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2,610 results for “tracking”
Data from: Tracking the popularity and outcomes of all bioRxiv preprints
<p>The data used to generate figures in the manuscript titled <a href="https://www.biorxiv.org/content/early/2019/01/13/515643">"Tracking the popularity and outcome of all bioRxiv preprints,"</a> posted to bioRxiv 13 Jan 2019.</p> <ul> <li><strong>22 Mar 2019:</strong> PDFs of each figure from the paper have been added to the repository. In addition, the license has been changed from CC-BY-NC to CC0.</li> </ul>
Kinematically collected reference fingerprint map (RFM) with the high precision tracking system for feature-based indoor positioning
<p>The offline referencing phase, one of the core phases of the fingerprinting-based indoor positioning system (FIPS), is the key stage for deploying the positioning system. The reference fingerprint map (RFM) is acquired for representing the relationship between location-relevant features and the corresponding locations and used for inferring the user’s location at the online stage. The kinematically collecting the RFM using the mobile device with the help of high precision tracking system is contributed to the community for benchmarking comparison of the indoor positioning performance. The detailed description of the data is cooming soon.<br> </p>
Rhodamine fluorometry tracking data Alfacs Bay
<p>Rhodamine dye was released from a waste water outfall in Alfacs Bay, Catalonia. This dye has a strong red/pink colour and can be used to trace the movement of the waster water plume. These fluorimetry data are combined with GPS geolocation data and were generated using a fluorometer towed from a boat. They show the progression of the plume over the course of a day.</p> <p>Times are Central European Time (GMT+1)</p>
Datasets and Supporting Materials for the IPIN 2017 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2017 Competition (Sapporo, Japan).</p> <p><strong>Contents:</strong></p> <ol> <li>Track3_LogfileDescription_and_SupplementaryMaterial.pdf: Description of the logfiles and supplemental materials.</li> <li>Track3_TechnicalAnnex.pdf: Technical annex describing the competition </li> <li>01-Logfiles: This folder contains a subfolder with the 25 training logfiles, a subfolder with the 9 validation logfiles, and a subfolder with the 7 blind evaluation logfiles as provided to competitors.</li> <li>02-Supplementary_Materials: This folder contains the Matlab/Octave parser, the raster maps, the visualization of the training routes and the location of the BLE beacon (CAR) and some Wi-Fi APs (UJIUB).</li> <li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 505 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li> </ol> <p><strong>Please, cite the following works when using the datasets included in this package:</strong></p> <ul> <li>Torres-Sospedra, J.; Jiménez, A. R.; Moreira, A.; Lungenstrass, T.; Lu, W.-C.; Knauth, S.; Mendoza-Silva, G.M.; Seco, F.; Perez-Navarro, A.; Nicolau, M.J.; Costa, A.; Meneses, F.; Farina, J.; Morales, J.P.; Lu, W.-C.; Cheng, H.-T.; Yang, S.-S.; Fang, S.-H.; Chien, Y.-R. and Tsao, Y. Off-line evaluation of mobile-centric Indoor Positioning Systems: the experiences from the 2017 IPIN competition Sensors Vol. 18(2), 2018. <a href="http://dx.doi.org/10.3390/s18020487">http://dx.doi.org/10.3390/s18020487</a></li> <li>Jimenez, A.R.; Mendoza-Silva, G.M.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2017 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2823924">http://dx.doi.org/10.5281/zenodo.2823924</a> </li> </ul> <p><strong>Additional information can be found at:</strong></p> <ul> <li><a href="http://evaal.aaloa.org/2017/2017-competition-home">http://evaal.aaloa.org/2017/2017-competition-home</a></li> <li><a href="http://indoorloc.uji.es/ipin2017track3/">http://indoorloc.uji.es/ipin2017track3/</a></li> </ul> <p><strong>For any further questions about the database and this competition track, please contact: </strong></p> <ul> <li>Joaquín Torres (<a href="mailto:jtorres@uji.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">jtorres@uji.es</a>) Institute of New Imaging Technologies, Universitat Jaume I, Spain. </li> <li>Antonio R. Jiménez (<a href="mailto:antonio.jimenez@csic.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">antonio.jimenez@csic.es</a>) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain. </li> </ul> <p><br> </p>
Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site)
<p>This package contains the datasets and supplementary materials used in the IPIN 2018 Competition (Nantes, France).</p> <p><strong>Contents:</strong></p> <ol> <li>IPIN2018_CallForCompetition_v2.1: Call for competition including the technical annex describing the competition </li> <li>01-Logfiles: This folder contains a subfolder with the 22 training logfiles, a subfolder with the 15 (13 + 2) validation logfiles, and a subfolder with the 1 blind evaluation logfile as provided to competitors.</li> <li>02-Supplementary_Materials: This folder contains the Matlab/octave parser, the raster maps, the vector maps and the visualization of the training routes.</li> <li>03-Evaluation: This folder contains the scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li> <li>03-Evaluation_alternative: This folder contains the alternative scripts used to calculate the competition metric, the 75th percentile on the 99 evaluation points. This version is compatible with MatLab and Octave and does not require any toolbox. In some cases, the differences in the reported errors might be around 10 cm with respect to the script used in the competition. The ground truth is also provided in MatLab format and as a CSV file. Since the results must be provided with a 2Hz freq. starting from apptimestamp 0, the GT includes the closest timestamp matching the timing provided by competitors.</li> </ol> <p><strong>Please, cite the following works when using the datasets included in this package:</strong></p> <ul> <li>Jimenez, A.R.; Mendoza-Silva, G.M.; Ortiz, M.; Perez-Navarro, A.; Perul, J.; Seco, F.; Torres-Sospedra, J. Datasets and Supporting Materials for the IPIN 2018 Competition Track 3 (Smartphone-based, off-site). <a href="http://dx.doi.org/10.5281/zenodo.2823964">http://dx.doi.org/10.5281/zenodo.2823964</a></li> <li>Renaudin, V.; Ortiz, M.; Perul, J.; Torres-Sospedra, J.; Ramón Jimenez, A.; Pérez-Navarro, A.; Martín Mendoza-Silva, G.; Seco, F.; Landau, Y.; Marbel, R.; Ben-Moshe, B.; Zheng, X.; Ye, F.; Kuang, J.; Li, Y.; Niu, X.; Landa, V.; Hacohen, S.; Shvalb, N.; Lu, C.; Uchiyama, H.; Thomas, D.; Shimada, A.; Taniguchi, R.; Ding, Z.; Xu, F.; Kronenwett, N.; Vladimirov, B.; Lee, S.; Cho, E.; Jun, S.; Lee, C.; Park, S.; Lee, Y.; Rew, J.; Park, C.; Jeong, H.; Han, J.; Lee, K.; Zhang, W.; Li, X.; Wei, D.; Zhang, Y.; Park, S. Y.; Park, C. G.; Knauth, S.; Pipelidis, G.; Tsiamitros, N.; Lungenstrass, T.; Pablo Morales, J.; Trogh, J.; Plets, D.; Opiela, M.; Shih-Hau Fang Tsao, Y.; Chien, Y.-R.; Yang, S.-S.; Ye, S.-J.; Ali, M. U.; Hur, S.; and Park, Y. Evaluating Indoor Positioning Systems in a Shopping Mall: The Lessons Learned from the IPIN 2018 Competition IEEE Access Vol. 7, pp. 148594-148628, 2019. http://dx.doi.org/10.1109/ACCESS.2019.2944389</li> </ul> <p><strong>Additional information can be found at:</strong></p> <ul> <li><a href="http://evaal.aaloa.org/2018/call-for-competitions">http://evaal.aaloa.org/2018/call-for-competitions</a></li> <li><a href="http://ipin-conference.org/2018/ipincompetition/">http://ipin-conference.org/2018/ipincompetition/</a></li> </ul> <p><strong>For any further questions about the database and this competition track, please contact: </strong></p> <ul> <li>Joaquín Torres (<a href="mailto:jtorres@uji.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">jtorres@uji.es</a>) Institute of New Imaging Technologies, Universitat Jaume I, Spain. </li> <li>Antonio R. Jiménez (<a href="mailto:antonio.jimenez@csic.es?subject=IPIN%202016%20Competition%20Dataset%20(Zenodo)">antonio.jimenez@csic.es</a>) Center of Automation and Robotics (CAR)-CSIC/UPM, Spain. </li> </ul>
An Analysis of the Probabilistic Track of the IPC 2018
<p>This archive contains data and results used for the paper 'An Analysis of the Probabilistic Track of the IPC 2018'. See the attached README for more details.<br> </p>
Food Tracking Device data
<p>This dataset includes data from the Food Tracking Device from several pilot cases within the URBAN-WASTE project. In particular, a few hotels and restaurants were supplied with a food waste tracking device where the kitchen could record their food waste data. The statistics would then be transformed into simple figures to give the kitchen feedback on their development in terms of food waste generation.</p>
Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset
<p>Version 4 of the dataset is available (Sep 19 2019)!</p> <p>Note this version has significantly more data than Version 2. </p> <p>Dataset description paper (full version) is available!</p> <p>https://arxiv.org/pdf/1903.06754.pdf (updated Sep 7 2019)</p> <p>Tools for visualizing the data is available!</p> <p>https://github.com/corgiTrax/Gaze-Data-Processor</p> <p> </p> <p><strong>=========================== Dataset Description ===========================</strong></p> <p>We provide a large-scale, high-quality dataset of human actions with simultaneously recorded eye movements while humans play Atari video games. The dataset consists of 117 hours of gameplay data from a diverse set of 20 games, with 8 million action demonstrations and 328 million gaze samples. We introduce a novel form of gameplay, in which the human plays in a semi-frame-by-frame manner. This leads to near-optimal game decisions and game scores that are comparable or better than known human records. For every game frame, its corresponding image frame, the human keystroke action, the reaction time to make that action, the gaze positions, and immediate reward returned by the environment were recorded.</p> <p> </p> <p>Q & A: Why frame-by-frame game mode?</p> <p><strong>Resolving state-action mismatch</strong>: Closed-loop human visuomotor reaction time is around 250-300 milliseconds. Therefore, during gameplay, state (image) and action that are simultaneously recorded at time step t could be mismatched. Action at time t could be intended for a state 250-300ms ago. This effect causes a serious issue for supervised learning algorithms, since label at and input st are no longer matched. Frame-by-frame game play ensures states and actions are matched at every timestep.</p> <p><strong>Maximizing human performance</strong>: Frame-by-frame mode makes gameplay more relaxing and reduces fatigue, which could normally result in blinking and would corrupt eye-tracking data. More importantly, this design reduces sub-optimal decisions caused by inattentive blindness.</p> <p><strong>Highlighting critical states that require multiple eye movements</strong>: Human decision time and all eye movements were recorded at every frame. The states that could lead to a large reward or penalty, or the ones that require sophisticated planning, will take longer and require multiple eye movements for the player to make a decision. Stopping gameplay means that the observer can use eye-movements to resolve complex situations. This is important because if the algorithm is going to learn from eye-movements it must contain all “relevant” eye-movements.</p> <p> </p> <p><strong>============================ Readme ============================</strong></p> <p>1. meta_data.csv: meta data for the dataset., including:</p> <ul> <li> <p>GameName: String. Game name. e.g., “alien” indicates the trial is collected for game Alien (15 min time limit). “alien_highscore” is the trajectory collected from the best player’s highest score (2 hour limit). See dataset description paper for details.</p> </li> </ul> <ul> <li> <p>trial_id: Integer. One can use this number to locate the associated .tar.bz2 file and label file.</p> </li> <li> <p>subject_id: Char. Human subject identifiers.</p> </li> <li> <p>load_trial: Integer. 0 indicates that the game starts from scratch. If this field is non-zero, it means that the current trial continues from a saved trial. The number indicates the trial number to look for.</p> </li> <li> <p>highest_score: Integer. The highest game score obtained from this trial.</p> </li> <li> <p>total_frame: Number of image frames in the .tar.bz2 repository.</p> </li> <li> <p>total_game_play_time: Integer. game time in ms. </p> </li> <li> <p>total_episode: Integer. number of episodes in the current trial. An episode terminates when all lives are consumed.</p> </li> <li> <p>avg_error: Float. Average eye-tracking validation error at the end of each trial in visual degree (1 visual degree = 1.44 cm in our experiment). See our paper for the calibration/validation process.