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4,669 results for “environment”
S35 | INDOORCT16 | Indoor Environment Substances from 2016 Collaborative Trial
<p>This is the collection associated with list S35 INDOORCT16 on the NORMAN Suspect List Exchange.</p> <p><a href="https://www.norman-network.com/nds/SLE/">https://www.norman-network.com/nds/SLE/</a></p> <p>S35 INDOORCT16 <strong>Indoor Environment Substances from 2016 Collaborative Trial</strong></p> <p>Lists of GC-MS and LC-MS compounds and DSFP output, plus merged files from the Indoor Dust Collaborative Trial, 2016 provided by Peter Haglund (UMU) and Pawel Rostkowski (NILU). Details in Rostkowski <em>et al</em>. 2019 DOI: <a href="https://link.springer.com/article/10.1007/s00216-019-01615-6">10.1007/s00216-019-01615-6</a></p> <p>Update 6 Feb 2020: two NA SMILES removed in CSV for PubChem upload. 17/7/2022: NA and N/A SMILES removed from CSV and XLSX, most replaced with structures; some are representative structures for classes.</p>
Using machine learning to integrate genetic and environmental data to model genotype-by-environment interactions
<p>Files generated from the study described in <a href="https://doi.org/10.1101/2024.02.08.579534">Fernandes et. al (2024)</a> .</p> <p>The file "cvs_h2s.csv" comprises the coefficient of variation and the Cullis heritability for each environment.</p> <p>The file "all_predictions.csv" contains the predictions from all the models evaluated, in different cross-validation (CV) scenarios.</p> <p>The file "coincidence_index.csv" has the Coincidence Index (CI) for each CV and models evaluated in our study.</p> <p>Our study used the multi-environment maize yield trials data from the Genomes to Fields 2022 initiative (<a href="https://doi.org/10.1186/s13104-023-06421-z">Lima et. al 2024</a>).</p>
The Red Queen in the Repository: metadata quality in an ever-changing environment (preprint of paper, presentation slides and dataset collection with validation schemas to IDCC2019 conference paper)
<p>This fileset contains a preprint version of the conference paper (.pdf), presentation slides (as .pptx) and the dataset(s) and validation schema(s) for the IDCC 2019 (Melbourne) conference paper: <em>The Red Queen in the Repository: metadata quality in an ever-changing environment. </em>Datasets and schemas are in .xml, .xsd , Excel (.xlsx) and .csv (two files representing two different sheets in the .xslx -file). The <em>validationSchemas.zip</em> holds the additional validation schemas (.xsd), that were not found in the schemaLocations of the metadata xml-files to be validated. The schemas must all be placed in the same folder, and are to be used for validating the Dataverse <em>dcterms</em> records (with <em>metadataDCT.xsd</em>) and the Zenodo <em>oai_datacite</em> feeds respectively (<em>schema.datacite.org_oai_oai-1.0_oai.xsd</em>). In the latter case, a simpler way of doing it might be to replace the incorrect URL "<em>http://schema.datacite.org/oai/oai-1.0/ oai_datacite.xsd</em>" in the <em>schemaLocation </em>of these xml-files by the CORRECT: <em>schemaLocation="http://schema.datacite.org/oai/oai-1.0/ http://schema.datacite.org/oai/oai-1.0/oai.xsd"</em> as has been done already in the sample files here. The sample file folders <em>testDVNcoll.zip </em>(Dataverse), <em>testFigColl.zip </em>(Figshare)<em> </em>and <em>testZenColl.zip </em>(Zenodo)<em> </em>contain all the metadata files tested and validated that are registered in the spreadsheet with objectIDs.<br> In the case of Zenodo, one original file feed,<br> <em>zen2018oai_datacite3orig-https%20_zenodo.org_oai2d%20verb=ListRecords%26metadata<br> Prefix=oai_datacite%26from=2018-11-29%26until=2018-11-30.xml</em> ,<br> is also supplied to show what was necessary to change in order to perform validation as indicated in the paper.</p> <p>For Dataverse, a corrected version of a file,<br> <em>dvn2014ddi-27595<strong>Corr</strong>_https%20_dataverse.harvard.edu_api_datasets_export%20<br> exporter=ddi%26persistentId=doi%253A10.7910_DVN_27595<strong>Corr</strong>.xml</em> ,<br> is also supplied in order to show the changes it would take to make the file validate without error.</p>
Impact of medical radionuclide discharges on people and the environment: scenario data used in the non-human biota impact assessment
