{"data":[{"id":"10.7941/d18907","type":"dois","attributes":{"doi":"10.7941/d18907","identifiers":[],"creators":[{"name":"Liebschner, Dorothee","nameType":"Personal","givenName":"Dorothee","familyName":"Liebschner","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-3921-3209","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Current limits on determination of protonation state using neutron macromolecular crystallography"}],"publisher":"Dryad","container":{},"publicationYear":2020,"subjects":[{"subject":"neutron diffraction"},{"subject":"xray-diffraction"}],"contributors":[],"dates":[{"date":"2020-01-14T23:05:03Z","dateType":"Submitted"},{"date":"2020-02-07T00:00:00Z","dateType":"Issued"},{"date":"2020-02-07T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1016/bs.mie.2020.01.008","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["85245 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":"Neutron diffraction is a technique used to locate hydrogen and and\n deuterium atoms at medium data resolution in macromolecular\n structures. Typically, the structures are not determined de novo; instead,\n the structure has been previously determined with X-ray diffraction. The\n Protein Data Bank (PDB) was parsed to find homologue models for all\n neutron structures. The data table lists PDB IDs for\n X-ray structures that are similar to neutron\n models deposited  in the PDB (as of October\n 2019).  ","descriptionType":"Abstract"},{"description":"The\n homologue protein sequences were obtained with the program BLAST. The\n X-ray model with the highest sequence identity and highest resolution\n (cut-offs: minimum sequence identity of 90% and minimum data resolution of\n 2Å) was considered as homologue. The BLAST search\n provided 156 homologues for all 161 neutron models. ","descriptionType":"Methods"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.7941/D18907","contentUrl":null,"metadataVersion":15,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":136,"downloadCount":3,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2020-02-07T16:53:21Z","registered":"2020-02-07T16:53:22Z","published":null,"updated":"2026-03-25T19:30:55Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7941/d1gd1h","type":"dois","attributes":{"doi":"10.7941/d1gd1h","identifiers":[],"creators":[{"name":"Mishra, Umakant","nameType":"Personal","givenName":"Umakant","familyName":"Mishra","affiliation":["Sandia National Laboratories California"],"nameIdentifiers":[]},{"name":"Gautam, Sagar","nameType":"Personal","givenName":"Sagar","familyName":"Gautam","affiliation":["Sandia National Laboratories California"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0828-1631","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Spatial heterogeneity and environmental predictors of permafrost region soil organic carbon stocks"}],"publisher":"Dryad","container":{},"publicationYear":2022,"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":"2022-04-02T14:37:05Z","dateType":"Submitted"},{"date":"2022-05-28T00:00:00Z","dateType":"Issued"},{"date":"2022-05-28T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1126/sciadv.aaz5236","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.6408933","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["13695925712 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":"Large stocks of soil organic carbon (SOC) have accumulated in the Northern\n Hemisphere permafrost region, but their current amounts and future fate\n remain uncertain. By analyzing dataset combining \u0026gt;2700 soil\n profiles with environmental variables in a geospatial framework, we\n generated spatially explicit estimates of permafrost-region SOC stocks,\n quantified spatial heterogeneity, and identified key environmental\n predictors. We estimated that 1014+194−175 Pg C are\n stored in the top 3 m of permafrost region soils. The greatest\n uncertainties occurred in circumpolar toe-slope positions and in flat\n areas of the Tibetan region. We found that soil wetness index and\n elevation are the dominant topographic controllers and surface air\n temperature (circumpolar region) and precipitation (Tibetan region) are\n significant climatic controllers of SOC stocks. Our results provide first\n high-resolution geospatial assessment of permafrost region SOC stocks and\n their relationships with environmental factors, which are crucial for\n modeling the response of permafrost affected soils to changing climate.\n  ","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"funderName":"\n        Office of Biological and Environmental Research of the U.S. Department\n        of Energy*\n      "}],"url":"https://datadryad.org/dataset/doi:10.7941/D1GD1H","contentUrl":null,"metadataVersion":9,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":261,"downloadCount":48,"referenceCount":0,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-05-28T07:45:00Z","registered":"2022-05-28T07:45:01Z","published":null,"updated":"2026-03-13T23:05:47Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7941/d1sp93","type":"dois","attributes":{"doi":"10.7941/d1sp93","identifiers":[],"creators":[{"name":"Sytwu, Katherine","nameType":"Personal","givenName":"Katherine","familyName":"Sytwu","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1125-2943","nameIdentifierScheme":"ORCID"}]},{"name":"Rangel DaCosta, Luis","nameType":"Personal","givenName":"Luis","familyName":"Rangel DaCosta","affiliation":["University of California, Berkeley"],"nameIdentifiers":[]},{"name":"Scott, Mary","nameType":"Personal","givenName":"Mary","familyName":"Scott","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[]}],"titles":[{"title":"Segmented high-resolution transmission electron microscopy images of nanoparticles"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"FOS: Materials engineering","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Materials engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"Transmission electron microscopy","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Nanoparticles","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"}],"contributors":[],"dates":[{"date":"2023-07-11T18:06:01Z","dateType":"Created"},{"date":"2023-07-11T18:34:38Z","dateType":"Submitted"},{"date":"2023-07-31T00:00:00Z","dateType":"Issued"},{"date":"2023-07-31T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"http://arxiv.org/abs/2306.11853","relatedIdentifierType":"URL"},{"relationType":"IsSupplementedBy","relatedIdentifier":"10.18126/z4mr-xwk5","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1093/micmic/ozae001","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["33690074287 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":"A collection of high-resolution transmission electron microscopy (HRTEM)\n images of crystalline nanoparticles on amorphous substrates and their\n corresponding segmentation maps, including 407 raw camera images and\n segmentation maps; as well as 13 curated datasets created from this larger\n repository. Images cover a variety of microscope magnifications\n (0.02-0.042 nm/pixel), electron dosages (80-884 e/Å2), nanoparticle\n diameters (2.2-20 nm), nanoparticle material (Au, Ag, CdSe), and substrate\n background (ultrathin C, SiN), and have been acquired over multiple\n microscope sessions. Segmentation maps were created by a single human\n labeler. Digital images are provided to aid in viewing data. \n Datasets have each been curated by metadata characteristics, including\n acquisition date, microscope magnification, electron dosage, nanoparticle\n material, and nanoparticle diameter. The datasets have been processed from\n raw image data and converted into a format conducive for machine learning\n training, including flat field correction, pixel value standardization,\n and image patching.","descriptionType":"Abstract"},{"description":"TEM samples were created by dropcasting commercially purchased\n nanoparticles on either an ultrathin C or SiN grid. HRTEM images of\n nanoparticles were collected on a TEAM 0.5 aberration-corrected\n transmission electron microscope with a OneView (Gatan) camera at full\n resolution (4096x4096 pixels). Segmentation labels were created by a\n single human labeler using LabelBox. Digital images of the data (under a\n variety of color mapping protocols) were created and uploaded to LabelBox,\n where they were then labeled.  To create datasets,\n images were first selected using metadata criteria (i.e. microscope\n parameters, nanoparticle characteristics), and then processed into a\n dataset. Processing included (in order): 1) Removal of x-ray artifacts. 2)\n Flat-field correction. 3) Image standardization. 4) Dividing into smaller\n patches. 5) Removal of majority-background patches. X-ray artifacts were\n removed by averaging the surrounding pixels of outlier points above a\n certain threshold (1500 counts) above the image mean. For flat field\n correction, the uneven illumination was estimated using iterative weighted\n linear regression to a 2D Bezier basis (n=2, m=2) and divided out. Images\n were then individually standardized (mean=0, std=1). The original\n 4096x4096 pixel images and corresponding labels were then divided into 64\n 512x512 pixel patches, and patches that consisted of mostly substrate were\n removed. For more details, see our code here. ","descriptionType":"Methods"},{"description":"Raw image data are saved under the dm3 format, which can be\n opened by Digital Micrograph (also known as Gatan Microscopy Suite),\n ImageJ/Fiji, or using the ncempy Python package. The raw data are stored\n in folders, where each folder generally corresponds to a single microscope\n session. raw_data_metadata.csv provides the metadata and locations for all\n of the image files.  Datasets and their corresponding\n labels are stored as h5 files, which can be opened using the h5py Python\n package. Image files are stored under the key 'images' and\n segmentation labels are stored under the key 'labels'.\n Processed_datasets_metadata.csv provides the metadata for the processed\n datasets and their associated segmentation label files. For an example of\n opening and using these datasets, see our code here.