</p> </li> <li> <p>max_error: Float. Max eye-tracking validation error. </p> </li> <li> <p>low_sample_rate: Percentage. Percentage of frames with less than 10 gaze samples. The most common reason for this is blinking.</p> </li> <li> <p>frame_averaging: Boolean. The game engine allows one to turn this on or off. When turning on (TRUE), two consecutive frames are averaged, this alleviates screen flickering in some games.</p> </li> <li> <p>fps: Integer. Frame per second when an action key is held down.</p> </li> </ul> <p> </p> <p>2. [game_name].zip files: these include data for each game, including:</p> <p>*.tar.bz2 files: contains game image frames. The filename indicates its trial number.</p> <p>*.txt files: label file for each trial, including:</p> <ul> <li> <p>frame_id: String. The ID of a frame, can be used to locate the corresponding image frame in .tar.bz2 file.</p> </li> <li> <p>episode_id: Integer (not available for some trials). Episode number, starting from 0 for each trial. A trial could contain a single trial or multiple trials.</p> </li> <li> <p>score: Integer (not available for some trials). Current game score for that frame.</p> </li> <li> <p>duration(ms): Integer. Time elapsed until the human player made a decision. </p> </li> <li> <p>unclipped_reward: Integer. Immediate reward returned by the game engine.</p> </li> <li> <p>action: Integer. See action_enums.txt for the mapping. This is consistent with the Arcade Learning Environment setup.</p> </li> <li> <p>gaze_positions: Null/A list of integers: x0,y0,x1,y1,...,xn,yn. Gaze positions for the current frame. Could be null if no gaze. (0,0) is the top-left corner. x: horizontal axis. y: vertical.</p> </li> </ul> <p> </p> <p>3. action_enums.txt: contains integer to action mapping defined by the Arcade Learning Environment. </p> <p> </p> <p><strong>============================ Citation ============================</strong></p> <p>If you use the Atari-HEAD in your research, we ask that you please cite the following:</p> <p>@misc{zhang2019atarihead,</p> <p> title={Atari-HEAD: Atari Human Eye-Tracking and Demonstration Dataset},</p> <p> author={Ruohan Zhang and Calen Walshe and Zhuode Liu and Lin Guan and Karl S. Muller and Jake A. Whritner and Luxin Zhang and Mary M. Hayhoe and Dana H. Ballard},</p> <p> year={2019},</p> <p> eprint={1903.06754},</p> <p> archivePrefix={arXiv},</p> <p> primaryClass={cs.LG}</p> <p>}</p> <p>Zhang, Ruohan, Zhuode Liu, Luxin Zhang, Jake A. Whritner, Karl S. Muller, Mary M. Hayhoe, and Dana H. Ballard. "AGIL: Learning attention from human for visuomotor tasks." In Proceedings of the European Conference on Computer Vision (ECCV), pp. 663-679. 2018.</p> <p>@inproceedings{zhang2018agil,</p> <p> title={AGIL: Learning attention from human for visuomotor tasks},</p> <p> author={Zhang, Ruohan and Liu, Zhuode and Zhang, Luxin and Whritner, Jake A and Muller, Karl S and Hayhoe, Mary M and Ballard, Dana H},</p> <p> booktitle={Proceedings of the European Conference on Computer Vision (ECCV)},</p> <p> pages={663--679},</p> <p> year={2018}</p> <p>}</p> <p><br> <br> </p>
PEPT data for Understanding the effect of fluid viscosity in Vertical Stirred Mills using the Positron Emission Particle Tracking (PEPT) approach
<p>The raw PEPT data collected for the paper "Understanding the effect of fluid viscosity in vertical stirred mills using the positron emission particle tracking (PEPT) approach." The paper is the first to use the PEPT technique to investigate the effect of fluid viscosity on the efficiency of the grinding process.</p> <p>This data can be post-processed using the PEPT-ML library and used in isolation or it can be used to calibrate an equivalent simulation. The simulation template is available on GitHub and the link to this is under the Software tab. Each file is labelled by the fluid viscosity and attritor speed used in the experiment, The data for a single run is often split across files but can be combined by the PEPT-ML library.</p>
ALICE Pb-Pb Run 2 event display with red/blue tracks
<p>This event display shows tracks in a Pb-Pb event recorded during Run 2 of the LHC. Individual tracks are shown following a red/blue colour code according to their charge.</p>
Data supporting "Burn Period: A use-inspired metric to track wildfire risk across the southwest U.S."
