\\_total\\_score | Total HABS score | \n \n | \n \n | | dds\\_emotional\\_burden | DDS emotional burden subscore |\n dds\\_emotional\\_burden | \n \n | | dds\\_doc\\_related\\_distress | DDS physician-related distress subscore\n | dds\\_doc\\_related\\_distress | \n \n | | dds\\_regimen\\_related\\_distress | DDS regimen-related distress\n subscore | dds\\_regimen\\_related\\_distress | \n \n | | dds\\_interpersonal\\_distress | DDS interpersonal distress subscore |\n dds\\_interpersonal\\_distress | \n \n | | dds\\_total\\_score | DDS total score | dds\\_total\\_score | \n \n | | mnsi\\_quest\\_score | MNSI questionnaire score | 0-13 points | \n \n | | mnsi\\_exam\\_score | MNSI physical exam score | 0-8 points | \n \n | | albumin | Albumin (g/dL) | Numeric | \n \n | | total\\_protein | Total protein (g/dL) | Numeric | \n \n | | bun\\_post | BUN (post-dialysis) (mg/dL) | Numeric | \n \n | | bun\\_pre | BUN (pre-dialysis) (mg/dL) | Numeric | \n \n | | creatinine | Creatinine (mg/dL) | Numeric | \n \n | | sodium | Sodium (mEq/L) | Numeric | \n \n | | potassium | Potassium (mEq/L) | Numeric | \n \n | | co2 | Bicarbonate (mEq/L) | Numeric | \n \n | | chloride | Chloride (mEq/L) | Numeric | \n \n | | calcium | Calcium (mg/dL) | Numeric | \n \n | | phosphorus | Phosphorus (mg/dL) | Numeric | \n \n | | pth\\_quart | PTH (quarterly) (pg/mL) | Numeric | \n \n | | glucose | Glucose (mg/dL) | Numeric | \n \n | | a1c\\_nkc | HbA1c (%), measured clinically | Numeric | \n \n | | hgb | Hgb (g/dL) | Numeric | \n \n | | wbc | WBC (x10\\^9/L) | Numeric | \n \n | | platelets | Platelets (x10\\^9/L) | Numeric | \n \n | | ferritin\\_quart | Ferritin (quarterly) (µg/L) | Numeric | \n \n | | fe\\_sat\\_quart | Fe saturation (quarterly) (%) | Numeric | \n \n | | pna | PNA (protein nitrogen appearance) | Numeric | \n \n | | kt\\_v | Kt/V (Fractional urea clearance) | Numeric | \n \n | | urea\\_reduction\\_ratio | Urea reduction ratio | Numeric | \n \n | | kt\\_v\\_residual\\_weekly | Kt/V (Fractional urea clearance),\n calculated from residual renal function | Numeric | Evaluated in PD ppts\n only | | kt\\_v\\_weekly\\_dialysate | Kt/V (Fractional urea clearance),\n calculated from dialysate | Numeric | Evaluated in PD ppts only | |\n npna\\_pd | Normalized protein nitrogen appearance (nPNA) | Numeric |\n Evaluated in PD ppts only | ### 2. Longitudinal data (N = 1,511 rows,\n multiple rows per participant) | Name | Label/Description | Format | Notes\n | | :------------------ |\n :----------------------------------------------------------------------- |\n :-------------------------------------- |\n :------------------------------------------------------------------------------------- | | blossom\\_id\\_deid | De-identified BLOSSOM ID number | Numeric | \n \n | | visit | Visit | Baseline, baseline 2, 3-month, 12-month | \n \n | | a1c | HbA1c (%), measured in Kabytaev lab at University of Missouri |\n Numeric | \n \n | | ga | Glycated albumin (%), measured in Kabytaev lab at University of\n Missouri | Numeric | \n \n | | fruct | Frutosamine (µmol/L), measured in Kabytaev lab at University\n of Missouri | Numeric | \n \n | | cgm\\_days | Days of valid CGM | Numeric | \n \n | | sdglu | SD of CGM blood glucose (mg/dL) | Numeric | \n \n | | iqrglu | IQR of CGM blood glucose (mg/dL) | Numeric | \n \n | | cvglu | CV% of CGM blood glucose (mg/dL) | Numeric | \n \n | | ptimein | Proportion of time in range | Numeric | Range is 70-140\n mg/dL for those without diabetes; 70-180 mg/dL for those with diabetes | |\n ptimein\\_140 | Proportion of time 70-140 mg/dL | Numeric | \n \n | | ptimein\\_180 | Proportion of time 70-180 mg/dL | Numeric | \n \n | | ptimeabove | Proportion of time above range | Numeric | Range is\n 70-140 mg/dL for those without diabetes; 70-180 mg/dL for those with\n diabetes | | ptimeabove\\_140 | Proportion of time above 140 mg/dL |\n Numeric | \n \n | | ptimeabove\\_180 | Proportion of time above 180 mg/dL | Numeric | \n \n | | ptimeabove\\_250 | Proportion of time above 250 mg/dL | Numeric | \n \n | | ptimebelow\\_70 | Proportion of time below 70 mg/dL | Numeric | \n \n | | ptimebelow\\_54 | Proportion of time below 54 mg/dL | Numeric | \n \n | | meanglu | Mean of CGM blood glucose (mg/dL) | Numeric | \n \n | | gmi | Glucose Management Indicator (%) | Numeric | \n \n | | hypo\\_mins\\_70 | Total number of hypoglycemic minutes \u0026lt;70 mg/dL |\n Numeric | \n \n | | num\\_hypoep\\_70 | Number of hypoglycemic events \u0026lt;70 mg/dL |\n Numeric | \n \n | | hypo\\_mins\\_54 | Total number of hypoglycemic minutes \u0026lt;54 mg/dL |\n Numeric | \n \n | | num\\_hypoep\\_54 | Number of hypoglycemic events \u0026lt;54 mg/dL |\n Numeric | \n \n | | rx\\_any | Any medication use | 0 = no, 1 = yes | \n \n | | rx\\_db | Antidiabetic medication use | 0 = no, 1 = yes | \n \n | | rx\\_ins | Insulin use | 0 = no, 1 = yes | \n \n | | rx\\_ins\\_rapid | Rapid-acting insulin use | 0 = no, 1 = yes | \n \n | | rx\\_ins\\_short | Short-acting insulin use | 0 = no, 1 = yes | \n \n | | rx\\_ins\\_intermed | Intermediate-acting insulin use | 0 = no, 1 = yes\n | \n \n | | rx\\_ins\\_long | Long-acting insulin use | 0 = no, 1 = yes | \n \n | | rx\\_sglt2i | SGLT2i use | 0 = no, 1 = yes | \n \n | | rx\\_glp1a | GLP-1a use | 0 = no, 1 = yes | \n \n | | rx\\_dpp4i | dPP4-i use | 0 = no, 1 = yes | \n \n | | rx\\_alphaglui | Alpha-glucosidase inhibitor use | 0 = no, 1 = yes | \n \n | | rx\\_amylina | Amylin analog use | 0 = no, 1 = yes | \n \n | | rx\\_biguanides | Biguanide use | 0 = no, 1 = yes | \n \n | | rx\\_meglitinides | Meglitinide use | 0 = no, 1 = yes | \n \n | | rx\\_su | Sulfonylurea use | 0 = no, 1 = yes | \n \n | | rx\\_tzd | Thiazolidinediones use | 0 = no, 1 = yes | \n \n | | rx\\_antihtn | Antihypertensive medication use | 0 = no, 1 = yes | \n \n | | rx\\_raas | RAASi use | 0 = no, 1 = yes | \n \n | | rx\\_ace | ACEi use | 0 = no, 1 = yes | \n \n | | rx\\_arb | ARB use | 0 = no, 1 = yes | \n \n | | rx\\_aa | Aldosterone antagonist use | 0 = no, 1 = yes | \n \n | | rx\\_ri | Renin inhibitor use | 0 = no, 1 = yes | \n \n | | rx\\_diuretic | Diuretic use | 0 = no, 1 = yes | \n \n | | rx\\_diuretic\\_loop | Loop diuretic use | 0 = no, 1 = yes | \n \n | | rx\\_diuretic\\_thiaz | Thiazide diuretic use | 0 = no, 1 = yes | \n \n | | rx\\_diuretic\\_k | Potassium-sparing diuretic use | 0 = no, 1 = yes | \n \n | | rx\\_betablk | Beta-blocker use | 0 = no, 1 = yes | \n \n | | rx\\_ccb | Calcium-channel blocker use | 0 = no, 1 = yes | \n \n | | rx\\_vasodilators | Vasodilator use | 0 = no, 1 = yes | \n \n | | rx\\_statin | Statin use | 0 = no, 1 = yes | \n \n | | rx\\_fibrate | Fibrate use | 0 = no, 1 = yes | \n \n | | rx\\_aspirin | Aspirin use | 0 = no, 1 = yes | \n \n | | rx\\_opiate | Opiate use | 0 = no, 1 = yes | \n \n | | num\\_antihtn\\_meds | Number of antihypertensive medications | Numeric\n | \n \n | ## Human subjects data All participants provided informed consent prior\n to participation in the study. The consent form included the following:\n [From the consent form]: \"We may present the results of this research\n study at meetings or in a medical journal, but we will not identify you.\n Information gathered from you is pooled with the information from other\n participants in this study and no information that identifies you will be\n revealed.\" [From the HIPAA authorization]: \"If the results of\n this study are made public, information that identifies you will not be\n used. The researcher will use your patient information only in the ways\n that are described in the research consent form that you sign and as\n described in this HIPAA authorization.\""}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"National Institute of Diabetes and Digestive and Kidney Diseases","funderIdentifier":"https://ror.org/00adh9b73","awardNumber":"R01DK126373"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.vx0k6dk6h","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":1,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-10-06T12:10:03Z","registered":"2026-10-06T12:10:04Z","published":null,"updated":"2026-10-06T12:10:04Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.2z34tmq32","type":"dois","attributes":{"doi":"10.5061/dryad.2z34tmq32","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Pennsylvania"],"name":"Hart, Michael","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8865-3062"}]},{"nameType":"Personal","affiliation":["University of Pennsylvania"],"name":"Haskell, Dustin","nameIdentifiers":[]}],"titles":[{"title":"Data from: \u003cem\u003eIn vivo\u003c/em\u003e dissection of human NRXN1 isoforms reveals gain-of-function pathogenicity of schizophrenia-associated 3' deletions"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Schizophrenia","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Genetics","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Neuroscience","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[{"nameType":"Personal","affiliation":["University of Pennsylvania"],"name":"Hart, Michael","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8865-3062"}],"contributorType":"ProjectLeader"},{"nameType":"Personal","affiliation":["University of Pennsylvania"],"name":"Hart, Michael","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8865-3062"}],"contributorType":"ProjectManager"},{"nameType":"Personal","affiliation":["University of Pennsylvania"],"name":"Hart, Michael","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8865-3062"}],"contributorType":"Supervisor"},{"nameType":"Personal","affiliation":["University of Pennsylvania"],"name":"Hart, Michael","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8865-3062"}],"contributorType":"Editor"},{"nameType":"Personal","affiliation":["University of Pennsylvania"],"name":"Haskell, Dustin","contributorType":"ProjectLeader","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Pennsylvania"],"name":"Haskell, Dustin","contributorType":"DataCurator","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Pennsylvania"],"name":"Haskell, Dustin","contributorType":"DataCollector","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Pennsylvania"],"name":"Haskell, Dustin","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Pennsylvania"],"name":"Haskell, Dustin","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Pennsylvania"],"name":"Hart, Michael","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8865-3062"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2026-09-10T13:07:21Z","dateType":"Created"},{"date":"2026-09-10T13:07:21Z","dateType":"Submitted"},{"date":"2026-10-06T00:00:00Z","dateType":"Issued"},{"date":"2026-10-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1093/hmg/ddag085","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["34014 bytes"],"formats":[],"version":"4","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Heterozygous deletions in NRXN1, encoding the presynaptic adhesion\n molecule Neurexin 1, are among the most frequently identified rare\n variants in schizophrenia and other neuropsychiatric disorders. Patient\n sequencing has revealed that 3′ deletions within NRXN1 generate novel\n isoforms not produced from the intact locus, yet whether these isoforms\n are functional, passively non-functional, or actively pathogenic in vivo\n is still not clear. To address this, we expressed eight human NRXN1\n isoforms in C. elegans neurons including four control isoforms and four 3′\n deletion variant isoforms identified in schizophrenia patient cell lines,\n and characterized their effects on protein localization and two\n independent behaviors: a food deprivation response and social feeding\n behaviors. Most human isoforms showed expression and localization within\n the nerve ring and neurons similar to the C. elegans ortholog, NRX-1;\n however, several isoforms, particularly among the 3′ deletion variants,\n displayed aberrant accumulation in neuronal cell bodies or as puncta in\n neuropil. Functionally, isoforms fell into one of three categories: no\n effect on nrx-1 loss-of-function behavioral phenotypes, partial rescue, or\n gain of function, with multiple isoforms showing differences between the\n behaviors. Strikingly, two 3′ deletion isoforms produced gain-of-function\n behavioral phenotypes more severe than the nrx-1 null mutant,\n demonstrating that these patient-derived variants can be actively\n pathogenic. These results establish C. elegans as a tractable in vivo\n platform for dissecting the isoform-specific functional consequences of\n NRXN1 variants and suggest that strategies for NRXN1-associated\n neuropsychiatric diseases must account for both loss-of-function and\n gain-of-function isoform mechanisms."},{"descriptionType":"TechnicalInfo","description":"# Data from: *In vivo* dissection of human NRXN1 isoforms reveals\n gain-of-function pathogenicity of schizophrenia-associated 3'\n deletions Dataset DOI:\n [10.5061/dryad.2z34tmq32](https://doi.org/10.5061/dryad.2z34tmq32) ##\n Description of the data and file structure Behavioral and qRT-PCR data\n associated with expression of human NRXN1 isoforms in C. elegans. This\n file includes 4 spreadsheets that are the data used to generate Figures 3\n and 4, and Supplemental Figures 2 \u0026amp; 3. ### File:\n Haskell_HMG_data_dryad_.xlsx **Description:** 4 spreadsheets of data used\n to generate graphs in Figures 3 and 4, and Supplemental Figures 2 \u0026amp; 3\n for the associated publication. #### Sheet: Figure 3 **Description**:\n Comparison of control, nrx-1(wy778) mutants, and nrx-1(wy778) mutants\n expressing indicated NRXN1 isoforms on food-deprived activity levels,\n measured from single animals in wells of a WorMotel behavioral setup.\n Values are the nomalized average activity per well (single animal) over 8\n hours. Normalized activity values for animals without food (normalized to\n the average of control animal activity levels for that replicate - set to\n 1). All *nrx-1(wy778)* and *nrx-1(wy778)* with *NRXN1* expression animals\n were normalized within replicates to allow for pooling of replicates\n across days and equipment setups. \\ Blank cells represent differences in\n total number of animals included across conditions/replicates. \\ Columns\n C-F represent wildtype human NRXN1 isoforms defined in the manuscript,\n whereas columns G-I represent human NRXN1 isoforms that are generated from\n the 3' deletion allele. ##### Variables: * N2 control animals (used\n for normalization to 1) * nrx-1(wy778) mutant animals alone * nrx-1(wy778)\n expressing human NRXN1 (SS39) * nrx-1(wy778) expressing human NRXN1 (SS47)\n * nrx-1(wy778) expressing human NRXN1 (SS71) * nrx-1(wy778) expressing\n human NRXN1 (SS75) * nrx-1(wy778) expressing human NRXN1 (SS73) *\n nrx-1(wy778) expressing human NRXN1 (SS77) * nrx-1(wy778) expressing human\n NRXN1 (SS84) * nrx-1(wy778) expressing human NRXN1 (SS89) #### Sheet:\n Figure 4 **Description**: NRXN1 isoforms differentially impact social\n feeding behavior. (A) In comparison to *npr-1(ad609)* controls with high\n aggregation, *npr-1(ad609);nrx-1(wy778)* controls have significantly\n reduced levels of aggregation. Values of controls and mutants alone are\n compared to *npr-1(ad609);nrx-1(wy778)*  with human NRXN1 isoform\n expression as indicated. Values are the number of animals out of 50\n animals per well that were in contact with 2 or more other animals\n (definition of social animals based on literature). \\ Blank cells\n represent differences in total number of animals included across\n conditions/replicates.\\ Columns C-F represent wildtype human NRXN1\n isoforms defined in the manuscript, whereas columns G-I represent human\n NRXN1 isoforms that are generated from the 3' deletion allele. #####\n Variables: * npr-1(ad609) social control animals * npr-1(ad609);\n nrx-1(wy778) mutant animals alone * npr-1(ad609); nrx-1(wy778) expressing\n human NRXN1 (SS39) * npr-1(ad609); nrx-1(wy778) expressing human NRXN1\n (SS47) * npr-1(ad609); nrx-1(wy778) expressing human NRXN1 (SS71) *\n npr-1(ad609); nrx-1(wy778) expressing human NRXN1 (SS75) * npr-1(ad609);\n nrx-1(wy778) expressing human NRXN1 (SS73) * npr-1(ad609); nrx-1(wy778)\n expressing human NRXN1 (SS77) * npr-1(ad609); nrx-1(wy778) expressing\n human NRXN1 (SS84) * npr-1(ad609); nrx-1(wy778) expressing human NRXN1\n (SS89) #### Sheet: Supplemental Figure 2 **Description**: Expression of\n NRXN1 isoforms does not induce aggregation behavior. In addition to\n control (N2 Bristol) and *nrx-1(wy778)*, 8 NRXN1 isoforms (4 control\n isoforms and 4 3’ deletion isoforms) were tested for aggregation behavior\n in the solitary feeding N2 Bristol strain background. Values of controls\n and mutants alone are compared to *nrx-1(wy778)*  with human NRXN1 isoform\n expression as indicated. Values are the number of animals out of 50\n animals per well that were in contact with 2 or more other animals\n (definition of social animals based on literature). Blank cells represent\n differences in total number of animals included across\n conditions/replicates. Columns C-F represent wildtype human NRXN1 isoforms\n defined in the manuscript, whereas columns G-I represent human NRXN1\n isoforms that are generated from the 3' deletion allele. #####\n Variables: * N2 control animals (used for normalization to 1) *\n nrx-1(wy778) mutant animals alone * nrx-1(wy778) expressing human NRXN1\n (SS39) * nrx-1(wy778) expressing human NRXN1 (SS47) * nrx-1(wy778)\n expressing human NRXN1 (SS71) * nrx-1(wy778) expressing human NRXN1 (SS75)\n * nrx-1(wy778) expressing human NRXN1 (SS73) * nrx-1(wy778) expressing\n human NRXN1 (SS84) * nrx-1(wy778) expressing human NRXN1 (SS89) *\n nrx-1(wy778) expressing human NRXN1 (SS77) #### Sheet: Supplemental Figure\n 3 **Description**: Relative expression levels of human *NRXN1* transgenes.\n RT-qPCR was performed on control, *nrx-1(wy778)*, and human *NRXN1*\n transgenic animals. Expression levels were normalized to *tba-1* (tubulin)\n control primers (each dot represents 1-2 technical replicate wells from 2\n independent replicates). Blank cells represent difference in 1 vs 2\n technical replicates included. Columns C-F represent wildtype human NRXN1\n isoforms defined in the manuscript, whereas columns G-I represent human\n NRXN1 isoforms that are generated from the 3' deletion allele. #####\n Variables: * N2 control animals (used for normalization to 1) *\n nrx-1(wy778) mutant animals alone * nrx-1(wy778) expressing human NRXN1\n (SS39) * nrx-1(wy778) expressing human NRXN1 (SS47) * nrx-1(wy778)\n expressing human NRXN1 (SS71) * nrx-1(wy778) expressing human NRXN1 (SS75)\n * nrx-1(wy778) expressing human NRXN1 (SS73) * nrx-1(wy778) expressing\n human NRXN1 (SS77) * nrx-1(wy778) expressing human NRXN1 (SS84) *\n nrx-1(wy778) expressing human NRXN1 (SS89)"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"National Institute of Mental Health","funderIdentifier":"https://ror.org/04xeg9z08","awardTitle":"Dissecting neural mechanisms integrating multiple inputs in C. elegans","awardNumber":"5R56MH096881-12","awardUri":"https://reporter.nih.gov/project-details/10898814"},{"funderIdentifierType":"ROR","funderName":"National Institute of General Medical Sciences","funderIdentifier":"https://ror.org/04q48ey07","awardTitle":"Molecular coordination of adhesion molecules in foraging behaviors and circuits","awardNumber":"5R35GM146782-05","awardUri":"https://reporter.nih.gov/project-details/11399840"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.2z34tmq32","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":1,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-10-06T12:04:06Z","registered":"2026-10-06T12:04:07Z","published":null,"updated":"2026-10-06T12:04:07Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.q83bk3jzc","type":"dois","attributes":{"doi":"10.5061/dryad.q83bk3jzc","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Pennsylvania","Boston University"],"name":"Glass, Benjamin","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-2288-6389"}]},{"nameType":"Personal","affiliation":["Boston University"],"name":"Davies, Sarah","nameIdentifiers":[]}],"titles":[{"title":"Data and code from: Effects of temperature on development in \u003cem\u003eOrbicella faveolata\u003c/em\u003e"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Coral reefs","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"coral larvae"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Climate change","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"development"}],"contributors":[{"nameType":"Personal","affiliation":["University of Pennsylvania","Boston University"],"name":"Glass, Benjamin","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-2288-6389"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2026-04-14T17:24:33Z","dateType":"Created"},{"date":"2026-09-28T13:21:54Z","dateType":"Submitted"},{"date":"2026-10-06T00:00:00Z","dateType":"Issued"},{"date":"2026-10-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["1787009 bytes"],"formats":[],"version":"5","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This repository contains data associated with experiments investigating\n the effects of various seawater temperatures on developmental outcomes in\n the reef-building coral Orbicella faveolata. Larvae of O.\n faveolata were exposed to cool (20°C) and warm (30°C) conditions,\n and outcomes including survival, settlement (i.e., the transition from\n pelagic larva to benthic juvenile), and gene expression under both\n treatments were compared to larvae from the same cohort reared at ambient\n (27°C) temperature. Files in this repository contain data pertaining to a\n number of metrics quantified in larvae including survival and settlement\n rates, with each file containing relevant metadata such as the\n experimental treatment group from which measurements originate. These data\n are openly available for reuse without restriction, and the authors are\n not aware of any legal or ethical considerations."},{"descriptionType":"TechnicalInfo","description":"# Data and code from: Effects of temperature on development in *Orbicella\n faveolata* Repository contains data associated with experiments\n investigating the effects of various seawater temperatures on\n developmental outcomes in the reef-building coral *Orbicella faveolata*.\n The overall aim of this study was to determine how exposure to abnormally\n cool or warm seawater temperatures influences outcomes including survival\n and settlement in *O. faveolata*. To achieve this, larvae were exposed to\n cool, ambient, or warm conditions, and various metrics were quantified in\n addition to sampling of larvae for analysis of gene expression via RNA\n sequencing. ## Data and file structure ### File 1: Survival_data.csv\n **Description**: This file is for data pertaining to the survival of O.\n faveolata larvae exposed to different temperatures over time, which was\n determined by counting under a light microscope. Survival was compared\n between temperature treatment as well as over time. ##### Variables: *\n Temperature_C: temperature in °C at which larvae were cultured * Group:\n unique identifier for groups of animals in the experiment (i.e.,\n biological replicates) * Time_h: time in hours after which survival was\n determined * Animals_original: theoretical maximum number of animals that\n could have been surviving at a given time * Animals_surviving: actual\n number of animals surviving at a given time * Survival_percent:\n Animals_surviving/Animals_original*100, rounded to the nearest integer ###\n File 2: Settlement_data.csv **Description: **This file\n is for data pertaining to the settlement of O. faveolata exposed to\n different temperatures over time, which was determined by counting under a\n light microscope. Settlement refers to the transition from the motile\n planula stage to the benthic juvenile, and was determined via\n morphological observations. Settlement rates were compared between\n temperature treatments. ##### Variables: * Temperature_C: temperature in\n °C at which larvae were cultured * Group: unique identifier for groups of\n animals in the experiment (i.e., biological replicates) *\n Animals_original: theoretical maximum number of animals that could have\n been alive and settled at a given time * Animals_settled: actual number of\n animals settled at a given time * Settled_percent:\n Animals_settled/Animals_original100, rounded to the nearest integer *\n Animals_alive: total number of surviving animals (unsettled larvae and\n settled juveniles) * Settlement_capacity:\n Animals_settled/Animals_alive100, rounded to the nearest integer ### File\n 3: Count_table.txt **Description: **This file is a table\n containing read counts for each RNA sequencing sample (columns) across all\n genes (rows). Columns are named by the temperature treatment (20, 27, or\n 30) and replicate (A–E), with full metadata present in File 4\n (Sample_list.csv). ### File 4: Sample_list.csv\n **Description: **This file contains metadata for samples\n processed for RNA sequencing. ##### Variables: * Sample_name: unique\n identifier for name of RNAseq samples; this column matches the header of\n File 3 (Count_table.txt) * Temperature_C: temperature in °C at which\n larvae were cultured * Group: unique identifier for groups of animals in\n the experiment (i.e., biological replicates) ## Sharing/Access information\n Links to other publicly accessible locations of the data: * NA Data was\n derived from the following sources: * NA ## Code/Software ### File 1:\n Ofav_larvae_hot_cold_code.Rmd **Description: **This file\n contains code in R markdown format for producing analyses and\n visualizations of the data. Running the script will require downloading\n gene annotations for the O. faveolata genome from Young et al. (2024) BMC\n Genomics. Save the file as \"Annotations.txt\" and place the file\n in the same directory as the R script, then the script can be run in full."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Boston University","funderIdentifier":"https://ror.org/05qwgg493","awardTitle":"Startup to S.W.D."},{"funderIdentifierType":"ROR","funderName":"U.S. National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62","awardTitle":"Transcription Factors in Cnidarian Immunity, Symbiosis, and Bleaching","awardNumber":"1937650"},{"funderIdentifierType":"ROR","funderName":"U.S. National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62","awardTitle":"\n        Postdoctoral Fellowship: OCE-PRF: Effects of dual anthropogenic\n        stressors across life history in a reef-building coral\n      ","awardNumber":"2506815"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.q83bk3jzc","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-10-06T11:19:11Z","registered":"2026-10-06T11:19:13Z","published":null,"updated":"2026-10-06T11:19:13Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.69p8cz9k6","type":"dois","attributes":{"doi":"10.5061/dryad.69p8cz9k6","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["South Australian Museum"],"name":"Leijs, Remko","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6909-5659"}]},{"nameType":"Personal","affiliation":["Queensland Museum"],"name":"King, Judith","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Adelaide University"],"name":"Hogendoorn, Katja","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4942-8062"}]}],"titles":[{"title":"A revision of the Australian leafcutter bees, previously placed in \u003cem\u003eMegachile\u003c/em\u003e (\u003cem\u003eEutricharaea\u003c/em\u003e) (Hymenoptera: Megachilidae), with descriptions of seven new species"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"subject":"taxonomic revision"},{"subject":"Megachile"},{"subject":"Eutricharaea"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Australia","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[{"nameType":"Personal","affiliation":["South Australian Museum"],"name":"Leijs, Remko","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6909-5659"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2026-08-05T05:12:42Z","dateType":"Created"},{"date":"2026-08-20T05:54:26Z","dateType":"Submitted"},{"date":"2026-09-07T00:00:00Z","dateType":"Issued"},{"date":"2026-09-07T00:00:00Z","dateType":"Available"},{"date":"2026-10-06T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["291852 bytes"],"formats":[],"version":"6","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"The species in the subgenus Eutricharaea Thompson, 1872 (genus Megachile)\n construct brood cells from pieces of leaves or petals cut from a diverse\n array of plants. Commonly bees in this group are called ’leafcutter bees’.\n The Australian species, as currently placed in Eutricharaea, are revised\n and redescribed, and seven additional species are described. New\n combinations are proposed for two species: Chalicodoma (Hackeriapis)\n atrella Cockerell, Michener 1965, comb. nov. = Megachile (Eutricharaea)\n atrella Cockerell 1906; Megachile (Eutricharaea) hampsoni Cockerell 1906.\n comb. nov.= Megachile (unplaced) hampsoni Cockerell 1906. The following 13\n names were synonymized: Megachile adelaidae Cockerell 1910a, syn. nov. =\n Megachile (Eutricharaea) atrella Cockerell 1906; Megachile phenacopyga\n Cockerell 1910a, syn. nov. = Megachile (Eutricharaea) chrysopyga Smith\n 1853; Megachile australasiae Dalla Torre 1896, syn. nov., and Megachile\n cygnorum Cockerell 1906. syn. nov. = Megachile (Eutricharaea) macularis\n Dalla Torre 1896; Megachile austeni Cockerell 1906, syn. nov., and\n Megachile rowlandi Cockerell 1930a, syn. nov. = Megachile (Eutricharaea)\n pictiventris Smith 1879; Megachile ignescens Cockerell 1929, syn. nov. =\n Megachile (Eutricharaea) rhodogastra Cockerell, 1910; Megachile cetera\n Cockerell 1912, syn. nov., and Megachile subserricauda Rayment 1939, syn.\n nov. = Megachile (Eutricharaea) serricauda Cockerell 1910c; Megachile\n ciliatipes Cockerell 1921, syn. nov., Megachile detersa Cockerell 1910b,\n syn. nov., and Megachile quinquelineata Cockerell 1906 = Megachile\n (Eutricharaea) simplex Smith 1853. We now recognize 33 species.\n Phylogenetic analyses of DNA barcode data indicate that the Australian\n leafcutter bees, as previously placed in Eutricharaea, are not\n monophyletic and are associated with other Megachile subgenera that also\n occur in South-East Asia. All descriptions in this paper are accompanied\n by high resolution diagnostic images and distribution maps. Data on flower\n visitation and phenology are given. Dichotomous keys to both sexes of the\n species are provided."},{"descriptionType":"TechnicalInfo","description":"# A revision of the Australian leafcutter bees, previously placed in\n *Megachile* (*Eutricharaea*) (Hymenoptera: Megachilidae), with\n descriptions of seven new species Dataset DOI:\n [10.5061/dryad.69p8cz9k6](https://doi.org/10.5061/dryad.69p8cz9k6) ##\n Description of the data and file structure File\n name: examined_specimens_Eutricharaea_Corrected_for_Dryad.xlsx\n and examined_specimens_Eutricharaea_Corrected_for_Dryad.csv Description of\n Examined Specimens column headers: * Collection specimen repository (State\n Museum): For acronyms, see paper [text] * reg.no.: Repository-unique\n specimen registration number [text] * genus: *Megachile* (Hymenoptera)\n [text] * subgenus: *Megachile* (subgenus *Eutricharaea*) [text] * species:\n Known or newly described species name [text] * sex: Male or female; also\n indicated if specimen is a holotype or allotype [text] * locality:\n Locality description [text] * state: Acronym for Australian state [text] *\n date: Collecting date [date dd/mm/yyyy] * dec lat: Latitude [in decimal\n degrees] * dec long: Longitude [in decimal degrees] * collectors: Specimen\n collector(s) [text] * host plant: Host plant genus or species [text] *\n host plant family: Host plant family [text] empty cells n/a indicate data\n unavailable *Specimens examined* This study is based on examination of\n specimens from the following institutions. Their acronyms are used in the\n database of the examined specimens, which is available as supplementary\n information associated with this paper: * AM                  Australian\n Museum, Sydney * ANIC              Australian National Insect Collection,\n Canberra * BMNH            The Natural History Museum, London, United\n Kingdom * HU                  Institute für Spezielle Zoology und\n Zoologisches Museum der Humboldt Universität, Berlin, Germany *\n BPBM             Bernice Pauahi Bishop Museum, Honolulu, USA *\n OUM               Hope department of Entomology, Oxford University, United\n Kingdom * MV                  Museum of Victoria Entomology, Melbourne *\n QM                  Queensland Museum, Brisbane * SAMA             South\n Australian Museum, Adelaide * UQIC              University of Queensland\n Insect Collection, Brisbane, at QM * WADA            Department of Primary\n Industries, Perth, Western Australia * WAM              Western Australia\n Museum, Perth * WINC             Waite Insect Collection, Adelaide"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Australian Biological Resources Study","funderIdentifier":"https://ror.org/00289aa83","awardTitle":"Bush Blitz Tactical Taxonomy Grant from the Australian Government"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.69p8cz9k6","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":35,"downloadCount":11,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-09-07T10:27:41Z","registered":"2026-09-07T10:27:42Z","published":null,"updated":"2026-10-06T10:48:58Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.7pvmcvf6n","type":"dois","attributes":{"doi":"10.5061/dryad.7pvmcvf6n","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Hanyang University"],"name":"Kim, Yong-Jun","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0009-6826-0259"}]},{"nameType":"Personal","affiliation":["Ewha Womans University"],"name":"Yeh, Sang-Wook","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4549-1686"}]},{"nameType":"Personal","affiliation":["Ocean University of China","Laoshan Laboratory","Shandong University"],"name":"Wang, Guojian","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8881-7394"}]},{"nameType":"Personal","affiliation":["CSIRO Environment"],"name":"N.G., Benjamin","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4458-4592"}]}],"titles":[{"title":"Nonlinear increase of compound drought-heatwave events since the early 2000s"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Earth and related environmental sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Climate change","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Anthropogenic climate change","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Atmospheric science","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[{"nameType":"Personal","affiliation":["Hanyang University"],"name":"Kim, Yong-Jun","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0009-6826-0259"}],"contributorType":"ProjectLeader"},{"nameType":"Personal","affiliation":["Hanyang University"],"name":"Kim, Yong-Jun","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0009-6826-0259"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["Hanyang University"],"name":"Kim, Yong-Jun","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0009-6826-0259"}],"contributorType":"DataCollector"},{"nameType":"Personal","affiliation":["Hanyang University"],"name":"Kim, Yong-Jun","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0009-6826-0259"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["Hanyang University"],"name":"Kim, Yong-Jun","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0009-6826-0259"}],"contributorType":"DataCurator"},{"nameType":"Personal","affiliation":["Hanyang University"],"name":"Kim, Yong-Jun","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0009-6826-0259"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["Hanyang University"],"name":"Kim, Yong-Jun","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0009-6826-0259"}],"contributorType":"Editor"},{"nameType":"Personal","affiliation":["Ewha Womans University"],"name":"Yeh, Sang-Wook","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4549-1686"}],"contributorType":"ProjectLeader"},{"nameType":"Personal","affiliation":["Ewha Womans University"],"name":"Yeh, Sang-Wook","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4549-1686"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["Ewha Womans University"],"name":"Yeh, Sang-Wook","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4549-1686"}],"contributorType":"Supervisor"},{"nameType":"Personal","affiliation":["Ewha Womans University"],"name":"Yeh, Sang-Wook","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4549-1686"}],"contributorType":"Editor"},{"nameType":"Personal","affiliation":["Ewha Womans University"],"name":"Yeh, Sang-Wook","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4549-1686"}],"contributorType":"DataCollector"},{"nameType":"Personal","affiliation":["Ewha Womans University"],"name":"Yeh, Sang-Wook","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4549-1686"}],"contributorType":"ProjectManager"},{"nameType":"Personal","affiliation":["Ocean University of China","Laoshan Laboratory","Shandong University"],"name":"Wang, Guojian","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8881-7394"}],"contributorType":"ProjectLeader"},{"nameType":"Personal","affiliation":["Ocean University of China","Laoshan Laboratory","Shandong University"],"name":"Wang, Guojian","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8881-7394"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["Ocean University of China","Laoshan Laboratory","Shandong University"],"name":"Wang, Guojian","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8881-7394"}],"contributorType":"Supervisor"},{"nameType":"Personal","affiliation":["Ocean University of China","Laoshan Laboratory","Shandong University"],"name":"Wang, Guojian","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8881-7394"}],"contributorType":"Editor"},{"nameType":"Personal","affiliation":["Ocean University of China","Laoshan Laboratory","Shandong University"],"name":"Wang, Guojian","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8881-7394"}],"contributorType":"DataCollector"},{"nameType":"Personal","affiliation":["CSIRO Environment"],"name":"N.G., Benjamin","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4458-4592"}],"contributorType":"Editor"},{"nameType":"Personal","affiliation":["Hanyang University"],"name":"Kim, Yong-Jun","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0009-6826-0259"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":["Ewha Womans University"],"name":"Yeh, Sang-Wook","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4549-1686"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":["Ocean University of China","Laoshan Laboratory","Shandong University"],"name":"Wang, Guojian","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8881-7394"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":["CSIRO Environment"],"name":"N.G., Benjamin","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4458-4592"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2025-10-10T01:43:10Z","dateType":"Created"},{"date":"2025-10-16T06:52:01Z","dateType":"Submitted"},{"date":"2026-01-16T00:00:00Z","dateType":"Issued"},{"date":"2026-01-16T00:00:00Z","dateType":"Available"},{"date":"2026-10-06T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1126/sciadv.aea3038","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.23101781","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["636574107 bytes"],"formats":[],"version":"16","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Compound drought-heatwave events (CDHEs) have substantially increased\n since the early 2000s, posing elevated risks to socio-ecosystems. However,\n the physical characteristics of drought- and heatwave-leading CDHEs and\n their relative contributions to the overall increase remain unexplored.\n Using a multihazard pair generation algorithm with daily reanalysis data,\n we show that both drought- and heatwave-leading CDHEs increased\n nonlinearly over the past two decades relative to earlier decades. This\n pattern is evident at the global scale but also shows considerable\n regional variation. Focusing on heatwave-leading CDHEs which are\n associated with stronger intensification of the subsequent hazard than\n drought-leading events, we find that the nonlinear amplification of\n land-atmosphere coupling since the late 1990s has not only induced the\n emergence of statistically significant positive sensitivities in\n previously unresponsive regions, but also markedly enhanced sensitivities\n in high-occurrence regions. These findings highlight the importance of\n considering the disproportionate regional risks associated with\n heatwave-leading CDHEs when adapting to climate change."