{"data":[{"id":"10.6078/d1wt6w","type":"dois","attributes":{"doi":"10.6078/d1wt6w","identifiers":[],"creators":[{"name":"Albright, Ashley","nameType":"Personal","givenName":"Ashley","familyName":"Albright","affiliation":["University of California, San Francisco"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7163-9640","nameIdentifierScheme":"ORCID"}]},{"name":"Marshall, Wallace","nameType":"Personal","givenName":"Wallace","familyName":"Marshall","affiliation":["University of California, San Francisco"],"nameIdentifiers":[]}],"titles":[{"title":"Parent dataset: Single and half-cell RNA-sequencing in \u003cem\u003eStentor coeruleus\u003c/em\u003e control, beta-tubulin, and dynein knockdown cells"}],"publisher":"Dryad","container":{},"publicationYear":2026,"subjects":[{"subject":"Stentor coeruleus"},{"subject":"RNA-sequencing"},{"subject":"Microtubule"},{"subject":"RNAi"},{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2024-10-07T03:33:44Z","dateType":"Created"},{"date":"2023-01-10T01:08:58Z","dateType":"Submitted"},{"date":"2023-01-12T00:00:00Z","dateType":"Issued"},{"date":"2023-01-12T00:00:00Z","dateType":"Available"},{"date":"2026-05-08T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","relatedIdentifier":"https://github.com/aralbright/2022_AADAWM","relatedIdentifierType":"URL"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1101/2023.01.09.523364","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.5061/dryad.zpc866tjd","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.5061/dryad.9zw3r22r0","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.5061/dryad.6djh9w1c1","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.5061/dryad.pnvx0k71j","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["259038559464 bytes"],"formats":[],"version":"10","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Stentor coeruleus are giant single-celled ciliates that reach\n lengths of up to 1 mm. Because of their large size, these cells are easy\n to manipulate and they also possess the ability to heal wounds and\n regenerate. These properties are ideal for investigating subcellular\n pattern formation, as we can physically dissect the cells into anterior\n and posterior halves. We used half-cell RNA-sequencing to assay for\n changes in transcript skew along the cell's anterior posterior axis\n upon knocking down beta-tubulin, an important component of the microtubule\n cytoskeleton, and dynein intermediate chains, a component of the molecular\n motor. ","descriptionType":"Abstract"},{"description":"After conducting RNAi knockdown of beta-tubulin or dynein\n in \u003cem\u003eStentor coeruleus\u003c/em\u003e, we bisected the cells using a\n glass needle. Within 20 minutes of bisection, the whole cells and bisected\n cells were lysed and prepped for sequencing with the NEBNext Single\n Cell/Low Input Library Prep Kit for Illumina (Cat. No. E6420). Samples\n were pooled and submitted for sequencing on an Illumina NovaSeq6000\n (SP200) or NovaSeqX (SP300).","descriptionType":"Methods"},{"description":"# Single and half-cell RNA-sequencing in Stentor coeruleus control,\n beta-tubulin, and dynein knockdown cells\n [https://doi.org/10.6078/D1WT6W](https://doi.org/10.6078/D1WT6W) ##\n Description of the data and file structure These 'half-cell' RNA\n sequencing data accompany our preprint: **Genome-wide analysis of\n anterior-posterior mRNA regionalization in Stentor coeruleus reveals a\n role for the microtubule cytoskeleton** Link:\n [https://www.biorxiv.org/content/10.1101/2023.01.09.523364v3](https://www.biorxiv.org/content/10.1101/2023.01.09.523364v3) Each file is named for a treatment (con/control, tub/tubulin, G04/dynein construct #1, G05/dynein construct #2) and for which half of the cell (A/anterior, P/posterior) the fragment came from. The tubulin and dynein experiments were conducted separately, and the controls are different. For example, files beginning with con1A_S15 and con1A_S197 are from different experiments. This README lists all file names grouped by category. metadata.csv contains the file name, the experiment the file is from (bulk, tubulin, or dynein), the condition (control or knockdown), the region (anterior or posterior), and the biological replicate.  ## Tubulin ### First run: This run also contains whole cell samples (file name contains W rather than A or P) that were not analyzed in the accompanying manuscript. con1A_S15_L002_R1_001.fastq.gz con1A_S15_L002_R2_001.fastq.gz con1P_S16_L002_R1_001.fastq.gz con1P_S16_L002_R2_001.fastq.gz con2A_S17_L002_R1_001.fastq.gz con2A_S17_L002_R2_001.fastq.gz con2P_S18_L002_R1_001.fastq.gz con2P_S18_L002_R2_001.fastq.gz con4A_S19_L002_R1_001.fastq.gz con4A_S19_L002_R2_001.fastq.gz con4P_S20_L002_R1_001.fastq.gz con4P_S20_L002_R2_001.fastq.gz con5A_S21_L002_R1_001.fastq.gz con5A_S21_L002_R2_001.fastq.gz con5P_S22_L002_R1_001.fastq.gz con5P_S22_L002_R2_001.fastq.gz conW1_S23_L002_R1_001.fastq.gz conW1_S23_L002_R2_001.fastq.gz conW2_S24_L002_R1_001.fastq.gz conW2_S24_L002_R2_001.fastq.gz conW3_S25_L002_R1_001.fastq.gz conW3_S25_L002_R2_001.fastq.gz conW4_S26_L002_R1_001.fastq.gz conW4_S26_L002_R2_001.fastq.gz conW5_S27_L002_R1_001.fastq.gz conW5_S27_L002_R2_001.fastq.gz tub1A_S1_L001_R1_001.fastq.gz tub1A_S1_L001_R2_001.fastq.gz tub1P_S2_L001_R1_001.fastq.gz tub1P_S2_L001_R2_001.fastq.gz tub2A_S3_L001_R1_001.fastq.gz tub2A_S3_L001_R2_001.fastq.gz tub2P_S4_L001_R1_001.fastq.gz tub2P_S4_L001_R2_001.fastq.gz tub3A_S5_L001_R1_001.fastq.gz tub3A_S5_L001_R2_001.fastq.gz tub3P_S6_L001_R1_001.fastq.gz tub3P_S6_L001_R2_001.fastq.gz tub4A_S7_L001_R1_001.fastq.gz tub4A_S7_L001_R2_001.fastq.gz tub4P_S8_L001_R1_001.fastq.gz tub4P_S8_L001_R2_001.fastq.gz tub5A_S9_L001_R1_001.fastq.gz tub5A_S9_L001_R2_001.fastq.gz tub5P_S10_L001_R1_001.fastq.gz tub5P_S10_L001_R2_001.fastq.gz tubW1_S11_L001_R1_001.fastq.gz tubW1_S11_L001_R2_001.fastq.gz tubW3_S12_L001_R1_001.fastq.gz tubW3_S12_L001_R2_001.fastq.gz tubW4_S13_L001_R1_001.fastq.gz tubW4_S13_L001_R2_001.fastq.gz tubW5_S14_L001_R1_001.fastq.gz tubW5_S14_L001_R2_001.fastq.gz ### resequenced: The following samples from the initial tubulin experiment were resequenced. con1A_S15 and con1A_S157 are from the same experiment, but two separate sequencing runs. The following files were merged with their corresponding file from the first run for the final analysis. con1A_S157_L007_R1_001.fastq.gz con1A_S157_L007_R2_001.fastq.gz con1P_S158_L007_R1_001.fastq.gz con1P_S158_L007_R2_001.fastq.gz con2A_S159_L007_R1_001.fastq.gz con2A_S159_L007_R2_001.fastq.gz con2P_S160_L007_R1_001.fastq.gz con2P_S160_L007_R2_001.fastq.gz con4A_S161_L007_R1_001.fastq.gz con4A_S161_L007_R2_001.fastq.gz con4P_S162_L007_R1_001.fastq.gz con4P_S162_L007_R2_001.fastq.gz con5A_S163_L007_R1_001.fastq.gz con5A_S163_L007_R2_001.fastq.gz con5P_S164_L007_R1_001.fastq.gz con5P_S164_L007_R2_001.fastq.gz tub1A_S147_L007_R1_001.fastq.gz tub1A_S147_L007_R2_001.fastq.gz tub1P_S148_L007_R1_001.fastq.gz tub1P_S148_L007_R2_001.fastq.gz tub2A_S149_L007_R1_001.fastq.gz tub2A_S149_L007_R2_001.fastq.gz tub2P_S150_L007_R1_001.fastq.gz tub2P_S150_L007_R2_001.fastq.gz tub3A_S151_L007_R1_001.fastq.gz tub3A_S151_L007_R2_001.fastq.gz tub3P_S152_L007_R1_001.fastq.gz tub3P_S152_L007_R2_001.fastq.gz tub4A_S153_L007_R1_001.fastq.gz tub4A_S153_L007_R2_001.fastq.gz tub4P_S154_L007_R1_001.fastq.gz tub4P_S154_L007_R2_001.fastq.gz tub5A_S155_L007_R1_001.fastq.gz tub5A_S155_L007_R2_001.fastq.gz tub5P_S156_L007_R1_001.fastq.gz tub5P_S156_L007_R2_001.fastq.gz ## Dynein: ### control: con1A_S197_L006_R1_001.fastq.gz con1A_S197_L006_R2_001.fastq.gz con1P_S198_L006_R1_001.fastq.gz con1P_S198_L006_R2_001.fastq.gz con2A_S199_L006_R1_001.fastq.gz con2A_S199_L006_R2_001.fastq.gz con2P_S200_L006_R1_001.fastq.gz con2P_S200_L006_R2_001.fastq.gz con3A_S201_L006_R1_001.fastq.gz con3A_S201_L006_R2_001.fastq.gz con3P_S202_L006_R1_001.fastq.gz con3P_S202_L006_R2_001.fastq.gz con4A_S203_L006_R1_001.fastq.gz con4A_S203_L006_R2_001.fastq.gz con4P_S204_L006_R1_001.fastq.gz con4P_S204_L006_R2_001.fastq.gz con5A_S205_L006_R1_001.fastq.gz con5A_S205_L006_R2_001.fastq.gz con5P_S206_L006_R1_001.fastq.gz con5P_S206_L006_R2_001.fastq.gz ### G04: G046A_S207_L007_R1_001.fastq.gz G046A_S207_L007_R2_001.fastq.gz G046P_S208_L007_R1_001.fastq.gz G046P_S208_L007_R2_001.fastq.gz G047A_S209_L007_R1_001.fastq.gz G047A_S209_L007_R2_001.fastq.gz G047P_S210_L007_R1_001.fastq.gz G047P_S210_L007_R2_001.fastq.gz G048A_S211_L007_R1_001.fastq.gz G048A_S211_L007_R2_001.fastq.gz G048P_S212_L007_R1_001.fastq.gz G048P_S212_L007_R2_001.fastq.gz G049A_S213_L007_R1_001.fastq.gz G049A_S213_L007_R2_001.fastq.gz G049P_S214_L007_R1_001.fastq.gz G049P_S214_L007_R2_001.fastq.gz G0410A_S215_L007_R1_001.fastq.gz G0410A_S215_L007_R2_001.fastq.gz G0410P_S216_L007_R1_001.fastq.gz G0410P_S216_L007_R2_001.fastq.gz ### G05: G0511A_S217_L008_R1_001.fastq.gz G0511A_S217_L008_R2_001.fastq.gz G0511P_S218_L008_R1_001.fastq.gz G0511P_S218_L008_R2_001.fastq.gz G0512A_S219_L008_R1_001.fastq.gz G0512A_S219_L008_R2_001.fastq.gz G0512P_S220_L008_R1_001.fastq.gz G0512P_S220_L008_R2_001.fastq.gz G0513A_S221_L008_R1_001.fastq.gz G0513A_S221_L008_R2_001.fastq.gz G0513P_S222_L008_R1_001.fastq.gz G0513P_S222_L008_R2_001.fastq.gz G0514A_S223_L008_R1_001.fastq.gz G0514A_S223_L008_R2_001.fastq.gz G0514P_S224_L008_R1_001.fastq.gz G0514P_S224_L008_R2_001.fastq.gz G0515A_S225_L008_R1_001.fastq.gz G0515A_S225_L008_R2_001.fastq.gz G0515P_S226_L008_R1_001.fastq.gz G0515P_S226_L008_R2_001.fastq.gz ### Bulk: A1_S1_L001_R1_001.fastq.gz\\ A1_S1_L001_R2_001.fastq.gz\\ A2_S2_L001_R1_001.fastq.gz\\ A2_S2_L001_R2_001.fastq.gz\\ A5_S5_L001_R1_001.fastq.gz\\ A5_S5_L001_R2_001.fastq.gz\\ A6_S6_L001_R1_001.fastq.gz\\ A6_S6_L001_R2_001.fastq.gz\\ P1_S3_L001_R1_001.fastq.gz\\ P1_S3_L001_R2_001.fastq.gz\\ P2_S4_L001_R1_001.fastq.gz\\ P2_S4_L001_R2_001.fastq.gz\\ P5_S7_L001_R1_001.fastq.gz\\ P5_S7_L001_R2_001.fastq.gz\\ P6_S8_L001_R1_001.fastq.gz\\ P6_S8_L001_R2_001.fastq.gz ## Code/software All scripts and analyses are available here: [https://github.com/aralbright/2022_AADAWM](https://github.com/aralbright/2022_AADAWM)","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Institute of General Medical Sciences","funderIdentifier":"https://ror.org/04q48ey07","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6078/D1WT6W","contentUrl":null,"metadataVersion":9,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":142,"downloadCount":14,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-01-12T18:57:15Z","registered":"2023-01-12T18:57:16Z","published":null,"updated":"2026-05-08T05:31:51Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d1941t","type":"dois","attributes":{"doi":"10.6078/d1941t","identifiers":[],"creators":[{"name":"Schwartz, Andrew","nameType":"Personal","givenName":"Andrew","familyName":"Schwartz","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-2623-5962","nameIdentifierScheme":"ORCID"}]},{"name":"Osterhuber, Randall","nameType":"Personal","givenName":"Randall","familyName":"Osterhuber","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]}],"titles":[{"title":"Snowpack, precipitation, and temperature measurements at the Central Sierra Snow Laboratory for water years 1971 to 2025"}],"publisher":"Dryad","container":{},"publicationYear":2025,"subjects":[{"subject":"FOS: Earth and related environmental sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Earth and related environmental sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"snowpack"},{"subject":"snowfall"},{"subject":"federal sampler"},{"subject":"Central Sierra Snow Lab"}],"contributors":[{"name":"Central Sierra Snow Laboratory","affiliation":[],"contributorType":"Sponsor","nameIdentifiers":[]}],"dates":[{"date":"2025-08-05T18:20:05Z","dateType":"Created"},{"date":"2021-06-15T17:26:03Z","dateType":"Submitted"},{"date":"2021-06-22T00:00:00Z","dateType":"Issued"},{"date":"2021-06-22T00:00:00Z","dateType":"Available"},{"date":"2025-10-01T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1016/j.isci.2022.104240","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["987904 bytes"],"formats":[],"version":"13","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The snowpack of the Sierra Nevada Mountains is an indispensable freshwater\n resource for large portions of western North America. The Central Sierra\n Snow Laboratory (CSSL) has had an integral role in the measurement of\n snowfall and snowpack properties within the Sierra Nevada Mountains, and\n has worked to develop a physical understanding of the processes that\n govern snow since 1946. This dataset contains measurements of temperature,\n precipitation quantity, snowfall, and snowpack characteristics, including\n 24-hour snowfall, snowpack depth, and snow water equivalent for each water\n year (October 1 to September 30) from 1971 to 2025 at CSSL except for\n Water Year 2020. Measurements were made at the same location at CSSL for\n the entirety of the 53-year measurement period to ensure continuity of\n record with minimal effects from differences in measurement location.","descriptionType":"Abstract"},{"description":"Data on snowpack depth and snow water equivalent (SWE) were\n collected using a federal sampler and/or a large snow stake at 9:00\n Pacific Standard Time (PST) each day. New snowfall measurements used a\n snow board and ruler at 8:00 and 16:00 PST on days when snowfall\n occurred and were then totaled for daily new snow quantity.","descriptionType":"Methods"},{"description":"Snowfall amounts \u0026lt; 0.5 cm when measured with the snow\n board and ruler were labelled as \"Trace\" or \"T\"\n accumulations. Periods with no measurement and/or missing values are\n recorded as \"--\". References to \"patchy\",\n \"patches\", \"small patches\", or \"large\n patches\" are in reference to snow cover on the site. \n Water Years 1971 through 2019 include SWE measurements from a\n federal sampler on the site. Beginning in Water Year 2021, SWE values that\n are used are obtained from the USDA NRCS SNOTEL site at the\n lab. ","descriptionType":"Other"},{"description":"# Snowpack, precipitation, and temperature measurements at the Central\n Sierra Snow Laboratory for water years 1971 to 2025 Dataset DOI:\n [10.6078/D1941T](10.6078/D1941T) ## Description of the data and file\n structure Water Years 1971 through 2019 include data on snow depth and SWE\n measurements that were collected using a federal sampler and/or a large\n snow stake at 9:00 Pacific Standard Time (PST) each day. New snowfall\n measurements used a snow board and ruler at 8:00 and 16:00 Pacific\n Standard Time on days when snowfall occurred and were then totaled for\n daily new snow quantity. Beginning in Water Year 2021, SWE values were\n obtained from the USDA NRCS SNOTEL site at the lab at 00:00 local time and\n snow depth measurements were obtained from a snow depth pole permanently\n installed at the site measurement location at 8am PST. New snowfall\n measurements used a snow board and ruler at 8:00 and 16:00 Pacific\n Standard Time on days when snowfall occurred and were then totaled for\n daily new snow quantity. Measurement Notes: Measurements of snowfall were\n rounded to the nearest 0.5 cm and measurements of precipitation were\n rounded to the nearest mm. Snowfall amounts \u0026lt; 0.5 cm when measured with\n the snow board and ruler were labelled as \"Trace\" or\n \"T\" accumulations. Periods with no measurement and/or missing\n values are recorded as \"--\". References to \"patchy\",\n \"patches\", \"small patches\", or \"large\n patches\" are in reference to snow cover on the site.  ### Change log:\n 2025-08-05 1. Expanded dataset by including .csv files for 2021-2024 water\n years. 2. Standardized date format between all files. 3. Previously,\n absent values were reported with either 'NA' or '--'.\n These have all been changed to '--' for continuity.  4. Remarks\n were checked and additional context was added to existing daily remarks\n for clarification. 5. A typo was corrected in Water Year 2006 where a\n value was \"209\" and was changed to \"290\". 