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424 results for “data challenge”
Chemical data accompanying the manuscript "Chromium cycling in redox-stratified basins challenges δ53Cr paleoredox proxy applications" in Geophysical research Letters
<p>Water column and sediment chromium concentration and stable isotope data and ancillary metal data from Lake Cadagno, Switzerland. These data accompany a manuscript by the same authors in Geophysical Research Letters (doi: 10.1029/2022GL099154).</p> <p> </p> <p>The associated CTD data are available in the following Zenodo dataset: Sepúlveda Steiner, O., Carlino, C., Haizmann, E., Roman, S., Wüest, A., & Bouffard, D. (2022). Lake Cadagno 2017 CTD and water quality monitoring [Data set]. Zenodo. <a href="http://doi.org/10.5281/zenodo.7127882">http://doi.org/10.5281/zenodo.7127882</a></p>
CryoEM Maps and Associated Data Submitted to the 2015/2016 EMDataBank Map Challenge
<p>Files and metadata associated with the EMDataBank/Unified Data Resource for 3DEM 2015/2016 Map Challenge hosted at challenges.emdatabank.org are deposited.</p> <p>All members of the Scientific Community--at all levels of experience--were invited to participate as Challengers, and/or as Assessors.</p> <p>Seven benchmark raw image datasets were selected for the challenge. Six are selected from recently described single particle structure determinations with image data collected as multi-frame movies; one is based on simulated (in silico) images. All of the raw image datasets are archived at pdbe.org/empiar.</p> <p>27 Challengers created 66 single particle reconstructions from the targets, and then uploaded their results with associated details. 15 of the reconstructions were calculated using the SDSC Gordon supercomputer.</p> <p>This map challenge was one of two community-wide challenges sponsored by EMDataBank in 2015/2016 to critically evaluate 3DEM methods that are coming into use, with the ultimate goal of developing validation criteria associated with every 3DEM map and map-derived model.</p> <p> </p>
Supplementary data for the article: Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges
<p>This repository provides the supplementary data to the paper titled <a href="https://doi.org/10.1016/j.resconrec.2024.107572" target="_blank" rel="noopener"><em>"Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges"</em></a>, published 2024 in <em>Resources, Conservation and Recycling</em>.</p> <h4><strong>Contents</strong></h4> <p>The repository is split in 3 parts and comprises the following files (more details are provided in the <em>README.md</em>):</p> <p><strong>A_Database of reviewed studies:</strong></p> <ul> <li>contains the detailed review data, meant for readers to use as an overview file to gather studies relevant to them. It also includes an overview of all data sources that the reviewed studies used.</li> </ul> <p><strong>B_Scientific supplement to paper:</strong></p> <ul> <li>Contains all data relevant to the related publication Harpprecht et al. (2024), such as studies screened , FAIR data analysis, or analyzed impact trends.</li> </ul> <p><strong>C_Data for figures in paper:</strong></p> <ul> <li>This file contains all the data for Figures 3, 4 and 5 in tabular form, representing impact trends, scenario variables, scenario modelling approaches and data sources used.</li> </ul> <h4><strong>Summary</strong></h4> <p>These files allow to reproduce the results of our study. In this work, we systematically reviewed studies which assessed future environmental impacts of metal supply chains. Our review yielded 40 publications covering 15 metals: copper, iron, aluminium, nickel, zinc, lead, cobalt, lithium, gold, manganese, neodymium, dysprosium, praseodymium, terbium, and titanium. We evaluated their results regarding future impact trends, and their methods, i.e., modelling approaches, scenario variables, and data sources of scenario variables. We identified 15 scenario variables. The most common variables are background electricity mix, ore grade, recycling shares, demand, and energy efficiency. We identified 229 unique data sources for the reviewed scenario variables.</p> <h4><strong>Related publication</strong></h4> <p>More details on the data and its interpretation as well as the scientific context are provided in the publication itself:</p> <p><a href="https://doi.org/10.1016/j.resconrec.2024.107572" target="_blank" rel="noopener">Harpprecht, C., Miranda Xicotencatl, B., van Nielen, S., van der Meide, M., Li, C. , Li, Z., Tukker, A., Steubing, B. (2024). <em>Future environmental impacts of metals: a systematic review of impact trends, modelling approaches, and challenges.</em> Resources, Conservation and Recycling.</a></p> <h4><strong>Funding </strong></h4> <p>Carina Harpprecht received funding from the Energy Program of the German Aerospace Center in 2022. Zhijie Li received funding from the European Institute of Innovation and Technology (EIT) under the project Valomag (Project No. 14049).</p> <h4><strong>License</strong></h4> <p>CC-BY 4.0 license for DLR (German Aerospace Center)</p>
