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20 results for “crosswalks”
European Collaboration for Healthcare Optimisation (ECHO) Indicators Definition Crosswalks
<p><strong>European Collaboration for Healthcare Optimisation (ECHO) Indicators Definition Crosswalks</strong></p> <p>ECHO indicators rationale and code definition mapped out in ICD-9 and ICD-10 (for diagnoses) and ICD-9, NOMESCO, OPCS-4, ACHI and Leustungkatalog. </p> <p> </p>
Software Contributor Roles Crosswalk
<p>Initial version of a crosswalk of software contributor roles taxonomies, models, schemes, etc.</p> <p>Created during a <a href="https://software.ac.uk/cw23">Collaborations Workshop 2023</a> hack activity.</p>
Crosswalk between CESSDA Data Catalogue (CDC) Metadata Profile and ECRIN Metadata Schema.
<p>This dataset contains two files: (1) a crosswalk between CESSDA Data Catalogue (CDC) DDI2.5 Metadata Profile (<a href="https://cmv.cessda.eu/profiles/cdc/ddi-2.5/1.0.4/profile.html">https://cmv.cessda.eu/profiles/cdc/ddi-2.5/1.0.4/profile.html</a>) and ECRIN Metadata Schema for Clinical Research Data Objects Version 6.0 (August 2021) (<a href="https://zenodo.org/record/5554961">https://zenodo.org/record/5554961</a>) with an extension to “geographical data” and (2) vice versa.</p>
Crosswalk of most used metadata schemes and guidelines for metadata interoperability
<p>This resource provides crosswalks among the most commonly used metadata schemes and guidelines to describe digital objects in Open Science, including:</p> <ul> <li>RDA metadata IG recommendation of the metadata element set,</li> <li>EOSC Pilot - EDMI metadata set,</li> <li>Dublin CORE Metadata Terms,</li> <li>Datacite 4.3 metadata schema,</li> <li>DCAT 2.0 metadata schema and DCAT 2.0 application profile,</li> <li>EUDAT B2Find metadata recommendation,</li> <li>OpenAIRE Guidelines for Data Archives,</li> <li>OpenAire Guidelines for literature repositories 4.0,</li> <li>OpenAIRE Guidelines for Other Research Products,</li> <li>OpenAIRE Guidelines for Software Repository Managers,</li> <li>OpenAIRE Guidelines for CRIS Managers,</li> <li>Crossref 4.4.2 metadata XML schema,</li> <li>Harvard Dataverse metadata schema,</li> <li>DDI Codebook 2.5 metadata XML schema,</li> <li>Europeana EDM metadata schema, </li> <li>Schema.org, </li> <li>Bioschemas,</li> <li>The PROV Ontology.</li> </ul>
Crosswalks IUCLID 6 v9 EU PPP Microorganisms - active substance application (product) to Data Requirements
<p>The <strong>Excel file</strong> provides detailed crosswalks from the current Table of Content (ToC) for Microbial Plant Protection Product (PPP) dossier in <a href="https://iuclid6.echa.europa.eu/it/home">IUCLID 6 v.9</a> to Commission Regulation (EU) 283/2013 and Commission Regulation (EU) 284/2013 as amended by Commission Regulation (EU) 2022/1439 & Commission Regulation (EU) 2022/1440.</p> <p>There are two worksheets:</p> <ul> <li><strong>ACTIVE SUBSTANCE</strong> (283-2013): mapping between the current IUCLID working context "EU PPP Microorganisms - active substance information" to the Commission Regulation (EU) No 283/2013 as amended by Commission Regulation (EU) 2022/1439.</li> <li><strong>PRODUCT</strong> (284-2013): mapping between the the current IUCLID working context "EU PPP Microorganisms - active substance application (product)" to the Commission Regulation (EU) No 284/2013 as amended by Commission Regulation (EU) 2022/1440.</li> </ul> <p>The spreadsheets contain the following columns:</p> <ul> <li><strong>Data Requirements Section (Commission Reguation (EU) 2022/1439 or 2022/1440)</strong>: the name of the ToC section (in accordance with the new data requirements).</li> <li><strong>IUCLID section</strong>: the name of the ToC section in IUCLID.</li> <li><strong>Endpoint study record</strong>: name of the document template used to report individual studies of the section. These usually correspond to <a href="https://www.oecd.org/en/topics/sub-issues/assessment-of-chemicals/harmonised-templates.html">OECD Harmonised Templates (OHT)</a>. </li> <li><strong>Endpoint summary</strong>: name of the document template used to report the summary information for the section endpoints.