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"United States Department of Energy","awardNumber":"DE-AC02-05CH11231","funderIdentifier":"https://ror.org/01bj3aw27","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7941/D1SP93","contentUrl":null,"metadataVersion":7,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":935,"downloadCount":324,"referenceCount":1,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-08-01T05:22:47Z","registered":"2023-08-01T05:22:48Z","published":null,"updated":"2026-01-28T14:41:08Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7941/d1xc97","type":"dois","attributes":{"doi":"10.7941/d1xc97","identifiers":[],"creators":[{"name":"Bandstra, Mark","nameType":"Personal","givenName":"Mark","familyName":"Bandstra","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6403-7895","nameIdentifierScheme":"ORCID"}]},{"name":"Ghawaly, James","nameType":"Personal","givenName":"James","familyName":"Ghawaly","affiliation":["Oak Ridge National Laboratory"],"nameIdentifiers":[]},{"name":"Peplow, Douglas","nameType":"Personal","givenName":"Douglas","familyName":"Peplow","affiliation":["Oak Ridge National Laboratory"],"nameIdentifiers":[]},{"name":"Archer, Daniel","nameType":"Personal","givenName":"Daniel","familyName":"Archer","affiliation":["Oak Ridge National Laboratory"],"nameIdentifiers":[]},{"name":"Prins, Nicholas","nameType":"Personal","givenName":"Nicholas","familyName":"Prins","affiliation":["Oak Ridge National Laboratory"],"nameIdentifiers":[]},{"name":"Joshi, Tenzing","nameType":"Personal","givenName":"Tenzing","familyName":"Joshi","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[]},{"name":"Curtis, Joseph","nameType":"Personal","givenName":"Joseph","familyName":"Curtis","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[]},{"name":"Jones, Chandler","nameType":"Personal","givenName":"Chandler","familyName":"Jones","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[]},{"name":"Quiter, Brian","nameType":"Personal","givenName":"Brian","familyName":"Quiter","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-7001-7455","nameIdentifierScheme":"ORCID"}]},{"name":"Nachtsheim, Abigael","nameType":"Personal","givenName":"Abigael","familyName":"Nachtsheim","affiliation":["Los Alamos National Laboratory"],"nameIdentifiers":[]}],"titles":[{"title":"Synthetic urban gamma-ray spectra for training spectral detection and identification models"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[{"subject":"Gamma radiation","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Radiation detection"},{"subject":"gamma-ray detection"},{"subject":"gamma-ray spectroscopy"},{"subject":"Machine learning","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"FOS: Physical sciences","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Physical sciences","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2023-05-28T03:58:13Z","dateType":"Submitted"},{"date":"2023-05-30T00:00:00Z","dateType":"Issued"},{"date":"2023-05-30T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsDerivedFrom","relatedIdentifier":"https://gitlab.com/lbl-anp/radai/radai","relatedIdentifierType":"URL"},{"relationType":"IsDerivedFrom","relatedIdentifier":"https://gitlab.com/lbl-anp/radai/explanations","relatedIdentifierType":"URL"},{"relationType":"IsCitedBy","relatedIdentifier":"https://www.osti.gov/biblio/1897855","relatedIdentifierType":"URL"},{"relationType":"IsCitedBy","relatedIdentifier":"https://www.osti.gov/biblio/1897851","relatedIdentifierType":"URL"}],"relatedItems":[],"sizes":["2322097558 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":"This dataset contains training, validation, and testing data that consist\n of individual gamma-ray spectra from a synthetic urban radiological\n dataset. The spectra were generated by the Radiological Detection and\n Identification (RADAI) project and are from a simulated 2x4x16\"\n NaI(Tl) detector traveling down a street in a simulated urban area. The\n background consists of realistic benchmarked terrestrial (K-40, U-238\n series, Th-232 series), fallout (Cs-137), rain (Pb-214 and Bi-214), and\n cosmic gamma-ray events. The 24 anomalous sources are simulated point-like\n sources of various types, including enhanced naturally occurring\n radioactive material (NORM), medical isotopes, industrial isotopes, and\n special nuclear material (SNM). All simulations are performed in 3-D, so\n the effects of scattering from nearby objects and shielding by clutter are\n all included. The dataset is prepared so that all sources are encountered\n at a number of different locations and at a wide variety of strengths.","descriptionType":"Abstract"},{"description":"The dataset consists of individual gamma-ray spectra generated\n from a synthetic urban model by the Radiological Detection and\n Identification (RADAI) project. The spectra are not continuous in time,\n and some are background-only while others contain a single anomalous\n source of any of 24 kinds. The data were generated by random selections of\n spectra from over 100 hours of data. Some high signal-to-noise (SNR)\n source encounters were in the dataset, and their strengths were randomly\n downsampled using binomial selection to cover SNR ranges of orders of\n magnitude for each source. The tools used to generate this dataset from\n the larger dataset are contained in the RADAI code repository (https://gitlab.com/lbl-anp/radai/radai).","descriptionType":"Methods"},{"description":"The dataset is stored in a single HDF5 file. Global attributes\n such as the spectral bin edges, integration time, and spectrum label names\n are provided as HDF5 datasets and attributes, and the training,\n validation, and testing data are stored in data groups. Each set contains\n the spectra (\u003cstrong\u003eX\u003c/strong\u003e), the same spectra with\n background-tagged events only (\u003cstrong\u003eX_b\u003c/strong\u003e), the same\n spectra with source-tagged events only (\u003cstrong\u003eX_s\u003c/strong\u003e),\n the true source labels (\u003cstrong\u003ey_label\u003c/strong\u003e), the\n fractional amount of gross counts from each source type\n (\u003cstrong\u003ey_s_over_bs\u003c/strong\u003e), and the gross-counts\n signal-to-noise ratio (SNR) of the sources if present\n (\u003cstrong\u003ey_snr\u003c/strong\u003e). The latter two datasets\n (\u003cstrong\u003ey_s_over_bs\u003c/strong\u003e and\n \u003cstrong\u003ey_snr\u003c/strong\u003e) are calculated from the other datasets\n and provided for convenience.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"National Nuclear Security Administration","awardNumber":"LB21-ML-Rad Nuc Datasets-PD3SS","funderIdentifier":"https://ror.org/03sk1we31","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7941/D1XC97","contentUrl":null,"metadataVersion":6,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":532,"downloadCount":123,"referenceCount":0,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-05-30T17:47:22Z","registered":"2023-05-30T17:47:22Z","published":null,"updated":"2026-01-28T14:20:17Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7941/d1k62r","type":"dois","attributes":{"doi":"10.7941/d1k62r","identifiers":[],"creators":[{"name":"Baral, Nawa Raj","nameType":"Personal","givenName":"Nawa Raj","familyName":"Baral","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0942-9183","nameIdentifierScheme":"ORCID"}]},{"name":"Mishra, Shruti","nameType":"Personal","givenName":"Shruti","familyName":"Mishra","affiliation":["Sandia National Laboratories"],"nameIdentifiers":[]},{"name":"George, Anthe","nameType":"Personal","givenName":"Anthe","familyName":"George","affiliation":["Sandia National Laboratories"],"nameIdentifiers":[]},{"name":"Gautam, Sagar","nameType":"Personal","givenName":"Sagar","familyName":"Gautam","affiliation":["Sandia National Laboratories"],"nameIdentifiers":[]},{"name":"Mishra, Umakant","nameType":"Personal","givenName":"Umakant","familyName":"Mishra","affiliation":["Sandia National Laboratories"],"nameIdentifiers":[]},{"name":"Scown, Corinne","nameType":"Personal","givenName":"Corinne","familyName":"Scown","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-2078-1126","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Data for: Multifunctional landscapes for dedicated bioenergy crops lead to low-carbon market-competitive biofuels"}],"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":"2023-06-16T21:11:57Z","dateType":"Submitted"},{"date":"2023-06-20T00:00:00Z","dateType":"Issued"},{"date":"2023-06-20T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1016/j.rser.2022.112857","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.6819232","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["17202424 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":"Switchgrass is a promising feedstock for cellulosic biorefineries, due to\n its ability to maintain comparatively high biomass yields across a wide\n range of soil and climatic conditions. However, there is an incomplete\n understanding of the economic and environmental tradeoffs associated with\n cultivating switchgrass on low-productivity land for conversion to\n biofuels. This study surveys prior literature and demonstrates a new\n integrated assessment framework, including agroecological, ecosystem\n services valuation, technoeconomic, and life-cycle assessment models, to\n quantify and contextualize the economic and environmental impacts of\n switchgrass cultivation on marginal land with downstream conversion to\n biofuels. Monetizing and incorporating the value of ecosystem services,\n such as improved water quality benefits from nitrate and sediment\n reductions, climate change mitigation benefits from CO2 emission\n reduction, and recreational and pollination benefits from increased\n biodiversity, the modeled multifunctional landscape reduces the ethanol\n production cost by 33.3–58.9 cents/L-gasoline-equivalent ($1.3–2.2/gge).\n Planting switchgrass in low-productivity land improves soil health,\n resulting in the carbon footprint reduction credit of 12.8–20.2 gCO2e/MJ.\n For an improved switchgrass-to-ethanol conversion pathway with the maximum\n benefits from ecosystem services, the minimum ethanol selling price and\n carbon footprint of ethanol, respectively, could reach to 31\n cents/L-gasoline-equivalent (47% reduction relative to average gasoline\n price) and 3 gCO2e/MJ (97% reduction relative to gasoline). This\n low-carbon renewable ethanol leads to substantial State and/or Federal\n policy incentives (~$1/L-gasoline-equivalent) providing a large benefit to\n biorefinery operators, farmers, and the public as a whole.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"United States Department of Energy","awardNumber":"DE-AC02-05CH11231","funderIdentifier":"https://ror.org/01bj3aw27","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7941/D1K62R","contentUrl":null,"metadataVersion":6,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":144,"downloadCount":11,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-06-21T00:10:09Z","registered":"2023-06-21T00:10:10Z","published":null,"updated":"2026-01-28T10:44:00Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7941/d1432p","type":"dois","attributes":{"doi":"10.7941/d1432p","identifiers":[],"creators":[{"name":"Gautam, Sagar","nameType":"Personal","givenName":"Sagar","familyName":"Gautam","affiliation":["Sandia National Laboratories California"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0828-1631","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Continental United States may lose 1.8 petagrams of soil organic carbon under climate change by 2100"}],"publisher":"Dryad","container":{},"publicationYear":2022,"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":"earth system model"},{"subject":"environmental factors"},{"subject":"climate"},{"subject":"future projection"},{"subject":"soil organic carbon"}],"contributors":[],"dates":[{"date":"2022-02-22T22:34:05Z","dateType":"Submitted"},{"date":"2022-03-31T00:00:00Z","dateType":"Issued"},{"date":"2022-03-31T00:00:00Z","dateType":"Available"},{"date":"2022-09-12T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/geb.13489","relatedIdentifierType":"DOI"},{"relationType":"IsDerivedFrom","relatedIdentifier":"10.5281/zenodo.6231005","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["9336126449 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":"Aims: High-resolution information on soils’ vulnerability to\n climate-induced soil organic carbon (SOC) loss can enable environmental\n scientists, land managers, and policy makers to develop targeted\n mitigation strategies. This study aims to estimate baseline and decadal\n changes in continental US surface SOC stocks under future emission\n scenarios.   