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2,863 results for “Challenge”
GECCO Industrial Challenge 2017 Dataset: A water quality dataset for the 'Monitoring of drinking-water quality' competition at the Genetic and Evolutionary Computation Conference 2017, Berlin, Germany.
<p>Dataset of the 'Industrial Challenge: Monitoring of drinking-water quality' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 15th-19th 2017, Berlin, Germany</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of water quality data</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>M. Friese, J. Stork, A. Fischbach, M. Rebolledo, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided and prepared by:</p> <p>Thüringer Fernwasserversorgung,</p> <p>IMProvT research project (S. Moritz)</p> <p><br> </p> <p>Industrial Challenge: Monitoring of drinking-water quality</p> <p> </p> <p>Description:</p> <p>Water covers 71% of the Earth's surface and is vital to all known forms of life. The provision of safe and clean drinking water to protect public health is a natural aim. Performing regular monitoring of the water-quality is essential to achieve this aim.</p> <p>Goal of the GECCO 2017 Industrial Challenge is to analyze drinking-water data and to develop a highly efficient algorithm that most accurately recognizes diverse kinds of changes in the quality of our drinking-water.</p> <p> </p> <p>Submission deadline:</p> <p>June 30, 2017</p> <p>Official webpage:</p> <p><a href="http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/">http://www.spotseven.de/gecco-challenge/gecco-challenge-2017/</a></p>
CURE-OR: Challenging Unreal and Real Environments for Object Recognition
<p>As one of the research directions at <a href="https://ghassanalregib.info/">OLIVES Lab @ Georgia Tech</a>, we focus on the robustness of data-driven algorithms under diverse challenging conditions where trained models can possibly be depolyed. To achieve this goal, we introduced a large-sacle (1.M images) object recognition dataset (<a href="https://github.com/olivesgatech/CURE-OR">CURE-OR</a>) which is among the most comprehensive datasets with controlled synthetic challenging conditions. In <a href="https://github.com/olivesgatech/CURE-OR">CURE-OR</a> dataset, there are 1,000,000 images of 100 objects with varying size, color, and texture, captured with multiple devices in different setups. The majority of images in the dataset were acquired with smartphones and tested with off-the-shelf applications to benchmark the recognition performance of devices and applications that are used in our daily lives. Please refer to our <a href="https://github.com/olivesgatech/CURE-OR">GitHub page</a> for code, papers, and more information. Some data specifications are provided below:</p> <p><strong>Image Name Format : </strong></p> <p>"backgroundID_deviceID_objectOrientationID_objectID_challengeType_challengeLevel.jpg"</p> <p><strong>Background ID: </strong></p> <p>1: White 2: Texture 1 - living room 3: Texture 2 - kitchen 4: 3D 1 - living room 5: 3D 2 – office</p> <p><strong>Object Orientation ID: </strong></p> <p>1: Front (0 º) 2: Left side (90 º) 3: Back (180 º) 4: Right side (270 º) 5: Top</p> <p><strong>Object ID:</strong></p> <p> 1-100</p> <p><strong>Challenge Type:</strong> </p> <p>No challenge 02: Resize 03: Underexposure 04: Overexposure 05: Gaussian blur 06: Contrast 07: Dirty lens 1 08: Dirty lens 2 09: Salt & pepper noise 10: Grayscale 11: Grayscale resize 12: Grayscale underexposure 13: Grayscale overexposure 14: Grayscale gaussian blur 15: Grayscale contrast 16: Grayscale dirty lens 1 17: Grayscale dirty lens 2 18: Grayscale salt & pepper noise</p> <p><strong>Challenge Level: </strong></p> <p>A number between [0, 5], where 0 indicates no challenge, 1 the least severe and 5 the most severe challenge. Challenge type 1 (no challenge) and 10 (grayscale) has a level of 0 only. Challenge types 2 (resize) and 11 (grayscale resize) has 4 levels (1 through 4). All other challenges have levels 1 to 5.</p>
Figure 7 in A new subterranean Maraenobiotus (Crustacea: Copepoda) from Slovenia challenges the concept of polymorphic and widely distributed harpacticoids
Figure 7. Maraenobiotus slovenicus sp. nov., line drawings, (A–C) holotype female; (D, E) allotype male: (A) genital segment with attached spermatophore, ventral; (B) last urosomite, anal somite and furcal rami, ventral; (C) last urosomite, anal somite and furcal rami, dorsal; (D) last urosomite, anal somite and furcal rami, ventral; (E) anal somite and furcal rami, dorsal.
