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1,580 results for “Vulnerability”
45 Vulnerability Discoverability Timelines from the 2019 Collegiate Penetration Testing Competition
<p><em><strong>Description</strong></em></p> <p>This is a collection of manually curated timelines from the 2019 Collegiate Penetration Testing Competition (CPTC). Collection and annotation are described in detail in this publication:</p> <ul> <li>Benjamin S. Meyers, Sultan Fahad Almassari, Brandon N. Keller, and Andrew Meneely. Examining Penetration Tester Behavior in the Collegiate Penetration Testing Competition. Forthcoming at Transactions on Software Engineering and Methodology. https://dl.acm.org/doi/10.1145/3514040</li> </ul> <p><em><strong>Included Files</strong></em></p> <ul> <li><strong><em>2019_cptc_timelines.csv</em>:</strong> Completed timelines for ten teams from the 2019 CPTC nationals competition.</li> <li><strong><em>2019_cptc_timeline_columns.csv</em>:</strong> Descriptions of the columns in <strong><em>2019_cptc_</em></strong><em><strong>timelines.csv</strong></em>.</li> <li><strong><em>2019_cptc_vulnerabilities.csv</em>:</strong> Brief vulnerability descriptions and CWE mappings.</li> </ul> <p><em><strong>Other Resources</strong></em></p> <ul> <li>Complete Splunk log data dumps are available <a href="http://mirrors.rit.edu/cptc/2019/mirrors/nationals/">here</a>. These must be ingested and viewed with a Splunk instance.</li> <li>To request access to the CPTC team reports, please contact Brock Wagehoft (<a href="mailto:bew1127@rit.edu">email</a>).</li> </ul> <p><em><strong>Contact</strong></em></p> <p>Please contact Benjamin S. Meyers (<a href="mailto:bsm9339@rit.edu">email</a>) with questions about this data and its collection.</p> <p><em><strong>Acknowledgments</strong></em></p> <p>Collection of this data has been sponsored in part by the National Science Foundation grant 1922169, and by a Department of Defense DARPA SBIR program (grant 140D63-19-C-0018).</p>
Indicators and socio-spatial vulnerability index
<p>This table contains all variables used to compute the socio-spatial vulnerability index and the values of this index, at the dristrict scale, on the coastal zone of Bangladesh (16 districts).</p>
Data and Material for 'Less is More: Supporting Developers in Vulnerability Detection during Code Review'
<p>Data and Material supporting the paper 'Less is More: Supporting Developers in Vulnerability Detection during Code Review'.</p>
Deciphering the Neurosensory Olfactory Pathway and Associated Neo-Immunometabolic Vulnerabilities Implicated in COVID-Associated Mucormycosis (CAM) and COVID-19 in a Diabetes Backdrop—A Novel Perspective
<p>Raw data files of transcriptomic profiling experiments, which form the basis for our publication (https://www.mdpi.com/2673-4540/3/1/13).</p>
La force des positions vulnerables : hégémonie financière et potentiel disruptif
<p>Dataset and R script related to the paper <a href="https://arcs.episciences.org/9233">La force des positions vulnérables : Hégémonie financière et potentiel disruptif</a>, <em>ARCS</em>, 2018.</p>
Data from: Elevated fires during COVID-19 lockdown and the vulnerability of protected areas
<p><strong>Related article:</strong> Johanna Eklund, Julia P G Jones, Matti Räsänen, Jonas Geldmann, Ari-Pekka Jokinen, Adam Pellegrini, Domoina Rakotobe, O. Sarobidy Rakotonarivo, Tuuli Toivonen, and Andrew Balmford. Elevated fires during COVID-19 lockdown and the vulnerability of protected areas. Nature Sustainability (2022) https://doi.org/10.1038/s41893-022-00884-x.