Location: Continental United States   Time\n Period: 2014-2100   Results: Baseline SOC projections from ML\n approaches captured more than 50% of variability in SOC observations,\n whereas ESMs represented only 6-16% of observed SOC variability. ML\n estimates showed a mean total loss of 1.8 Pg C from US surface soils under\n the high-emission scenario by 2100, whereas ESMs showed no significant\n change in SOC stocks with wide variation among ESMs. Both ML and ESM\n predictions agree on the direction of SOC change (net emissions or\n sequestration) across 46%–51% of continental US land area. These\n differences are attributable to the high-resolution site-specific data\n used in ML model compared to the relatively coarse grid represented in\n CMIP6 ESMs.   Main conclusions: Our high-resolution estimates of\n baseline SOC stocks, identification of key environmental controllers, and\n projection of SOC changes from US land cover types under future climate\n scenarios suggest the need for high-resolution simulations of SOC in ESMs\n to represent the heterogeneity of SOC. We found that the SOC change is\n sensitive to key soil related factors (e.g. soil drainage and soil order)\n that have not been historically considered as input parameters in ESMs,\n because currently more than 95% variability in the SOC of CMIP6 ESMs are\n controlled by net primary productivity, temperature, and precipitation.\n Using additional environmental factors to estimate the baseline SOC stocks\n and predict the future trajectory of SOC change can provide more accurate\n results.","descriptionType":"Abstract"},{"description":"We used recent SOC field observations (n = 6,213 sites),\n environmental factors (n = 32), and an ensemble machine learning (ML)\n approach to estimate baseline SOC stocks in surface soils across the\n continental United States at 100-m spatial resolution, and decadal changes\n under the projected climate scenarios of Coupled Model Intercomparison\n Project Phase Six (CMIP6) Earth System Models (ESMs).","descriptionType":"Methods"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.7941/D1432P","contentUrl":null,"metadataVersion":11,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":188,"downloadCount":50,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-03-31T19:08:22Z","registered":"2022-03-31T19:08:24Z","published":null,"updated":"2026-01-28T08:47:32Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7941/d17w62","type":"dois","attributes":{"doi":"10.7941/d17w62","identifiers":[],"creators":[{"name":"Gao, Xiulin","nameType":"Personal","givenName":"Xiulin","familyName":"Gao","affiliation":["Texas Tech University"],"nameIdentifiers":[]},{"name":"Schwilk, Dylan","nameType":"Personal","givenName":"Dylan","familyName":"Schwilk","affiliation":["Texas Tech University"],"nameIdentifiers":[]}],"titles":[{"title":"Burn hot or tolerate trees: flammability decreases with shade tolerance in grasses"}],"publisher":"Dryad","container":{},"publicationYear":2022,"subjects":[{"subject":"fire"},{"subject":"grass"},{"subject":"flammability"},{"subject":"shade tolerance"},{"subject":"post-fire response"},{"subject":"ecological strategy"},{"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":"2022-01-12T18:42:05Z","dateType":"Submitted"},{"date":"2022-01-25T00:00:00Z","dateType":"Issued"},{"date":"2022-01-25T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/oik.08930","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["11580125 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":"In ecosystems where trees and grasses coexist, some grass species are\n found only in open habitats and others persist under trees. The\n persistence of shade intolerant grasses in ecosystems such as open\n woodlands and savannas depends on recurrent fires to open the tree canopy.\n Therefore, grasses that depend on open sites might benefit from high\n flammability. We tested if shade intolerant grasses are more flammable\n than shade tolerant grasses and if flammability differences affected\n post-fire grass growth. We examined the relationship between shade\n tolerance and flammability by determining individual-level flammability\n and species shade tolerance of 17 grass species. We also measured grass\n traits to determine trait effects on flammability and the post-fire\n response. Grass species varied in flammability, mainly in the amount of\n heat produced during burning. Shade tolerant species produced less heat at\n 50 cm above the ground. Biomass and live fuel moisture had the greatest\n effects on heat release. However, the negative effect of live fuel\n moisture on heat release at the soil surface was weakened in plants with\n high specific leaf area. In addition, grass bulk density negatively\n influenced heat release at 50 cm height. Heat release at the soil surface\n negatively influenced post-fire growth. However, the influences of soil\n heating and species-specific traits on individual survival were more\n complex with 2- and 3-way interactions. Shade tolerance was negatively\n correlated with a major axis of flammability variation: shade tolerant\n grasses produced less heat where that heat could damage tree boles. Such\n heterogeneity in grass flammability may help maintain the tree-grass\n mixture in natural plant communities. If shade tolerant grasses near trees\n cause less fire damage to woody plants, especially tree saplings, this may\n weaken positive grass-fire feedbacks and thus aid the long-term\n coexistence of trees and grasses.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.7941/D17W62","contentUrl":null,"metadataVersion":9,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":124,"downloadCount":10,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-01-26T03:40:28Z","registered":"2022-01-26T03:40:30Z","published":null,"updated":"2026-01-28T08:13:07Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7941/d1n33q","type":"dois","attributes":{"doi":"10.7941/d1n33q","identifiers":[],"creators":[{"name":"Hong, Tianzhen","nameType":"Personal","givenName":"Tianzhen","familyName":"Hong","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-1886-9137","nameIdentifierScheme":"ORCID"}]},{"name":"Luo, Na","nameType":"Personal","givenName":"Na","familyName":"Luo","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[]},{"name":"Blum, David","nameType":"Personal","givenName":"David","familyName":"Blum","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[]},{"name":"Wang, Zhe","nameType":"Personal","givenName":"Zhe","familyName":"Wang","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[]}],"titles":[{"title":"A three-year building operational performance dataset for informing energy efficiency"}],"publisher":"Dryad","container":{},"publicationYear":2022,"subjects":[{"subject":"Building Energy Efficiency"}],"contributors":[],"dates":[{"date":"2022-01-18T16:50:06Z","dateType":"Submitted"},{"date":"2022-02-02T00:00:00Z","dateType":"Issued"},{"date":"2022-02-02T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1038/s41597-022-01257-x","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["263391386 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":"This dataset was curated from an office building constructed in 2015 in\n Berkeley, California, which includes whole-building and end-use energy\n consumption, HVAC system operating conditions, indoor and outdoor\n environmental parameters, and occupant counts. The data was collected in\n three years from more than 300 sensors and meters for two office floors\n (each 2,325 m2) of the building. A three-step data curation strategy is\n applied to transform the raw data into the research-grade data: (1)\n cleaning the raw data to detect and adjust the outlier values and fill the\n data gaps; (2) creating the metadata model of the building systems and\n data points using the Brick schema; (3) describing the metadata of the\n dataset using a semantic JSON schema. This dataset can be used for various\n types of applications, including building energy benchmarking, load shape\n analysis, energy prediction, occupancy prediction and analytics, and HVAC\n controls to improve understanding and efficiency of building operations\n for reducing energy use, energy costs, and carbon emissions.","descriptionType":"Abstract"},{"description":"This dataset includes data of whole-building and end-use energy\n consumption, HVAC system operating conditions, indoor and outdoor\n environmental parameters, and occupant counts. The data was collected in\n three years from more than 300 sensors and meters for two office floors of\n the building. A three-step data curation strategy is applied to transform\n the raw data into the research-grade data: (1) cleaning the raw data to\n detect and adjust the outlier values and fill the data gaps; (2) creating\n the metadata model of the building systems and data points using the Brick\n schema; (3) describing the metadata of the dataset using a semantic JSON\n schema.","descriptionType":"Methods"},{"description":"The time-series data is in CSV format and has a size of 2.38 GB.\n A more detailed note about the data cleaning strategy is available at the\n dataset’s GitHub page - https://github.com/LBNL-ETA/Data-Cleaning. We\n recommend users to explore the metadata of equipment and sensors in the\n Brick model by using the Brick TTL viewer. Users can obtain the high-level\n metadata about the building and dataset in the metadata JSON\n file.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"United States Department of Energy","awardNumber":"DE-AC02-05CH11231","funderIdentifier":"https://ror.org/01bj3aw27","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7941/D1N33Q","contentUrl":null,"metadataVersion":10,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":7557,"downloadCount":2748,"referenceCount":0,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2022-02-02T22:34:29Z","registered":"2022-02-02T22:34:30Z","published":null,"updated":"2026-01-28T06:58:36Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7941/d1wk8m","type":"dois","attributes":{"doi":"10.7941/d1wk8m","identifiers":[],"creators":[{"name":"Less, Brennan","nameType":"Personal","givenName":"Brennan","familyName":"Less","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-6134-3926","nameIdentifierScheme":"ORCID"}]},{"name":"Walker, Iain","nameType":"Personal","givenName":"Iain","familyName":"Walker","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9667-1797","nameIdentifierScheme":"ORCID"}]},{"name":"Lorenzetti, David","nameType":"Personal","givenName":"David","familyName":"Lorenzetti","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[]},{"name":"Sohn, Michael","nameType":"Personal","givenName":"Michael","familyName":"Sohn","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[]}],"titles":[{"title":"SVACH - Development of advanced smart ventilation controls for residential applications"}],"publisher":"Dryad","container":{},"publicationYear":2021,"subjects":[{"subject":"FOS: Engineering and technology","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Engineering and technology","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"},{"subject":"mechanical ventilation"},{"subject":"indoor air quality"},{"subject":"smart ventilation controls"},{"subject":"contam"},{"subject":"energyplus"},{"subject":"cosimulation"}],"contributors":[],"dates":[{"date":"2021-08-25T19:24:02Z","dateType":"Submitted"},{"date":"2021-09-01T00:00:00Z","dateType":"Issued"},{"date":"2021-09-01T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsSourceOf","relatedIdentifier":"https://bitbucket.org/berkeleylab/eetd_svach/src/master/","relatedIdentifierType":"URL"},{"relationType":"IsCitedBy","relatedIdentifier":"https://escholarship.org/uc/item/5nk6t61r","relatedIdentifierType":"URL"},{"relationType":"IsSourceOf","relatedIdentifier":"https://svach.lbl.gov/","relatedIdentifierType":"URL"},{"relationType":"IsCitedBy","relatedIdentifier":"10.3390/en14175257","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["191455382 