},{"descriptionType":"TechnicalInfo","description":"# Nonlinear increase of compound drought-heatwave events since the early\n 2000s Dataset DOI:\n [10.5061/dryad.7pvmcvf6n](https://doi.org/10.5061/dryad.7pvmcvf6n) ##\n Description of the data and file structure This repository contains the\n corrected datasets and scripts required to reproduce the main figures\n presented in the article, **“Nonlinear increase of compound\n drought–heatwave events since the early 2000s.”** The dataset has been\n updated to correct an error identified in the previously published\n version. The corrected data were used to repeat the affected analyses\n associated with the correction and erratum of the article. ### Files and\n variables #### File: preprocessed_data__(updated_26.10.02).zip\n **Description:** This zip file contains the datasets required to reproduce\n the main figures. #### File: hw_dr_pair_num_rev.nc **Description:** This\n file includes the detected heatwave-leading CDHE information over study\n period. #### File: dr_hw_pair_num_rev.nc **Description:** This file\n includes the detected drought-leading CDHE information over study period.\n #### File: hw_dr_pi_rev.nc **Description:** This file includes the\n detrended daily land-atmosphere coupling strength during heatwave-leading\n CDHEs #### File: dr_hw_pi_rev.nc **Description:** This file includes the\n detrended daily land-atmosphere coupling strength during drought-leading\n CDHEs #### File: pi_detrend_all_warm_ann_from_daily.nc\n **Description:** This file includes the calculated land-atmosphere\n coupling strength (warm season averaged for each year) #### File:\n pi_detrend_all_warm_daily.nc **Description:** This file includes the\n calculated daily land-atmosphere coupling strength. #### File:\n t2m_ann_mean_1980_2023.nc **Description:** This file includes the annual\n mean 2m temperature over the study period. (Derived from ERA5-Land\n dataset) #### File: IPCC_region_id_hexagon.csv **Description:** This file\n includes the hexagon information for each IPCC subregion. This was used\n for generating the regional hexagon plot. #### File: pi_slope_rev.csv\n **Description:** This file includes the calculated regional sensitivity\n value of heatwave- and drought-leading CDHEs to land-atmosphere coupling.\n #### File: pi_slope_change_rev.csv **Description:** This file includes the\n change of calculated regional sensitivity value of heatwave- and\n drought-leading CDHEs to land-atmosphere coupling. #### File:\n skewness_alpha_11y_m_affected_area_rev.csv **Description:** This file\n includes the nonlinearity index of each subregion. (heatwave-leading CDHE\n case) #### File: t2m_region_11y_m_affected_area_rev.csv\n **Description:** This file includes the regional 11-year running window\n averaged temperature. #### File: hw_dr_region_11y_m_affected_area_rev.csv\n **Description:** This file includes the regional 11-year running window\n averaged affected area of heatwave-leading CDHEs. #### File:\n temp_thres_11yr_m_affected_area_rev.csv **Description:** This file\n includes the regional temperature threshold which detected from the\n Pettitt's test. #### File: thres_year_11y_m_affected_area_rev.csv\n **Description:** This file includes the regional threshold year which\n detected from the Pettitt's test. #### Folder:\n IPCC_subregion_masked_file **Description:** This folder contains files\n listed below. * subregion_masked_file subfolder : contains each IPCC\n subregion's masked file. * subregion_shapefile subfolder : contains\n each IPCC subregion's shapefile. #### ## Code/software We used Python\n version 3.10.16\n ([https://www.python.org/downloads/release/python-31016/](https://www.python.org/downloads/release/python-31016/)) for all data processing, analysis, and graphical representation. #### File: 1_poly_hw_1980_2023_p90.py **Description:** This file contains the code used to generate heatwave polygons from the daily heatwave mask. #### File: 2_hw_end_time_1980_2023_p90.py **Description:** This file contains the code used to identify heatwave events lasting at least five consecutive days and determine their start and end dates. #### File: 3_MYRIAD-HESA_hw_p90_5d_SM_dr_p10_5d_shapely_compat.py **Description:** This file contains the code used to identify compound drought–heatwave events based on the spatial and temporal overlap between heatwave and drought events. #### File: 4_Counting_each_type_and_save.py **Description:** This file contains the code used to classify compound drought–heatwave events into heatwave-leading and drought-leading events and calculate their annual gridded occurrence. #### File: Figure_1.py **Description:** This file contains the code required to reproduce Figure 1. #### File: Figure_2.py **Description:** This file contains the code required to reproduce Figure 2. #### File: Figure_3.py **Description:** This file contains the code required to reproduce Figure 3. #### File: Figure_4.py **Description:** This file contains the code required to reproduce Figure 4. #### File: Figure_5.py **Description:** This file contains the code required to reproduce Figure 5. ## Access information Other publicly accessible locations of the data: * ERA5-Land and ERA5 dataset can be downloaded from Copernicus Climate Data Store (CDS) at [https://cds.climate.copernicus.eu/datasets](https://cds.climate.copernicus.eu/datasets). * GLEAM 4.2a dataset is freely available online at [https://www.gleam.eu/#downloads](https://www.gleam.eu/#downloads)."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Korea Meteorological Administration","funderIdentifier":"https://ror.org/04nrmrg07","awardNumber":"RS-2025-02313090"},{"funderIdentifierType":"ROR","funderName":"Korea Meteorological Administration","funderIdentifier":"https://ror.org/04nrmrg07","awardNumber":"RS-2024-00403698"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.7pvmcvf6n","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":267,"downloadCount":84,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-01-16T12:25:03Z","registered":"2026-01-16T12:25:04Z","published":null,"updated":"2026-10-06T08:00:08Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.h44j0zq16","type":"dois","attributes":{"doi":"10.5061/dryad.h44j0zq16","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Washington"],"name":"Ellingboe, Ethan","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0007-5856-0886"}]},{"nameType":"Personal","affiliation":["University of Washington"],"name":"Huang, Monica","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Washington"],"name":"Sultana Priyota, Azeezah","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Washington"],"name":"Simonen, Kathrina","nameIdentifiers":[]}],"titles":[{"title":"Data from: A proxy method to bridge LCA data gaps using automated material classification and probabilistic under-specification"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"subject":"life cycle assessment"},{"subject":"Data Gap"},{"subject":"proxy data"},{"subject":"chemical classification"},{"subject":"probabilistic under-specification"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Earth and related environmental sciences","subjectScheme":"fos"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Chemical sciences","subjectScheme":"fos"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Materials engineering","subjectScheme":"fos"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Environmental engineering","subjectScheme":"fos"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Engineering and technology","subjectScheme":"fos"}],"contributors":[{"nameType":"Personal","affiliation":["University of Washington"],"name":"Ellingboe, Ethan","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0007-5856-0886"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2026-01-17T01:13:38Z","dateType":"Created"},{"date":"2026-01-20T18:09:24Z","dateType":"Submitted"},{"date":"2026-04-22T00:00:00Z","dateType":"Issued"},{"date":"2026-04-22T00:00:00Z","dateType":"Available"},{"date":"2026-10-06T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsSourceOf","relatedIdentifier":"10.6084/m9.figshare.30996661","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1021/acs.est.5c12282","relatedIdentifierType":"DOI"},{"relationType":"IsSourceOf","relatedIdentifier":"10.5281/zenodo.19694242","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["124875 bytes"],"formats":[],"version":"10","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Life cycle assessments (LCAs) are essential for understanding the\n environmental impacts of material production. However, gaps in life cycle\n inventory (LCI) data for material and chemical inputs present a key\n challenge for LCA practitioners, especially in the early design stages.\n Strategies for filling in these gaps require additional time and\n expertise, which can hinder the LCA’s completion. This dataset is the\n result of classifying existing LCI data for chemical and material\n production processes by the product’s chemical structure, then evaluating\n environmental impact distributions of all chemicals and materials within a\n chemical group. LCI data for this dataset was collected from the Federal\n LCA Commons, and the open-source web tool ClassyFire was used to classify\n products by their chemical structure into the ChemOnt chemical taxonomy.\n This results in a dataset of environmental impact distributions based on\n existing LCI data and chemical structure, appropriate to be used as proxy\n environmental impact data for chemicals or specialty materials when data\n specific to that product does not exist. This dataset includes descriptive\n statistics, specifically the minimum, 20th percentile, median, 80th\n percentile, and maximum environmental impact for each environmental impact\n and chemical entity group. The environmental impacts evaluated are Global\n Warming Potential (GWP, evaluated using IPCC AR5 characterization\n factors), Acidification Potential (AP), Eutrophication Potential (EP),\n Ozone Depletion Potential (ODP), and Photochemical Oxidant Creation\n Potential (POCP). Input materials with data gaps may be classified into\n the same chemical taxonomy, where proxy environmental impact values can be\n selected from the available distributions to quickly fill in any data\n gaps. For an example of how this data may be used, please see the\n associated spreadsheet tool in the Supplemental Information section. The\n methods used to create dataset were applied to classify material\n production processes available in the Federal LCA Commons in this dataset,\n however could be similarly applied to other LCA databases. "},{"descriptionType":"Methods","description":"Evaluation of life cycle impact assessment (LCIA) results from\n life cycle inventory (LCI) data was conducted using the open-source LCA\n software OpenLCA (version 2.4.0). To demonstrate the methods and framework\n described in the associated journal article, the five core impact\n assessment categories required by ISO 21930:2017 were selected for\n evaluation: 100-year global warming potential (GWP), acidification\n potential (AP), eutrophication potential (EP), ozone depletion potential\n (ODP), and photochemical oxidant creation potential (POCP). The US EPA\n ISO21930-LCIA-US (v0.1) LCIA method was used to evaluate these impact\n categories for product systems generated from Federal LCA Commons\n processes using OpenLCA. Please see the associated journal article for\n further details of the calculation and classification methods.\n This dataset was developed as part of the Parametric Open Data\n for Life Cycle Assessment Project (POD|LCA) Project which contains a suite\n of open-source methods, models, data, and a variety of tools for\n screening-level (early-stage) building sector life cycle assessments\n (LCAs). This dataset can be used to fill data gaps in LCA studies and is\n being integrated into tools developed by the POD|LCA project. This results\n in a dataset of proxy environmental impact data appropriate to be used to\n fill data gaps for chemicals or specialty materials when data specific to\n that product does not exist. Please refer to the corresponding article for\n additional details. For more information on the POD|LCA project, please\n visit https://podlca.uw.edu/ and https://www.lifecyclelab.org/project/pod-lca/."},{"descriptionType":"TechnicalInfo","description":"# Data from: A proxy method to bridge LCA data gaps using automated\n material classification and probabilistic under-specification Dataset DOI:\n [10.5061/dryad.h44j0zq16](https://doi.org/10.5061/dryad.h44j0zq16) ##\n Description of the data and file structure Supporting information for *A\n proxy method to bridge LCA data gaps using automated material\n classification and probabilistic under-specification.* This dataset\n includes: 1. Environmental impact results for chemical entities with\n process life cycle inventory (LCI) data available in the Federal LCA\n Commons, as well as the InChI key corresponding to the product chemical\n entity from each process, and ClassyFire classification results for each\n InChI key.(\"Process_environmental_impacts_FLCAC.csv\") 2. Proxy\n underspecified environmental impact descriptive statistics for process\n environmental impacts grouped by ClassyFire classification. For each\n ClassyFire group at each taxonomic level having at least one constituent\n process, the minimum, 20th percentile, median, 80th percentile, and\n maximum environmental impact values are provided. The number of Federal\n LCA Commons processes with products classified into each group is also\n provided. (\"Proxy_environmental_impacts_FLCAC.csv\")\n Additionally, a spreadsheet tool containing formulas for automatic InChI\n key search by chemical name and lookup of proxy environmental impact\n distributions by chemical class corresponding to the dataset files\n described above has been uploaded for access on Zenodo. Evaluation of life\n cycle impact assessment (LCIA) results from life cycle inventory (LCI)\n data was conducted using the open-source LCA software OpenLCA (version\n 2.4.0). To demonstrate the methods and framework described in the\n associated journal article, the five core impact assessment categories\n required by ISO 21930:2017 were selected for evaluation: 100-year global\n warming potential (GWP), acidification potential (AP), eutrophication\n potential (EP), ozone depletion potential (ODP), and photochemical oxidant\n creation potential (POCP). The US EPA ISO21930-LCIA-US (v0.1) LCIA method\n was used to evaluate these impact categories for product systems generated\n from Federal LCA Commons processes using OpenLCA. Please see the\n associated journal article for further details of the calculation and\n classification methods. ### Files and variables #### File:\n Process_environmental_impacts_FLCAC.csv **Description:** This file\n contains environmental impact results for chemical entities with process\n life cycle inventory (LCI) data available in the Federal LCA Commons, as\n well as the InChI key corresponding to the product chemical entity from\n each process, and ClassyFire classification results for each InChI key.\n ##### Variables * Process Name: Name of process dataset from the Federal\n LCA Commons * UUID: A Universally Unique Identifier code corresponding to\n the process dataset from the Federal LCA Commons * Process Type: Either a\n unit process (requires product system linking) or an LCI result (already\n aggregated for the full product system). * Amount: Quantity of the product\n output from the process under study for which environmental impact results\n were evaluated. * Unit: Unit corresponding to the amount. * GWP [kg CO2\n eq]: Global warming potential, in units of kg CO~2~ eq. * AP [kg SO2\n eq]: Acidification potential, in units of kg SO~2~ eq. * EP [kg N\n eq]: Eutrophication potential, in units of kg N eq. * ODP [kg CFC-11\n eq]: Ozone depletion potential, in units of kg CFC-11 eq. * POCP [kg O3\n eq]: Photochemical oxidant creation potential, in units of kg O~3~ eq. *\n NAICS Category: North American Industrial Classification System category *\n NAICS Subcategory: North American Industrial Classification System\n subcategory * Compound/Substance Name: Chemical entity name of the product\n compound or substance * InChIKey: International Chemical Identifier Key *\n Kingdom: ClassyFire Kingdom-level classification  * Superclass: ClassyFire\n Superclass-level classification  * Class: ClassyFire Class-level\n classification  * Subclass: ClassyFire Subclass-level classification  *\n Parent level 1:  ClassyFire Parent level 1 classification  * Parent level\n 2: ClassyFire Parent level 2 classification * Parent level 3: ClassyFire\n Parent level 3 classification #### File:\n Proxy_environmental_impacts_FLCAC.csv **Description:** This file contains\n proxy underspecified environmental impact descriptive statistics for\n process environmental impacts grouped by ClassyFire classification. For\n each ClassyFire group at each taxonomic level having at least one\n constituent process, the minimum, 20th percentile, median, 80th\n percentile, and maximum environmental impact values are provided. The\n number of Federal LCA Commons processes with products classified into each\n group is also provided. ##### Variables * ChemOnt Taxonomic Level: Level\n of group in ChemOnt (ClassyFire) taxonomy: Kingdom, Superclass, Class, or\n Subclass. * Category Name: ClassyFire chemical entity group * Processes\n Classified: Number of Federal LCA Commons processes with product chemical\n entities classified into this group. * GWP Min. [kgCO2eq/kg]: Minimum GWP\n of product chemical entities classified into this group. * GWP 20th%\n [kgCO2eq/kg]: 20th percentile GWP of product chemical entities classified\n into this group. * GWP Q1 [kgCO2eq/kg]: Quartile 1 (25th percentile) GWP\n of product chemical entities classified into this group. * GWP Median\n [kgCO2eq/kg]: Median GWP of product chemical entities classified into this\n group. * GWP Q3 [kgCO2eq/kg]: Quartile 3 (75th percentile) GWP of product\n chemical entities classified into this group. * GWP 80th% [kgCO2eq/kg]:\n 80th percentile GWP of product chemical entities classified into this\n group. * GWP Max. [kgCO2eq/kg]: Maximum GWP of product chemical entities\n classified into this group. * AP Min. [kgSO2eq/kg]: Minimum AP of product\n chemical entities classified into this group. * AP 20th%\n [kgSO2eq/kg]: 20th percentile AP of product chemical entities classified\n into this group. * AP Q1 [kgSO2eq/kg]: Quartile 1 (25th percentile) AP\n of product chemical entities classified into this group. * AP Median\n [kgSO2eq/kg]: Median AP of product chemical entities classified into this\n group. * AP Q3 [kgSO2eq/kg]: Quartile 3 (75th percentile) AP of product\n chemical entities classified into this group. * AP 80th% [kgSO2eq/kg]:\n 80th percentile AP of product chemical entities classified into this\n group. * AP Max. [kgSO2eq/kg]: Maximum AP of product chemical entities\n classified into this group. * EP Min. [kgNeq/kg]: Minimum EP of product\n chemical entities classified into this group. * EP 20th% [kgNeq/kg]: 20th\n percentile EP of product chemical entities classified into this group. *\n EP Q1 [kgNeq/kg]: Quartile 1 (25th percentile) EP of product chemical\n entities classified into this group. * EP Median [kgNeq/kg]: Median EP\n of product chemical entities classified into this group. * EP Q3\n [kgNeq/kg]: Quartile 3 (75th percentile) EP of product chemical entities\n classified into this group. * EP 80th% [kgNeq/kg]: 80th percentile EP\n of product chemical entities classified into this group. * EP Max.\n [kgNeq/kg]: Maximum EP of product chemical entities classified into this\n group. * ODP Min. [kgCFC-11eq/kg]: Minimum ODP of product chemical\n entities classified into this group. * ODP 20th% [kgCFC-11eq/kg]: 20th\n percentile ODP of product chemical entities classified into this group. *\n ODP Q1 [kgCFC-11eq/kg]: Quartile 1 (25th percentile) ODP of product\n chemical entities classified into this group. * ODP Median\n [kgCFC-11eq/kg]: Median ODP of product chemical entities classified into\n this group. * ODP Q3 [kgCFC-11eq/kg]: Quartile 3 (75th percentile) ODP\n of product chemical entities classified into this group. * ODP 80th%\n [kgCFC-11eq/kg]: 80th percentile ODP of product chemical entities\n classified into this group. * ODP Max. [kgCFC-11eq/kg]: Maximum ODP\n of product chemical entities classified into this group. * POCP Min.\n [kgO3eq/kg]: Minimum POCP of product chemical entities classified into\n this group. * POCP 20th% [kgO3eq/kg]: 20th percentile POCP of product\n chemical entities classified into this group. * POCP Q1 [kgO3eq/kg]:\n Quartile 1 (25th percentile) POCP of product chemical entities classified\n into this group. * POCP Median [kgO3eq/kg]: Median POCP of product\n chemical entities classified into this group. * POCP Q3\n [kgO3eq/kg]: Quartile 3 (75th percentile) POCP of product chemical\n entities classified into this group. * POCP 80th% [kgO3eq/kg]: 80th\n percentile POCP of product chemical entities classified into this group. *\n POCP Max. [kgO3eq/kg]: Maximum POCP of product chemical entities\n classified into this group. ## Code/software No custom code or software is\n required to access or use the data files in this repository. The included\n .csv data files can be opened using standard spreadsheet software or\n imported into programming environments. The supplementary Microsoft Excel\n (.xlsx) spreadsheet tool hosted on Zenodo requires enabling Excel's\n built-in WEBSERVICE functions to retrieve InChI key data via the Pubchem\n PUG-REST API\n ([https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest](https://pubchem.ncbi.nlm.nih.gov/docs/pug-rest)) and the ClassyFire API ([http://classyfire.wishartlab.com/access)](http://classyfire.wishartlab.com/access). ## Access information Other publicly accessible locations of the data: * This data is also accessible via the associated journal article. Data was derived from the following sources: * Software: OpenLCA (version 2.4.0) LCI Data: * National Renewable Energy Laboratory/USLCI Database Public (version 1.2025-06.0) [https://www.lcacommons.gov/lca-collaboration/National_Renewable_Energy_Laboratory/USLCI_Database_Public/datasets](https://www.lcacommons.gov/lca-collaboration/National_Renewable_Energy_Laboratory/USLCI_Database_Public/datasets)? commitId=c46436821cedb106a7eebd8220117d00b6590a43 [Accessed: 27 February 2025] - US Forest Service Forest Products Laboratory/Forestry and Forest Products (version 1.2025-08.0) [https://www.lcacommons.gov/lca-collaboration/US_Forest_Service_Forest_Products_Lab/Woody_biomass/datasets\\[Accessed](https://www.lcacommons.gov/lca-collaboration/US_Forest_Service_Forest_Products_Lab/Woody_biomass/datasets[Accessed): 21 July 2025] - NIST / Construction Materials (version 1.2025-07.0) [https://www.lcacommons.gov/lca-collaboration/NIST/construction_materials/datasets](https://www.lcacommons.gov/lca-collaboration/NIST/construction_materials/datasets) [Accessed July 21 2025] * US Environmental Protection Agency / USEEIOv2.0 (version 1.2022-06.0) [https://www.lcacommons.gov/lca-](https://www.lcacommons.gov/lca-) collaboration/US_Environmental_Protection_Agency/USEEIO_v2/datasets [Accessed August 22 2025] LCIA Methods: * US EPA / ISO21930-LCIA-US (version 1.2024-6.0) [https://www.lcacommons.gov/lca-collaboration/US_Environmental_Protection_Agency/construction_epd_indicators/datasets](https://www.lcacommons.gov/lca-collaboration/US_Environmental_Protection_Agency/construction_epd_indicators/datasets) [Accessed July 21 2025]          Chemical entity classification: * Djoumbou Feunang Y, Eisner R, Knox C, Chepelev L, Hastings J, Owen G, Fahy E, Steinbeck C, Subramanian S, Bolton E, Greiner R, and Wishart DS. ClassyFire: Automated Chemical Classification With A Comprehensive, Computable Taxonomy. Journal of Cheminformatics, 2016, 8:61.\\ DOI: [10.1186/s13321-016-0174-y](http://jcheminf.springeropen.com/articles/10.1186/s13321-016-0174-y) ## Acknowledgment This material is based on work developed as part of the Parametric Open Data for Life Cycle Assessment (POD|LCA) project supported by the Advanced Research Projects Agency-Energy (ARPA-E) in the U.S. Department of Energy, under award number DE-AR0001624 as part of the program under program number DE-FOA-0001953 (Topic V: Life Cycle Assessment for Carbon Negative Buildings). The views and opinions of the authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof.  Team members who contributed to this work include Ethan Ellingboe, Monica Huang, Azeezah Sultana Priyota, and Kathrina Simonen. "}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Advanced Research Projects Agency - Energy","funderIdentifier":"https://ror.org/03q1rgc19","awardNumber":"DE-AR0001624"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.h44j0zq16","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":26,"downloadCount":2,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-04-22T11:19:10Z","registered":"2026-04-22T11:19:11Z","published":null,"updated":"2026-10-06T07:47:42Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.tdz08kqfd","type":"dois","attributes":{"doi":"10.5061/dryad.tdz08kqfd","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Baker, Jacob","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8113-6898"}]},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Kogan, Emily","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9905-7928"}]},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Ma, Shaorong","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4643-8393"}]},{"nameType":"Personal","affiliation":["Lehigh University"],"name":"Lu, Ju","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Zuo, Yi","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9020-0003"}]}],"titles":[{"title":"Data from: 5-HT2ARs in layer 5 pyramidal neurons mediate the neuroplastic but not the hallucinogenic effects of psilocybin"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"subject":"psilocybin"},{"subject":"neuroplasticity"},{"subject":"dendrite"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"}],"contributors":[{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Baker, Jacob","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8113-6898"}],"contributorType":"ProjectLeader"},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Baker, Jacob","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8113-6898"}],"contributorType":"Editor"},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Baker, Jacob","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8113-6898"}],"contributorType":"DataCurator"},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Baker, Jacob","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8113-6898"}],"contributorType":"DataCollector"},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Baker, Jacob","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8113-6898"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Kogan, Emily","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9905-7928"}],"contributorType":"DataCollector"},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Kogan, Emily","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9905-7928"}],"contributorType":"Editor"},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Ma, Shaorong","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4643-8393"}],"contributorType":"DataCollector"},{"nameType":"Personal","affiliation":["Lehigh University"],"name":"Lu, Ju","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Lehigh University"],"name":"Lu, Ju","contributorType":"ProjectLeader","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Lehigh University"],"name":"Lu, Ju","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Lehigh University"],"name":"Lu, Ju","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Lehigh University"],"name":"Lu, Ju","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Lehigh University"],"name":"Lu, Ju","contributorType":"DataCollector","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Zuo, Yi","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9020-0003"}],"contributorType":"ProjectLeader"},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Zuo, Yi","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9020-0003"}],"contributorType":"Editor"},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Zuo, Yi","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9020-0003"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Zuo, Yi","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9020-0003"}],"contributorType":"Supervisor"},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Zuo, Yi","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9020-0003"}],"contributorType":"ProjectManager"},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Zuo, Yi","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9020-0003"}],"contributorType":"DataCollector"},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Zuo, Yi","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9020-0003"}],"contributorType":"DataCurator"},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Zuo, Yi","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9020-0003"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Baker, Jacob","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8113-6898"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Kogan, Emily","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9905-7928"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":["Lehigh University"],"name":"Lu, Ju","contributorType":"ContactPerson","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Santa Cruz"],"name":"Zuo, Yi","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9020-0003"}],"contributorType":"ContactPerson"},{"name":"University of California, Santa Cruz","contributorType":"Sponsor","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-09-16T18:46:13Z","dateType":"Created"},{"date":"2026-09-16T18:46:16Z","dateType":"Submitted"},{"date":"2026-09-22T00:00:00Z","dateType":"Issued"},{"date":"2026-09-22T00:00:00Z","dateType":"Available"},{"date":"2026-10-06T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.64898/2026.04.06.716778","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["63195 bytes"],"formats":[],"version":"5","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This data was collected to investigate the role of serotonin 2A receptors\n (5-HT2ARs) expressed in cortical layer 5 pyramidal neurons (L5 PyrNs) in\n psilocybin-induced hallucination-like behavior and synaptic\n neuroplasticity in mice. The dataset contains head-twitch response (HTR)\n counts recorded after psilocybin or saline injection, and dendritic spine\n dynamics measured by longitudinal in vivo two-photon imaging of apical\n tuft dendrites of L5 PyrNs in the somatosensory cortex of Thy1-GFP-M mice."},{"descriptionType":"TechnicalInfo","description":"# 5-HT2ARs in layer 5 pyramidal neurons mediate the neuroplastic but not\n the hallucinogenic effects of psilocybin Dataset DOI:\n [10.5061/dryad.XXXXXXX](https://doi.org/10.5061/dryad.XXXXXXX) ##\n Description of the data and file structure This data was collected to\n investigate the role of serotonin 2A receptors (5-HT2ARs) expressed in\n cortical layer 5 pyramidal neurons (L5 PyrNs) in psilocybin-induced\n hallucination-like behavior and synaptic neuroplasticity in mice. The\n dataset contains head-twitch response (HTR) counts recorded after\n psilocybin or saline injection, and dendritic spine dynamics measured by\n longitudinal *in vivo* two-photon imaging of apical tuft dendrites of L5\n PyrNs in the somatosensory cortex of Thy1-GFP-M mice. Psi: psilocybin;\n intraperitoneal doses are given in mg/kg body weight, with 0.9% saline as\n the vehicle control. HTR: head-twitch response, counted during the 20 min\n after injection. UMS: unpredictable mild stress (21 days). Genotypes: WT\n (wild type), FKO (5-HT2AR full knockout, htr2a stop/stop), CR (conditional\n rescue, Rbp4-Cre+; htr2a stop/stop), fl/fl (floxed control, htr2a\n flox/flox), CKO (conditional knockout, Rbp4-Cre+; htr2a flox/flox). Mouse\n IDs beginning with 'a' refer to mice with cranial window\n implantations for longitudinal *in vivo* two-photon imaging experiments\n listed in dendritic_imaging_mouse_list.csv; IDs beginning with\n 'b' refer to HTR behavior mice listed in\n mouse_behavior_list.csv. In all spine dynamics files, 'spine d0'\n is the number of spines counted at the imaging session at the start of the\n imaging interval, 'formation' is the number of new spines\n present at the end of the imaging interval, 'elimination' is the\n number of spines present on d0 not present at the end of the imaging\n interval, form % =formation/spinee d0 x 100, elim % =elimination/spinee d0\n x 100, and density change % = form % - elim %. Each row corresponds to one\n mouse unless noted otherwise. NA indicates that data is not available for\n that mouse during that session. ### Files and variables #### File:\n mouse_behavior_list.csv **Description:** Master list of all mice used in\n HTR behavior experiments **Column descriptions** * ID: Unique mouse\n identifier (b001-b100). * genotype: Mouse genotype; WT, FKO, CR, fl/fl, or\n CKO. * dose: Treatment received; saline, 0.3 mg/kg, 1 mg/kg, or 3 mg/kg\n psilocybin. * DOB: Date of birth (M/D/YYYY). * Exp Date: Date of the HTR\n recording (M/D/YYYY). * sex: Biological sex of the mouse; male (M) or\n female (F). #### File: dendritic_imaging_mouse_list.csv **Description:**\n Master list of all mice used in *in vivo* two-photon spine imaging\n experiments (Fig. 1c-s, Fig. 2c-g, Fig. 3b-f, Extended Data Fig. 2-6).\n **Column descriptions** * ID: Unique mouse identifier (a001-a079). * UMS?:\n Whether the mouse underwent 21 d of unpredictable mild stress; yes (Y) or\n no (N). * PSI dose: Psilocybin dose received; saline, 0.3 mg/kg, 1 mg/kg,\n or 3 mg/kg. * genotype: Mouse genotype; WT, FKO, CR, fl/fl, or CKO. * sex:\n male (M) or female (F). * DOB: Date of birth (M/D/YYYY). * imaging d0:\n Date of the first (baseline, day 0) imaging session (M/D/YYYY). ####\n Folder: Fig1 * **Description:** Data underlying Figure 1. One CSV per\n figure panel. * **Fig1A.csv**: HTR counts after saline or psilocybin\n injection in WT mice (Fig. 1a). Please see mouse_behavior_list.csv for the\n corresponding mouse information. **Column descriptions** * ID: Unique\n mouse identifier. * genotype: Mouse genotype; WT for all mice in this\n file. * dose: Treatment received; saline, 0.3 mg/kg, 1 mg/kg, or 3 mg/kg\n psilocybin. * sex: male (M) or female (F). * HTR: Number of head twitches\n during the 20 min after injection. **Fig1C.csv**: 2d spine dynamics (d0 to\n d2; injection on d1) in WT mice receiving saline or psilocybin. **Column\n descriptions** * ID: Unique mouse identifier. * dose: Treatment received;\n saline, 0.3 mg/kg, 1 mg/kg, or 3 mg/kg psilocybin. * spine d0: Total\n number of spines counted at the d0 imaging session. * formation: Number of\n new spines formed between d0 and d2. * form %: formation / spine d0 x 100.\n * elimination: Number of spines present on d0 that were absent on d2. *\n elim %: elimination / spine d0 x 100. **Fig1E.csv**: 2d spine dynamics at\n different times before and after 1 mg/kg psilocybin treatment in WT mice.\n **Column descriptions** * ID: Unique mouse identifier. * interval: Imaging\n interval; d-2-0 (day -2 to day 0, pre-treatment baseline), d0-2 (day 0 to\n day 2, spanning psilocybin injection on day 1), d7-9 (day 7 to day 9), or\n d21-23 (day 21 to day 23). * spine d0: Total number of spines counted at\n the first imaging session of the interval. * formation: Number of new\n spines formed over the interval. * form %: formation / spine d0 x 100. *\n elimination: Number of spines eliminated over the interval. * elim %:\n elimination / spine d0 x 100. * **Fig1F.csv**: 7d spine dynamics (d0 to\n d7) in control versus 1 mg/kg psilocybin-treated WT mice. **Column\n descriptions** * ID: Unique mouse identifier. * dose: Treatment received;\n saline or 1 mg/kg psilocybin. * spine d0: Total number of spines counted\n at the d0 imaging session. * formation: Number of new spines formed\n between d0 and d7. * form %: formation / spine d0 x 100. * elimination:\n Number of spines present on d0 that were absent on d7. * elim %:\n elimination / spine d0 x 100. * **Fig1G.csv**: 21d spine dynamics (d0 to\n d21) in control versus 1 mg/kg psilocybin-treated WT mice. **Column\n descriptions** * ID: Unique mouse identifier. * dose: Treatment received;\n saline or 1 mg/kg psilocybin. * spine d0: Total number of spines counted\n at the d0 imaging session. * formation: Number of new spines formed\n between d0 and d21. * form %: formation / spine d0 x 100. * elimination:\n Number of spines present on d0 that were absent on d21. * elim %:\n elimination / spine d0 x 100. * **Fig1H.csv**: Spine density changes in 1\n mg/kg psilocybin-treated WT mice measured over 2d (d0-d2), 7d (d0-d7), or\n 21d (d0-d21) intervals. **Column descriptions** * ID: Unique mouse\n identifier. * interval: Measurement interval starting at d0; 2d (d0-d2),\n 7d (d0-d7), or 21d (d0-d21). * formation %: Number of new spines formed\n over theinterval/spinee count at d0 x 100. * elimination %: Number of\n spines eliminated over theinterval/spinee count at d0 x 100. * density\n change %: formation % - elimination %. * **Fig1K.csv**: 7d survival of\n newly formed versus pre-existing spines in control versus 1 mg/kg\n psilocybin-treated WT mice. New spines are those present on d2 but not d0;\n pre-existing spines are those present on both d0 and d2. **Column\n descriptions** * ID: Unique mouse identifier. * dose: Treatment received;\n saline or 1 mg/kg psilocybin. * total new spines: Number of new spines\n formed between d0 and d2. * new d7 stable: Number of those new spines\n still present on d7. * new spine survival %: new d7 stable / total new\n spines x 100. * preexisting spines: Number of spines present on both d0\n and d2. * preexisting d7 stable: Number of pre-existing spines still\n present on d7. * preexisting spine survival %: preexisting d7 stable /\n preexisting spines x 100. * **Fig1L.csv**: Morphological categories\n (stubby, mushroom, thin) of new spines formed between d0 and d2 in control\n versus 1 mg/kg psilocybin-treated WT mice. **Column descriptions** * ID:\n Unique mouse identifier. * dose: Treatment received; saline or 1 mg/kg\n psilocybin. * total new spines: Number of new spines formed between d0 and\n d2. * stubby: Number of new spines classified as stubby. * %\n stubby:stubby/totall new spines x 100. * mushroom: Number of new spines\n classified as mushroom. * % mushroom: mushroom / total new spines x 100. *\n thin: Number of new spines classified as thin. * % thin:thin/totall new\n spines x 100. * **Fig1M.csv**: 7d fate of new spines by morphological\n category in control versus 1 mg/kg psilocybin-treated WT mice. Morphology\n was assigned at d2 (formation). **Column descriptions** * ID: Unique mouse\n identifier. * dose: Treatment received; saline or 1 mg/kg psilocybin. *\n total new spines: Number of new spines formed between d0 and d2. * total\n new spines lost: Number of those new spines eliminated by d7. * spines\n survived: Number of those new spines still present on d7. * elim stubby:\n Number of eliminated new spines whose d2 morphology was stubby. * % stubby\n survival: Computed as (stubby - elim stubby) / stubby x 100, where stubby\n is the number of new stubby spines. * elim mushroom: Number of eliminated\n new spines whose d2 morphology was mushroom. * % mushroom survival:\n Computed as (mushroom - elim mushroom) / mushroom x 100, where mushroom is\n the number of new mushroom spines. * elim thin: Number of eliminated new\n spines whose d2 morphology was thin. * % thin survival: Computed as (thin\n - elim thin) / thin x 100, where thin is the number of new thin spines. *\n **Fig1O.csv**: 2d spine formation in post-UMS versus unstressed WT mice\n receiving saline or 1 mg/kg psilocybin. **Column descriptions** * ID:\n Unique mouse identifier. * dose: Treatment received; saline or 1 mg/kg\n psilocybin. * treatment: Stress condition; unstressed or post-UMS\n (injected 1d after cessation of 21d UMS). * spine d0: Total number of\n spines counted at the imaging session immediately preceding injection. *\n formation: Number of new spines formed over the 2 d interval spanning the\n injection. * form %: formation / spine d0 x 100. * **Fig1P.csv**: 2d spine\n elimination in post-UMS versus unstressed WT mice receiving saline or 1\n mg/kg psilocybin. **Column descriptions** * ID: Unique mouse identifier. *\n dose: Treatment received; saline or 1 mg/kg psilocybin. * treatment:\n Stress condition; unstressed or post-UMS (injected 1d after cessation of\n 21d UMS). * spine d0: Total number of spines counted at the imaging\n session immediately preceding injection. * elimination: Number of spines\n eliminated over the 2 d interval spanning the injection. * elim %:\n elimination / spine d0 x 100. * **Fig1Q.csv**: 2d spine density changes in\n post-UMS versus unstressed WT mice receiving saline or 1 mg/kg psilocybin.