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Fig. 1 in A Case of winter activity of the Hermann's Tortoise (Testudo hermanni Gmelin, 1789) (Reptilia: Testudinidae) from Bulgaria
Fig. 1. The plantation of Quercus suber and the adult female T. hermanni recorded in it.
Fig. 3 in A White Stork (Ciconia ciconia (Linnaeus, 1758)) nest - an unique case of multiple nesting commensalism of five species from Dragoman (W Bulgaria)
Fig. 3. Spanish sparrows nesting in a nest of White Stork, Dragoman, 19.05.2019. Photo: Z. Boev.
Fig. 2 in A White Stork (Ciconia ciconia (Linnaeus, 1758)) nest - an unique case of multiple nesting commensalism of five species from Dragoman (W Bulgaria)
Fig. 2. House sparrows nesting in a nest of White Stork, Dragoman, 19.05.2019. Photo: Z. Boev.
Fig. 1 in Case of alarm vocalization in a colony of Microtus guentheri (Danford & Alston, 1880) (Mammalia, Rodentia, Arvicolidae) from Southern Bulgaria
Fig. 1. Spectrogram of the alarm whilst of Guenter's vole Microtus guentheri.
Diversity's Role in Collaboration and Conflict: A Case-Study of React.js
Open the record for dataset details and reuse information.
A case study on the impact of interplanetary coronal mass ejection on the Martian O(1S) 557.7 nm dayglow emission using ExoMars TGO/NOMAD-UVIS observations: First Results
<p>This study presents the impact of interplanetary coronal mass ejection (ICME) on the O(1S) 557.7 nm dayglow emission in the Martian atmosphere using instruments onboard ExoMars-TGO and MAVEN spacecrafts.</p>
Methodology data of "A qualitative and quantitative citation analysis toward retracted articles: a case of study"
<p>This document contains the datasets and visualizations generated after the application of the methodology defined in our work: <em>"A qualitative and quantitative citation analysis toward retracted articles: a case of study"</em>. The methodology defines a citation analysis of the Wakefield et al. [1] retracted article from a quantitative and qualitative point of view. The data contained in this repository are based on the first two steps of the methodology. The first step of the methodology (i.e. “Data gathering”) builds an annotated dataset of the citing entities, this step is largely discussed also in [2]. The second step (i.e. "Topic Modelling") runs a topic modeling analysis on the textual features contained in the dataset generated by the first step. </p> <p><strong>Note:</strong> the data are all contained inside the "<strong><em>method_data.zip"</em> </strong>file. You need to unzip the file to get access to all the files and directories listed below.</p> <p> </p> <p><strong>Data gathering</strong></p> <p>The data generated by this step are stored in <strong>"<em>data/</em>"</strong>:</p> <ol> <li><em><strong>"cits_features.csv": </strong></em>a dataset containing all the entities (rows in the CSV) which have cited the Wakefield et al. retracted article, and a set of features characterizing each citing entity (columns in the CSV). The features included are: DOI ("doi"), year of publication ("year"), the title ("title"), the venue identifier ("source_id"), the title of the venue ("source_title"), yes/no value in case the entity is retracted as well ("retracted"), the subject area ("area"), the subject category ("category"), the sections of the in-text citations ("intext_citation.section"), the value of the reference pointer ("intext_citation.pointer"), the in-text citation function ("intext_citation.intent"), the in-text citation perceived sentiment ("intext_citation.sentiment"), and a yes/no value to denote whether the in-text citation context mentions the retraction of the cited entity ("intext_citation.section.ret_mention").