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":"This study examined the use of zoned ventilation systems using a coupled\n CONTAM/EnergyPlus model for new California dwellings. Several smart\n control strategies were developed with a target of halving\n ventilation-related energy use, largely through reducing dwelling\n ventilation rates based on zone occupancy. The controls were evaluated\n based on the annual energy consumption relative to continuously operating\n non-zoned, code-compliant mechanical ventilation systems. The systems were\n also evaluated from an indoor air quality perspective using the\n equivalency approach, where the annual personal concentration of a\n contaminant for a control strategy is compared to the personal\n concentration that would have occurred using a continuously operating,\n non-zoned system. Individual occupant personal concentrations were\n calculated for the following contaminants of concern: moisture, CO2,\n particles and a generic contaminant. Zonal controls that saved energy by\n reducing outside airflow achieved typical reductions in\n ventilation-related energy of 10 to 30%, compared to the 7% savings from\n the unzoned control. However, this was at the expense of increased\n personal concentrations for some contaminants in most cases. In addition,\n care is required in the design and evaluation of zonal controls, because\n control strategies may reduce exposure to some contaminants while\n increasing exposure to others.","descriptionType":"Abstract"},{"description":"This data file represents summary results for 2,967 annual\n co-simulations of EnergyPlus and CONTAM at one-minute time-steps,\n including data outputs addressing zone and whole-dwelling ventilation\n airflows (fan flows, infiltration, exfiltration), zone and personal\n contaminant concentrations (PM\u003csub\u003e2.5\u003c/sub\u003e,\n CO\u003csub\u003e2\u003c/sub\u003e, Generic and water vapor), and energy use. Each\n simulation represents a unique combination of California climate zone (CEC\n CZ 1, 3, 10 and 16), building prototype (apartment, 1-story and 2-story),\n envelope air leakage (0.6, 2 and 3 ACH\u003csub\u003e50\u003c/sub\u003e),\n ventilation fan type/configuration (multi- and single-point configurations\n of exhaust, supply and balanced fans), and ventilation control type (10\n smart control types and 2 baseline types). Detailed descriptions of the\n simulation effort can be found in the project\n final report. Annual, one-minute time-series outputs were\n recorded from EnergyPlus, and each time-series file was post-processed to\n produce a one-line summary of the results. All data pre- and\n post-processing is characterized in detail at the project bitbucket\n repository available here.     ","descriptionType":"Methods"},{"description":"Three files are included in this dataset are:\n \"SVACH_zonalSims_allCases.csv\" is the data\n file containing annual summary output values for each of 2,967\n simulations. Each simulation is represented by a single row, including the\n case data and all corresponding reference data (see column descriptions in\n \"dataFileColumnDescriptions.xlsx\" and\n in \"README.txt\"). \n \"dataFileColumnDescriptions.xlsx\" contains a field\n representing each column in the data file (n=2,901), along with a variable\n type (input/output) and a human-readable description of the\n variable. \"README.txt\" describes additional\n details about column names, including alias values in the column names\n spreadsheet. ","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"United States Department of Energy","awardNumber":"DE-AC02-05CH11231","funderIdentifier":"https://ror.org/01bj3aw27","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"California Energy Commission","awardNumber":"EPC-15-037","funderIdentifier":"https://ror.org/05eaakg28","funderIdentifierType":"ROR"},{"funderName":"Aereco SA*","awardNumber":"FP00003428"}],"url":"https://datadryad.org/dataset/doi:10.7941/D1WK8M","contentUrl":null,"metadataVersion":12,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":163,"downloadCount":5,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2021-09-01T19:20:38Z","registered":"2021-09-01T19:20:39Z","published":null,"updated":"2026-01-28T06:22:18Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7941/d1hg9j","type":"dois","attributes":{"doi":"10.7941/d1hg9j","identifiers":[],"creators":[{"name":"Chan, Wanyu","nameType":"Personal","givenName":"Wanyu","familyName":"Chan","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0361-0085","nameIdentifierScheme":"ORCID"}]},{"name":"Tang, Hao","nameType":"Personal","givenName":"Hao","familyName":"Tang","affiliation":["Chongqing University"],"nameIdentifiers":[]},{"name":"Sohn, Michael","nameType":"Personal","givenName":"Michael","familyName":"Sohn","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[]}],"titles":[{"title":"Automating the interpretation of PM2.5 time-resolved measurements using a data-driven approach"}],"publisher":"Dryad","container":{},"publicationYear":2020,"subjects":[{"subject":"FOS: Environmental engineering","schemeUri":"https://web-archive.oecd.org/2012-06-15/138575-38235147.pdf","subjectScheme":"fos"},{"subject":"FOS: Environmental engineering","schemeUri":"http://www.oecd.org/science/inno/38235147.pdf","subjectScheme":"Fields of Science and Technology (FOS)"}],"contributors":[],"dates":[{"date":"2020-12-07T17:49:03Z","dateType":"Submitted"},{"date":"2020-12-10T00:00:00Z","dateType":"Issued"},{"date":"2020-12-10T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/ina.12780","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["74211719 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":"The rapid development of automated measurement equipment enables\n researchers to collect greater quantities of time-resolved data from\n indoor and outdoor environments. The interpretation of the resulting data\n can be a time-consuming effort. This dataset contains the R code and\n time-resolved indoor and outdoor PM2.5 data to illustrate a machine\n learning approach called Random Forest (RF). The method is used\n to study a dataset of 836 emission events that occurred over a\n two-week period, each in 18 apartments in California. The resulting RF\n model is applied to analyze PM2.5 data of an entirely separate dataset\n collected from 65 new homes in California. The RF model identifies 442\n indoor emission events, with a few misidentifications. In the accompanying\n paper, we present the RH model development and evaluate\n its performance as the sample size and source vary. We discuss the\n characteristics of the dataset that tended to help the source\n identification and why. For example, we show that data from many events\n and from different apartments are essential for the model to be suitable\n for analyzing the new separate dataset. We also show that longitudinal\n data appears to be more helpful than the time frequency of measurements in\n a given apartment.","descriptionType":"Abstract"},{"description":"The ‘Dataset’ directory contains two datasets of indoor and\n outdoor PM2.5 data that were previously collected from field studies\n conducted by our research group at the Lawrence Berkeley National\n Laboratory. Dataset 1 contains PM2.5 data that were collected by Noris et\n al. (2013) from two-weeks of monitoring in 18 low-income apartments in\n California. Dataset 1 is used as the training dataset, where the indoor PM\n emission events were previously analyzed by Chan et al. (2018) using a\n rule-based method. Dataset 2 contains PM2.5 data that were collected by\n Singer et al. (2020) from 65 new California single-family homes for one\n week each. The 18 apartments in Dataset 1 were\n identified by building number (‘Bldg’ = 1, 2, or 3), apartment number\n (‘Apt’ = 1 to 6), and whether the data was collected before (‘Period = 1)\n or after (‘Period = 2’) retrofit. The 65 single-family homes in Dataset 2\n were identified by building number (‘Bldg’). An adjustment factor of 1.23\n was applied to the indoor PM2.5 concentration “data_value_raw” measured\n using a photometer for Dataset 2, see Singer et al. (2020) for more\n details. The PM2.5 concentrations in Dataset 1 already incorporated an\n adjustment factor, see Chan et al. (2018) for more details.\n Both datasets were processed to calculate the following\n “features”, some of which were used in the Random Forest model.\n Indoor_value is the indoor\n PM\u003csub\u003e2.5\u003c/sub\u003e concentration\n (ug/m\u003csup\u003e3\u003c/sup\u003e) Back_diff_x, where x =\n 1, 2, 3, 4, 5, and 10, corresponding to the backward-difference in indoor\n PM\u003csub\u003e2.5\u003c/sub\u003e (ug/m\u003csup\u003e3\u003c/sup\u003e) in relation to\n the value at x timestep before it. Front_diff_x,\n where x = 1, 2, 3, 4, 5, and 10, corresponding to the frontward-difference\n in indoor PM\u003csub\u003e2.5\u003c/sub\u003e (ug/m\u003csup\u003e3\u003c/sup\u003e) in\n relation to the value at x timestep after it.\n Variance_y_min, where y = 4, 8, 12, and 16, corresponding to the\n standard deviation of y minutes of indoor\n PM\u003csub\u003e2.5\u003c/sub\u003e (ug/m\u003csup\u003e3\u003c/sup\u003e) centering at\n the current timestep. Outdoor_value is the\n outdoor PM\u003csub\u003e2.5\u003c/sub\u003e concentration\n (ug/m\u003csup\u003e3\u003c/sup\u003e) Outdoor_hourly is the\n 1-hour average outdoor\n PM\u003csub\u003e2.5\u003c/sub\u003e (ug/m\u003csup\u003e3\u003c/sup\u003e) calculated\n using data from the previous hour ending at the current\n timestep. Extreme_point is a data flag: 1 means the\n current timestep of indoor PM\u003csub\u003e2.5\u003c/sub\u003e is a local minimum\n or maximum, 0 = no Extreme_forward is the indoor\n PM\u003csub\u003e2.5\u003c/sub\u003e concentration\n (ug/m\u003csup\u003e3\u003c/sup\u003e) at the next local minimum or maximum\n datapoint Extreme_backward is the indoor\n PM\u003csub\u003e2.5\u003c/sub\u003e concentration\n (ug/m\u003csup\u003e3\u003c/sup\u003e) at the previous local minimum or maximum\n datapoint Extreme_diff = Extreme_forward\n Extreme_backward, is the difference in indoor\n PM\u003csub\u003e2.5\u003c/sub\u003e (ug/m\u003csup\u003e3\u003c/sup\u003e) between two\n local minimum or maximum datapoint\n Extreme_forward_outdoor is the outdoor\n PM\u003csub\u003e2.5\u003c/sub\u003e (ug/m\u003csup\u003e3\u003c/sup\u003e) at the next\n local minimum or maximum datapoint\n Extreme_backward_outdoor is the\n outdoor PM\u003csub\u003e2.5\u003c/sub\u003e (ug/m\u003csup\u003e3\u003c/sup\u003e) at the\n previous local minimum or maximum datapoint \n In addition to the above, the training Dataset 1 also contains\n the following data flags that were determined previously by Chan et al.\n (2018) using the rule-based method. \n Emission is a data flag indicating whether the current datapoint\n was part of an indoor emission event: 1 = yes, 0 = no\n Backward_E is a data flag indicating whether the pervious local\n minimum or maximum datapoint was part of an indoor emission event: 1 =\n yes, 0 = no Forward_E is a data flag indicating\n whether the next local minimum or maximum datapoint was part of an indoor\n emission event: 1 = yes, 0 = no Decay is a data flag\n indicating whether the current datapoint was part of a decay period\n following an indoor emission: 1 = yes, 0 = no \n The ‘Dataset’ directory contains a third input file\n ‘Dataset2_Volume.csv’. The file provides data about the approximate\n well-mixed air volume of the 65 single-family homes, which is needed to\n compute indoor PM\u003csub\u003e2.5\u003c/sub\u003e emission rates for Dataset 2.