Figure 4 in A new subterranean Maraenobiotus (Crustacea: Copepoda) from Slovenia challenges the concept of polymorphic and widely distributed harpacticoids
Figure 4. Maraenobiotus slovenicus sp. nov., SEM micrographs, (A–D) damaged paratype male 1; (E–H) paratype male 2: (A) mouth appendages, ventral; (B) maxillule and maxilla, ventral; (C) central part of left antennule, ventral; (D) antenna, ventral; (E) left antennule, lateral; (F) first three urosomites, lateral; (G) anal somite and caudal rami, lateral; (H) detail of first urosomite, with cuticular window, large pore, and sensillum, lateral.
Figure 3 in A new subterranean Maraenobiotus (Crustacea: Copepoda) from Slovenia challenges the concept of polymorphic and widely distributed harpacticoids
Figure 3. Maraenobiotus slovenicus sp. nov. (A, B) SEM micrographs, paratype female 3; (C–H) damaged paratype male 1: (A) first two urosomites and P5; (B) P1–P3, ventrolateral; (C) habitus with several large epibiotic ciliates, ventral; (D) P4–P6, ventral; (E) last two urosomites and caudal rami, ventral; (F) antennule, ventral; (G) distal part of right antennule, ventral; (H) central part of right antennule, ventral.
Figure 1 in A new subterranean Maraenobiotus (Crustacea: Copepoda) from Slovenia challenges the concept of polymorphic and widely distributed harpacticoids
Figure 1. Maraenobiotus slovenicus sp. nov., SEM micrographs, paratype female 1: (A) habitus, dorsal; (B) cephalothorax, dorsal; (C) anterior part of cephalothorax, dorsal; (D) right antennule, dorsal; (E) free pedigerous somites, dorsal; (F) first three urosomites, dorsal; (G) last three urosomites and caudal rami, dorsal; (H) right caudal ramus, dorsal.
[2019 QSM Reconstruction Challenge] Submissions Stage 2
<p>This repository contains the original, unaltered files submitted to Stage 2 of the 2019 Quantitative Susceptibility Mapping Reconstruction Challenge.</p> <p>The data provided to applicants of the challenge along with the scripts used to obtain the evaluation metrics are available <a href="https://doi.org/10.5281/zenodo.4559540">here</a>. Information about the submitted solutions and resulting analysis metrics are available <a href="https://doi.org/10.5281/zenodo.3687196">here</a>.</p> <p>The results of the challenge are fully reported in the journal article "<a href="http://doi.org/10.1002/mrm.28754">QSM Reconstruction Challenge 2.0: Design and Report of Results</a>".</p>
Analysing the Yeast Complexome - The Complex Portal rising to the challenge
<p>Datasets of complexes and co-complexes used in the analysis for this paper: Analysing the Yeast Complexome - The Complex Portal rising to the challenge.</p> <p> </p>
Data and code for the paper Atmospheric Observations with E-band Microwave Links – Challenges and Opportunities
<p>Raw and preprocessed data for the paper Atmospheric Observations with E-band Microwave Links – Challenges and Opportunities accepted for publication in the journal Atmospheric Measurement Techniques.</p> <p>The dataset includes total losses (transmitted - received power levels) of commercial microwave links and observations of rainfall, air temperature, and air relative humidity. In addition, theoretical gaseous attenuation calculated from air temperature and relative humidity is provided.</p> <p>Data are stored in semicolon-delimited csv files. Time stamps are in UTC time in the format yyyy-mm-dd HH:MM:SS. Raw data contain not regular time series, preprocessed data contain regular time series at 1-min and 5-min temporal resolution. Metadata are stored in textfiles.</p> <p>Dataset contains also R code for processing and analyzing the data as presented in the paper Atmospheric Observations with E-band Microwave Links – Challenges and Opportunities. The code is in the form of R Markdown files and interactive html notebooks.</p>
GECCO Industrial Challenge 2019 Dataset: A water quality dataset for the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition at the Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic.