<br> <strong>Note: </strong>this dataset is licensed under a <a href="https://creativecommons.org/publicdomain/zero/1.0/legalcode">Creative Commons public domain dedication (CC0)</a>.<br> </li> <li><em><strong>"cits_text.csv": </strong>this dataset stores the abstract ("abstract") and the in-text citations context ("intext_citation.context") </em>for each citing entity identified using the DOI value ("doi").<br> <strong>Note: </strong>the data keep their original license (the one provided by their publisher). This dataset is provided in order to favor the reproducibility of the results obtained in our work.</li> </ol> <p> </p> <p><strong>Topic modeling</strong><br> We run a topic modeling analysis on the textual features gathered (i.e. abstracts and citation contexts). The results are stored inside the <em><strong>"topic_modeling/"</strong></em> directory. The topic modeling has been done using MITAO, a tool for mashing up automatic text analysis tools, and creating a completely customizable visual workflow [3]. The topic modeling results for each textual feature are separated into two different folders, <em><strong>"abstracts/"</strong></em> for the abstracts, and <em><strong>"intext_cit/"</strong></em> for the in-text citation contexts. Both the directories contain the following directories/files: <strong> </strong></p> <ol> <li> <p><em><strong>"mitao_workflows/"</strong></em>: the workflows of MITAO. These are JSON files that could be reloaded in MITAO to reproduce the results following the same workflows.</p> </li> <li> <p><em><strong>"corpus_and_dictionary/": </strong></em>it contains the dictionary and the vectorized corpus given as inputs for the LDA topic modeling.</p> </li> <li> <p><em><strong>"coherence/coherence.csv":</strong></em> the coherence score of several topic models trained on a number of topics from 1 - 40.</p> </li> <li> <p><em><strong>"datasets_and_views/": </strong></em>the datasets and visualizations generated using MITAO. </p> </li> </ol> <p> </p> <p><strong>References</strong></p> <ol> <li>Wakefield, A., Murch, S., Anthony, A., Linnell, J., Casson, D., Malik, M., Berelowitz, M., Dhillon, A., Thomson, M., Harvey, P., Valentine, A., Davies, S., & Walker-Smith, J. (1998). RETRACTED: Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children. <em>The Lancet</em>, <em>351</em>(9103), 637–641. <a href="https://doi.org/10.1016/S0140-6736(97)11096-0">https://doi.org/10.1016/S0140-6736(97)11096-0</a></li> <li> <p>Heibi, I., & Peroni, S. (2020). A methodology for gathering and annotating the raw-data/characteristics of the documents citing a retracted article v1 (protocols.io.bdc4i2yw) [Data set]. In protocols.io. ZappyLab, Inc. <a href="https://doi.org/10.17504/protocols.io.bdc4i2yw">https://doi.org/10.17504/protocols.io.bdc4i2yw</a></p> </li> <li> <p> </p> Ferri, P., Heibi, I., Pareschi, L., & Peroni, S. (2020). MITAO: A User Friendly and Modular Software for Topic Modelling [JD]. PuntOorg International Journal, 5(2), 135–149. <a href="https://doi.org/10.19245/25.05.pij.5.2.3">https://doi.org/10.19245/25.05.pij.5.2.3</a> <p> </p> </li> </ol>
Data from: Non-inheritable risk factors during pregnancy for congenital heart defects in offspring: a matched case-control study
<p>Data analyzed in "Non-inheritable risk factors during pregnancy for congenital heart defects in offspring: a matched case-control study". The data provided by the authors to benefit other researchers. The posted materials are not copyedited and are the sole responsibility of the authors, so questions should be addressed to the corresponding author.</p>
Do we similarly assess diversity with microscopy and High Throughput Sequencing? Case of microalgae in lakes.