<p>This dataset contains the input data for the D-DAT model: activity concentrations in water for the simulated Molse Nete scenario. It also contains the dynamic model-calculated activity concentrations in sediment and the non-human biota. These are the primary data upon which the dose calculations werte performed, and they can be used to reproduce these calculations. The related preprint article is also given in this repository: https://zenodo.org/records/10488393.</p> <p> </p> <p> </p> <p> </p>
The Immunomodulatory Effect of Silver Nanoparticles in a Retinal Inflammatory Environment
<p>Activation of immune response plays an important role in the development of retinal diseases. One of the main populations of immune cells contributing to the retinal homeostasis are microglia, which represent a population of residential macrophages. However, under pathological conditions, microglia become activated and rather support a harmful inflammatory reaction and retinal angiogenesis. Therefore, targeting these cells could provide protection against retinal neuroinflammation and neovascularization. In the recent study, we analyzed effects of silver nanoparticles (AgNPs) on microglia in vitro and in vivo. We showed that the AgNPs interact in vitro with stimulated mouse CD45/CD11b positive cells (microglia/macrophages), decrease their secretion of nitric oxide and vascular endothelial growth factor, and regulate the expression of genes for Iba-1 and interleukin-1β (IL-1β). In our in vivo experimental mouse model, the intravitreal application of a mixture of proinflammatory cytokines tumor necrosis factor-α, IL-1β and interferon-γ induced local inflammation and increased local expression of genes for inducible nitric oxide synthase, IL-α, IL-1β and galectin-3 in the retina. This stimulation of local inflammatory reaction was significantly inhibited by intravitreal administration of AgNPs. The application of AgNPs also decreased the presence of CD11b/Galectin-3 positive cells in neuroinflammatory retina, but did not influence viability of cells and expression of gene for rhodopsin in the retinal tissue. These data indicate that AgNPs regulate reactivity of activated microglia in the diseased retina and thus could provide a beneficial effect for the treatment of several retinal diseases.</p>
Data Report: "Health care of Persons Deprived of Liberty" Course from Brazil's Unified Health System Virtual Learning Environment
<p><strong>Dataset name: </strong>asppl-dataset.csv</p> <p><strong>Version: </strong>1.0</p> <p><strong>Dataset period: </strong>06/07/2018- 05/25/2021</p> <p><strong>Dataset Characteristics: </strong>Multivalued</p> <p><strong>Number of Instances: </strong>4861</p> <p><strong>Number of Attributes: </strong>33</p> <p><strong>Missing Values: </strong>Yes</p> <p><strong>Area(s): </strong>Health and education </p> <p><strong>Sources: </strong></p> <ul> <li> <p><strong>Primary</strong>: Unified Health System Virtual Learning Environment (AVASUS, in Portuguese: Ambiente Virtual de Aprendizagem do Sistema Único de Saúde) [1];</p> </li> <li> <p><strong>Secondary: </strong></p> <ol> <li> <p>Brazilian Classification of Occupations (CBO, in Portuguese: Classificação Brasileira de Ocupação) [2];</p> </li> <li> <p>National Registry of Health Establishments (CNES, in Portuguese: Cadastro Nacional de Estabelecimentos de Saúde) [3]; and </p> </li> <li> <p>Brazilian Institute of Geography and Statistics (IBGE, in Portuguese: Instituto Brasileiro de Geografia e Estatística) [4].</p> </li> </ol> </li> </ul> <p><strong>Description: </strong>The data contained on the asppl-dataset.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health care of Persons Deprived of Liberty”. The course is available on the Unified Health System Virtual Learning Environment [1]. This dataset provides elementary data for analyzing the course’s impact and reach, as well as the profile of its participants.</p> <p> </p>
Supplementary data for the article Linguistic system and sociolinguistic environment as competing factors in linguistic variation: A typological approach
<p>This material contains the dataset and the R-scripts from the <a href="https://version.helsinki.fi/gramadapt/linguistic-system-and-sociolinguistic-environment">gitlab repository</a> of the following article. Please cite the article when using the data.</p> <p>Sinnemäki, Kaius 2020. Linguistic system and sociolinguistic environment as competing factors in linguistic variation: A typological approach. <em>Journal of Historical Sociolinguistics</em> 6(2): 20190101. <a href="https://doi.org/10.1515/jhsl-2019-1010">https://doi.org/10.1515/jhsl-2019-1010</a></p>