\n **Column descriptions** * ID: Unique mouse identifier. * dose: Treatment\n received; saline or 1 mg/kg psilocybin. * treatment: Stress condition;\n unstressed or post-UMS (injected 1d after cessation of 21d UMS). * spine\n d0: Total number of spines counted at the imaging session immediately\n preceding injection. * formation: Number of new spines formed over the 2d\n interval spanning the injection. * form %: formation / spine d0 x 100. *\n elimination: Number of spines eliminated over the 2d interval spanning the\n injection. * elim %: elimination / spine d0 x 100. * density change %:\n form % - elim %. * **Fig1R.csv**: Regrowth of spines lost during UMS in\n post-UMS mice receiving saline or 1 mg/kg psilocybin. **Column\n descriptions** * ID: Unique mouse identifier. * dose: Treatment received;\n saline or 1 mg/kg psilocybin. * treatment: Stress condition; post-UMS for\n all mice in this file. * total lost spines: Number of spines eliminated\n during the 21d UMS period. * regrow spines: Number of those lost spines\n that reappeared at the same site within 2d after injection. * relocalize\n %: regrow spines / total lost spines x 100. * **Fig1S.csv**: Fraction of\n post-treatment new spines that emerged at sites of prior spine elimination\n in post-UMS mice receiving saline or 1 mg/kg psilocybin. **Column\n descriptions** * ID: Unique mouse identifier. * dose: Treatment received;\n saline or 1 mg/kg psilocybin. * treatment: Stress condition; post-UMS for\n all mice in this file. * total new spines: Number of new spines formed\n within 2d after injection. * regrow spines: Number of those new spines\n that emerged at sites where a spine had been eliminated during UMS. *\n relocalize %: regrow spines / total new spines x 100. #### Folder: Fig2 *\n **Description:** Data underlying Figure 2. **Fig2B.csv**: HTR counts after\n 1 mg/kg psilocybin in FKO and CR mice. **Column descriptions** * ID:\n Unique mouse identifier. * genotype: Mouse genotype; FKO or CR. * dose:\n Treatment received; 1 mg/kg psilocybin for all mice in this file. * sex:\n male (M) or female (F). * HTR: Number of head twitches during the 20 min\n after injection. * **Fig2C.csv**: Baseline spine density on apical tuft\n dendrites of L5 PyrNs in WT, FKO, and CR mice. **Column descriptions** *\n ID: Unique mouse identifier. * genotype: Mouse genotype; WT, FKO, or CR. *\n density d0 (spines per 10 microns): Spine density at the d0 imaging\n session, in spines per 10 micrometers of dendrite. * **Fig2E.csv**: 2d\n spine dynamics at baseline and with 1 mg/kg psilocybin treatment in WT,\n FKO, and CR mice. Baseline columns cover the pre-treatment interval (day\n -2 to day 0); Psi columns cover the interval spanning the injection (day 0\n to day 2, injection on day 1). **Column descriptions** * ID: Unique mouse\n identifier. * genotype: Mouse genotype; WT, FKO, or CR. * baseline spine\n d0: Total number of spines counted at the start of the baseline interval.\n * baseline formation: Number of new spines formed over the baseline\n interval. * baseline form %: baseline formation / baseline spine d0 x 100.\n * baseline elimination: Number of spines eliminated over the baseline\n interval. * baseline elim %: baseline elimination / baseline spine d0 x\n 100. * baseline density change %: baseline form % - baseline elim %. * Psi\n spine d0: Total number of spines counted at the start of the treatment\n interval. * Psi formation: Number of new spines formed over the treatment\n interval. * Psi form %: Psi formation / Psi spine d0 x 100. * Psi\n elimination: Number of spines eliminated over the treatment interval. *\n Psi elim %: Psi elimination / Psi spine d0 x 100. * Psi density change %:\n Psi form % - Psi elim %. * **Fig2F.csv**: 7d spine dynamics (d0 to d7)\n with 1 mg/kg psilocybin treatment in FKO versus CR mice. **Column\n descriptions** * ID: Unique mouse identifier. * genotype: Mouse genotype;\n FKO or CR. * spine d0: Total number of spines counted at the d0 imaging\n session. * formation: Number of new spines formed between d0 and d7. *\n form %: formation / spine d0 x 100. * elimination: Number of spines\n present on d0 that were absent on d7. * elim %: elimination / spine d0 x\n 100. * density change %: form % - elim %. * **Fig2G.csv**: 7d survival of\n newly formed versus pre-existing spines with 1 mg/kg psilocybin treatment\n in FKO versus CR mice. New spines are those present on d2 but not d0;\n pre-existing spines are those present on both d0 and d2. **Column\n descriptions** * ID: Unique mouse identifier. * genotype: Mouse genotype;\n FKO or CR. * total new spines: Number of new spines formed between d0 and\n d2. * new d7 stable: Number of those new spines still present on d7. * new\n spine survival %: new d7 stable / total new spines x 100. * preexisting\n spines: Number of spines present on both d0 and d2. * preexisting d7\n stable: Number of pre-existing spines still present on d7. * preexisting\n spine survival %: preexisting d7 stable / preexisting spines x 100. ####\n Folder: Fig3 * **Description:** Data underlying Figure 3. * **Fig3A.csv**:\n HTR counts after 1 mg/kg psilocybin in fl/fl and CKO mice. **Column\n descriptions** * ID: Unique mouse identifier. * genotype: Mouse genotype;\n fl/fl or CKO. * dose: Treatment received; 1 mg/kg psilocybin for all mice\n in this file. * sex: male (M) or female (F). * HTR: Number of head\n twitches during the 20 min after injection. * **Fig3B.csv**: Baseline\n spine density on apical tuft dendrites of L5 PyrNs in fl/fl and CKO mice.\n **Column descriptions** * ID: Unique mouse identifier. * genotype: Mouse\n genotype; fl/fl or CKO. * density d0 (spines per 10 microns): Spine\n density at the d0 imaging session, in spines per 10 micrometers of\n dendrite. **Fig3D.csv**: 2d spine dynamics at baseline and with 1 mg/kg\n psilocybin treatment in fl/fl and CKO mice. Baseline columns cover the\n pre-treatment interval (day -2 to day 0); Psi columns cover the interval\n spanning the injection (day 0 to day 2, injection on day 1). **Column\n descriptions** * ID: Unique mouse identifier. * genotype: Mouse genotype;\n fl/fl or CKO. * baseline spine d0: Total number of spines counted at the\n start of the baseline interval. * baseline formation: Number of new spines\n formed over the baseline interval. * baseline form %: baseline formation /\n baseline spine d0 x 100. * baseline elimination: Number of spines\n eliminated over the baseline interval. * baseline elim %: baseline\n elimination / baseline spine d0 x 100. * baseline density change %:\n baseline form % - baseline elim %. * Psi spine d0: Total number of spines\n counted at the start of the treatment interval. * Psi formation: Number of\n new spines formed over the treatment interval. * Psi form %: Psi formation\n / Psi spine d0 x 100. * Psi elimination: Number of spines eliminated over\n the treatment interval. * Psi elim %: Psi elimination / Psi spine d0 x\n 100. * Psi density change %: Psi form % - Psi elim %. * **Fig3E.csv**: 7d\n spine dynamics (d0 to d7) with 1 mg/kg psilocybin treatment in fl/fl\n versus CKO mice. **Column descriptions** * ID: Unique mouse identifier. *\n genotype: Mouse genotype; fl/fl or CKO. * spine d0: Total number of spines\n counted at the d0 imaging session. * formation: Number of new spines\n formed between d0 and d7. * form %: formation / spine d0 x 100. *\n elimination: Number of spines present on d0 that were absent on d7. * elim\n %: elimination / spine d0 x 100. * density change %: form % - elim %. *\n **Fig3F.csv**: 7d survival of newly formed versus pre-existing spines with\n 1 mg/kg psilocybin treatment in fl/fl versus CKO mice. **Column\n descriptions** * ID: Unique mouse identifier. * genotype: Mouse genotype;\n fl/fl or CKO. * total new spines: Number of new spines formed between d0\n and d2. * new d7 stable: Number of those new spines still present on d7. *\n new spine survival %: new d7 stable / total new spines x 100. *\n preexisting spines: Number of spines present on both d0 and d2. *\n preexisting d7 stable: Number of pre-existing spines still present on d7.\n * preexisting spine survival %: preexisting d7 stable / preexisting spines\n x 100. #### Folder: Extended_Data **Description:** Data underlying the\n Extended Data figures. **Extended_Data_Fig1A.csv**: HTR counts by sex\n after different psilocybin doses in WT mice. **Column descriptions** * ID:\n Unique mouse identifier. * genotype: Mouse genotype; WT for all mice in\n this file. * dose: Treatment received; 0.3 mg/kg, 1 mg/kg, or 3 mg/kg\n psilocybin. * DOB: Date of birth (M/D/YYYY). * Exp Date: Date of the HTR\n recording (M/D/YYYY). * sex: male (M) or female (F). * HTR: Number of head\n twitches during the 20 min after injection. * **Extended_Data_Fig1B.csv**:\n Time course of HTRs after 1 mg/kg psilocybin, counted in 5-min bins.\n **Column descriptions** * ID: Unique mouse identifier. * genotype: Mouse\n genotype; WT for all mice in this file. * dose: Treatment received; 1\n mg/kg psilocybin for all mice in this file. * DOB: Date of birth\n (M/D/YYYY). * Exp Date: Date of the HTR recording (M/D/YYYY). * sex: male\n (M) or female (F). * 0-5 min: Number of head twitches 0-5 min after\n injection. * 5-10 min: Number of head twitches 5-10 min after injection. *\n 10-15 min: Number of head twitches 10-15 min after injection. * 15-20 min:\n Number of head twitches 15-20 min after injection.\n **Extended_Data_Fig2B.csv**: 2d spine density changes in WT mice receiving\n saline or psilocybin. **Column descriptions** * ID: Unique mouse\n identifier. * dose: Treatment received; saline, 0.3 mg/kg, 1 mg/kg, or 3\n mg/kg psilocybin. * density change %: form % - elim %. *\n **Extended_Data_Fig2C.csv**: 2d spine dynamics at baseline and after 1\n mg/kg psilocybin in WT mice. Baseline columns cover the pre-treatment\n interval (day -2 to day 0); Psi columns cover the interval spanning the\n injection (day 0 to day 2, injection on day 1). **Column descriptions** *\n ID: Unique mouse identifier. * baseline spine d0: Total number of spines\n counted at the start of the baseline interval. * baseline formation:\n Number of new spines formed over the baseline interval. * baseline form %:\n baseline formation / baseline spine d0 x 100. * baseline elimination:\n Number of spines eliminated over the baseline interval. * baseline elim %:\n baseline elimination / baseline spine d0 x 100. * baseline density change\n %: baseline form % - baseline elim %. * Psi spine d0: Total number of\n spines counted at the start of the treatment interval. * Psi formation:\n Number of new spines formed over the treatment interval. * Psi form %: Psi\n formation / Psi spine d0 x 100. * Psi elimination: Number of spines\n eliminated over the treatment interval. * Psi elim %: Psi elimination /\n Psi spine d0 x 100. * Psi density change %: Psi form % - Psi elim %. *\n **Extended_Data_Fig4B.csv**: 21d survival of newly formed versus\n pre-existing spines in control versus 1 mg/kg psilocybin-treated WT mice.\n New spines are those present on d2 but not d0; pre-existing spines are\n those present on both d0 and d2. **Column descriptions** * ID: Unique\n mouse identifier. * dose: Treatment received; saline or 1 mg/kg\n psilocybin. * total new spines: Number of new spines formed between d0 and\n d2. * new d21 stable: Number of those new spines still present on d21. *\n new spine survival %: new d21 stable/total new spines x 100. * preexisting\n spines: Number of spines present on both d0 and d2. * preexisting d21\n stable: Number of pre-existing spines still present on d21. * preexisting\n spine survival %: preexisting d21 stable/preexisting spines x 100.\n **Extended_Data_Fig5.csv**: Morphological categories of new spines formed\n within 2d after saline or 1 mg/kg psilocybin injection in post-UMS mice\n **Column descriptions** * ID: Unique mouse identifier. * dose: Treatment\n received; saline or 1 mg/kg psilocybin. * total new spines: Number of new\n spines formed over the 2d interval spanning the injection. * stubby:\n Number of new spines classified as stubby. * % stubby:stubby/totall new\n spines x 100. * mushroom: Number of new spines classified as mushroom. * %\n mushroom: mushroom/total new spines x 100. * thin: Number of new spines\n classified as thin. * % thin:thin/total new spines x 100. *\n **Extended_Data_Fig6A.csv**: 2d spine formation and elimination at\n baseline and with 1 mg/kg psilocybin treatment in WT, FKO, and CR mice.\n **Column descriptions** * ID: Unique mouse identifier. * baseline spine\n d0: Total number of spines counted at the start of the baseline interval.\n * baseline formation: Number of new spines formed over the baseline\n interval. * baseline form %: baseline formation / baseline spine d0 x 100.\n * baseline elimination: Number of spines eliminated over the baseline\n interval. * baseline elim %: baseline elimination / baseline spine d0 x\n 100. * Psi spine d0: Total number of spines counted at the start of the\n treatment interval. * Psi formation: Number of new spines formed over the\n treatment interval. * Psi form %: Psi formation / Psi spine d0 x 100. *\n Psi elimination: Number of spines eliminated over the treatment interval.\n * Psi elim %: Psi elimination / Psi spine d0 x 100. *\n **Extended_Data_Fig6B.csv**: 7d survival of new spines in WT (baseline and\n psilocybin-treated), FKO, and CR mice. **Column descriptions** * ID:\n Unique mouse identifier. * genotype: Mouse genotype; WT, FKO, or CR. *\n total new spines: Number of new spines formed between d0 and d2. * d7 new\n spine survival: Number of those new spines still present on d7. * new\n spine survival %: d7 new spine survival / total new spines x 100. *\n **Extended_Data_Fig6C.csv**: 7d survival of pre-existing spines in WT\n (baseline and psilocybin-treated), FKO, and CR mice. **Column\n descriptions** * ID: Unique mouse identifier. * genotype: Mouse genotype;\n WT, FKO, or CR. * total old spines: Number of pre-existing spines (present\n on both d0 and d2). * d7 old spine survival: Number of pre-existing spines\n still present on d7. * preexisting spine survival %: d7 old spine survival\n / total old spines x 100. #### Folder:\n Extended_Data/Extended_Data_Fig3_code_and_data **Description:**\n Dendrite-level spine formation data and the MATLAB code for the\n permutation test of the uniformity of spine formation across dendritic\n segments. * **Extended_Data_Fig3.csv**: Dendrite-level 2d spine formation\n in WT mice with and without 1 mg/kg psilocybin treatment. Each row\n corresponds to one dendritic segment; only segments longer than 10\n micrometers are included, and the permutation-test code further restricts\n the analysis to segments with at least 4 spines at d0. **Column\n descriptions** * ID: Identifier of the mouse the dendritic segment belongs\n to. * treatment: Imaging interval condition; baseline (untreated 2 d\n interval) or 1 mg/kg PSI (2 d interval spanning a 1 mg/kg psilocybin\n injection). * sex: Biological sex of the mouse; male (M) or female (F). *\n d0: Number of spines on the dendritic segment at the start of the\n interval. * form: Number of new spines formed on the dendritic segment\n over the interval. * **mainfcn.m**: Main MATLAB script. Loads the dendrite\n data, runs the permutation test for each dataset, and plots the results. *\n **simulate.m**: Function performing the permutation test (10,000 trials).\n New spines are randomly assigned to dendrites with probability\n proportional to each dendrite's initial spine count, and the\n Kullback-Leibler (KL) divergence of each simulated distribution from the\n null distribution is compared with the empirical KL divergence to obtain a\n one-tailed p-value. * **KL_Divergence_test.m**: Function computing the KL\n divergence between the observed distribution of new spines across\n dendrites and the null distribution proportional to initial spine counts.\n * **plot_sim_stats.m**: Function plotting the distribution of simulated KL\n divergences with the empirical value marked as a vertical line. *\n **README.txt**: Instructions for running the code, input data format,\n expected outputs, and system requirements. * **MIT LICENSE.txt**: MIT\n license covering the MATLAB code in this folder. ## Code/software The\n MATLAB code in Extended_Data/Extended_Data_Fig3_code_and_data reproduces\n the permutation test in Extended Data Fig. 3. It was tested on MATLAB\n R2025b (Windows 11) and requires the MATLAB Statistics and Machine\n Learning Toolbox. Please see the README.txt in that folder for usage\n instructions. The code is released under the MIT license and is also\n available at\n [https://github.com/ZuoLabUCSC/branch-specific-spine-formation](https://github.com/ZuoLabUCSC/branch-specific-spine-formation). All CSV files can be opened with any spreadsheet software or text editor; no proprietary software is required to access the data. Statistical analyses in the manuscript were performed in GraphPad Prism 10. ## Access information Other publicly accessible locations of the data: * NA Data was derived from the following sources: * NA"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"National Institute of Mental Health","funderIdentifier":"https://ror.org/04xeg9z08","awardNumber":"R01MH127737"},{"funderIdentifierType":"ROR","funderName":"National Institute of Mental Health","funderIdentifier":"https://ror.org/04xeg9z08","awardNumber":"R01MH136381"},{"funderIdentifierType":"ROR","funderName":"National Institute on Aging","funderIdentifier":"https://ror.org/049v75w11","awardNumber":"R01AG071787"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.tdz08kqfd","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-09-22T12:54:33Z","registered":"2026-09-22T12:54:34Z","published":null,"updated":"2026-10-06T07:44:21Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.15dv41pc1","type":"dois","attributes":{"doi":"10.5061/dryad.15dv41pc1","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Colorado State University"],"name":"Lee, Casey","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0005-1003-2588"}]},{"nameType":"Personal","affiliation":["Colorado State University"],"name":"Angeloni, Lisa","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Colorado State University"],"name":"Crooks, Kevin","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Colorado State University"],"name":"Wittemyer, George","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1640-5355"}]}],"titles":[{"title":"Data and code from: Traffic and noise are associated with increased vigilance in urban black-tailed prairie dogs"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Animal behavior","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Conservation biology","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"acoustic ecology"}],"contributors":[{"nameType":"Personal","affiliation":["Colorado State University"],"name":"Lee, Casey","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0005-1003-2588"}],"contributorType":"ContactPerson"},{"name":"Colorado State University","contributorType":"Sponsor","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-07-18T18:58:45Z","dateType":"Created"},{"date":"2026-09-29T05:20:13Z","dateType":"Submitted"},{"date":"2026-10-06T00:00:00Z","dateType":"Issued"},{"date":"2026-10-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["29013 bytes"],"formats":[],"version":"5","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"There is growing evidence for the impacts of anthropogenic noise on\n natural systems. The risk-disturbance hypothesis predicts that human\n disturbance, including noise, has behavioural impacts analogous to\n predation risk, eliciting antipredator responses. However, these effects\n may be reduced in urban areas with chronic disturbance if animals become\n tolerant, are unable to sustain the costs of antipredator behaviour, or\n experience lower risk because predators avoid anthropogenic disturbance.\n We studied the effects of traffic and its associated noise on the\n behaviour of black-tailed prairie dogs (Cynonyms ludovicianus), a native\n keystone species in North American prairie ecosystems. Prior\n work found that rural prairie dogs exhibited elevated\n antipredator behaviour in response to 1-hour recordings of traffic noise,\n consistent with the risk-disturbance hypothesis. To investigate whether\n urban prairie dogs exhibit similar responses to temporal variation in\n chronic traffic and noise on real roads, we measured prairie dog\n behaviour, vehicle numbers, and sound levels at five colonies in the urban\n Front Range of Colorado during periods that varied in traffic levels. We\n found that prairie dogs were significantly more likely to be vigilant\n during observations with higher vehicle traffic counts and louder traffic\n noise. These findings indicate that despite the potential for habituation,\n chronically exposed prairie dogs may still perceive greater risk with\n increases in traffic and associated noise in a manner consistent with the\n risk-disturbance hypothesis."},{"descriptionType":"TechnicalInfo","description":"# Data and code from: Traffic and noise are associated with increased\n vigilance in urban black-tailed prairie dogs Dataset DOI:\n [10.5061/dryad.15dv41pc1](https://doi.org/10.5061/dryad.15dv41pc1) ##\n Description of the data and file structure To investigate the behavioral\n responses of urban prairie dogs to variation in chronic traffic and noise,\n we measured prairie dog behavior, vehicle numbers, and sound levels during\n rush-hour periods on weekdays and on quieter weekends at five colonies in\n the urban Front Range of Colorado. ### Files and variables #### File:\n LND_AB_June2026_Final.R **Description:** This script constructs derived\n variables, sets customs controls, generates our graphics, and runs our\n analyses.  #### File: LND_public_observations.csv **Description:**  This\n is the the experimental data used in our R script analyses. Note that a\n few variables used in analyses are derived in-script. Any NA represents\n missing data.\\ \\ Variable names are as follows:  * USID: Unique name given\n to each visit, listed as Site_Date combination * WE.WD: Indicates whether\n a visit occurred on a weekend or weekday * AM_PM: Indicates whether a\n visit occurred in the morning or afternoon * Scan: Indicates whether a\n scan took place at 0, 5, or 10 minutes into the observation period * Temp:\n temperature on that day in Farenheit * Wind: wind speed in MPH * dB.avg:\n the a-weighted average decibel reading per visit * Foraging/Vigilant: The\n number of prairie dogs engaged in these behaviors in a scan * Total.PD:\n The total number of prairie dogs seen in a scan * ObsArea: The total area\n (in hectares) of the observation area of a given site ## Code/software\n Statistical analyses were performed using R, version 4.3.1 using the\n following packages: library(dplyr) library(tidyr) library(lme4)\n library(emmeans) library(ggbeeswarm) library(ggplot2) library(patchwork)\n Script covers derivation of variables and construction of GLMMs and\n visuals."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"U.S. National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62"},{"funderIdentifierType":"ROR","funderName":"Animal Behavior Society","funderIdentifier":"https://ror.org/031nh9x49"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.15dv41pc1","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-10-06T07:01:16Z","registered":"2026-10-06T07:01:17Z","published":null,"updated":"2026-10-06T07:01:17Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.547d7wmnt","type":"dois","attributes":{"doi":"10.5061/dryad.547d7wmnt","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Maine"],"name":"Pahadi, Pratima","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-2388-0966"}]},{"nameType":"Personal","affiliation":["University of Maine"],"name":"Wason, Jay","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1338-881X"}]},{"nameType":"Personal","affiliation":["University of Maine","Xishuangbanna Tropical Botanical Garden"],"name":"Zhang, Yongjiang","nameIdentifiers":[]}],"titles":[{"title":"Data from: Future northeastern U.S. forests: Seedling survival and growth is shaped by species-specific responses to climate, canopy openness and soil quality"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Agriculture, forestry, and fisheries","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Global warming","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"forest resilience"},{"subject":"climate gradient"}],"contributors":[{"nameType":"Personal","affiliation":["University of Maine"],"name":"Pahadi, Pratima","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-2388-0966"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":["University of Maine"],"name":"Wason, Jay","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1338-881X"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":["University of Maine","Xishuangbanna Tropical Botanical Garden"],"name":"Zhang, Yongjiang","contributorType":"ContactPerson","nameIdentifiers":[]},{"name":"Maine Agricultural and Forest Experiment Station","contributorType":"Sponsor","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-08-17T19:06:25Z","dateType":"Created"},{"date":"2026-09-28T16:54:46Z","dateType":"Submitted"},{"date":"2026-10-06T00:00:00Z","dateType":"Issued"},{"date":"2026-10-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1016/j.agrformet.2026.111482","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["572057 bytes"],"formats":[],"version":"2","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"These are the data associated with the project related to examining the\n future of northeastern U.S. forests: Seedling survival and growth are\n shaped by species-specific responses to climate, canopy openness and soil\n quality. These datasets were generated from a common garden experiment\n examining how climate, soil type, canopy openness, and species identity\n influence the survival and growth of tree seedlings representing twelve\n tree species common to northeastern U.S. forests. The datasets include\n seedling-level measurements of growth (relative height growth, relative\n diameter growth, absolute height growth, absolute diameter growth) and\n survival along with other variables including soil type (native\n versus garden soil), canopy openness (forest edge versus forest interior),\n and site climate. Additional datasets include measurements of canopy\n openness, soil characteristics, and other environmental variables used to\n evaluate species-specific regeneration responses across contrasting\n climatic and microsite conditions."},{"descriptionType":"TechnicalInfo","description":"# Data from: Future northeastern U.S. forests: Seedling survival and\n growth is shaped by species-specific responses to climate, canopy openness\n and soil quality Dataset DOI:\n [10.5061/dryad.547d7wmnt](https://doi.org/10.5061/dryad.547d7wmnt) ##\n Description of the data and file structure **Dataset\n DOI:** 10.5061/dryad.547d7wmnt **Description of the data and file\n structure** **Project**: Future of northeastern U.S. forests: Seedling\n survival and growth is shaped by species-specific responses to climate,\n canopy openness and soil quality **Authors:** Pratima Pahadi, Nicholas\n Fisichelli, Jay Wason and Yongjiang Zhang  **Date compiled**: August\n 7^th^, 2026 Data compiled by Pratima Pahadi **Files and variables**  Note:\n Blank cells= Not applicable or absent information ** ** **File:\n CC_Survival.csv** **Description:** These data contain the final survival\n of all tree seedlings at the end of the experiment used in survival\n analysis. ***Variables*** *Range:* Geographic region of the study site\n within Maine. Levels include, Central= Central sites, Northern= Northern\n sites, Southern= Southern sites *Site:* Study sites where seedlings were\n planted. Sites include Colby= Colby college, UMKF= University of Maine at\n Fort Kent (UMFK), UMO= University of Maine at Orono, UMF= University of\n Maine at Farmington, UNE= University of New England (UNE), Schoodic=\n Schoodic Institute. All sites are located in Maine, U.S.A. *Position:*\n Canopy position where seedlings were planted. Gap= indicates planting\n location at the interior of the forest, and Edge= indicates planting\n location at the exterior of the forest *Soiltype:* Soil types used for\n planting. Native= Native soil and Garden= Garden soil *Species:* Tree\n species planted in the experiment. welve northeastern US tree species used\n in this study. They are named with their common names. Namely; Eastern red\n cedar, Yellow poplar, Loblolly pine*,* Virginia pine, Chestnut\n oak*,* Black oak, Balsam fir, White spruce, Eastern white pine,\n Red maple, Black cherry, Red oak *Survival:* Seedling survival status\n recorded as a binary variable, 0=dead, 1=alive *Temp:* Five-year average\n annual air temperature (°C) for each study site *SPEI:* Five-year average\n annual Standardized Precipitation Evapotranspiration Index named as SPEI\n for each study site \n \n **File: RHt.csv** **Description:** These data contain the relative height\n growth rate of all seedlings included in the height growth analysis.\n ***Variables*** *Range:* Geographic region of the study site within Maine.\n Levels include, Central= Central sites, Northern= Northern sites,\n Southern= Southern sites *Site:* Study sites where seedlings were planted.\n Sites include Colby= Colby college, UMKF= University of Maine at Fort Kent\n (UMFK), UMO= University of Maine at Orono, UMF= University of Maine at\n Farmington, UNE= University of New England (UNE), Schoodic= Schoodic\n Institute. All sites are located in Maine, U.S.A. *Position:* Canopy\n position where seedlings were planted. Gap= indicates planting location at\n the interior of the forest, and Edge= indicates planting location at the\n exterior of the forest *Soiltype:* Soil types used for planting. Native=\n Native soil and Garden= Garden soil *Species:* Tree species planted in the\n experiment. Twelve northeastern US tree species used in this study. They\n are named with their common names. Namely; Eastern red cedar, Yellow\n poplar, Loblolly pine*,* Virginia pine, Chestnut oak*,*\n Black oak, Balsam fir, White spruce, Eastern white pine, Red maple, Black\n cherry, Red oak *RHt:* Relative height growth rate of each seedling,\n calculated from the initial and final height measurements over the\n duration of the experiment. *Temp:* Five-year average annual air\n temperature (°C) for each study site *SPEI:* Five-year average annual\n Standardized Precipitation Evapotranspiration Index named as SPEI for each\n study site \n \n **File: RD.csv** **Description:** These data contain the relative\n diameter growth rate of all seedlings included in the diameter growth\n analysis. ***Variables*** *Range:* Geographic region of the study site\n within Maine. Levels include, Central= Central sites, Northern= Northern\n sites, Southern= Southern sites *Site:* Study sites where seedlings were\n planted. Sites include Colby= Colby college, UMKF= University of Maine at\n Fort Kent (UMFK), UMO= University of Maine at Orono, UMF= University of\n Maine at Farmington, UNE= University of New England (UNE), Schoodic=\n Schoodic Institute. All sites are located in Maine, U.S.A. *Position:*\n Canopy position where seedlings were planted. Gap= indicates planting\n location at the interior of the forest, and Edge= indicates planting\n location at the exterior of the forest *Soiltype:* Soil types used for\n planting. Native= Native soil and Garden= Garden soil *Species:* Tree\n species planted in the experiment. Twelve northeastern US tree species\n used in this study. They are named with their common names. Namely;\n Eastern red cedar, Yellow poplar, Loblolly pine*,* Virginia pine,\n Chestnut oak*,* Black oak, Balsam fir, White spruce, Eastern\n white pine, Red maple, Black cherry, Red oak *RD:* Relative diameter\n growth rate of each seedling, calculated from the initial and final\n diameter measurements over the duration of the experiment. *Temp:*\n Five-year average annual air temperature (°C) for each study site *SPEI:*\n Five-year average annual Standardized Precipitation Evapotranspiration\n Index named as SPEI for each study site    **File: AAHt.csv**\n **Description:** These data contain the annual absolute height growth of\n all seedlings included in the absolute height growth analysis.\n ***Variables*** *Range:* Geographic region of the study site within Maine.\n Levels include, Central= Central sites, Northern= Northern sites,\n Southern= Southern sites *Site:* Study sites where seedlings were planted.\n Sites include Colby= Colby college, UMKF= University of Maine at Fort Kent\n (UMFK), UMO= University of Maine at Orono, UMF= University of Maine at\n Farmington, UNE= University of New England (UNE), Schoodic= Schoodic\n Institute. All sites are located in Maine, U.S.A. *Position:* Canopy\n position where seedlings were planted. Gap= indicates planting location at\n the interior of the forest, and Edge= indicates planting location at the\n exterior of the forest *Soiltype:* Soil types used for planting. Native=\n Native soil and Garden= Garden soil *Species:* Tree species planted in the\n experiment. Twelve northeastern US tree species used in this study. They\n are named with their common names. Namely; Eastern red cedar, Yellow\n poplar, Loblolly pine*,* Virginia pine, Chestnut oak*,*\n Black oak, Balsam fir, White spruce, Eastern white pine, Red maple, Black\n cherry, Red oak *AAHt:* Annual absolute height growth of each seedling,\n calculated from the initial and final diameter measurements over the\n duration of the experiment. *Temp:* Five-year average annual air\n temperature (°C) for each study site *SPEI:* Five-year average annual\n Standardized Precipitation Evapotranspiration Index named as SPEI for each\n study site    **File: AAD.csv** **Description:** These data contain the\n annual absolute diameter growth of all seedlings included in the absolute\n diameter growth analysis. ***Variables*** *Range:* Geographic region of\n the study site within Maine. Levels include, Central= Central sites,\n Northern= Northern sites, Southern= Southern sites *Site:* Study sites\n where seedlings were planted. Sites include Colby= Colby college, UMKF=\n University of Maine at Fort Kent (UMFK), UMO= University of Maine at\n Orono, UMF= University of Maine at Farmington, UNE= University of New\n England (UNE), Schoodic= Schoodic Institute. All sites are located in\n Maine, U.S.A. *Position:* Canopy position where seedlings were planted.\n Gap= indicates planting location at the interior of the forest, and Edge=\n indicates planting location at the exterior of the forest *Soiltype:* Soil\n types used for planting. Native= Native soil and Garden= Garden soil\n *Species:* Tree species planted in the experiment. Twelve northeastern US\n tree species used in this study. They are named with their common names.\n Namely; Eastern red cedar, Yellow poplar, Loblolly pine*,*\n Virginia pine, Chestnut oak*,* Black oak, Balsam fir, White\n spruce, Eastern white pine, Red maple, Black cherry, Red oak *AAD:* Annual\n absolute height growth of each seedling, calculated from the initial and\n final diameter measurements over the duration of the experiment. *Temp:*\n Five-year average annual air temperature (°C) for each study site *SPEI:*\n Five-year average annual Standardized Precipitation Evapotranspiration\n Index named as SPEI for each study site \n \n **File: SitewiseAbsoluteHeightDecline.csv** **Description:** These data\n contain the percent decline of annual absolute height growth for different\n study sites. ***Variables*** *Sites:* Study sites where seedlings were\n planted. Sites include Colby= Colby college, UMKF= University of Maine at\n Fort Kent (UMFK), UMO= University of Maine at Orono, UMF= University of\n Maine at Farmington, UNE= University of New England (UNE), Schoodic=\n Schoodic Institute. All sites are located in Maine, U.S.A. *AAHt:* Percent\n decline of annual absolute height growth of seedlings of different sites.\n *Temp:* Five-year average annual air temperature (°C) for each study site\n *SPEI:* Five-year average annual Standardized Precipitation\n Evapotranspiration Index named as SPEI for each study site   **File:\n SpecieswiseAbsoluteHeightDecline.csv** **Description:** These data contain\n the percent decline of annual absolute height growth of different study\n species across different study sites. ***Variables*** *Sites:* Study sites\n where seedlings were planted. Sites include Colby= Colby college, UMKF=\n University of Maine at Fort Kent (UMFK), UMO= University of Maine at\n Orono, UMF= University of Maine at Farmington, UNE= University of New\n England (UNE), Schoodic= Schoodic Institute. All sites are located in\n Maine, U.S.A. *Species:* Tree species planted in the experiment. Twelve\n northeastern US tree species used in this study. They are named with their\n common names. Namely; Eastern red cedar, Yellow poplar, Loblolly\n pine*,* Virginia pine, Chestnut oak*,* Black oak, Balsam\n fir, White spruce, Eastern white pine, Red maple, Black cherry, Red oak\n *AAHt:* Percent decline of annual absolute height growth of seedlings of\n different tree species included in our study. *Temp:* Five-year average\n annual air temperature (°C) for each study site *SPEI:* Five-year average\n annual Standardized Precipitation Evapotranspiration Index named as SPEI\n for each study site   \n \n **File: Canopy_Openness.csv** **Description:** These data contain the\n canopy openness (%) measurements collect at the forest edge and forest\n interior plots across six study sites. ***Variables*** *Site:* Study sites\n where seedlings were planted. Sites include Colby= Colby college, UMKF=\n University of Maine at Fort Kent (UMFK), UMO= University of Maine at\n Orono, UMF= University of Maine at Farmington, UNE= University of New\n England (UNE), Schoodic= Schoodic Institute. All sites are located in\n Maine, U.S.A. *Position:* Canopy position where seedlings were planted.\n Interior= indicates planting location at the interior of the forest, and\n Edge= indicates planting location at the exterior of the forest *Corner:*\n Corner of the sampling plot where canopy openness was measured.