<p>These are the repository files of the paper "Do we similarly assess diversity with microscopy and High Throughput Sequencing? Case of microalgae in lakes" published in Organisms Diversity and Evolution</p> <p>In these files are given:</p> <p>- the sampling sites descrptions (coordinates, lake names)</p> <p>- species relative abundances obtained with light microscopy for each sampling site</p> <p>- OTUs amounts and relative abundances obtained High-Throughput Sequencing for each sampling site</p> <p>- code correspondence between lake codes and FastQ files codes</p> <p>- FastQ files of the sampling sites</p>
Example results for use-cases of DAVOS toolkit (dependability assessment, verification, optimisation and selection of hardware models)
<p>This dataset exemplifies the results that can be obtained for several basic experimentation scenarios by means of DAVOS toolkit, available under MIT licence at https://github.com/IlyaTuzov/DAVOS</p> <p>Particular experimentation scenarios are:</p> <p>- Dependability assessment (LEON3 processor core)<br> - Dependability benchmarking of implementation alternatives (MC8051 processor core)<br> - Dependability-aware design space exploration (when implementing PIC core by Xilinx ISE toolkit)</p> <p><br> <strong>Installation steps.</strong></p> <p>1. Ensure to have python ver. 2.x installed. Type in terminal (cmd console in Windows): “python --version” – if the output looks like > Python 2.x.x – python is installed. <br> Otherwise download and install 2.x.x distribution: https://www.python.org/<br> Add python installation path to environment path variable.</p> <p><br> 2. Ensure to have Web-Server installed (Apache preferable). For instance, XAMPP: https://www.apachefriends.org/index.html</p> <p> <br> 3. Ensure that Web-server is configured to execute CGI scripts, particularly python-scripts:<br> In the 'httpd.conf' file (XAMMP control panel – button config in front of apache module):<br> </p> <p>– search for line Options Indexes FollowSymLinks and add ExecCGI, so the resulting line looks like this: <br> Options Indexes FollowSymLinks ExecCGI<br> – search for #AddHandler cgi-script .cgi, uncomment (remove #), and append “.py” to this line, so the results looks like:<br> AddHandler cgi-script .cgi .pl .asp .py </p> <p>4. Unpack the contents of *.zip package into the folder on the Web Server. <br> For instance into 'Web-server root folder'/Dataset.<br> The Web-Server root can be configured in the ‘httpd.conf’ file in the DocumentRoot section, for instance: <br> DocumentRoot "F:/HTWEB"<br> <Directory "F:/HTWEB"><br> ...</p> <p>5. In the web-browser navigate to the root directory of extracted package:<br> http://localhost/Dataset/index.html<br> </p>
MPAS-Albany Land Ice v6.0 Test Cases
<p>This archive includes test case for the MPAS-Albany Land Ice v6.0 model.</p> <p>See http://mpas-dev.github.io/ for links to model source code and User's Guide.</p>
International Relations Articles Clustering Case - Data File
<p>Data file in CSV format of "Perceptions Journal" articles (2010-2012)</p>
Robustness assessment through iterative statistical fault injection: LEON3 processor as a case study
<p><strong>Dataset exemplifies an approach of iterative statistical fault injection to assess the robustness of HDL models.</strong></p> <p>Contents:<br> 1. Results of exhaustive fault injection experiments (bit-flip faults) into LEON3 processor model;<br> 2. Interactive querying interface, allowing to obtain custom samples from exhaustive results, and visualize them;<br> 3. Python scripts simulating 3 approaches to statistical fault injection: conservative, error-driven, time-driven.</p> <p> </p> <p><strong>Installation guide:</strong></p> <p> 1. Ensure to have python ver. 2.x installed. Type in terminal (cmd console in Windows): “python --version” – if the output looks like > Python 2.x.x – python is installed. <br> Otherwise download and install 2.x.x distribution: https://www.python.org/<br> Add python installation path to environment path variable.</p> <p><br> 2. Ensure to have Web-Server installed (Apache preferable). For instance, XAMPP: https://www.apachefriends.org/index.html</p> <p> <br> 3. Ensure that Web-server is configured to execute CGI scripts, particularly python-scripts:<br> In the 'httpd.conf' file (XAMMP control panel – button config in front of apache module):<br> </p> <p> – search for line Options Indexes FollowSymLinks and add ExecCGI, so the resulting line looks like this: <br> Options Indexes FollowSymLinks ExecCGI<br> – search for #AddHandler cgi-script .cgi, uncomment (remove #), and append “.py” to this line, so the results looks like:<br> AddHandler cgi-script .cgi .pl .asp .py </p> <p> 4. Unpack the contents of *.zip package into the folder on the Web Server. <br> For instance into 'Web-server root folder'/Dataset.<br> The Web-Server root can be configured in the ‘httpd.conf’ file in the DocumentRoot section, for instance: <br> DocumentRoot "F:/HTWEB"<br> <Directory "F:/HTWEB"><br> ...</p> <p> 5. In the web-browser navigate to the root directory of extracted package:<br> http://localhost/Dataset/index.html</p>
Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: A Case Study of the PyPI Ecosystem - the dataset
<pre><em>Replication pack, FSE2018 submission #164: </em><em>------------------------------------------ </em></pre> <pre><strong>**</strong>Working title:<strong>** </strong>Ecosystem-Level Factors Affecting the Survival of Open-Source Projects: A Case Study of the PyPI Ecosystem <strong>**</strong>Note:<strong>** </strong>link to data artifacts is already included in the paper. Link to the code will be included in the Camera Ready version as well. <em>Content description </em><em>=================== </em> <strong>- **</strong>ghd-0.1.0.zip<strong>** </strong>- the code archive. This code produces the dataset files described below <strong>- **</strong>settings.py<strong>** </strong>- settings template for the code archive. <strong>- **</strong>dataset_minimal_Jan_2018.zip<strong>** </strong>- the minimally sufficient version of the dataset. This dataset only includes stats aggregated by the ecosystem (PyPI) <strong>- **</strong>dataset_full_Jan_2018.tgz<strong>** </strong>- full version of the dataset, including project-level statistics. It is ~34Gb unpacked. This dataset still doesn't include PyPI packages themselves, which take around 2TB. <strong>- **</strong>build_model.r, helpers.r<strong>** </strong>- R files to process the survival data (`survival_data.csv` in <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>**</strong>, `common.cache/survival_data.pypi_2008_2017-12_6.csv` in <strong>**</strong>dataset_full_Jan_2018.tgz<strong>**</strong>) <strong>- **</strong>Interview protocol.pdf<strong>** </strong>- approximate protocol used for semistructured interviews. <strong>- </strong>LICENSE - text of GPL v3, under which this dataset is published <strong>- </strong>INSTALL.md - replication guide (~2 pages)</pre> <pre><em>Replication guide </em><em>================= </em> <em>Step 0 - prerequisites </em><em>---------------------- </em> <strong>- </strong>Unix-compatible OS (Linux or OS X) <strong>- </strong>Python interpreter (2.7 was used; Python 3 compatibility is highly likely) <strong>- </strong>R 3.4 or higher (3.4.4 was used, 3.2 is known to be incompatible) Depending on detalization level (see Step 2 for more details): <strong>- </strong>up to 2Tb of disk space (see Step 2 detalization levels) <strong>- </strong>at least 16Gb of RAM (64 preferable) <strong>- </strong>few hours to few month of processing time <em>Step 1 - software </em><em>---------------- </em> <strong>- </strong>unpack <strong>**</strong>ghd-0.1.0.zip<strong>**</strong>, or clone from gitlab: git clone https://gitlab.com/user2589/ghd.git git checkout 0.1.0 `cd` into the extracted folder. All commands below assume it as a current directory. <strong>- </strong>copy `settings.py` into the extracted folder. Edit the file: <strong> * </strong>set `DATASET_PATH` to some newly created folder path <strong> * </strong>add at least one GitHub API token to `SCRAPER_GITHUB_API_TOKENS` <strong>- </strong>install docker. For Ubuntu Linux, the command is `sudo apt-get install docker-compose` <strong>- </strong>install libarchive and headers: `sudo apt-get install libarchive-dev` <strong>- </strong>(optional) to replicate on NPM, install yajl: `sudo apt-get install yajl-tools` Without this dependency, you might get an error on the next step, but it's safe to ignore. <strong>- </strong>install Python libraries: `pip install --user -r requirements.txt` . <strong>- </strong>disable all APIs except GitHub (Bitbucket and Gitlab support were not yet implemented when this study was in progress): edit `scraper/init.py`, comment out everything except GitHub support in `PROVIDERS`. <em>Step 2 - obtaining the dataset </em><em>----------------------------- </em> The ultimate goal of this step is to get output of the Python function `common.utils.survival_data()` and save it into a CSV file: # copy and paste into a Python console from common import utils survival_data = utils.survival_data('pypi', '2008', smoothing=6) survival_data.to_csv('survival_data.csv') Since