\n The well-mixed air volume (ft\u003csup\u003e3\u003c/sup\u003e) is computed by\n ‘FloorArea_sqft’ x ‘CeilingHgt_ft x ‘Factor’. ‘Factor’ is the % of the\n house air volume in the vicinity of the photometer used to measure indoor\n PM\u003csub\u003e2.5\u003c/sub\u003e, where\n the PM\u003csub\u003e2.5\u003c/sub\u003e concentration was assumed to be\n well-mixed during the indoor emission event and decay period.","descriptionType":"Methods"},{"description":"In addition to the two PM2.5 datasets and the computed features,\n there are two additional directories: ‘Code’ and ‘Results.’\n ‘Code’ contains four R scripts that build a Random Forest model\n using Dataset 1 as the training dataset, then apply the model to Dataset 2\n and compute statistics of indoor PM\u003csub\u003e2.5\u003c/sub\u003e emission\n events. ‘Results’ contains outputs from the R scripts.\n 'Code' files are: \n ‘1-Building random forest using dataset1.R’\n reads ‘Training_Dataset1.csv’, builds Random Forest models, and saves the\n results in two R objects files: ‘Emission.RData’ and\n ‘Decay.RData’. ‘2-Apply resulting model to\n dataset2.R’ reads ‘Dataset2.csv’, and applies the Random Forest\n models from two R objects files: ‘Emission.RData’ and ‘Decay.RData’, to\n identify indoor PM\u003csub\u003e2.5\u003c/sub\u003e emission events and decay\n periods. The results are output to ‘Dataset2_analysis.csv’.\n ‘3-Dataset 2 summary.R’ reads\n ‘Dataset2_analysis.csv’ and outputs indoor\n PM\u003csub\u003e2.5\u003c/sub\u003e emission events (‘Sum_E.csv’) and decay\n periods (‘Sum_D.csv’) identified by the Random Forest models.\n ‘Selected_events.csv’ are a subset of emission events meeting the criteria\n outlined in the accompanying paper.\n ‘4-Emission rate calculation.R’ computes\n emission rates for the indoor PM\u003csub\u003e2.5\u003c/sub\u003e events and\n saves the results in two R objects files: ‘Pre_E.RData’ and ‘Pre_D.RData’.\n Summary statistics of emission rates and other event characteristics are\n written in ‘Dataset2_result.csv’. Users of\n the codes are reminded to change the file paths to the working directory\n prior to running R scripts. R version 3.6.3 was used to produce the\n results. Two R libraries are needed: ‘lubridate’ version 1.7.8 and\n ‘randomForest’ version 4.6-14.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"United States Department of Energy","awardNumber":"DE-AC02-05CH11231","funderIdentifier":"https://ror.org/01bj3aw27","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"California Energy Commission","awardNumber":"PIR-16-012","funderIdentifier":"https://ror.org/05eaakg28","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7941/D1HG9J","contentUrl":null,"metadataVersion":11,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":314,"downloadCount":25,"referenceCount":0,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2020-12-10T15:19:56Z","registered":"2020-12-10T15:19:57Z","published":null,"updated":"2026-01-28T01:20:03Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7941/d1t050","type":"dois","attributes":{"doi":"10.7941/d1t050","identifiers":[],"creators":[{"name":"Zhao, Haoran","nameType":"Personal","givenName":"Haoran","familyName":"Zhao","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0002-0802-0431","nameIdentifierScheme":"ORCID"}]},{"name":"Chan, Wanyu","nameType":"Personal","givenName":"Wanyu","familyName":"Chan","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0361-0085","nameIdentifierScheme":"ORCID"}]},{"name":"Cohn, Sebastian","nameType":"Personal","givenName":"Sebastian","familyName":"Cohn","affiliation":["Association for Energy Affordability","CollegeAmerica"],"nameIdentifiers":[]},{"name":"Delp, William W.","nameType":"Personal","givenName":"William W.","familyName":"Delp","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[]},{"name":"Walker, Iain S.","nameType":"Personal","givenName":"Iain S.","familyName":"Walker","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9667-1797","nameIdentifierScheme":"ORCID"}]},{"name":"Singer, Brett C.","nameType":"Personal","givenName":"Brett C.","familyName":"Singer","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5665-4343","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Indoor air quality in new and renovated low‐income apartments with mechanical ventilation and natural gas cooking in California"}],"publisher":"Dryad","container":{},"publicationYear":2020,"subjects":[{"subject":"Multi-family buildings"},{"subject":"nitrogen dioxide"},{"subject":"Fine particulate matter"},{"subject":"Formaldehyde","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Range hood"},{"subject":"Codes and standards"}],"contributors":[],"dates":[{"date":"2020-09-29T23:08:03Z","dateType":"Submitted"},{"date":"2020-10-12T00:00:00Z","dateType":"Issued"},{"date":"2020-10-12T00:00:00Z","dateType":"Available"},{"date":"2020-10-23T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/ina.12764","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["25410462 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":"This study presents pollutant concentrations and performance data for\n code-required mechanical ventilation equipment in 23 low-income apartments\n at 4 properties constructed or renovated 2013-2017. All apartments had\n natural gas cooking burners. Occupants pledged to not use windows for\n ventilation during the study but several did. Measured airflows of range\n hoods and bathroom exhaust fans were lower than product specifications.\n Only eight apartments operationally met all ventilation code requirements.\n Pollutants measured over one week in each apartment included time-resolved\n fine particulate matter (PM2.5), nitrogen dioxide (NO2), formaldehyde and\n carbon dioxide (CO2) and time-integrated formaldehyde, NO2 and nitrogen\n oxides (NOX). Compared to a recent study of California houses\n with code-compliant ventilation, apartments were smaller, had fewer\n occupants, higher densities, and higher mechanical ventilation rates. Mean\n PM2.5, formaldehyde, NO2, and CO2 were 7.7 µg/m3, 14.1 ppb, 18.8 ppb, and\n 741 ppm in apartments; these are 4% lower, 25% lower, 165% higher, and 18%\n higher compared to houses with similar cooking frequency. Four apartments\n had weekly PM2.5 above the California annual outdoor standard of 12 µg/m3\n and also discrete days above the World Health Organization 24-h guideline\n of 25 µg/m3. Two apartments had weekly NO2 above the California annual\n outdoor standard of 30 ppb.","descriptionType":"Abstract"},{"description":"This study evaluated IAQ in 23 low-income apartments at 4\n properties with natural gas cooking burners and mechanical ventilation\n equipment having specifications that met state building code requirements.\n The inclusion criteria were for apartment units to have (1) mechanical\n ventilation (MV) equipment that met the requirements of California’s Title\n 24 residential building code and (2) a natural gas cooking appliance.\n Required MV equipment were an exhaust fan in each bathroom, a kitchen\n exhaust fan or range hood, and equipment providing regular ventilation to\n the dwelling unit – each having specifications that met the code-minimum\n airflow requirements. Each property was visited in\n advance of the week of monitoring to confirm the presence of compliant MV\n equipment; this was done by inspecting 2-4 unoccupied units per site.\n Recruitment commenced following this visit. During the first visit, teams\n provided the participant with a paper version of the survey to obtain\n information about satisfaction with air quality and thermal conditions in\n the home and routine activities that impact ventilation and IAQ.\n Characteristics of mechanical ventilation equipment, cooking appliances,\n and thermal conditioning systems were documented and unit airtightness and\n ventilation equipment airflows were measured. Temperature, humidity,\n carbon dioxide and air pollutant concentrations were measured inside each\n apartment and air pollutant concentrations were measured outdoors on site.\n Sensors were installed to monitor use of gas cooking burners, ventilation\n equipment, and natural ventilation. Participants were asked to record\n occupancy and activities during each day of monitoring. Surveys and\n activity logs were collected and equipment was removed after one week of\n monitoring in each apartment. The study was led by\n Lawrence Berkeley National Laboratory (LBNL). Association for Energy\n Affordability recruited study buildings and completed a large portion of\n the field work. All study protocols involving interactions and collection\n of data from private individuals and monitoring in occupied homes were\n reviewed and approved by the LBNL Human Subjects Committee. Research\n Funding and technical contributions of collaborators are noted below in\n the acknowledgements.","descriptionType":"Methods"},{"description":"What is contained in this dataset? The\n dataset contains the most relevant information collected about the\n apartments and their mechanical equipment, results of the participant\n survey, results of air leakage and airflow measurements at the homes,\n pollutant concentrations measured by time-integrated passive samplers\n inside and outside of the home, usage of cooktop and oven, external door\n and window open state, and time-series or air pollutants and environmental\n indicators measured within and outside of the apartments\n Organization of Dataset\n Home_Equipment_Data This folder contains\n data about the house, including basic characteristics, air leakage test\n results, and measured airflow rates of mechanical ventilation equipment.\n There is one EXCEL file containing the data for all homes. The home\n characteristics form used by the field team is also included in the folder\n to explain the data parameters used in the EXCEL file.\n IAQ_Activity_Monitoring This folder contains\n time-resolved indoor and outdoor air quality data, including raw and\n adjusted PM2.5 as measured by DustTrak and PDR photometry (PM), ultrafine\n particles number concentration (UFP), PM concentrations measured by\n low-cost sensor (PM1, PM25, PM10), carbon dioxide (CO2), nitrogen dioxide\n (NO2), formaldehyde (FRM), total volatile organic compound (tVOC),\n temperature (T), and relative humidity (RH). Data also included T and RH\n measured at the supply air register (AS). Data also include hourly outdoor\n PM2.5 and NO2 concentration form the closest regulatory air monitoring\n stations (AQS). This folder also contains time series\n data of cooktop burners and oven monitored using iButton temperature\n sensors, other cooking devices monitored using AC power logger, kitchen\n range hood and bathroom fan on/off monitored using either an anemometer or\n a motor sensor, and open/close status of doors and windows monitored with\n state sensors. There is one csv file of 1-minute time-series data for each\n home, total 23 csv files. See\n KV_IAQ_Activity_Monitoring_ReadMe for data header definitions and data\n issues. Most instruments had internal logging and\n special software to download data from the field instruments and convert\n the data files to csv format. One-minute resolution time-series data files\n were created for each house using a python script that pulled data from\n multiple csv files, aligned data by time, executed unit conversions, and\n interpolate data that were measured at different time resolutions. Visual\n review was conducted on the compiled files to check for translation or\n writing errors, indications of instrument malfunction, mislabeled units or\n unit conversion errors, mislabeled location, and time stamp errors. More\n detailed information about data issues identified are explained in the\n ReadMe file. IAQ_Sample This\n folder contains the results of time-integrated air quality samples,\n including passive measurements of formaldehyde, nitrogen dioxide and\n nitrogen oxides, and PM2.5 gravimetric filter measurements. There is one\n EXCEL file containing all data. Detailed information about chemical\n analysis of air samples are provided elsewhere in the journal\n paper. Occupant_Activity This\n folder contains tabulated information provided by study participants from\n their daily activity logs. There is one EXCEL file containing data\n transcribed by a researcher, which was independently spot checked by\n another researcher to confirm accuracy. The PDF file shows the format of\n the daily activity log used.