</p> <p><strong>In this dataset:</strong></p> <p>This dataset contains information about monthly fire incidence and precipitation for the protected areas of Madagascar from January 2012 to December 2020. The fire data is sourced from NASA’s Visible Infrared Imaging Radiometer Suite (VIIRS) 375 m active fire product and the precipitation data from the Global Precipitation Measurement (GPM) mission (for years 2016-2020) and its predecessor The Tropical Rainfall Measuring Mission (TRMM) (for years 2011-2015) at spatial resolution 10 km. The fire and precipitation data was overlayed with the protected area polygons of the June 2020 release of the World Database of Protected Areas. For sources and more details on how the data was compiled see the related article. The data can be used to inspect temporal dynamics of wildfires inside protected areas and for informing adaptive protected area management and planning.</p> <p><strong>Please cite this dataset as:</strong></p> <p>Johanna Eklund, Julia P G Jones, Matti Räsänen, Jonas Geldmann, Ari-Pekka Jokinen, Adam Pellegrini, Domoina Rakotobe, O. Sarobidy Rakotonarivo, Tuuli Toivonen, and Andrew Balmford. Elevated fires during COVID-19 lockdown and the vulnerability of protected areas. Nature Sustainability (2022) https://doi.org/10.1038/s41893-022-00884-x.</p> <p><strong>Column names</strong></p> <p>NAME: Name of protected area</p> <p>Fires_sum: Number of observed fires (VIIRS)</p> <p>Month: Month</p> <p>Year: Year</p> <p>Precipitation: Precipitation (mm)</p> <p>Plag_1:Plag_12: Precipitation during previous month; 2 months ago; 3 months ago…12 months ago</p> <p>YEAR_CREAT: Year of establishment of protected area</p> <p>Biome: Biome</p> <p>REP_AREA: Area of protected area (km<sup>2</sup>)</p> <p>Fires_per_km2: Fires per km<sup>2</sup></p> <p>Prec_acc_12m: Accumulated precipitation during the last 12 months</p> <p>fBiome: Biome as factor</p> <p>fNAME: Name as factor</p> <p>sPrecipitation: Precipitation (scaled; see Methods section of article)</p> <p>sPlag_1: Precipitation in previous month (scaled; see Methods section of article)</p> <p>sPrec_acc_12m: Accumulated precipitation during the last 12 months (scaled; see Methods section of article)</p> <p>Pred_Zinb_1a: Predicted fires (see Methods section of article)</p> <p>Diff_Zinb_1a: Difference: Observed fires - predicted fires</p> <p>Year_pred: Year for prediction</p> <p><strong>License</strong><br> Creative Commons Attribution 4.0 International.</p>
Dataset: A Fly in the Ointment: An Empirical Study on the Characteristics of Ethereum Smart Contracts Code Weaknesses and Vulnerabilities
<p>Dataset: A Fly in the Ointment: An Empirical Study on the Characteristics of Ethereum Smart Contracts Code Weaknesses and Vulnerabilities</p> <p>Majd Soud, Grischa Liebel, Mohammad Hamdaqa<br> majd18@ru.is, grischal@ru.is, mhamdaqa@polymtl.ca.</p> <p>This Dataset includes the following: </p> <p>1. "labeling.xml" files that represents the data for Categories of vulnerabilities in Smart Contracts for four data sources (i.e., Common Vulnerability and Exposure (CVE), Smart Contract Weakness Classification Registry (SWC), Stack Overflow, and GitHub)<br> XML files structure:<br> The XML files can be opened used any editor or any code editor (e.g. Visual Studio Code).</p> <p>1. Each file has a root that is <Card_Table></Card_Table> which contains all the cards we labeled. <br> 2. Each card is represented by the <Card></Card> and contains the following:<br> - The tag marked by <Tag> represents the keyword that was used to search and collect the card from StackOverflow. <br> - The URL marked by <URL> of the URL link which contains all the information of the labeled vulnerability. <br> - The other tages marked by <OtherTags> that shows all the tags used in the post on Stack Overflow. <br> - The expert labeling for the categories of vulnerabilities in each card is represented by <CategoryExpertLabel><br> - In more details, some records has the <SecondExpertCategoryLabel> that represents the second expert labeling for the categories of vulnerabilities.<br> - The <CategoryAgreement> used to calculate the inter-rater agreement between the two labelers. <br> </p>