</li> <li><strong>Other IUCLID document</strong>: name of any other document template in IUCLID used to report information of the section. </li> <li><strong>OHT</strong>: number of the OECD Harmonised Template used in the section.</li> <li><strong>Additional context</strong>: fulI IUCLID paths indicating the section of the respective document where information needs to be provided and/or specific values to be indicated. </li> </ul> <p>Note: <span>in cases where an IUCLID document is not included in the updated ToC this will be found in a specific section 'Documents applicable to the former data requirements' which can be found at the end of the dataset.</span></p> <p><strong>Version 5 </strong>includes changes in the table of contents of <a href="https://iuclid6.echa.europa.eu/it/home">IUCLID 6 v9</a>.</p> <p> </p> <p> </p>
Crosswalks IUCLID 6 v9 EU PPP Active substance application (product) to KCA&KCP
<p>This Excel file provides detailed crosswalks from the <a href="https://esubmission.croplifeeurope.eu/registration-for-download/">EU Table of Contents (SANCO/10181/2013)</a> for plant protection product (PPP) dossiers to <a href="https://iuclid6.echa.europa.eu/it/home">IUCLID 6 v9</a>. It includes two spreadsheets, containing the mappings for active substance (as laid out in Commission Regulation (EU) No 283/2013) and representative product (Commission Regulation (EU) No 284/2013):</p> <ul> <li><strong>EU_PPP_ActiveSubstance</strong>: mapping between the "KCA" ToC and IUCLID 6 v9 working context "EU PPP Active substance information"</li> <li><strong>EU_PPP_Product</strong>: mapping between the "KCP" ToC and IUCLID 6 v9 working context "EU PPP Active substance application (product)"</li> </ul> <p>The spreadsheets map each section of the original EU ToC to:</p> <ul> <li><strong><em>IUCLID section</em></strong>: name of the section in IUCLID where to input the corresponding data/information</li> <li><em><strong>Endpoint study record</strong></em>: name of the document template used to report individual studies of the section (if exists). These usually correspond to <a href="https://www.oecd.org/en/topics/sub-issues/assessment-of-chemicals/harmonised-templates.html">OECD Harmonised Templates (OHT)</a>.</li> <li><em><strong>Endpoint summary</strong></em>: name of the document template used to report the summary information of the studies presented in the section (if available)</li> <li><em><strong>Other IUCLID document</strong></em>: name of any other document template in IUCLID used to report information of the section (if available)</li> <li><em><strong>OHT</strong>:</em> name/id of the OECD Harmonised Template used for the endpoint study record document (if available)</li> <li><em><strong>Additional context</strong></em>: fulI IUCLID paths indicating the section of the respective document where information needs to be provided and/or specific values to be indicated. If more than one path is provided, these are separated by ";". Operators "=" and "!=" are used to indicate values to be used and to be avoided, respectively. When more than one value is possible, "IN" and "NOT IN" operators are used, followed by the exhaustive list of possible values. This nomenclature was defined with the aim of making contents machine-readable.</li> </ul> <p><strong>Version 6 </strong>includes changes in the table of contents of IUCLID 6 v9.</p>
Towards metadata for machine learning - Crosswalk tables
<p><strong>Crosswalks for Machine Learning models and datasets used for training</strong></p> <p>Here we present a collection of crosswalks for ML models (in TSV and XLSX formats) and datasets used for training (in TSV and XLSX formats). These crosswalks were created during an <a href="https://www.nfdi4datascience.de/">NFDI4DataScience</a> hackathon organized by the Semantic Technologies team (SemTec) at<a href="https://www.zbmed.de/en/"> ZB MED Information Centre for Life Sciences (ZB MED)</a> with the aim of provindg a starting point for a common proposal towards a metadata schema for ML models based on <a href="http://schema.org">schema.org</a>.</p> <p><strong>Files</strong></p> <ul> <li>2023.11.23 Metadata for ML - ML dataset Union.tsv: Crosswalks for datasets in TSV format</li> <li>2023.11.23 Metadata for ML - ML dataset Union.xlsx: Crosswalks for datasets in XLSX format</li> <li>2023.11.23 Metadata for ML - ML model union.tsv: Crosswalks for ML models in TSV format</li> <li>2023.11.23 Metadata for ML - ML model union.xlsx: Crosswalks for ML models in XLSX format</li> </ul>