\n Measurements were collected at the four plot corners (Corner 1, Corner 2,\n Corner 3, and Corner 4) *Direction:* Cardinal direction in which canopy\n openness was measured at each plot corner. Measurements were recorded\n facing North (N), East (E), South (S), and West (W) *CanopyOpenness:*\n Canopy openness expressed as a percentage (%), measured using a convex\n spherical densiometer  "}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Maine Agricultural and Forest Experiment Station","funderIdentifier":"https://ror.org/03q3mt530","awardNumber":"ME0-42121"},{"funderIdentifierType":"ROR","funderName":"Maine Agricultural and Forest Experiment Station","funderIdentifier":"https://ror.org/03q3mt530","awardNumber":"ME0-22502"},{"funderIdentifierType":"ROR","funderName":"Office of Integrative Activities","funderIdentifier":"https://ror.org/04k9mqs78","awardTitle":"\n        Collaborative Research: E-RISE RII: Enhancing Maine Forest Economy,\n        Sustainability, and Technology Ecosystem To Accelerate Innovation\n      ","awardNumber":"2416915"},{"funderIdentifierType":"ROR","funderName":"Center for Discrete Mathematics and Theoretical Computer Science","funderIdentifier":"https://ror.org/00k551w06","awardNumber":"CAFS.21.92"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.547d7wmnt","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-10-06T03:51:10Z","registered":"2026-10-06T03:51:11Z","published":null,"updated":"2026-10-06T03:51:11Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.f1vhhmhch","type":"dois","attributes":{"doi":"10.5061/dryad.f1vhhmhch","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Seoul National University","Wageningen University \u0026 Research"],"name":"Kaiser, Elias","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9081-9604"}]},{"nameType":"Personal","affiliation":["Seoul National University"],"name":"Sun, Rui","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Seoul National University"],"name":"Ryu, Youngryel","nameIdentifiers":[]}],"titles":[{"title":"Data from: Overlooked sunfleck properties and potential implications for crop improvement"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Agricultural sciences","subjectScheme":"fos"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Photosynthesis","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Crops","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Sunlight","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[{"nameType":"Personal","affiliation":["Seoul National University","Wageningen University \u0026 Research"],"name":"Kaiser, Elias","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9081-9604"}],"contributorType":"ProjectLeader"},{"nameType":"Personal","affiliation":["Seoul National University","Wageningen University \u0026 Research"],"name":"Kaiser, Elias","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9081-9604"}],"contributorType":"DataCurator"},{"nameType":"Personal","affiliation":["Seoul National University","Wageningen University \u0026 Research"],"name":"Kaiser, Elias","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9081-9604"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["Seoul National University","Wageningen University \u0026 Research"],"name":"Kaiser, Elias","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9081-9604"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["Seoul National University"],"name":"Sun, Rui","contributorType":"DataCurator","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Seoul National University"],"name":"Sun, Rui","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Seoul National University"],"name":"Sun, Rui","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Seoul National University"],"name":"Sun, Rui","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Seoul National University"],"name":"Ryu, Youngryel","contributorType":"ProjectManager","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Seoul National University"],"name":"Ryu, Youngryel","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Seoul National University"],"name":"Ryu, Youngryel","contributorType":"Supervisor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Seoul National University","Wageningen University \u0026 Research"],"name":"Kaiser, Elias","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9081-9604"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2026-09-28T06:21:25Z","dateType":"Created"},{"date":"2026-09-28T06:22:12Z","dateType":"Submitted"},{"date":"2026-10-06T00:00:00Z","dateType":"Issued"},{"date":"2026-10-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["1380647774 bytes"],"formats":[],"version":"4","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Sunflecks are ubiquitous in crop canopies, yet their properties are\n understudied. Recent data indicates that on sunny days, wind-induced\n sunflecks within crop canopies (windflecks) occur roughly twice per second\n and at amplitudes of roughly 10% of full sunlight. However, studies on the\n effects of fluctuating light on photosynthesis and plant growth often use\n fluctuations that do not resemble sunflecks, possibly resulting in\n irrelevant crop improvement targets. In this review, first, we quantify\n this mismatch between experiments and reality, showing that most studies\n measuring light intensities in crop canopies did so too infrequently to\n capture all sunfleck properties, while studies either simulating dynamic\n photosynthesis or growing plants under fluctuating light did so using much\n slower fluctuations than those often encountered in the field. Second, we\n attempted to estimate the losses of photosynthesis due to sunflecks: using\n light intensity fluctuations recorded inside a rice canopy at high\n frequency (100 Hz) on a sunny day, a dynamic photosynthesis model based\n analysis calculated minimal losses (0.85-1.24% diurnally; relative to\n steady-state simulations), suggesting that sunflecks are too frequent,\n short, and weak to cause substantial losses due to slow dynamics of\n photosynthesis. We found that the choice of light intensity pattern\n matters enormously, as estimated losses reached 13% diurnally when using a\n less realistic light intensity pattern. We also introduce a framework for\n determining photosynthesis under windflecks based on pulsed light\n experiments. Thirdly, we present an overview of effects of global climate\n change on dynamic photosynthesis and crop growth under fluctuating light,\n concluding that likely, rising [CO2] will benefit photosynthesis under\n sunflecks, whereas heat, VPD and drought may be detrimental. Finally, we\n suggest future research directions, noting that new targets for crop\n improvements under sunflecks will require a much better characterization\n of sunflecks, their windfleck and cloudfleck components, and their effects\n on photosynthesis and plant growth."},{"descriptionType":"TechnicalInfo","description":"# Data from: Overlooked sunfleck properties and potential implications for\n crop improvement Dataset DOI:\n [10.5061/dryad.f1vhhmhch](https://doi.org/10.5061/dryad.f1vhhmhch) ##\n Description of the data and file structure Data belonging to Kaiser et\n al., 2026, Global Change Biology: *Overlooked sunfleck properties and\n potential implications for crop improvement* ### Files and variables ####\n File: Fig1a.csv **Description:** Photosynthetically active radiation (PAR;\n umol m-2 s-1) logged at 100 Hz above and inside a rice canopy #####\n Variables * time: Time (yyyy-mm-dd hh:mm:ss) * above_canopy: PAR logged\n above the canopy * 25_cm_below: PAR logged at 25 cm below the top of the\n canopy * 40_cm_below: PAR logged at 40 cm below the top of the canopy ####\n File: Fig1bc.csv **Description:** Photosynthetically active radiation\n (PAR; umol m-2 s-1) logged at 100 Hz above and inside a maize canopy #####\n Variables * time: Time (yyyy-mm-dd hh:mm:ss) * 25_cm_below: PAR logged at\n 40 cm below the top of the canopy * 40_cm_below: PAR logged at 90 cm below\n the top of the canopy #### **Files: Fig1DEF.m; Fig1d.csv; Fig1e.csv;\n Fig1f.csv; Fig1def_README.md** `code/Fig1DEF.m` plots the three 100-s PPFD\n intervals shown in Figure 1D–F and applies the Durand et al. 2021\n event-detection method. | Panel | Weather | Date | DOY | Sensor | Data\n file | Detected events | | ----- | -------- | ---------- | --: |\n ---------------- | ---------------- | --------------: | | D | Sunny |\n 2025-07-28 | 209 | 50 cm (sensor 2) | `data/Fig1d.csv` | 72 | | E | Cloudy\n | 2025-07-25 | 206 | 50 cm (sensor 2) | `data/Fig1e.csv` | 0 | | F |\n Overcast | 2025-07-19 | 200 | 50 cm (sensor 2) | `data/Fig1f.csv` | 0 |\n Each CSV contains 10,000 observations at 100 Hz. Columns are\n `time_within_window_s`, `time_since_midnight_s`, and\n `ppfd_umol_photons_m2_s1`. The values were extracted from\n `PAR_rice_2025.mat` without smoothing, averaging, or clipping. #### File:\n Fig3c.csv **Description:** Net photosynthesis rate as a function of\n frequency of pulsed light at a duty ratio of 0.25. ##### Variables *\n Dataset: Publication that data was derived from * Pulse_frequency:\n Frequency at which light intensity pulses were applied (Hz) *\n Photosynthesis_percentage: Percentage of steady-state photosynthesis (%)\n #### Files: Fig4.m; Fig4a.csv; Fig4c.csv; Fig4e.csv; Fig4_README.md\n `code/Fig4.m` contains the simulation used for Figure 4. ## Input data |\n File | Description | |---|---| | `data/Fig4a.csv` | Full-resolution light\n sequence (100 Hz). | | `data/Fig4c.csv` | Point-sampled from the Figure 4a\n sequence every 60 s (one original point per minute; no averaging). | |\n `data/Fig4e.csv` | Light sequence digitized from Taylor and Long (2017),\n Figure 6a, using WebPlotDigitizer. | The first two files use `time_s`; the\n Fig. 4e file uses `time_h`. In all files, `ppfd_umol_photons_m2_s1` is\n PPFD in µmol photons m⁻² s⁻¹. Data values and row order are preserved; no\n smoothing or clipping was applied. **Source for Fig. 4e:** Taylor, S. H.,\n \u0026amp; Long, S. P. (2017). Slow induction of photosynthesis on shade to sun\n transitions in wheat may cost at least 21% of productivity. *Philosophical\n Transactions of the Royal Society B: Biological Sciences, 372*, 20160543.\n [https://doi.org/10.1098/rstb.2016.0543](https://doi.org/10.1098/rstb.2016.0543) `Fig4.m` retains the original local paths and MAT/XLSX input readers. To run the downloaded copy, update the paths and input-reading section for the CSV files listed above. #### File: FigS1.csv **Description:** Percentage of studies mentioning photosynthesis under fluctuating light per year. ##### Variables * Year: year number between 1980 and 2025 * Photosynthesis_fluctuating_light: hits in Google scholar when searching for \u0026gt;photosynthesis, \"fluctuating light\"\u0026lt; * Photosynthesis: hits in Google scholar when searching for \u0026gt;photosynthesis\u0026lt; #### File: FigS4.m **Description:** MATLAB code used to construct Fig. S4, which shows the behaviour of the dynamic photosynthesis and stomatal conductance model under different light intensities (light response curves), during photosynthetic induction, and during lightflecks. #### Files: fitTau_gs_Yamori_Vico_separate.m; gs_PAR_rice_m.; RubiscoTau_rice.m; yamori2020_fig1b_a_vs_ci.csv; yamori2020_fig1b_a_vs_ci.csv; yamori2020_fig1b_a_vs_ci.csv; yamori2020_fig2b_gs_induction.csv; yamori2020_fig2c_a_induction.csv; yamori2020_fig4b_gs_low_light.csv; yamori2020_fig4c_a_low_light.csv; yamori2020_figS2c_rubisco_activation.csv; TableS5_README.md #### Description: Yamori et al. (2020) digitized data and fitting code Digitized by Rui Sun on 2026-02-06 using WebPlotDigitizer. Coordinates and scales follow the published figures. No interpolation or smoothing was applied to the CSV data. Source: Yamori, W., Kusumi, K., Iba, K., \u0026amp; Terashima, I. (2020). Increased stomatal conductance induces rapid changes to photosynthetic rate in response to naturally fluctuating light conditions in rice. *Plant, Cell \u0026amp; Environment*, 43, 1230–1240. [https://doi.org/10.1111/pce.13725](https://doi.org/10.1111/pce.13725) ## Data | CSV file | Source | | --- | --- | | yamori2020_fig1b_a_vs_ci.csv | Figure 1b | | yamori2020_fig1c_gs_vs_par.csv | Figure 1c | | yamori2020_fig1d_a_vs_par.csv | Figure 1d | | yamori2020_figS2c_rubisco_activation.csv | Figure S2c | | yamori2020_fig2b_gs_induction.csv | Figure 2b | | yamori2020_fig2c_a_induction.csv | Figure 2c | | yamori2020_fig4b_gs_low_light.csv | Figure 4b | | yamori2020_fig4c_a_low_light.csv | Figure 4c | Units are included in the CSV column names. `gs` is stomatal conductance, `A` is net CO2 assimilation, `Ci` is intercellular CO2 concentration, and `PAR` is photosynthetically active radiation. ## Code The scripts retain the original local file paths. Update the input paths before running the downloaded copies. The `code` folder contains the parameter-fitting scripts: * `gsPAR_rice.m`: steady-state stomatal parameters (`gmin`, `gmax`, `alpha_gs`, and `theta_gs`). * `RubiscoTau_rice.m`: Rubisco activation and deactivation time constants (`tau_i,R` and `tau_d,R`). * `fitTau_gs_Yamori_Vico_separate.m`: stomatal opening and closing time constants (`tau_op` and `tau_cl`). #### File: TableS6.m **Description:** MATLAB code used to generate data shown in Table S6 (sensitivity analysis of dynamic photosynthesis model). ### Code/software MATLAB"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Korea Institute of Science and Technology","funderIdentifier":"https://ror.org/05kzfa883","awardNumber":"RS-2024-00348585"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.f1vhhmhch","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-10-06T00:11:54Z","registered":"2026-10-06T00:11:55Z","published":null,"updated":"2026-10-06T00:11:55Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.v6wwpzhcp","type":"dois","attributes":{"doi":"10.5061/dryad.v6wwpzhcp","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Washington Applied Physics Laboratory"],"name":"Vladoiu, Anda","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0006-4539-915X"}]},{"nameType":"Personal","affiliation":["University of Washington Applied Physics Laboratory"],"name":"Lien, Ren-Chieh","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Washington Applied Physics Laboratory","University of Washington Applied Physics Laboratory"],"name":"Ma, Barry","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["National Taiwan University"],"name":"Hsu, Je-Yuan","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["National Taiwan University"],"name":"Chang, Ming-Huei","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Stanford University"],"name":"Thomas, Leif","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["National Taiwan University"],"name":"Jan, Sen","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["National Taiwan Ocean University"],"name":"Cheng, Yu-Hsin","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["National Taiwan University"],"name":"Yang, Yiing Jang","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Eastern Fishery Research Center, Fisheries Research Institute"],"name":"Chiang, Wei-Chuan","nameIdentifiers":[]}],"titles":[{"title":"Data from: Kuroshio-induced island wake instabilities and turbulent mixing from EM-APEX floats"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Earth and related environmental sciences","subjectScheme":"fos"},{"subject":"turbulent mixing"},{"subject":"island wake dynamics"},{"subject":"Kuroshio instabilities"}],"contributors":[{"nameType":"Personal","affiliation":["University of Washington Applied Physics Laboratory"],"name":"Vladoiu, Anda","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0006-4539-915X"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2026-09-24T20:58:05Z","dateType":"Created"},{"date":"2026-09-24T20:58:08Z","dateType":"Submitted"},{"date":"2026-10-05T00:00:00Z","dateType":"Issued"},{"date":"2026-10-05T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1175/jpo-d-26-0075.1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["51782575 bytes"],"formats":[],"version":"4","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Clusters of 6 to 9 EM-APEX profiling floats measured CTD, horizontal\n velocity, and microstructure thermal variance during four deployments at\n the same locations in the Green Island wake induced by the Kuroshio. Large\n turbulent kinetic energy dissipation rates ~O(10-7-10-6) W/kg were\n observed in the near-field wake. Enhanced turbulent mixing is robustly\n explained by vertical shear instability with subcritical Richardson\n number. Horizontal divergence, relative vorticity, lateral strain, and\n potential vorticity were estimated from float clusters. Strong negative\n relative vorticity O(-10f) potentially suggests inertial instability and\n strong negative potential vorticity, symmetric instability, but the\n measurements are in a nonlinear transitional imbalance so conventional\n submesoscale instability criteria, assuming geostrophic balance, are not\n sufficient. Observed dissipation rates increase with decreasing Richardson\n number and increasing magnitude of negative relative vorticity, suggesting\n inertial instability leading to secondary vertical shear instability.\n Dissipation rates, strain, and negative relative and potential vorticity\n magnitudes decrease from near- to far-field. In the upper 200 m,\n temperature-salinity deviations from typical Kuroshio properties coincide\n with large dissipation rate and thermal eddy diffusivity, implying\n significant watermass modifications. Turbulent mixing and wake eddy\n formation are modulated by the semidiurnal tides. The origin of the\n observed strong negative vorticity, effects of internal tidal modulation\n on the formation of anticyclones/cyclones and their downstream evolution,\n and the generation and evolution of near-inertial waves in the wake\n warrant further investigation."},{"descriptionType":"TechnicalInfo","description":"# Data from: Kuroshio-induced island wake instabilities and turbulent\n mixing from EM-APEX floats Dataset DOI:\n [10.5061/dryad.v6wwpzhcp](https://doi.org/10.5061/dryad.v6wwpzhcp) Data to\n accompany the article \"Kuroshio-induced island wake instabilities and\n turbulent mixing from EM-APEX floats\" (Journal of Physical\n Oceanography, DOI 10.1175/JPO-D-26-0075.1) by Anda Vladoiu, Ren-Chieh\n Lien, Barry Ma, Je-Yuan Hsu, Ming-Huei Chang, Leif Thomas, Sen Jan,\n Yu-Hsin Cheng, Yiing Jang Yang, Wei-Chuan Chiang. The SupportingData.zip\n archive contains .mat processed data files used in creating the figures in\n the article. Please see article for detailed data description and\n processing methods. Descriptions of each .mat file, corresponding to\n distinct figures, are below, with units in square brackets. Each file\n contains structures pertaining to variables plotted in each figure panel,\n named in alphabetical order. Missing values (due to quality-control,\n gridding or analysis procedures, as described in the manuscript) are\n retained as NaN. Contact Anda Vladoiu\n ([avladoiu@uw.edu](mailto:avladoiu@uw.edu)) for additional information. ##\n Description of data ### Fig. 1 Fig. 1. (a-d) EM-APEX floats trajectories\n during each of the four deployments (surface GPS positions). (e) Sea level\n timeseries at Chenggong port. Time is in GMT. Fig1.mat Structure\n \"Fig1a\" contains variables: \"lat\" = section latitude\n [degN] \"lon\" = section longitude [degE] Structure\n \"Fig1b\" contains variables: \"lat\" = section latitude\n [degN] \"lon\" = section longitude [degE] Structure\n \"Fig1c\" contains variables: \"lat\" = section latitude\n [degN] \"lon\" = section longitude [degE] Structure\n \"Fig1d\" contains variables: \"lat\" = section latitude\n [degN] \"lon\" = section longitude [degE] Structure\n \"Fig1e\" contains variables: \"SeaLevel\" = sea level [m]\n \"TimeSeaLevel\" = sea level time [matlab time]\n \"DeplBegTime\" = deployments beginning time [matlab time]\n \"DeplEndTime\" = deployments end time [matlab time] ### Fig. 2\n Fig. 2. Time-latitude-depth plots of salinity (a1-a4), temperature\n (b1-b4), zonal velocity (c1-c4), and meridional velocity (d1-d4) from all\n EM-APEX floats. Fig2.mat Variable 'floats' contains the serial\n numbers for each of the nine floats. Structures\n \"Fig2*a1-a4*.float*1-9*\" (where *a1-a4* pertains to each\n deployment and *1-9* pertains to each float) contains variables:\n \"time\" = time [matlab time] \"lat\" = latitude [degN]\n \"depth\" = depth [m] \"S\" = salinity [psu] Structures\n \"Fig2*b1-b4*.float*1-9*\" (where *b1-b4* pertains to each\n deployment and *1-9* pertains to each float) contains variables:\n \"time\" = time [matlab time] \"lat\" = latitude [degN]\n \"depth\" = depth [m] \"T\" = temperature [degC]\n Structures \"Fig2*c1-c4*.float*1-9*\" (where *c1-c4* pertains to\n each deployment and *1-9* pertains to each float) contains variables:\n \"time\" = time [matlab time] \"lat\" = latitude [degN]\n \"depth\" = depth [m] \"u\" = zonal velocity [m/s]\n Structures \"Fig2*d1-d4*.float*1-9*\" (where *d1-d4* pertains to\n each deployment and *1-9* pertains to each float) contains variables:\n \"time\" = time [matlab time] \"lat\" = latitude [degN]\n \"depth\" = depth [m] \"v\" = meridional velocity [m/s]\n ### Fig. 3 Fig. 3. Time-latitude-depth plots of zonal vertical shear\n (a1-a4), meridional vertical shear (b1-b4), vertical shear magnitude\n squared (c1-c4), and Richardson number (d1-d4) from all EM-APEX floats,\n for each of the four deployments. Fig3.mat Variable 'floats'\n contains the serial numbers for each of the nine floats. Structures\n \"Fig3*a1-a4*.float*1-9*\" (where *a1-a4* pertains to each\n deployment and *1-9* pertains to each float) contains variables:\n \"time\" = time [matlab time] \"lat\" = latitude [degN]\n \"depth\" = depth [m] \"dudz\" = zonal vertical shear\n [1/s] Structures \"Fig3*b1-b4*.float*1-9*\" (where *b1-b4*\n pertains to each deployment and *1-9* pertains to each float) contains\n variables: \"time\" = time [matlab time] \"lat\" =\n latitude [degN] \"depth\" = depth [m] \"dvdz\" =\n meridional vertical shear [1/s] Structures\n \"Fig3*c1-c4*.float*1-9*\" (where *c1-c4* pertains to each\n deployment and *1-9* pertains to each float) contains variables:\n \"time\" = time [matlab time] \"lat\" = latitude [degN]\n \"depth\" = depth [m] \"S2\" = vertical shear magnitude\n [1/s^2] Structures \"Fig3*d1-d4*.float*1-9*\" (where *d1-d4*\n pertains to each deployment and *1-9* pertains to each float) contains\n variables: \"time\" = time [matlab time] \"lat\" =\n latitude [degN] \"depth\" = depth [m] \"Ri\" = Richardson\n number [] ### Fig. 4 Fig. 4. Time-latitude-depth plots of buoyancy\n frequency squared (a1-a4), thermal variance dissipation rate (b1-b4),\n turbulent kinetic energy dissipation rate (c1-c4), and thermal diffusivity\n (d1-d4) from all EM-APEX floats, for each of the four deployments.\n Fig4.mat Variable 'floats' contains the serial numbers for each\n of the nine floats. Structures \"Fig4*a1-a4*.float*1-9*\" (where\n *a1-a4* pertains to each deployment and *1-9* pertains to each float)\n contains variables: \"time\" = time [matlab time] \"lat\"\n = latitude [degN] \"depth\" = depth [m] \"N2\" = buoyancy\n frequency squared [1/s^2] Structures \"Fig4*b1-b4*.float*1-9*\"\n (where *b1-b4* pertains to each deployment and *1-9* pertains to each\n float) contains variables: \"time\" = time [matlab time]\n \"lat\" = latitude [degN] \"depth\" = depth [m]\n \"chi\" = thermal variance dissipation rate [degC/s] Structures\n \"Fig4*c1-c4*.float*1-9*\" (where *c1-c4* pertains to each\n deployment and *1-9* pertains to each float) contains variables:\n \"time\" = time [matlab time] \"lat\" = latitude [degN]\n \"depth\" = depth [m] \"epsilon\" = turbulent kinetic\n energy dissipation rate [W/kg] Structures\n \"Fig4*d1-d4*.float*1-9*\" (where *d1-d4* pertains to each\n deployment and *1-9* pertains to each float) contains variables:\n \"time\" = time [matlab time] \"lat\" = latitude [degN]\n \"depth\" = depth [m] \"KT\" = thermal diffusivity [m^2/s]\n ### Fig. 5 Fig. 5. Temperature-Salinity diagrams of bin-median depth\n (a1-a4), buoyancy frequency squared (b1-b4), vertical shear magnitude\n squared (c1-c4), Richardson number (d1-d4), turbulent kinetic energy\n dissipation rate (e1-e4), and thermal diffusivity (f1-f4), for each of the\n four deployments between 10-300 m depth. Fig5.mat Structures\n \"Fig5*a1-a4*\" (where *a1-a4* pertains to each deployment)\n contains variables: \"T\" = temperature [degC] \"S\" =\n salinity [psu] \"depth\" = depth [m] Structures\n \"Fig5*b1-b4*\" (where *a1-a4* pertains to each deployment)\n contains variables: \"T\" = temperature [degC] \"S\" =\n salinity [psu] \"N2\" = buoyancy frequency squared [1/s^2]\n Structures \"Fig5*c1-c4*\" (where *a1-a4* pertains to each\n deployment) contains variables: \"T\" = temperature [degC]\n \"S\" = salinity [psu] \"S2\" = vertical shear magnitude\n [1/s^2] Structures \"Fig5*d1-d4*\" (where *a1-a4* pertains to each\n deployment) contains variables: \"T\" = temperature [degC]\n \"S\" = salinity [psu] \"Ri\" = Richardson number []\n Structures \"Fig5*e1-e4*\" (where *a1-a4* pertains to each\n deployment) contains variables: \"T\" = temperature [degC]\n \"S\" = salinity [psu] \"epsilon\" = turbulent kinetic\n energy dissipation rate [W/kg] Structures \"Fig5*f1-f4*\" (where\n *a1-a4* pertains to each deployment) contains variables: \"T\" =\n temperature [degC] \"S\" = salinity [psu] \"KT\" = thermal\n diffusivity [m^2/s] ### Fig. 6 Fig. 6. Depth profiles of median\n temperature (a), salinity (b), potential density (c), buoyancy frequency\n squared (d) vertical shear (e), Richardson number (f), dissipation rate\n (g), lateral strain (h), Rossby number (i) and potential vorticity (j),\n for near-field and far-field profiles, from all four deployments. Fig6.mat\n Structure \"Fig6a\" contains variables: \"NF\" =\n near-field temperature [degC] \"FF\" = far-field temperature\n [degC] \"z\" = depth [m] Structure \"Fig6b\" contains\n variables: \"NF\" = near-field salinity [psu] \"FF\" =\n far-field salinity [psu] \"z\" = depth [m] Structure\n \"Fig6c\" contains variables: \"NF\" = near-field\n potential density [kg/m^3] \"FF\" = far-field potential density\n [kg/m^3] \"z\" = depth [m] Structure \"Fig6d\" contains\n variables: \"NF\" = near-field buoyancy frequency squared [1/s^2]\n \"FF\" = far-field buoyancy frequency squared [1/s^2]\n \"z\" = depth [m] Structure \"Fig6e\" contains variables:\n \"NF\" = near-field vertical shear magnitude [1/s^2]\n \"FF\" = far-field vertical shear magnitude [1/s^2] \"z\"\n = depth [m] Structure \"Fig6f\" contains variables: \"NF\"\n = near-field Richardson number [] \"FF\" = far-field Richardson\n number [] \"z\" = depth [m] Structure \"Fig6g\" contains\n variables: \"NF\" = near-field turbulent kinetic energy\n dissipation rate [W/kg] \"FF\" = far-field turbulent kinetic\n energy dissipation rate [W/kg] \"z\" = depth [m] Structure\n \"Fig6h\" contains variables: \"NF\" = near-field lateral\n strain [] \"FF\" = far-field lateral strain [] \"z\" =\n depth [m] Structure \"Fig6i\" contains variables: \"NF\" =\n near-field Rossby number [] \"FF\" = far-field Rossby number []\n \"z\" = depth [m] Structure \"Fig6j\" contains variables:\n \"NF\" = near-field potential vorticity [] \"FF\" =\n far-field potential vorticity [] \"z\" = depth [m] ### Fig. 7 Fig.\n 7. Bin-median turbulent kinetic energy dissipation rate in buoyancy\n frequency squared and vertical shear squared space, for four deployments\n (a-d) between 10-300 m depth. Fig7.mat Structures \"Fig7*a-d*\"\n (where *a-d* pertains to each deployment)contain variables: \"N2\"\n = buoyancy frequency squared [1/s^2] \"S2\" = vertical shear\n magnitude [1/s^2] \"epsilon\" = turbulent kinetic energy\n dissipation rate [W/kg] ### Fig. 8 Fig. 8. Timeseries of sea level at\n Chenggong port, and isopycnal layer depth, Richardson number, turbulent\n kinetic energy dissipation rate, and latitude, from all floats and all\n four deployments. Fig8.mat Structure \"Fig8\" contains variables:\n \"time_SeaLevel\" = time sea level [matlab time]\n \"SeaLevel\" = sea level [m] \"time_z\" = time depth\n [matlab time] \"z\" = depth [m] \"time_Ri\" = time\n Richardson number [matlab time] \"Ri\" = Richardson number []\n \"time_epsilon\" = time dissipation rate [matlab time]\n \"epsilon\" = turbulent kinetic energy dissipation rate [W/kg]\n \"time_lat\" = time latitude [matlab time] \"lat\" =\n latitude [deg N] ### Fig. 9 Fig. 9. Histograms for relative vertical\n vorticity (a1-a4), horizontal divergence (b1-b4), lateral strain (c1-c4)\n and potential vorticity (d1-d4), from each of the four deployments,\n between 10-300 m depth. Fig9.mat Structure \"Fig9\" containing\n fields: *a1-a4* (pertaining to each deployment), with variables:\n \"y\" = histogram \"x\" = relative vertical vorticity []\n *b1-b4* (pertaining to each deployment), with variables: \"y\" =\n histogram \"x\" = horizontal divergence [] *c1-c4* (pertaining to\n each deployment), with variables: \"y\" = histogram \"x\"\n = lateral strain [] *d1-d4* (pertaining to each deployment), with\n variables: \"y\" = histogram \"x\" = potential vorticity\n [s^-3] ### Fig. 10 Fig. 10. Sep 7 deployment timeseries of: 100-m depth\n float cluster shapes (a); cluster-mean temperature (b); cluster-mean\n salinity (c); cluster-mean turbulent kinetic energy dissipation rate (d);\n cluster-mean Richardson number (e); horizontal divergence (f); relative\n vertical vorticity (g); lateral strain (h); potential vorticity (i) and\n its horizontal (j), (k) and vertical (l) components. Fig10.mat Structure\n \"Fig10\" containing fields *a-l* pertaining to each panel:\n \"Fig10.a\" contains variables: \"lon\" = longitude [degE]\n \"lat\" = latitude [degN] \"Fig10.b\" contains variables:\n \"time\" = time [matlab time] \"depth\" = depth [m]\n \"var\" = temperature [degC] \"Fig10.c\" contains\n variables: \"time\" = time [matlab time] \"depth\" = depth\n [m] \"var\" = salinity [psu] \"Fig10.d\" contains\n variables: \"time\" = time [matlab time] \"depth\" = depth\n [m] \"var\" = turbulent kinetic energy dissipation rate [W/kg]\n \"Fig10.e\" contains variables: \"time\" = time [matlab\n time] \"depth\" = depth [m] \"var\" = Richardson number []\n \"Fig10.f\" contains variables: \"time\" = time [matlab\n time] \"depth\" = depth [m] \"var\" = horizontal\n divergence [] \"Fig10.g\" contains variables: \"time\" =\n time [matlab time] \"depth\" = depth [m] \"var\" =\n relative vorticity [] \"Fig10.h\" contains variables:\n \"time\" = time [matlab time] \"depth\" = depth [m]\n \"var\" = lateral strain [] \"Fig10.i\" contains\n variables: \"time\" = time [matlab time] \"depth\" = depth\n [m] \"var\" = potential vorticity [] \"Fig10.j\" contains\n variables: \"time\" = time [matlab time] \"depth\" = depth\n [m] \"var\" = zonal potential vorticity [] \"Fig10.k\"\n contains variables: \"time\" = time [matlab time]\n \"depth\" = depth [m] \"var\" = meridional potential\n vorticity [] \"Fig10.l\" contains variables: \"time\" =\n time [matlab time] \"depth\" = depth [m] \"var\" =\n vertical potential vorticity [] ### Figs. 11, 12, 13 Figs. 11, 12, 13:\n same as Fig. 10 but for the Sep 9, Sep 13 and Sep 15 deployments,\n respectively. Fig11.mat, Fig12.mat, Fig13.mat: same as Fig10.mat ### Fig.\n 14 Fig. 14. Bin-median divergence (a, b) and turbulent kinetic energy\n dissipation rate (c, d) in relative vertical vorticity and lateral strain\n space, for positive (a, c) and negative (b, d) potential vorticity. Median\n lateral strain (e, f) and turbulent kinetic energy dissipation rate (g, h)\n in relative vertical vorticity and horizontal divergence space, for\n positive (e, g) and negative (f, h), positive vorticity. Fig14.mat\n Structure \"Fig14\" containing fields *a-h* pertaining to each\n panel: \"Fig14.a\" contains variables: \"x\" = relative\n vorticity [] \"y\" = lateral strain [] \"var\" =\n horizontal divergence [degC] \"Fig14.b\" contains variables:\n \"x\" = relative vorticity [] \"y\" = lateral strain []\n \"var\" = horizontal divergence [degC] \"Fig14.c\"\n contains variables: \"x\" = relative vorticity [] \"y\" =\n lateral strain [] \"var\" = turbulent kinetic energy dissipation\n rate [W/kg] \"Fig14.d\" contains variables: \"x\" =\n relative vorticity [] \"y\" = lateral strain [] \"var\" =\n turbulent kinetic energy dissipation rate [W/kg] \"Fig14.e\"\n contains variables: \"x\" = relative vorticity [] \"y\" =\n horizontal divergence [degC] \"var\" = lateral strain []\n \"Fig14.f\" contains variables: \"x\" = relative vorticity\n [] \"y\" = horizontal divergence [degC] \"var\" = lateral\n strain [] \"Fig14.g\" contains variables: \"x\" = relative\n vorticity [] \"y\" = horizontal divergence [degC] \"var\"\n = turbulent kinetic energy dissipation rate [W/kg] \"Fig14.h\"\n contains variables: \"x\" = relative vorticity [] \"y\" =\n horizontal divergence [degC] \"var\" = turbulent kinetic energy\n dissipation rate [W/kg] ### Fig. 15 Fig. 15. Bin-median turbulent kinetic\n energy dissipation rate in (Rossby number, Richardson number) space for\n positive (a) and negative (b) potential vorticity, and as a function of\n Richardson number (c) and Rossby number (d), from all four deployments,\n between 10-300 m depth. \\ In (c)-(d), data for positive potential\n vorticity are shown in red and for negative potential vorticity in blue;\n black squares in (c) show bin-median values computed directly from\n vertical profiles. Fig15.mat Structure \"Fig15\" containing fields\n *a-d* pertaining to each panel: \"Fig15.*a-b*\" contain variables:\n \"Ro\" = Rossby number [] \"Ri\" = Richardson number []\n \"eps\" = turbulent kinetic energy dissipation rate [W/kg]\n \"Fig15.c\" contains variables: \"Ri\" = Richardson number\n [] \"black\" = turbulent kinetic energy dissipation rate [W/kg]\n \"blue\" = turbulent kinetic energy dissipation rate [W/kg]\n \"red\" = turbulent kinetic energy dissipation rate [W/kg]\n \"Fig15.d\" contains variables: \"Ro\" = Rossby number []\n \"blue\" = turbulent kinetic energy dissipation rate [W/kg]\n \"red\" = turbulent kinetic energy dissipation rate [W/kg] ###\n Fig. A1 Fig. A1. Vertical wavenumber spectra for: (a1-a4)\n buoyancy-frequency normalized vertical shear, (b1-b4) vertical strain, and\n (c1-c4) buoyancy-frequency normalized rotary vertical shear, for each of\n the four deployments. Spectra are bin-averaged for: all profiles (black),\n high profile-mean dissipation rate (red), low profile-mean dissipation\n rate (blue), latitudes above 22.85N (orange) and latitudes below 22.85N\n (light blue). FigA1.mat Structure \"FigA1\" containing fields\n *a1-c4* pertaining to each panel: \"FigA1.*a1-a4*\" contain\n variables: \"kz\" = vertical wavenumber [cpm] \"black\" =\n vertical shear spectrum [1/cpm] \"red\" = vertical shear spectrum\n [1/cpm] \"orange\" = vertical shear spectrum [1/cpm]\n \"blue\" = vertical shear spectrum [1/cpm] \"lightblue\" =\n vertical shear spectrum [1/cpm] \"FigA1.*b1-b4*\" contain\n variables: \"kz\" = vertical wavenumber [cpm] \"black\" =\n vertical strain spectrum [1/cpm] \"red\" = vertical strain\n spectrum [1/cpm] \"orange\" = vertical strain spectrum [1/cpm]\n \"blue\" = vertical strain spectrum [1/cpm] \"lightblue\"\n = vertical strain spectrum [1/cpm] \"FigA1.*c1-c4*\" contain\n variables: \"kz\" = vertical wavenumber [cpm] \"black\" =\n clockwise vertical shear spectrum [1/cpm] \"red\" = clockwise\n vertical shear spectrum [1/cpm] \"orange\" = clockwise vertical\n shear spectrum [1/cpm] \"blue\" = clockwise vertical shear\n spectrum [1/cpm] \"lightblue\" = clockwise vertical shear spectrum\n [1/cpm] \"blackdotted\" = counterclockwise vertical shear spectrum\n [1/cpm] \"reddotted\" = counterclockwise vertical shear spectrum\n [1/cpm] \"orangedotted\" = counterclockwise vertical shear\n spectrum [1/cpm] \"bluedotted\" = counterclockwise vertical shear\n spectrum [1/cpm] \"lightbluedotted\" = counterclockwise vertical\n shear spectrum [1/cpm] ### Fig. C1 Fig. C1. Bin-median turbulent kinetic\n energy dissipation rate in (Rossby number, Richardson number) space for\n positive (a, c) and negative (b, d) potential vorticity, for thermal wind\n balance (a, b) and gradient wind balance (c, d).  FigC1.mat Structure\n \"FigC1\" containing fields *a-d* pertaining to each panel:\n \"FigC1.*a-b*\" contain variables: \"Ro\" = Rossby number\n [] \"Ri\" = Richardson number [] \"eps\" = turbulent\n kinetic energy dissipation rate [W/kg] ### Fig. C2 Fig. C2. Sep 7\n deployment timeseries of: 100-m depth float cluster shapes (a); curvature\n Rossby number (b); potential vorticity times absolute angular momentum\n (c); barotropic component (d); baroclinic component (e); Rossby number\n (f);Richardson number (g); cluster mean dissipation rate (h); gradient\n wind balance instability types (i). FigC2.mat Structure \"FigC2\"\n containing fields *a-i* pertaining to each panel: \"FigC2.a\"\n contains variables: \"lon\" = longitude [degE] \"lat\" =\n latitude [degN] \"FigC2.b\" contains variables: \"time\" =\n time [matlab time] \"depth\" = depth [m] \"var\" =\n curvature Rossby number [] \"FigC2.c\" contains variables:\n \"time\" = time [matlab time] \"depth\" = depth [m]\n \"var\" = potential vorticity times angular momentum []\n \"FigC2.d\" contains variables: \"time\" = time [matlab\n time] \"depth\" = depth [m] \"var\" = barotropic component\n [] \"FigC2.e\" contains variables: \"time\" = time [matlab\n time] \"depth\" = depth [m] \"var\" = baroclinic component\n [] \"FigC2.f\" contains variables: \"time\" = time [matlab\n time] \"depth\" = depth [m] \"var\" = Rossby number []\n \"FigC2.g\" contains variables: \"time\" = time [matlab\n time] \"depth\" = depth [m] \"var\" = Richardson number []\n \"FigC2.h\" contains variables: \"time\" = time [matlab\n time] \"depth\" = depth [m] \"var\" = turbulent kinetic\n energy dissipation rate [W/kg] \"FigC2.i\" contains variables:\n \"time\" = time [matlab time] \"depth\" = depth [m]\n \"var\" = gradient wind instability type [] ### Figs. C3, C4, C5\n Figs. C3, C4, C5. Same as Fig. C2 but for the Sep 9, Sep 13 and Sep 15\n deployments, respectively. FigC3.mat, FigC4.mat, FigC5.mat: same as\n FigC2.mat"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Office of Naval Research","funderIdentifier":"https://ror.org/00rk2pe57"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.v6wwpzhcp","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-10-05T23:56:45Z","registered":"2026-10-05T23:56:49Z","published":null,"updated":"2026-10-05T23:56:49Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.d7wm37qhc","type":"dois","attributes":{"doi":"10.5061/dryad.d7wm37qhc","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Universidad Nacional Autónoma de México"],"name":"Benitez-Malvido, Julieta","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6180-1651"}]},{"nameType":"Personal","affiliation":["Universitat de Miguel Hernández d'Elx"],"name":"Graciá, Eva","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Universitat de Miguel Hernández d'Elx"],"name":"Gímenez, Andrés","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-5248-9819"}]},{"nameType":"Personal","affiliation":["Universidad Nacional Autónoma de México"],"name":"Ávila Eulogio, Iraís","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Universidad Nacional Autónoma de México"],"name":"Méndez-Rojas, Diana María","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Universitat de Miguel Hernández d'Elx"],"name":"Rodríguez-Caro, Roberto Carlos","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2321-9497"}]},{"nameType":"Personal","affiliation":["Mediterranean Institute for Advanced Studies"],"name":"Travesset, Anna","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-1816-1334"}]}],"titles":[{"title":"Sex-biased interactions between spur-thighed tortoises (\u003cem\u003eTestudo graeca\u003c/em\u003e) and hindgut nematodes"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"subject":"ecological networks"},{"subject":"hingut nematodes"},{"subject":"Testudo graeca"}],"contributors":[{"nameType":"Personal","affiliation":["Universidad Nacional Autónoma de México"],"name":"Benitez-Malvido, Julieta","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6180-1651"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":["Universitat de Miguel Hernández d'Elx"],"name":"Graciá, Eva","contributorType":"ContactPerson","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Universitat de Miguel Hernández d'Elx"],"name":"Gímenez, Andrés","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-5248-9819"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":["Universidad Nacional Autónoma de México"],"name":"Ávila Eulogio, Iraís","contributorType":"ContactPerson","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Universidad Nacional Autónoma de México"],"name":"Méndez-Rojas, Diana María","contributorType":"ContactPerson","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Universitat de Miguel Hernández d'Elx"],"name":"Rodríguez-Caro, Roberto Carlos","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2321-9497"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":["Mediterranean Institute for Advanced Studies"],"name":"Travesset, Anna","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-1816-1334"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2026-08-20T01:45:03Z","dateType":"Created"},{"date":"2026-08-24T16:08:33Z","dateType":"Submitted"},{"date":"2026-09-21T00:00:00Z","dateType":"Issued"},{"date":"2026-09-21T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.7717/peerj.8076","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.22541/au.177070879.99878872/v1","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1002/ece3.74377","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["13924 bytes"],"formats":[],"version":"11","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Spur-thighed tortoises (Testudo graeca) were sampled in southern Spain.\n For male and female tortoises, we assessed the relationships between\n nematode infestation and body traits (weight, height, plastron width, and\n carapace length) and growth rates. Further, differences in species\n composition and diversity of hindgut nematode communities between sexes\n were investigated. Finally, the structure and diversity of\n tortoise-nematode interactions were evaluated with an intrapopulation\n ecological network approach."},{"descriptionType":"TechnicalInfo","description":"# Sex-biased interactions between spur-thighed tortoises (*Testudo\n graeca*) and hindgut nematodes Dataset DOI:\n [10.5061/dryad.d7wm37qhc](https://doi.org/10.5061/dryad.d7wm37qhc) ##\n Description of the data and file structure Nematode species abbreviations\n are as follows *Alaeuris numidica* (A. num), *Mehdiella stylosa* (M.\n styl), *M. uncinata* (M. unci), *M. microstoma* (M. micro), *Tachygonetria\n longicollis* (T. long), *T. dentata* (T. dent), *T. conica* (T. coni), *T.