<p>Comma delimited data file of derived daily meteorological metrics from hourly, gap filled and quality controlled Remote Automated Weather Station (RAWS) data for Arizona and New Mexico (southwest U.S.) provided by the Climate, Ecosystems, and Fire Applications (CEFA) program at the Desert Research Institute (Brown, 2022, unpublished data). Data file contains daily average dewpoint temperature, air temperature, maximum Hot-Dry-Windy Index, maximum Fosberg Fire Weather Index, maximum vapor pressure deficit, and total number of hours/day with relative humidity below 20% for 124 RAWS from 2000-2022.</p>
Data and Code for "Extracting reproductive parameters from GPS tracking data for a nesting raptor in Europe"
<p>Understanding population dynamics requires estimation of demographic parameters. We build on existing approaches to develop a new tool that uses GPS tracking data to estimate breeding propensity and breeding success, and show that this tool yielded accurate predictions for two red kite populations in Central Europe. The tool is available as an R package at <a href="https://github.com/Vogelwarte/NestTool">https://github.com/Vogelwarte/NestTool</a> and will facilitate the estimation of demographic parameters from tracking data to inform population assessments. The files in this repository contain the data and analytical code to replicate the results of the publication in the Journal of Avian Biology (DOI: 10.1111/jav.03246). The version contained in this repository does not include updates and improvements that occurred after the 29 August 2024.</p>
Feral Muscovy and Mallard Tracking Data at USF
<p>Tracking data collected as a part of published study. </p> <p> </p> <p>Joni Downs, Mehrdad Vaziri, Lucy Deba Enomah, and Zachary J. Smith. 2021. Habitat use and movements of feral Mallards (Anas platyrhynchos) and invasive Muscovy ducks (Cairina moschata) in Tampa, Florida. Florida Field Naturalist 49(2): 35-45.</p>
25 years of high-frequency ground penetrating radar measurements of snow studies in Svalbard - metadata and GPS tracks
<p><strong>Surveys by ground penetrating radar (GPR) are accurate and cost-efficient, and have been conducted on Svalbard for more than 25 years, thus permitting the assessment of long term changes. The campaigns so far have covered various areas and the data is dispersed. The purpose of this report is to collect information about the conducted GPR snow cover measurements. The activities initiated in this project will be continued in the coming years and extended with a comprehensive data analysis.</strong></p> <p><strong>The dataset includes a description of metadata from GPR snow cover measurements in 1997-2022 (.CSV file) and GPS traces (.SHP files) of measurements taken in Svalbard.</strong></p> <p><strong>This study is part of the State of Environmental Science in Svalbard Report 2022 published by Svalbard Integrated Arctic Earth Observing System (SIOS).</strong></p>
Data for the "Does prognostic seeding along flight tracks produce the desired effects of cirrus cloud thinning?" manuscript
<p>Tar file of the data used to prepare the plots and write the text in: "Does prognostic seeding along flight tracks produce the desired effects of cirrus cloud thinning?" manuscript for submission to ACP.</p> <p>A description of each netcdf file is provided in the README file. The format of each file is in netcdf4</p>
Document-to-document relevant assessment for TREC Genomics Track 2005
<p>Here we present a table with document-to-document relevance assessment judgements on a subset of the TREC Genomics Track 2005 which corresponds to document-to-topic relevance assessments. This data was produced by four annotators to make it possible to analyze inter-annotator agreements as part of our future work. The data was produced with and in-house annotation tool tailored to the initial TREC data and the task at hand. The "raw data document evaluation" contains six columns, first row consecutive id, second original TREC topic, third PubMed Id used as reference document, fourth PMID used to evaluate the relevance wrt the reference document, fifth the relevance score (2 definitely relevant, 1 partially relevant, 0 non-relevant), and sixth annotator id.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This work is part of the STELLA project funded by DFG (project no. 407518790). This work was supported by the BMBF-funded de.NBI Cloud within the German Network for Bioinformatics Infrastructure (de.NBI) (031A532B, 031A533A, 031A533B, 031A534A, 031A535A, 031A537A, 031A537B, 031A537C, 031A537D, 031A538A).</p>