<p>Dataset of the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 13th-17th 2019, Prague, Czech Republic</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>1. Original train dataset of water quality data provided to participants (identical to gecco2019_train_water_quality.csv)</p> <p>2. Call for Participation</p> <p>3. Rules and Description of the Challenge</p> <p>4. Resource Package provided to participants</p> <p>5. The complete dataset, consisting of train, test and validation merged together (gecco2019_all_water_quality.csv)</p> <p>6. The test dataset, which was used for creating the leaderboard on the server (gecco2019_test_water_quality.csv)</p> <p>7. The train dataset, which participants had available for training their models (gecco2019_train_water_quality.csv)</p> <p>8. The validation dataset, which was used for the end results for the challenge (gecco2019_valid_water_quality.csv)</p> <p> </p> <p>The challenge required the participants to submit a program for event detection. A training dataset was available to the participants (gecco2019_train_water_quality.csv). During the challenge the participants were able to upload a version of their program to out online platform, where this version was scored against the testing dataset (gecco2019_test_water_quality.csv), thus an intermediate leaderboard was available. To avoid overfitting against this dataset, at the end of the challenge, the end result was created from scoring with the validation dataset (gecco2019_valid_water_quality.csv). </p> <p>Train, Test, Validation dataset are from the same measuring station and are in chronological order. So the timestamps from the test dataset begin directly after the train timestamps, while the validation timestamps begin directly after the test timestamps. </p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, S. Moritz, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p> </p> <p>Internet of Things: Online Event Detection for Drinking Water Quality Control</p> <p> </p> <p>Description:</p> <p>For the 8th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2018 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.</p> <p><br> Competition Opens: End of January/Start of February 2019<br> Final Submission: 30 June 2019</p> <p>Official webpage:</p> <p><a href="https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php">https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php</a></p> <p> </p>
Dataset Challenges and Opportunities for Academic Parents during COVID-19
<p>Anonimized dataset of the survey on the impact of COVID-19 on academic parents. Participants who did not give consent were filtered out as well.</p>
Dataset: Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges
<p>This repository contains supplementary materials for the following journal paper:</p> <p>Valdemar Švábenský, Pavel Čeleda, Jan Vykopal, Silvia Brišáková.<br> <em>Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges.</em><br> In Elsevier Computers & Security. 2020.<br> <a href="https://doi.org/10.1016/j.cose.2020.102154">https://doi.org/10.1016/j.cose.2020.102154</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/2101.01421">https://arxiv.org/abs/2101.01421</a></p> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <pre><code>@article{Svabensky2020cybersecurity, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and \v{C}eleda, Pavel and Vykopal, Jan and Bri\v{s}\'{a}kov\'{a}, Silvia}, title = {{Cybersecurity Knowledge and Skills Taught in Capture the Flag Challenges}}, journal = {Computers \& Security}, publisher = {Elsevier}, volume = {102}, year = {2020}, issn = {0167-4048}, url = {https://www.sciencedirect.com/science/article/pii/S0167404820304272}, doi = {10.1016/j.cose.2020.102154}, }</code></pre> <p><strong>Attached content</strong></p> <p>See the README.md file inside the attached ZIP file for more details.</p>
Planetary defence challenge dataset
<p>Inspired by the European Space Agency's <a href="https://www.heramission.space/">HERA</a> mission to investigate the Didymos binary asteroid system after NASA's <a href="https://www.nasa.gov/planetarydefense/dart">DART</a> mission kinetic impactor test, this dataset was created as the "<strong>Planetary defence challenge</strong>". Hosted on ESAs competition platform <a href="http://kelvins.esa.int">Kelvins</a>, the task is to analyze measurements in form of simulated lightcurves that have been collected pre and post-impact. In total, 300 pairs of such pre- and post-impact measurements are provided. The goal is to estimate the β-factor, which is related to the momentum-transfer of the impactor and the axial ratio a/c of the secondary asteroid.</p> <p>The data of the lightcurve measurements contain</p> <ul> <li>time of measurement (in minutes)</li> <li>normalized total luminosity (maximum value: 1.0)</li> </ul> <p>For the first 200 lightcurve-pairs, the following information is given in <em>parameters.csv</em>:</p> <ul> <li>value of the β-factor (>= 1)</li> <li>J2 value of the primary</li> <li>a/c axial ratio of the secondary</li> </ul> <p>The goal of the challenge is to estimate the β-factor and a/c for the remaining 100 lightcurve pairs.</p> <p>For more details on the dataset and the competition, please visit <a href="https://kelvins.esa.int/planetary-defence/">https://kelvins.esa.int/planetary-defence/</a>.</p>