2025-10-01\n 1. Added Water Year 2025 data (WY2025.csv)","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.6078/D1941T","contentUrl":null,"metadataVersion":12,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":7682,"downloadCount":1855,"referenceCount":0,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-06-23T00:47:06Z","registered":"2021-06-23T00:47:07Z","published":null,"updated":"2026-05-07T15:36:11Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d1v88t","type":"dois","attributes":{"doi":"10.6078/d1v88t","identifiers":[],"creators":[{"name":"Wittenberg, Jamie","nameType":"Personal","givenName":"Jamie","familyName":"Wittenberg","affiliation":["Indiana University Bloomington"],"nameIdentifiers":[]},{"name":"Sackmann, Anna","nameType":"Personal","givenName":"Anna","familyName":"Sackmann","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-3852-6951","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Educational Resources from UC Berkeley RDM Librarian Training Program"}],"publisher":"Dryad","container":{},"publicationYear":2016,"subjects":[{"subject":"library professional development"},{"subject":"open educational resources"},{"subject":"research data management"}],"contributors":[],"dates":[{"date":"2016-09-21T11:50:43Z","dateType":"Issued"},{"date":"2016-09-21T11:50:43Z","dateType":"Available"},{"date":"2016-09-21T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1016/j.acalib.2018.04.004","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["9049569 bytes"],"formats":[],"version":"2","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.6078/D1V88T","contentUrl":null,"metadataVersion":15,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":226,"downloadCount":38,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2016-09-21T18:50:25Z","registered":"2016-09-21T18:50:26Z","published":null,"updated":"2026-04-29T18:54:43Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d16x1t","type":"dois","attributes":{"doi":"10.6078/d16x1t","identifiers":[],"creators":[{"name":"Zhu, Qindan","nameType":"Personal","givenName":"Qindan","familyName":"Zhu","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2173-4014","nameIdentifierScheme":"ORCID"}]},{"name":"Laughner, Joshua","nameType":"Personal","givenName":"Joshua","familyName":"Laughner","affiliation":["California Institute of Technology"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8599-4555","nameIdentifierScheme":"ORCID"}]},{"name":"Cohen, Ron","nameType":"Personal","givenName":"Ron","familyName":"Cohen","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6617-7691","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Berkeley High Resolution (BEHR) OMI NO2 v3.0C - Gridded pixels, daily profiles"}],"publisher":"Dryad","container":{},"publicationYear":2019,"subjects":[{"subject":"Atmospheric chemistry","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Berkeley High Resolution NO2"},{"subject":"NO2"},{"subject":"OMI"},{"subject":"ozone monitoring instrument"}],"contributors":[],"dates":[{"date":"2022-03-23T19:30:07Z","dateType":"Submitted"},{"date":"2019-03-06T00:22:14Z","dateType":"Issued"},{"date":"2019-03-06T00:22:14Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.5194/essd-10-2069-2018","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.6078/d1bm2b","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.6078/d12d5x","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.6078/d1rq3g","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.6078/d1wh41","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.6078/d1n086","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["23675288208 bytes"],"formats":[],"version":"2","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The BEHR reprocesses tropospheric NO2 columns from the Ozone\n Monitoring Instrument (OMI) satellite using high resolution a\n priori NO2 profiles, surface reflectivity, and surface\n elevation data. This product uses NO2 profiles for the day\n retrieved, simulated by the WRF-Chem model at 12 km spatial resolution.\n The use of high spatial resolution NO2 profiles has been shown to\n better resolve urban/rural differences in NO2 column densities,\n and the use of day-to-day (rather than monthly average) profiles is\n especially important in applications that preferentially select\n observations upwind or downwind of a NOx source. The BEHR OMI\n NO2 v3.0C product is an experimental branch of BEHR v3.0B\n (10.6078/D1WH41, 10.6078/D12D5X, 10.6078/D1RQ3G,\n 10.6078/D1N086).  The time range covers May 13-June 23, 2012 and\n August 1-September 23, 2013.","descriptionType":"Abstract"},{"description":"The methods for the BEHR product v3.0C are described in detail in\n Qindan et al. (in prep).","descriptionType":"Methods"},{"description":"Each month of BEHR data is stored as a .tar.gz file (i.e. a tar\n archive compressed with gzip). These can be decompressed using 7zip or (on\n Mac/Linux) the command \"tar -xzvf \u0026lt;filename\u0026gt;\",\n e.g. \"tar -xzvf OMI_BEHR-DAILY_US_v3-0A_200501.tar.gz\" will\n expand OMI_BEHR-DAILY_US_v3-0A_200501.tar.gz  In\n addition to downloading the full dataset or manually downloading\n individual files, a Python program is available at https://github.com/CohenBerkeleyLab/BEHRDownloader that can perform bulk downloads of a subset of the files, as well as automatically uncompress the tar files. See the Readme in that repo for further usage information. For details on best practices for using the BEHR data, see the user guide PDF included in this repository.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Aeronautics and Space Administration","awardNumber":"NNX14AK89H","funderIdentifier":"https://ror.org/027ka1x80","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Aeronautics and Space Administration","awardNumber":"NNX15AE37G","funderIdentifier":"https://ror.org/027ka1x80","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Aeronautics and Space Administration","awardNumber":"80NSSC18K0624","funderIdentifier":"https://ror.org/027ka1x80","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Smithsonian Astrophysical Observatory","awardNumber":"SV3-83019","funderIdentifier":"https://ror.org/04mh52z70","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6078/D16X1T","contentUrl":null,"metadataVersion":16,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":226,"downloadCount":96,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2019-03-06T00:35:19Z","registered":"2019-03-06T00:35:20Z","published":null,"updated":"2026-04-28T17:54:39Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d1bm2b","type":"dois","attributes":{"doi":"10.6078/d1bm2b","identifiers":[],"creators":[{"name":"Zhu, Qindan","nameType":"Personal","givenName":"Qindan","familyName":"Zhu","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2173-4014","nameIdentifierScheme":"ORCID"}]},{"name":"Laughner, Josh","nameType":"Personal","givenName":"Josh","familyName":"Laughner","affiliation":["California Institute of Technology"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8599-4555","nameIdentifierScheme":"ORCID"}]},{"name":"Cohen, Ron","nameType":"Personal","givenName":"Ron","familyName":"Cohen","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6617-7691","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Berkeley High Resolution (BEHR) OMI NO2 v3.0C - Native pixels, daily profiles"}],"publisher":"Dryad","container":{},"publicationYear":2019,"subjects":[{"subject":"Atmospheric chemistry","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Berkeley High Resolution NO2"},{"subject":"NO2"},{"subject":"OMI"},{"subject":"ozone monitoring instrument"}],"contributors":[],"dates":[{"date":"2022-03-23T17:03:04Z","dateType":"Submitted"},{"date":"2019-03-05T20:03:44Z","dateType":"Issued"},{"date":"2019-03-05T20:03:44Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.5194/essd-10-2069-2018","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.6078/d16x1t","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.6078/d12d5x","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.6078/d1rq3g","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.6078/d1wh41","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.6078/d1n086","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["4639604938 bytes"],"formats":[],"version":"2","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The BEHR reprocesses tropospheric NO2 columns from the Ozone\n Monitoring Instrument (OMI) satellite using high resolution a\n priori NO2 profiles, surface reflectivity, and surface\n elevation data. This product uses NO2 profiles for the day\n retrieved, simulated by the WRF-Chem model at 12 km spatial resolution.\n The use of high spatial resolution NO2 profiles has been shown to\n better resolve urban/rural differences in NO2 column densities,\n and the use of day-to-day (rather than monthly average) profiles is\n especially important in applications that preferentially select\n observations upwind or downwind of a NOx source. The BEHR OMI\n NO2 v3.0C product is an experimental branch of BEHR v3.0B\n (10.6078/D1WH41, 10.6078/D12D5X, 10.6078/D1RQ3G,\n 10.6078/D1N086).  The time range covers May 13-June 23, 2012 and\n August 1-September 23, 2013.","descriptionType":"Abstract"},{"description":"The methods for the BEHR product v3.0C are described in detail in\n Qindan et al. (in prep).","descriptionType":"Methods"},{"description":"Each month of BEHR data is stored as a .tar.gz file (i.e. a tar\n archive compressed with gzip). These can be decompressed using 7zip or (on\n Mac/Linux) the command \"tar -xzvf \u0026lt;filename\u0026gt;\",\n e.g. \"tar -xzvf OMI_BEHR-DAILY_US_v3-0A_200501.tar.gz\" will\n expand OMI_BEHR-DAILY_US_v3-0A_200501.tar.gz  In\n addition to downloading the full dataset or manually downloading\n individual files, a Python program is available at https://github.com/CohenBerkeleyLab/BEHRDownloader that can perform bulk downloads of a subset of the files, as well as automatically uncompress the tar files. See the Readme in that repo for further usage information. For details on best practices for using the BEHR data, see the user guide PDF included in this repository.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Aeronautics and Space Administration","awardNumber":"NNX14AK89H","funderIdentifier":"https://ror.org/027ka1x80","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Aeronautics and Space Administration","awardNumber":"NNX15AE37G","funderIdentifier":"https://ror.org/027ka1x80","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Aeronautics and Space Administration","awardNumber":"80NSSC18K0624","funderIdentifier":"https://ror.org/027ka1x80","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Smithsonian Astrophysical Observatory","awardNumber":"SV3-83019","funderIdentifier":"https://ror.org/04mh52z70","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6078/D1BM2B","contentUrl":null,"metadataVersion":15,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":299,"downloadCount":169,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2019-03-05T20:14:22Z","registered":"2019-03-05T20:14:23Z","published":null,"updated":"2026-04-28T17:54:37Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d1ks3m","type":"dois","attributes":{"doi":"10.6078/d1ks3m","identifiers":[],"creators":[{"name":"Laughner, Joshua","nameType":"Personal","givenName":"Joshua","familyName":"Laughner","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8599-4555","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"BErkeley High Resolution (BEHR) OMI NO2 Prototype High Temporal Resolution Product"}],"publisher":"Dryad","container":{},"publicationYear":2016,"subjects":[{"subject":"BEHR"},{"subject":"Berkeley High Resolution NO2"},{"subject":"nitrogen dioxide"},{"subject":"NO2"},{"subject":"OMI"},{"subject":"ozone monitoring instrument"}],"contributors":[],"dates":[{"date":"2016-12-01T20:25:36Z","dateType":"Issued"},{"date":"2016-12-01T20:25:36Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.5194/acp-16-15247-2016","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["44288924724 bytes"],"formats":[],"version":"1","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The BErkeley High Resolution (BEHR) OMI NO2 Prototype High Temporal\n Resolution Product makes use of high spatial and temporal resolution a\n priori NO2 profiles for a limited domain in the southeast United States,\n covering 1 June-30 Aug 2013. These retrievals have been used to evaluate\n the impact of daily temporal resolution of the high-spatial-resolution a\n priori NO2 profiles needed for the retrieval","descriptionType":"Abstract"},{"description":"Tropospheric slant columns are obtained from the NASA OMI\n Standard Product v2.1 (SP2). Tropospheric air mass factors (AMFs) are\n computed using the scattering weights from the SP2 look up table and a\n priori NO2 profiles simulated with WRF-Chem at 12 km spatial resolution.\n Inputs to the scattering weight look up table are the sun-satellite\n geometry, surface albedo from the MODIS combined black-sky albedo product\n (MCD43C3) and the terrain heights from the Global Land One-km Base\n Elevation (GLOBE) project database (https://www.ngdc.noaa.gov/mgg/topo/globe.html). The AMF for each pixel is the average of clear and cloudy AMFs; weighted by the cloud radiance fraction for that pixel. Cloudy AMFs use the OMI O2-O2 cloud pressure as the surface pressure and 0.8 as the cloud albedo. These AMFs are used to compute the BEHR tropospheric vertical column densities (VCDs).","descriptionType":"Methods"},{"description":"There are nine .zip archives containing retrievals for the period 1\n June-30 Aug 2013. The three .zip files marked \"pseudo\" contain\n pseudo-retrievals for this period; these retrievals use the same set of\n OMI pixels for all days. Thus, VCDs are not included in these files, only\n the BEHR AMFs. The purpose is to show the impact of daily vs. NO2 profiles\n on the AMFs. The three pseudo-retrieval zip archives differ only in the\n NO2 profiles used: daily profiles, monthly average profiles, or hybrid\n profiles, which use daily profiles below 750 hPa and monthly average\n above. The other six .zip archives (marked \"prototype\") contain\n full retrievals with the actual pixels for each day. Again, three\n different sets of NO2 a priori profiles are used, daily, monthly average,\n and coarse monthly average profiles (simulated at 108 km spatial\n resolution). The \"native\" files contain data at the native OMI\n pixel resolution; the \"gridded\" files contain this data\n oversampled to a 0.05x0.05 degree grid. The latter is easier to average\n over time, since the geographic coordinates of each grid cell is fixed\n from day to day. This data should be filtered for quality by the\n vcdQualityFlags and XTrackQualityFlags fields. Only pixels where\n vcdQualityFlags is an even integer and XTrackFlags is 0 should be used.\n Cloud filtering is also recommended for most applications; we require that\n CloudFraction is less than or equal to 0.2. Although the full prototype\n retrievals contain pixels throughout the continental US, only pixels\n within the approximate domain 89 to 79 deg. W and 30 to 38 deg. N have\n valid data.