Artifacts for the ISSTA 2022 Paper: An Empirical Study on the Effectiveness of Static C Code Analyzers for Vulnerability Detection
<p>This repository contains the evaluation script and the corresponding data of the ISSTA'22 paper "An Empirical Study on the Effectiveness of Static C Code Analyzers for Vulnerability Detection".</p>
Global patterns in endemicity and vulnerability of soil fungi
<p>This repository contains the data associated with the paper Tedersoo et al. (2022) <em>Global patterns in endemicity and vulnerability of soil fungi</em> // <strong>Global Change Biology</strong>. DOI:10.1111/gcb.16398</p> <p>Fungi are highly diverse organisms and provide a wealth of ecosystem functions. However, distribution patterns and conservation needs of fungi have been very little explored compared to charismatic animals and plants. Here we assess endemicity patterns, global change vulnerability and conservation priority areas for functional groups of soil fungi based on six global surveys using a high-resolution, long-read metabarcoding approach. Endemicity of all fungi and most functional groups peaks in tropical habitats, including Amazonia, Yucatan, West-Central Africa, Sri Lanka and New Caledonia, with a negligible island effect compared with plants and animals. We also found that fungi are vulnerable mostly to drought, heat and land cover change, particularly in dry tropical regions with high human population density. Fungal conservation areas of highest priority include herbaceous wetlands, tropical forests and woodlands. We suggest that there should be more attention focused on the conservation of fungi, especially tropical root symbiotic arbuscular mycorrhizal and ectomycorrhizal fungi, unicellular early-diverging groups and macrofungi in general. Given the low overlap between endemicity of fungi and macroorganisms, but high matching in conservation needs, detailed analyses on distribution and conservation requirements are warranted for other microorganisms and soil organisms in general.</p> <p>This repository contains the following data associated with the publication:</p> <ul> <li>Supplementary tables S1 - S6 (`<strong>Tables_S1-S6.xlsx</strong>`):</li> </ul> <p>- Table S1. Definition of ecoregions and assignment of samples to ecoregions<br> - Table S2. GSMc dataset used for endemicity analyses<br> - Table S3. Dataset used for modeling endemicity values<br> - Table S4. Dataset used for calculating and mapping vulnerability scores<br> - Table S5. Dataset used for calculating and mapping conservation value<br> - Table S6. Additional funding sources by authors</p> <ul> <li>OTU distribution by samples and ecoregions (`<strong>Data_taxon_assignment_to ecoregions.xlsx</strong>`)</li> </ul> <p>Gridded maps:</p> <ul> <li>Conservation priorities for all fungi and fungal groups</li> </ul> <p>- ConservationPriority_AllFungi.tif<br> - ConservationPriority_AM.tif<br> - ConservationPriority_EcM.tif<br> - ConservationPriority_Moulds.tif<br> - ConservationPriority_NonEcMAgaricomycetes.tif<br> - ConservationPriority_OHPs.tif<br> - ConservationPriority_Pathogens.tif<br> - ConservationPriority_Unicellular.tif<br> - ConservationPriority_Yeasts.tif</p> <ul> <li>The average vulnerability of all fungi and fungal groups and the model uncertainty estimates</li> </ul> <p>- AverageVulnerability_AllFungi.tif<br> - AverageVulnerability_AM.tif<br> - AverageVulnerability_EcM.tif<br> - AverageVulnerability_Moulds.tif<br> - AverageVulnerability_NonEcMAgaricomycetes.tif<br> - AverageVulnerability_OHPs.tif<br> - AverageVulnerability_Pathogens.tif<br> - AverageVulnerabilityUncertainty_AllFungi.tif<br> - AverageVulnerabilityUncertainty_AM.tif<br> - AverageVulnerabilityUncertainty_EcM.tif<br> - AverageVulnerabilityUncertainty_Moulds.tif<br> - AverageVulnerabilityUncertainty_NonEcMAgaricomycetes.tif<br> - AverageVulnerabilityUncertainty_OHPs.tif<br> - AverageVulnerabilityUncertainty_Pathogens.tif<br> - AverageVulnerabilityUncertainty_Unicellular.tif<br> - AverageVulnerabilityUncertainty_Yeasts.tif<br> - AverageVulnerability_Unicellular.tif<br> - AverageVulnerability_Yeasts.tif</p> <ul> <li>The