Bridge2AI Grand Challenge AI-Readiness Evaluation Data Year 2 of 4
<p>This excel workbook and set of radar plots contains current and projected AI-readiness evaluation datasets of four NIH Bridge2AI Program Grand Challenges in Functional Genomics, Clinical Care Informatics, Precision Public Health, and Return to Health (Salutogenesis). These evaluations were collected in late 2024, at the conclusion of Year 2 of the 4-year Bridge2AI program, by Grand Challenge (GC) representatives on the Bridge2AI Standards Working Group, in consultation with their GC leadership team, and will be updated in subsequent years and the program progresses. They assess biomedical AI readiness of existing collected data only. </p>
Tractography Challenge ISMRM 2015 b=3000s/mm² Data.
<p>This archive contains a version of the ISMRM 2015 Tractography Challenge data simulated with a b-value of 3000s/mm², 64 non-zero gradient directions and 1 baseline volume. The other simulation parameters are the same as for the original phantom.</p>
Unblinded Data for PLAsTiCC Classification Challenge
<p>For classification challenge (https://plasticc.org), the unblinded data files are included here. See PDF note above for more information. The original challenge (Sep 28, 2018 - Dec 17, 2018) was hosted at https://www.kaggle.com/c/PLAsTiCC-2018 .</p>
Voice Conversion Challenge 2020 Listening Test Data
<pre>Voice conversion (VC) is a technique to transform a speaker identity included in a source speech waveform into a different one while preserving linguistic information of the source speech waveform. In 2016, we have launched the Voice Conversion Challenge (VCC) 2016 [1][2] at Interspeech 2016. The objective of the 2016 challenge was to better understand different VC techniques built on a freely-available common dataset to look at a common goal, and to share views about unsolved problems and challenges faced by the current VC techniques. The VCC 2016 focused on the most basic VC task, that is, the construction of VC models that automatically transform the voice identity of a source speaker into that of a target speaker using a parallel clean training database where source and target speakers read out the same set of utterances in a professional recording studio. 17 research groups had participated in the 2016 challenge. The challenge was successful and it established new standard evaluation methodology and protocols for bench-marking the performance of VC systems. In 2018, we have launched the second edition of VCC, the VCC 2018 [3]. In the second edition, we revised three aspects of the challenge. First, we educed the amount of speech data used for the construction of participant's VC systems to half. This is based on feedback from participants in the previous challenge and this is also essential for practical applications. Second, we introduced a more challenging task refereed to a Spoke task in addition to a similar task to the 1st edition, which we call a Hub task. In the Spoke task, participants need to build their VC systems using a non-parallel database in which source and target speakers read out different sets of utterances. We then evaluate both parallel and non-parallel voice conversion systems via the same large-scale crowdsourcing listening test. Third, we also attempted to bridge the gap between the ASV and VC communities. Since new VC systems developed for the VCC 2018 may be strong candidates for enhancing the ASVspoof 2015 database, we also asses spoofing performance of the VC systems based on anti-spoofing scores. In 2020, we launched the third edition of VCC, the VCC 2020 [4][5]. In this third edition, we constructed and distributed a new database for two tasks, intra-lingual semi-parallel and cross-lingual VC. The dataset for intra-lingual VC consists of a smaller parallel corpus and a larger nonparallel corpus, where both of them are of the same language. The dataset for cross-lingual VC consists of a corpus of the source speakers speaking in the source language and another corpus of the target speakers speaking in the target language. As a more challenging task than the previous ones, we focused on cross-lingual VC, in which the speaker identity is transformed between two speakers uttering different languages, which requires handling completely nonparallel training over