Research data supporting "Considerations for Implementing Electronic Laboratory Notebooks in an Academic Research Environment"
<p><em>Research data supporting the publication:</em></p> <p><em>Higgins SG, Nogiwa-Valdez AA, Stevens MM, Considerations for Implementing Electronic Laboratory Notebooks in an Academic Research Environment, Nature Protocols, 2021.</em></p> <p>This repository contains the raw survey data of 172 current and historic electronic laboratory notebook (ELN) software packages.</p> <p>Main files:</p> <ul> <li>"ELN_Review_Higgins_2021_Survey.csv" = raw survey data in 'tidy' data format</li> <li>"ELN_Review_Higgins_2021.Rmd" = an R Markdown File (R Notebook) that takes the survey data as input and produces summary statistics and plots. This file was written using R Studio as the IDE.</li> </ul> <p>Derived files, generated from those above:</p> <ul> <li>"ELN_Review_Higgins_2021.nb.html" = a self-contained HTML file that is automatically generated by R Studio, based on the markdown file. This can be opened in any web browser to allow manual inspection of the code and comments without the need for specialist software. Embedded within this file is also the original markdown script (i.e. a copy of the code in "ELN_Review_Higgins_2021.Rmd")</li> <li>"ELN_Review_Higgins_2021_Lifetimes_Interactive_Figure1.html" = an HTML file generated by the script above via the plotly package. It contains an interactive version of the ELN survey data, allowing the user to hover over the timeline and explore the data.</li> <li>"ELN_Review_Higgins_2021_Timeline.pdf" = static version of ELN timeline, used to generate figure in main manuscript.</li> <li>"ELN_Review_Higgins_2021_Releases-Per-Year.pdf" = static version of number of new ELNs per year, used to generate figure in main manuscript.</li> </ul> <p>This survey was generated from a mixture of primary and secondary sources (see references for secondary sources).</p>
Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments
<p><strong>Context</strong></p><p>This dataset is part of the paper "Sonar-to-RGB Image Translation for Diver Monitoring in Poor Visibility Environments" presented at Oceans 2022, Hampton Roads,<strong> </strong>DOI: <a href="https://doi.org/10.1109/OCEANS47191.2022.9977024">10.1109/OCEANS47191.2022.9977024</a></p><p>This dataset consists of paired camera and multi-beam sonar images of technical divers performing different underwater tasks in two locations: an indoor test basin and a lake. The general goal is to assist emergency operators that monitor the safety of divers operating in bad visibility conditions.</p><p>This data was used to train image-to-image translation models in order to generate realistic optical-like images given only sonar images as input or a combination of a sonar image and a dark or turbid optical image.</p><p> </p><p><strong>Content</strong></p><p>This repository contains three .zip folders each containing data collected in a different lab or field trial.</p><ul><li>'basin-dataset-1.zip' and 'basin-dataset-2.zip' contain data that were collected in an indoor testing facility at DFKI - Robotics Innovation Center, Bremen, Germany.</li><li>'lake-dataset-1.zip' and 'lake-dataset-2.zip' contains data collected at lake Kreidesee, Hemmoor, Germany.</li></ul><p>Each .zip file contains two subfolders labelled as 'camera' and 'sonar', each containing the images in png format. Data files under these subfolders with matching names composes a pair of time-synchronized images. For example, 'camera/0001.png' corresponds to 'sonar/0001.png'. The acquisition timestamp represented in seconds since epoch for every data file is recorded in 'sample.csv' include in each .zip file.</p><p>For more details and meta-information on the collected data please refer to "data_description.json" included in this repository.</p><p>Additional tools for handling and preparing the data can be found under <a href="https://github.com/DeeperSense/oceans_2022">https://github.com/DeeperSense/oceans_2022</a></p><p> </p><p><strong>Acknowledgements</strong></p><p>The data in this repository were collected as a joint effort between the German Center for Artificial Intelligence (DFKI), the German Federal Agency for technical Relief (THW), and Kraken Robotics GmbH. This work is part of the project DeeperSense that received funding from the European Commission. Program H2020-ICT-2020-2 ICT-47-2020 Project Number: 101016958.</p><p>The authors would like to thank the Federal Government and the Heads of Government of the Länder, as well as the Joint Science Conference (GWK), for their initiative within the framework of the NFDI4Ing consortium (German Research Foundation (DFG) - project number 442146713).</p>