\n Occupant_Survey This folder contains survey\n results about the occupants, their general activities related to\n ventilation and IAQ satisfaction, completed by study participants. There\n is one EXCEL file containing data transcribed by a researcher. Two homes\n did not complete surveys, as indicated by \"No survey\" in the\n response. The MS Word file contains questions of the occupant\n surveys.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"California Energy Commission","awardNumber":"PIR-16-012","funderIdentifier":"https://ror.org/05eaakg28","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"United States Department of Energy","awardNumber":"DE-AC02-05CH11231","funderIdentifier":"https://ror.org/01bj3aw27","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"Environmental Protection Agency","awardNumber":"DW-89-9232201-7","funderIdentifier":"https://ror.org/03tns0030","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7941/D1T050","contentUrl":null,"metadataVersion":13,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":1953,"downloadCount":347,"referenceCount":0,"citationCount":2,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2020-10-13T01:57:30Z","registered":"2020-10-13T01:57:32Z","published":null,"updated":"2026-01-28T01:08:44Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7941/d1zs7x","type":"dois","attributes":{"doi":"10.7941/d1zs7x","identifiers":[],"creators":[{"name":"Chan, Wanyu","nameType":"Personal","givenName":"Wanyu","familyName":"Chan","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0361-0085","nameIdentifierScheme":"ORCID"}]},{"name":"Kim, Yang-Seon","nameType":"Personal","givenName":"Yang-Seon","familyName":"Kim","affiliation":["Wichita State University"],"nameIdentifiers":[]},{"name":"Delp, William","nameType":"Personal","givenName":"William","familyName":"Delp","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[]},{"name":"Walker, Iain","nameType":"Personal","givenName":"Iain","familyName":"Walker","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9667-1797","nameIdentifierScheme":"ORCID"}]},{"name":"Singer, Brett","nameType":"Personal","givenName":"Brett","familyName":"Singer","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-5665-4343","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Data from: Indoor air quality in California homes with code-required mechanical ventilation"}],"publisher":"Dryad","container":{},"publicationYear":2020,"subjects":[{"subject":"ASHRAE 62.2"},{"subject":"Healthy Efficient New Gas Home Study"},{"subject":"Residential energy efficiency"},{"subject":"Carbon dioxide","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Fine particulate matter"},{"subject":"Formaldehyde","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"Air quality","schemeUri":"https://github.com/PLOS/plos-thesaurus","subjectScheme":"PLOS Subject Area Thesaurus"},{"subject":"indoor air quality"},{"subject":"Indoor air pollution"},{"subject":"ventilation"}],"contributors":[],"dates":[{"date":"2020-02-07T18:19:02Z","dateType":"Submitted"},{"date":"2020-04-22T00:00:00Z","dateType":"Issued"},{"date":"2020-04-22T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1111/ina.12676","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["104811667 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":"Data were collected in 70 detached houses built in 2011-2017 in compliance\n with the mechanical ventilation requirements of California’s building\n energy efficiency standards. Each home was monitored for a one-week period\n with windows closed and the central mechanical ventilation system\n operating. Pollutant measurements included time-resolved fine particulate\n matter (PM2.5) indoors and outdoors and formaldehyde and carbon dioxide\n (CO2) indoors. Time-integrated measurements were made for formaldehyde,\n NO2 and nitrogen oxides (NOX) indoors and outdoors. Operation of the\n cooktop, range hood and other exhaust fans was continuously recorded\n during the monitoring period. One-time diagnostic measurements included\n mechanical airflows and envelope and duct system air leakage. All homes\n met or were very close to meeting the ventilation requirements. On average\n the dwelling unit ventilation fan moved 50% more airflow than the minimum\n requirement. Pollutant concentrations were similar or lower than those\n reported in a 2006-2007 study of California new homes built in 2002-2005.\n Mean and median indoor concentrations were lower by 44% and 38% for\n formaldehyde and 44% and 54% for PM2.5. Ventilation fans were operating in\n only 26% of homes when first visited and the control switches in many\n homes did not have informative labels as required by building standards.","descriptionType":"Abstract"},{"description":"Overview of HENGH\n Study The HENGH study was\n conceived, designed and implemented for the purpose of evaluating impacts\n of residential mechanical ventilation equipment requirements that have\n been part of the California’s Building Energy Efficiency Standards since\n 2008. Starting in 2009, these standards have required bath and kitchen\n exhaust fans and dwelling unit mechanical ventilation with sizing and\n performance levels based on the residential ventilation standard (62.2) of\n the ASHRAE organization. The ventilation standards are intended to help\n maintain indoor air quality as homes are constructed with tighter shells\n to reduce uncontrolled outdoor air infiltration for energy\n efficiency. The study was led by\n Lawrence Berkeley National Laboratory (LBNL). All study protocols\n involving interactions and collection of data from private individuals and\n monitoring in occupied homes were reviewed and approved by the LBNL Human\n Subjects Committee. Research Funding and technical contributions of\n collaborators are noted below in the acknowledgements.  The field study included the following data\n collection elements: \n Homeowner survey about household demographics, ventilation\n practices, activities that can impact indoor air quality, and satisfaction\n with environmental conditions in the home.\n Compilation of basic data about the houses (location, size,\n number of bedrooms, etc.) and gas appliances and mechanical ventilation\n equipment (technology type, make, model, etc.)\n Measurements of air leakiness of the building envelope and\n forced air system ductwork. Measurements of the\n following parameters over a weeklong monitoring period: \n Airflows of all mechanical ventilation equipment;\n Air pollutants and environmental parameters indoors and\n outdoors; Cooktop and oven surface temperatures to\n identify burner use. \n Participants were expected to complete a daily activity\n log.\u003cb\u003e \u003c/b\u003e What is contained in this\n dataset? The dataset\n contains the most relevant information collected about the 70 houses and\n their mechanical equipment, results of the participant survey, results of\n air leakage and airflow measurements at the homes, pollutant\n concentrations measured by time-integrated passive samplers inside and\n outside of the home, usage of cooktop and oven, external door open state,\n and time-series or air pollutants and environmental indicators measured\n within and outside of the houses.  Organization of\n Dataset  \n Airflow This folder contains time series\n data of monitored mechanical ventilation equipment, estimates of air\n infiltration rate, and overall air exchange rate.  There is one csv file\n for each home. See HENGH_Airflow_ReadMe for more details. \n Ambient_PM This\n folder contains a summary of PM2.5 data reported by one or more ambient\n air monitoring stations nearest to each study home. There is one EXCEL\n file containing PM2.5 data reported from up to three closest regulatory\n monitoring sites. A composite estimate of ambient PM2.5 was calculated for\n each home using an inverse distance weighing method.  \n Home_Equipment_Data \n This folder contains data about the house, including basic\n characteristics, air leakage test results, and measured airflow rates of\n mechanical ventilation equipment. There is one EXCEL file containing the\n data for all homes. The EXCEL file has ReadMe information about the data\n provided and a note about data quality issue concerning exhaust airflow\n measurements of over-the-range microwaves.    \n IAQ_Monitoring This folder\n contains time-resolved air quality data, including estimated PM2.5 as\n measured by photometry (PM), carbon dioxide (CO2), nitrogen dioxide (NO2),\n formaldehyde (FRM), temperature (T), and relative humidity (RH). There is\n one csv file of 1-minute time-series data for each home. See\n HENGH_IAQ_Monitoring_ReadMe for data header definitions and data\n issues.  IAQ_Sample\n This folder contains the results of time-integrated\n air quality samples, including passive measurements of formaldehyde,\n nitrogen dioxide and nitrogen oxides, and PM2.5 gravimetric filter\n measurements. There is one EXCEL file containing all data. Detail\n information about chemical analysis of air samples are provided elsewhere\n in the journal paper and report.  \n Occupant_Activity This folder contains\n tabulated information provided by study participants from their daily\n activity logs. There is one EXCEL file containing data transcribed by a\n staff member, which was independently spot checked by another staff to\n confirm accuracy. The PDF file is the daily activity log used. \n Occupant_Survey \n This folder contains results of a survey about the occupants,\n their general activities related to ventilation and IAQ\n satisfaction, completed by study participants. There is one EXCEL file\n containing data transcribed by a staff member. Two homes did not complete\n surveys; these homes have \"No survey\" in each data file.\n Questions for the occupant surveys are provided in MS Word and PDF\n formats.  State_Monitoring\n This folder contains time series data of cooking\n burners monitored with iButton temperature sensors and open/close status\n of external (mostly patio) doors monitored with state sensors. There is\n one csv file for each home. See HENGH_State_Monitoring_ReadMe for more\n details. ","descriptionType":"Methods"},{"description":"Time Series Data Handling and\n Quality Assurance Review Most instruments had internal logging and special\n software to download data from the field instruments as binary files or\n ascii/csv files. The instruments for which files downloaded as binary\n provide software to view the data or export the data to csv\n files.  One-minute resolution\n time-series data files were created for each house using an R script that\n pulled data from the csv files, aligned data by time, executed unit\n conversions, and translated from instruments with longer or different data\n intervals (e.g. 30 min formaldehyde data and 1.5 min for anemometer data).