\n robusta* (T. rob), *T. macrolaimus* (T. mac), *T. numidica* (T. numi), *T.\n setosa* (T. set), *T. palearticus* (T. pale), *T. pusilla* (T. pusi), *T.\n seurati* (T. seur) and *Thaparia thapari* (T. thap).  ### Files and\n variables #### File: Tortoise-NematodeInteractions1.csv\n **Description:** Body traits of male and female tortoises (*Testuto\n graeca*) and the nematode species abundance infesting their hindguts in\n Southern Spain. Variables * Tortoise sex (MALE/FEMALE) * Sampling sites *\n Tortoise age (years) * Tortoise carapace length (mm) * Tortoise\n height (mm) * Tortoise plastron width (mm) * Tortoise weight (g) *\n Tortoise growth rate(mm/year) * Nematode density: total number of\n nematodes per individual tortoise * Tortoise sex: the numbers after the\n sex indicate the ID of each male and female adult tortoise * T_long\n through T_seur: Nematode species abreviations that indicate the taxonomic\n name and number of each nematode species infecting individual male and\n female tortoises **Notes:** * n/a: indicates no available data, as data\n were lost while processing individual tortoises * null: indicates no\n value, as only infected individual tortoises were considered to construct\n the ecological networks and to perform the nematode diversity analyses"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Universidad Nacional Autónoma de México","funderIdentifier":"https://ror.org/01tmp8f25","awardTitle":"Programa de Apoyos para la Superación del Personal Académico de la UNAM"},{"funderIdentifierType":"ROR","funderName":"Ministerio de Ciencia, Innovación y Universidades","funderIdentifier":"https://ror.org/05r0vyz12","awardTitle":"MICIU/AEI/10.13039/501100011033","awardNumber":"TED2021-130381B-I00"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.d7wm37qhc","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":8,"downloadCount":6,"referenceCount":0,"citationCount":3,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-09-21T23:52:27Z","registered":"2026-09-21T23:52:27Z","published":null,"updated":"2026-10-05T23:56:35Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.jdfn2z3sh","type":"dois","attributes":{"doi":"10.5061/dryad.jdfn2z3sh","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Louisiana State University"],"name":"Rougeau, Kale","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Louisiana State University"],"name":"Elderd, Bret","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5853-1136"}]}],"titles":[{"title":"Data and code from: We are dying to eat you: Cannibalism and disease transmission under global climate change"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Disease ecology","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Baculoviruses","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Spodop"},{"subject":"resource quality"}],"contributors":[{"nameType":"Personal","affiliation":["Louisiana State University"],"name":"Rougeau, Kale","contributorType":"ContactPerson","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Louisiana State University"],"name":"Elderd, Bret","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5853-1136"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2026-07-31T16:40:52Z","dateType":"Created"},{"date":"2026-09-13T14:25:09Z","dateType":"Submitted"},{"date":"2026-09-17T00:00:00Z","dateType":"Issued"},{"date":"2026-09-17T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1002/ece3.74403","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["77634 bytes"],"formats":[],"version":"3","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"In terrestrial ecosystems, climate change may alter host-pathogen dynamics\n along with interactions between hosts due to rising global temperatures\n and carbon dioxide levels. As carbon dioxide levels increase, plants may\n decrease their nitrogen-carbon ratio, which may decrease plant quality for\n herbivores and alter species interactions. Here, we examined\n disease transmission, a host-pathogen interaction, and cannibalism, an\n interaction between hosts, using the fall armyworm (Spodoptera\n frugiperda), a cannibalistic agricultural pest, and its species-specific\n lethal baculovirus, Spodoptera frugiperda multiple nucleopolyhedrovirus\n (SfMNPV), under various potential challenges of climate change. We used\n three temperature treatments (cooler, optimal, warmer) based on the fall\n armyworm’s thermal performance curve to simulate a changing climate and\n four artificial diets with various protein-to-carbohydrate ratios (low,\n equal, high, standard) to manipulate resource quality due to changes in\n the nitrogen-to-carbon ratio from increasing carbon dioxide levels. After\n molting to the fourth instar, experimental larvae were presented with a\n virus-infected or uninfected conspecific.  Cannibalism incidence\n and final infection status of the experimental larvae were\n recorded.  For the experimental larvae that survived and did not\n become infected, pupal mass was measured as a fitness proxy. We found that\n higher temperatures increased cannibalism in larvae on all resource types.\n Larvae cannibalized more when provided with a lower protein diet. Warmer\n temperatures led to slightly higher disease transmission, though diet type\n did not influence infection results in cannibals. Changes in global\n temperatures and carbon dioxide levels could cause more frequent\n cannibalism in the fall armyworm and similar species, though disease\n transmission from cannibalism alone may not increase substantially. We\n also found significant differences in fitness estimates between each\n temperature and diet treatment; individuals fed more carbohydrates (lower\n protein) and kept at cooler temperatures had larger pupal masses,\n indicating potentially higher fitness, while individuals fed more protein\n and kept at warmer temperatures had lower fitness\n potential. Under climate change, increases in temperature and\n carbon dioxide levels affect both host-pathogen dynamics and intraspecific\n interactions between hosts with varied effects on host fitness. By\n examining the multiple potential effects of climate change instead of\n examining each factor in isolation, a clearer picture of how climate\n change will affect ecological systems can be more thoroughly developed."},{"descriptionType":"TechnicalInfo","description":"# Data and code from: We are dying to eat you: Cannibalism and disease\n transmission under global climate change Dataset DOI:\n [10.5061/dryad.jdfn2z3sh](https://doi.org/10.5061/dryad.jdfn2z3sh) ##\n Description of the data and file structure List of files associated with\n the Rougeau and Elderd manuscript: We are dying to eat you: cannibalism\n and disease transmission under global climate change ### Files and\n variables #### File: RougeauElderd-Data.csv\n **Description:** RougeauElderd-Data.csv is a comma-delimited file of the\n data used in the analysis file RougeauElderd-AnalysisGraphics.R. Missing\n data code: NA ##### Variables * ID: unique ID of all of the individuals\n used in the analysis * Temp: Day/Night temperature used in the experiment\n (degree C) * DayTemp: Day time temperature used in the experiment (degree\n C) * ConStatus: conspecific status: infected or uninfected * Diet: Type of\n diet used in the experiment trial * InitialMass:  Initial mass of the\n individual (grams) * FinalMass: Final mass of the individual (grams) *\n NetMass: Change in mass (FinalMass - InitialMass) * X1H: Check of\n cannibalism after one hour (0 - no cannibalism, 1 - cannibalism) * X2H:\n Check of cannibalism after two hours (0 - no cannibalism, 1 - cannibalism)\n * X4H: Check of cannibalism after four hours (0 - no cannibalism, 1 -\n cannibalism) * X8H: Check of cannibalism after eight hours (0 - no\n cannibalism, 1 - cannibalism) * X16H: Check of cannibalism after sixteen\n hours (0 - no cannibalism, 1 - cannibalism) * X24H: Check of cannibalism\n after twenty-four hours (0 - no cannibalism, 1 - cannibalism) * StartDate:\n Start of the experiment * EndDate: End of the experiment * Days: Total\n days of the experiment * EvalStat: Evaluation Status of the individual\n after experiment (P - Pupa, V - Virus Death, O - Other Death) * Infected:\n Virus infected death (1 - death by virus, 0 - other outcome) * PupSex: Sex\n of individuals that pupated (M - male, F - female) * PupMass: Pupal mass\n (grams) * Moth: Date pupated individual eclosed * MothStat: Status of moth\n (M - Moth, DM - Deformed moth, FTE - Failed to eclose, NA - not\n applicable) * ConCube: Did the conspecific consume the diet cube? (0 - no,\n 1 - yes) #### File: RougeauElderd-AnalysisGraphics.R\n **Description:** Contains the code for analyzing the data and creating\n figures associated with the manuscript. ``` Fig 1 - Prob. of Cannibalism\n Fig 2 - Prob of Disease Transmission Fig 3 - Pupal Mass ``` ##\n Code/software All analyses were performed in R, version 4.6.0.  The\n following libraries are used to analyze the data and construct the\n graphics: stringr, dplyr, and MASS. ## Access information Other publicly\n accessible locations of the data: * None Data was derived from the\n following sources: * Experiments"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"United States Department of Agriculture","funderIdentifier":"https://ror.org/01na82s61","awardNumber":"2019-­ 67014-­ 29919"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.jdfn2z3sh","contentUrl":null,"metadataVersion":1,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":63,"downloadCount":26,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-09-17T05:17:36Z","registered":"2026-09-17T05:17:37Z","published":null,"updated":"2026-10-05T23:56:17Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.w3r228154","type":"dois","attributes":{"doi":"10.5061/dryad.w3r228154","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Instituto de Ecología"],"name":"Calahorra Oliart, Adriana","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0003-5613-5131"}]},{"nameType":"Personal","affiliation":["University of North Carolina at Charlotte","North Carolina Research Campus"],"name":"Yohe, Laurel R.","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1567-8749"}]},{"nameType":"Personal","affiliation":["University of North Carolina at Charlotte"],"name":"Hutagalung, Alisa","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of North Carolina at Charlotte"],"name":"Kane, Alexa","nameIdentifiers":[]}],"titles":[{"title":"Data and code from: Natural and artificial selection generate distinct cranial diversification pathways in bats and domestic dogs"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Evolutionary biology","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Chiroptera","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Canidae"}],"contributors":[{"nameType":"Personal","affiliation":["Instituto de Ecología"],"name":"Calahorra Oliart, Adriana","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0003-5613-5131"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":["University of North Carolina at Charlotte","North Carolina Research Campus"],"name":"Yohe, Laurel R.","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1567-8749"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2025-09-02T19:28:00Z","dateType":"Created"},{"date":"2026-08-05T23:34:11Z","dateType":"Submitted"},{"date":"2026-08-17T00:00:00Z","dateType":"Issued"},{"date":"2026-08-17T00:00:00Z","dateType":"Available"},{"date":"2026-10-05T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1093/evolut/qpag151","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["275144987 bytes"],"formats":[],"version":"13","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Code and files for examining and comparing cranial shape patterns in\n domestic dogs, wild canids and phyllostomid bats."},{"descriptionType":"TechnicalInfo","description":"# Natural versus artificial selection generate distinct cranial\n diversification pathways in bats and domestic dogs [Access this dataset on\n Dryad](https://doi.org/10.5061/w3r228154) This dataset contains the data\n and R code used to examine and compare patterns of cranial shape\n diversification in domestic dogs, wild canids, and phyllostomid bats.\n Cranial shape was quantified using three-dimensional geometric\n morphometrics. The main dataset includes 33 canid and 62 phyllostomid bat\n specimens, with cranial shape represented by 26 landmarks. The dataset\n also contains phylogenetic trees used in the comparative analyses and data\n from an observer test conducted to assess landmark digitization error. ##\n Description of the data and file structure The dataset in the compressed\n file batdog_evolution.zip contains specimen metadata, three-dimensional\n cranial landmark coordinates, corrected centroid-size data, phylogenetic\n trees, and the data used for the observer-error analysis. ###\n `batdog_data.csv` This file contains metadata for each specimen included\n in the main dataset. Each row represents one specimen. Variables: *\n `Order`: Taxonomic order. Values are `Carnivora` or `Chiroptera`. *\n `Subfamily`: Taxonomic subfamily. * `Genus`: Taxonomic genus. * `Species`:\n Species. * `Name`: Specimen identifier used in the analyses. For bat and\n wild-canid specimens, this generally corresponds to the species name with\n an underscore between genus and species; for domestic dogs, it corresponds\n to the breed name with spaces replaced by underscores. * `Clean`:\n Human-readable specimen or taxon name without underscores. * `Diet`: Main\n dietary category for phyllostomid bats, following Rojas et al. (2012).\n Domestic dog specimens are classified as `Domestic dog`. * `File`: Name of\n the corresponding `.nts` landmark file. ### `fixCS.csv` This file contains\n corrected centroid-size (CS) values used in the main analyses, together\n with taxonomic and ecological metadata for each specimen. Centroid size is\n calculated as the square root of the summed squared distances of all\n landmarks from their centroid and is used as a measure of overall cranial\n size in geometric morphometrics. Initial centroid-size values are\n calculated from the landmark coordinates during Generalized Procrustes\n Analysis using `gpagen()` in the R package `geomorph`. For some specimens,\n the original three-dimensional models were represented at different\n spatial scales, causing centroid-size values to differ by orders of\n magnitude for reasons unrelated to biological size. These affected values\n were identified by inspection and manually rescaled in `fixCS.csv` before\n the file was re-imported into R for analysis. The uncorrected\n centroid-size values are not retained in a separate tabular file in this\n dataset. They can be regenerated from the original landmark coordinates in\n `nts_final/` by running the corresponding section of `batdog.R`. Because\n the original three-dimensional models were not all represented on a common\n spatial scale, a single physical unit of measurement is not assigned to\n the centroid-size values in this dataset. Variables: * `[unnamed first\n column]`: Duplicate specimen identifier created when row names were\n exported to the CSV file. This column duplicates `Name` and is not used in\n the analyses. * `CS`: Corrected centroid size, used as the measure of\n overall cranial size. * `Name`: Specimen identifier corresponding to the\n morphological data. * `Order`: Taxonomic order. Values are `Carnivora` or\n `Chiroptera`. * `Subfamily`: Taxonomic subfamily. * `Genus`: Taxonomic\n genus. * `Species`: Species. * `Clean`: Human-readable specimen or taxon\n name without underscores. * `Diet`: Dietary or ecological category used in\n the analyses. ### `fixCS_ph_all.csv` This file contains corrected\n centroid-size (CS) values for the tree-matched dataset used in analyses\n that include phyllostomid bats and canids together. Centroid size is\n calculated as the square root of the summed squared distances of all\n landmarks from their centroid and is used as a measure of overall cranial\n size in geometric morphometrics. Two dog specimens, `Feral` and `Clumber\n Spaniel`, are present in the shape dataset but absent from the\n phylogenetic tree and are therefore excluded from this dataset. After\n restricting the landmark data to specimens represented in the combined\n phylogeny, the coordinates are realigned and centroid size is\n recalculated. As in `fixCS.csv`, values affected by differences in the\n spatial scale of the original three-dimensional models were identified and\n manually rescaled before analysis. The uncorrected centroid-size values\n are not retained in a separate tabular file. They can be regenerated from\n the corresponding landmark coordinates by running the relevant section of\n `batdog.R`. Because the original three-dimensional models were not all\n represented on a common spatial scale, a single physical unit of\n measurement is not assigned to these centroid-size values. This\n tree-matched dataset is used to construct the combined allometry-corrected\n PCA and for analyses requiring correspondence between the morphological\n data and the combined phylogeny. Variables: * `[unnamed first column]`:\n Duplicate specimen identifier created when row names were exported to the\n CSV file. This column duplicates `Name` and is not used in the analyses. *\n `CS`: Corrected centroid size, used as the measure of overall cranial\n size. * `Name`: Specimen/species identifier corresponding to the `.nts`\n landmark data. * `Order`: Taxonomic order. Values are `Carnivora` or\n `Chiroptera`. * `Subfamily`: Taxonomic subfamily. * `Genus`: Taxonomic\n genus. * `Species`: Species. * `Clean`: Complete species or specimen name\n without underscores. * `Diet`: Dietary or ecological category used in the\n analyses. ### `nts_final/` This folder contains the `.nts` files used in\n the main geometric morphometric analyses. It contains landmark\n configurations for 33 canid and 62 phyllostomid bat specimens. Cranial\n shape is represented by 26 three-dimensional landmarks. Each `.nts` file\n corresponds to a specimen listed in `batdog_data.csv`, with the\n corresponding filename given by the `File` variable. The files contain the\n original, unaligned three-dimensional landmark coordinates. The\n coordinates are imported into R using `readland.nts()` and subsequently\n subjected to Generalized Procrustes Analysis (GPA) using `gpagen()` in the\n R package `geomorph`. GPA is used to remove variation associated with\n position, orientation, and scale prior to shape analyses. ### Phylogenetic\n trees The dataset contains three phylogenetic tree files in `.tre` format.\n #### `batDog_bat_tree_v5.tre` Time-calibrated phylogenetic tree used for\n the phyllostomid bat analyses. The original tree was obtained from Rojas\n et al. (2016) and trimmed to the species represented in this dataset using\n the `treedata()` function in the R package `geiger`. ####\n `batDog_dog_tree_v5.tre` Phylogenetic tree used for the domestic dog and\n wild canid analyses. The dog phylogeny was obtained from Parker et al.\n (2017) and grafted onto the time-calibrated carnivore supertree of\n Nyakatura and Bininda-Emonds (2012). #### `batDog_total_tree_v1.tre`\n Time-calibrated phylogenetic tree containing the phyllostomid bat and\n canid taxa represented in analyses requiring both groups within a common\n phylogenetic framework. Two domestic dog specimens present in the\n morphological dataset, `Feral` and `Clumber Spaniel`, are absent from this\n tree and are therefore excluded from the corresponding tree-matched\n morphological dataset. This tree is used for analyses requiring\n correspondence between the combined morphological and phylogenetic\n datasets, including phylogenetic analyses of allometry and combined\n phylomorphospace analyses. ### `observer_test/` This folder contains the\n data and code used to evaluate landmark digitization observer error. The\n observer test was conducted using an initial configuration of 28\n landmarks. Each selected skull was landmarked three times by each\n observer. Following identification and removal of two unreliable\n landmarks, the final configuration used in the main analyses contained 26\n landmarks. #### `observer_test/error_experiment.csv` This file contains\n metadata for the specimens and repeated digitizations included in the\n observer test. Variables: * `Order`: Taxonomic order. Values are\n `Carnivora` or `Chiroptera`. * `Subfamily`: Taxonomic subfamily. *\n `Genus`: Taxonomic genus. * `Species`: Species. * `Mesh_file`: Name of the\n three-dimensional mesh file of the landmarked skull. * `Nts_file`: Name of\n the `.nts` file created after landmarking. * `Name`: Identifier associated\n with the specimen and corresponding landmark data. * `Clean`:\n Human-readable specimen or taxon name. * `Diet`: Main dietary or\n ecological category. * `Observer`: Observer who performed the landmark\n digitization. Observer codes used in the analyses correspond to ACO, AH,\n and AK. * `Digitalization`: Replicate digitization number. Each skull was\n landmarked three times by each observer; values range from 1 to 3. ####\n `observer_test/fixCsTest28.csv` This file contains corrected centroid-size\n values and associated metadata for the observer test using the original\n 28-landmark configuration. Centroid size is calculated as the square root\n of the summed squared distances of all landmarks from their centroid and\n is used as a measure of overall cranial size. As in the main dataset,\n values affected by differences in the spatial scale of the original\n three-dimensional models were identified and manually rescaled before\n analysis. Variables: * `[unnamed first column]`: Duplicate specimen\n identifier created when row names were exported to the CSV file. This\n column duplicates `Name` and is not used in the analyses. * `CS`:\n Corrected centroid size. * `Name`: Specimen and digitization identifier\n corresponding to the landmark data. * `Order`: Taxonomic order. Values are\n `Carnivora` or `Chiroptera`. * `Subfamily`: Taxonomic subfamily. *\n `Genus`: Taxonomic genus. * `Species`: Species. * `Clean`: Human-readable\n specimen or taxon name. * `Type`: Ecological group used in the observer\n test. #### `observer_test/fixCsTest26.csv` This file contains corrected\n centroid-size values and associated metadata for the observer test using\n the final 26-landmark configuration after removal of two unreliable\n landmarks. Centroid size and scale corrections follow the same definitions\n and procedure described for `observer_test/fixCsTest28.csv`. Variables: *\n `[unnamed first column]`: Duplicate specimen identifier created when row\n names were exported to the CSV file. This column duplicates `Name` and is\n not used in the analyses. * `CS`: Corrected centroid size. * `Name`:\n Specimen and digitization identifier corresponding to the landmark data. *\n `Order`: Taxonomic order. Values are `Carnivora` or `Chiroptera`. *\n `Subfamily`: Taxonomic subfamily. * `Genus`: Taxonomic genus. * `Species`:\n Species. * `Clean`: Human-readable specimen or taxon name. * `Type`:\n Ecological group used in the observer test. #### `observer_test/ntss28/`\n This folder contains the `.nts` landmark files used for the observer-error\n analysis with the original 28-landmark configuration. Each file contains\n the three-dimensional landmark coordinates for one repeated digitization\n of a skull. Filenames identify the corresponding taxon, observer, and\n digitization replicate. #### `observer_test/ntss26/` This folder contains\n the corresponding `.nts` landmark files after removal of two unreliable\n landmarks, resulting in the final 26-landmark configuration. These files\n are used to repeat the observer-error analyses after landmark removal. ##\n Sharing/Access information Data were derived from the following published\n sources: * Rojas et al. (2012): Dietary information for phyllostomid bats.\n * Rojas et al. (2016): Phylogenetic tree for phyllostomid bats. * Parker\n et al. (2017): Phylogenetic information for domestic dogs. * Nyakatura and\n Bininda-Emonds (2012): Time-calibrated carnivore phylogeny. * Navalón et\n al. (2022): Functions for the disparity-through-time (DTT) analyses. ##\n Code/Software All analyses were conducted in R version 4.4.1 (\"Race\n for Your Life\") using RStudio version 2024.12.1+563. ### `batdog.R`\n This is the main R script used to conduct the analyses and generate\n figures for the study. Needed inputs include: * `batdog_data.csv` *\n `fixCS.csv` * `fixCS_ph_all.csv` * files contained in `nts_final/` *\n `batDog_bat_tree_v5.tre` * `batDog_dog_tree_v5.tre` *\n `batDog_total_tree_v1.tre` * `Navalon2022_functions.R` The script imports\n and aligns the three-dimensional landmark data and performs the geometric\n morphometric and phylogenetic comparative analyses used in the study. The\n main analytical workflow includes: * Import of three-dimensional landmark\n coordinates and matching of specimens to taxonomic and ecological\n metadata; * Generalized Procrustes Analysis (GPA); * Correction and log\n transformation of centroid size; * Assessment of cranial modularity\n between the rostrum and cranium; * Comparison of rates of shape evolution\n between cranial modules; * Assessment and correction of cranial shape\n allometry; * Principal Component Analysis (PCA) of allometry-corrected\n cranial shape; * Visualization and comparison of bat and canid morphospace\n occupation; * Comparison of bat and canid morphospace using\n multidimensional hypervolumes; * Comparison of major axes of cranial shape\n variation using PCA eigenvectors; * Calculation and comparison of\n morphological disparity; * Phylogenetic comparative analyses, including\n phylogenetic signal and evolutionary-rate analyses; * Phylomorphospace\n visualization; * Comparison of observed morphological disparity with\n expectations generated by Brownian-motion simulations; and *\n Disparity-through-time (DTT) analyses. Outputs include statistical\n summaries and R objects generated during the analyses, as well as plots\n used to construct the main and supplementary figures. ###\n `Navalon2022_functions.R` This file contains functions from Navalón et al.\n (2022) used by `batdog.R` for the disparity-through-time (DTT) analyses.\n In particular, the main analysis uses the `disparity.phylo()` and\n `trend.through.time()` functions to calculate phylogenetic disparity\n through time and compare empirical disparity trajectories with\n expectations generated by Brownian-motion simulations. ###\n `observer_test/observer_test.R` This script performs the observer-error\n analyses used to assess the repeatability of cranial landmark placement\n and to compare the original 28-landmark configuration with the final\n 26-landmark configuration. The script should be run separately for the two\n landmark configurations: * For the original 28-landmark analysis, use the\n `.nts` files contained in `observer_test/ntss28/` together with\n `observer_test/fixCsTest28.csv`. * For the final 26-landmark analysis, use\n the `.nts` files contained in `observer_test/ntss26/` together with\n `observer_test/fixCsTest26.csv`. Needed inputs include: *\n `observer_test/error_experiment.csv` * `observer_test/fixCsTest28.csv` or\n `observer_test/fixCsTest26.csv` * the corresponding `.nts` landmark files\n in `observer_test/ntss28/` or `observer_test/ntss26/` The script imports\n the repeated three-dimensional landmark configurations, performs\n Generalized Procrustes Analysis, incorporates corrected centroid-size\n values, and evaluates variation in landmark placement among observers and\n repeated digitizations. Procrustes linear models are used to quantify\n observer effects on cranial shape. The script also performs allometry\n correction and Principal Component Analysis to visualize\n observer-associated variation in morphospace. Outputs include statistical\n summaries of observer effects and plots showing landmark-coordinate and\n morphospace variation among observers and repeated digitizations. ### R\n packages The main analysis and observer-test scripts use the following R\n packages: * `abind` * `ape` * `cowplot` * `datawizard` * `dplyr` *\n `geiger` * `geomorph` * `ggdist` * `ggplot2` * `ggpubr` * `ggrepel` *\n `gridExtra` * `ggridges` * `hypervolume` * `Morpho` * `mvMORPH` *\n `patchwork` * `phytools` * `psych` * `stringr` * `tidyquant` * `tidyverse`\n The analyses also use the base R package `grid`."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Division of Information and Intelligent Systems","funderIdentifier":"https://ror.org/053a2cp42","awardTitle":"\n        CRII: III: Harnessing Deep-Learning to Simplify Biological Inference\n        from Complex Imaging Data\n      ","awardNumber":"2246064"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.w3r228154","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-08-17T21:00:30Z","registered":"2026-08-17T21:00:31Z","published":null,"updated":"2026-10-05T23:39:36Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.69p8cz9b0","type":"dois","attributes":{"doi":"10.5061/dryad.69p8cz9b0","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Bard College"],"name":"Collins, Cathy","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7205-7247"}]},{"nameType":"Personal","affiliation":["Bard College"],"name":"Skinner-Sloan, Ella","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Bard College"],"name":"Douglas, Mary","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Bard College"],"name":"Lagunes, Yadriel","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Bard College"],"name":"Pasatiempo, Martha","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Campbell Scientific, Inc."],"name":"Baker, Dirk","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Sarah Lawrence College"],"name":"Hersh, Michelle","nameIdentifiers":[]}],"titles":[{"title":"Warming increases disease severity and host range for soil-borne pathogens"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Fungal pathogens","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"host range"},{"subject":"soilborne fungi"}],"contributors":[{"nameType":"Personal","affiliation":["Bard College"],"name":"Collins, Cathy","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7205-7247"}],"contributorType":"ContactPerson"},{"name":"University of Kansas","contributorType":"Sponsor","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-09-21T21:05:37Z","dateType":"Created"},{"date":"2026-09-21T21:19:45Z","dateType":"Submitted"},{"date":"2026-10-05T00:00:00Z","dateType":"Issued"},{"date":"2026-10-05T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["405846 bytes"],"formats":[],"version":"4","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Soil-borne fungal pathogens impact productivity of both agricultural and\n wild plant ecosystems. Climate warming is predicted to increase disease\n risk by shifting abundance, composition, and geographic range of fungal\n pathogens. However, the direct impacts of warming on disease severity and\n host range of fungal pathogens remain poorly understood. We studied ten\n putative fungal pathogens that attack seeds. We show that at higher\n temperatures, pathogens reduced germination success, indicating more\n severe disease. Pathogens also attacked a greater number of plant species\n under warmer conditions. Increases in fungal growth rates outpaced\n warming-induced shifts toward faster germination, providing an advantage\n to pathogens. Climate change may thus threaten food security via more\n severe seed losses for plant species, and expanding the range of plant\n species each pathogen attacks. Because plant species coexistence is in\n part mediated by specialized pathogens, generalized pathogens that attack\n a wider range of plant species will likely disrupt mechanisms for\n maintaining biodiversity and ecosystem function in natural systems."},{"descriptionType":"TechnicalInfo","description":"# Warming increases disease severity and host range for soil-borne\n pathogens Dataset DOI:\n [10.5061/dryad.69p8cz9b0](https://doi.org/10.5061/dryad.69p8cz9b0) ##\n Description of the data and file structure We experimentally tested the\n hypotheses that fungi growing in higher temperatures would kill more seeds\n (cause more severe disease), and kill seeds of a wider variety of plant\n species (increase host range). Fungi in our study were cultured from seeds\n buried for one year in Kansas forest patches. We selected 10 fungal taxa\n for which we had 8-12 unique isolates from different locations in the\n landscape, and generated replicate cultures for each isolate for seed\n germination trials. For 12 different plant species, we placed 20 seeds of\n a single species on each fungal culture, and quantified germination under\n two different temperature conditions. Germination data from this\n experiment were used to quantify disease severity (proportion of seeds\n that failed to germinate in fungal trials relative to controls) for each\n fungal x plant species combination, and host range (number of fungal taxa\n for which disease was severe) for each fungal taxon. ### Files and\n variables #### File: Data.zip **Description:** This data file contains two\n folders: 1_Raw_Data 2_Processed_Data_and_Model_Output #### File: Code.zip\n **Description:** The code file contains two folders:\n 3_Data_processing_and_modeling 4_Figure_scripts. All code #### File:\n Collins_et_al_ReadMe_File.txt **Description:** Text version of ReadMe\n file. ## Code/software Data were analyzed using R, v. 4.5.2. ``` Package\n Version car car 3.1-5 cowplot cowplot 1.2.0 DHARMa DHARMa 0.4.7 dplyr\n dplyr 1.2.0 emmeans emmeans 2.0.1 flextable flextable 0.9.11\n germinationmetrics germinationmetrics 0.1.9 ggh4x ggh4x 0.3.1 gghighlight\n gghighlight 0.5.0 ggplot2 ggplot2 4.0.2 ggpubr ggpubr 0.6.3 glmmTMB\n glmmTMB 1.1.14 gridExtra gridExtra 2.3 gt gt 1.3.0 officer officer 0.7.4\n patchwork patchwork 1.3.2 performance performance 0.16.0 purrr purrr 1.2.1\n scales scales 1.4.0 sjPlot sjPlot 2.9.0 tibble tibble 3.3.1 tidyr tidyr\n 1.3.2 tidyverse tidyverse 2.0.0 viridisLite viridisLite 0.4.3 grid grid\n 4.5.2 ``` This README file describes the raw and processed data files used\n for analyses, as well as script for all data processing, modeling, and\n figure generation. Each folder and its contents are described in detail\n below, and in their respective folders. ### Folder: Data/1_Raw_Data\n Contains files: ``` 1. DailyEmergence_Raw.csv 2.\n CultureInventory_SH_all.csv 3. Lookup2.csv 4.\n CultureInventory_SH_focal.csv 5. Fungal_Growth_Raw_data.xls 6.\n FocalSp.Seedbank.csv 7. Focal_Plants_over_time.csv ``` **1.\n DailyEmergence_Raw.csv:** Data file containing raw numbers for germination\n each day, for each seed species, on each fungus. Data were collected Jan\n 2021- Jan 2022. * Wave = the group (different months, or named groups)\n when germination trial occurred.\"Redo\" indicates a culture that\n was off-timing with our \"waves\". * Temperature = mean\n temperature of chamber (20 degrees C or 25 degrees C). * Loc = unique\n numbered location on shelves in chambers. * Rep = replicate number\n (typically 1-12 within a taxon) for each parent fungus/plant\n species/temperature combination. Cultures that represent redos (e.g., to\n situations where culture with a designated replicate number didn't\n grow initially) are indicated with a period (\".\"). In situations\n where the redo resulted in a duplicate culture, this is noted in the\n \"Duplicate\" column (below) and randomly selected duplicate\n cultures were eliminated in R code prior to analysis. Rep was used for\n internal record-keeping and aside from eliminating duplicates, is not used\n in analysis. * SeedSp = first three letters of genus for plant species in\n germination trial: ACH (*Achillea millefolium*); AGE (*Ageratina\n altissima*); AMB (*Ambrosia artemisiifolia*); AND (*Andropogon\n virginicus*); GEU (*Geum canadense*); BRO (*Bromus inermis*); CHA\n (*Chamaechrista fasciculata*); DES (*Desmodium illinoense*); MON (*Monarda\n fistulosa*); POA (*Poa pratensis*); PYC (*Pycnanthemum tenuifolium*); RUD\n (*Rudbeckia hirta*). - LTSNum = Long-term storage code, assigned by\n researchers for curation. Control = trials with no fungus. * Duplicate =\n duplicates of a particular fungus/seed combination. These are duplicate\n trials, not duplicated data. Randomly chosen duplicates omitted at the\n beginning of code for models and figures. Y = Yes, trial is a duplicate.\n N= No, trial is not a duplicate. * Day01-Day65: Columns containing number\n of newly germinated seeds on each day. * Total: Total number of germinates\n seeds, summed across all days. This number is the basis of the Totals.csv\n data in processed data file. **2. CultureInventory_SH_all.csv** *\n LTSnumber: Long-term storage number for internal use. *\n SpeciesHypothesisCode: Numerical code for isolate assigned by Unite\n database. NAs represent cultures for which there was no code matched in\n database. These isolates were not used in statistical analyses, and are\n omitted in code for rank abundance diagram (Fig. S1). * LTSinKochs: 0 =\n not used in this study; 1 = used in this study (which we informally dubbed\n Kochs). * LTSinKochs.1: Y=Yes, isolate was used in this study; N= No,\n isolate was not included in this study (which we informally and internally\n dubbed Kochs). * Name.Unite: taxonomy assigned based on Unite species\n hypothesis **3. Lookup2.csv:** Lookupfile for Species Hypothesis. *\n SpeciesHypothesisCode: Numerical code for isolate assigned by Unite\n Database. * SH_name: Taxonomic name assigned by Unite Database. *\n InKochsStudy: whether or not we used the isolate in this study (Y = Yes, N\n = No). **4. CultureInventory_SH_focal.csv**: Rows in this data file\n contain unique fungal cultures isolated from seeds buried in the\n experimental landscape for a year. Taxonomy assignments using species\n hypotheses in Unite database were completed 2023. * UID: Unique identifier\n for each fungus. * PlotID: plot identifiers indicating which 1-m2 plot in\n the landscape seeds were buried in, and fungi were cultured from. * patch:\n designation for each unique Cluster of patches in the landscape. * LL:\n landscape location, i.e., habitat type. Sampling plots are characterized\n as being in a small patch (S), the matrix between patches (M), or in the\n interior (LI) or edge (LE) of a large patch. Field site is pictured in\n Fig.1 and described in Supplemental Methods. - BuriedHost = first three\n letters of genus and species for plant species from which a fungus was\n originally isolated. If germination occurred without fungus,\n \"Control\" ; ANDVIR (*Andropogon virginicus*); DESILL (*Desmodium\n illinoense*); EUPRUG (Formerly named *Eupatorium rugosum*, changed to new\n taxonomic name *Ageratina altissima*, AGE, in script); FRAAME (*Fraxinus\n americana*); GEUCAN (*Geum canadense*); JUNVIR (*Juniperus\n virginiana*);POAPRA (*Poa pratensis*). * LocHost: Combination of\n BuriedHost and PlotID. * LocHostFun: Combination of BuriedHost, PlotID,\n and Fungal taxon. * LTSnumber: Long-term storage number for internal use.\n * SpeciesHypothesisCode: Numerical code for isolate assigned by Unite\n database. * SH_name: taxonomic name assigned to isolate by Unite databse\n based on species hypothesis code. * LTSinKochs: 0 = not used in this\n study; 1 = used in this study (which we informally dubbed Kochs). *\n LTSinKochs.1: Y=Yes, isolate was used in this study; N= No, isolate was\n not included in this study (which we informally and internally dubbed\n \"Kochs\"). * Name.Unite: taxonomy assigned based on Unite species\n hypothesis **5. Fungal_Growth_Raw_Data.xls:** Tab 1. Each row contains a\n fungal isolate grown on media and measured with two perpendicular measures\n on days 3-7. One cell contains an NA where data were not collected for\n vertical measure of a fungus; for analysis, we calculated average size for\n that day as if vertical and horizontal growth were equal. * Temperature:\n Fungi were grown at mean temperatures of 20 or 25 degrees C. * LTS:\n Long-term storage number associated with isolate, for internal use. *\n Sub.num: 3 replicates were subbed from each parent isolate. Column\n contains 1, 2, or 3 to indicate replicate. * Unite.Name: taxonomy assigned\n based on Unite species hypothesis * Day3.H.cm: horizontal measurement of\n fungus (cm) * Day3.V.cm: perpendicular, vertical, measurement of fungus\n (cm) * Day3.Ave.mm: average of horizontal and vertical growth, converted\n to mm * Day3.Growth.mm: Daily growth, here calculated as Ave size in mm/3\n days * Day4.H.cm: horizontal measurement of fungus (cm) * Day4.V.cm:\n perpendicular, vertical, measurement of fungus (cm) * Day4.Ave.mm: average\n of horizontal and vertical growth, converted to mm * Day4.Growth.mm: Daily\n growth, here calculated as the difference in Day 4- Day3 * Day5.H.cm:\n horizontal measurement of fungus (cm) * Day5.V.cm: perpendicular,\n vertical, measurement of fungus (cm) * Day5.Ave.mm: average of horizontal\n and vertical growth, converted to mm * Day5.Growth.mm: Daily growth, here\n calculated as the difference in Day 5- Day4 * Day6.H.cm: horizontal\n measurement of fungus (cm) * Day6.V.cm: perpendicular, vertical,\n measurement of fungus (cm) * Day6.Ave.mm: average of horizontal and\n vertical growth, converted to mm * Day6.Growth.mm: Daily growth, here\n calculated as the difference in Day 6- Day5 * Day7.H.cm: horizontal\n measurement of fungus (cm) * Day7.V.cm: perpendicular, vertical,\n measurement of fungus (cm) * Day7.Ave.mm: average of horizontal and\n vertical growth, converted to mm * Day7.Growth.mm: Daily growth, here\n calculated as the difference in Day 7- Day6 * Ave.Daily: the average daily\n growth calculated as the mean of all daily growth measures (mm). Tab2. The\n average daily growth for the 3 subs from each parent isolate were averaged\n via a pivot table on this tab. The average daily growth for each fungus\n provide the means in the processed data file, Mean_Fungal_GR.csv,\n contained in the folder: 3_Processed_Data_and_Model_Output **6.