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339 results for “Fairness”
Fair For Fusion Demonstrator I
<p>Demonstration video of the Fair for Fusion Demonstrator I.</p>
Distribution. Ethiopia, fairly widespread in the W Ethiopian highlands and at least one locality (Hirna) E of Ethiopian Rift. in Muridae
Distribution. Ethiopia, fairly widespread in the W Ethiopian highlands and at least one locality (Hirna) E of Ethiopian Rift.
Nomad-FAIR North-Remote-Tool Example Dataset Electron Microscopy
<p><strong>Dataset.Objective.Aperture.Energy.Slit.cdf.nx5.zip<br> Dataset.Objective.Aperture.Energy.Slit.dm3.cdf.zip<br> parser-focalseries-gatandm-cdf2nx5.py</strong><br> An example dataset for focus-series reconstruction (in-line holography).<br> The dataset supplements/comes from a research study <a href="https://doi.org/10.1016/j.ultramic.2021.113264">https://doi.org/10.1016/j.ultramic.2021.113264</a></p> <p><strong>em-sprint09-example.zip</strong></p> <p>This archive contains an example YAML file how metadata to e.g. 1613_Si_HAADF_610_kx.emd<br> (see below) are stored internally in a NOMAD OASIS when saving entries into the customized<br> ELN schema of the EM example that was developed in Sprint09 Area B in the FAIRmat project.<br> This eln_data.yaml file serves as an input to the dataconverter/em reader tool to supplement<br> the numerical data from the 1613_Si_HAADF_610_kx.emd file with metadata during the creation<br> of the emd.nxs file.</p> <p>Furtermore, this archive contains a Bruker BCF, a Velox EMD, and a Digital Micrograph v3 DM3<br> file. These file formats can be used as input to the dataconverter/em reader tool to inject<br> specific numerical data while creating an instance of an NXem-compliant NeXus file.</p> <p>The files have the following SHA256 checksum to verify their integriety:</p> <p>sha256sum eln_data.yaml<br> c0fd230336ce7cd8e2715445ea6af404202b4ab9d7e7186838748bd0559d536f</p> <p>sha256sum 46_ES-LP_L1_brg.bcf<br> bf5ca0f862af49726b848a8a60d73af6f1669990131288ac4b4ca6cb57e58aff</p> <p>sha256sum 1613_Si_HAADF_610_kx.emd<br> 6ed0e8b8c170a0ed307e5c877fd891767a38e636f4680632e9b00c46a2b963b9</p> <p>sha256sum EELS_map_2_ROI_1_location_4.dm3<br> b97850f6c6b100740813c34d20eda294ef66a6130daa42a6cf975c4572ff599c</p> <p> </p>
Hur FAIR är svensk digitiserad kulturarvsdata idag Appendix Tabeller
<p>Tabelldata för artikeln "Hur FAIR är svensk digitiserad kulturarvsdata idag?"</p>
Supplementary_FAIR_clay
Open the record for dataset details and reuse information.