different languages. As for listening test, we subcontracted the crowd-sourced perceptual evaluation with English and Japanese listeners to Lionbridge TechnologiesInc. and Koto Ltd., respectively. Given the extremely large costs required for the perceptual evaluation, we selected 5 utterances (E30001, E30002, E30003,E30004, E30005) only from each speaker of each team. To evaluate the speaker similarity of the cross-lingual task, we used audio in both the English language and in the target speaker’s L2language as reference. For each source-target speaker pair, we selected three English recordings and two L2 language recordings as the natural reference for the converted five utterances. </pre> <p>This data repository includes the audio files used for the crowd-sourced perceptual evaluation and raw listening test scores. </p> <pre>[1] Tomoki Toda, Ling-Hui Chen, Daisuke Saito, Fernando Villavicencio, Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "The Voice Conversion Challenge 2016" in Proc. of Interspeech, San Francisco. [2] Mirjam Wester, Zhizheng Wu, Junichi Yamagishi "Analysis of the Voice Conversion Challenge 2016 Evaluation Results" in Proc. of Interspeech 2016. [3] Jaime Lorenzo-Trueba, Junichi Yamagishi, Tomoki Toda, Daisuke Saito, Fernando Villavicencio, Tomi Kinnunen, Zhenhua Ling, "The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods", Proc Speaker Odyssey 2018, June 2018. [4] Yi Zhao, Wen-Chin Huang, Xiaohai Tian, Junichi Yamagishi, Rohan Kumar Das, Tomi Kinnunen, Zhenhua Ling, and Tomoki Toda. "Voice conversion challenge 2020: Intra-lingual semi-parallel and cross-lingual voice conversion" Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 80-98, DOI: 10.21437/VCC_BC.2020-14. [5] Rohan Kumar Das, Tomi Kinnunen, Wen-Chin Huang, Zhenhua Ling, Junichi Yamagishi, Yi Zhao, Xiaohai Tian, and Tomoki Toda. "Predictions of subjective ratings and spoofing assessments of voice conversion challenge 2020 submissions." Proc. Joint Workshop for the Blizzard Challenge and Voice Conversion Challenge 2020, 99-120, DOI: 10.21437/VCC_BC.2020-15. </pre>
Tractography Challenge ISMRM 2015 High-resolution Data
<p>The ground truth of this tractography validation data set was defined based on the fiber bundle geometry of a high-quality Human Connectome Project (HCP) dataset, constructed from multiple whole-brain global tractography maps. An expert radiologist extracted 25 major tracts (i.e., bundles of streamlines) from the tractogram. These association, projection and commissural fibers covered more than 70% of the white matter across the whole brain. The dataset features a brain-like macro-structure of long-range connections, mimicking an <em>in vivo</em> HCP-quality acquisition based on a simulated diffusion signal. </p>
ForensicVM Windows 11 Virtualization Dataset: Cloud and Local Application Data Retrieval with Virtualization and Password Bypass Challenge
<p>A Windows 11 Pro dataset designed in VirtualBox, complete with local and cloud apps, is to be virtually analyzed for crucial evidence. Bypassing the straightforward 'Bart' password is essential for access, yet original passwords should remain unchanged for others to attempt the same challenge. The task involves determining the password's nature, Bart's motives, identifying involved cloud applications, and extracting data both offline and online. The viability of dead box forensics for complete data retrieval is questioned, alongside what additional information network access could unveil. The challenge includes identifying two financial applications, extracting their data, and gathering cryptocurrency-related information, presented as an engaging forensicVM showcase by Nuno Mourinho, Mario Candeias, and Rogerio Bravo (Escola Superior de Tecnologia e Gestão de Beja, Instituto Politécnico de Beja).</p>
A challenging data set for evaluating part-of-speech taggers
<p>This data set contains 2,227 sentences, with a part-of-speech (POS) tag specified for a single word in the sentence. The data file is a tab-separated text file where each row (after the header row) is formatted as follows:</p><p><i>sentence <TAB> POS tag <TAB> (optional) motivation</i></p><p>Note that, in the sentence (= a string of space-separated characters), the POS-tagged word is indicated by the POS tag in brackets, placed just after the word to which it refers. Example:</p><p><i>The road bends [VERB] to the right . VERB</i></p><p>In this example, the optional motivation is not included, as the tagged word can easily be identified as being a verb.</p>
Numerical data analysed to produce Figure 3a of Nature Climate Change submission "Five challenges for subseasonal to decadal prediction research " by Merryfield et al.