\n FocalSp.Seedbank.csv:** Each row contains plant species. Species are a\n subset of data set of all species collected in the larger (unpublished)\n study. * Species: Five letter codes for each plant species: AMBAR\n (Ambrosia artemesiifolia); ANDVI (Andropogon virginicus); BROIN (*Bromus\n inermis*); EUPAL (Formerly named *Eupatorium rugosum*), changed to new\n taxonomic name *Ageratina altissima*, AGEAL, in script); GEUCA (*Geum\n canadense*); MONFI (*Monarda fistulosa*); POAPR (*Poa pratensis*); PYCTE\n (*Pynanthemum tenufolium*); RUDHI (*Rudbeckia hirta*). - Site: Independent\n sampling locations within a patch where seedbank samples were collected. -\n Abundance: number of individuals of that species that emerged from soil\n samples collected at that site. - PatchLoc: habitat type of Site: LI =\n Large-patch interior; LE = Large-patch edge; S = Small patch; M=matrix\n between patches - PatchSz: whether Site is located in small (S) or large\n patch (L), or matrix (M) - Cluster: ID for each large patch, or cluster of\n small patches (cluster-scale ID) 7\\. Focal_Plants_over_time.csv: Each row\n contains plant species detected in each year for which we have data.\n Species in this file are a subset of data set for all species collected in\n the larger (unpublished) study. * LOCATION: Unique plot identifier for\n permanent 1-m2 plots. * CODE: Five letter codes for each plant species:\n AMBAR (Ambrosia artemesiifolia); ACHMI (*Achillea millefolium*); ANDVI\n (*Andropogon virginicus*); CASCH (taxonomy now *Chamaecrista\n fasciculata*); BROIN (*Bromus inermis*); DESIL (*Desmodium illinoense*);\n EUPAL (Formerly named *Eupatorium rugosum*, changed to new taxonomic name\n *Ageratina altissima*, AGEALT, in script); GEUCA (*Geum canadense*); MONFI\n (*Monarda fistulosa*); POAPR (*Poa pratensis*); PYCTE (*Pynanthemum\n tenufolium*); RUDHI (*Rudbeckia hirta*). * COV1x1: percent cover of a\n plant species.Two rows with NAs (Line 3546 and 3741) that represent\n missing cover values were excluded when compiling Fig. S3. * Cluster: ID\n for each large patch, or cluster of small patches (cluster-scale ID) . *\n PermPlots: whether plot is located in small (S) or large patch (L), or\n matrix (M) . * Sp.Code: shortened version of longer species code first\n three letters of genus only. * Latin: Latin taxonomic name for the plant\n species. ### Folder: Data/2_Processed data files and model output Contains\n files: 1\\. Total_Dryad.csv 2\\. Mean_Fungal_GR.csv 3\\. Fungus_summary.csv\n 4\\. all_species_contrasts.csv 5\\. GermStatResults.csv **1.\n Total_Dryad.csv**: Data file containing germination data for seeds of 10\n plant species grown with or without (controls) a fungus, at two\n temperatures. This data uses the \"Total\" column from raw data\n file \"DailyEmergence_Raw.csv\", with information on fungal taxa\n added. This file is used as the basis for statistical modeling. * Wave =\n the group (named groups, some indicating month, others named arbitrarily)\n during which germination trial occurred. * Temperature = mean temperature\n of chamber (20 C or 25 C). * Loc = unique numbered location on shelves in\n chambers. * Rep = replicate number (typically 1-12 within a taxon) for\n each parent fungus. Cultures that represent redos (e.g. to situations\n where culture with a designated replicate number didn't grow\n initially) are indicated with a period (\".\"). In situations\n where the redo resulted in a duplicate culture, this is noted in the\n \"Duplicate\" column (below) and randomly selected duplicate\n cultures were eliminated in R code prior to analysis. Rep was used for\n internal record-keeping and aside from eliminating duplicates, is not used\n in analysis. - SeedSp = first three letters of genus for seed species in\n germination trial: ACH (*Achillea millefolium*); AGE (*Ageratina\n altissima*); AMB (*Ambrosia artemisiifolia*); AND (*Andropogon\n virginicus*); BRO (*Bromus inermis*); CHA (*Chamaechrista fasciculata*);\n DES (*Desmodium illinoense*); GEU (*Geum canadense*); MON (*Monarda\n fistulosa*); POA (*Poa pratensis*); PYC (*Pycnanthemum tenuifolium*); RUD\n (*Rudbeckia hirta*). * LTSNum = Long-term storage code for each fungal\n isolate. * TotalEmerge = total number of seeds that germinated. *\n Duplicate = in some cases where had duplicates of a particular fungus/seed\n combination-these are duplicate trials, not data errors. Randomly chosen\n duplicates are omitted at the beginning of code for models and figures,\n but retained here for transparency. * Omit = delineation assigned randomly\n to select among duplicates for analysis. * PropEmerg=proportion of seeds\n that germinated, out of 20. * BuriedHost = first three letters of genus\n and species for plant species from which a fungus was originally isolated.\n If row represents germination without fungus, \"Control\": ANDVIR\n (*Andropogon virginicus*); DESILL (*Desmodium illinoense);* EUPRUG\n (Formerly named *Eupatorium altissimum*, changed to new taxonomic name\n *Ageratina altissima*, AGE, in script); GEUCAN (*Geum canadense*); POAPRA\n (*Poa pratensis*); Control (no fungus present). - Unite.Name = Unite Name\n (taxonomy) assignment for fungus. - Unite.Name2 = Unite Name (taxonomy)\n assignment with no spaces - Pl.Family = plant family for seed in\n germination trial. - Fun.Family = fungus family for fungus in germination\n trials. **2. Mean_Fungal_GR.csv**: Mean fungal growth rates under two\n temperature conditions (calculated from raw growth rate file). * LTSNum:\n Long-term storage ID for fungal isolate. * AveDaily: Average daily growth\n (mm) over 7 days. * Name.Unite: Fungal taxonomy assigned by Unite\n database. * Temperature: Mean temperature growth condition (20 or 25\n degrees C). * Name.Unite.Withn: column containing fungal taxon and sample\n size. **3. Fungus_summary.csv**: Tally of the number of plant victims\n (species, families) and mean effect size. Tallied from model results\n reported in all_species_contrasts.csv (and analyzed in Models_Dryad.R). *\n Unite.Name: Fungal taxonomy assigned by Unite database. * Temperature:\n Mean temperature growth condition (20 or 25 degrees C). * N: sample size.\n * NumSpeciesImpacted.Thres: tally of plant species experiencing \u0026gt;10%\n increase or decrease in germination with fungus present. *\n NumPlFamiliesIMpacted.Thres: tally of plant families experiencing \u0026gt;10%\n increase or decrease in germination with fungus present. * NumSpeciesSig:\n tally of plant species whose increase or decrease in germination with\n fungus present relative to control yielded p\u0026lt;0.05 (after correcting for\n multiple tests). * NumbPlFamiliesSig: tally of plant families whose\n increase or decrease in germination with fungus present relative to\n control yielded p\u0026lt;0.05 (after correcting for multiple tests). *\n NumSpeciesBoth: tally of plant species who experienced change in\n germination \u0026gt; 10% and also p\u0026lt;0.05. * NumPlFamiliesBoth: tally of\n plant families who experienced change in germination \u0026gt; 10% and also\n p\u0026lt;0.05. * MeanEffect: Mean effect sizes from glmms. * SE: Standard\n error of effect size from glmms. * MeanLogRatio: Mean log ratios from\n glmms. * NumPositiveEffects: Tally of species where fungi yielded positive\n impacts on germination. * NumNegativeEffects: Tally of species where fungi\n yielded negative impacts on germination. **4. all_species_contrasts.cs**v\n Data file containing output from glmm examining seed germination with and\n without fungi, at two temperatures, for 10 fungal taxa and 12 plant taxa.\n Analyses completed in R code file called Models_Dryad.R. * SeedSp = first\n three letters of genus for seed species in germination trial: ACH\n (*Achillea millefolium*); AGE (*Ageratina altissima*); AMB (*Ambrosia\n artemisiifolia*); AND (*Andropogon virginicus*); BRO (*Bromus inermis*);\n CHA (*Chamaechrista fasciculata*); DES (*Desmodium illinoense*); GEU\n (*Geum canadense*); MON (*Monarda fistulosa*); POA (*Poa pratensis*); PYC\n (*Pycnanthemum tenuifolium*); RUD (*Rudbeckia hirta*). - Temperature =\n mean temperature of chamber (mean of 20 or 25 degrees Celcius). -\n Prob_Control = germination probability for control seeds (without fungus)-\n output from glmm. - Fungus = germination probability for seeds (with\n fungus)- output from glmm. - SE_Control = Standard error for\n probabilities. - SE_Fungus = Standard error for probabilities. - Prob_diff\n = Difference in germination probability for seeds with and without fungus.\n - SE_diff = Standard error for the difference in probability. -\n Ymin_Fungus = lower 95% CI around probability difference. - Ymas_Fungus =\n upper 95% CI around probability difference. - Abs_prob_diff = absolute\n value of effect (probability difference). - Affected = Whether absolute\n value of effect exceeds 0.1, or 10% (threshold in this study). TRUE =\n exceeds 10%. FALSE = less than 10%. - Log_ratio = Log-ratio output from\n glmms. - Unite.Name = Fungal taxonomy assigned by Unite database. -\n P.value = result from posthoc tests (treatment vs control), corrected for\n multiple tests. - PlantFamily = plant family of seeds used in germination\n trial. - FungalFamily = fungal family of fungus used in germination trial.\n - Abbrev = abbreviation for figures. - P.sig = TRUE = P\u0026lt;0.05, p\u0026gt;0.05\n = FALSE. **5. GermStatResults.csv** Columns A:BV are the original raw\n germination data returned; Columns after BV contain many outputs provided\n by Germinationmetrics package. NAs represent cells for which indices could\n not be computed du to having only 1 germinated seed. The only columns used\n in subsequent analyses for this paper is T50 Coolbear, a metric for\n germination timing (time to reach 50% of maximum germinated seeds). Full\n information on all indices provided by Germinatrionmetrics can be found\n at:\n [https://cran.r-project.org/web/packages/germinationmetrics/index.html](https://cran.r-project.org/web/packages/germinationmetrics/index.html) and [https://cran.ms.unimelb.edu.au/web/packages/germinationmetrics/vignettes/Introduction.pdf](https://cran.ms.unimelb.edu.au/web/packages/germinationmetrics/vignettes/Introduction.pdf) (Table 3 lists the indices and their computation). ### Folder: Code/3_Data processing and modeling scripts Contains files: **1. Models_Dryad.R:** R script for the GLMMs that comprise the bulk of the key results in Figure 2 and 3, as well as output contained in statistical tables in Appendix S1. * R packages: gt, glmmTMB, emmeans, tidyr, dplyr, DHARMa, car, flextable, officer * Calls: Total_Dryad.csv - Output from code: Tables, S3, S4, S6, all_species_contrasts.csv **2. GerminationStats_Dryad.R**: R script for germination statistics (timing, overall success). * R packages: germinationmetrics, dplyr, tidyverse, purrr * Calls: DailyEmergence_Raw.csv * Output from code: GermStatResults.csv, and results from two-sample t-tests in Figure S8. ### Folder: Code/4_Figure_Scripts Contains files: **Fig_2_Dryad.R** * Calls: Total_Dryad.csv * R packages: gt, glmmTMB, emmeans, tidyr, dplyr, DHARMa, car, flextable, officer * Output: Figure 2, and Table S5 **Fig_3_Dryad.R** * Calls: all_species_contrasts.csv * R packages: ggplot2, grid, tidyr, gplyr, cowplot **Fig_4_Dryad.R** * Calls: Mean_Fungal_GR.csv, Fungal_Summary.csv * R packages: ggplot2, grid, tidyr, gplyr, cowplot * Output: Figure 4 a,b,c, and regression statistics **Fig_S1_Ranked fungi abundance.R** * Calls: CultureInventory_SH_all.csv, Lookup2.csv * R packages: ggplot2, dplyr ghghlight **Fig_S2_Location and host for focal isolates.R** * Calls: CultureInventory_SH_Focal.csv * R packages: ggplot2, dplyr **Fig_S3_Plants_and_seedbank.R** * Calls: FocalSp.Seedbank.csv, Focal_Plants_over_time.csv * R packages: ggplot2, dplyr, viridsLite, cowplot, grid, gridExtra **Fig_S4_raw_proportions.R** * Calls: Total_Dryad.csv * R packages: dplyr, ggplot2, grid **Fig_S5_Fusarium.R** * Calls: Total_Dryad.csv * R packages: ggplot2, ggh4x, dplyr **Fig_S6_Victim_Effect_size_v_pvalue.R** * Calls: all_species_contrasts.csv * R packages: ggplot2, tidyr, dplyr **Fig_S7_HostvVictim.R** * Calls: all_species_contrasts.csv, Total_Dryad.csv * Rpackages: ggplot2, dpylr, tidyr **Fig_S8_Germination_Timing_Success.R** * Calls: Germ.StatResults.csv * Rpackages: ffplot2, dplyr, ggpubr, tidyverse"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Division of Environmental Biology","funderIdentifier":"https://ror.org/03g87he71","awardTitle":"\n        Collaborative Research: RUI: how landscape fragmentation interferes with\n        plant-pathogen interactions that maintain local plant diversity\n      ","awardNumber":"1655972"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.69p8cz9b0","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":1,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-10-05T23:35:17Z","registered":"2026-10-05T23:35:18Z","published":null,"updated":"2026-10-05T23:35:18Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.2v6wwpzwr","type":"dois","attributes":{"doi":"10.5061/dryad.2v6wwpzwr","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Washington"],"name":"Alberti, Marina","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Hohenheim","Staatliches Museum für Naturkunde Stuttgart","KomBioTa – Center for Biodiversity and Integrative Taxonomy"],"name":"Allhoff, Korinna","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Université de Sherbrooke"],"name":"Barbour, Matthew","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Leibniz Institute of Freshwater Ecology and Inland Fisheries"],"name":"Govaert, Lynn","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Washington"],"name":"Malesis-Dahm, Anna","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0002-7989-597X"}]},{"nameType":"Personal","affiliation":["Swiss Federal Institute of Aquatic Science and Technology"],"name":"Matthews, Blake","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Hohenheim"],"name":"Palmer, Malina","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Université Marie et Louis Pasteur"],"name":"Pantel, Jelena","nameIdentifiers":[]}],"titles":[{"title":"Data and code from: Emergent properties of urban landscapes shape urban eco-evolutionary dynamics"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Evolutionary ecology","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Urban ecology","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"eco-evolutionary dynamics"},{"subject":"Urban eco-evo"}],"contributors":[{"nameType":"Personal","affiliation":["University of Washington"],"name":"Malesis-Dahm, Anna","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0002-7989-597X"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2024-09-09T21:35:39Z","dateType":"Created"},{"date":"2026-09-28T18:48:08Z","dateType":"Submitted"},{"date":"2026-10-05T00:00:00Z","dateType":"Issued"},{"date":"2026-10-05T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1007/s10980-026-02450-8","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.13738270","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["484865528 bytes"],"formats":[],"version":"3","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Urbanization transforms natural ecosystems into heterogeneous and\n fragmented landscapes, creating novel selective pressures that can drive\n eco-evolutionary dynamics. However, the specific role of urban spatial\n structure in shaping these dynamics remains poorly understood in landscape\n ecology. With these data, we test the hypothesis that urban landscapes\n differ systematically from non-urban landscapes, exhibiting lower\n landscape heterogeneity, lower forest aggregation, and higher\n developed-land aggregation across U.S. metropolitan regions. Using data\n from 100 U.S. metropolitan areas, we empirically quantify how urbanization\n reshapes landscape heterogeneity and connectivity."},{"descriptionType":"Methods","description":"We tested the hypothesis that urban landscapes exhibit unique\n landscape patterns by quantifying landscape heterogeneity and connectivity\n across urban and non-urban areas using two metrics to characterize\n landscape structures. Landscape heterogeneity was measured using the\n Shannon diversity index (SHDI) of land cover, while connectivity was\n assessed using the aggregation index (AI) for both natural forested areas\n and urban development. We then contrasted the distributions of these\n landscape metrics across urban and non-urban landscapes within selected\n drainage basins intersecting 100 US metropolitan areas (refer to the\n sampling method). We compared urban and non-urban landscape metrics using\n Bayesian generalized linear mixed models with urban status as a fixed\n effect and metropolitan region as a random effect."},{"descriptionType":"TechnicalInfo","description":"# Data and code from: Emergent properties of urban landscapes shape urban\n eco-evolutionary dynamics\n [https://doi.org/10.5061/dryad.2v6wwpzwr](https://doi.org/10.5061/dryad.2v6wwpzwr) This dataset was used to compare the landscape characteristics of urban and nonurban areas. We tested the hypothesis that urban landscapes exhibit unique landscape patterns by quantifying landscape heterogeneity and connectivity across urban and non-urban areas using two metrics to characterize landscape structures. Landscape heterogeneity was measured using the Shannon diversity index (SHDI) of land cover, while connectivity was assessed using the aggregation index (AI) for both natural forested areas and urban development. We employed a Bayesian approach as a complementary analytical technique to enhance the reliability of our findings. Code for statistical analysis, including Bayesian analysis, is provided. ## Description of the data and file structure The file `metric_metro.csv` includes the landscape metrics and other information for each grid cell. * shdi = Shannon's diversity index * sq_km = grid cell area * urban = if the grid cell is urban (y) or not (n) * ai2 = Aggregation index of developed cover (contains NA if no developed cover present) * ai4 = Aggregation index of forest cover (contains NA if no forest cover present) * HUC10 = Watershed code * NAME = Watershed name * NAMELSAD = Metro area name The file `cell_locate.csv` includes the location of each grid cell. It is used for calculating the Moran's I of model residuals. * cellid = Unique id of each grid cell * NAME = Watershed name * lat_First = Latitude of the centroid of the grid cell * long_First = Longitude of the centroid of the grid cell ## Code/Software The file `Bayes_GLMM_parallel.R` uses packages `remotes` and `cmdstanr` to run a Bayesian GLMM controlling for metropolitan area.  The ZIP folder `ModelWorkspaces.zip` contains the results from Bayesian modeling. `BayesGLMMDevAI.RData` contains results for AIof developed cover, `BayesGLMMForAI_Beta.RData` contains results for AI of forest cover, and `BayesGLMMshdi_2.RData` contains results for SHDI. The ZIP folder `MoransI.zip` contains three files to prepare the distributions of residuals of each model (`SHDI.R`, `ForAI.R`, and `DevAi.R`), the RStudio Project File `NewAnalyses`, and the file `MoranI.R`, which reattaches spatial information from `cell_locate.csv` to the data from `metric_metro.csv` and the model residual using the package `dplyr`, and then uses the package '[moranfast](https://github.com/mcooper/moranfast)' to calculate Moran's I of the residuals."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Division of Environmental Biology","funderIdentifier":"https://ror.org/03g87he71","awardTitle":"\n        RCN: Eco-Evolutionary Dynamics in an Urban Planet: Underlying Mechanisms\n        and Ecosystem Feedbacks\n      ","awardNumber":"1840663"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.2v6wwpzwr","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-10-05T22:34:46Z","registered":"2026-10-05T22:34:47Z","published":null,"updated":"2026-10-05T22:34:47Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.c59zw3rqz","type":"dois","attributes":{"doi":"10.5061/dryad.c59zw3rqz","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Toronto","Stanford Medicine"],"name":"Tom, Julia","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0002-4801-2230"}]},{"nameType":"Personal","affiliation":["University of Toronto"],"name":"Manzone, Joseph X.","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Toronto"],"name":"Chen, Joyce L.","nameIdentifiers":[]}],"titles":[{"title":"Data from: Rethinking predictive suppression: expert pianists tend to show less, not more, tactile suppression than musical novices"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Health sciences","subjectScheme":"fos"},{"subject":"tactile perception"},{"subject":"predictive suppression"},{"subject":"fine motor performance"},{"subject":"expertise"},{"subject":"musician"}],"contributors":[{"nameType":"Personal","affiliation":["University of Toronto","Stanford Medicine"],"name":"Tom, Julia","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0002-4801-2230"}],"contributorType":"ProjectLeader"},{"nameType":"Personal","affiliation":["University of Toronto","Stanford Medicine"],"name":"Tom, Julia","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0002-4801-2230"}],"contributorType":"DataCurator"},{"nameType":"Personal","affiliation":["University of Toronto","Stanford Medicine"],"name":"Tom, Julia","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0002-4801-2230"}],"contributorType":"DataCollector"},{"nameType":"Personal","affiliation":["University of Toronto","Stanford Medicine"],"name":"Tom, Julia","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0002-4801-2230"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["University of Toronto","Stanford Medicine"],"name":"Tom, Julia","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0002-4801-2230"}],"contributorType":"ProjectManager"},{"nameType":"Personal","affiliation":["University of Toronto","Stanford Medicine"],"name":"Tom, Julia","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0002-4801-2230"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["University of Toronto","Stanford Medicine"],"name":"Tom, Julia","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0002-4801-2230"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["University of Toronto","Stanford Medicine"],"name":"Tom, Julia","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0002-4801-2230"}],"contributorType":"Editor"},{"nameType":"Personal","affiliation":["University of Toronto"],"name":"Manzone, Joseph X.","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Toronto"],"name":"Manzone, Joseph X.","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Toronto"],"name":"Chen, Joyce L.","contributorType":"ProjectLeader","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Toronto"],"name":"Chen, Joyce L.","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Toronto"],"name":"Chen, Joyce L.","contributorType":"Supervisor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Toronto"],"name":"Chen, Joyce L.","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Toronto"],"name":"Chen, Joyce L.","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Toronto","Stanford Medicine"],"name":"Tom, Julia","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0009-0002-4801-2230"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":["University of Toronto"],"name":"Chen, Joyce L.","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2026-05-18T18:19:17Z","dateType":"Created"},{"date":"2026-05-19T00:35:20Z","dateType":"Submitted"},{"date":"2026-06-02T00:00:00Z","dateType":"Issued"},{"date":"2026-06-02T00:00:00Z","dateType":"Available"},{"date":"2026-10-05T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsSourceOf","relatedIdentifier":"10.5281/zenodo.22718962","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["22736 bytes"],"formats":[],"version":"15","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Expert performance is often attributed to automatization that relies on\n predictive models rather than sensory feedback. The role of tactile\n feedback in expert performance, however, has been little studied. Current\n literature points in two directions. On one hand, predictable external\n tactile inputs are perceived less (i.e., suppressed). Experts, running\n stronger predictive models than novices, should thus suppress tactile\n inputs more. On the other hand, reducing tactile inputs impairs fine motor\n performance: opposite to what expertise should yield. To probe this\n paradox, we examined expert pianists and musical novices performing piano\n key presses. We measured tactile detection thresholds to electrical\n stimuli in three conditions: at rest, during planning of a key press\n (‘planning’), and during key press execution (‘execution’). Tactile\n suppression was defined as an increase in threshold during planning or\n execution relative to rest. We separately measured fine motor performance\n as participants’ accuracy at matching a key press velocity target. Experts\n demonstrated greater tactile sensitivity than novices in every condition.\n Contrary to the predictive suppression account, experts did not show\n greater tactile suppression than novices, exhibiting instead a trend\n towards less suppression during both planning and execution. Experts were\n more accurate than novices at matching a slow velocity target. Across both\n groups, we found a trend towards more planning suppression relating to\n worse slow velocity accuracy and more variable movement onset timing.\n Together, these findings suggest that expertise may preserve, rather than\n suppress, tactile inputs, consistent with their benefit to performance\n rather than their predictability."},{"descriptionType":"Methods","description":"\u003cstrong\u003eMATERIALS AND METHODS\u003c/strong\u003e\n \u003cstrong\u003e1.     Participants\u003c/strong\u003e\n 48 young adults (18-29 years) performed the tactile perception\n test. The complete dataset from three participants were excluded: two\n participants due to sweat preventing electrodes from adhering to the skin\n for a full trial block, and one following post-testing disclosure of\n musical self-study, rendering the participant ineligible for the novice\n group.  Participants were additionally excluded on a\n condition-wise basis (see Table 1). Three musical novices were excluded\n from movement-related conditions (‘Planning’ and ‘Execution’: see 4.\n Tactile perception test), as testing occurred without Optotrak motion\n detection (see 2. Materials, apparatus, stimuli). Fourteen additional\n participants (7 expert pianists, 7 musical novices) were excluded from the\n Execution condition, due to detection rates not reaching 50% at the two\n strongest intensities presented (31).  Participants who\n completed both tests did so in a single-day session, starting with the\n fine motor test, followed immediately by the tactile perception test. As\n this was, to our knowledge, the first study to test expert pianists and\n musical novices using these measures, sample size was determined by a\n sample of convenience (for sample size determination, see Supplemental\n Materials). All expert pianists were current or former\n students of a post-secondary piano program requiring performance audition\n to enter. For this study, all experts were currently playing the piano as\n active performers and/or as piano teachers, spending an average of 17.87\n hours per week playing the piano. All musical novices indicated 3 years or\n less of formal musical training or self-study on any instrument over a\n lifetime. To determine eligibility, participants were\n pre-screened over the phone. Participants were included if they were\n right-handed according to the Edinburgh Handed Inventory (32) and had no\n self-reported current severe pain or history of neurological impairment.\n Participants were excluded who were 30 years or older, due to large-scale\n studies showing age-related declines in perceptual, cognitive and motor\n performance starting as early as age 30 (33,34). All\n participants provided informed written consent prior to commencement of\n experimental procedures, and received compensation of $15 per hour for\n their study participation. The experiment was approved by the University\n of Toronto Research Ethics Board (#44450).\n \u003cstrong\u003e2.     Materials, apparatus,\n stimuli\u003c/strong\u003e Participants performed two\n separate tests: the tactile perception test and fine motor test. For both\n tests, participants were seated at a digital piano (Yamaha AvantGrand N1)\n and were tested while pressing a piano key with their left ring finger.\n The left ring finger was selected as the best finger for demonstrating the\n effects of expert training (23), due to ring finger enslavement creating\n the greatest difficulty for independent muscle control (35). The same\n piano key was selected for both tests, due to variability of weight\n between different keys. Throughout the experiment, the\n volume on the digital piano was set to silent to reduce potential effects\n of auditory feedback on tactile perception (36). For\n the tactile perception test, perception (i.e., perceptual threshold) was\n measured by recording participants’ responses to variable intensities of\n electrical stimuli delivered to the left ring finger. Electrical stimuli\n were generated by a constant current stimulator (Digitimer DS4 Bi-Phasic\n Current Stimulator) (37), which transmitted square wave pulses lasting 10\n ms to a pair of surface electrodes (3M Red Dot 2650 repositionable\n monitoring electrodes). The electrodes were trimmed to fit finger surfaces\n without impeding joint movement. The anodal electrode was attached to the\n tip of the distal phalanx (i.e., fingertip), and the cathodal electrode to\n the palmar surface of the middle phalanx of the left ring finger (see\n Figure 1a). Participants adjusted placement of the anodal electrode to\n ensure the electrode rested flat on the piano key while the fingertip\n rested on the key.           For the fine motor test,\n motor performance was measured by comparing a key press peak velocity\n (i.e., actual velocity) to a target velocity. Peak velocity and the\n corresponding piano key being pressed were recorded via Musical Instrument\n Digital Interface (MIDI; Roland UM-ONE mk2; velocity range 1-127,\n dimensionless scale), with keystroke data captured by the digital piano’s\n non-contact, optical key sensors. All MIDI data were collected and\n analyzed using custom scripts in Python (PyCharm version\n 11.0.15+). For both tactile and motor tests, Optotrak\n motion detection cameras (Optotrak Certus, Northern Digital Inc.,\n Waterloo, ON, Canada) positioned above the piano key collected kinematic\n data of the piano key press. An IRED marker taped to the nail of\n participants’ left ring finger provided positional data on the finger at\n the piano key. The IRED was sampled at 200 Hz. Position data were then\n low-pass filtered using a second-order Butterworth filter (40 Hz cutoff)\n using the TAT-HUM trajectory analysis toolkit (38). We applied a higher\n filter than is typical to human voluntary movement studies, due to the\n steep velocity change characterizing the piano key press\n movement. Auditory beeps through a piezoelectric\n speaker served as movement cues in both tactile and motor tests (see 4.\n Tactile perception test and 5. Fine motor test). The speaker was attached\n to a custom-made device case resting approximately 20 centimeters away\n from participants’ left forearm. Python and National\n Instruments multi-function devices (NI USB-6251 and NI SCB-68) controlled\n all experimental events, and custom Python scripts collected all\n perceptual and movement data. \u003cstrong\u003e3. Data\n collection cohorts\u003c/strong\u003e Data were collected\n in two cohorts recruited sequentially: Cohort 1 and Cohort 2. Cohort 1\n testing sessions only collected data for the present study, while Cohort 2\n sessions collected data for an additional study after the present study.\n The main differences between cohorts were the different lengths for\n testing sessions (Cohort 2: ~2.5 hours, Cohort 1: ~1.5 hours), due to the\n additional study tested in Cohort 2. To minimize effects of fatigue for\n Cohort 2, its trial list was shortened. Further adaptations were made to\n the Cohort 2 study design (see Table 2), based upon observations made\n during Cohort 1 testing sessions. An overview of cohort\n differences can be found in Table 2, with further details provided in\n Table 3, 4.1 Conditions and 4.2 Trial structure.\n \u003cstrong\u003e4. Tactile perception\n test \u003c/strong\u003e To test tactile perception, brief\n electrical stimuli (i.e., 10 ms) of variable intensities were sent through\n electrodes (i.e., anode and cathode) attached to participants’ left ring\n finger (see Figure 1a). Participants recorded responses of whether or not\n they felt the tactile stimulus (‘Y’ = yes, I felt it; ‘N’ = no, I did not\n feel it) through right hand inputs on a computer keyboard. The anode\n electrode was attached to the left ring fingertip and rested flush on the\n selected, fixed piano key (i.e., C3: 3\u003csup\u003erd\u003c/sup\u003e to lowest\n ‘C’ on piano) throughout the experiment. The stimulating electrode’s flush\n position on the piano key minimized new contact events (i.e., artifacts)\n disrupting tactile perception, and allowed for piano key presses to be\n performed unimpeded. To minimize fatigue, participants’ left forearm\n rested on a pillow positioned adjacent to the C3 piano key.\n Tactile thresholds in different movement conditions were tested\n through stimuli being administered at the following timepoints: at rest\n (Baseline: see Figure 1b), shortly before a piano key press (Planning: see\n Figure 1c), or during a piano key press (Execution: see Figure 1c). Each\n trial commenced with a sequence of four auditory beeps of regular temporal\n spacing (interval between beeps = 750 milliseconds (ms); auditory beep\n duration = 100 ms). During Baseline trials, the fingertip remained at rest\n on the piano key. During Planning and Execution trials, participants were\n instructed to press the piano key simultaneous with the fourth auditory\n beep, using the previous three, temporally-regular beeps as a\n synchronizing cue. A single tactile stimulus was presented over the course\n of the four beeps (for auditory-stimulus relationship, see 4.1\n Conditions), or no stimulus was presented in catch trials. Each trial\n ended with participants indicating whether they felt the stimulus or not\n (Y/N). To derive tactile thresholds, cumulative\n Gaussian functions were fit to detection responses (i.e., numbers of ‘yes’\n responses and trials per stimulus level) using the \u003cem\u003epsignifit\n 4\u003c/em\u003e toolbox (39: https://github.com/wichmann-lab/psignifit/wiki) in MATLAB (MathWorks, Natick, MA), with the lower and upper bound of the function allowed to vary independently (37). The tactile threshold was defined as the stimulus intensity at the midpoint (50%) on the Gaussian curve (i.e., representing 50% detection when the two tails were symmetric and reached 0% and 100% detection). Higher tactile thresholds indicated less tactile sensitivity; lower tactile thresholds indicated greater tactile sensitivity (10). Psychometric curves for all participants in all conditions can be found in Supplemental Materials. Each condition had catch trials with no stimulation that were distributed randomly across each trial block. Participants’ data was excluded from analysis when participants answered more than 11% percent ‘Yes’ (i.e., more than 1 out of 9 catch trials per condition during catch trials in the Baseline blocks) and more than 20% ‘Yes’ during catch trials in the Planning and Execution block (i.e., more than 3 out of 18 catch trials in the Planning and Execution block) (10). The inclusion criteria for catch trials in the Planning and Execution block was wider due to expected increased noise during movement (40,41). All participants’ data fell within these inclusion criteria. \u003cstrong\u003e4.1 Conditions\u003c/strong\u003e The experiment started and ended with a testing block of tactile thresholds (i.e., sensitivity) in the Baseline condition (Baseline 1, Baseline 2), in which participants kept their left-hand ring finger at rest on the C3 piano key (see Figure 1b). To account for the potential effects of time on our measure of tactile sensitivity (e.g., adaptation, skin conductivity), we utilized the average of Baseline 1 and Baseline 2 as our measure of resting tactile sensitivity (‘Baseline’). The middle block consisted of joint testing of tactile sensitivity in the Planning and Execution conditions (see Figure 1c). For this block, participants were instructed to press the piano key down simultaneous with the fourth auditory beep, using the preceding three, temporally regular beeps as a synchronizing cue. No instruction was given regarding the velocity of the key press. For Planning trials, the tactile stimulus was fixed at a timepoint prior to the fourth auditory beep and subsequent key press. To determine the timing for measuring planning-related suppression, we turned to previous literature, which showed planning-related tactile suppression occurring up to 300 ms before movement initiation (42,43). For Cohort 1, the Planning stimulus timepoint was thus set to 300 ms prior to the fourth beep. However, analysis of Optotrak data from Cohort 1 showed movement initiation regularly occurring more than 50 ms prior to the fourth beep (see 4.3 Kinematic parameters). As a result, Phase II Planning stimulus was moved 50 seconds earlier (i.e., to 350 ms prior to the fourth beep) to better align it to the movement timing (see Table 2). For Execution trials, Optotrak motion detection was used to time-lock the tactile stimulus to key press movement. Optotrak triggered stimuli to be delivered approximately 55 ms after movement onset, corresponding with 45% of the average movement duration (i.e., average movement duration = 121 ± 20ms). Movement duration was calculated as the amount of time between movement onset and movement end (i.e., movement end = velocity \u0026lt;10 mm/s). Prior to commencement of the Planning and Execution block, we instructed participants to direct their attention in the following sequence: first, focus on pressing the piano key simultaneous with the fourth beep; next, recall whether they felt a stimulus or not during the trial; last, input their response (Y/N). This instruction was given to prevent participants from waiting for the stimuli before making the piano key press, resulting in late and thus invalid trials. Within this block, Planning and Execution trials were balanced and randomized to prevent participants from being able to anticipate when the stimulus would be presented. Additionally, to avoid tactile feedback from the fingertip striking the piano key surface, participants were asked to keep the anode electrode flush and in contact with the piano key throughout all trials, and to keep the fingertip motionless except when depressing the piano key (44). Participants were instructed to keep their eyes open throughout the experiment, and were informed that the piano’s volume was set to silent. Further kinematic instructions relating to the Optotrak motion detection were given for the Planning and Execution block. In order for the infrared diode marker (IRED: see 2. Materials, apparatus, stimuli) attached to the left ring fingernail to remain visible to the Optotrak motion detection camera overhead (see 2. Materials, apparatus, stimuli), participants were asked to avoid: 1) letting the ring fingernail tilt forward; 2) letting the middle finger cross over the ring fingernail. To familiarize participants with each condition, participants performed practice trials at the start of each new condition. We did not specify the number of practice trials to be performed, due to pilot data showing wide individual variation (e.g., 1-25 trials) in the number of practice trials needed to successfully perform the protocol within kinematic parameters (i.e., tactile stimulus \u0026lt;300 ms and \u0026gt;65 ms before key press). The data from practice trials were not analyzed. The experiment began only after we observed participants’ ability to perform the new condition accurately and comfortably three times consecutively. \u003cstrong\u003e4.2 Trial structure\u003c/strong\u003e To determine perceptual thresholds, we first calculated each participant’s estimated threshold by using a staircase procedure, stepping up or down stimulus intensity according to response to the preceding stimulus (see Figure 2). The initial stimulus intensity (i.e., first staircase step) was 1.0 mA, lowered to 0.8 mA for Cohort 2 after Cohort 1 data confirmed all participants detected stimuli at this lower level. After each stimulus, participants responded on a computer keyboard whether they felt the stimulus or not (Y = Yes / N = No). Their responses determined both the direction of the staircase and size of the step, with responses finally narrowing down to the ‘estimated threshold’ (see Figure 2).  