Fairness in Diffusion Recommender Models: Insights from an Empirical Study of DiffRec
<p>Dataset for the experiments of the paper submission `Fairness in Diffusion Recommender Models: Insights from an Empirical Study of DiffRec`. The included datasets are Foursquare Tokyo (FKTY), MovieLens 1M with all attributes and users (ML1M).</p>
Fair Graph Augmentation for Graph Collaborative Filtering
<p>Dataset for the paper submission `Fair Graph Augmentation for Graph Collaborative Filtering`. The included datasets are Foursquare New York City (FNYC), Foursquare Tokyo (FKTY), MovieLens 1M (ML1M), Last.FM 1M (LF1M), Rent The Runway (RENT)</p>
FAIRness of Repositories & Their Data: A Report from LIBER's Research Data Management Working Group
<p>Data repositories play a crucial role in the evolution of Open Science. The FAIR Data Principles establish how to make data Findable, Accessible, Interoperable and Reusable (Wilkinson et al., 2016). The FAIR principles are as follows: </p> <p><strong>To Be Findable</strong></p> <ul> <li>F1. (meta)data are assigned a globally unique and eternally persistent identifier.</li> <li>F2. data are described with rich metadata.</li> <li>F3. (meta)data are registered or indexed in a searchable resource.</li> <li>F4. metadata specify the data identifier.</li> </ul> <p><strong>To Be Accessible:</strong></p> <ul> <li>A1 (meta)data are retrievable by their identifier using a standardized communications protocol.</li> <li>A1.1 the protocol is open, free, and universally implementable.</li> <li>A1.2 the protocol allows for an authentication and authorization procedure, where necessary.</li> <li>A2 metadata are accessible, even when the data are no longer available.</li> </ul> <p><strong>To Be Interoperable</strong></p> <ul> <li>I1. (meta)data use a formal, accessible, shared, and broadly applicable language for knowledge representation.</li> <li>I2. (meta)data use vocabularies that follow FAIR principles.</li> <li>I3. (meta)data include qualified references to other (meta)data.</li> </ul> <p><strong>To Be Reusable</strong></p> <ul> <li>R1. meta(data) have a plurality of accurate and relevant attributes.</li> <li>R1.1. (meta)data are released with a clear and accessible data usage license.</li> <li>R1.2. (meta)data are associated with their provenance.</li> <li>R1.3. (meta)data meet domain-relevant community standards. </li> </ul> <p><strong>Methodology</strong></p> <p>Based on the FAIR Data Principles, two questionnaires were created. The first (hereafter #Q1 - see Appendix #1) targeted repository managers and/or librarians and consisted of 40 questions. The second (hereafter #Q2 - see Appendix #2) targeted technical staff responsible for repository development and maintenance and consisted of 25 questions. </p> <p>Members of LIBER’s <a href="https://libereurope.eu/strategy/research-infrastructures/rdm/">Research Data Management (RDM) Working Group</a> circulated the questionnaires between December 2018 and February 2019. Responses were collected from managers and/or librarians of 29 repositories for the first (#Q1) questionnaire. </p> <p>In addition, technical staff responsible for the development and maintenance of 14 repositories (Table 1) responded to the second (#Q2) questionnaire. In 11 cases, repositories filled out both #Q1 and #Q2. </p> <p>In this report, the responses for both questionnaires have been merged and analyzed to gain a comprehensive picture about FAIRness at the level of repositories and their data.<br> </p>
Lezione su FAIR data
<p>Registrazione video della lezione tenuta il 19 settembre - no editing [potrebbero esserci pause]</p>
Perceived fairness and perceived transparency of AI systems according to system's characteristics, personality traits and demographic characteristics