full replication will take several months, here are some ways to speedup the process: <em>####Option 2.a, difficulty level: easiest </em> Just use the precomputed data. Step 1 is not necessary under this scenario. <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018.zip<strong>** </strong><strong>- </strong>get `survival_data.csv`, go to the next step <em>####Option 2.b, difficulty level: easy </em> Use precomputed longitudinal feature values to build the final table. The whole process will take 15..30 minutes. <strong>- </strong>create a folder `<DATASET_PATH>/common.cache`, where `<DATASET_PATH>` is the value of the variable `DATASET_PATH` in `settings.py` <strong>- </strong>extract <strong>**</strong>dataset_minimal_Jan_2018<strong>** </strong>to the newly created folder <strong>- </strong>rename files: mv backporting.csv monthly_data.pypi_backporting.csv mv cc_degree.csv monthly_data.pypi_cc_degree.csv mv commercial.csv monthly_data.pypi_commercial.csv mv commits.csv monthly_data.pypi_commits.csv mv contributors.csv monthly_data.pypi_contributors.csv mv dc_katz.csv monthly_data.pypi_dc_katz.csv mv downstreams.csv monthly_data.pypi_downstreams.csv mv d_upstreams.csv monthly_data.pypi_d_upstreams.csv mv github_user_info.csv user_info.pypi.csv mv issues.csv monthly_data.pypi_issues.csv mv non_dev_issues.csv monthly_data.pypi_non_dev_issues.csv mv non_dev_submitters.csv monthly_data.pypi_non_dev_submitters mv package_urls.csv package_urls.pypi.csv mv q90.csv monthly_data.pypi_q90.csv # raw_dependencies.csv is not required # raw_packages_info.csv is not required # Feel free to read README.md for more details about the data mv submitters.csv monthly_data.pypi_submitters.csv # In this scenario we'll generate a new survival_data.csv mv university.csv monthly_data.pypi_university.csv mv upstreams.csv monthly_data.pypi_upstreams.csv <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` Then, use the Python code above to obtain `survival_data.csv`. <em>####Option 2.c, difficulty level: medium </em> Use predownloaded raw data to build longitudinal feature values, and then the dataset. Despite most of the data is cached, some functions will pull up updates which might take anywhere from days to couple weeks to run. <strong>- </strong>Download <strong>**</strong>dataset_full_Jan_2018.tgz<strong>** </strong>from http://k.soberi.us/dataset_full_Jan_2018.tgz . This file is not included in this archive because of its size (5.4Gb compressed, 34Gb unpacked). <strong>- </strong>edit `common/decorators.py`, set `DEFAULT_EXPIRY` to some higher value, e.g. `DEFAULT_EXPIRY = float('inf') # cache never expires` <strong>- </strong>extract the content of this archive into `<DATASET_PATH>`. <strong>- </strong>clean up `<DATASET_PATH>/common.cache` (otherwise you'll get Step 2.a. You can reproduce Step 2.b by deleting only `survival_data.pypi_2008_2017-12_6.csv`) Run the Python code above to obtain `survival_data.csv`. <em>####Option 2.d, difficulty level: hard </em> Build the dataset from scratch. Although most of the processing is parallelized, it will take at least couple months on a reasonably powerful server (32 cores, 512G of RAM, 2Tb+ of HDD space in our setup). <strong>- </strong>ensure the `<DATASET_PATH>` is empty <strong>- </strong>add more GitHub tokens (borrow from your coworkers) to `settings.py`. Run the Python code above to obtain `survival_data.csv`. <em>Step 3 - run the regression </em><em>--------------------------- </em> install R libraries: install.packages(c("htmlTable", "OIsurv", "survival", "car", "survminer", "ggplot2", "sqldf", "pscl", "texreg", "xtable")) Use `build_model.r` (e.g. in RStudio) and produced `survival_data.csv` to build the regressions used in the paper. This process takes at least 16Gb of RAM and takes few hours to run due to the gigantic size of the dataset. </pre>
CATCH-EyoU: Meanings and Practices of Youth Participation and Cases of Successful Participation: Cross-national Ethnography Cases