Crosswalks IUCLID 6.6 EU PPP Microorganisms - active substance application (product) to KMA&KMP
<p>This Excel file provides detailed crosswalks from the <a href="https://esubmission.ecpa.eu/toc/EU">EU Table of Contents (SANCO/10181/2013)</a> for microbial plant protection product (PPP) dossiers to <a href="https://iuclid6.echa.europa.eu/">IUCLID 6.6</a>. It includes two spreadsheets, containing the mappings for active substance (as laid out in Commission Regulation (EU) No 283/2013) and representative product (Commission Regulation (EU) No 284/2013):</p> <ul> <li><strong>EU_PPP_Micro_ActiveSubstance</strong>: mapping between the "KMA" ToC and IUCLID 6.5's working context "EU PPP Microorganisms - active substance information"</li> <li><strong>EU_PPP_Micro_Product</strong>: mapping between the "KMP" ToC and IUCLID 6.5's working context "EU PPP Microorganisms - active substance application (product)"</li> </ul> <p>The spreadsheets map each section of the original EU ToC to:</p> <ul> <li><strong><em>IUCLID section</em></strong>: name of the section in IUCLID where to input the corresponding data/information</li> <li><em><strong>Endpoint study record</strong></em>: name of the document template used to report individual studies of the section (if exists). These usually correspond to OECD Harmonised Templates (OHT).</li> <li><em><strong>Endpoint summary</strong></em>: name of the document template used to report the summary information of the studies presented in the section (if exists)</li> <li><em><strong>Other IUCLID document</strong></em>: name of any other document template in IUCLID used to report information of the section (if exists)</li> <li><em><strong>OHT</strong>:</em> name/id of the OECD Harmonised Template used for the endpoint study record document (if exists)</li> <li><em><strong>Additional context</strong></em>: fulI IUCLID paths indicating the section of the respective document where information needs to be provided and/or specific values to be indicated. If more than one path is provided, these are separated by ";". Operators "=" and "!=" are used to indicate values to be used and to be avoided, respectively. When more than one value is possible, "IN" and "NOT IN" operators are used, followed by the exhaustive list of possible values. This nomenclature was defined with the aim of making contents machine-readable.</li> </ul> <p><strong>Version 3 </strong>includes changes in the table of contents of IUCLID6.6.</p>
Social Science Preregistration Metadata Crosswalk
<p>This spreadsheet tries to create a crosswalk for the main four social science study preregistration platfroms:</p> <ul> <li>OSF (Based on the fields for the preregistration challenge)</li> <li>EGAP</li> <li>The AEA's Social Science Registry</li> <li>RIDIE</li> </ul> <p>as well as the DataCite 4.0 Metadata Kernel</p>
Crosswalk between Source Classification Codes (SCCs) and 14 sectors of economy
<p>This dataset is a crosswalk between US EPA Source Classification Codes (SCCs) and sectors of economy. The 14 sectors include all anthropogenic emission sources, and exclude biogenic, wildfire, and international sources. The 14 sectors are: 1) agriculture (“Ag”); 2) coal electricity utility (“Coal Elec.”); 3) noncoal electricity utility (“Non-coal Elec.”); 4) commercial cooking (“Cooking”); 5) construction (“Const.”); 6) diesel heavy-duty vehicle (“Diesel HD Veh.”); 7) gasoline light-duty vehicle (“Gas LD Veh.”); 8) industrial; 9) road dust (“Road Dst”); 10) residential gas combustion (“Res. Gas”); 11) residential wood combustion (“Res. Wood”); 12) residential others (“Res. Other”); 13) off-highway vehicle and equipment (“Offroad”); and 14) miscellaneous (“Misc.”). </p> <p>The SCC list are downloaded from <a href="https://ofmpub.epa.gov/sccwebservices/sccsearch/">https://ofmpub.epa.gov/sccwebservices/sccsearch/</a> [Accessed Sep 22, 2022]. </p> <p>In the data table, the first column is the SCC; the second column is the classification of the 14 sectors; and the reset of the columns includes detailed description of each SCC from EPA. </p> <p> </p>