Performance Data of an Ice-Melting Probe from Field Tests in two Different Ice Environments
<p>This dataset was acquired at field tests of the steerable ice-melting probe "EnEx-IceMole" (Dachwald et al., 2014). A field test in summer 2014 was used to test the melting probe's system, before the probe was shipped to Antarctica, where, in international cooperation with the MIDGE project, the objective of a sampling mission in the southern hemisphere summer 2014/2015 was to return a clean englacial sample from the subglacial brine reservoir supplying the Blood Falls at Taylor Glacier (Badgeley et al., 2017, German et al., 2021).</p> <p>The standardized log-files generated by the IceMole during melting operation include more than 100 operational parameters, housekeeping information, and error states, which are reported to the base station in intervals of 4 s. Occasional packet loss in data transmission resulted in a sparse number of increased sampling intervals, which where compensated for by linear interpolation during post processing. The presented dataset is based on a subset of this data: The penetration distance is calculated based on the ice screw drive encoder signal, providing the rate of rotation, and the screw's thread pitch. The melting speed is calculated from the same data, assuming the rate of rotation to be constant over one sampling interval. The contact force is calculated from the longitudinal screw force, which es measured by strain gauges. The used heating power is calculated from binary states of all heating elements, which can only be either switched on or off. Temperatures are measured at each heating element and averaged for three zones (melting head, side-wall heaters and back-plate heaters).</p>
Data to reproduce analysis in "Systematic analysis of transcriptional and epigenetic effects of genetic variation in Kupffer cells enables discrimination of cell intrinsic and environment-dependent mechanisms"
<p>Here you can find the datasets necessary to reproduce all analyses described in the Glass lab paper by <a href="https://www.biorxiv.org/content/10.1101/2022.09.22.509046v1">Bennett et al</a>. The python and R code for reproducing analysis and figures can be found on our linked <a href="https://github.com/HunterBennett/KupfferCell_NaturalGeneticVariation">github repository.</a></p> <p>Briefly, this paper explores the effect of natural genetic variation <em>in vivo</em>, using Kupffer cells as a model cell type. We collect and analyze transcriptional and epigenetic data (ATAC-seq, H3K27Ac ChIP-seq) to identify putative <em>trans</em> regulators driving differential gene expression across inbred strains of mice. Additionally, we provide evidence that <em>trans</em> effects control a majority of strain differential genes at homeostasis while <em>cis</em> effects dominate the transcriptional response to an external signal (lipopolysaccharide).</p> <p>References:</p> <p>Hunter Bennett, Ty D. Troutman, Enchen Zhou, Nathanael J. Spann, Verena M. Link, Jason S. Seidman, Christian K. Nickl, Yohei Abe, Mashito Sakai, Martina P. Pasillas, Justin M. Marlman, Carlos Guzman, Mojgan Hosseini, Bernd Schnabl, Christopher K. Glass bioRxiv 2022.09.22.509046; doi: <a href="https://doi.org/10.1101/2022.09.22.509046">https://doi.org/10.1101/2022.09.22.509046</a></p> <p> </p>
Data in support of Primack et al. 2022 Frontiers in Ecology & Environment: Historically excluded groups in ecology are undervalued and poorly treated
Hostile workplaces undermine efforts to make the ecological sciences more inclusive and welcoming. A survey sent to the Ecological Society of America membership and ECOLOG-L listserv subscribers provides a snapshot of a range of workplace experiences in ecology. The results of this survey are published as Primack et al. 2022. Historically excluded groups in ecology are undervalued and poorly treated. Frontiers in Ecology and the Environment. This dataset includes the survey results and code for data analysis.