relative importance of predicted vulnerability of all fungi</li> </ul> <p>- RelativeImportanceOfVulnerability_AllFungi.tif</p> <ul> <li>Vulnerability to drought, heat, and land cover change for all fungi</li> </ul> <p>- Vulnerability_AllFungi_Heat-Drought-LandCoverChange.tif<br> - VulnerabilityUncertainty_AllFungi_Heat-Drought-LandCoverChange.tif</p> <ul> <li> Human footprint index based on the Land-Use Harmonisation (LUH2; Hurtt et al., 2020, doi:10.5194/gmd-13-5425-2020) - `<strong>LandCoverChange_1960-2015.tif</strong>`</li> <li> MD5 checksums for all files (`<strong>MD5.md5</strong>`)</li> </ul> <p>Fungal groups:<br> - <strong>AM</strong>, arbuscular mycorrhizal fungi (including all Glomeromycota but excluding all Endogonomycetes)<br> - <strong>EcM</strong>, ectomycorrhizal fungi (excluding dubious lineages)<br> - <strong>NonEcMAgaricomycetes</strong>, non-EcM Agaricomycetes (mostly saprotrophic fungi with usually macroscopic fruiting bodies)<br> - <strong>Moulds</strong> (including Mortierellales, Mucorales, Umbelopsidales and Aspergillaceae and Trichocomaceae of Eurotiales and Trichoderma of Hypocreales)<br> - Putative <strong>pathogens</strong> (including plant, animal and fungal pathogens as primary or secondary lifestyles)<br> - <strong>OHPs</strong>, opportunistic human parasites (excluding Mortierellales)<br> - <strong>Yeasts</strong> (excluding dimorphic yeasts)<br> - <strong>Unicellular</strong>, other unicellular (non-yeast) fungi (including chytrids, aphids, rozellids and other early-diverging fungal lineages)</p> <p>Detailed processing steps can be found here:<br> <a href="https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability">https://github.com/Mycology-Microbiology-Center/Fungal_Endemicity_and_Vulnerability</a></p>
VulnMiner: A Comprehensive Framework for Vulnerability Collection from C/C++ Source Code Projects
<p>In this repository, we present an initial release of the VulnMiner vulnerability dataset, curated from prevalent projects and annotated with vulnerable and benign instances. This dataset incorporates projects with vulnerabilities labeled as Common Weakness Enumeration (CWE) categories. The developed open-source extraction tool collects vulnerability data utilizing static security analyzers. The study also fosters the machine learning (ML) and natural language processing (NLP) model's effectiveness in accurately classifying vulnerabilities, evidenced by its identification of numerous weaknesses in open-source projects.</p>
VulneraCity - The urban vulnerability drivers database
<p>VulneraCity is a database of unique urban vulnerability drivers for six different hazards (Coastal flooding, Pluvial flooding, Earthquakes, Heatwaves, Drought, Waterborne diseases), providing descriptions, classifications, and sources. The drivers are collected from over 450 individual studies, based on a systematic literature review. For more info, please see our accompanying paper (please cite this when using VulneraCity in your own work): </p> <p><strong>Stolte, T. R., Koks, E. E., De Moel, H., Reimann, L., Van Vliet, J., De Ruiter, M. C., & Ward, P. J. (2024). VulneraCity–drivers and dynamics of urban vulnerability based on a global systematic literature review. <em>International Journal of Disaster Risk Reduction, 108,</em> 104535. <a href="https://doi.org/10.1016/j.ijdrr.2024.104535">https://doi.org/10.1016/j.ijdrr.2024.104535</a> </strong></p> <p> </p>
Replication Package for the Paper Titled "How Well Do Software Practitioners Fix Code Vulnerabilities with Different Types of Explanations?"