\n Visual review was conducted on the compiled files (and primary csv or\n binary files were consulted as needed) to check for translation or writing\n errors (especially from terminal emulator), indications of instrument\n malfunction, mislabeled units or unit conversion errors, mislabeled\n location, and time stamp errors.  The draft final set of time-series data were\n visually reviewed by a second researcher by creating multi-panel,\n time-series plots by monitoring period. Panel combinations included (1)\n indoor CO2 and indoor and outdoor PM, T, and RH; (2) indoor PM, NO2, and\n formaldehyde along with cooktop and oven temperatures, and range hood\n operation (focused on checking formaldehyde and NO2). The time series data\n were visually reviewed to search again for examples of possible instrument\n malfunction or errors introduced via data transfer. More detail\n information about data issues identified are explained in the ReadMe\n information provided in each data folder.  Acknowledgements In addition to research funding noted below, the\n Southern California Gas Company (SoCalGas) provided direct financial\n support to the Gas Technology Institute (GTI) to purchase equipment and to\n conduct field data collection. Staff support was contributed by the\n Pacific Gas \u0026amp; Electric Company (PG\u0026amp;E) which funded Misti\n Bruceri \u0026amp; Associates (MBA) to provide a staff person to support\n the study, and by SoCalGas, which allocated engineering and technical\n staff to contribute to the field work in SoCalGas service territory under\n GTI direction. SoCalGas and PG\u0026amp;E also supported the project by\n allocating Gas Service Technicians to conduct gas appliance safety\n inspections in study homes. The\n dataset presented here would not exist without the committed work of the\n field research teams in PG\u0026amp;E and SoCalGas service territories; the\n authors are deeply appreciative of their efforts. The field work for this\n project was conducted by Luke Bingham, Erin Case, and Shawn Scott of GTI;\n Guy Lawrence of MBA; and Eric Barba, Mary Nones, Ara Arouthinounian, and\n Ricardo Torres of SoCalGas; and Randy Maddalena, Marion Russell, and\n student interns of LBNL. Rick Chitwood also assisted with field data\n collection and provided guidance on measuring airflow rates in supply\n ventilation systems.","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"California Energy Commission","awardNumber":"PIR-14-007","funderIdentifier":"https://ror.org/05eaakg28","funderIdentifierType":"ROR"},{"schemeUri":"https://ror.org","funderName":"United States Department of Energy","awardNumber":"DE-AC02-05CH11231","funderIdentifier":"https://ror.org/01bj3aw27","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7941/D1ZS7X","contentUrl":null,"metadataVersion":16,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":1528,"downloadCount":287,"referenceCount":0,"citationCount":4,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2020-04-22T16:02:30Z","registered":"2020-04-22T16:02:31Z","published":null,"updated":"2026-01-27T21:07:34Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7941/d1v63m","type":"dois","attributes":{"doi":"10.7941/d1v63m","identifiers":[],"creators":[{"name":"Tokunaga, Tetsu","nameType":"Personal","givenName":"Tetsu","familyName":"Tokunaga","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0003-0861-6128","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Simplified Green-Ampt model, imbibition-based estimates of permeability, and implications for leak-off in shale reservoirs"}],"publisher":"Dryad","container":{},"publicationYear":2020,"subjects":[],"contributors":[],"dates":[{"date":"2020-06-08T19:17:03Z","dateType":"Submitted"},{"date":"2020-07-14T00:00:00Z","dateType":"Issued"},{"date":"2020-07-14T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1029/2019wr026919","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["161681 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":"Predicting water imbibition into porous materials is important in a wide\n variety of fields, yet is often challenging because of uncertainties in\n both the permeability and capillary pressure driving force. Here, this\n uncertainty is reduced through evaluating correlations between the\n permeability and the effective capillary pressure associated with the\n wetting front, Pc,f. These correlations allow elimination of Pc,f from the\n Green and Ampt equation, and concentrates all uncertainties in fluxes on\n the effective permeability k. Over a wide range of k and n, imbibition\n scales approximately with k1/3. Although Leverett k1/4 scaling for\n predicting Pc,f is shown to be inferior when tested with data spanning a\n wide range of porosities n, it nevertheless predicted imbibition fairly\n well. From simple imbibition measurements, both the empirical and Leverett\n scaling approaches allow estimates of k that have root mean-square\n deviations of about 1 order of magnitude relative to measurements that\n ranged over 10 orders of magnitude in k.","descriptionType":"Abstract"},{"description":"These data were largely compiled from literature sources listed\n in the reference section of the Supporting Information. A few data are\n from the author's previously unpublished laboratory measurements.\n Data processing is decribed in detail in the manuscript.","descriptionType":"Methods"}],"geoLocations":[],"fundingReferences":[{"schemeUri":"https://ror.org","funderName":"Office of Fossil Energy","awardNumber":"DE-AC02-05CH11231","funderIdentifier":"https://ror.org/03ery9d53","funderIdentifierType":"ROR"}],"url":"https://datadryad.org/dataset/doi:10.7941/D1V63M","contentUrl":null,"metadataVersion":10,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":134,"downloadCount":6,"referenceCount":1,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2020-07-14T20:24:08Z","registered":"2020-07-14T20:24:10Z","published":null,"updated":"2026-01-27T20:36:03Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7941/d14p62","type":"dois","attributes":{"doi":"10.7941/d14p62","identifiers":[],"creators":[{"name":"Chang, Chun","nameType":"Personal","givenName":"Chun","familyName":"Chang","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9805-9067","nameIdentifierScheme":"ORCID"}]},{"name":"Kneafsey, Timothy","nameType":"Personal","givenName":"Timothy","familyName":"Kneafsey","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[]},{"name":"Wan, Jiamin","nameType":"Personal","givenName":"Jiamin","familyName":"Wan","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[]},{"name":"Tokunaga, Tetsu","nameType":"Personal","givenName":"Tetsu","familyName":"Tokunaga","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[]},{"name":"Nakagawa, Seiji","nameType":"Personal","givenName":"Seiji","familyName":"Nakagawa","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[]}],"titles":[{"title":"Impacts of Mixed-Wettability on Brine Drainage and Supercritical CO2 Storage Efficiency in a 2.5-D Heterogeneous Micromodel"}],"publisher":"Dryad","container":{},"publicationYear":2020,"subjects":[],"contributors":[],"dates":[{"date":"2020-06-01T16:56:04Z","dateType":"Submitted"},{"date":"2020-07-10T00:00:00Z","dateType":"Issued"},{"date":"2020-07-10T00:00:00Z","dateType":"Available"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1029/2019wr026789","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["195646306 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":"Geological carbon storage (GCS) involves unstable drainage processes that\n affect CO2 storage efficiency and plume distribution. Drainage is greatly\n complicated by the mixed-wet nature of rock surfaces common in hydrocarbon\n reservoirs where supercritical CO2 (scCO2) is used in enhanced oil\n recovery. We performed scCO2 injection (brine drainage) experiments at 8.5\n MPa and 45 °C in heterogeneous micromodels, two mixed-wet with varying\n water- and intermediate-wet patches, and one water-wet. The flow regime\n changes from capillary fingering through crossover to viscous fingering in\n the micromodels of same pore geometry but different wetting surfaces at\n displacement rates with logCa (capillary number) increasing from\n −8.1 to −4.4. While the mixed-wet micromodel with uniformly distributed\n intermediate-wet patches yields ~0.15 scCO2 saturation increase at both\n capillary fingering and crossover flow regimes (–8.1 ≤logCa≤−6.1), the one\n heterogeneous wetting to scCO2 results in ~0.09 saturation increase only\n at the crossover flow regime (−7.1 ≤logCa≤−6.1). The interconnected flow\n paths in the former are quantified and compared to the channelized scCO2\n flow through intermediate-wet patches in the latter by topological\n analysis. At logCa\u0026gt;−6.1 (near well), the effects of wettability and\n pore geometry are suppressed by strong viscous force. Both scCO2\n saturation and distribution suggest the importance of wettability on CO2\n storage efficiency and plume shape in reservoirs, and capillary leakage\n through caprock at GCS conditions.","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org/dataset/doi:10.7941/D14P62","contentUrl":null,"metadataVersion":11,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":148,"downloadCount":20,"referenceCount":1,"citationCount":1,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2020-07-11T03:48:52Z","registered":"2020-07-11T03:48:54Z","published":null,"updated":"2026-01-27T20:25:27Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7941/d1f63w","type":"dois","attributes":{"doi":"10.7941/d1f63w","identifiers":[],"creators":[{"name":"Yen, Ethan","nameType":"Personal","givenName":"Ethan","familyName":"Yen","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[]},{"name":"Williams, Quentin","nameType":"Personal","givenName":"Quentin","familyName":"Williams","affiliation":["University of California, Santa Cruz"],"nameIdentifiers":[]},{"name":"Kunz, Martin","nameType":"Personal","givenName":"Martin","familyName":"Kunz","affiliation":["Lawrence Berkeley National Laboratory"],"nameIdentifiers":[{"schemeUri":"https://orcid.org","nameIdentifier":"https://orcid.org/0000-0001-9769-9900","nameIdentifierScheme":"ORCID"}]}],"titles":[{"title":"Thermal pressure in the laser heated diamond anvil cell: a quantitative study and implications for the density vs. mineralogy correlation of the mantle"}],"publisher":"Dryad","container":{},"publicationYear":2020,"subjects":[],"contributors":[],"dates":[{"date":"2020-08-25T16:37:03Z","dateType":"Submitted"},{"date":"2020-08-24T00:00:00Z","dateType":"Issued"},{"date":"2020-08-24T00:00:00Z","dateType":"Available"},{"date":"2020-08-28T00:00:00Z","dateType":"Updated"}],"language":"en","types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[{"relationType":"IsCitedBy","relatedIdentifier":"10.1029/2020jb020006","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1002/essoar.10503889.1","relatedIdentifierType":"DOI"},{"relationType":"IsCitedBy","relatedIdentifier":"10.1002/essoar.10502086.1","relatedIdentifierType":"DOI"}],"relatedItems":[],"sizes":["5908151998 bytes"],"formats":[],"version":"12","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":"Thermal pressure is an inevitable thermodynamic consequence of heating a\n volumetrically constrained sample in the diamond anvil cell. Its possible\n influences on experimentally determined density-mineralogy correlations\n are widely appreciated, yet the effect itself has never been\n experimentally measured. We present here the first