Crosswalk between ISRM grid and 2010 census block
<p>This dataset contain two files: (1) a crosswalk between <a href="https://zenodo.org/record/2589760">InMAP Source-Receptor Matrix (ISRM)</a> grid and census block (based on 2010 census); and (2) a shapefile of ISRM grid. </p>
CDSet: Crosswalk DataSet for Zebra Crossing Detection
<p>This dataset is a collection of crosswalk (zebra crossing) images collected on vehicle-mounted cameras, used to study advanced neural network algorithms for crosswalk detection and analysis of car crossing behavior.</p> <p>The dataset contains 3434 images, including real scenes such as daytime, rainy days, occlusion, deformation, truncation, night, damaged, dazzling, etc. The targets includes two categories: crosswalk and guide arrows. The dataset is divided into 3080 training sets and 354 test sets. In addition, this dataset also provides additional 1770 images labeled with or without crosswalk for testing.</p> <p>The correspoding paper is CDNet: a real-time and robust crosstalk detection network on Jetson nano based on YOLOv5, which can be found in <a href="https://doi.org/10.1007/s00521-022-07007-9">doi: 10.1007/s00521-022-07007-9</a> (<a href="https://rdcu.be/cHuc8">Read Paper</a>). The benchmark model is based on the previous SOTA model YOLOv5 and has undergone multiple improvements such as ROI, SE, NST, SSVM, Synthetic Fog, etc. The open source code can be found on <a href="https://github.com/zhangzhengde0225/CDNet">Github-CDNet</a>.</p> <p> </p>
cu-boulder/ceeb_nces_crosswalk: first release of ceeb_nces_crosswalk
<p>first release of ceeb crosswalk</p>
D6.1 Review, analysis and crosswalk of the data requirements of key policies
Open the record for dataset details and reuse information.
Metadata crosswalks for software management plans at NFDI4DS hackathon maSMP 2023
<p>Software Management Plans (SMPs) help formalize a set of structures and goals that ensure the research software is accessible and reusable in the short, medium and long term. Although not as common as the Data Management Plans, SMPs are gaining attention, with different communities providing examples and guidance on around it (e.g., ELIXIR SMPs, eScience Center in the Netherlands SMP Guidance and the Max Plank Digital Libraries SMP).</p><p>Machine-actionable SMPs (maSMPs) aim at providing a semantic layer on top of SMPs in the form of metadata schemas. Based on the <a href="https://doi.org/10.37044/osf.io/k8znb">ELIXIR SMPs</a> and inspired by the work done on the Research Data Alliance (RDA) <a href="https://github.com/RDA-DMP-Common/RDA-DMP-Common-Standard">machine-actionable Data Management Plan (maDMP)</a> application profile, the SemTec team at ZB MED has created an <a href="https://zenodo.org/doi/10.5281/zenodo.7806638">maSMP metadata schema</a> using <a href="https://schema.org/">schema.org</a> as base vocabulary. </p><p>The latest version of such maSMP metadata schema takes advantage of the crosswalks created at the NFDI4DS hackathon on maSMPs 2023.11.28 to 2023-12-01 organized by the SemTec team at ZB MED. The crosswalks data corresponding to Max Plank Digital Library SMP, <a href="https://doi.org/10.37044/osf.io/k8znb">ELIXIR SMPs</a>, and <a href="https://doi.org/10.14454/3w3z-sa82">DataCite Metadata Schema 4.4 - OutputManagementPlan</a> are included in this dataset collection. The files in the dataset collection are:</p><ul><li>"2023.11.29 maSMP crosswalk - NFDI4DS hackathon - Software.csv" - Crosswalk from the maSMP vr 2.0.0 to the Max Plank Digital Library SMP, <a href="https://github.com/KnowledgeCaptureAndDiscovery/somef">SOMEF</a>, and <a href="https://doi.org/10.37044/osf.io/k8znb">ELIXIR SMPs</a></li><li>"2023.11.29 maSMP crosswalk - NFDI4DS hackathon - MngPlan.csv" - Crosswalk from the maSMP to <a href="https://doi.org/10.14454/3w3z-sa82">DataCite Metadata Schema 4.4 - OutputManagementPlan</a> and the Max Plank Digital Library SMP</li><li>"2023.11.29 maSMP crosswalk - NFDI4DS hackathon - RDMO-SMP.csv" - Crosswalk from the Max Plank Digital Library SMP to metadata properties from different vocabularies</li><li>"Tables description.png" - Image showing a crosswalk template</li></ul><p>We acknowledge the