2010 ISMRM Recon Challenge, Stockholm, Sweden
<p>This data was presented at the ISMRM-ESMRMB Joint Annual Meeting in Stockholm, Sweden, 2010. It can be found in the program under:</p> <p>PLENARY SESSION<br /> Room AI</p> <p> The Eye of the Beholder: An Image Reconstruction Challenge<br /> Organizers: Margaret A. Hall-Craggs, M.D., Douglas C. Noll, Ph.D., and James G. Pipe, Ph.D.</p> <p> 08:15 If I Am So Good at This, Why Do I Miss So Much? Jeremy M. Wolfe, Ph.D.<br /> 08:40 Reconstruction Challenge: So Many Algorithms, So Few Data<br /> Award presentations, panel discussion</p> <p>---</p> <p>Please read the instructions for each data set before downloading. The first three data sets are from the 2010 ISMRM Recon Challenge. The available <strong>poster</strong> (2010RCPoster.pdf) gives examples of the entries. Registrants to the 2010 ISMRM Annual Meeting can hear radiologist’s comments and see the results by <strong>visiting here</strong> (http://www.ismrm.org/PRES_W_A1_0840/)<strong>.</strong></p> <p>1. <strong><em>Need for Speed</em></strong>. Data were simulated using collected projection X-ray of an arterial bolus injection in a patient with an AVM. X-ray data were collected 3 frames per second, for a total of 10 seconds (31 collected frames) which span wash-in to wash-out. These were linearly interpolated in time between frames to create a total of 200 temporal images, each with 512x512 resolution. B1-maps (provided with data) derived from an axial slice through a water phantom using an 8-channel head coil were superimposed on the image to create 8 “coil” images. Independent noise was added to each channel. The data were synthesized over 200 trajectories, each with 2000 points. Data for each trajectory are synthesized from one temporal frame of the time series.<em> </em><br /> <br /> <strong>NFS_materials.zip:</strong> descriptions of trajectories (the pdf files), read/write code for C and Matlab, and B<sub>1 </sub>maps. <br /> <strong>NFS_truth.dat:</strong> for the NFS truth data set, which has no header, is floating point (4 byte) real data, and arranged as a 3D array of 512x512x37.</p> <p>You may then choose from any of the following sets:</p> <p>A. <strong>NFS_NZWA_09AUG06.zip:</strong> a <strong>Spiral</strong> trajectory coordinates and data. (trajectory designed by Nick Zwart, <br /> Barrow Neurological Institute)</p> <p>B. <strong>NFS_ASAM_09AUG04.zip:</strong> a <strong>PR</strong> trajectory coordinates and data. (trajectory designed by Alexey Samsonov, University of Wisconsin)</p> <p>2. <strong><em>Double Vision</em></strong>. Data originate from 12 axial images in the abdomen (respiratory gated T2 FSE with Fat Sat) collected using a torso phase-array coil, collected at 320x320 matrix (40cm FOV). Field maps (breatheld, end-exhilation, with Fat Sat) were collected using gradient echo images at TE = 3 and 5, with a 96x96 collected matrix. Synthesized data were corrupted by off-resonance phase. Data from each of the 8 coils were generated. B0 and B1 maps are provided with the data. (B0 maps are in units of Hz). The data were synthesized over 8 trajectories, each with 20,000 points. The dwell time was 1usec, e.g. the time for each acquisition was 20msec. <strong> </strong><br /> <br /> <strong>DV_materials.zip:</strong> descriptions of trajectories (the pdf files), read/write code for C and Matlab, B<sub>0</sub> maps, and B<sub>1 </sub>maps.</p> <p><strong>DV_truth.dat:</strong> the DV truth data set, which has no header, is floating point (4 byte) real data, and arranged as a 3D array of 320x320x12.</p> <p>You may choose from any one of the following sets:</p> <p>A. <strong>DV_CMEY_09JUL06.zip:</strong> a <strong>Spiral</strong> trajectory coordinates and data. (trajectory designed by Craig Meyer, University of Virginia)</p> <p>B. <strong>DV_JPIP_09SEP08.zip:</strong> an <strong>EPI</strong> trajectory coordinates and data. (trajectory designed by Jim Pipe, Barrow Neurological Institute)</p> <p>3. <strong>Piece of the Puzzle</strong>. Data are from axially collected 3D unspoiled Gradient-Echo images of the knee, 320(X) x 320(Y) x 220(Z), with 0.5mm resolution in each direction (FOV = 160mm x 160mm x 110mm). Data are from an 8-channel phased-array, with coils in the x-y plane, i.e. very little coil orthogonality in Z. Contestants will get separate data for each coil. Independent noise was added to each channel. The data were synthesized over 4,000 trajectories, each with 2,000 points, which results in total undersampling of rough R=5, depending on the trajectory set. <br /> <br /> <strong>POP_materials.zip:</strong> to get descriptions of trajectories (the pdf files), read/write code for C and Matlab, and B<sub>1 </sub>maps.</p> <p><strong>POP_truth.dat: </strong>for the POP truth data set, which has no header, is floating point (4 byte) real data, and arranged as a 3D array of 320x320x220.