<p>We collected data of 3197 users' fairness perception regarding various configurations of a AI-based system in the recruitment domain, as well as, the demographic and personally characteristics of the participants.</p> <p>The dataset includes the following columns:</p> <p><strong>:System characteristics</strong></p> <p> :Certification</p> <p>Uncertificated system (U)</p> <p>Certificated system (C)</p> <p>:Input data</p> <p>High quality input data (H)</p> <p>Low quality input data (L)</p> <p>:Output</p> <p>Positive outcome (P)</p> <p>Borderline outcome (B)</p> <p>Negative outcome (N)</p> <p>:Explanation style</p> <p>Control- no explanation (CON)</p> <p>Case-based (CAS)</p> <p>Certification-based (CER)</p> <p>Demographic-based (DEM)</p> <p>Input influence-based (INP)</p> <p>Sensitivity-based (SEN)</p> <p><strong>:Demographic characteristics</strong></p> <p>:Gender</p> <p>Female</p> <p>Male</p> <p>:Age</p> <p>18-34</p> <p>35-50</p> <p>50+</p> <p>:Residence</p> <p>Unites states of America</p> <p>India</p> <p>Other</p> <p>:Education level</p> <p>High school degree or less</p> <p>Bachelor's degree</p> <p>Master's or doctoral degree</p> <p>:Employment status</p> <p>Not employed</p> <p>Employed</p> <p>:Income level</p> <p>Above average</p> <p>Average</p> <p>Below average</p> <p><strong>:Personality characteristics</strong></p> <p>(TIPI questionnaire)</p> <p>Extraverted, enthusiastic</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Critical, quarrelsome</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Dependable, self-disciplined</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Anxious, easily upset</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Open to new experiences, complex</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Reserved, quiet</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Sympathetic, warm</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Disorganized, careless</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Calm, emotionally stable</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p>Conventional, uncreative</p> <p>1-7 (1= disagree strongly up to 7= agree strongly)</p> <p><strong>:Participants responses</strong></p> <p>:Fairness evaluation</p> <p>The participants were requested to report their level of perceived fairness (their view about the fairness of the system - at what level they consider the system as a fair system) on a 6-point Likert scale, from "Extremely fair" (represented as 3) to "Extremely unfair" (represented as -3). The option of "neither fair or unfair" (represented as 0) was excluded from the scale.</p> <p>:Transparency evaluation</p> <p>the participants were requested to report their level of perceived transparency (their understanding why the system produced the specific output - at what level they understand why this output was given) on a 6-point Likert scale, from " Thoroughly understand" (represented as 3) to " Thoroughly don't understand" (represented as -3). The option of "neither understand or don't understand" (represented as 0) was excluded from the scale.</p> <p>:Output Expectation</p> <p>The participants were requested to report their expectation for the specific output based on the input they received according to the system's scale, 5-point Likert scale from "Strongly recommended" (represented as 2) to "Strongly not recommended" (represented as -2).</p> <p> </p>
Phylogenetic diversity rankings in the face of extinctions: The robustness of the fair proportion index
<p>Planning for the protection of species often involves difficult choices about which species to prioritize, given constrained resources.<br> One way of prioritizing species is to consider their "evolutionary distinctiveness'', i.e. their relative evolutionary isolation on a phylogenetic tree. Several evolutionary isolation metrics or phylogenetic diversity indices have been introduced in the literature, among them the so-called Fair Proportion index (also known as the "evolutionary distinctiveness" score). This index apportions the total diversity of a tree among all leaves, thereby providing a simple prioritization criterion for conservation. </p> <p>Here, we focus on the prioritization order obtained from the Fair Proportion index and analyze the effects of species extinction on this ranking. More precisely, we analyze the extent to which the ranking order may change when some species go extinct and the Fair Proportion index is re-computed for the remaining taxa. We show that for each phylogenetic tree, there are edge lengths such that the extinction of one leaf per cherry completely reverses the ranking. Moreover, we show that even if only the lowest ranked species goes extinct, the ranking order may drastically change. <br> We end by analyzing the effects of these two extinction scenarios (extinction of the lowest ranked species and extinction of one leaf per cherry) for a collection of empirical and simulated trees. In both cases, we can observe significant changes in the prioritization orders, highlighting the empirical relevance of our theoretical findings.</p>