<p>NetCDF4-formatted files containing daily sea ice concentration data from Environment and Climate Change Canada's CanSIPSv2 seasonal forecasting system described in Lin et al. (2020) https://doi.org/10.1175/WAF-D-19-0259.1 </p> <ul> <li>2 models, CanCM4i and GEM-NEMO</li> <li>10 ensemble members for each model, each in separate files as indicated by suffixes _1 to _10</li> <li>initialized May 1, 1980 to 2021</li> <li>840 files total (42 predicted years x 10 ensemble members x 2 models)</li> <li>model outputs interpolated to common 1-degree grid</li> </ul> <p>The calibrated probabilistic forecast map shown in Figure 3a is based on the nonhomogeneous censored Gaussian regression (NCGR) method described in Dirkson et al, (2021) https://doi.org/10.1175/WAF-D-20-0066.1 and produced using scripts available at https://github.com/adirkson/sea-ice-timing </p> <p>The procedure uses as inputs</p> <ul> <li>freeze-up dates calculated from the provided model outputs as described in Sigmond et al. (2016) https://doi.org/10.1002/2016GL071396</li> <li> <p>NOAA/NSIDC Climate Data Record of Passive Microwave Sea Ice Concentration, Version 3: https://nsidc.org/data/G02202/versions/3</p> </li> </ul>
LSSTC AGN Data Challenge 2021
<p>This repository hosts the dataset used in the LSSTC AGN Data Challenge (DC) 2021 (PI: Gordon Richards). More information about the data challenge can be found in the DC GitHub repository @ https://github.com/RichardsGroup/AGN_DataChallenge. </p> <p><strong>Dataset Versions: </strong></p> <p><strong>1.0: </strong>The initial dataset used in the DC, as well as the blinded dataset (ObjectTable_Blinded.parquet) that was used to evaluate submissions. Note that the image cutouts are not included here due to the large size, but the script used to generate those cutouts using SDSS archive services is included in the DC GitHub repository.</p> <p><strong>1.1: </strong>The same dataset as in v1.0 but with the following updates:</p> <ul> <li>Uncovered the true coordinates of each source in the dataset</li> <li>Added E(B-V) for every source using the SFD1998 dust map</li> <li>Added spectrum source information (i.e., SDSS fiberid, plate, mjd) if available. </li> </ul> <p> </p> <p><strong>Caveat:</strong> </p> <ul> <li>The optical (grizY) and NIR photometry of sources in the XMM-LSS field is a product of the HSC/VISTA pixel-level joint processing initiative led by Raphael Shirley and Manda Banerji. Thus, it is an early prototype dataset and is still subject to testing and characterization.</li> </ul> <p> </p> <p><strong>Citation:</strong></p> <p>The DC dataset released here is a compilation of data from various sources. If you find the DC dataset useful for your research and would like to acknowledge it, please also reference the original sources of the data. Below is a list of publications that you should consider citing. </p> <p><em>X-ray in XMM-LSS (XMM-SERVS): </em>2018MNRAS.478.2132C</p> <p><em>UV Photometry (GALEX): </em>2017ApJS..230...24B</p> <p><em>Optical Photometry (in the object/source tables): </em></p> <ul> <li>DES: 2021ApJS..255...20A</li> <li>SDSS Stripe 82 Coadd: 2014ApJ...794..120A</li> <li>HSC DR2: 2019PASJ...71..114A</li> </ul> <p><em>Optical Light Curves (in the ForcedSource table):</em></p> <ul> <li>SDSS DR7: 2009ApJS..182..543A</li> <li>SDSS II Supernova Survey: 2008AJ....135..338F</li> </ul> <p><em>Astrometry (i.e., parallax, proper motion):</em></p> <ul> <li>Gaia EDR3: 2021A&A...649A...1G</li> <li>NOIRLab Source Catalog DR2: 2021AJ....161..192N</li> </ul> <p><em>NIR in XMM-LSS (VISTA/VIDEO): </em>2013MNRAS.428.1281J</p> <p><em>NIR in Stripe 82 (UKIDSS):</em></p> <ul> <li> <p>2006MNRAS.367..454H</p> </li> <li> <p>2007MNRAS.379.1599L</p> </li> <li> <p>2008MNRAS.384..637H</p> </li> <li> <p>2009MNRAS.394..675H</p> </li> </ul> <p><em>Optical u-band in XMM-LSS (CFHTLS):</em> 2012yCat.2317....0H</p> <p><em>MIR in XMM-LSS </em>(Spitzer DeepDrill): 2021MNRAS.501..892L</p> <p><em>MIR in Stripe 82 </em>(SpIES): 2016ApJS..225....1T</p> <p><em>FIR</em> (Hershel/HELP): 2019MNRAS.490..634S</p> <p><em>Radio</em> (FIRST): 1994ASPC...61..165B</p> <p><em>HighZ QSOs: </em></p> <ul> <li>2016ApJ...819...24W</li> <li>2016ApJ...829...33Y</li> </ul> <p><em>SDSS Spectroscopy:</em></p> <ul> <li>SDSS DR16: 2020ApJS..249....3A</li> <li>SDSS DR16 Quasar Catalog: 2020ApJS..250....8L</li> </ul> <p> </p> <p> </p>