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Aeronautics and Space Administration","awardNumber":"NNX14AK89H","funderIdentifier":"https://ror.org/027ka1x80","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Aeronautics and Space Administration","awardNumber":"NNX15AE37G","funderIdentifier":"https://ror.org/027ka1x80","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Aeronautics and Space Administration","awardNumber":"NNX14AH04G","funderIdentifier":"https://ror.org/027ka1x80","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Smithsonian Institution","awardNumber":"SV3-83019","funderIdentifier":"https://ror.org/01pp8nd67","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6078/D1KS3M","contentUrl":null,"metadataVersion":24,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":1151,"downloadCount":973,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2016-12-01T20:25:41Z","registered":"2016-12-01T20:25:42Z","published":null,"updated":"2026-04-10T12:44:28Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d1w30c","type":"dois","attributes":{"doi":"10.6078/d1w30c","identifiers":[],"creators":[{"name":"Hirschfeld, Daniella","nameType":"Personal","givenName":"Daniella","familyName":"Hirschfeld","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9664-7594","nameIdentifierScheme":"ORCID"}]},{"name":"Hill, Kristina","nameType":"Personal","givenName":"Kristina","familyName":"Hill","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]},{"name":"Plane, Ellen","nameType":"Personal","givenName":"Ellen","familyName":"Plane","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]}],"titles":[{"title":"SanFranciscoBay_Adapt2SeaLevelRise_RegionalData"}],"publisher":"Dryad","container":{},"publicationYear":2017,"subjects":[{"subject":"Regional strategies"},{"subject":"Storm Surge"},{"subject":"Unit cost"}],"contributors":[],"dates":[{"date":"2017-03-07T14:59:38Z","dateType":"Issued"},{"date":"2017-03-07T14:59:38Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSupplementedBy","relatedIdentifier":"http://www.sfei.org/data/sf-bay-shore-inventory-gis-data#sthash.Sofh5BLG.dpbs","relatedIdentifierType":"URL"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1890/150088","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.6078/d11s3n","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.6078/d19596","relatedIdentifierType":"DOI"},{"relationType":"IsSourceOf","relatedIdentifier":"http://data.pointblue.org/apps/ocof/cms/","relatedIdentifierType":"URL"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.6078/d1kk59","relatedIdentifierType":"DOI"},{"relationType":"IsSourceOf","relatedIdentifier":"http://sfport.com/file/16667","relatedIdentifierType":"URL"},{"relationType":"IsSourceOf","relatedIdentifier":"\n      http://www.spn.usace.army.mil/Portals/68/docs/FOIA%20Hot%20Topic%20Docs/SSF%20Bay%20Shoreline%20Study/Final%20Shoreline%20Main%20Report.pdf\n    ","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["50282843 bytes"],"formats":[],"version":"1","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"As awareness of climate change increases government agencies, non profits\n and community groups are working to develop strategic plans for sea level\n rise adaptation. To better understand specific choices for sea level rise\n adaptation we examined the relationship between current protective\n infrastructure and projected water levels from sea level rise and storm\n events.","descriptionType":"Abstract"},{"description":"Our analysis is based on three primary steps. First we reclassified the\n shoreline infrastructure data from San Francisco Estuary Institute (SFEI).\n We used the following dictionary to reclassify the data in terms of the\n Landform / Wall value: {'Berm': 'Landform',\n 'Channel or Opening': 'Landform', 'Shoreline\n Protection Structure': 'Landform','Embankment':\n 'Landform', 'Engineered Levee': 'Landform',\n 'Floodwall': 'Wall', 'Natural Shoreline':\n 'Landform','Transportation Structure':\n 'Wall', 'Water Control Structure': 'Wall',\n 'Wetland': 'Landform'}. We used the following\n dictionary to reclassify the data in terms of the Dynaic / Static value\n {'Berm': 'Static', 'Channel or Opening':\n 'Static', 'Shoreline Protection Structure':\n 'Static','Embankment': 'Static',\n 'Engineered Levee': 'Static', 'Floodwall':\n 'Static', 'Natural Shoreline':\n 'Dynamic','Transportation Structure':\n 'Static', 'Water Control Structure':\n 'Dynamic', 'Wetland': 'Dynamic'}.\n Additionally, for some of the shoreline protection structure sites we used\n google earth and site visits to shift their categorization from landform\n to wall. Second we conducted a rapid assessment of overtopping by\n subtracting the current shoreline heights from USGS’s projected future\n water levels. Finally we generated three potential shorelines. The first\n called “Edge_Current_SFEI” is based on SFEI’s Bayshore_Defense category\n with the values “First line of shoreline defense” or “Wetland on Bay\n shore.” The other two are based on SFEI’s Bay Area Aquatic Resources\n Inventory and capture the saltwater and freshwater habitat zones.","descriptionType":"Methods"}],"geoLocations":[{"geoLocationBox":{"eastBoundLongitude":-121.783447,"northBoundLatitude":38.23818,"southBoundLatitude":37.40071,"westBoundLongitude":-122.838135}}],"fundingReferences":[{"funderName":"McQuown Fellowship"}],"url":"https://datadryad.org/dataset/doi:10.6078/D1W30C","contentUrl":null,"metadataVersion":14,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":197,"downloadCount":18,"referenceCount":3,"citationCount":5,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2017-03-07T15:00:03Z","registered":"2017-03-07T15:00:05Z","published":null,"updated":"2026-04-10T12:34:04Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d1g010","type":"dois","attributes":{"doi":"10.6078/d1g010","identifiers":[],"creators":[{"name":"Baldwin, Bruce G.","nameType":"Personal","givenName":"Bruce G.","familyName":"Baldwin","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]},{"name":"Thornhill, Andrew H.","nameType":"Personal","givenName":"Andrew H.","familyName":"Thornhill","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]},{"name":"Freyman, William A.","nameType":"Personal","givenName":"William A.","familyName":"Freyman","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]},{"name":"Ackerly, David. D.","nameType":"Personal","givenName":"David. D.","familyName":"Ackerly","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]},{"name":"Kling, Matthew M.","nameType":"Personal","givenName":"Matthew M.","familyName":"Kling","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]},{"name":"Morueta-Holme, Naia","nameType":"Personal","givenName":"Naia","familyName":"Morueta-Holme","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]},{"name":"Mishler, Brent D.","nameType":"Personal","givenName":"Brent D.","familyName":"Mishler","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]}],"titles":[{"title":"Six subsets of all native vascular plant species of California used by Baldwin et al. (2017), and the R script to use to extract the subsets from the master spatial file"}],"publisher":"Dryad","container":{},"publicationYear":2017,"subjects":[],"contributors":[],"dates":[{"date":"2017-02-21T01:57:26Z","dateType":"Issued"},{"date":"2017-02-21T01:57:26Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.3732/ajb.1600326","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.6078/d1b885","relatedIdentifierType":"DOI"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.6078/d16k5w","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["481076 bytes"],"formats":[],"version":"4","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Spatial patterns of diversity and endemism across California were examined\n by Baldwin et al. (2017 Amer. J. Bot.) for six subsets of all native\n vascular plant species: (1) only vascular plants that are completely\n restricted (absolutely endemic) to California, (2) only vascular plants\n native to the California Floristic Province, (3) only angiosperms, (4)\n only gymnosperms, (5) only ‘pteridophytes’ (i.e., ferns and lycophytes),\n and (6) the set of California species belonging to the diverse genera\n examined by Stebbins and Major (1965 Ecol. Monogr.). A CSV file is\n provided for each of the 6 subsets, plus the R script to use to extract\n the subsets from the master spatial file, which is linked below.","descriptionType":"Abstract"},{"description":"From: Baldwin, B.G., A.H. Thornhill, W.A. Freyman, D.D. Ackerly,\n M.M. Kling, N. Morueta-Holme, and B.D. Mishler. 2017. Species richness and\n endemism in the native flora of California. American Journal of Botany.\n 104: 487–501. http://www.amjbot.org/content/104/3/487.fullUse with the other Baldwin (2017) data sets linked below.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DEB-1354552","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6078/D1G010","contentUrl":null,"metadataVersion":22,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":262,"downloadCount":47,"referenceCount":4,"citationCount":3,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2017-02-21T01:57:32Z","registered":"2017-02-21T01:57:33Z","published":null,"updated":"2026-04-10T12:14:56Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d1xt19","type":"dois","attributes":{"doi":"10.6078/d1xt19","identifiers":[],"creators":[{"name":"Fajans, Joel","nameType":"Personal","givenName":"Joel","familyName":"Fajans","affiliation":["Acupuncture \u0026 Integrative Medicine College"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-4403-6027","nameIdentifierScheme":"ORCID"}]},{"name":"Zhong, Mike","nameType":"Personal","givenName":"Mike","familyName":"Zhong","affiliation":["Acupuncture \u0026 Integrative Medicine College"],"nameIdentifiers":[]},{"name":"Zukor, Abraham","nameType":"Personal","givenName":"Abraham","familyName":"Zukor","affiliation":["Acupuncture \u0026 Integrative Medicine College"],"nameIdentifiers":[]}],"titles":[{"title":"Simulated antihydrogen orbits in an octupole-based Minimum-B trap"}],"publisher":"Dryad","container":{},"publicationYear":2018,"subjects":[],"contributors":[],"dates":[{"date":"2018-03-05T03:07:18Z","dateType":"Issued"},{"date":"2018-03-05T03:07:18Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1088/1367-2630/aabb84","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["851544615 bytes"],"formats":[],"version":"1","rightsList":[{"rights":"Creative Commons Attribution 4.0 International","rightsUri":"https://creativecommons.org/licenses/by/4.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc-by-4.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"These movies show simulations of the trajectories of antihydrogen atoms\n confined in a Minimum-B trap similar to the \"flat\" trap\n used by CERN's ALPHA collaboration.","descriptionType":"Abstract"},{"description":"See associated paper.","descriptionType":"Methods"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"United States Department of Energy","awardNumber":"DE-FG02-06ER54904","funderIdentifier":"https://ror.org/01bj3aw27","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"1500538-PHY","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"funderName":"U.C. Berkeley Summer Undergraduate Research Fellowship"},{"funderName":"Berkeley Research Computing program"}],"url":"https://datadryad.org/dataset/doi:10.6078/D1XT19","contentUrl":null,"metadataVersion":17,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":222,"downloadCount":85,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2018-03-05T03:11:11Z","registered":"2018-03-05T03:11:12Z","published":null,"updated":"2026-04-06T15:13:32Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d11h7d","type":"dois","attributes":{"doi":"10.6078/d11h7d","identifiers":[],"creators":[{"name":"Maffre, Pierre","nameType":"Personal","givenName":"Pierre","familyName":"Maffre","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-5766-476X","nameIdentifierScheme":"ORCID"}]},{"name":"Chiang, John","nameType":"Personal","givenName":"John","familyName":"Chiang","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]},{"name":"Swanson-Hysell, Nicholas","nameType":"Personal","givenName":"Nicholas","familyName":"Swanson-Hysell","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]}],"titles":[{"title":"Data for: The effect of Pliocene regional climate changes on silicate weathering"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Earth and related environmental sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Earth and related environmental sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2023-01-04T11:39:36Z","dateType":"Submitted"},{"date":"2023-01-06T00:00:00Z","dateType":"Issued"},{"date":"2023-01-06T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.5194/cp-19-1461-2023","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["35410021289 bytes"],"formats":[],"version":"4","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"This dataset stores the data of the article \"The effect of Pliocene\n regional climate changes on silicate weathering: a potential amplifier of\n Pliocene-Pleistocene cooling\",(P. Maffre, J. Chiang \u0026amp; N.\n Swanson-Hysell, submitted to Climate of the Past). This study uses a\n climate model (GCM) to reproduce an estimate of Pliocene Sea Surface\n Temperature (SST). The main GCM outputs of this modeling (with a slab\n ocean model) are stored in \"GCM_outputs_for_GEOCLIM/\", as well\n as the climatologies from ERA5 reanalysis. The other GCM outputs that were\n used in intermediary steps (coupled ocean-atmosphere, and fixed SST\n simulations) are stored in \"other_GCM_outputs/\". The forcing\n files (Q-flux) and other boundary conditions to run the \"main\"\n GCM simulations can be found in\n \"other_GCM_outputs/Q-flux_derivation/\", as well as the scripts\n used to generate them. Secondly, the mentioned study uses the GCM outputs\n in \"GCM_outputs_for_GEOCLIM/\" as inputs for the silicate\n weathering model GEOCLIM-DynSoil-Steady-State\n (https://github.com/piermafrost/GEOCLIM-dynsoil-steady-state/tree/PEN), to\n investigate weathering and equilibrium CO2 changes due to Pliocene SST\n conditions. The results of these simulations are stored in\n \"GEOCLIM-DynSoil-Steady-State_outputs/\". The purpose of this\n dataset is to provide the raw outputs used to draw the conclusions of\n Maffre et al. (submitted to Climate of the Past), and to allow the\n experiments to be reproduced, by providing the scripts to generate the\n boundary conditions.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6078/D11H7D","contentUrl":null,"metadataVersion":8,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":195,"downloadCount":33,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-01-06T17:51:01Z","registered":"2023-01-06T17:51:02Z","published":null,"updated":"2026-04-02T20:02:12Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/m7x0656s","type":"dois","attributes":{"doi":"10.6078/m7x0656s","identifiers":[],"creators":[{"name":"Rauh, Nicholas K.","nameType":"Personal","givenName":"Nicholas K.","familyName":"Rauh","affiliation":[],"nameIdentifiers":[]}],"titles":[{"title":"Rough Cilicia"}],"publisher":"Open Context","container":{},"publicationYear":2012,"subjects":[],"contributors":[],"dates":[{"date":"2012","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[],"descriptions":[],"geoLocations":[],"fundingReferences":[],"url":"https://opencontext.org/projects/295b5bf4-0f44-4698-80cd-7a39cb6f133d","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":"2026-03-24T22:25:42Z","registered":"2026-03-24T22:25:43Z","published":null,"updated":"2026-03-24T22:25:43Z"},"relationships":{"client":{"data":{"id":"cdl.ucb","type":"clients"}}}},{"id":"10.6078/d1vh87","type":"dois","attributes":{"doi":"10.6078/d1vh87","identifiers":[],"creators":[{"name":"Del Bonis-O'Donnell, Jackson","nameType":"Personal","givenName":"Jackson","familyName":"Del Bonis-O'Donnell","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9135-2102","nameIdentifierScheme":"ORCID"}]},{"name":"Yang, Sarah","nameType":"Personal","givenName":"Sarah","familyName":"Yang","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]},{"name":"Landry, Markita","nameType":"Personal","givenName":"Markita","familyName":"Landry","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]}],"titles":[{"title":"nIRCat characterization and acute brain slice imaging"}],"publisher":"Dryad","container":{},"publicationYear":2021,"subjects":[],"contributors":[],"dates":[{"date":"2021-04-13T14:52:02Z","dateType":"Submitted"},{"date":"2021-04-22T00:00:00Z","dateType":"Issued"},{"date":"2021-04-22T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1038/s41596-021-00530-4","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["292859445 bytes"],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Dopamine neuromodulation of neural synapses is a process implicated in a\n number of critical brain functions and diseases. Development of protocols\n to visualize this dynamic process is essential to understanding how\n dopamine modulates neurochemical brain function. We have developed a\n non-genetically encoded, near infrared (nIR) catecholamine nanosensor\n (nIRCat) capable of identifying ~2 µm dopamine release hotspots in the\n dorsal striatal brain slices. This platform is readily introduced into\n both genetically tractable and intractable organisms and is compatible\n with a number of dopamine receptor agonist and antagonists. In the\n following work, we describe the synthesis, characterization, and\n implementation of nIRCat in brain slices. We show how to image\n electrically and optogenetically stimulated dopamine release using nIRCat,\n and how these imaging protocols can be adapted to study the effects of\n dopamine receptor pharmacology. We also detail the development of\n video-analysis software to identify and track the location and kinetics of\n dopamine release hot spots over the course of nIRCat imaging experiments.