Quantifying representativeness in RCTs using ML fairness metrics - Data and codes
<p>The "Quantifying representativeness in RCTs using ML fairness metrics - Data and codes" is used to quantify representativeness in randomized clinical trials (RCTs) and provide insights to improve the clinical trial equity and health equity. We developed RCT representativeness metrics based on Machine Learning (ML) Fairness Research. Visualizations and statistical tests based on proposed metrics enable researchers and physicians to rapidly visualize and assess subgroup representation in RCTs. The approach enables users to determine underrepresentation, absence, or other misrepresentation of subgroups indicating potential limitations of RCTs. The method could help support generalizability evaluation of existing RCT cohorts, enrollment target decisions for new RCTs (if eligibility criteria are included), and monitoring of RCT enrollment, ultimately contributing to more equitable public health outcomes. We apply the proposed RCT representativeness metrics to three landmark clinical trials released in the last decade: Action to Control Cardiovascular Risk in Diabetes (ACCOD), Antihypertensive and Lipid-Lowering Treatment to Prevent Heart Attack Trial (ALLHAT), and Systolic Blood Pressure Intervention Trial (SPRINT). This dataset contains the processed data and results for the experiments and visualization codes in the paper titled "Quantifying representativeness in randomized clinical trials using machine learning fairness metrics."</p>
Trusted World of Corona (TWOC) - WHO COVID-19 Case Report Form (CRF) Synthetic Data - FAIR
<p>The synthetic dataset that models COVID-19 real world observations from WHO COVID-19 RAPID Version CRFs of hospitalized patients for the hypothesis under study, originally created by the TWOC project.</p>
Supplementary material 1 from: Penev L, Koureas D, Groom Q, Lanfear J, Agosti D, Casino A, Miller J, Arvanitidis C, Cochrane G, Barov B, Hobern D, Banki O, Addink W, Kõljalg U, Ruch P, Copas K, Mergen P, Güntsch A, Benichou L, Benito Gonzalez Lopez J (2021) Towards Interlinked FAIR Biodiversity Knowledge: The BiCIKL perspective. Biodiversity Information Science and Standards 5: e74233. https://doi.org/10.3897/biss.5.74233
The BiCIKL (Biodiversity Community Integrated Knowledge Library) Project Presentation: Goals and Ambitions
The eWaterCycle platform for Open and FAIR Hydrological collaboration Video Abstract
<p>In this video we introduce The eWaterCycle platform for Open and FAIR Hydrological collaboration. With the eWaterCycle platform we are providing the hydrological community with a platform to conduct their research fully compatible with the principles of Open Science as well as FAIR science.</p> <p>This is a video abstract with the publication "The eWaterCycle platform for Open and FAIR Hydrological collaboration." in the journal Geoscientific Model Development (GMD) (under review, link will be provided when available).</p> <p>For more information on the eWaterCycle platform see:</p> <ul> <li>the eWaterCycle package on Zenodo: https://doi.org/10.5281/zenodo.5119389</li> <li>the 5 use cases presented in the paper and video, on Zenodo: https://doi.org/10.5281/zenodo.5543899</li> <li>the eWaterCycle website at https://www.ewatercycle.org/</li> </ul>
Nomad-FAIR North-Remote-Tool Example Dataset Atom Probe Microscopy
<p>Excerpt of a dataset from Huan Zhao, R76-26120-OA<br> https://www.nature.com/articles/s41524-020-00486-1</p>
fair calibration data