Exoplanet imaging data challenge, phase 2
<p><strong>Datasets for the second phase of the Exoplanet Imaging Data Challenge</strong> (<a href="https://exoplanet-imaging-challenge.github.io/">https://exoplanet-imaging-challenge.github.io</a>). </p> <p>The second phase of the Exoplanet Imaging Data Challenge is focused on the characterisation of exoplanet signals in high-contrast imaging data. The participants must perform two tasks: provide (1) astrometry of the detected signals, and (2) spectrophotometry of the detected signals.</p> <p>For this phase, we therefore provide with <strong>8</strong> high-contrast data sets, taken with two integral field spectrographs: SPHERE-IFS installed at the Very Large Telescope (VLT, Chile) and GPI installed at the Gemini-South telescope (Chile). Each data set consists of the following files (in <em>.fits</em> format):</p> <p>(a) <em>image_cube_instxx.fits</em>: the coronagraphic multispectral image cube, acquired in pupil-stabilized mode;<br> (b) <em>parallactic_angles_instxx.fits</em>: the corresponding parallactic angle and airmass variation during the observation sequence;<br> (c) <em>wavelength_vect_instxx.fits</em>: the corresponding wavelength vector for each spectral channel;<br> (d) <em>psf_cube_instxx.fits</em>: the non-coronagraphic (point spread function) multispectral image of the target star;<br> (e) <em>first_guess_astrometry_instxx.fits</em>: a first guess position w.r.t the star of the (2 or 3) injected planetary signals.</p> <p>Each data set has 2 to 3 injected planetary signals in various locations.<br> Each data set has very different observing conditions, from very good to very bad.</p> <p><em>Additional information and ressources can be found on the <a href="https://exoplanet-imaging-challenge.github.io/">website</a> and dedicated <a href="https://github.com/exoplanet-imaging-challenge/phase2">Github repository.</a></em></p> <p><em>The results of the data challenge must be submitted directly by participants on the <a href="https://eval.ai/web/challenges/challenge-page/1717/overview">EvalAI platform</a>.</em></p>
LISA Data Challenge Sangria (LDC2a)
<p><strong>Sangria</strong> includes two main datasets: each contains Gaussian instrumental noise and simulated waveforms from 30 million Galactic white dwarf binaries, from 17 verification Galactic binaries, and from merging massive black-hole binaries with parameters derived from an astrophysical model. The first dataset includes the full specification used to generate it: source parameters, a description of instrumental noise with the corresponding power spectral density, LISA's orbit, etc. We also release noiseless data for each type of source, for waveform validation purposes. The second dataset is <em>blinded</em>: the level of istrumental noise and number of sources of each type are not disclosed (except for the known parameters of the verification binaries).</p> <p>See <a href="https://lisa-ldc.lal.in2p3.fr/challenge2a">LDC website</a> for more details.</p> <p> </p>
CryoEM Models and Associated Data Submitted to the 2015/2016 EMDataBank Model Challenge