Regional Heat Vulnerability Map and Cooling Solutions: A webtool of the Healthy Urban Environments Initiative
## Regional Heat Vulnerability Map and Cooling Solutions The regional heat vulnerability map and cooling solutions webtool offers two data sources for equitable heat mitigation. The dashboard layers vulnerability data onto land surface temperature regional rankings to identify areas with high and low heat exposure and vulnerability as well as the existing assets in each census block group. Additional layers can be added into the heat vulnerability map to highlight how heat affects critical infrastructures including schools, mobile home parks, parking lots, public transportation stops, pedestrian thoroughfares, and bikeways. The solutions tab showcases a variety of heat mitigation solutions and the research behind them. Heat-related solutions and resources from urban Maricopa County are included, including solutions funded through the Healthy Urban Environment Initiative. The data catalogued here are the underlying data that populate the webtool. ## Healthy Urban Environment (HUE) Initiative - Overview HUE is a solutions-focused research, policy and technology incubator to create healthier communities across Maricopa County (central Arizona, USA) through collaboration between researchers, practitioners and community members. As such, HUE funded rapid development, testing and deployment of heat-mitigation and air-quality improvement strategies and technologies. Heat emerged as the urgent focus, as urban centers across the desert Southwest continue to grow in size and density, aggravating existing challenges posed by the expansion of the built environment. In Phoenix, AZ, this expansion of the built environment creates conditions which magnify the intensity and duration of heat – making it difficult for residents to achieve thermal comfort throughout the day and night. Further, the legacies of urban sprawl and transportation planning in the Phoenix, Arizona metropolitan area have contributed to challenges with atmospheric pollutants. Importantly, urban heat and air qua
Sampling sites where ecological indices were used to assess the impact of different environmental stressors in aquatic environments in Argentina
Dataset is a compilation of all sampling sites of articles where ecological indices were used to assess the impact of different environmental stressors in aquatic environments from Argentina. Points of this dataset were extracted from 78 papers published between 1996 and 2018. We selected articles that use ecological indices to analyze some local environmental problematics or stressors. Using the type of index from each article we performed the kml file, which contained the categorized sampling sites by different symbols according to the ecological index: physico-chemical, biological, geomorphological and multimetric. We carried out a map (shapefile) with all the sampling sites referenced to the ecoregions of Argentina proposed by Burkart (1999).
Annual summer single-day measurements of the thermal environment with a bio-meteorological sensor under trees, shade structures, and sun-exposed areas in the Rio Salado Park in Tempe, AZ, USA
We have measured the thermal environment/bio-meteorological conditions under trees, shade structures, and at sun-exposed locations in the Rio Salado Park in Tempe, AZ, USA annually since 2018 on a clear sky, hot, sunny day in June/July to track shade performance of newly planted trees over time. Measurements were taken with a mobile bio-meteorological weather station, known as MaRTy (Middel & Krayenhoff, 2019; DOI: 10.1016/j.scitotenv.2019.06.085). Since the measurement campaign began, some of the trees have died, were removed, or their environment has changed due to external events such as a bridge collapse (2020-07-29).