The estimated threshold determined stimulus intensity levels for the main experimental trials (41), with intensities fixed at increments (‘steps’: see Table 3) above and below the estimated threshold. To reduce trial numbers, allowing for timely completion of the experiment, trial numbers per step followed a bell curve: lowest (i.e., four) at the highest and lowest intensity steps, and increasing towards the middle (i.e., six, eight) to provide a robust measure of perception (see Table 3). In all conditions for Cohort 1, thirteen stimulus intensity ‘steps’ were delivered. For Cohort 1 Baseline and Planning, five steps above and below the estimated threshold were delivered, plus a double step at the range ends (see Table 3), confirming perception (i.e., absence and presence) at intensity extremes (41). For Execution across both cohorts, the stimulus range was shifted four steps higher than the Baseline and Planning range to capture the expected higher perceptual thresholds during movement (42,43). For Cohort 2, the trial lists per condition were shortened (see Table 3) to reduce the effects of fatigue from the extended testing session: steps per condition were reduced from thirteen to nine, and the Baseline trial list was halved, dividing trials between Baseline 1 and Baseline 2. For Cohort 2’s Execution trials, to keep the highest Execution stimulus intensity matched to the highest stimulus level as for Cohort 1 (i.e., despite the reduction in steps), the Execution step size was changed to 0.061 mA. This matched the upper bound of the Execution range across cohorts; however, because Cohort 2 used fewer steps (9 vs. 13), the lower bound of the Execution range for Cohort 2 was higher than for Cohort 1 (missing the lowest steps sampled for Cohort 1). Each condition had nine catch trials (i.e., with no stimulation) distributed randomly throughout each trial block. The one exception was Cohort 2’s Baseline, where ten catch trials were issued, allowing for an equal division of catch trials across Baseline 1 and Baseline 2 (i.e., five each).\u003cstrong\u003e \u003c/strong\u003e \u003cstrong\u003e4.3 Kinematic parameters\u003c/strong\u003e An Optotrak motion capture system was used to capture kinematic data from the key press movements. The kinematic data was used during the Planning condition to exclude trials where: 1) movement occurred too close to the stimulus, potentially masking stimulus perception (i.e., ‘backward masking’) (40,41); 2) movement occurred too far from the stimulus, beyond when planning suppresion has been found to occur (42,43). Planning trials where the stimulus occurred too close (i.e., \u0026lt;65 ms) to the key press movement were deemed ‘out-of-bound’ and excluded. This minimum bound reflects previous literature indicating that backward masking of tactile perception for single-digit movements occurs within 49 ms prior to movement onset (40,41), with an additional 16 ms (i.e., 33% of the backward masking maximum) added as a buffer. Planning trials where the stimulus occurred too far in advance of the movement (i.e., \u0026gt;300 ms) were also deemed ‘out-of-bound’ and excluded. This maximum bound reflects previous literature indicating that tactile suppression of an upcoming, planned movement begins 300-400 ms prior to movement onset (42,43). Out-of-bound trials were repeated by the experimenter. See Supplemental Figure S2 for a histogram of Planning trial numbers by the latency between stimulus and movement onset. \u003cstrong\u003e5. Fine motor test\u003c/strong\u003e The parameters that were the same as in the tactile perception test (see 4. Tactile perception test) were: participants’ position seated at the piano, the selected finger and piano key for making key presses, the piano volume setting on silent. The parameters that differed from the tactile perception test were: no electrodes were connected to the left hand ring finger, so the finger directly touched the piano key surface during the key press; participants’ right hands rested in their laps, as no perceptual responses needed to be input; the left forearm was unsupported to best represent the context of naturalistic piano performance, with the short duration of this test posing minimal risk of fatigue. \u003cstrong\u003e5.1 Conditions\u003c/strong\u003e First, to determine each participant’s maximum velocity keypress (MVK), participants performed five trials with the instruction: “Make the piano key travel from key top to key bottom as quickly as possible.” For each trial, the peak velocity of the keystroke was recorded as a MIDI peak velocity value (‘velocity’: see 2. Materials, apparatus, stimuli). The highest velocity achieved across the five trials was defined as the individual's MVK. Next, the participants were tested in their ability to match a key press velocity target defined relative to their individual MVK. Slow (30% MVK) and fast (70% MVK) targets (29) were presented in separate blocks, with block order (i.e., slow first vs. fast first) counter-balanced within each group to control for order effects. Each target velocity block started with ten ‘with feedback’ trials (WithFB), where participants received verbal feedback after each trial on the peak velocity of their key press (i.e., actual velocity). The WithFB trials served to familiarize participants with the relationship between key press movement and actual velocity. Subsequently, participants performing 10 trials with no feedback (NoFB). Data from the NoFB trials were used to analyze participants’ performance. All velocities were measured and presented in MIDI peak velocity output (see 2. Materials, apparatus, stimuli). Before the slow velocity block, participants were informed that key presses below a velocity threshold (i.e., the minimum velocity for the piano to sound when the volume is on) would not be registered, and were instructed to try to remain above this threshold. When key press velocities were too slow to register, the experimenter pressed a non-C3 piano key to forward the experiment to the next trial. Non-registering trials were skipped, with no negative impact on measured performance. \u003cstrong\u003e5.2 Trial structure\u003c/strong\u003e Each trial started with a single beep (100 ms), which served as a ‘Go’ signal for participants to press the piano key. Optotrak recording started with the beep and stopped 1.5 seconds later. If key presses occurred prior to the beep or after the Optotrak recording window, the trial was repeated. \u003cstrong\u003e6. Data analysis\u003c/strong\u003e All data analyses were conducted using RStudio (version 2024.04.2.764, PBC, Boston, MA). \u003cem\u003eP\u003c/em\u003e-values were adjusted using the Benjamini-Hochberg procedure (RStudio: p.adjust (), method = “BH”) to control the false discovery rate, with statistical significance set at α = 0.05. When Shapiro-Wilks test confirmed non-normality of data, we performed Mann-Whitney U tests for group comparisons (RStudio: wilcox.text()), and Spearman’s rank correlation for correlation analyses. Tactile thresholds were analyzed using a linear model with an unstructured, within-subject covariance (RStudio: gls(), nlme package) to accommodate uneven samples across conditions (see 1. Participants). The unstructured covariance structure was selected because likelihood-ratio testing and an AIC comparison rejected compound symmetry (χ²(4) = 83.35, ΔAIC = 75.35). Degrees of freedom are residual-based, and fixed effects were tested using Type III ANOVA. Group differences were tested using Welch's t-tests, and condition differences using estimated marginal means (RStudio: emmeans()), with degrees of freedom approximated by the appx-Satterthwaite method. For correlation analyses, we bootstrapped data with 10,000 samples (RStudio: boot (), R = 10,000 type “bca”) to derive 95% bias-corrected and accelerated (BCa) confidence intervals (CI). All data tested with Pearson’s correlation were continuous and normal. Non-significant effects were followed up with Bayesian analyses (45: RStudio: BayesFactor ()) to assess the relative support for the null versus the alternative hypothesis (46). Bayesian \u003cem\u003et\u003c/em\u003e-tests used the default Cauchy prior (47: \u003cem\u003er\u003c/em\u003e = 0.707), and Bayesian correlation analyses used the default Jeffreys-beta* prior (48: \u003cem\u003er\u003c/em\u003e = 1/3). For directional hypotheses (49, BF₀₊ indicates evidence for the null relative to the hypothesized direction, and BF₋₊ evidence for the opposite direction relative to the hypothesized one. For non-directional testing, BF\u003csub\u003e01\u003c/sub\u003e indicates evidence favoring the null, and BF\u003csub\u003e10\u003c/sub\u003e the alternative hypothesis. Bayes factor values greater than 1 are reported, with interpretation of Bayes factors following conventional benchmarks: weak/anecdotal: 1–3; moderate: 3–10; strong: 10–30 (50). \u003cstrong\u003e6.1 Dependent measures\u003c/strong\u003e \u003cstrong\u003e6.1.1 Movement kinematics\u003c/strong\u003e We first analyzed movement kinematics during the tactile test recorded by Optotrak to check for potential confounds to tactile suppression, as slower movement velocity relates to less tactile suppression (51). We performed an independent t-test (Expert vs. Novice) on key press peak velocity (‘Velocity’), predicting no group difference, as previous studies showed no difference in movement velocity between expert musicians and musical novices (52). We also analyzed kinematic data to test whether experts differed from novices in motor performance during the tactile test. Previous studies showed earlier movement onset (‘Onset’: 53), greater velocity consistency (‘VelocityConsistency’: 54) and greater movement onset timing consistency (‘OnsetConsistency’: 19,54) in experts compared to novices. We performed t-tests on these kinematic measures, predicting earlier onset and greater onset and velocity consistency in experts. All consistency was measured as variable error (i.e., root mean summed square of difference from the mean). \u003cstrong\u003e6.1.2 Tactile perception\u003c/strong\u003e Our main measure of central tactile suppression was planning suppression, or the difference in thresholds between planning and rest (Planning\u003csub\u003esupp\u003c/sub\u003e = Planning-Baseline) (10,55). As previous literature showed that tactile suppression increases during movement execution (40,41), we also measured tactile suppression during movement execution, or the difference in thresholds between execution and rest (Execution\u003csub\u003esupp\u003c/sub\u003e = Execution-Baseline). Positive suppression values represent reduced tactile sensitivity, and negative suppression values represent enhanced sensitivity. \u003cem\u003e\u003cstrong\u003eMain research question #1: Do experts show more central tactile suppression than novices when performing a trained fine motor task?\u003c/strong\u003e\u003c/em\u003e To answer our first research question, we performed a linear model (see 6. Data analysis) with the factors Group (Expert vs. Novice) and Condition (Baseline, Planning, Execution). In our following report of the analyses and results, the components of the ANOVA (i.e., main effects and interactions) and post-hoc analyses are addressed according to the research question being addressed, rather than in contiguous order. First, due to our hypothesis of greater central tactile suppression in experts compared to novices, we predicted a Group by Condition interaction, indicating a larger increase in thresholds from rest to movement in experts. Next, confirming the presence of tactile suppression during movement (10,40-44), we predicted a main effect of Condition, with higher thresholds at movement-related timepoints than at rest. To directly test our hypothesis that experts show greater central tactile suppression than novices, we conducted an \u003cem\u003ea priori\u003c/em\u003e t-test of group differences (Expert vs. Novice) in planning suppression (Planning\u003csub\u003esupp\u003c/sub\u003e). We predicted a significant difference, indicating greater suppression in experts compared to novices. As an exploratory analysis, we conducted an \u003cem\u003ea priori\u003c/em\u003e t-test of group differences in execution suppression (Execution\u003csub\u003esupp\u003c/sub\u003e). We predicted no significant group difference, as previous research examining simple finger movements similar to the present study design (i.e., discrete, single digit movement) found execution suppression to result from general, peripheral reafference, rather than central, predictive motor plans (40,41). \u003cem\u003e\u003cstrong\u003eMain research question #2a: Does greater central tactile suppression correlate with worse fine motor performance?\u003c/strong\u003e\u003c/em\u003e \u003cstrong\u003eCorrelation of fine motor performance and tactile suppression\u003c/strong\u003e \u003cstrong\u003e6.2.1 Fine motor performance\u003c/strong\u003e To examine the relationship between central tactile suppression and fine motor performance, we first analyzed fine motor performance. As we sought to determine whether tactile suppression relates specifically to expert skill, we measured fine motor performance using a task central to expert pianists’ training: accuracy at targeting piano key presses velocities (i.e., constant error). Because key press velocity directly determines sound intensity, it plays a central role in expert pianists' control of musical expression (30). Constant error (CE) was measured as the difference between actual (i.e., key press peak velocity) and target velocities. Larger CE indicated less accurate velocity targeting. Positive CE indicated actual velocities faster than the target, and negative CE, velocities slower than the target. To determine the effects of expertise and target velocity on CE, we performed a 2 Group (Expert vs. Novice) by 2 Velocity (30 vs. 70) mixed repeated measures ANOVA. Due to the task being selected to reflect expert training, we predicted an effect of Group. As previous literature showed expert pianists performing better than musical novices specifically at slow key press velocity control (32), we predicted an interaction between Group and Velocity, with greater accuracy in experts at the slow target velocity. \u003cstrong\u003e6.2.2 Correlation fine motor performance and tactile suppression\u003c/strong\u003e Based on prior literature indicating expert pianists perform better than musical novices at the slow piano key press velocity (23,29), we selected CE.30 as our measure of fine motor performance, conducting a Pearson’s correlational analysis between planning suppression (Planning\u003csub\u003esupp\u003c/sub\u003e) and CE.30 with bootstrapped confidence intervals. We analyzed the correlation across the Overall sample (Expert and Novice collapsed). \u003cem\u003e\u003cstrong\u003eMain research question #2b: Do experts more weakly couple central tactile suppression to fine motor performance than novices?\u003c/strong\u003e\u003c/em\u003e To test whether Experts more weakly couple central tactile suppression and fine motor performance than Novices, we conducted a correlation analysis using bootstrapped confidence intervals between Planning\u003csub\u003esupp\u003c/sub\u003e and CE.30 for each group separately (Expert, Novice). We then examined the effect of expertise by conducting a Fisher’s z test, comparing the correlations of Expert and Novice groups."},{"descriptionType":"TechnicalInfo","description":"# Data from: Rethinking predictive suppression: expert pianists tend to\n show less, not more, tactile suppression than musical novices Dataset DOI:\n [10.5061/dryad.c59zw3rqz](https://doi.org/10.5061/dryad.c59zw3rqz) ##\n **GENERAL INFORMATION** **Author Information:** Corresponding Author:\n Julia Tom (University of\n Toronto): [j.tom@utoronto.ca](mailto:j.tom@utoronto.ca) Co-authors: Joseph\n X. Manzone (University of Toronto), Joyce L. Chen (University of Toronto)\n **Funding Information:** Natural Sciences and Engineering Council of\n Canada Discovery Grant (to J.L. Chen). ## **SHARING/ACCESS INFORMATION**\n **Recommended citation for this dataset:** Tom, J., Manzone, J. X., \u0026amp;\n Chen, J. L. (2026). Data from: Rethinking predictive suppression: expert\n pianists do not show greater tactile suppression than musical novices. ##\n **DATA \u0026amp; FILE OVERVIEW** **Data_ExpertTactileSuppression.csv**: Main\n experimental trial data, including tactile thresholds in different\n movement conditions, tactile suppression in different movement conditions,\n and fine motor performance. **METHODOLOGICAL INFORMATION** **Methods used\n for data collection:** See Methods section of the associated manuscript\n for full experimental design. **Human Subjects \u0026amp; Privacy Compliance:**\n All participants provided explicit consent for their de-identified data to\n be published. Direct personal identifiers (such as names or participant\n IDs) were removed, and indirect identifiers were aggregated to ensure\n anonymity. ### Files and variables #### File:\n Data_ExpertTactileSuppression.csv ##### Variables * P#: Unique numerical\n identifier assigned to each participant (e.g., 1, 2, 3...). *\n Group: Expertise group assignment: 1 == Expert; 2 == Novice * CE.30: slow\n velocity accuracy (Musical Instrument Digital Interface: MIDI) * CE.70:\n fast velocity accuracy (MIDI) * avg_peak_velocity: mean peak velocity\n (millimeters/second: mm/s) * ve_peak_velocity: variable error\n (consistency) peak velocity (mm/s) * avg_onset: mean movement onset\n timing (second: s) * ve_onset: variable error (consistency) movement onset\n timing (s) * Zero: zero value * Baseline1: tactile perceptual threshold\n from Baseline 1 trials (pre-test rest; milliamperes: mA) *\n planning: tactile perceptual threshold from movement planning trials (mA)\n * execution: tactile perceptual threshold from movement execution block\n (mA) * Baseline2: tactile perceptual threshold from Baseline 2 trials\n (post-test rest; mA) * BaselineAverage: mean Baseline1 and Baseline2 (mA)\n * P-Bavg: planning suppression (planning - BaselineAverage: mA) *\n E-Bavg: execution suppression (execution - BaselineAverage: mA) *\n P-B1: planning suppression (planning - Baseline1: mA) * E-B1: execution\n suppression (execution - Baseline1: mA) * Age: age on date of testing\n (years) * Sex: female == 1; male == 2 * Catch B1: count of false positive\n catch trials during Baseline 1 block * Catch PE: count of false positive\n catch trials during joint planning and execution block * Catch B2: count\n of false positive catch trials during Baseline 2 block \n \n ## Code/software **Required Software for Viewing Data** The primary\n dataset is provided in a standard, open format (.csv) to ensure long-term\n accessibility. It can be viewed using: * **Spreadsheet Software:**\n Microsoft Excel, Google Sheets, or LibreOffice Calc. * **Text/Code\n Editors:** VS Code, Notepad++, or TextEdit (for raw text inspection). ###\n Statistical Analysis Environment All data processing, statistical\n analysis, and data visualizations were executed using the following\n environment: * **Software:** RStudio (Version 2024.04.2.764) ### Loaded R\n Packages The data analysis was performed in R 4.4.1 using CRAN packages.\n Statistical tests use base R (`t.test`, `wilcox.test`, `cor.test`,\n `p.adjust`). The packages below supply the models, effect sizes and\n figures. * **Data manipulation:** `dplyr`, `tidyr` * **Models:** `nlme`\n 3.1-166 (`gls()` with unstructured within-subject covariance, for the\n tactile threshold model); `afex` 1.4-1 (mixed repeated-measures ANOVA, for\n constant error) * **Marginal means and contrasts:** `emmeans`\n 1.11.0 (estimated marginal means and pairwise condition contrasts from the\n threshold model, with Satterthwaite degrees of freedom) * **Effect\n sizes:** `effectsize` 1.0.1 (Cohen's *d*, rank-biserial correlation,\n partial eta squared) * **Assumption testing:** `car` 3.1-3 (Levene's\n test) * **Bayesian statistics:** `BayesFactor` 0.9.12.4.7 (Bayes factors\n for *t*-tests and correlations) * **Resampling:** `boot` 1.3-31 (bootstrap\n confidence intervals for correlations) * **Figures:** `ggplot2` 3.5.1,\n `ggpattern` 1.2.1, `patchwork` 1.2.0, `ragg` 1.3.2 (TIFF export) *\n **Tables:** `knitr` 1.49 ### Workflow \u0026amp; Relationship of Files 1.\n **Data Input:** The primary tabular data file\n (`Data_ExpertTactileSuppression.csv`) contains the de-identified raw\n metrics used for the study. 2. **Analysis Execution:** The data was\n processed and analyzed using RStudio (Version 2024.04.2.764). The R\n packages listed above were utilized to run the primary statistical tests\n and generate the figures presented in the manuscript directly from this\n `.csv` file. Please note that this script is not included here. ## Access\n information Other publicly accessible locations of the data: * None. This\n is the primary and exclusive repository for this dataset. Data was derived\n from the following sources: * Not applicable. This dataset consists\n entirely of original data collected and produced by the authors for the\n associated study. ## Human subjects data All participants provided\n explicit consent for their de-identified data to be published in the\n public domain. To ensure anonymity, all direct personal identifiers (e.g.,\n names, contact information) were completely removed from the dataset, and\n indirect identifiers were aggregated into broader categories where\n necessary."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Natural Sciences and Engineering Research Council of Canada","funderIdentifier":"https://ror.org/01h531d29","awardTitle":"Discovery Grant"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.c59zw3rqz","contentUrl":null,"metadataVersion":3,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":19,"downloadCount":3,"referenceCount":8,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-06-02T00:26:51Z","registered":"2026-06-02T00:26:52Z","published":null,"updated":"2026-10-05T20:44:39Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.2fqz6135z","type":"dois","attributes":{"doi":"10.5061/dryad.2fqz6135z","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Leipzig University"],"name":"Richter, Vincent","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8009-0357"}]},{"nameType":"Personal","affiliation":["Leipzig University"],"name":"Triphan, Tilman","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["MRC Laboratory of Molecular Biology"],"name":"Cardona, Albert","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Leipzig University"],"name":"Thum, Andreas S","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-3830-6596"}]}],"titles":[{"title":"Data from: Morphology and ultrastructure of pharyngeal sense organs of \u003cem\u003eDrosophila\u003c/em\u003e larvae"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Electron microscopy","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Sensory systems","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Drosophila","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[{"nameType":"Personal","affiliation":["Leipzig University"],"name":"Richter, Vincent","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8009-0357"}],"contributorType":"ContactPerson"},{"name":"Leipzig University","contributorType":"Sponsor","affiliation":[],"nameIdentifiers":[]}],"dates":[{"date":"2026-09-24T11:06:42Z","dateType":"Created"},{"date":"2026-09-24T11:09:54Z","dateType":"Submitted"},{"date":"2026-10-05T00:00:00Z","dateType":"Issued"},{"date":"2026-10-05T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1101/2025.06.07.657386","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.7554/elife.108036.2","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["5442301987 bytes"],"formats":[],"version":"3","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This study provides a comprehensive ultrastructural analysis of the\n pharyngeal sensory system in Drosophila melanogaster larvae, focusing on\n the four major pharyngeal sense organs: the ventral pharyngeal sensilla\n (VPS), dorsal pharyngeal sensilla (DPS), dorsal pharyngeal organ (DPO),\n and posterior pharyngeal sensilla (PPS). Our analysis revealed 15 sensilla\n across these organs, comprising four mechanosensory, nine chemosensory,\n and two dual-function sensilla. We identified 35 Type I neurons (six\n mechanosensory and 29 chemosensory) and six Type II neurons with putative\n chemosensory functions. Additional sensory structures, including papilla\n sensilla and chordotonal organs in the cephalopharyngeal region, were\n characterized. The EM volumes of these organs and sensilla were extracted\n from the whole larval L1 dataset published in Schoofs et al. 2024\n (https://doi.org/10.1016/j.cub.2024.08.025) and Peale et al. 2024\n (https://doi.org/10.1101/2024.07.02.601671)."},{"descriptionType":"TechnicalInfo","description":"# Data from: Morphology and ultrastructure of pharyngeal sense organs of\n *Drosophila* larvae A whole Drosophila melanogaster first instar larva was\n sectioned into 4,866 sections. The sections were 34nm thick, and placed 3\n to a grid on 1,622 grids. The EM volume of a whole Drosophila melanogaster\n 1st instar larva used in this study has been previously reported (Peale et\n al. 2024; Schoofs et al. 2024). Briefly, the genotype of this female larva\n was Canton S G1 [iso] × w1118 [iso] 5905. The resulting EM volume contains\n 4,866 z-slices with an x,y,z resolution of 5 × 5 × 34 nm. This dataset\n includes the complete central and peripheral nervou system, including all\n neurons, synapses, and accessory structures. ## Description of the data In\n the ZIP folder 'EM_volumes_Dryad.7z', there are several folders\n with the scanning transmission electron microscopy (**STEM**) data of the\n morphology of the different organs and sensilla. These include the volumes\n of the four main pharyngeal organs: the ventral pharyngeal sensilla\n (**VPS**), the dorsal pharyngeal sensilla (**DPS**), the posterior\n pharyngeal sensilla (**PPS**), and the dorsal pharyngeal organ (**DPO**).\n Additionally, there is a folder including the EM images used in the\n publication to describe the sensilla occurring in the pharyngeal region.\n Note: The images were resized to reduce the storage space required. The\n original volumes or images can be obtained upon request. ### File\n structure EM_volumes_Dryad     DPO --\u0026gt; contains an image sequence of\n the DPO (773 z-slices, TIF Files, 1620x1080 pixels, resized from 5616x3744\n pixels, 1 pixel = 5 nm)     DPS --\u0026gt; contains an image sequence of the\n DPS (1,351 z-slices, TIF Files, 2000x2000 pixels, resized from 8000x8000\n pixels, 1 pixel = 5 nm)     Pharynx_single_sensilla_images --\u0026gt; contains\n representative images of pharyngeal sensilla in the four main pharnygeal\n organs and solitary papilla sensilla and chordotonal organs (359 images,\n JPEG Files, not resized, 1 pixel = 5 nm)     PPS --\u0026gt;  contains an image\n sequence of the PPS (401 z-slices, TIF Files, 1920x1080 pixels, resized\n from 7680x4320 pixels, 1 pixel = 5 nm)     VPS --\u0026gt; contains an image\n sequence of the VPS (1,073 z-slices, TIF Files, 1080x1080 pixels, resized\n from 12000x12000 pixels, 1 pixel = 5 nm)"}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Deutsche Forschungsgemeinschaft","funderIdentifier":"https://ror.org/018mejw64","awardNumber":"441181781"},{"funderIdentifierType":"ROR","funderName":"Deutsche Forschungsgemeinschaft","funderIdentifier":"https://ror.org/018mejw64","awardNumber":"426722269"},{"funderIdentifierType":"ROR","funderName":"Deutsche Forschungsgemeinschaft","funderIdentifier":"https://ror.org/018mejw64","awardNumber":"432195391"},{"funderIdentifierType":"ROR","funderName":"Directorate-General for Employment, Social Affairs and Inclusion","funderIdentifier":"https://ror.org/01qanyf14","awardNumber":"100649752"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.2fqz6135z","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":0,"referenceCount":0,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-10-05T20:26:50Z","registered":"2026-10-05T20:26:51Z","published":null,"updated":"2026-10-05T20:26:51Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.0rxwdbsg4","type":"dois","attributes":{"doi":"10.5061/dryad.0rxwdbsg4","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Jacquotte, Elisa","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Holzwarth, James A.","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Mota, Ana Zotta","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Centro Agronomico Tropical De Investigacion Y Ensenanza Catie"],"name":"Leandro-Muñoz, Mariela E.","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Centre de Coopération Internationale en Recherche Agronomique pour le Développement"],"name":"Rhoné, Bénédicte","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Todd, Evelyn T.","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Lepelley, Maud","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Bellanger, Laurence","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Descombes, Patrick","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Arigoni, Fabrizio","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Fillodeau, Audrey","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Beche, Eduardo","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2318-3615"}]}],"titles":[{"title":"Data and code from: Association mapping and genome prediction reveals a complex genetic architecture for resistance to black and frosty pod rot in cacao (\u003cem\u003eTheobroma cacao\u003c/em\u003e L.) populations"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Agricultural sciences","subjectScheme":"fos"},{"subject":"Genomic prediction"},{"subject":"GWAS"},{"subject":"Cacao diseases"}],"contributors":[{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Jacquotte, Elisa","contributorType":"DataCurator","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Jacquotte, Elisa","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Jacquotte, Elisa","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Jacquotte, Elisa","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Holzwarth, James A.","contributorType":"DataCurator","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Holzwarth, James A.","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Holzwarth, James A.","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Mota, Ana Zotta","contributorType":"DataCurator","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Mota, Ana Zotta","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Mota, Ana Zotta","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Centro Agronomico Tropical De Investigacion Y Ensenanza Catie"],"name":"Leandro-Muñoz, Mariela E.","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Centro Agronomico Tropical De Investigacion Y Ensenanza Catie"],"name":"Leandro-Muñoz, Mariela E.","contributorType":"DataCollector","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Centre de Coopération Internationale en Recherche Agronomique pour le Développement"],"name":"Rhoné, Bénédicte","contributorType":"DataCollector","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Centre de Coopération Internationale en Recherche Agronomique pour le Développement"],"name":"Rhoné, Bénédicte","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Centre de Coopération Internationale en Recherche Agronomique pour le Développement"],"name":"Rhoné, Bénédicte","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Todd, Evelyn T.","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Todd, Evelyn T.","contributorType":"DataCurator","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Lepelley, Maud","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Bellanger, Laurence","contributorType":"DataCollector","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Bellanger, Laurence","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Descombes, Patrick","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Arigoni, Fabrizio","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Fillodeau, Audrey","contributorType":"ProjectManager","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Fillodeau, Audrey","contributorType":"ProjectLeader","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Fillodeau, Audrey","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Beche, Eduardo","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2318-3615"}],"contributorType":"ProjectManager"},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Beche, Eduardo","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2318-3615"}],"contributorType":"ProjectLeader"},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Beche, Eduardo","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2318-3615"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Beche, Eduardo","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2318-3615"}],"contributorType":"Editor"},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Beche, Eduardo","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2318-3615"}],"contributorType":"Supervisor"},{"nameType":"Personal","affiliation":["Nestlé (Switzerland)"],"name":"Beche, Eduardo","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2318-3615"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2026-03-16T12:10:35Z","dateType":"Created"},{"date":"2026-09-27T13:36:42Z","dateType":"Submitted"},{"date":"2026-10-05T00:00:00Z","dateType":"Issued"},{"date":"2026-10-05T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":["132012930 bytes"],"formats":[],"version":"6","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This dataset contains genomic and phenotypic data generated from cacao\n (Theobroma cacao L.) breeding populations evaluated for resistance to\n black pod rot (Phytophthora spp.) and frosty pod rot (Moniliophthora\n roreri), two of the most economically important diseases affecting cacao\n production worldwide. The resource includes genotypic information derived\n from high-density single nucleotide polymorphism (SNP) markers, phenotypic\n measurements collected from multi-year field evaluations, and associated\n trait data related to disease resistance and productivity. Variables are\n provided in standard tabular formats with accompanying metadata describing\n genotype identifiers, marker positions, phenotypic traits, disease\n assessments, and population assignments. All data were generated from\n plant materials and field evaluations and do not contain personally\n identifiable information or data from human participants. No special\n ethical restrictions apply beyond compliance with the terms of use\n associated with the repository. The dataset is intended to support open,\n reproducible research and facilitate the development of disease-resistant\n and productive cacao varieties through data-driven breeding approaches."},{"descriptionType":"TechnicalInfo","description":"# Data and code from: Association mapping and genome prediction reveals a\n complex genetic architecture for resistance to black and frosty pod rot in\n cacao (*Theobroma cacao* L.) populations Dataset DOI:\n [10.5061/dryad.0rxwdbsg4](https://doi.org/10.5061/dryad.0rxwdbsg4) ###\n Files and variables #### File: Phenotypic.data.csv **Description:** BLUES\n of the phenotypes used for the GWAS and genomic prediction ##### Variables\n * id: unique identification for the genotypes * FPRe: Frosty pod rot\n external * FPRi: Frosty pod rot internal * BPR: Black Pod Rot *\n NOP: Number of Pods * NOHP: Number of Healthy pods * MI: Moniliasis\n incidence * Pop:  population/family * env: environment  #### File:\n geno_012_cacao.QC.csv **Description:** Genomic data used for the GWAS\n analysis and Testing number of markers and Population size for genomic\n prediction #### File: geno_012_cacao.QC.prunned.csv\n **Description:** Genomic data prunned by LD and used for the genomic\n selection prediction models Rows are the marker ids, like chr1:9786:SG\n (Chromosome 1, position in base pairs 9786) Columns are the Genotype id,\n like S374 (Genotype id S374) Genotypic data is in a dosage format (012)\n #### File: geno_012_cacao.QC.prunned.csv **Description:** Genomic data\n prunned by LD and used for the genomic selection prediction models Rows\n are the marker ids, like chr1:9786:SG (Chromosome 1, position in base\n pairs 9786) Columns are the Genotype ids, like S374 (Genotype id S374)\n Genotypic data is in a dosage format (012) #### File: Marker_map.csv\n **Description:** File with marker name, chromosome and base pair position\n #### File: GS_models_Publish.R **Description:** R codes for additive and\n dominance genomic prediction models (GBLUP) #### File:\n GS_Test_number_of_Markers_and_Pop_size_Publish.R **Description:** R code\n for testing genomic prediction (GBLUP) under different number of markers\n and population size #### File: Multi_trait_GS_GBLUP_ASReml_Publish.R\n **Description:** R codes for comparting MT-GP and ST-GP ## Code/software\n All data analysis was conducted in R."}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.0rxwdbsg4","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":1,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-10-05T16:15:56Z","registered":"2026-10-05T16:15:57Z","published":null,"updated":"2026-10-05T16:15:57Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.547d7wmp0","type":"dois","attributes":{"doi":"10.5061/dryad.547d7wmp0","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["University of Iowa"],"name":"Nayak, Manasa","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Iowa"],"name":"Flora, Gagan","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Iowa"],"name":"Budnik, Ivan","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Iowa"],"name":"Barbhuyan, Tarun","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Iowa"],"name":"Patel, Rakesh","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Iowa"],"name":"Ghatge, Madankumar","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Iowa"],"name":"Kumskova, Mariia","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Iowa"],"name":"Jain, Aditi","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Iowa"],"name":"Chauhan, Neelam","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Iowa"],"name":"Kumar, Jitendra","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Colorado Anschutz"],"name":"Jewell, Megan","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Iowa"],"name":"Kumar, Santosh","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Iowa"],"name":"Lentz, Steven","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Colorado Anschutz"],"name":"Neeves, Keith","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Iowa"],"name":"Chauhan, Anil","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8554-0898"}]}],"titles":[{"title":"Data from: Pyruvate kinase M2 promotes venous thrombosis by enhancing SNAP23-mediated platelet exocytosis and consequent NETosis"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Platelets","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Metabolism","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Pyruvate kinase M2"},{"subject":"SNAP23"},{"subject":"Venous thrombosis"},{"subject":"NETosis"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Biological sciences","subjectScheme":"fos"}],"contributors":[{"nameType":"Personal","affiliation":["University of Iowa"],"name":"Chauhan, Anil","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8554-0898"}],"contributorType":"ContactPerson"}],"dates":[{"date":"2026-04-13T16:57:51Z","dateType":"Created"},{"date":"2026-04-13T16:57:54Z","dateType":"Submitted"},{"date":"2026-05-28T00:00:00Z","dateType":"Issued"},{"date":"2026-05-28T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1182/bloodadvances.2025017414","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["1012986 bytes"],"formats":[],"version":"5","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"Little is known about the role of metabolic regulatory\n mechanisms in the pathobiology of deep vein thrombosis (DVT).