<p>Files and metadata associated with the EMDataBank/Unified Data Resource for 3DEM 2015/2016 Models Challenge hosted at challenges.emdatabank.org are deposited.</p> <p>All members of the Scientific Community--at all levels of experience--were invited to participate as Challengers, and/or as Assessors.</p> <p>Eight recently determined target structures were selected for the challenge. All of the maps were archived in the EM Data Bank (EMDB; http://emdatabank.org).</p> <p>In total 16 Challengers created 106 models and uploaded their results with associated details. In the zip files, each entry is represented in a folder containing the original deposition upload (deposited_EM.pdb), initial processing at RCSB/Rutgers (deposited_EM_edited.pdb, maxit.cif, maxit.cif.pdb) and final model version evaluated (model-compare.pdb) at UC Davis (http://model-compare.emdatabank.org).</p> <p>This model challenge was one of two community-wide challenges sponsored by EMDataBank in 2015/2016 to critically evaluate 3DEM methods that are coming into use, with the ultimate goal of developing validation criteria associated with every 3DEM map and map-derived model.</p> <p> </p>
Supplementary data: Breeding wheat for organic farming: can the high grain protein gene Gpc-B1 help to tackle challenges in view of end-use quality?
<p>Agronomic and quality data of organic wheat (<em>Triticum aestivum</em>), mean comparisons and supplementary figures related to the publication "Breeding wheat for organic farming: can the high grain protein gene Gpc-B1 help to tackle challenges in view of end-use quality?" by Grausgruber et al. (2024) published in the Journal of Cereal Science.</p>
Supplementary data to Challenges and Opportunities for the Recovery of Critical Raw Materials from Electronic Waste
<p>Content of the excell file:</p> <p>Table S1: Relationship between UNU-keys and MINCOTUR codes</p> <p>Table S2: UNU-Key composition and alpha and beta values for Weibull distributions</p> <p>Table S3: Metal prices used in this study.</p>
Codes and Data for 'Cost-effective Planning of Decarbonized Power-Gas Infrastructure to Meet the Challenges of Heating Electrification'
<p>The codes and data used in the followng paper</p> <p>''Khorramfar, R., Santoni-Calvin, M., Mallapragada, D., Amin, S., Botterud, A.,<br>Norfork L., (2025) Cost-effective Planning of Power-Gas Infrastructure to Meet the Challenges<br>of Heating Electrification, Cell Reports Sustainability</p> <p> </p> <p>Link (open source): https://www.cell.com/cell-reports-sustainability/fulltext/S2949-7906(25)00003-5</p> <p> </p>
Data and Code from: On-farm land management strategies and production challenges in United States Organic Agricultural Systems.
<p>This repository contains data and code used in:</p> <p>Isaac Mpanga, Russel Trondstad, Jessica Guo, David LeBauer, and John Omololu, 2021. On-farm land management strategies and production challenges in United States Organic Agricultural Systems. Current Research in Environmental Sustainability.</p> <p>It provides USDA Surveys of Agricultural Production from 2008-2019 to investigate state and national trends by state in organic farm area, number, and sales, as well to evaluate national trends in on-farm land-use practices and challenges facing US organic production.</p> <p>It also includes code used to transform, visualize, and analyze the data, and derived data products - notably organic farm area and sales with values imputed to correct for redacted state level measures.</p>
Data for: Mercury contamination challenges the behavioral response of a keystone species to Arctic climate change
<p>Combined effects of multiple, climate change-associated stressors are of mounting concern, especially in Artic ecosystems. Elevated mercury (Hg) exposure in Arctic animals could affect behavioural responses to changes in foraging landscapes linked to climate change, generating interactive effects on behaviour and population resilience. We investigated this hypothesis in the little auk (<em>Alle alle</em>), a keystone Artic seabird. We compiled behavioural data using accelerometers, and quantified blood mercury and environmental conditions (sea surface temperature (SST), sea ice coverage (SIC)) across multiple years. These datasets contain the behavioral, blood Hg and environmental data (SST, SIC) used in our analyses. Details about the datasets are found in the accompanying word document.</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.