\n Altogether, synthesis and characterization of nIRCat takes 5 hours and\n imaging live brain slices takes 6 hours. ","descriptionType":"Abstract"},{"description":"These data were collected using absorbance spectroscopy,\n fluorescence spectroscopy, and wide-field near-infrared fluorescence\n microscopy","descriptionType":"Methods"},{"description":"All data includes metadata files outlining the details of the\n data and experiment.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.6078/D1VH87","contentUrl":null,"metadataVersion":12,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":129,"downloadCount":9,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-04-22T17:36:04Z","registered":"2021-04-22T17:36:05Z","published":null,"updated":"2026-03-24T19:10:28Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d1cb04","type":"dois","attributes":{"doi":"10.6078/d1cb04","identifiers":[],"creators":[{"name":"Adams, Seira","nameType":"Personal","givenName":"Seira","familyName":"Adams","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1882-2806","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Chemical data of Hawaiian Tetragnatha spiders"}],"publisher":"Dryad","container":{},"publicationYear":2024,"subjects":[{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Speciation","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"reproductive isolation"},{"subject":"Pheromones","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Lipids","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"methyl ethers"},{"subject":"Silk","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2023-10-15T04:52:34Z","dateType":"Created"},{"date":"2022-05-17T22:59:04Z","dateType":"Submitted"},{"date":"2022-05-19T00:00:00Z","dateType":"Issued"},{"date":"2022-05-19T00:00:00Z","dateType":"Available"},{"date":"2024-01-24T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.10565055","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1098/rspb.2023.2340","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["3774312 bytes"],"formats":[],"version":"7","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Studies of adaptive radiations have played a central role in our\n understanding of reproductive isolation. Yet the focus has been on\n human-biased visual and auditory signals, leaving gaps in our knowledge of\n other modalities. To date, studies on chemical signals in adaptive\n radiations have focused on systems with multimodal signaling, making it\n difficult to isolate the role chemicals play in reproductive isolation. In\n this study we examine the use of chemical signals in the species\n recognition and adaptive radiation of Hawaiian Tetragnatha spiders by\n focusing on entire communities of co-occurring species, and conducting\n behavioral assays in conjunction with chemical analysis of their silks\n using gas chromatography-mass spectrometry. Male spiders significantly\n preferred the silk extracts of conspecific mates over those of sympatric\n heterospecifics. The compounds found in the silk extracts, long chain\n alkyl methyl ethers, were remarkably species-specific in the combination\n and quantity. The differences in the profile were greatest between\n co-occurring species and between closely related sibling species. Lastly,\n there were significant differences in the chemical profile between two\n populations of a particular species. These findings provide key insights\n into the role chemical signals play in the attainment and maintenance of\n reproductive barriers between closely related co-occurring species.","descriptionType":"Abstract"},{"description":"# GENERAL INFORMATION 1\\. Title of Dataset: Chemical Data of Hawaiian\n Tetragnatha Spiders 2\\. Author Information: A. Principal Investigator\n Contact Information Name: Seira Ashley Adams Institution: University of\n California, Berkeley Address: 130 Mulford Hall, #3114, University of\n California-Berkeley, Berkeley CA 94720 Email:\n B. Associate or Co-investigator Contact\n Information Name: Rosemary Gillespie Institution: University of\n California, Berkeley Address: 130 Mulford Hall, #3114, University of\n California-Berkeley, Berkeley CA 94720 Email:\n 3\\. Date of data collection: 2016 - 2020\n 4\\. Geographic location of data collection: \\- Waikamoi Preserve, Maui,\n Hawaii \\- Haleakala Crater, Maui, Hawaii \\- Angelo Coast Range Reserve,\n California \\- Bodega Marine Reserve, California 5\\. Information about\n funding sources that supported the collection of the data: National\n Science Foundation Graduate Research Fellowships Program (Grant #\n DGE 1106400) # SHARING/ACCESS INFORMATION 1\\. Licenses/restrictions placed\n on the data: Please cite paper published from this dataset when available\n (publication in review) 2\\. Links to publications that cite or use the\n data: (publication in review) 3\\. Recommended citation for this dataset:\n (publication in review) # DATA \u0026amp; FILE OVERVIEW 1\\. File List: * Methyl\n Ether Matrix = Abundance data of the methyl ether compounds found in each\n specimen * Metadata = Additional information of each specimen * S6 =\n Derivatization information of the most common methyl ethers * S17 = 3-D\n traitgram * 3d_traitgram_cleaned = Code to generate 3-D traitgram *\n Chemical_Analysis_cleaned = Code used for chemical analysis *\n group_analysis = Code used for group analysis * priority_order = Code used\n to generate priority order # METHODOLOGICAL INFORMATION 1\\. Description of\n methods used for collection/generation of data: (Publication in review)","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DGE 1106400","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6078/D1CB04","contentUrl":null,"metadataVersion":11,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":162,"downloadCount":23,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-05-19T14:30:20Z","registered":"2022-05-19T14:30:22Z","published":null,"updated":"2026-03-23T19:33:04Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d1bx2f","type":"dois","attributes":{"doi":"10.6078/d1bx2f","identifiers":[],"creators":[{"name":"Poppe, Andrew","nameType":"Personal","givenName":"Andrew","familyName":"Poppe","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-8137-8176","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Particle-in-cell modeling of Martian magnetic cusps"}],"publisher":"Dryad","container":{},"publicationYear":2020,"subjects":[{"subject":"space physics"}],"contributors":[],"dates":[{"date":"2020-11-30T21:17:03Z","dateType":"Submitted"},{"date":"2020-12-03T00:00:00Z","dateType":"Issued"},{"date":"2020-12-03T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1029/2020gl090763","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["37830957 bytes"],"formats":[],"version":"4","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Datasets supporting the manuscript, \"Particle-in-cell modeling of\n Martian magnetic cusps and their role in enhancing nightside ionospheric\n ion escape\", published in Geophysical Research Letters. All\n datasets are output from a 1.5-dimensional, particle-in-cell model of\n magnetospheric-ionospheric particle interactions along an open magnetic\n field line in a Martian magnetic cusp region. Particles are\n self-consistently traced according to the combined magnetic mirror force,\n parallel electrostatic fields, and Martian gravity. Particle densities,\n velocity distributions, and domain potentials are printed out at the end\n of simulations, after the establishment of steady-state conditions.","descriptionType":"Abstract"},{"description":"All files are in simple ASCII format, with descriptive\n headers.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Aeronautics and Space Administration","awardNumber":"NNX16AR94G","funderIdentifier":"https://ror.org/027ka1x80","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6078/D1BX2F","contentUrl":null,"metadataVersion":12,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":141,"downloadCount":12,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2020-12-03T21:35:47Z","registered":"2020-12-03T21:35:49Z","published":null,"updated":"2026-03-23T19:25:38Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d1s99x","type":"dois","attributes":{"doi":"10.6078/d1s99x","identifiers":[],"creators":[{"name":"McGuire, Jimmy","nameType":"Personal","givenName":"Jimmy","familyName":"McGuire","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-9562-5585","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Datasets for Draco lineatus phylogenomics study"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Natural sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Natural sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Genomics","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Introgression","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Mitochondria","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Phylogeography","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Phylogenetics","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Population genetics","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Reptiles","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Indonesia","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[{"name":"Museum of Vertebrate Zoology and Department of Integrative Biology*","affiliation":[],"contributorType":"Sponsor","nameIdentifiers":[]}],"dates":[{"date":"2023-10-09T17:58:11Z","dateType":"Created"},{"date":"2023-10-09T17:59:05Z","dateType":"Submitted"},{"date":"2023-10-11T00:00:00Z","dateType":"Issued"},{"date":"2023-10-11T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSourceOf","relatedIdentifier":"10.5281/zenodo.6869477","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1093/sysbio/syad020","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["370525900 bytes"],"formats":[],"version":"8","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"The biota of Sulawesi is noted for its high degree of endemism and for its\n substantial levels of in situ biological diversification. While the\n island’s long period of isolation and dynamic tectonic history have been\n implicated as drivers of regional diversification, this has rarely been\n tested in the context of an explicit geological framework. Here we provide\n a tectonically-informed biogeographical framework that we use to explore\n the diversification history of Sulawesi flying lizards (the Draco lineatus\n Group), a radiation that is endemic to Sulawesi and its surrounding\n islands. We employ a framework for inferring cryptic speciation that\n involves phylogeographic and genetic clustering analyses as a means of\n identifying potential species followed by population demographic\n assessment of divergence-timing and rates of bi-directional migration as\n means of confirming lineage independence (and thus species status). Using\n this approach, phylogenetic and population genetic analyses of\n mitochondrial sequence data obtained for 613 samples, a 50-SNP data set\n for 370 samples, and a 1249-locus exon-capture data set for 106 samples\n indicate that the current taxonomy substantially understates the true\n number of Sulawesi Draco species, that both cryptic and arrested\n speciation have taken place, and that ancient hybridization confounds\n phylogenetic analyses that do not explicitly account for reticulation. The\n Draco lineatus Group appears to comprise 15 species – nine on Sulawesi\n proper and six on peripheral islands. The common ancestor of this group\n colonized Sulawesi ~11 Ma when proto-Sulawesi was likely composed of two\n ancestral islands, and began to radiate ~6 Ma as new islands formed and\n were colonized via overwater dispersal. The enlargement and amalgamation\n of many of these proto-islands into modern Sulawesi, especially during the\n past 3 Ma, set in motion dynamic species interactions as once-isolated\n lineages came into secondary contact, some of which resulted in lineage\n merger, and others surviving to the present.\n                                 ","descriptionType":"Abstract"},{"description":"We generated a mitochondrial sequence data\n set for 593 individuals representing the nine currently-recognized species\n in the \u003cem\u003eDraco lineatus\u003c/em\u003e Group (McGuire et al. 2007)\n plus 20 additional individuals representing this clade’s sister taxon,\n \u003cem\u003eDraco bimaculatus,\u003c/em\u003e from the Philippines (see McGuire\n and Alcala 2000). This sampling regime allowed for many individuals to be\n screened for each species with particularly dense coverage of Sulawesi\n proper. The mitochondrial data\n set was subjected to partitioned Bayesian phylogenetic analysis using\n MrBayes 3.2.7 (Ronquist et al. 2012), with 5 \u003cem style=\"color:#1f1f1f;font-family:'Times New Roman' , serif;font-size:12pt;text-indent:0.5in;\"\u003ea\n priori\u003c/em\u003e designated partitions\n (ND2 codon positions 1, 2, and 3, 12S rRNA+ND2 flanking tRNAs, and 16S\n rRNA). The most appropriate nucleotide substitution model for each\n partition was selected using likelihood ratio-tests implemented in\n MrModelTest (Nylander 2004). Four separate analyses were undertaken, each\n with Metropolis-coupling (four chains), and 50 million generations.\n Convergence was assessed by confirming that all four analyses settled on\n the same tree topology and parameter estimates and that all model\n parameters had ESS values over 200 using Tracer v1.7.2 (Rambaut et al.\n 2018). The tree was rooted using the outgroup\u003cem\u003e\n \u003c/em\u003e\u003cem style=\"color:#1f1f1f;font-family:'Times New Roman' , serif;font-size:12pt;text-indent:0.5in;\"\u003eDraco\n bimaculatus\u003c/em\u003e.\n We also\n obtained a more comprehensive multi-locus data set comprising 1249\n sequence loci for 106 individual \u003cem\u003eDraco\u003c/em\u003e\n \u003cem\u003elineatus\u003c/em\u003e Group samples using an exon-capture approach\n plus two \u003cem\u003eD. bimaculatus\u003c/em\u003e that serve as the outgroup.\n These data were analyzed using RAxML and IQTree, ASTRAL-III, StarBEAST,\n and BPP v. 4.3.8 with a number of combinations of the full set of 108\n samples included. For example, the full data set was too large for\n StarBEAST, so subsets of samples for each mitochondrial lineage were\n included (35, 38, or 41 samples).\n  ","descriptionType":"Methods"},{"description":"The data files can be opened with any text editor, such as BBEdit\n and can be analyzed using programs compatible with the nexus and phylip\n file formats.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DEB 0328700","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Geographic Society","funderIdentifier":"https://ror.org/04bqh5m06","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DEB 0640967","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DEB 1258185","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DEB 1457845","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DEB 1652988","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6078/D1S99X","contentUrl":null,"metadataVersion":8,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":114,"downloadCount":29,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-10-04T22:14:30Z","registered":"2023-10-04T22:14:31Z","published":null,"updated":"2026-03-23T18:54:27Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d1fm8j","type":"dois","attributes":{"doi":"10.6078/d1fm8j","identifiers":[],"creators":[{"name":"Quenum, Jerome","nameType":"Personal","givenName":"Jerome","familyName":"Quenum","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]},{"name":"Zenyuk, Iryna","nameType":"Personal","givenName":"Iryna","familyName":"Zenyuk","affiliation":["University of California, Irvine"],"nameIdentifiers":[]},{"name":"Ushizima, Daniela","nameType":"Personal","givenName":"Daniela","familyName":"Ushizima","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7363-9468","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"3D microCT of lithium metal battery after charge and discharge"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"lithium battery"},{"subject":"Deep learning","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Computer vision","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Neural networks","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Image segmentation"},{"subject":"Quality control","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Electrical engineering, electronic engineering, information engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2023-02-10T23:20:50Z","dateType":"Submitted"},{"date":"2023-03-27T00:00:00Z","dateType":"Issued"},{"date":"2023-03-27T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.3390/jimaging9060111","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["5421815057 bytes"],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Lithium metal battery (LMB) has the potential to be the next-generation\n battery system because of their high theoretical energy density. However,\n defects known as dendrites are formed by heterogeneous lithium (Li)\n plating, which hinder the development and utilization of LMBs.