quantitative\n measurements of the spatial distribution of thermal pressure in a laser\n heated diamond anvil cell (LHDAC) in both olivine and AgI. The observed\n thermal pressure is strongly localized and closely follows the\n distribution of the laser hotspot. The magnitude of the thermal pressure\n is of the order of the thermodynamic thermal pressure (αKTDT) with\n gradients between 0.5 – 1.0 GPa/10 μm. Remarkably, we measure a steep\n gradient in thermal pressure even in a sample that is heated close to its\n melting line. This generates consequences for pressure determinations in\n pressure-volume-temperature (PVT) equation of state measurements when\n using an LHDAC. We show that an incomplete account of thermal pressure in\n PVT experiments can lead to biases in the coveted depth versus mineralogy\n correlation. However, the ability to spatially resolve thermal pressure in\n an LHDAC opens avenues to measure difficult-to-constrain thermodynamic\n derivative properties, which are important for comprehensive thermodynamic\n descriptions of the interior of planets.","descriptionType":"Abstract"},{"description":"\u003cstrong\u003eSynchrotron X-ray\n diffraction\u003c/strong\u003e Angle-dispersive in situ\n X-ray powder diffraction patterns at high pressure and high temperature\n were collected at beamline 12.2.2 at the Advanced Light Source at the\n Lawrence Berkeley National ­Laboratory using an X-ray wavelength of \n \u003ci\u003eλ=0.5166\u003c/i\u003eÅ (24 keV) and \u003ci\u003eλ=0.4969\u003c/i\u003eÅ (25\n keV) for the silver iodide and San Carlos olivine experiments,\n respectively. The X-ray energy for the AgI was lowered to 24 keV to be at\n a safe distance from the Ag-K-a-absorption edge. At each spatial position,\n X-ray diffraction patterns were taken both before and during the IR laser\n heating to yield ambient and heated diffraction patterns. The X-ray beam\n size was 10 mm. Patterns were collected with exposure times of 30 sec on a\n MAR3450 image plate. The detector distance and orientation were calibrated\n using a CeO\u003csub\u003e2\u003c/sub\u003e standard at the sample\n position. \u003cstrong\u003eLaser heating and\n temperature measurement\u003c/strong\u003e Laser heating of the LHDAC was\n conducted using a 1090 nm IR fiber laser system [\u003ci\u003eKunz et\n al.\u003c/i\u003e, 2018], with a beam size of \u003ci\u003e30\u003c/i\u003eμm FWHM in\n diameter. The silver iodide sample was heated with\n \u003ci\u003e0\u003c/i\u003e.9-1.0W\u003ci\u003e \u003c/i\u003ein both the upstream and\n downstream directions. The San Carlos olivine sample was heated with\n powers of \u003ci\u003e2.5\u003c/i\u003e-3.2W\u003ci\u003e \u003c/i\u003eupstream and\n \u003ci\u003e4.5\u003c/i\u003e-5.7W\u003ci\u003e \u003c/i\u003edownstream. To probe the\n sample across the hot spot, the sample had to be moved relative to the\n stationary X-ray beam, and with it, the laser hot spot which in turn was\n kept centered on the gasket hole (see Figure 1 in main text). The center\n of the gasket hole served as the reference for positioning the laser hot\n spot. As a result, this procedure created an individual hot spot for every\n diffraction measurement. The laser heating set-up on beamline 12.2.2\n [\u003ci\u003eKunz et al.\u003c/i\u003e, 2018] allows for quasi real-time\n temperature mapping of the sample chamber during a heating event.\n Temperatures were measured using the double sided spectroradiometric\n pyrometry set up on beamline 12.2.2, which employs a modified peak scaling\n approach [\u003ci\u003eKunz et al.\u003c/i\u003e, 2018; \u003ci\u003eRainey and\n Kavner\u003c/i\u003e, 2014]. This approach avoids the notorious chromatic\n aberration artifacts and also produces full absolute temperature maps in\n real time, thus enabling the spatial mapping of the thermal pressure\n effects presented here. The pyrometry setup produces\n upstream and downstream 74mm x 74mm square temperature maps centered at\n the peak of the laser hotspot. As a result, radial temperature readings\n from the center of the sample exist from 0 to 37mm for the full azimuthal\n range, but disregarding radial completeness, temperature data exist from 0\n to 52.3mm from the center. We plotted the upstream and downstream\n temperatures against radial distance by averaging the temperatures of\n pixels with the same Euclidian distance (within floating point error) from\n the center of the 74mm x 74mm temperature maps. The upstream and\n downstream graphs were averaged to produce an average temperature vs.\n radial distance plot. Due to the large thermal\n conductivity of the diamond anvils, it has been shown that at the\n diamond/sample interface, the sample has a temperature close to room\n temperature [\u003ci\u003eKiefer and Duffy\u003c/i\u003e, 2005]. To construct the\n temperatures between 52.3mm and 80mm (the sample edge), we use a simple\n linear decrease between the points at \u003ci\u003e(44.5\u003c/i\u003eum\u003ci\u003e,\n \u003c/i\u003eavg\u003ci\u003eT\u003c/i\u003e\u003csub\u003e\u003ci\u003e37um\u003c/i\u003e\u003c/sub\u003e\u003ci\u003e,\u003c/i\u003e\u003ci\u003eT\u003c/i\u003e\u003csub\u003e\u003ci\u003e52.3um\u003c/i\u003e\u003c/sub\u003e\u003ci\u003e)\u003c/i\u003e and \u003ci\u003e(80\u003c/i\u003eum\u003ci\u003e, 298\u003c/i\u003e\u003ci\u003eK\u003c/i\u003e\u003ci\u003e)\u003c/i\u003e. To construct the first point of the linear decrease, we considered the temperature points between 37mm and 52.3mm because 360-degree azimuthal averaging is only possible between 0 and 37mm. The average distance and temperature of the points between 37mm and 52.3mm gives us the starting point for the linear decrease. The average beam temperatures of sections centered between 0 and 47.3mm (52.3mm – 5mm) was obtained by averaging the corresponding 10mm section (our beam size) of the average temperature vs. radial distance graphs. Average beam temperatures of sections centered between 52.3mm and 80mm were obtained by taking the average temperature-value of the linear decrease over the corresponding 10mm radial section. \u003cstrong\u003ePressure Determination\u003c/strong\u003e Scattering intensity versus 2θ plots were obtained by azimuthal integration of the 2-dimensional powder diffraction patterns using DIOPTAS [\u003ci\u003ePrescher and Prakapenka\u003c/i\u003e, 2015]. From the intensity versus 2θ plots for the silver iodide sample, lattice spacings with Miller indices (200), (220), (311), (222), (400), (420), and (422) were used to refine the unit-cell parameters of silver iodide’s cubic crystal structure. From the intensity versus 2θ plots for the San Carlos olivine, lattice spacings with Miller indices (020), (021), (101), (002), (130), (131), (112), and (211) were analyzed using Celref 3 [\u003ci\u003eLaugier and Bochu\u003c/i\u003e, 2002] to yield orthorhombic unit-cell parameters. ----- Kiefer, B., and T. S. Duffy (2005), Finite element simulations of the laser-heated diamond-anvil cell, \u003ci\u003eJournal of Applied Physics\u003c/i\u003e, \u003ci\u003e97\u003c/i\u003e(11), 114902. Kunz, M., J. Yan, E. Cornell, E. E. Domning, C. E. Yen, A. Doran, C. M. Beavers, A. Treger, Q. Williams, and A. A. MacDowell (2018), Implementation and application of the peak scaling method for temperature measurement in the laser heated diamond anvil cell, \u003ci\u003eReview of Scientific Instruments\u003c/i\u003e, \u003ci\u003e89\u003c/i\u003e(8), 083903. Laugier, J., and B. Bochu (2002), CELREF V3: Cell parameters refinement program from powder diffraction diagram. Laboratoire des Matériaux et du Génie Physique, Institut National Polytechnique de Grenoble, France, edited. Prescher, C., and V. B. Prakapenka (2015), DIOPTAS: a program for reduction of two-dimensional X-ray diffraction data and data exploration, \u003ci\u003eHigh Pressure Research\u003c/i\u003e, \u003ci\u003e35\u003c/i\u003e(3), 223-230. Rainey, E., and A. Kavner (2014), Peak scaling method to measure temperatures in the laser‐heated diamond anvil cell and application to the thermal conductivity of MgO, \u003ci\u003eJournal of Geophysical Research: Solid Earth\u003c/i\u003e, \u003ci\u003e119\u003c/i\u003e(11), 8154-8170.","descriptionType":"Methods"},{"description":"Calculations have been performed using the following\n softwares: Eosfit (http://www.rossangel.com/text_eosfit.htm) Origin (https://www.originlab.com/) Dioptas (http://www.clemensprescher.com/programs/dioptas) Microsoft Excel a self-written python script for Temperature maps","descriptionType":"Other"}],"geoLocations":[],"fundingReferences":[{"funderName":"\n        Office of Science, Office of Basic Energy Sciences, Materials Sciences\n        Division, of the US Department of Energy\n      ","awardNumber":"DE-AC03-76SF00098"},{"funderName":"COMPRES NSF Cooperative Agreement","awardNumber":"EAR 16-49658"}],"url":"https://datadryad.org/dataset/doi:10.7941/D1F63W","contentUrl":null,"metadataVersion":17,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":197,"downloadCount":46,"referenceCount":0,"citationCount":3,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2020-08-24T20:52:05Z","registered":"2020-08-24T20:52:06Z","published":null,"updated":"2026-01-27T19:58:46Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7941/d1j63t","type":"dois","attributes":{"doi":"10.7941/d1j63t","identifiers":[],"creators":[{"name":"Dryad Digital Repository","nameType":"Organizational","affiliation":["Dryad Digital Repository"],"nameIdentifiers":[]}],"titles":[{"lang":"en","title":"Dryad dataset awaiting publication"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[],"contributors":[],"dates":[{"date":"2023","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[],"descriptions":[{"description":":unas","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org","contentUrl":null,"metadataVersion":0,"schemaVersion":"http://datacite.org/schema/kernel-4","source":"mds","isActive":true,"state":"findable","reason":null,"viewCount":1,"downloadCount":2,"referenceCount":0,"citationCount":0,"partCount":0,"partOfCount":0,"versionCount":0,"versionOfCount":0,"created":"2023-10-04T22:46:30Z","registered":"2023-10-04T22:46:31Z","published":null,"updated":"2025-02-01T01:02:06Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7941/d1263g","type":"dois","attributes":{"doi":"10.7941/d1263g","identifiers":[],"creators":[{"name":"Dryad Digital Repository","nameType":"Organizational","affiliation":["Dryad Digital Repository"],"nameIdentifiers":[]}],"titles":[{"lang":"en","title":"Dryad dataset awaiting publication"}],"publisher":"Dryad","container":{},"publicationYear":2023,"subjects":[],"contributors":[],"dates":[{"date":"2023","dateType":"Issued"}],"language":null,"types":{"ris":"DATA","bibtex":"misc","citeproc":"dataset","schemaOrg":"Dataset","resourceType":"Dataset","resourceTypeGeneral":"Dataset"},"relatedIdentifiers":[],"relatedItems":[],"sizes":[],"formats":[],"version":null,"rightsList":[],"descriptions":[{"description":":unas","descriptionType":"Abstract"}],"geoLocations":[],"fundingReferences":[],"url":"https://datadryad.org","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":"2023-10-04T22:19:14Z","registered":"2023-10-04T22:19:15Z","published":null,"updated":"2023-11-14T19:44:46Z"},"relationships":{"client":{"data":{"id":"dryad.dryad","type":"clients"}}}},{"id":"10.7941/d15w4p","type":"dois","attributes":{"doi":"10.7941/d15w4p","identifiers":[],"creators":[{"name":"Dryad Digital Repository","nameType":"Organizational","affiliation":["Dryad Digital Repository"],"nameIdentifiers":[]}],"titles":[{"lang":"en","title":"Dryad dataset awaiting 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