feedback provided by other participants in the NFDI4DS hackathon on maSMPs (Esteban Gonzalez <a href="https://orcid.org/0000-0003-4112-6825">ORCID:0000-0003-4112-6825</a>, Yves Vincent Grossmann <a href="https://orcid.org/0000-0002-2880-8947">ORCID:0000-0002-2880-8947</a>, Carlos Utrilla Guerrero <a href="https://orcid.org/">ORCID:0000-0002-9994-1462</a>, Mariaisabel Gonzalez-Ocanto <a href="https://orcid.org/0000-0001-5485-9724">ORCID:0000-0001-5485-9724</a>,Thomas Pronk <a href="https://orcid.org/0000-0001-9334-7190">ORCID:0000-0001-9334-7190</a>, David Wallace <a href="https://orcid.org/0000-0001-8958-4601">ORCID:0000-0001-8958-4601</a>, Jürgen Windeck <a href="https://orcid.org/0000-0003-1909-4353">ORCID:0000-0003-1909-4353</a>).</p><p>The maSMP project is part of the NFDI4DataScience project funded by the German Research Foundation (DFG), project number 460234259. This dataset was created during the NFDI4DS hackathon on maSMPs at ZB MED 2023.</p><p> </p>
National Emissions Inventory crosswalk for the InMAP Source-Receptor Matrix (ISRM) dataset
<p>The InMAP Source-Receptor Matrix (ISRM) estimates the air quality impacts of emissions released from any source location in the contiguous United States to any receptor location. Specifically, the values in the ISRM dataset are the change in PM<sub>2.5</sub> concentration (µg m<sup>-3</sup>) in any receptor grid cell per unit of emissions (µg sec<sup>-1</sup>) in any source grid cell. ISRM was created from repeated runs of the Intervention Model for Air Pollution (<a href="http://spatialmodel.com/inmap/">InMAP</a>), isolating the impact of emissions from every grid cell in InMAP and from three emission heights representing ground-level, low-stack, and high-stack emissions. A file of the marginal impacts of emissions from each source location is also included ("marginal_values.csv"), which summarizes the estimates in ISRM by each source grid cell in terms of monetary damages ($ tonne<sup>-1</sup>), increased mortality (deaths tonne<sup>-1</sup>), and population exposure (population*µg m<sup>-3</sup> tonne<sup>-1</sup>).</p> <p>These files join the US EPA National Emissions Inventory to the ISRM to facilitate future analyses. The original ISRM file can be found here: <a href="https://zenodo.org/record/3590127#.Xt0Z4GhKhPb">https://zenodo.org/record/3590127#.Xt0Z4GhKhPb</a></p>
Pedestrian simulation results used for choosing optimal location for crosswalks
<p>Pedestrian simulation results generated by AntRoadPlanner simulation system for 2 areas in Saint-Petersburg. Used in article "Using Multi-Agent Simulation to Predict Natural Crossing Points for Pedestrians and Choose Locations for Mid-Block Crosswalks"</p> <p> </p>
Marked crosswalks in US transit-oriented station areas, 2007–2020: A computer vision approach using street view imagery
<p>This is the supplementary dataset for the article “Marked crosswalks in US transit-oriented station areas, 2007–2020: A computer vision approach using street view imagery” (<a href="https://journals.sagepub.com/doi/10.1177/23998083221112157">https://journals.sagepub.com/doi/10.1177/23998083221112157</a>) published on Environment and Planning B: Urban Analytics and City Science. The dataset contains comprehensive information of the presence of marked crosswalks (i.e., parallel-line and high-visibility) at each street intersection within a 250-meter buffer of all US TOD stations for each consecutive year between 2007 and 2020. We describe each field of the dataset below. Most of the column station variable names are taken directly from the National TOD Database from the <a href="https://toddata.cnt.org/">Center for Transit-Oriented Development</a>. </p> <ul> <li>intersecton_id: numeric, unique identifier of each intersection.