\n Non-destructive techniques to observe the dendrite morphology often use\n computerized X-ray tomography (XCT) imaging to provide cross-sectional\n views. To retrieve three-dimensional structures inside a battery, image\n segmentation becomes essential to quantitatively analyze XCT images. This\n work proposes a new binary semantic segmentation approach using a\n transformer-based neural network (T-Net) model capable of segmenting out\n dendrites from XCT data. In addition, we compare the performance of the\n proposed T-Net with three other algorithms, such as U-Net, Y-Net, and\n E-Net, consisting of an Ensemble Network model for XCT analysis. Our\n results show the advantages of using T-Net when evaluating\n over-segmentation metrics, such as mean Intersection over Union (mIoU) and\n mean Dice Similarity Coefficient (mDSC) as well as through several\n qualitatively comparative visualizations. This data record contains a\n relevant crop of the original XCT data as well as the corresponding result\n of using our proposed transformer-based neural network (T-Net)\n for semantic segmentation.","descriptionType":"Abstract"},{"description":"Synchrotron-based hard X-ray computed tomography (XCT) for\n high-resolution. This deposit record contains a relevant crop of the\n original XCT data as well as the corresponding result of using our\n proposed transformer-based neural network (T-Net) for semantic\n segmentation.","descriptionType":"Methods"},{"description":"Any multi-tif reader, such as Gimp, Preview, ImageJ, among\n others","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"funderName":"U.S. Department of Energy*"},{"schemeUri":"https://ror.org","funderName":"Lawrence Berkeley National Laboratory","awardNumber":"Bridges Fellowship","funderIdentifier":"https://ror.org/02jbv0t02","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6078/D1FM8J","contentUrl":null,"metadataVersion":8,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":299,"downloadCount":56,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-03-27T20:26:01Z","registered":"2023-03-27T20:26:02Z","published":null,"updated":"2026-03-18T15:44:23Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d1m41m","type":"dois","attributes":{"doi":"10.6078/d1m41m","identifiers":[],"creators":[{"name":"Akman, Melis","nameType":"Personal","givenName":"Melis","familyName":"Akman","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-7274-0521","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Protea repens whole transcriptome count data for control and drought treatment for 8 populations, climatic data for the 8 populations and phenotypic data collected, and data used for linear mixed models for climate gene expression/trait correlation testing"}],"publisher":"Dryad","container":{},"publicationYear":2020,"subjects":[{"subject":"Evolutionary and ecological transcriptomics"}],"contributors":[],"dates":[{"date":"2020-10-14T18:58:02Z","dateType":"Submitted"},{"date":"2020-10-26T00:00:00Z","dateType":"Issued"},{"date":"2020-10-26T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/mec.15705","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["107243034 bytes"],"formats":[],"version":"5","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Long term environmental variation often drives local adaptation and leads\n to trait differentiation across populations. Additionally, when traits\n change in an environment-dependent way through phenotypic plasticity, the\n genetic variation underlying plasticity will also be under selection.\n These processes could create a landscape of differentiation across\n populations in traits and their plasticity. Here, we performed a dry-down\n experiment under controlled conditions to measure responses in seedlings\n of a shrub species from the Cape Floristic Region, the common sugarbush\n (Protea repens). We measured morphological and physiological traits, and\n sequenced whole transcriptomes of leaf tissues from 8 populations that\n represent both the climatic and the geographic distribution of this\n species. We found that there is substantial variation in how populations\n respond to drought, but we also observed common patterns such as reduced\n leaf size and leaf thickness, and upregulation of stress- and\n down-regulation of growth-related gene groups. Both high environmental\n heterogeneity and milder source site climates were associated with higher\n plasticity in various traits and co-expression gene networks. Associations\n between traits, trait plasticity, gene networks and the source site\n climate suggests that temperature may play a bigger role in shaping these\n patterns when compared to precipitation, in line with recent changes in\n the region due to climate change. We also found that traits respond to\n climatic variation in an environment dependent manner: some associations\n between traits and climate were apparent only under certain growing\n conditions. Together, our results uncover common responses of P. repens\n populations to drought, and climatic drivers of population differentiation\n in functional traits, gene expression and their plasticity.","descriptionType":"Abstract"},{"description":"\u003cb\u003eField sampling and\n climate data\u003c/b\u003e In 2011, we collected seeds from\n 8 wild populations of \u003ci\u003eP. repens\u003c/i\u003e spanning its geographic\n distribution in the CFR. From 40-50 plants per population (mean n= 45\n plants), we collected 1-2 mature seed heads from the previous year’s\n growth. Plants were sampled along transects through the population centre,\n with at least 1 m between each sampled plant for small populations\n (\u0026lt;100 plants), and 5 m for larger populations. Seed heads were\n allowed to air-dry until achenes were released. GPS coordinates of each site near\n the population center were used to extract elevation and six climate\n metrics for rainfall and temperature from GIS layers. The climate layers\n used were (1) mean annual rainfall, (2) average daily maximum temperature\n for summer (January), (3) average daily minimum temperature for winter\n (July), (4) precipitation concentration (a seasonality indicator we refer\n to as “rainfall seasonality”), measured as Markham’s concentration index\n (Markham 1970), which ranges from 0 to 100, with a value of 0 indicating perfectly even precipitation across all months, and 100 indicating that all precipitation falls in a single month (5) inter-annual rainfall variability, measured as the coefficient of variation in mean annual rainfall across years, and (6) extreme temperature range within a year, measured as the difference between summer maximum and winter minimum temperatures. Our seventh variable was elevation, which was extracted separately from ASTER GDEM (Daac 2011). All climate data layers were from the South African Atlas of Agrohydrology and Climatology (Schulze 1997) with higher resolution for 4 and 5 below, and (Schulze 2007) for the rest, and were based on 50 year (collected between 1950-1999 when data was available from field stations) climate averages, extremes, or variances. \u003cb\u003eExperimental setup and dry-down treatments\u003c/b\u003e In July 2014, we sowed seeds from 8 \u003ci\u003eProtea repens\u003c/i\u003e populations on low nutrient mix (50% bark, 50% sand) in trays of 100 individual plugs 2x2 cm in size with a complete random design. Trays were placed in a long-day growth chamber (8 hours in dark at 8℃, 16 hours in light at 20℃). The trays were randomly moved across the growth chambers every 3 days with each watering. After 1.5 months, we transplanted germinated seedlings into small, deep pots (5 cm diameter x 17 cm deep) in trays of 50 each, in the same low nutrient mix. After transplanting, seedlings were rested for two weeks indoors under growth lights before being transferred to a single greenhouse bench at Nicholls State University Farm, Thibodaux, LA. Two weeks post-greenhouse transfer and two weeks prior to the start of the dry-down experiment, we selected a total of 316 out of 419 seedlings (19-20 seedlings/population x 8 populations x 2 treatments) for uniform size for the experiment. We then reorganized the 2.5 month-old seedlings into trays containing 20-25 seedlings each, in a complete random design. In the experiment, seedlings were assigned to one of two treatments: control, in which we applied regular watering (~every 3 days, equal amounts of water was given to each plant) and drought, in which we stopped watering entirely for 12 days. Because we had a variable number of seedlings per maternal plant, we could not perfectly balance maternal lines across populations and treatments. To the extent possible, however, we included at least one seedling per maternal family per treatment; for 82% of the 83 maternal families, we were able to include at least one seedling per maternal family in each treatment, and for 51% of families, we included multiple seedlings per treatment. The drought experiment began in late October 2014, when greenhouse conditions were warm but relatively dry, and thus most similar to summer conditions in the native range. \u003cb\u003eTrait measurements and gene expression sampling\u003c/b\u003e At 6 days and 12 days into the drought experiment, plants were measured for functional traits and leaves were sampled for transcriptomics. Functional traits that were measured at both timepoints included plant height (cm), stomatal conductance, leaf area, leaf length:width ratio (LWR), specific leaf area (fresh leaf area/dry leaf mass; SLA), stomatal density (number per mm\u003csup\u003e2\u003c/sup\u003e), stomatal pore length (mm) and stem pigmentation. Stem pigmentation, measured as stem hue (H4), was derived using Endler’s segment classification (Maia \u003ci\u003eet al. \u003c/i\u003e2013), based on the average of two reflectance spectra readings (400-700 nm) taken at mid-stem (JAZ, Ocean Optics). On the segment classification scale for hue, higher values represent redder stems that contain higher levels of anthocyanin. Stomatal conductance was measured using a steady-state leaf porometer (SC-1, Decagon) on one leaf per plant, with an additional adjacent leaf measured simultaneously if the chamber was not filled by a single leaf due to small leaf size. All other leaf measurements were taken from the 2nd or 3rd youngest expanding leaf from the stem apex. Immediately following conductance measurement, the leaf was removed to measure leaf area, width, length and stomatal traits. Fresh leaves were digitally scanned shortly after harvest at 400 dpi on a flatbed scanner. After scanning, acrylic peels were taken from the adaxial leaf surface and placed on cellophane, which were later viewed under a light microscope at 40x for stomatal measurements (see Carlson \u003ci\u003eet al. \u003c/i\u003e2016). Although height and pigmentation were measured on all plants on both timepoints (mean sample size per population=39), all other traits were measured on only a subset of plants on each of these timepoints, typically in association with the transcriptomic data (10-20 individuals per population for each timepoint). Two additional variables were calculated by subtracting day 6 from day 12 values on the same plant: pigment accumulation (day 12 hue - day 6 hue) and growth (day 12 height - day 6 height). \u003cb\u003eTranscriptome sampling \u003c/b\u003e A smaller subset of plants compared to functional trait measurements had one leaf per plant harvested for whole transcriptome sequencing and gene expression analysis (n=120 plants total, or 4 per population per treatment per day, except no ALC on day 12 due to insufficient number of individuals because of lower germination in this population). Plants with leaves removed on day 6 were not sampled for transcriptomics on day 12, to avoid possible shifts in gene expression caused by leaf removal. As a consequence, gene expression and most traits were measured in one set of plants on day 6 and in a separate set on day 12. Transcriptome sampling took place prior to any other measurements on a given day and consisted of collecting one leaf for RNA extraction per plant (the 2nd or 3rd youngest leaf from the tip) into a 1 mL tube and snap-freezing immediately in liquid nitrogen. These samples were stored at -80°C until library preparation. All sampling for transcriptome sequencing was done between 10 AM and 11 AM, and functional trait measurements were done between 10AM and 2PM each day. Both transcriptomics and other trait measurements were done in a random order. On day 12, we harvested a subset of plants for root length and carbohydrate storage measurements (n=9 per population including the 4 transcriptome plants and 5 additional plants). After completing all trait and transcriptome sampling, we cut plants at the root- shoot junction and placed aboveground tissues in a 10 mL tube and snap-froze the samples in liquid nitrogen for future carbohydrate analyses. Belowground tissues were gently removed from the soil, rinsed clean and stored in a separate tube and also snap-frozen. For these 9 plants per population, we also measured the length of the longest root. Tubes containing plant tissues were stored in a -80°C freezer for ~1 month, then freeze-dried for biomass and carbohydrate analyses. Dried tissues for above- and belowground material was measured and ground. Ten to hundred and fifty mg of ground material was used for carbohydrate analyses as described in (Akman \u003ci\u003eet al. \u003c/i\u003e2012). Briefly, carbohydrates and starch were extracted with 70% methanol solution. The extract was used for soluble carbohydrate content measurements and the pellet including starch was treated enzymatically with α-amylase and amyloglucosidase resulting in hexose sugars.  Soluble carbohydrates and hexose sugar concentration was measured with a modified version of anthrone method using fixed glucose standards. \u003cb\u003eSurvival measurements \u003c/b\u003e After all trait and gene expression data had been collected, the remaining plants in the drought treatment (those that had not been harvested at the end of the experiment) were maintained in the greenhouse under drought stress with regular watering. Plants were checked weekly to score survival and days until mortality was recorded.  \u003cb\u003eTranscriptome library construction and sequencing \u003c/b\u003e Transcriptome sequencing samples were homogenized in a Beadbeater-96 (Biospec Inc, Bartlesville, OK, USA) with 2.3 mm Chrome-Steel Beads (Biospec Inc, Bartlesville, OK, USA). Lysis/binding buffer (Life Technologies, Grand Island, NY, USA, with addition of %3 PVP-40) was added and the solution was homogenized again for 2 min. Tubes were transferred to a 60°C water bath for 30 min with occasional shaking every 10 min. The solution was centrifuged for 3 min at 12 000 x g and the supernatant was used for mRNA isolation and sequencing library preparation by the procedures explained in Kumar et al. (2012). Sequencing libraries were pooled and quality control of the libraries were done with Bioanalyzer (Agilent Technologies Inc., Santa Clara, CA, USA). Before pooling, concentration of each library was measured in a plate reader and appropriate amounts were pooled in order to balance reads obtained from each library from the pooled samples. A total of 120 samples were sequenced; 60 in each of 2 Illumina Hiseq 2500 lanes, yielding 756 million single-end reads (100 bp long). A randomized block design was used for sequencing: two samples per population per treatment per time-point were included in each lane.    \u003cb\u003eGene expression analyses and bioinformatics \u003c/b\u003e Raw reads were trimmed and demultiplexed with trimmomatic (Bolger \u003ci\u003eet al.\u003c/i\u003e 2014) and fastx toolkit (Hannon). By using bowtie (Langmead \u0026amp; Salzberg 2012), trimmed reads were mapped to a previously published de-novo transcriptome of \u003ci\u003eP. repens\u003c/i\u003e (Akman \u003ci\u003eet al.\u003c/i\u003e 2016) assembled by using sequences of individuals that also include all of our focal populations. Before reads were mapped to the transcriptome, a further clustering of the contigs were done using cd-hit (Fu \u003ci\u003eet al.\u003c/i\u003e 2012), which reduced number of contigs from 120406 to 106403 (settings -c 0.95 -n 8 -r 1; N\u003csub\u003e50\u003c/sub\u003e slightly increased from 1130 to 1132). Read counts, extracted using eXpress (Pachter 2011), averaged 6.02 million reads per sample (ranged between 2.46-21.80 million). Mapping rate was 92.4% on average (ranged between 89.5%-94.5%). Contigs with \u0026lt;1 counts per million (cpm) for at least 24 samples were excluded from the analyses (reducing the library size range to 2.35-20.46, average = 5.73 million reads), which reduced the number of contigs used to 45019 (29507 of which had hits to Arabidopsis genes). Differential gene expression analyses were performed using edgeR. For each time point, differentially expressed genes per population were revealed using pairwise comparisons using negative binomial models. The common set of differentially expressed genes was obtained by extracting the overlap of differentially expressed genes for each population at day 12. We then used linear models in edgeR, including sequencing lane as a fixed factor, to detect contigs that showed variation between treatments and among populations (population x treatment). Co-expression gene networks were computed using WGCNA (Langfelder \u0026amp; Horvath 2008) with consensus clustering option for the 2 timepoints together. Normalized and cpm-filtered read counts were transformed with “voom” in limma package. A soft-threshold of 6 was used for clustering and 14 co-expressed gene networks were constructed. The number of genes within each gene network varied between 49 (GN13) to 7682 (GN1). The gene network “grey” (called GN0 in this study) as constructed by WGCNA for genes that do not fit any gene network has 3124 genes. A representative “eigen gene” value, which is the first principal component axis for all genes within the network, was used for further analyses of climate and trait associations.   Markham CG (1970) Seasonality of precipitation in the United States. \u003ci\u003eAnnals of the Association of American Geographers. Association of American Geographers\u003c/i\u003e, \u003cb\u003e60\u003c/b\u003e, 593–597. Daac LP (2011) ASTER L1B. \u003ci\u003eSioux Falls, South Dakota: USGS/Earth Resources Observation and Science (EROS) Center\u003c/i\u003e. Schulze RE (1997) \u003ci\u003eSouth African atlas of agrohydrology and-climatology\u003c/i\u003e. Water Research Commission, Pretoria, South Africa. Schulze RE (2007) \u003ci\u003eSouth African atlas of climatology and agrohydrology: WRC Report 1489/1/06\u003c/i\u003e. Water Resource Commission. Maia R, Eliason CM, Bitton P, Doucet SM, Shawkey MD. 2013. Pavo: An R package for the analysis, visualization and organization of spectral data. Methods in Ecology and Evolution\u003cb\u003e, 10\u003c/b\u003e: 1097-1107. Kumar R, Ichihashi Y, Kimura S \u003ci\u003eet al.\u003c/i\u003e (2012) A high-throughput method for Illumina RNA-Seq library preparation. \u003ci\u003eFrontiers in plant science\u003c/i\u003e, \u003cb\u003e3\u003c/b\u003e\u003cb\u003e, \u003c/b\u003e202. Akman M, Bhikharie AV, McLean EH \u003ci\u003eet al.\u003c/i\u003e (2012) Wait or escape? Contrasting submergence tolerance strategies of \u003ci\u003eRorippa amphibia, Rorippa sylvestris\u003c/i\u003e and their hybrid. \u003ci\u003eAnnals of botany\u003c/i\u003e, \u003cb\u003e109\u003c/b\u003e, 1263–1276. Bolger AM, Lohse M, Usadel B (2014) Trimmomatic: A flexible trimmer for Illumina Sequence Data. \u003ci\u003eBioinformatics, \u003c/i\u003e30, 2114-2120. Hannon GJ Fastx-toolkit. 2010. \u003ci\u003eGNU Affero General Public License\u003c/i\u003e, \u003cb\u003e706\u003c/b\u003e. Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. \u003ci\u003eNature methods\u003c/i\u003e, \u003cb\u003e9\u003c/b\u003e, 357–359. Akman M, Carlson JE, Holsinger KE, Latimer AM (2016) Transcriptome sequencing reveals population differentiation in gene expression linked to functional traits and environmental gradients in the South African shrub Protea repens. \u003ci\u003eThe New phytologist\u003c/i\u003e, \u003cb\u003e210\u003c/b\u003e, 295–309. Fu L, Niu B, Zhu Z, Wu S, Li W (2012) CD-HIT: accelerated for clustering the next-generation sequencing data. \u003ci\u003eBioinformatics \u003c/i\u003e, \u003cb\u003e28\u003c/b\u003e, 3150–3152. Langfelder P, Horvath S (2008) WGCNA: an R package for weighted correlation network analysis. \u003ci\u003eBMC bioinformatics\u003c/i\u003e, \u003cb\u003e9\u003c/b\u003e, 559.","descriptionType":"Methods"},{"description":"Seeds of 8 Protea repens\n were collected in the wild. Population location coordinates were used to\n extract climatic data from Schultze 1997 and 2007. Seeds were sown on soil\n and grown in climate rooms and a green house. After plants were 2.5 months\n old, we started our experiment in a green house. Half the plants were used\n in a dry-down treatment while the other half were watered regularly. Leaf\n tissues were collected and phenotypic measurements were done 6 and 12 days\n in to the treatment. Leaves were used in transcriptome library\n preparation. The libraries were then sequenced on Illumina Hiseq. After\n quality assessment, reads were mapped using bowtie and read counts were\n extracted using eXpress. After voom transformation, WGCNA was used for\n consensus clustering for the two timepoints. Here we include, raw count\n data (file 1) together with WGCNA eigen gene values, phenotypes and\n climate data for each population split into two timepoints (file2 and\n file3).","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"1046328","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"1045985","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.6078/D1M41M","contentUrl":null,"metadataVersion":12,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":160,"downloadCount":9,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2020-10-26T18:46:15Z","registered":"2020-10-26T18:46:16Z","published":null,"updated":"2026-03-18T15:26:05Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d13t3m","type":"dois","attributes":{"doi":"10.6078/d13t3m","identifiers":[],"creators":[{"name":"Treidel, Lisa","nameType":"Personal","givenName":"Lisa","familyName":"Treidel","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9390-8306","nameIdentifierScheme":"ORCID"}]},{"name":"Clark, Rebecca","nameType":"Personal","givenName":"Rebecca","familyName":"Clark","affiliation":["Sienna College"],"nameIdentifiers":[]},{"name":"Lopez, Melissa","nameType":"Personal","givenName":"Melissa","familyName":"Lopez","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]},{"name":"Williams, Caroline","nameType":"Personal","givenName":"Caroline","familyName":"Williams","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3112-0286","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Diet sensitivity in field crickets"}],"publisher":"Dryad","container":{},"publicationYear":2021,"subjects":[],"contributors":[],"dates":[{"date":"2021-02-08T09:10:04Z","dateType":"Submitted"},{"date":"2021-02-25T00:00:00Z","dateType":"Issued"},{"date":"2021-02-25T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1242/jeb.237834","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["268715 bytes"],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Animals adjust resource acquisition throughout life to meet changing\n physiological demands of growth, reproduction, activity, and somatic\n maintenance. Wing-polymorphic crickets invest in either dispersal or\n reproduction during early adulthood, providing a system in which to\n determine how variation in physiological demands, determined by sex and\n life history strategy, impact nutritional targets, plus the consequences\n of nutritionally imbalanced diets across life stages. We hypothesized that\n high demands of biosynthesis (especially oogenesis in females) drive\n elevated resource acquisition requirements and confer vulnerability to\n imbalanced diets. Nutrient targets and allocation into key tissues\n associated with life history investments were determined for juvenile and\n adult male and female field crickets (Gryllus lineaticeps)  when\n given a choice between two calorically equivalent but nutritionally\n imbalanced (protein- or carbohydrate-biased) artificial diets, or when\n restricted to one imbalanced diet. Flight muscle synthesis drove elevated\n general caloric requirements for juveniles investing in dispersal, but\n flight muscle quality was robust to imbalanced diets. Testes synthesis was\n not costly, and life history investments by males were insensitive to diet\n composition. In contrast, costs of ovarian synthesis drove elevated\n caloric and protein requirements for adult females. When constrained to a\n carbohydrate-biased diet, ovary synthesis was reduced in reproductive\n females, eliminating their advantage in early life fecundity over the\n dispersal morph. Our findings demonstrate that nutrient acquisition\n modulates dispersal-reproduction trade-offs in an age- and sex-specific\n manner. Declines in food quality will thus disproportionately affect\n specific cohorts, potentially driving demographic shifts and altering\n patterns of life history evolution.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.6078/D13T3M","contentUrl":null,"metadataVersion":12,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":183,"downloadCount":23,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-02-25T19:41:47Z","registered":"2021-02-25T19:41:48Z","published":null,"updated":"2026-03-17T16:01:15Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d1j99r","type":"dois","attributes":{"doi":"10.6078/d1j99r","identifiers":[],"creators":[{"name":"Fournier, Robert","nameType":"Personal","givenName":"Robert","familyName":"Fournier","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9494-2776","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"The effects of drought and nutrients on stream communities"}],"publisher":"Dryad","container":{},"publicationYear":2022,"subjects":[],"contributors":[],"dates":[{"date":"2021-11-04T03:47:04Z","dateType":"Submitted"},{"date":"2022-05-24T00:00:00Z","dateType":"Issued"},{"date":"2022-05-24T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSourceOf","relatedIdentifier":"10.5281/zenodo.5719121","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1371/journal.pone.0269222","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["384448 bytes"],"formats":[],"version":"3","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Drought and nutrient pollution can affect the dynamics of stream\n ecosystems in diverse ways. While the individual effects of both stressors\n are broadly examined in the literature, we still know relatively little\n about if and how these stressors interact. Here, we performed a mesocosm\n experiment that explores the compounded effects of seasonal drought via\n water withdrawals and nutrient pollution (1.0 mg/L of N and 0.1 mg/L of P)\n on a subset of Ozark stream community fauna and ecosystem processes. We\n observed biological responses to individual stressors as well as both\n additive and antagonistic stressor interactions. We found that drying\n negatively affected periphyton assemblages, macroinvertebrate\n colonization, and leaf litter decomposition in shallow habitats. However,\n in deep habitats, drought-based concentration effects caused trophic\n cascades that released algal communities from grazing pressures; while\n nutrient enrichment caused bottom-up cascades that influenced periphyton\n variables and crayfish growth rates. Finally, the combined effects of\n drought and nutrient enrichment interacted antagonistically to increase\n survival in longear sunfish; and stressors acted synergistically on\n grazers causing a trophic cascade that increased periphyton variables.\n Because stressors can differentially impact biota—and that the same\n stressor pairing can act both additively and antagonistically on different\n portions of the community simultaneously—our broad understanding of\n individual stressors might not adequately inform our knowledge of\n multi-stressor systems.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.6078/D1J99R","contentUrl":null,"metadataVersion":9,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":139,"downloadCount":18,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-05-24T19:35:09Z","registered":"2022-05-24T19:35:10Z","published":null,"updated":"2026-03-17T15:56:45Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d1799z","type":"dois","attributes":{"doi":"10.6078/d1799z","identifiers":[],"creators":[{"name":"Pak, Nina","nameType":"Personal","givenName":"Nina","familyName":"Pak","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-8252-6449","nameIdentifierScheme":"ORCID"}]},{"name":"Wu, Stephanie","nameType":"Personal","givenName":"Stephanie","familyName":"Wu","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]},{"name":"Gibson, Joel","nameType":"Personal","givenName":"Joel","familyName":"Gibson","affiliation":["Royal British Columbia Museum"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7786-0066","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Accepted marine Diptera from world register of marine species and aquatic annotations"}],"publisher":"Dryad","container":{},"publicationYear":2022,"subjects":[{"subject":"FOS: Natural sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Natural sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2021-05-10T20:43:02Z","dateType":"Submitted"},{"date":"2022-07-07T00:00:00Z","dateType":"Issued"},{"date":"2022-07-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1002/ece3.7935","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["461336 bytes"],"formats":[],"version":"6","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"List of Fly Families from Wiegmann et al. 2011 and annotations of marine\n and aquatic life histories based on World Register of Marine Species and\n other peer-reviewed sources.","descriptionType":"Abstract"},{"description":"These datasets are based on several peer-reviewed sources. We\n combined these peer reviewed sources into our dataset. Sources include\n Wiegmann et al. 2011, Adler and Courtney 2019, and records from the World\n Register of Marine Species (Downloaded file from Dec 2020).","descriptionType":"Methods"},{"description":"Not all infraorders/superfamilies are labeled.  