\n Recent studies have demonstrated the involvement of the metabolic enzyme\n pyruvate kinase M2 (PKM2) in platelet function; however, whether platelet\n PKM2 contributes to DVT has not been investigated yet. Using\n platelet-specific PKM2−/− (PKM2Plt-KO) or wild-type (WT) mice orally\n administered ML265 (a small molecule that limits PKM2 dimers by\n stabilizing PKM2 tetramers), we found reduced thrombus burden at 48 h\n post-surgery in the inferior vena cava (IVC) stenosis model compared with\n littermate controls. This reduction was associated with lower levels of\n CitH3, a marker of neutrophil extracellular traps (NETs), in the harvested\n thrombi and improved IVC wall contraction and relaxation responses\n (assessed by myography). Mechanistically, thrombin-stimulated platelets\n from PKM2Plt-KO mice or ML265-pretreated platelets from WT mice showed\n reduced SNAP23 phosphorylation and diminished PF4 release (a marker of\n α-granule exocytosis). The releasate collected from thrombin-stimulated\n platelets was less effective at inducing NETosis, compared to respective\n controls. Utilizing ML265-pretreated human whole blood perfused over a\n tissue factor-coated surface at a venous shear rate, we found that the\n area covered by platelet-leukocyte aggregates was profoundly reduced\n compared to vehicle control. Consistent with murine data, human platelets\n pretreated with ML265 and stimulated with thrombin exhibited decreased PF4\n release and generated releasates that were less potent in inducing\n NETosis. These findings reveal for the first time that targeting PKM2\n genetically or pharmacologically reduces SNAP23-mediated α-granule\n exocytosis in platelets, platelet releasate-induced NETosis, and\n susceptibility to DVT."},{"descriptionType":"Methods","description":"\u003cstrong\u003eMaterials\u003c/strong\u003e ML265\n was purchased from Cayman (CAS# 1221186-53-3). Apyrase, EGTA (ethylene\n glycol tetraacetic acid), prostaglandin I\u003csub\u003e2\u003c/sub\u003e\n (PGI\u003csub\u003e2\u003c/sub\u003e), thrombin, and protease inhibitors were\n purchased from Sigma (St. Louis, MO, USA). Convulxin (CAS # 37206-04-5)\n was purchased from SantaCruz Biotech. Primary anti-PF4 antibodies (rabbit\n anti-mouse #ab182988, 1:1000; mouse anti-human #ab49735, 1:1000),\n anti-CitH3 (#ab5103, 1:1000) from Abcam and goat anti-rabbit IgG,\n HRP-linked secondary antibody (#7074, 1:2500) were from Cell Signaling\n Technologies; goat anti-mouse IgG, HRP-linked secondary antibody (#P0447,\n 1:2500) from Dako. SNAP23 and phospho-SNAP23 antibodies were gifted by\n Makoto Itakura, Department of Biochemistry, Kitasato University School of\n Medicine, Japan. Super Signal West Pico chemiluminescent substrate\n (#34580), Femto Maximum Sensitivity Substrate (#34096), and PVDF membrane\n (#IPVH00010) were the products from Thermo Scientific and Millipore,\n respectively. Hoechst dye (#33342) was purchased from ThermoFisher\n Scientific. Sytox™ green (#S7020) and prolong gold antifade mountant\n (#P36934) were purchased from Invitrogen. Zombie Violet Fixable Viability\n Kit (#423114), BV605-conjugated anti-mouse CD41 (#133921), and Alexa Fluor\n 647–conjugated Annexin V (#640943) were purchased from Biolegend.\n FITC-conjugated CD62P (#553744) was purchased from BD Pharmingen. All\n other reagents were of analytical grade.\n \u003cstrong\u003eMethods\u003c/strong\u003e\n \u003cstrong\u003eHuman subjects\u003c/strong\u003e\n Human platelets were obtained from healthy volunteers who\n provided informed consent and had not taken any antiplatelet medication\n within the preceding two weeks. Blood collection was conducted in\n accordance with the Declaration of Helsinki and approved by the\n Institutional Review Board at the University of Iowa.\n \u003cstrong\u003eMice\u003c/strong\u003e Male mice,\n aged 12–13 weeks, on the C57BL/6J background were used throughout the\n study. Wild-type mice were obtained from the Jackson Laboratory.\n Platelet-specific PKM2-deficient mice\n (PKM2\u003csup\u003efl/fl\u003c/sup\u003ePF4Cre\u003csup\u003e+\u003c/sup\u003e) were\n generated by crossing PKM2\u003csup\u003efl/fl\u003c/sup\u003e with\n PF4Cre\u003csup\u003e+\u003c/sup\u003e mice at the University of Iowa animal\n facility; littermate\n PKM2\u003csup\u003efl/fl\u003c/sup\u003ePF4Cre\u003csup\u003e–\u003c/sup\u003e mice were\n used as control. For simplicity, from now on\n PKM2\u003csup\u003efl/fl\u003c/sup\u003ePF4Cre\u003csup\u003e+\u003c/sup\u003e mice will\n be referred to as PKM2\u003csup\u003ePlt-KO\u003c/sup\u003e mice and\n PKM2\u003csup\u003efl/fl\u003c/sup\u003e controls as\n PKM2\u003csup\u003eWT\u003c/sup\u003e. Mice were genotyped by polymerase chain\n reaction . Mice were kept in standard animal housing conditions with\n controlled temperature and humidity, and they had ad libitum access to a\n standard chow diet and water. The University of Iowa Animal Care and Use\n Committee approved all experiments. \u003cstrong\u003eHuman\n and mouse platelet isolation\u003c/strong\u003e Briefly,\n human venous blood was collected into 10 mL tubes containing acid citric\n dextrose solution A (ACD-A). Mouse blood from anaesthetized mice was drawn\n from the retro-orbital plexus and collected into 1.5 mL polypropylene\n tubes containing enoxaparin (0.3 mg/mL; Sanofi-Aventis, US LLC).\n \u003cstrong\u003eML265 preparation and administration\n for\u003c/strong\u003e \u003cem\u003e\u003cstrong\u003ein\n vivo\u003c/strong\u003e\u003c/em\u003e\n \u003cstrong\u003eexperiments\u003c/strong\u003e For\n \u003cem\u003ein vitro\u003c/em\u003e and \u003cem\u003eex vivo\u003c/em\u003e assays,\n ML265 was dissolved in DMSO and used at final concentrations of 50 or 100\n μM, with the final concentration of DMSO maintained at 0.1 % in all\n samples. For \u003cem\u003ein vivo\u003c/em\u003e studies, ML265 was suspended in\n 0.1 % v/v Tween-80 and 0.5 % w/v carboxymethyl cellulose (CMC). The mice\n received ML265 at a dose of 50 mg/kg body weight via oral gavage 30 min\n before inducing DVT and then every 12 h until the animals were sacrificed.\n Control mice received the same volume of the vehicle (0.1 % v/v Tween-80\n and 0.5 % w/v CMC) at the same time points. The appropriate concentrations\n of ML265 were determined based on our previous study.\n \u003cstrong\u003eMyography\u003c/strong\u003e\n Myography was performed using Myograph System 610M (Danish Myo\n technology) was used to determine the contraction and relaxation in veins.\n Briefly, IVC was excised, sufficiently cleaned, and placed into a dish\n filled with physiologic saline solution (PSS; prepared by dissolving 4.37\n g NaCl, 4.47 g KCl, 0.16 g\n KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, 0.29 g\n MgSO\u003csub\u003e4\u003c/sub\u003e·7H\u003csub\u003e2\u003c/sub\u003eO, 1.25 g\n NaHCO\u003csub\u003e3\u003c/sub\u003e, 1.00 g dextrose, 0.0097 g EDTA, and 0.235 g\n CaCl\u003csub\u003e2\u003c/sub\u003e in 1 L of water) to ensure vessel reactivity.\n Vessels were then cleaned and mounted on the wire myograph with the\n chambers filled with PSS and bubbled with a gas mixture of\n O\u003csub\u003e2\u003c/sub\u003e and CO\u003csub\u003e2\u003c/sub\u003e. Myograph\n channels were zeroed for force readings to establish a baseline, and a\n wake-up protocol was utilized to ensure viability. The vessels were then\n subjected to 2-mN tension and allowed to equilibrate for 30 min. Vessels\n were then exposed to high-potassium (96 mM) saline solution (KPSS;\n prepared by dissolving 2.26 g NaCl, 7.16 g KCl, 0.16 g\n KH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, 0.29 g\n MgSO\u003csub\u003e4\u003c/sub\u003e·7H\u003csub\u003e2\u003c/sub\u003eO, 1.25 g\n NaHCO\u003csub\u003e3\u003c/sub\u003e, 1.00 g dextrose, 0.0097 g EDTA, and 0.235 g\n CaCl\u003csub\u003e2\u003c/sub\u003e in 1 L of water) to elicit contraction\n responses. This response was carried out for 15 min. The vessels were then\n washed 3 times for 10 min each with PSS. The wake-up protocol was\n performed twice. Next, vessels were contracted utilizing phenylephrine\n (0.5 mM) and then subjected to acetylcholine (1.62 mM) to relax for 10\n minutes. The maximum relaxation point was considered when the curve\n plateau was maintained for 2 minutes.\n \u003cstrong\u003eNETosis assay\u003c/strong\u003e\n Freshly isolated neutrophils from the mouse bone marrow or human\n whole blood were seeded onto poly-L-lysine-coated coverslips (1 ×\n 10\u003csup\u003e4\u003c/sup\u003e cells/coverslip). The cells were incubated at\n 37 °C for 1 h in a CO\u003csub\u003e2\u003c/sub\u003e incubator and then\n co-incubated with thrombin-stimulated platelets (4 ×\n 10\u003csup\u003e8\u003c/sup\u003e cells/mL) or their releasates for an additional\n 4 h in the same conditions. In this assay, platelets were treated with 0.1\n U/mL thrombin for 10 min, centrifuged at 5000g for 5 mins at room\n temperature, and the resulting supernatant (releasate) was collected for\n further experiments. Ice-cold PBS was added to stop the reaction, and the\n coverslips were placed on ice for 10 min. The cells were fixed for 15 min\n in ice-cold PBS containing 2 % paraformaldehyde at room temperature. The\n fixed cells were then washed with ice-cold PBS. For specific staining of\n extracellular nuclear structures and nuclei, cells were incubated at room\n temperature with Sytox Green (Sigma) dye and Hoechst (Thermo Scientific),\n respectively, for 30 min at room temperature. Coverslips were washed with\n PBS and mounted onto glass slides using a drop of the mounting medium\n (ProLong™ Gold Antifade Mountant, ThermoFisher) before fluorescence\n microscopy analysis. \u003cstrong\u003eNETs\n quantification\u003c/strong\u003e: NETs were imaged using fluorescence\n microscopy and analyzed with ImageJ (NIH, Bethesda, MD, USA). Images were\n converted to 8-bit grayscale, and a global threshold was applied. The\n total number of neutrophils based on size (~20 μm in diameter) was\n quantified from Hoechst staining using the Analyze Particles tool with a\n size range of 20–2000 μm². NET-positive cells (forming extracellular\n traps; web-like structure) were identified as irregular structures with a\n size range of ~50–2000 μm². For quantification, two 20x magnification\n fields were analyzed (coverslip edges were avoided). The percentage of\n NET-positive neutrophils was calculated by dividing the number of\n NET-positive cells by the total neutrophil count and multiplying by\n 100. \u003cstrong\u003eFlow\n cytometry\u003c/strong\u003e Mouse whole blood samples were\n collected through the retro-orbital plexus into ACD-A 48 h post-DVT\n surgery. Samples were diluted 1:20 in the Tyrode buffer (20 mM HEPES, 138\n mM NaCl, 2.9 mM KCl, 1 mM MgCl\u003csub\u003e2\u003c/sub\u003e, 0.36 mM\n NaH\u003csub\u003e2\u003c/sub\u003ePO\u003csub\u003e4\u003c/sub\u003e, supplemented with 5\n mM glucose and 0.2 % bovine serum albumin, pH 7.4), incubated with 2.1 mM\n CaCl\u003csub\u003e2\u003c/sub\u003e for 10 min, stained with the antibody\n cocktail (Zombie Violet, CD41-BV605, CD62P-FITC, and Annexin V-Alexa Fluor\n 647) for 20 min in the dark, fixed in 0.2 % paraformaldehyde for 10 min in\n the dark, and analyzed using Cytek Aurora spectral flow cytometer (Cytek\n Biosciences Inc., Fremont, CA, USA). Platelet population of interest was\n characterized as Live\u003csup\u003e+CD41+CD62P+AnnexinV+\u003c/sup\u003e\n cells.\u003csup\u003e6\u003c/sup\u003e Antibodies were used at the final\n concentrations recommended by the manufacturers. All steps were performed\n at room temperature. \u003cstrong\u003eBioflux flow chamber\n assay\u003c/strong\u003e Thrombosis assays with human whole\n blood were performed using BioFluxTM 200 (Fluxion Biosciences, USA)\n microfluidics flow chamber. The channels were coated with tissue factor\n (TF) (Innovin stock diluted 1:9 in HBSS) for 1 h at room temperature and\n then blocked with 1 % Bovine serum albumin (BSA) for 20 min. The whole\n blood was incubated with DiOC6 (5 μM) for 10 min. The whole blood was then\n pretreated with ML265 or vehicle (DMSO) for 10 min. Next, whole blood was\n treated with 10 mM of CaCl\u003csub\u003e2\u003c/sub\u003e and immediately\n perfused over the TF-coated plate at a venous shear stress of 500\n s\u003csup\u003e−1\u003c/sup\u003e for 8 min. The fluorescently labeled\n platelets/thrombi on the TF-coated surface were analyzed with ImageJ (NIH,\n Bethesda, MD, USA). \u003cstrong\u003eWestern blot\n analysis\u003c/strong\u003e Briefly, immediately after\n harvesting, a DVT thrombus was placed in a Petri dish containing PBS on\n ice to prevent protein degradation, transferred to a pre-chilled\n microcentrifuge tube containing an ice-cold RIPA lysis buffer, and\n homogenized by using micro-tube homogenizer system. The homogenate was\n centrifuged at 6000 × g for 10 min at 4 °C to remove cellular debris, and\n the supernatant was collected for further analysis. Thrombus lysate and\n platelet lysate proteins were separated on precast 4–20 % SDS-PAGE (sodium\n dodecyl sulfate-polyacrylamide gel electrophoresis) gradient gels and\n electrophoretically transferred to PVDF (polyvinylidene fluoride) membrane\n using Bio-Rad western blotting system. For PKM2 dimer and tetramer study,\n handcast 6 % native gels (without SDS) are used. PVDF membranes were\n blocked with 5 % BSA or skimmed milk in 10 mM Tris-HCl, 150 mM NaCl, pH\n 8.0 (TBS) containing 0.05 % Tween-20 for one hour at room temperature.\n Blots were incubated with a primary antibody overnight at 4 °C, followed\n by incubation with a horseradish peroxidase-conjugated secondary antibody\n for 1 h at room temperature. Blots were developed using enhanced\n chemiluminescence and quantified using Image J software from NIH\n (Bethesda, MD, USA). \u003cstrong\u003ePonceau\n Staining:\u003c/strong\u003e PVDF membranes were rinsed with distilled water,\n then incubated in the Ponceau S solution for 30 min at room temperature on\n a shaker to stain the protein bands. After staining, membranes were washed\n in distilled water for 2–3 min until the red protein bands were clearly\n visible against the clear background.\n \u003cstrong\u003ePlasma recalcification\n assay\u003c/strong\u003e Briefly, the blood was collected\n from mice in 3.8% trisodium citrate. The platelet-poor plasma (PPP) was\n obtained by centrifuging the blood at 2650 rpm for 5 min and 30 s. The\n recalcification of plasma was initiated by adding 25 mM\n CaCl\u003csub\u003e2\u003c/sub\u003e. The optical density of plasma at 405 nm was\n observed using a spectrophotometer (Spectramax ID3, Molecular Devices)\n every minute up to 10 min to estimate fibrin formation in PPP.\n \u003cstrong\u003eDeep venous thrombosis (DVT)\n model\u003c/strong\u003e Briefly, mice were anesthetized\n and underwent a midline laparotomy. A spacer (30-gauge, 3-mm-long needle)\n was positioned on the exposed IVC, and a permanent narrowing ligature\n (with 5-0 nonabsorbable silk sutures) was tied around the IVC and spacer\n immediately caudal to the junction of the left renal vein. Next, the\n spacer was removed to restrict the IVC blood flow by 80-90 %. All visible\n side branches were ligated. Mice were then allowed to recover and were\n sacrificed 48 h post-surgery (the time required for complete thrombus\n formation in this model). Control and experimental mice were operated on\n in batches on the same day to minimize day-to-day variation. Thrombi were\n isolated from the vessels, and their length and weight were measured. In\n some mice, thrombi were further processed for western blotting.\n \u003cstrong\u003eMicrofluidic studies using custom-made DVT\n device\u003c/strong\u003e DVT was simulated \u003cem\u003eex\n vivo\u003c/em\u003e using a custom-fabricated microfluidic device consisting\n of a 150-µm wide channel expanding into a 300 µm-wide channel at an angle\n of 150° to define two model valve pockets. TF (undiluted) adsorbed into\n the wide channel and pockets by careful backfilling via pipette aspiration\n and allowed to incubate for 1 h at room temperature. The device was\n flushed with 2 % BSA and then blocked with 2 % BSA for 1 h. Citrated whole\n blood was pre-treated with ML265 (150 μM) or vehicle and incubated with\n DiOC6 (1 µM) to visualize platelets and leukocytes for 15 min at 37 °C.\n Whole blood and recalcification buffer (75 mM CaCl\u003csub\u003e2\u003c/sub\u003e\n and 37.5 mM MgCl\u003csub\u003e2\u003c/sub\u003e in HEPES-buffered saline, pH 7.4)\n were mixed upstream of the inlet of the device in T-junction at a\n volumetric ratio of 9:1 (blood : buffer) and perfused through the device\n at a flow rate of 372 µL/min for 30 min using two syringe pumps (Harvard\n Apparatus PhD 2000 and New Era NE300), corresponding to a Reynolds number\n (the ratio of inertial forces/viscous forces) of 10, enabling secondary\n flows into the valve pockets. Platelet and leukocyte accumulation was\n captured by confocal microscopy (Olympus IX83 with Yokogawa CSU-W1\n Confocal Scanner Unit, 20x) every 10 s. Kinetic data all utilized the same\n combined region of interest (ROI) based on the dimensions of the valve\n pockets in the device. Fluorescent images were thresholded using the ‘Max\n Entropy’ algorithm in ImageJ and processed into binary masks. The area\n covered over time within each valve pocket ROI was measured using ImageJ\n and normalized to the total area of each valve pocket ROI. Final images\n (20x) at the end of the experiment (25 min) were used for endpoint\n thrombus size measurements, and fraction of total valve pocket area was\n reported. \u003cstrong\u003eStatistical\n analysis\u003c/strong\u003e Continuous variables were\n presented as mean ± standard error (SEM) or as median with interquartile\n range and range, as specified. Categorical variables were presented as\n counts, proportions, and/or percentages. For continuous variables,\n differences between two groups were evaluated using the Mann–Whitney\n \u003cem\u003eU\u003c/em\u003e test or Student’s \u003cem\u003et\u003c/em\u003e-test, and\n differences between three or more groups using one- or two-way ANOVA\n followed by a post hoc multiple comparisons test. For categorical\n variables, differences between two groups were evaluated using the\n Fisher’s exact test. Two-tailed \u003cem\u003eP\u003c/em\u003e values less than\n 0.05 was considered statistically significant. All analyses were performed\n using GraphPad Prism software, version 10.6.0."},{"descriptionType":"TechnicalInfo","description":"# Data from: Pyruvate kinase M2 promotes venous thrombosis by enhancing\n SNAP23-mediated platelet exocytosis and consequent NETosis ## Description\n of the data and file structure This dataset contains the individual source\n data supporting figures and analyses in the associated article. ### Files\n and variables **File:** SourceData_Fig_1.xlsx **Description:** Source data\n for Figure 1, panels A, B, C and D. Sheet Fig. 1A: % of thrombus incidence\n in PKM2WT (PKM2fl/fl) and PKM2Plt-KO (PKM2fl/flPF4Cre+) mice 48 hours\n after IVC stenosis. Sheet Fig. 1B (left panel): quantified thrombus weight\n in PKM2WT and PKM2Plt-KO mice 48 hours after IVC stenosis. Sheet Fig. 1B\n (right panel): quantified thrombus length in PKM2WT and PKM2Plt-KO mice 48\n hours after IVC stenosis. Sheet Fig. 1C: Western blot analysis of CitH3\n levels normalised to β-actin was measured in the thrombus isolated from\n PKM2WT and PKM2Plt-KO mice. Sheet Fig. 1D (left panel): Fold increase in\n contraction measured by wire myography in IVCs isolated from PKM2WT and\n PKM2Plt-KO mice 48 hours after IVC stenosis. Sheet Fig. 1D (right panel):\n Percent of relaxation measured by wire myography in IVCs isolated from\n PKM2WT and PKM2Plt-KO mice 48 hours after IVC stenosis. **File:**\n SourceData_Fig_2.xlsx **Description:** Source data for Figure 2, panels A,\n B, and C. Sheet Fig. 2A: Quantification of the percentage of cells\n releasing NET (right) calculated in lower magnification, NETosis assay was\n performed by stimulating bone marrow–derived WT neutrophils with\n releasates collected from thrombin (0.1 U/mL)-activated platelets isolated\n from PKM2WT or PKM2Plt-KO mice. Sheet Fig 2B: Western blot analysis of\n p-SNAP23 levels normalised to total SNAP23 was measured in the Platelets\n isolated from PKM2WT and PKM2Plt-KO mice, in the absence (resting\n platelets) and presence of thrombin (activated). Sheet Fig 2C: Western\n blot analysis of PF4 in supernatants of Resting and activated platelets.\n Platelets isolated from PKM2WT and PKM2Plt-KO mice were stimulated with\n thrombin and centrifuged for 3 minutes. PF4 levels in the supernatant were\n measured by western blot and normalised to Ponceau. **File:**\n SourceData_Fig_3.xlsx **Description:** Source data for Figure 3, panels A,\n B, C and D. Sheet Fig. 3A: % of thrombus incidence in vehicle- or\n ML265-treated mice 48 hours after IVC stenosis. Sheet Fig. 3B (left\n panel): quantified thrombus weight in vehicle- or ML265-treated mice 48\n hours after IVC stenosis. Sheet Fig. 3B (right panel): quantified thrombus\n length in vehicle- or ML265-treated mice 48 hours after IVC stenosis.\n Sheet Fig. 3C: Western blot analysis of CitH3 levels normalised to β-actin\n was measured in the thrombus isolated from vehicle- or ML265-treated mice.\n Sheet Fig. 3D (left panel): Fold increase in contraction measured by wire\n myography in IVCs isolated from vehicle- or ML265-treated mice 48 hours\n after IVC stenosis. Sheet Fig. 3D (right panel): Percent of relaxation\n measured by wire myography in IVCs isolated from vehicle- or ML265-treated\n mice 48 hours after IVC stenosis. **File:** SourceData_Fig_4.xlsx\n **Description:** Source data for Figure 4, panels A, B and C. Sheet Fig.\n 4A: quantification of the percentage of cells releasing NET calculated in\n lower magnification, NETosis assay was performed by stimulating bone\n marrow–derived WT neutrophils with releasates collected from ML265- or\n vehicle-pretreated, thrombin-stimulated platelets. Sheet Fig. 4B: Western\n blot analysis of p-SNAP23 levels normalised to total SNAP23 was measured\n in the Platelets. WT platelets pretreated with vehicle or ML265 (50 and\n 100 μM; 10 minutes) were stimulated with thrombin. Sheet Fig. 4C: Western\n blot analysis of PF4 in supernatants of Resting and activated platelets.\n Vehicle and ML265 pre-treated platelets isolated from WT mice were\n stimulated with thrombin and centrifuged for 3 minutes; the level of PF4\n was measured in the supernatant by western blot and normalised to Ponceau.\n **File:** SourceData_Fig_5.xlsx Description: Source data for Figure 5,\n panels A and B. Sheet Fig. 5A: quantification of thrombus growth (right)\n on the TF-coated surface over time. Human whole blood pretreated with\n vehicle or ML265 was perfused over a TF-coated surface for 8 minutes at a\n shear rate of 500 s–1 in a BioFlux microfluidic flow chamber system. Sheet\n Fig 5B: quantification of thrombus growth (fraction of total area) at 25\n minutes (the end point of the assay). Human whole blood pretreated with\n vehicle or ML265 was perfused over a TF-coated surface for 25 minutes in a\n custom-made DVT device. Accumulation of blood cells after 25 minutes of\n blood flow. Blood cells were stained with DiOC6 in green. **File:**\n SourceData_Fig_6.xlsx **Description:** Source data for Figure 6, panels A\n and B. Sheet Fig. 6A: quantification of the percentage of cells releasing\n NET. NETosis assay was performed by stimulating human neutrophils with\n releasates collected from thrombin-activated human platelets in the\n presence or absence of ML265. Sheet Fig. 6B: Western blot analysis of PF4\n in supernatants of Resting and activated platelets. Vehicle and ML265\n pre-treated human platelets were stimulated with thrombin and centrifuged\n for 3 minutes; the level of PF4 was measured in the supernatant by western\n blot and normalised to Ponceau. **File:** SourceData_SupplFig_1.xlsx\n **Description:** Source data for SupplFig 1, panel B. Sheet SupplFig1B:\n The line graph shows the plasma recalcification time. The plasma\n recalcification time was measured in platelet-poor plasma from WT or\n PKM2Plt-KO mice following the addition of CaCl2. **File:**\n SourceData_SupplFig_2.xlsx **Description:** Source data for SupplFig 2\n Sheet SupplFig2: Percentage of procoagulant platelets (triple positive for\n CD41, phosphatidylserine, and P-selectin) in peripheral blood samples,\n measured by flow cytometry. **File:** SourceData_supplFig_4.xlsx\n **Description:** Source data for SupplFig 4 Sheet SupplFig4:\n Quantification of the percentage of cells releasing NETs calculated in\n lower magnification. NETosis assay was performed by stimulating bone\n marrow-derived WT neutrophils with thrombin-activated platelets isolated\n from PKM2WT or PKM2Plt-KO mice. **File:** SourceData_supplFig_5.xlsx\n **Description:** Source data for SupplFig 5, panel A, B and C. Sheet\n SupplFig5A: Sheet SupplFig1B: Western blot analysis of p-SNAP23 levels\n normalised to total SNAP23 was measured in the Platelets isolated from\n PKM2WT and PKM2Plt-KO mice, in the absence (resting platelets) and\n presence of Convulxin (activated). Sheet SupplFig5B: Western blot analysis\n of PF4 in supernatants of Resting and activated platelets. Platelets\n isolated from PKM2WT and PKM2Plt-KO mice were stimulated with convulxin\n and centrifuged for 3 minutes. PF4 levels in the supernatant were measured\n by western blot and normalised to Ponceau. Sheet SupplFig5C: The level of\n PF4 was measured in the whole cell lysate by western blot normalised to\n β-actin. Equal number of Platelets isolated from WT and PKM2Plt-KO mice\n and were lysed. **File:** SourceData_supplFig_6.xlsx **Description:**\n Source data for SupplFig 6, panel A and B. Sheet SupplFig6A: western blot\n analysis showing dimer/tetramer ratio densitometry, in mouse platelets\n isolated 48 hours after sham or DVT surgery. Sheet SupplFig6B:\n Quantification of the percentage of cells releasing NETs calculated in\n lower magnification. NETosis assay was performed by stimulating bone\n marrow-derived WT neutrophils co-incubated with ML265- or\n vehicle-pretreated thrombin-stimulated platelets. **File:**\n SourceData_supplFig_9.xlsx **Description:** Source data for SupplFig 9.\n Sheet SupplFig9: Quantification of the percentage of cells releasing NETs\n calculated in lower magnification. NETosis assay was performed by\n stimulating human neutrophils with thrombin-activated human platelets in\n the presence or absence of ML265. **File:**\n SourceData_Full_western_blots.pdf **Description:** Source data for Figure\n 1C, 2B (p-SNAP23), 2B (PF4), 3C, 4B, 4C, 6B, S1A, S5A, S5B, S5C, S6A. ##\n Human subjects data Human platelets were obtained from healthy volunteers\n who provided informed consent and had not taken any antiplatelet\n medication within the preceding two weeks. Blood collection was conducted\n in accordance with the Declaration of Helsinki and approved by the\n Institutional Review Board at the University of Iowa."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"National Heart, Lung, and Blood Institute","funderIdentifier":"https://ror.org/012pb6c26","awardTitle":"metabolic reprogramming in deep vein thrombosis and resolution","awardNumber":"1R01HL174460-01","awardUri":"https://reporter.nih.gov/project-details/10936247"},{"funderIdentifierType":"ROR","funderName":"American Heart Association","funderIdentifier":"https://ror.org/013kjyp64","awardTitle":"Novel role of Pyruvate Kinase M2 in experimental Deep Vein Thrombosis","awardNumber":"942168"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.547d7wmp0","contentUrl":null,"metadataVersion":2,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":48,"downloadCount":13,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-05-28T18:39:36Z","registered":"2026-05-28T18:39:37Z","published":null,"updated":"2026-10-05T16:14:01Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.5061/dryad.0gb5mkmhg","type":"dois","attributes":{"doi":"10.5061/dryad.0gb5mkmhg","identifiers":[],"creators":[{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Yadav, Ankit","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4326-6776"}]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Musa, Emmanuel","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Berkeley"],"name":"Song, Ah-Young","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7931-0148"}]},{"nameType":"Personal","affiliation":["University of California, Berkeley"],"name":"Pourghaderi, Alireza","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Hickethier, Micah","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Chang, Chun-Wai","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Choong, Kai Shen","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Oregon"],"name":"Simons, Casey","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Feng, Zhenxing","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Berkeley"],"name":"Reimer, Jeffrey","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Saudi Aramco (Saudi Arabia)"],"name":"Alahmed, Ammar","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Saudi Aramco (Saudi Arabia)"],"name":"Younes, Mourad","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Saudi Aramco (Saudi Arabia)"],"name":"Jamal, Aqil","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Gładysiak, Andrzej","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3302-1644"}]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Stylianou, Kyriakos","nameIdentifiers":[]}],"titles":[{"title":"NMR data for: Pore compartmentalization of CO\u003csub\u003e2\u003c/sub\u003e and H\u003csub\u003e2\u003c/sub\u003eO in a redox-active adsorbent"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Chemical engineering","subjectScheme":"fos"},{"schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subject":"FOS: Chemical sciences","subjectScheme":"fos"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Porous materials","subjectScheme":"PLOS Subject Area Thesaurus"},{"schemeUri":"https://github.com/PLOS/plos-thesaurus","subject":"Solid-state NMR spectroscopy","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"carbon capture"}],"contributors":[{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Yadav, Ankit","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4326-6776"}],"contributorType":"ProjectLeader"},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Yadav, Ankit","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4326-6776"}],"contributorType":"DataCollector"},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Yadav, Ankit","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4326-6776"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Yadav, Ankit","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-4326-6776"}],"contributorType":"Editor"},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Musa, Emmanuel","contributorType":"DataCollector","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Musa, 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Ah-Young","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7931-0148"}],"contributorType":"Editor"},{"nameType":"Personal","affiliation":["University of California, Berkeley"],"name":"Pourghaderi, Alireza","contributorType":"DataCollector","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Berkeley"],"name":"Pourghaderi, Alireza","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Berkeley"],"name":"Pourghaderi, Alireza","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Hickethier, Micah","contributorType":"DataCollector","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Hickethier, Micah","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Hickethier, Micah","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Chang, Chun-Wai","contributorType":"DataCollector","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Chang, Chun-Wai","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Chang, Chun-Wai","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Choong, Kai Shen","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Choong, Kai Shen","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Oregon"],"name":"Simons, Casey","contributorType":"DataCollector","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Oregon"],"name":"Simons, Casey","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of Oregon"],"name":"Simons, Casey","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Feng, Zhenxing","contributorType":"DataCollector","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Feng, Zhenxing","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Feng, Zhenxing","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Berkeley"],"name":"Reimer, Jeffrey","contributorType":"DataCollector","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["University of California, Berkeley"],"name":"Reimer, 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(Saudi Arabia)"],"name":"Younes, Mourad","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Saudi Aramco (Saudi Arabia)"],"name":"Jamal, Aqil","contributorType":"DataCollector","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Saudi Aramco (Saudi Arabia)"],"name":"Jamal, Aqil","contributorType":"Researcher","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Saudi Aramco (Saudi Arabia)"],"name":"Jamal, Aqil","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Gładysiak, Andrzej","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3302-1644"}],"contributorType":"DataCollector"},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Gładysiak, Andrzej","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3302-1644"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Gładysiak, Andrzej","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3302-1644"}],"contributorType":"Researcher"},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Gładysiak, Andrzej","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3302-1644"}],"contributorType":"Editor"},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Stylianou, Kyriakos","contributorType":"ProjectLeader","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Stylianou, Kyriakos","contributorType":"Editor","nameIdentifiers":[]},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Gładysiak, Andrzej","nameIdentifiers":[{"nameIdentifierScheme":"ORCID","schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3302-1644"}],"contributorType":"ContactPerson"},{"nameType":"Personal","affiliation":["Oregon State University"],"name":"Stylianou, Kyriakos","contributorType":"ContactPerson","nameIdentifiers":[]}],"dates":[{"date":"2026-09-19T15:14:15Z","dateType":"Created"},{"date":"2026-09-19T15:14:17Z","dateType":"Submitted"},{"date":"2026-10-05T00:00:00Z","dateType":"Issued"},{"date":"2026-10-05T00:00:00Z","dateType":"Available"}],"language":"en","types":{"schemaOrg":"Dataset","resourceTypeGeneral":"Dataset","citeproc":"dataset","bibtex":"misc","ris":"DATA","resourceType":"dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1002/anie.9809119","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["19162534 bytes"],"formats":[],"version":"3","rightsList":[{"rightsIdentifierScheme":"SPDX","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rights":"Creative Commons Zero v1.0 Universal","rightsIdentifier":"cc0-1.0"}],"descriptions":[{"descriptionType":"Abstract","description":"This dataset contains solid-state NMR data collected to investigate the\n mechanism of CO2/H2O co-adsorption in BVR-X. The dataset includes 1H\n and 13C spin-echo MAS NMR experiments examining changes in BVR-X\n upon H2O uptake. Two-dimensional 13C–1H HETCOR CP MAS NMR experiments on\n BVR-X exposed to H2O and 13CO2 reveal close spatial proximity between\n physisorbed CO2 and pyrazine moieties of the framework. These data support\n a pore-compartmentalized adsorption mechanism, where CO2 and H2O\n are preferentially adsorbed within the pores."},{"descriptionType":"TechnicalInfo","description":"# NMR data for: Pore compartmentalization of CO~2~ and H~2~O in a\n redox-active adsorbent Dataset DOI:\n [10.5061/dryad.0gb5mkmhg](https://doi.org/10.5061/dryad.0gb5mkmhg) ##\n Description of the data and file structure The compressed archive\n (Data_Pore_Compartmentalization.zip) contains solid-state NMR data\n associated with the manuscript, “Pore Compartmentalization of CO~2~ and\n H~2~O in a Redox-Active Adsorbent,” by Ankit K. Yadav et al., published\n in Angewandte Chemie International Edition (2026), e9809119\n ([https://doi.org/10.1002/anie.9809119](https://doi.org/10.1002/anie.9809119)). The dataset includes solid-state ^1^H and ^13^C magic-angle-spinning (MAS) NMR measurements of BVR-X samples, including activated, H~2~O-adsorbed, and ^13^CO~2~-dosed samples. BVR refers to the BEAVER MOFs discovered at Oregon State University. BVR-X is the material investigated in this study. The vanadium centers in BVR-X can exist in different oxidation states, denoted here as V(+4)-BVR-X and V(+5)-BVR-X. ### Files and variables The dataset is organized into separate folders for V(+4)-BVR-X, V(+5)-BVR-X, and ^13^CO~2~-dosed BVR-X. Within each sample folder, the data are organized according to the NMR experiment. * V(+4)-BVR-X: The folder contains ^1^H and ^13^C spin-echo MAS NMR data of activated BVR-X. * V(+5)-BVR-X: The folder contains ^1^H and ^13^C spin-echo MAS NMR data of H~2~O-BVR-X. The \"^1^H with varied echo time\" subfolder contains a series of ^1^H spin-echo measurements acquired with different echo times. * ^13^CO~2~-dosed : The folder contains ^13^C spin-echo MAS NMR and ^13^C-^1^H HETCOR NMR data of ^13^CO~2~-dosed, H~2~O-adsorbed BVR-X. The HETCOR (heteronuclear correlation) spectra were acquired with different ^1^H carrier-frequency positions, either in the diamagnetic proton region (labeled \"HETCOR_diamagnetic\") or in the paramagnetic proton region (labeled \"HETCOR_paramagnetic\"). ## Code/software The datasets are provided as Bruker NMR files and can be opened and processed using Bruker TopSpin. After extraction, the experimental folders can be opened directly in TopSpin. Detailed acquisition parameters are stored within the corresponding Bruker datasets and can be viewed through TopSpin. Alternatively, the data can be imported into MestReNova (Mnova) using the fid files in the corresponding experiment folders. One-dimensional processed spectra can also be imported into DMfit using the 1r files located in the corresponding folders."}],"geoLocations":[],"fundingReferences":[{"funderIdentifierType":"ROR","funderName":"Saudi Aramco (Saudi Arabia)","funderIdentifier":"https://ror.org/03ypap427"},{"funderName":"Murdock Charitable Trust","awardNumber":"SR-2017297"},{"funderName":"Pines Magnetic Resonance Center"},{"funderIdentifierType":"ROR","funderName":"U.S. National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62","awardTitle":"\n        MRI: Acquisition of a 400 MHz Nuclear Magnetic Resonance\n        (NMR)Spectrometer to Enable Material Science Research\n      ","awardNumber":"2018784"},{"funderIdentifierType":"ROR","funderName":"Oregon State University","funderIdentifier":"https://ror.org/00ysfqy60","awardTitle":"Stephen Slavens Faculty Scholar Endowment Fund"},{"funderIdentifierType":"ROR","funderName":"United States Department of Energy","funderIdentifier":"https://ror.org/01bj3aw27","awardNumber":"DE-AC02-06CH11357"}],"url":"https://datadryad.org/dataset/doi:10.5061/dryad.0gb5mkmhg","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":0,"downloadCount":1,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2026-10-05T15:35:15Z","registered":"2026-10-05T15:35:16Z","published":null,"updated":"2026-10-05T15:35:16Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}}],"meta":{"total":4417,"totalPages":177,"page":1},"links":{"self":"https://api.datacite.org/dois?client-id=dryad.dryad\u0026registered=2026","next":"https://api.datacite.org/dois?client-id=dryad.dryad\u0026page%5Bnumber%5D=2\u0026page%5Bsize%5D=25\u0026registered=2026"}}