</li> <li>tod_id: numeric, unique identifier of the associated TOD station. We retain all records of intersections that are associated with multiple TOD stations. </li> <li>year: numeric, one year between 2007 and 2020. </li> <li>plain: numeric, the number of presence of parallel-line crosswalks (bounding boxes) detected among the four 90-degree images at each intersection in that particular year. Zero for the years where GSV are not available. </li> <li>zebra: numeric, the number of presence of high-visibility crosswalks (bounding boxes) detected among the four 90-degree images at each intersection in that particular year. Zero for the years where GSV are not available. </li> <li>impute: binary, 1 if imputed; 0 otherwise. </li> <li>lon_intersection: longitude of each intersection obtained from OSM. </li> <li>lat_intersection: latitude of each intersection obtained from OSM. </li> <li>distance_intersection_to_tod: distance of each intersection from TOD station by meters. </li> <li>Agency: operating transit agency of the associated TOD station. </li> <li>Lines (s): transit lines of the associated TOD station. </li> <li>Station Name: station name of the associated TOD station. </li> <li>Year Opened: the year when the associated station opened. </li> <li>lat_tod: latitude of the associated TOD station obtained from the National TOD Database. </li> <li>lon_tod: longitude of the associated TOD station obtained from the Nation TOD Database. </li> <li>Both: numeric, the total number of marked (parallel-line + high-visibility) crosswalks detected among the four 90-degree images at each intersection in that particular year. </li> <li>plain_b: binary, 1 if presence of parallel-line crosswalk; 0 if otherwise. </li> <li>zebra_b: binary, 1 if presence of high-visibility crosswalk; 0 if otherwise. </li> <li>both_b: binary, 1 if presence of marked crosswalk; 0 if otherwise. </li> </ul>
New Ways of Mapping Knowledge Organization Systems. Using a SemiAutomatic MatchingProcedure for Building Up Vocabulary Crosswalks
<p>Abstract: Crosswalks between different vocabularies are an indispensable prerequisite for integrated and high­quality search scenarios in distributed data environments. Offered through the web and linked with each other they act as a central link so that users could move back and forth between different data sources being online available.<br> In the past, crosswalks between different thesauri have been primarily developed manually. In the long run the intellectual updating of such crosswalks requires huge personnel expenses. Therefore, an integration of automatic matching procedures, as for example Ontology Matching Tools, seems pretty obvious.<br> On the basis of computer­generated correspondences between the Thesaurus for Economics (STW) and the Thesaurus for the Social Sciences (TheSoz) our contribution will explore cross­border approaches between IT­assisted tools and procedures on the one hand<br> and external quality measurements via domain experts on the other hand. Thus, we will present techniques to semi­automatically perform vocabulary crosswalks. Due to intellectually evaluated results of multiple matching tools in the forerun, quality statements concerning the reliability of further computer­generated crosswalks can be made. This way, the application of various tools and procedures gradually contributes to an increase in quality. Moreover, on the long­term it facilitates a continuous update of high­quality vocabulary crosswalks.</p>
PUDL Raw EPA CAMD to EIA Data Crosswalk
<p>A file created collaboratively by EPA and EIA that connects EPA CEMS smokestacks (unitids) with corresponding EIA plant part ids reported in EIA Forms 860 and 923 (plant_id_eia, boiler_id, generator_id). This one-to-many connection is necessary because pollutants from various plant parts are collectively emitted and measured from one point-source. Archived from <a href="https://github.com/USEPA/camd-eia-crosswalk">https://github.com/USEPA/camd-eia-crosswalk</a></p> <p>This archive contains raw input data for the Public Utility Data Liberation (PUDL) software developed by <a href="https://catalyst.coop">Catalyst Cooperative</a>. It is organized into <a href="https://specs.frictionlessdata.io/data-package/">Frictionless Data Packages</a>. For additional information about this data and PUDL, see the following resources:</p> <ul> <li><a href="https://github.com/catalyst-cooperative/pudl">The PUDL Repository on GitHub</a></li> <li><a href="https://catalystcoop-pudl.readthedocs.io">PUDL Documentation</a></li> <li><a href="../communities/catalyst-cooperative/">Other Catalyst Cooperative data archives</a></li> </ul> <p> </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.