Blank cells under\n the marine, brackish, fresh, and terrestrial were replaced as\n NAs. ","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.6078/D1799Z","contentUrl":null,"metadataVersion":14,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":229,"downloadCount":5,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-07-07T16:28:07Z","registered":"2021-07-07T16:28:08Z","published":null,"updated":"2026-03-17T12:53:59Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d1dd85","type":"dois","attributes":{"doi":"10.6078/d1dd85","identifiers":[],"creators":[{"name":"Mayer, Allegra","nameType":"Personal","givenName":"Allegra","familyName":"Mayer","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3408-5906","nameIdentifierScheme":"ORCID"}]},{"name":"Silver, Whendee","nameType":"Personal","givenName":"Whendee","familyName":"Silver","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0372-8745","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"DayCent simulations for California annual grasslands: Monthly data outputs"}],"publisher":"Dryad","container":{},"publicationYear":2022,"subjects":[{"subject":"soil organic carbon"},{"subject":"DayCent"},{"subject":"compost"},{"subject":"nitrous oxide (N2O)"},{"subject":"California","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"CanESM2"},{"subject":"HadGEM2-ES"},{"subject":"RCP4.5"},{"subject":"RCP8.5"},{"subject":"FOS: Natural sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Natural sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[{"name":"University of California, Berkeley","nameType":"Personal","givenName":"Berkeley","familyName":"University of California","affiliation":[],"contributorType":"Sponsor","nameIdentifiers":[]}],"dates":[{"date":"2022-05-24T02:54:23Z","dateType":"Submitted"},{"date":"2022-05-26T00:00:00Z","dateType":"Issued"},{"date":"2022-05-26T00:00:00Z","dateType":"Available"},{"date":"2022-06-03T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1002/eap.2705","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["61409516 bytes"],"formats":[],"version":"5","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Composted manure and green waste amendments have been shown to increase\n net carbon (C) sequestration in rangeland soils and have been proposed as\n a means to help lower atmospheric CO2 concentrations. However, the effect\n of climate change on soil organic C (SOC) stocks and greenhouse gas\n emissions in rangelands is not well understood, and the viability of\n climate change mitigation strategies under future conditions is even less\n certain. We used a process-based biogeochemical model (DayCent) at a daily\n timestep to explore the long-term effects of potential future climate\n changes on C and greenhouse gas dynamics in annual grassland ecosystems.\n We then used the model to explore how the same ecosystems might respond to\n climate change following compost amendments to soils and determined the\n long-term viability of net SOC sequestration under changing climates. We\n simulated net primary productivity (NPP), SOC, and greenhouse gas fluxes\n across seven California annual grasslands with and without compost\n amendments. We drove the DayCent simulations with field data and with\n site-specific daily climate data from two Earth system models (CanESM2 and\n HadGEM-ES) and two representative concentration pathways (RCP4.5 and\n RCP8.5) through 2100. Net primary productivity and SOC stocks in unamended\n and amended ecosystems were surprisingly insensitive to projected climate\n changes. A one-time amendment of compost to rangeland acted as a\n slow-release organic fertilizer and increased NPP by up to 390–814 kg C\n ha-1 y-1 across sites. The amendment effect on NPP was not sensitive to\n Earth system model or emissions scenario and endured through the end of\n the century. Net SOC sequestration amounted to 1.96 ± 0.02 Mg C ha-1\n relative to unamended soils at the maximum amendment effect. Averaged\n across sites and scenarios, SOC sequestration peaked 22 ± 1 years after\n amendment and declined but remained positive throughout the century. While\n compost stimulated nitrous oxide (N2O) emissions, the cumulative net\n emissions (in CO2 equivalents) due to compost were far less than the\n amount of SOC sequestered. Compost amendments resulted in a net climate\n benefit of 69.6 ± 0.5 Tg CO2e 20 ± 1 years after amendment if applied to\n similar ecosystems across the state, amounting to 39% of California’s\n rangeland. These results suggest that the biogeochemical benefits of a\n single amendment of compost to rangelands in California is insensitive to\n future climate change and could contribute to decadal-scale climate\n mitigation goals alongside emissions reductions.","descriptionType":"Abstract"},{"description":"The DayCent Biogeochemical Model (Parton et al. 1998) was used to\n simulate the effects of climate and management on C and greenhouse gas\n dynamics in each rangeland ecosystem. DayCent is a widely utilized and\n well-established complex process model, developed using ecological\n concepts of grassland soil C and N dynamics (Parton et al. 1994). The\n model is parameterized for initial conditions using site-specific\n historical climate data, annual net primary productivity (NPP), and\n depth-specific measured values for soil texture and bulk density. The\n calibrated model provides a baseline from which the model can calculate\n trends with time and differences under changing conditions (e.g., climate\n and compost amendments in this study). DayCent partitions existing and\n added C into discrete soil pools based on estimated C turnover time:\n active (\u0026lt; 1 year), slow (decadal), and passive (millennial). Dead\n plant material is partitioned into active or slow cycling pools initially,\n depending upon tissue chemistry (e.g., lignin:N ratio), using first-order\n kinetics. Carbon can move among pools through decomposition and\n stabilization. The movement among pools mimics microbial activity and the\n mineral association of organic matter; it includes a separate pool for\n microbial biomass, but DayCent does not explicitly model specific\n mechanisms of microbial interactions or mineral stabilization (Parton et\n al. 1994). Modeled SOC flows and NPP are both strongly dependent on soil\n water availability in DayCent, which has been shown to be an important\n driver of ecosystem C dynamics in grasslands (Burke et al. 1997, Harpole\n et al. 2007). The nitrogen gas sub-model of DayCent uses a daily timestep\n to simulate N\u003csub\u003e2\u003c/sub\u003eO fluxes from nitrification and\n denitrification based on diffusivity parameters of soil (water-filled pore\n space, texture, bulk density, field capacity, temperature), pH, and soil\n NH\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e and\n NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e-\u003c/sup\u003e concentrations (Parton\n et al. 2001). The grassland CH\u003csub\u003e4\u003c/sub\u003e oxidation sub-model\n simulates methanotrophy at a daily timestep as a function of soil water\n content, field capacity, porosity, and temperature (Del Grosso et al.\n 2000). DayCent also models soil respiration and microbial respiration of\n CO\u003csub\u003e2\u003c/sub\u003e; here we report on total soil respiration which\n is more comparable with field data. DayCent facilitates the simulation of\n explicit management practices including grazing and compost amendments,\n and was originally developed, and has been used extensively, for modeling\n managed grassland and cropland ecosystems (Kelly et al. 2000; Parton et\n al. 1993; Parton et al. 1998; Ryals et al. 2015).\n \u003cstrong\u003eBiogeochemical model\n inputs\u003c/strong\u003e Field observations of soil\n texture, total organic C, bulk density, and biomass production from\n pre-treatment plots were used for the initial parameterization of the\n model for each site (Table 1). Total organic C was measured on five\n replicate soil cores at four depths down to 1 meter or point of refusal.\n The point of refusal was below one meter except for a minority of cores in\n Mendocino, Marin, and Tulare, where the mean points of refusal were 95.7 ±\n 2.7 cm, 92.2 ± 3.4 cm, and 99.5 ± 0.5 cm, respectively. Soil texture was\n measured on 3 samples from each transect (first, third, and fifth core)\n from the 0-10 cm depth at each site. Soil texture data for 10-100 cm soil\n depths were obtained from the SSURGO database (Soil Survey Staff et al.\n 2017). Bulk density samples were taken using a 6.35 cm diameter metal\n corer at 10 cm depth increments to 1 m or point of refusal from two soil\n pits per site. Aboveground NPP was measured by clipping vegetation at peak\n biomass from eight replicate 200 cm\u003csup\u003e2\u003c/sup\u003e subplots for\n both amended and unamended plots, oven drying at 65 ºC, and weighing;\n belowground NPP was measured in Marin and Yuba only by Ryals et al (2013).\n Soil subsamples were analyzed in duplicate for total C concentration at\n U.C. Berkeley on a Carlo Erba Elantech elemental analyzer (Lakewood, NJ,\n USA) using atropine as a standard at a rate of one per ten samples.\n Samples were re-run in if duplicates varied by more than 10%. Soils were\n tested for carbonates using 2M HCl; as no carbonates were found, results\n reported reflect only organic C concentrations. Bulk density was\n determined by calculating the rock volume and determining the oven dry\n (105°C) mass of soil per unit volume. Soil organic C contents were\n calculated by multiplying the C concentrations (%) by the oven-dry mass of\n the fine fraction (\u0026lt; 2 mm) and dividing by the bulk density and\n depth (Throop et al. 2012). Additional details can be found in Silver et\n al. (2018).  Livestock effects on biomass and biogeochemical cycling were\n represented using scheduled time- and intensity-specific grazing events.\n Grazing management was simulated to reflect site-specific historic and\n current practices (Appendix S1). Simulations of future\n conditions were driven by daily climate data from 2006 to 2100 extracted\n from the CanESM2 (Canadian Centre for Climate Modeling and Analysis,\n Canada) and HadGEM2-ES (Met Office Hadley Centre, UK) Earth System Models\n (ESMs). We chose not to simulate CO2 fertilization in order to isolate the\n role of climate. There remains debate as to which ESM most accurately\n represents future weather in California. We used CanESM2 and HadGEM2-ES\n because they yielded contrasting projections for future precipitation (see\n below). We used two Representative Concentration Pathway (RCP) scenarios:\n RCP4.5 that assumes some emissions reductions, and RCP8.5 that assumes\n business as usual societal behavior with minimal emissions reductions. We\n chose these two scenarios because California used RCP4.5 and RCP8.5 for\n emissions reduction targets in their 2018 assessment report (Franco et al.\n 2018). Data were extracted for the site-specific (2.8°x 2.8°) geographical\n grid of CanESM2 and HadGEM2-ES.","descriptionType":"Methods"},{"description":"Output units are described in the ReadMe.txt file. ","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"funderName":"California Climate Change 4th Assessment*"},{"funderName":"Betsy Taylor*"},{"funderName":"California Coastal Conservancy*"},{"funderName":"U.S. Department of Energy via Lawrence Livermore National Laboratory*","awardNumber":"DE-AC52-07NA27344"},{"schemeUri":"https://ror.org","funderName":"BAND foundation","funderIdentifier":"https://ror.org/05wr3m454","funderIdentifierType":"ROR"},{"funderName":"The Jena and Michael King Foundation*"},{"funderName":"The Rathmann Family Foundation*"},{"funderName":"The Trisons Foundation*"},{"funderName":"The V.K. Rasmussen Foundation*"}],"url":"https://datadryad.org/dataset/doi:10.6078/D1DD85","contentUrl":null,"metadataVersion":12,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":277,"downloadCount":32,"referenceCount":0,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-05-26T15:13:10Z","registered":"2022-05-26T15:13:11Z","published":null,"updated":"2026-03-16T22:14:04Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.6078/d18q68","type":"dois","attributes":{"doi":"10.6078/d18q68","identifiers":[],"creators":[{"name":"Martínez-Gómez, Jesús","nameType":"Personal","givenName":"Jesús","familyName":"Martínez-Gómez","affiliation":["University of California, Berkeley"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-6466-5659","nameIdentifierScheme":"ORCID"}]},{"name":"Song, Micheael","nameType":"Personal","givenName":"Micheael","familyName":"Song","affiliation":["Skyline College"],"nameIdentifiers":[]},{"name":"Tribble, Carrie","nameType":"Personal","givenName":"Carrie","familyName":"Tribble","affiliation":["University of Hawaiʻi at Mānoa"],"nameIdentifiers":[]},{"name":"Kopperud, Bjørn","nameType":"Personal","givenName":"Bjørn","familyName":"Kopperud","affiliation":["Ludwig-Maximilians-Universität München"],"nameIdentifiers":[]},{"name":"Freyman, William","nameType":"Personal","givenName":"William","familyName":"Freyman","affiliation":["23andMe (United States)"],"nameIdentifiers":[]},{"name":"Höhna, Sebastian","nameType":"Personal","givenName":"Sebastian","familyName":"Höhna","affiliation":["Ludwig-Maximilians-Universität München"],"nameIdentifiers":[]},{"name":"Specht, Chelsea","nameType":"Personal","givenName":"Chelsea","familyName":"Specht","affiliation":["Cornell University"],"nameIdentifiers":[]},{"name":"Rothfels, Carl","nameType":"Personal","givenName":"Carl","familyName":"Rothfels","affiliation":["Utah State University"],"nameIdentifiers":[]}],"titles":[{"title":"Commonly used Bayesian diversification methods lead to biologically meaningful differences in branch-specific rates on empirical phylogenies"}],"publisher":"Dryad","container":{},"publicationYear":2025,"subjects":[{"subject":"phylogenetics and evolutionary biology"},{"subject":"Biological science"},{"subject":"Statistical phylogenetics"},{"subject":"FOS: Biological sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Biological sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2023-09-02T00:03:09Z","dateType":"Created"},{"date":"2023-09-08T17:25:14Z","dateType":"Submitted"},{"date":"2025-01-15T00:00:00Z","dateType":"Issued"},{"date":"2025-01-15T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1101/2023.05.17.541228","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1093/evlett/qrad044","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["31151617025 bytes"],"formats":[],"version":"10","rightsList":[{"rights":"Creative Commons Zero v1.0 Universal","rightsUri":"https://creativecommons.org/publicdomain/zero/1.0/legalcode","schemeUri":"https://spdx.org/licenses/","rightsIdentifier":"cc0-1.0","rightsIdentifierScheme":"SPDX"}],"descriptions":[{"description":"Identifying along which lineages shifts in diversification rates occur is\n a central goal of comparative phylogenetics; these shifts may coincide\n with key evolutionary events such as the development of novel\n morphological characters, the acquisition of adaptive traits,\n polyploidization or other structural genomic changes, or dispersal to a\n new habitat and subsequent increase in environmental niche space. However,\n while multiple methods now exist to estimate diversification rates and\n identify shifts using phylogenetic topologies, the appropriate use and\n accuracy of these methods is hotly debated. Here we test whether five\n Bayesian methods—Bayesian Analysis of Macroevolutionary Mixtures (BAMM),\n two implementations of the Lineage-Specific Birth-Death-Shift model (LSBDS\n and PESTO), the approximate Multi-Type Birth-Death model (MTBD;\n implemented in BEAST2), and the cladogenetic diversification rate shift\n model (CLaDS2)—produce comparable results. We apply each of these methods\n to a set of 65 empirical time-calibrated phylogenies and compare\n inferences of speciation rate, extinction rate, and net diversification\n rate. We find that the five methods often infer different speciation,\n extinction, and net-diversification rates. Consequently, these different\n estimates may lead to different interpretations of the macroevolutionary\n dynamics. The different estimates can be attributed to fundamental\n differences among the compared models. Therefore, the inference of shifts\n in diversification rates is strongly method-dependent. We advise\n biologists to apply multiple methods to test the robustness of the\n conclusions or to carefully select the method based on the validity of the\n underlying model assumptions to their particular empirical system.","descriptionType":"Abstract"},{"description":"# Commonly used Bayesian diversification methods lead to biologically\n meaningful differences in branch-specific rates on empirical phylogenies\n ## Overview This directory contains the code and analysis used in the\n paper Martínez-Gómez, Song and Tribble et al. 2023. In short, this study\n tests whether different Bayesian evolutionary models which, aim to\n estimate branch specific shifts in diversification rates on a phylogeny,\n infer similar or different shifts using a set of empirically derived\n phylogenies. In this study we test five different methods, BAMM, PESTO,\n MTBD, LSBDS, and ClaDS2. Please see the corresponding publication in\n [Evolution Letter](https://doi.org/10.1093/evlett/qrad044) for more\n details. This repository