<p>This is a replication package for the article 'How Well Do Software Practitioners Fix Code Vulnerabilities with Different Types of Explanations?'. The survey questions can be found here, and we encourage the survey to be re-used.</p> <p>We also include survey data (with demographic data and qualitative responses removed for anonymity reasons).</p> <p>The project team consists of Tracy Hall, Emily Winter, Fahad Al Debeyan (Lancaster University) and Lech Madeyski (Wroclaw University of Science and Technology). If you have any questions about the re-use of this survey, feel free to contact Fahad at <a href="mailto:e.winter@lancaster.ac.uk">f.aldebeyan@lancaster.ac.uk</a>.</p>
Vul4J+: A Dataset of Vulnerabilities for Automated Vulnerability Repair
<div> <div><strong>Vul4J+</strong> is a dataset of vulnerability fixes for automated vulnerability repair (AVR) in Java. Each entry of the dataset represents a <strong>vulnerability</strong> affecting an open-source Java project, having reference to the commit (revision) containing the code affected by the vulnerability and its version fixed by a human developer (the "left" and "right" parts of the commit). Each vulnerability is equipped with at least one <strong>"oracle"</strong> that shows the presence of the vulnerability, and that can be used to validate the correctness of patches generated by AVR tools. This *"oracle"* might have the form of a:</div> <div>- <strong>Vulnerability-witnessing test</strong>, i.e., a JUnit test case that fails on the vulnerable version of the code but passes on the patched version.</div> <div>- <strong>Warning/report</strong> raised by a vulnerability static analyzer, i.e., SpotBugs, that is presented in the vulnerable version of the code but not in the patched version.</div> <br> <div>In essence, Vul4J+ is a cleaned up and extended version of Vul4J containing:</div> <div>- 106 known vulnerabilities with executable vulnerability-witnessing test cases in Docker containers and warnings (reports) from SpotBugs static analyzer (if found);</div> <div>- 79 come from the original Vul4J;</div> <div>- 27 result from the replication of the same protocol used in the original Vul4J;</div> <div>- 50 vulnerabilities stored in Docker containers with the warnings (reports) from SpotBugs static analyzer ;</div> <div>- 35 known vulnerabilities matched with vulnerability-witnessing test cases retrieved from projects in the wild.</div> <br> <div>In total, Vul4J+ points to <strong>191 vulnerabilities</strong>, each with at least one vulnerability oracle.</div> </div>
Dataset: Physical Vulnerability Database for Critical Infrastructure Hazard Risk Assessments
<p>The Physical Vulnerability Database for Critical Infrastructure Hazard Risk Assements is a database that contains fragility and vulnerability curves that can be used to evaluate the expected or potential damages to infrastructure assets due to flooding, earthquakes, windstorms and landslides. The database consists of three Excel-spreadsheets:</p> <ul> <li><em>Table_D1_Summary_CI_Vulnerability_Data:</em> summary table with information on hazard, exposure, and vulnerability characteristics as well as a number of details regarding reliability and reference purposes.</li> <li><em>Table_D2_Hazard_Fragility_and_Vulnerability Curves:</em> collection of fragility and vulnerability curves</li> <li><em>Table_D3_Costs:</em> cost values that can be used in combination with the curves for the estimation of asset damages</li> </ul> <p>Please consult the following publication for detailed information: Nirandjan, S., Koks, E. E., Ye, M., Pant, R., van Ginkel, K. C. H., Aerts, J. C. J. H., and Ward, P. J.: Review article: Physical Vulnerability Database for Critical Infrastructure Multi-Hazard Risk Assessments – A systematic review and data collection, Nat. Hazards Earth Syst. Sci. Discuss. [preprint], https://doi.org/10.5194/nhess-2023-208, in review, 2024.</p>
Replication Package for the Paper Titled "Emerging Results in Using Explainable AI to Improve Software Vulnerability Prediction"
<p>This is a replication package for the paper titled "Emerging Results in Using Explainable AI to Improve Software Vulnerability Prediction".</p>
DATASETS and OUTCOMES - Assessment of intrinsic aquifer vulnerability at continental scale through a critical application of the DRASTIC method: the case of South America
<p>A robust and comprehensive assessment of intrinsic aquifer vulnerability at continental scale map may represent an essential initial step towards a more sustainable land-use and water management.</p> <p>This repository contains the outcomes of an intrinsic aquifer vulnerability assessment of South America, performed by the DRASTIC method. The assets included in this repository are mainly raster maps (.tif, .geotif), created and georeferenced in QGIS (v3.16). Coordinate reference system (CRS) of the dataset is WGS84.</p> <p>Technical specifications of all graphical outcomes are stored in a dedicated file (README.txt).</p>