<p><strong>Note: please be careful selecting calibrations in your own work; the newest isn't always the "best", or the right one for your needs.</strong></p> <p><strong>If you're unsure, please contact me.</strong></p> <p>If you use fair calibrations in your own work, please cite:</p> <p>Smith, C., Cummins, D. P., Fredriksen, H.-B., Nicholls, Z., Meinshausen, M., Allen, M., Jenkins, S., Leach, N., Mathison, C., and Partanen, A.-I.: fair-calibrate v1.4.1: calibration, constraining, and validation of the FaIR simple climate model for reliable future climate projections, Geosci. Model Dev., 17, 8569–8592, https://doi.org/10.5194/gmd-17-8569-2024, 2024.</p> <p> </p> <p>This dataset contains the full data, input scripts and produced output data for the <strong>updated-2023 calibration</strong><strong> </strong>of <strong>fair v2.2.2</strong>.</p> <p>The zipfile contains everything, allowing you perform your own analysis. The <a href="https://github.com/chrisroadmap/fair-calibrate/tree/v1.4.3">GitHub version</a> contains enough for "bare bones" reproducibility. In addition, I have tried to provide all of the individual files that would be needed to run a historical run of fair.</p> <p><strong>fair v2.2.2</strong></p> <p>Obtainable from https://pypi.org/project/fair/</p> <p>From the command line:</p> <p><code>pip install fair==2.2.2</code></p> <p>or</p> <p><code>conda install -c conda-forge fair==2.2.2</code></p> <p><strong>Calibration v1.5</strong></p> <p>A 1.6 million member prior and 841 member posterior are implemented.</p> <ul> <li>1.6 million prior ensemble</li> <li>Climate response calibrated on 49 abrupt-4xCO2 experiments from CMIP6 and sampled using correlated kernel density estimates</li> <li>Methane lifetime calibrated on 4 AerChemMIP experiments for 1850 and 2014 (Thornhill et al. 2021a, 2021b). Unlike other variables which are sampled around some prior uncertainty, only the best estimate historical calibration is used. The base (1750) lifetime has been fixed and consistently used across projections</li> <li>Carbon cycle uses the parameters from Leach et al. 2021 calibrated for FaIR 2.0.0 using 11 C4MIP models.</li> <li>Aerosol cloud interactions depend on SO2, BC and OC, using new calibrations from 13 RFMIP and AerChemMIP models, with the APRP code fixed by Mark Zelinka (Zelinka et al. 2023). Prior is a trapezoidal distribution with vertices at (-2.2, -1.6, -0.4, +0.2) W/m2.</li> <li>Aerosol radiation interactions use prior values from AR6 Ch6, with best estimates and uncertainties scaled to create a prior in the range of -0.6 to 0.0 W/m2.</li> <li>Ozone uses the same methodology as AR6 (Smith et al. 2021).</li> <li>Effective radiative forcing uncertainty follows the distributions in AR6, with asymmetric distributions switched to skew-normal.</li> <li>Contrails are excluded from the calibration</li> <li>fair version bumped to v2.2.2</li> <li>many more individual files included in the calibration output which should make plugging and playing easier</li> </ul> <p><strong>Constraint sets</strong></p> <p>AR6_updated_no-contrails (v1.5.1)</p> <p>As v1.5.0, except</p> <ul> <li>Emissions and concentrations from CMIP6 historical</li> <li>Volcanic forcing time series from CMIP6.</li> <li>Solar forcing time series from CMIP6.</li> <li>Warming 1995-2014 relative to 1850-1900 range from AR6.</li> <li>CO2 concentrations constrained to AR6 estimate for 2014.</li> <li>Ocean heat content from IGCC (1971-2018), linear, from AR6.</li> <li>Aerosol ERF, ERFari and ERFaci as in the fair calibration from AR6 WG1 Chapter 7 (e.g. -1.68, -1.15, -0.60)</li> </ul> <p><strong>updated_2023 (v1.5.0)</strong></p> <ul> <li>841-member posterior (deliberately chosen).</li> <li>Emissions and concentrations from CMIP7 historical (Zeb Nicholls, Jarmo Kikstra et al.)</li> <li>Volcanic forcing time series updated to use CMIP7 (from Thomas Aubry and collaborators).</li> <li>Solar forcing time series updated to use CMIP7 (Funke et al. 2024).</li> <li>Temperature from IGCC 2024 (Forster et al. 2025) (1850-2024, mean of 4 datasets).</li> <li>Warming 2004-2023 relative to 1850-1900 range from IGCC 2023 (Forster et al. 2024).</li> <li>CO2 concentrations constrained to IGCC estimate for 2023.</li> <li>Ocean heat content from IGCC (1971-2020), linear, from IGCC.</li> <li>two step constraining procedure used: first RMSE of less than 0.19K, then 8-variable distribution fitting.