CS Track database - Dataset
<p>This is the main dataset which consist a list all relevant details of the CS Track database. The database contains information about 4949 Citizen Science (CS) projects extracted for more than 59 websites. This dataset contains the following information from the CS Track database: </p> <ul> <li>CS projects title </li> <li>the data extracted date</li> <li>the language of the CS projects informations</li> <li>the URL(s) of the website(s) from where the CS projects information was extracted. For other studies developed in CS Track consortium it might be useful to consult this data</li> <li>full list of assignments for research areas and SDGs for each CS project. </li> </ul>
Tracking data for breeding Curlew (Numenius arquata) in north Wales, UK
<p>This dataset of GPS tracking data for Eurasian Curlew (Numenius arquata) forms the basis of the analysis carried out in Bowgen, K. M., Dodd, S. G., Lindley, P., Burton, N. H. K., & Taylor, R. C. (2022). Curves for Curlew: Identifying Curlew breeding status from GPS tracking data. Ecology and Evolution, 12, e9509. https://doi.org/10.1002/ece3.9509.</p> <p>This paper details a new method to assign breeding status to individual birds during their full breeding season. All points are spatially anonymised but the analyses in the associated paper are replicable with this dataset as it only requires spatial and temporal relationships between the points rather than to a specific landscape.</p> <p>Data permissions are held jointly between BTO, National Resources Wales and Royal Society for the Protection of Birds as the funders and collaborators of the project. The study is open to collaboration with the full spatially referenced dataset following application to the BTO via the lead authors.</p> <p>........................................................................................</p> <p>We would also greatly appreciate if you could fill out <a href="https://forms.gle/DCc58VXpdmqnTmTk8" target="_blank" rel="noopener">this very short form</a> to tell us how you intend to use these data. Thanks in advance!</p>
CS Track Citizen Science Survey Data 2021
<p>CS Track is launching a survey to gather citizen scientists’ (16 year old and older) perspectives on activities and forms of participation, learning and knowledge-building in citizen science (CS) projects. The aim of CS Track is to broaden our knowledge about CS and the impact CS activities can have. CS Track will do this by investigating a large and diverse set of CS activities, disseminating best practices and formulating knowledge-based policy recommendations in order to maximise the potential benefits of CS activities for individual citizens, organisations and society. This multi-perspective approach will allow us to shed light on the role of citizen science in society and social attitudes and emerging cultures in communities that engage with science and technology challenges.</p> <p><strong>CSTrack_Citizen_Science_Survey_Data_Final_Anon.csv</strong>: CSV File. CS Track Citizen Science Survey Data in a CSV file.</p> <p><strong>CSTrack_Citizen_Science_Survey_Data_Final_Anon.xlsx</strong>: Excel datasheet. Same file as previous in an Excel datasheet.</p> <p><strong>CSTrack_Citizen_Science_Survey_Final.pdf</strong>: PDF-file. CS Track Citizen Science Survey.</p> <p> </p> <p> </p>
Flowchart on the methodology used to conduct the literature search on provenance representation models and change-tracking in RDF
<p>The image contains a flowchart on the methodology used to conduct the literature search on provenance representation models and change-tracking in RDF. This methodology was used in the Abstract submitted to the DH2023 conference in Graz.</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.