<p>This data set contains data related to the ethnographic qualitative study (participant observations and in-depth interviews) of citizens active in youth citizenship organizations. The selected participants are active in supporting, communicating and engaging young people’s active participation in diverse and varied causes (the environment, anti-austerity protests, pro-European vote, voting at 16, anti-fees and cuts campaigns, rights around civic spaces and youth centers, employment and jobs-related campaigns, issues around race and religion, volunteering, refugees, local housing and education). The ethnographic study was performed in Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia. </p> <p>The data set consists of: (1) eight national textual reports of detailed observations (ethnographic field notes) of youth civic and political organizations/individual youth. The reports are in English and are fully anonymized and redacted. (2) 48 carefully redacted and ethically permissioned English language transcripts of qualitative interviews (6 interviews from each partner country) and (3) 160 carefully redacted and ethically permissioned English language transcripts of participant-observation field notes (20 A4 pages per country).</p> <p>The names of organisations and personal data of participants or employees have been strictly redacted and anonymized in order to protect participants’ privacy and comply with ethical requirements. Instead generic descriptors such as ‘National Students Organisation’ or ‘Local Municipal Youth Volunteer Centre’ are used throughout.</p> <p>The data can be reused by researchers who want to compare our data with similar data collected in different countries, to perform textual analysis (content analysis and/or data mining) on our data. </p>
CATCH-EyoU: Meanings and Practices of Youth Participation and Cases of Successful Participation: Cross-national Documentary Evidence of Youth Civic and Political Participation Initiatives
<p>The data set integrates documentary evidence of national youth civic and political participation initiatives in Italy, Sweden, Germany, Greece, Portugal, Czech Republic, UK, and Estonia. The evidence are represented as pamphlets, brochures, leaflets, newsletters, social media and websites, you tube videos, meme and vine campaigns. The participation initiatives are selected with a focus on initiatives used by national youth citizenship organizations for generating, supporting, communicating and engaging young people’s active participation in diverse and varied causes (the environment, voting at 16, anti-fees and cuts campaigns, rights around civic spaces and youth centers, employment and jobs-related campaigns, issues around race and religion, volunteering, refugees, local housing and education).</p> <p> </p> <p>The data set consists of: (1) a spreadsheet integrating data from all partner countries, and containing qualitative descriptions of activities and cataloguing associated materials of youth civic and political organizations; (2) a textual report containing exemplar images of publicly available online newsletters, minutes, reports and campaign materials from across the consortium (where feasible), copyright cleared and creative commons screenshots of websites, invitation letters, records of personal conversations with members of youth civic teams, quotes from researcher-conversations on social media or f-2-f with members of teams with names redacted.</p> <p>Personal data and all non-public data has been anonymized in order to protect participants’ privacy.</p> <p>The data can be re-used by researchers who want to compare our cross- European data with similar data collected in different countries, to perform textual analysis (content analysis and/or data mining) on our data. Also other stakeholders may be interested in reanalyzing our data for comparative aims.</p>
Video of Juveniles of the Bluespotted Trevally, Caranx bucculentus (Teleostei: Carangidae), schooling with venomous catfishes (Plotosidae): a new case of mimicry
<p>Video of Juveniles of the Bluespotted Trevally, Caranx bucculentus (Teleostei: Carangidae), schooling with venomous catfishes (Plotosidae): a new case of mimicry: part of the publication:</p> <p>Smith-Vaniz, W.F., DeLoach, A. & DeLoach, N. (2018) Juveniles of the Bluespotted Trevally, Caranx bucculentus (Teleostei: Carangidae), schooling with venomous catfishes (Plotosidae): a new case of mimicry. Journal of the Ocean Science Foundation, 30, 82–84.<br> </p>
Schinias Case Study Area
<p>Data collected by the SUBSOL Data Monitoring System for the Schinias case study.</p>
The Binary Fraction of Stars in Dwarf Galaxies: The Cases of Draco and Ursa Minor
<p>supplementary data products, including all sky-subtracted spectra from individual targets, as well as random draws from posterior PDFs for model parameters (see enclosed README file)</p>
Leveraging Commercial Cubesat Constellations for Auroral Science: A Case Study
<p>Planet satellite magnetometer data to accompany the paper "Leveraging Commercial Cubesat Constellations for Auroral Science: A Case Study"</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.