</p> <p>You may choose from any one of the following sets:</p> <p>A. <strong>POP_MLUS_09AUG03.zip:</strong> a <strong>Random -</strong> <strong>Poisson Disk</strong> trajectory coordinates and data. (trajectory designed Miki Lustig, Stanford University)</p> <p>B. <strong>POP_ADEV_09AUG07.zip:</strong> a <strong>Spiral Projection (low resolution)</strong> trajectory coordinates and data. <br /> (trajectory designed Ajit Devaraj, Barrow Neurological Institute)</p> <p>C. <strong>POP_ADEV_09AUG10.zip:</strong> a <strong>Spiral Projection (high resolution)</strong> trajectory coordinates and data. <br /> (trajectory designed Ajit Devaraj, Barrow Neurological Institute)</p> <p>D. <strong>POP_BHAR_09AUG06.zip:</strong> a <strong>Stack-of-Spirals</strong> trajectory coordinates and data. <br /> (trajectory designed Brian Hargreaves, Stanford University)</p> <p>E. <strong>POP_HUWU_09SEP12.zip:</strong> a <strong>VIPR</strong> trajectory coordinates and data. <br /> (trajectory designed Huimin Wu, University of Wisconsin)</p> <p>Originally posted on: http://www.ismrm.org/mri_unbound/simulated.htm</p>
Interview with Barend Mons Open Science challenges
<p>Interview with Barend Mons Open Science challenges and the European Open Science Cloud, while attending the Dutch Presidency of the European Council meeting on Open Science, Amsterdam 5 April 2016.</p>
A distinct neurogenomic response to a tradeoff between social challenge and opportunity in male sticklebacks (Gasterosteus aculeatus)
<p>Animals frequently make adaptive decisions about what to prioritize when they are faced with multiple, conflicting demands at the same time. However, the proximate mechanisms of decision-making in the face of competing demands are not well understood. We explored this question using brain transcriptomics in a classic model system: threespined sticklebacks, where males face conflict between courtship and territorial defense. The outcome of decisions made by breeding males over whether to court mates or defend against territory intrusion are profoundly important to fitness. We characterized the behavior and brain gene expression profiles of males confronted by a tradeoff between courtship and territorial defense by comparing them to males not confronted by this tradeoff. When faced with the tradeoff, males behaviorally prioritized defense over courtship, and this decision was reflected in their brain gene expression profiles. A distinct set of genes and biological processes was recruited in the brain when males faced a tradeoff and these responses were largely non-overlapping across two brain regions. Combined, these results raise new questions about the interplay between the neural and molecular mechanisms involved in decision-making.</p>
FIGURE 4. Gammarus shirazinus n in tacea, Amphipoda) from warm springs in the south-east pre-alpine area of the Zagros, Iran: habitats with physiological challenges. Zootaxa, 2546, 31-51.
FIGURE 4. Gammarus shirazinus n. sp., holotype, ♂, 22 mm., from Pole-Berenji spring, S of Shiraz. A: pereopod 5, B: pereopod 6, C: pereopod 7, D: urosomites, E: epimeral plates, and F: uropod 3.
Quantification of soil organic carbon: the challenge of biochar-induced spatial heterogeneity
<p>R-script and output from model on spatially discrete biochar application and its influence on representative SOC sampling. An additional document to explain the data curation is also available ("Comment on Data curation").</p><p> </p>
Data from: Ability of seedlings to survive heat and drought portends future demographic challenges for five southwestern US conifers
<p>Climate change and disturbance are altering forests and the rates and locations of tree regeneration. We examined seedling survival of five southwestern United States (US) conifer species found in warmer and drier woodlands (<em>Pinus edulis</em>, <em>P. ponderosa</em>) and cooler and wetter subalpine forests (<em>Pseudotsuga menziesii</em>, <em>Abies concolor</em>, and <em>Picea engelmanii</em>) under hot and dry conditions in incubators. We constructed models that explained 53% to 76% of the species-specific survival variability, then applied these to recent climate (1980-2019) and projected climate (1980-2099) for the southwestern US. We found that lower elevations within species' range would have low survival under projected climate and that range contraction would be greatest for species that currently occupy warm-dry conditions. These results demonstrate that empirically derived physiological limitations can be used to identify where species composition or vegetation type change are likely to occur in the southwest US.</p>
Artifact for "Challenges in Empirically Testing Memory Persistency Models"
<p>Presented here are the litmus tests, auxiliary scripts, and output data constituting the artifact accompanying the paper titled 'Challenges in Empirically Testing Memory Persistency Models,' published in ICSE NIER'24. For detailed information, please refer to the README.</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.