AssureMOSS Vulnerability Statements Dataset (Tracer)
<p>This dataset contains 307 vulnerability statements found with <a href="https://sap.github.io/project-kb/prospector/">Prospector</a>, an open-source repository mining tool developed by SAP Security Research and the <a href="https://assuremoss.eu">AssureMOSS consortium</a>.</p> <p>The vulnerabilities covered by this dataset are a subset of those that appear in the "depth" dataset at <a href="https://patch-tracer.github.io/">https://patch-tracer.github.io/</a></p>
Assessment of the Vulnerability of Permafrost Carbon to Climate Change: A Sensitivity Analysis among Models
This activity is a comparison of how large-scale models represent permafrost carbon dynamics into the future (2010-2299). Model responses were evaluated at several temporal scales. To the extent possible, we standardized driver data and simulation procedures among the models. However, the protocol has been set up so that each model can build upon the procedures used to produce the outputs for historical analysis (1960- 2009) that was published in McGuire et al. 2016 (Global Biogeochemical Cycles 30:1015-1037, doi:10.1002/2016GB005405). Note that this comparison is an offline model comparison in which we assessed the sensitivity of the responses of the models to somewhat standardized forcing data. The activity compared among the models: Carbon dynamics: Predictions of average annual C fluxes (GPP, NPP, RH, CH4 fluxes, disturbance-related emissions, dissolved organic carbon export, lateral land used fluxes, etc.) and major pools for the northern permafrost region for the 2010-2299 period. Soil thermal dynamics: Predictions of annual soil thermal and hydrological dynamics at prescribed depths and the maximum annual active layer depth (in permafrost locations) for the 2010-2299 time period. The spatial simulation data for this project are are available through the National Snow and Ice Data Center (doi: 10.5067/ZRL5WJKN01XM).
Social and Heat Vulnerability Indices in Phoenix, Arizona
Vulnerability indices and maps are commonly employed by researchers and practitioners to assess hazard risk by combining variables that are theoretically or empirically associated with hazard outcomes and spatially visualizing those combined variables. For this dataset, we followed established methods to produce two vulnerability indices for 358 census tracts in the City of Phoenix, Arizona for the year 2016: the all-hazards Social Vulnerability Index (SoVI) and a specific hazards Heat Vulnerability Index (HVI). For SoVI, we compiled 27 social variables from the 2012-2016 American Community Survey (ACS); for HVI, we compiled seven social variables from the 2012-2016 ACS, one variable regarding residential air conditioning prevalence from the Maricopa County Assessor’s Office, and two variables related to vegetation density from Landsat 8 remote sensing imagery. Lastly, we conducted principal components analysis on each of the indices respective variables and then summed the resulting component scores for each census tract to produce the index values which we then spatially joined to the Phoenix census tracts.
Hydrodynamic and morphological information, and the absolute variations of the vulnerability indices for the period 2000-2015 of the Spanish Iberia Peninsula estuaries.
<p>The dataset included in this repository was obtained during the project entitled 'Sensibilidad física y biotic de los estuarios peninsulares al cambio global (SENSES)' funded by 'Fundación Biodiversidad', PRCV00487. The data were used in the research article 'Sensitivity of Iberian estuaries to changes in sea water temperature, salinity, river-flow, mean sea level, and tidal amplitudes' submitted to <em>Estuarine, Coastal and Shelf Science</em>.</p> <p>Brief description of dataset:</p> <p>For each estuary, the following parameters were calculated</p> <ul> <li>Fachade: the location of the estuary</li> <li>Area (km<sup>2</sup>) </li> <li>D (m): water depth at the mouth of the estuary in 2000 and 2015</li> <li>Tidal Prim (m<sup>3</sup>)</li> <li>Q<sub><em>f</em></sub> (m<sup>3</sup>/s): river flow in 2000 and 2015</li> <li><em>a </em>(m): tidal amplitude of the free surface elevation in 2000 and 2015</li> <li>∆<em>U</em> (m/s): absolute variation of the tidal current amplitude between 2000 and 2015</li> <li>∆<em>E</em> (W/m<sup>2</sup>): absolute variation of the tidal energy flux propagation index between 2000 and 2015</li> <li>∆<em>Ri </em>: absolute variation of the bulk Richardson number index between 2000 and 2015</li> <li>∆<em>SI</em>: absolute variation of the salinity intrusion index between 2000 and 2015</li> </ul> <p>A wide description of the parameters can be found in Serrano, M. A. et al (submitted to <em>Estuarine, Coastal and Shelf Science</em>)</p> <p>Contact person: mserranog@ugr.es</p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.