contains two main files: **Output.tar.gz** and\n **CompDiv.tar.gz**. The **Output.tar.gz** file contains the outputs from\n running each of the five methods investigated in the study. The\n **CompDiv.tar.gz** file contains files associated with the analysis of the\n outputs of **Output.tar.gz**. If you have any trouble with any aspect of\n this Dryad or publication please email Jesús Martínez-Gómez\n ([martinezg.jesus at gmail.com](mailto:martinezg.jesus@gmail.com)). ## A\n few notes * Each uncompressed file is quite large please have at least\n 200GB to comfortably open both files. * Throughout these repository we\n name of the phylogeny ([Phylogeny_name]) will follow the naming\n convention, first letter of first authors last name, followed by year of\n publication. This convention is from Heno Diaz et al. 2019. See Table S1\n for a key that will provide the corresponding taxonomic group and\n publication reference. The two exception to this naming convention are the\n phylogenies name \"Sample_Primates\" and\n \"Samples_Whale\". These two dataset often used as the example\n phylogeny in tutorials of these models. The corresponding reference for\n these phylogenies are both listed Table S1. * Due to licensing the\n phylogeny files themselves have not been included in this Dryad. You can\n find the phylogenies at the following [github\n repository](https://github.com/mwpennell/henaodiaz_2019_trees). * A number\n of the scripts used in this analysis been rewritten into functions to help\n user compare these analysis on there own. Specifically code associated\n with Markov chain Monte Carlo (MCMC) analysis (e.g., taking burnin),\n calculating summary statistics and plotting rates on trees. A tutorial\n with further explanation of those function can be found on\n [github/Jesusthebotanist/CompDiv_processing_and_plotting](https://github.com/Jesusthebotanist/CompDiv_processing_and_plotting) ## 1. Individual method output **Output.tar.gz**: This folders contains the the output of the analysis (i.e., ClaD2, LSBDS, MTBD, BAMM, PESTO). Each folder corresponds to a phylogeny directory (see note above on naming). Within each folder there will be a set of subdirectories corresponding to each method. To understand the output of each method we recommend you consult the respective method publication/ online resource, we've linked them below for your convenience. While most output files are common file types (e.g., .txt., .csv, .tsv), there are two file types of notes, .log and .tree files. LSBDS and MTBD both produce .log files. These are tab-delimited files that record the the iterations of a MCMC. While they can be opened in a spreadsheet program (e.g., Microsoft Excel, Google Sheet) it is more useful to analyze them in free program [Tracer](https://beast.community/tracer). Lastly, MSBD will produce a [Phylogeny_name]_default.rates.trees file. This file type stores phylogenies in either [Newick format](https://en.wikipedia.org/wiki/Newick_format) or [Nexus format](https://en.wikipedia.org/wiki/Nexus_file), and can generally be viewed with the free program such as [Icytree](https://icytree.org/) or [FigTree](http://tree.bio.ed.ac.uk/software/figtree/). However, in this case we do not recommend this, please see the description of the file type below. Proceeding the methods-specific reference is the description of the structure of **Output.tar.gz**. * BAMM * [BAMM Program Website](http://bamm-project.org/) * [BAMM Publication](https://www.nature.com/articles/ncomms2958) * [BAMMtools R package Publication](https://besjournals.onlinelibrary.wiley.com/doi/full/10.1111/2041-210X.12199) * MTBD * [BEAST2 Program website](https://www.beast2.org/) * [MSBD Program Website](https://taming-the-beast.org/tutorials/MSBD-tutorial/) * [MSBD Publication](https://academic.oup.com/sysbio/article/69/5/973/5762626) * ClaDS2 * [ClaDS2 Program Website](https://github.com/hmorlon/PANDA.jl) * [ClaDS2 Publication](https://www.nature.com/articles/s41559-019-0908-0) * [ClaDS2 implemented in Julia publication](https://academic.oup.com/sysbio/article/71/2/353/6316269) * LSBDS * [Revbayes Program Website](https://revbayes.github.io/) * [LSBDS Program Website](https://revbayes.github.io/tutorials/divrate/branch_specific.html) * [LSBDS Publication](https://www.biorxiv.org/content/10.1101/555805v1) * PESTO * [PESTO Program Website](https://github.com/kopperud/Pesto.jl) * Has yet to be published ### /\\[Phylogeny\\_name] #### /\\[Phylogeny\\_name]/BAMM\\_\\[Phylogeny\\_name]: This folder contains the following * output - Contains the BAMM output files. See [BAMM website](http://bamm-project.org/) for more information * run_info.txt - This file contains the run information on BAMM run. * control_[Phylogeny_name].cr.pr.txt - The BAMM input file. * cost.marg.csv - Branch specific rates on the tree. Generated by the getMarginalBranchRateMatrix() function in BAMMtools. * div.bamm.csv - Mean tip rates generated using getTipRates() function in [BAMMtools](https://doi.org/10.1111/2041-210X.12199) R package. #### /\\[Phylogeny\\_name]/MSBD\\_\\[Phylogeny\\_name]: There are three subdirectories, [Phylogeny_name], [Phylogeny_name]_2, and [Phylogeny_name]_3, these correspond to each of the three independent MCMC chain ran. Within each subdirectory the files are the same. The MSBD analyses, implemented in the program BEAST2, were run on [CIPRES](https://www.phylo.org/) a API specific for evolutionary analysis that computes on the University of California San Diego Super Computer cluster. Please see the the [CIPRES BEAST2](http://www.phylo.org/tools/beast2_xsede.html) for more information. * \\[Phylogeny_name]_default.log - This file contains the general BEAST2 log file. * \\[Phylogeny_name]_default.states.log - This file contains a BEAST2 log specific to MSBD, specifically estimate of the type change rate parameter gamma. See the [MSBD tutorial](https://taming-the-beast.org/tutorials/MSBD-tutorial/). * \\[Phylogeny_name]_default.branches.log - This file contains a BEAST2 log specific to MSBD, of interest this file contains the estimate of the shift for the tip edges. See the [MSBD tutorial](https://taming-the-beast.org/tutorials/MSBD-tutorial/). * \\[Phylogeny_name]_default.rates.trees - This contains the inferred rates of the tip sample in the MCMC. See 'MSBD data Wrangle' code snippit in the compProcessing.Rmd file located in our [github](https://github.com/Jesusthebotanist/CompDiv_processing_and_plotting) for more information. * \\[Phylogeny_name]_default.states.tree - This file records the lambda and mu rates from each sample of the MCMC. The rates are recorded in a annotated Newick file. See 'MSBD data Wrangle' code snippit in the compProcessing.Rmd file located in our [github](https://github.com/Jesusthebotanist/CompDiv_processing_and_plotting) the for more information. * \\[Phylogeny_name]_msbd_rates.csv - This file contains the individual lambda and mu rates sampled in the MCMC, extracted from the annotated newick file stored in [Phylogeny_name]_default.states.tree. * \\[Phylogeny_name]_msbd_rates_netDivOnly.csv - This file contain net-diversification ( net-div = lambda-mu) calculated for each sample of the MCMC, based on the [Phylogeny_name]_msbd_rates.csv file. * infile.xml, infile_altered.xml, infile_altered.xml.states - These three files contain the input files for the BEAST analysis. These included the phylogeny, specification of the MSBD model and MCMC parameters. * STDOUT - This contains the BEAST2 information typically printed to terminal. * STERR - This contains the BEAST2 information typically printed to terminal. * term.txt, start.txt, done.txt,_JOBINFO.TXT,scheduler.conf, \\_scheduler_stderr.txt - Are default CIPRES files regarding the run. Please reference [CIPRES](https://www.phylo.org/) for more information. #### /\\[Phylogeny\\_name]/LSBDS\\_\\[Phylogeny\\_name]: This folder contains a number of output files of the LSBDS model implemented in Revbayes. All files contain \"run[#]\" in there name that corresponds to the independent MCMC chain that was run. Below we explain the file types. * mcmc_LSBDS_[Phylogeny_name].Rev - This is the RevBayes script specificity the model and MCMC. * \\[Phylogeny_name]_LSBDS_rates.log - This contains the diversification rate parameters (i.e., lamba, mu and number shifts) tracked as part of this model. This is the key file used in the manuscript. * \\[Phylogeny_name]_LSBDS_model.log - This contains general parameters tracked by Revbayes including the the model posterior probability of the model and model model likelihood. * The following three files types are Revbayes specific files need to restart a MCMC. One of each is generated per run. For more information see Revbayes website. * \\[Phylogeny_name]_LSBDS__checkpoint_run[#].state * \\[Phylogeny_name]_LSBDS__checkpoint_run[#]_moves.state * \\[Phylogeny_name]_LSBDS__checkpoint_run[#]_mcmc.state * Occasionally one of the LSBDS_[Phylogeny_name folders will contain multiple subdirectories (e.g., \"chain_1\", \"chain_2\" ). This is indicative of MCMCs that were restarted. Since restarted Revbayes MCMC generated a number of new files, we placed these restarted run files in the aforementioned subdirectories. The files created by the restarted are the same as those listed above. #### /\\[Phylogeny\\_name]/ClaDS2\\_\\[Phylogeny\\_name] This folder contains: * \\[Phylogeny_name].RData - This folder contains a R data file that contains all the input and output information of the ClaDS2 run. ### /Bamm\\_Restart This directory contains BAMM outputs for phylogenies that did not converge the first time. The files organization follows the same as BAMM_[Phylogeny_name], see above. ### /PESTO\\_results This directory contains the PESTO output files. Each phylogeny has file [Phylogeny_name].tsv that records the lamda and mu rate estimates. ## 2. Analysis folder The **CompDiv.tar.gz** file contains the organizational structure for the post-method analysis largely done in R. It is organized as follows: ### scripts * compProcessing.Rmd: This is the main Rmarkdown for the analysis * read_newick_string_ex.R: A helper script used by compProcessing.Rmd * pesto_analyses: contains scripts used to run PESTO * MSN_reviewerResponse.R: R scrpit used to generate supplementary Fig. S7 * clads: This contains the scripts used to run ClaDS2 in [Julia](https://julialang.org/). The folders mainly consist of scripts for functions used by panda_scriptR.R. This is the principle script used to run ClaDS2. * panda_scriptR.R - is the main scripted used to generate phylogeny specific scripts to run CLaDS. The remainder of the scripts are small functions used by panda_scriptR * clads_treecode - This folder contains a series of phylogeny specific R script to run CLaDS2. ### compProcessing\\_output: This folder generates outputs and figures created by compProcessing.Rmd. There are multiple directiers and two files located (end of descriptoin) here. #### /convergencesAssessement: This folder contains files that contain information regarding convergence assessment. This information is summarized in Table S1. Importantly * BAMM_convergences.csv - Summarizes convergence for the program BAMM. Some of this information is also found in combined_convergence.csv file. * combined_convergence.csv - a CSV with information summarized in Table S1. * HenaoDiaz_legend.csv - a CSV matching the specific [Phylogeny_name] file with the reference in Henao Diaz 2019. * LSBDS_convergences_combinedOnly.csv - Summarizes convergences for combined MCMC of LSBDS. Some of this information is also found in combined_convergence.csv file. * LSBDS_convergences.csv - Summarizes convergences for individual MCMC chains of LSBDS. Some of this information is also found in combined_convergence.csv. * LSBDS_gelmanCI_of_point_estiamte.csv - Contains point estimates and MCMC convergence assessment statistics from LSBDS. * MSBD_conergences.csv - Summarizes convergence for the program MSBD. Some of this information is also found in combined_convergence.csv file. #### /diversification\\_summary\\_figure: This folder contains information regarding Fig. 2A, Table 1, Tables S2, Fig. S3 and Fig. S2 * AllMethods: Contains information regarding files in supplementary to include all 5 methods, see methods section of publication. * diversification_summary_dataforfigure__LSBDSfast_exceptRevbayes_median.csv - Individual data points used Fig. S4. * contrasts__LSBDSfast_exceptRevbayes_log_median.csv - Contrast with p-values used to plot significance in Fig. S4 * The remainder of the files are plots that of the residuls for each the summary statistic (i.e., averge or variance) for each model parameter investigated in the study (i.e., Speciation, Extinction, Diverisification) * ExcludesLSBDS: Contains information in the main text only focusing on 4/5 methods, specifically excluding LSBDS. * diversification_summary_dataforfigure__LSBDSfast_only_median.csv - Individual data points used Fig. 2A-C. * contrasts__LSBDSfast_only_log_median.csv - Significant contrast with p-values used to plot significance in Fig. 2A-C and Fig. S3. #### /individual\\_phylo\\_results: This information contains summarized posterior distributions * PosteriorSummaryStatistics: This a set of CSVs each corresponding to a phylogeny that summarize the posterior mean, posterior median, MAP, 95% HPD interval, quantile and other relevant information. There will be two files for every phylogeny. The files with names that include \"_LSBDSfast\" contain PESTO values as well. * Note in the compProcessing.Rmd this directory may referred to as \"point_estimates\" #### /MSN\\_summary\\_figure: This folder contains output files for the mean square analysis in Fig. 2B. Note: In compProcessing.Rmd this directory may be refered to as KF_summary_figure. * MSN_gmm_sigcontrastspartial.csv - Significant contrast with p-values corresponding to Fig. 2D-F * MSN_dataforfigurepartial.csv - Individual data points used Fig. 2D-F. * The remaining file are residual plots for the three parameters * lambda_partial_residuals.pdf - Speciation * mu_partial_residuals.pdf - Extinction * div_partial_residuals.pdf - net diversification #### /postburnin\\_Posteriors: This folder contains 4 directories one for each methods that use MCMC (LSBDS, CLaDS2, BAMM, MTBD) each containing the files corresponding to a combined MCMC chain,from the respective runs, and with a burnin removed. For each method, except BAMM, there are two files. The first with the suffix \"comindedPosterior\" this is contains MCMC estimates for speciation and speciation and extinction parameters. The file with suffix \"comindedPosterior_netDivOnly\" contains the parameters net diversification which is the difference between speciation and extinction. Net diversification was calculated for each generation of the the MCMC chain. BAMM only has a file corresponding to \"comindedPosterior_netDivOnly\". The summarized MCMC of speciation and extinction called 'cost.marg.csv' and is found in the respective phylogeny file of the Output.tar.gz folder * BAMM - postburnin posteriors for BAMM * ClaDS2 - postburnin posteriors for ClaDS2 * LSBDS - postburnin posteriors for LSBDS * MSBD - postburnin posteriors for MTBD #### /supplementalFigures: This folder contains one file for plotting Fig. S1 * FigsS1_data.csv - Contains data to plot Fig. S1. #### /uncertainty\\_overlap: This folder contains the materials for generating Fig. S6 and Fig. S8. Note: In comp_div_processing.Rmd this directory may be referred to as uncertainty. * HPDInterval_complete.csv - Data to plot Fig. S8 * HPDInterval_contrast.csv - Significant contrast with p-values used to plot Fig. S8 * HPDInterval_effectSize.csv - Effect size corresponding fo Fig. S8, cited in paper. * OverlapRatio_partial.csv - Data used to plot Fig. S7 #### /Summary\\_statistics\\_LSBDSfast\\_exceptRevbayes.csv: This file contains the MCMC summary statistics combined into a single file for all methods except LSBDS. #### /Summary\\_statistics\\_LSBDSfast\\_only.csv: This file contains the MCMC summary statistics combined into a single CSV for all method.","descriptionType":"TechnicalInfo"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Science Foundation","awardNumber":"DGE-1650441","funderIdentifier":"https://ror.org/021nxhr62","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"National Science 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