Aerial Photographs of the KBS LTER and Environs at the Kellogg Biological Station, Hickory Corners, MI (1938 to 2020)
Dataset Abstract Aerial photography is considered an important management tool in agriculture. Aerial photography allows researchers to detect spatial variability and understand the causes of the variability such as planter skips, drought stress, weeds and water erosion. In agricultural research it allows researchers to differentiate healthy vegetation from unhealthy and access plant biomass and moisture levels. The photographs are also useful to document trends and changes in the landscape. original data source http://lter.kbs.msu.edu/datasets/44
Effects of biotic (shrimp) and abiotic (discharge) factors on the depositional environment quantified in a montane stream in Puerto Rico. (Shrimp/Algae/Can J. Fish Aquat. Sci. (1994))
Effects of biotic (shrimp) and abiotic (discharge) factors on the depositional environment were quantified in a montane stream in Puerto Rico. Electricity was used experimentally to exclude large (approximately >1cm in length) biota without artificially increasing sedimentation as in cage enclosure/exclosure experiments in stream systems. Shrimp (>1cm in length) were excluded from substrata by semicircular fence hooked up to battery-powered fence chargers which emitted continuous pulses of electricity. Unelectrified control substrata had natural high densities of atyid shrimp. Significantly greater masses of total sediment, fine and large organic particles, and algal biovolume occurred in shrimp exclusion treatments relative to controls. Shrimp exclusion treatments experienced slow and steady accumulation of sediments under base flow conditions and a large stepwise increase in sediment following a storm. No measurable sediment accrued in the presence of natural densities of shrimp under base flow conditions. Shrimp rapidly removed sediments that accrued during the storm (440-620 g*m2 dry mass-1), decreasing sediment mass in control treatments to near prestorm levels (5-13 g*m2 dry mass-1) within 30 h. Atyid shrimp can significantly affect the accumulation of organic and inorganic materials on rock substrata in stream pools between high-discharge events. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Physical environment of the Luquillo Forest Dynamics Plot (LFDP), Puerto Rico
This file contains data that describe the physical and environmental attributes of the LFDP. The attributes include elevation, topography type, and percentage slope. All data are given for the 20 m by 20 m quadrat scale. Information on soils are taken form an interpolation of the soil map produced by the Natural Resources Conservation Service, US Department of Agriculture (Soil Survey 1995). Other information from the elevation of each of the corner posts defining the quadrats.The National Science Foundation requires that data from projects it funds are posted on the web two years after any data set has been organized and “cleanedâ€. The data from each census of the LFDP will be updated at intervals as each survey of the LFDP shows errors in the previous data collection. After posting on the web, researchers who are not part of the project are then welcome to use the data. Given the enormous amount of time, effort and resources required to manage the LFDP, obtain these data, and ensure data accuracy, LFDP Principal Investigators request that researchers intending to use this data comply with the requests below. Through complying with these requests we can ensure that the data are interpreted correctly, analyses are not repeated unnecessarily, beneficial collaboration between users is promoted and the Principle Investigators investment in this project is protected. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Germination in three R/FR light environments (El Verde)
Experiments were conducted in the Tabonuco forest at Luquillo Experimental Forest to determine the germination success of a number of species in different light environments. Species tested included: Byrsonima spicata, Calophyllum brasiliense, Carapa sp., Choven venosa, Guarea guidonia, Manilkara bidentata, Ochroma pyramidale and an unidentified species known locally as Jobo. Seeds were collected as they fell and placed on moist towling in horticulture trays at four sites. Seeds were kept moist and germination was recorded daily for 81 days and three times weekly for an additional 75 days. Light environments included a site exposed to full sun (FS) and sites with 55%, 75% and 80% cover. Instantaneous light measurements were made with a Licor 1800 spectroradiometer to determine the Red (660 nm) to Far-red (730 nm) ratio (R/FR) (Lee, 1987). Analysis of light data indicated that the four sites chosen provided three significantly different R/FR ratio environments, and that germination of some species was affected by the light environment (Smith, H. & Whitelam, G.C. 1990). Three species failed to germinate in any of the four light environments. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Functionally dissociable influences on learning rate in a dynamic environment
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.