</li> <li>Aerosol ERF, ERFari and ERFaci as in AR6 WG1</li> <li>No future warming constraints</li> </ul> <p><strong>Performance relative to AR6 assessed ranges</strong></p> <p>On request, as Zenodo seems to have removed the ability to format tables in the dataset description.</p> <p><strong>References</strong></p> <ul> <li>Forster et al. 2024: <a href="https://doi.org/10.5194/essd-16-2625-2024" rel="nofollow">https://doi.org/10.5194/essd-16-2625-2024</a></li> <li>Forster et al. 2025: <a href="https://doi.org/10.5194/essd-17-2641-2025" rel="nofollow">https://doi.org/10.5194/essd-17-2641-2025</a></li> <li>Funke et al. 2024: <a href="https://doi.org/10.5194/gmd-17-1217-2024" rel="nofollow">https://doi.org/10.5194/gmd-17-1217-2024</a></li> <li>Leach et al. 2021: <a href="https://doi.org/10.5194/gmd-14-3007-2021" rel="nofollow">https://doi.org/10.5194/gmd-14-3007-2021</a></li> <li>Smith et al. 2021: <a href="https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_FGD_Chapter07_SM.pdf" rel="nofollow">https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_FGD_Chapter07_SM.pdf</a></li> <li>Thornhill et al. 2021a: <a href="https://doi.org/10.5194/acp-21-853-2021" rel="nofollow">https://doi.org/10.5194/acp-21-853-2021</a></li> <li>Thornhill et al. 2021b: <a href="https://doi.org/10.5194/acp-21-1105-2021" rel="nofollow">https://doi.org/10.5194/acp-21-1105-2021</a></li> <li>Zelinka et al. 2023: <a href="https://doi.org/10.5194/acp-23-8879-2023" rel="nofollow">https://doi.org/10.5194/acp-23-8879-2023</a></li> </ul>
FAIR Data and Software Carpentries Lesson Mind Map
<p>The FAIR Data and Software Carpentries Concept Map is an instructional planning tool to help in the development of a FAIR Data and Software Carpentries lesson in Library Carpentry.</p>
Translating Non-Binary Coming-Out Reports: Gender-Fair Language Strategies and Use in News Articles
<p>Materials used for the "Translating Non-Binary Coming-Out Reports: Gender-Fair Language Strategies and Use in News Articles" paper appeared in the Journal of Specialised Translation, issue 40. </p> <p>The .xlsx files contain links to the analysed texts as well as a first annotation that was refined in SPSS (see .sav files). </p>
Nomad-FAIR North-Remote-Tool Example Dataset Orientation Microscopy
<p><strong>apm_sprint14_apav_usa_denton_smith.zip</strong><br> sha256sum R5038_00333-v02.epos<br> e17531c6dd1528016e2144e9c4b649f0ab385f26cffab5bac3ea3b0386a9ac67<br> sha256sum rng_5pj.rrng<br> 69833cfd4b7b76a54f08055393a4e264afed14b2de4c0bc8f66fd0bccf6cafb1<br> (These datasets were shared by the authors of the corresponding paper to the APAV package as is detailed here:<br> https://github.com/openjournals/joss-reviews/issues/4862 and here https://joss.theoj.org/papers/10.21105/joss.04862<br> The *.epos file is a typically dataset from a GBCO material, sort of which are described in the following publication:<br> https://doi.org/10.1017/S1431927621012794 the APAV documentation mentions that this dataset can be used for<br> exploring e.g. multihit capabilities of APAV. Enabling users to explore APAV especially coupled to the apmtools<br> container in the NOMAD Oasis / NOMAD Remote Tools Hub is the main motivation to have the dataset curated here.)</p> <p><strong>em_om_sprint14_01.zip</strong><br> sha256sum Forsterite.ctf.mtex<br> c028333173d8d9d9094c8121d854ffbb638d94746612743f262a586ee07a61d8 Forsterite.ctf.mtex<br> (This is example was generated from the classical Matlab/MTex texture toolbox Forsterite dataset using an MTex script)<br> <br> sha256sum H5OINA_examples_Specimen_1_Map_EDS_+_EBSD_Map_Data_2.h5oina<br> 163ce6ae95c373727785287ddcdce30c71b83e717ef8914c5212d8f8a2248c93<br> (This is an example dataset that was shared by P. Pinard to support the development of EDX/EBSD parsing for NOMAD)<br> sha256sum PrcShanghaiShi.EBSPs70deg.zip<br> d614d2db2e54e03ec9cb2eede7e6f66d029dc82ad64cc099d3c7fb1b2be3c5ec<br> (This is a very small subset of the here published dataset https://zenodo.org/record/7528088#.ZFFybnZBy38)</p> <p><strong>em_om_sprint14_02.zip</strong><br> sha256sum SmallIN100_Final.dream3d<br> aea101e5e4cc5f67613a1d041c1979e5bba60c6c6ec305718b2afdf96d47d14b<br> (This is dataset was processed with the SmallIN100 example included and referred to in 5DREAM3D-6.5.163-Win64)</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.