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764 results for “Usage”
Are Static Analysis Violations Really Fixed? A Closer Look at Realistic Usage of SonarQube. Dataset for OSS organizations
<p>Dataset containing all rules, files and issues mined for Apache Software Foundation and Eclipse Foundation.</p>
Feature count data for Love et al. 2019 analysis for "Using equivalence class counts for fast and accurate testing of differential transcript usage" paper
<p>Feature count data for Love et al. 2019 analysis used in the "Using equivalence class counts for fast and accurate testing of differential transcript usage" paper. For reproducing the analyses and figures using the <a href="https://github.com/Oshlack/ec-dtu-paper/">ec-dtu-paper</a> code.</p> <p>Contains:</p> <ul> <li>Equivalence class count matrix for all 24 samples (using counts from Salmon)</li> <li>Salmon quantification results for all 24 samples</li> <li>Exon counts for all 24 samples using DEXSeq-count</li> </ul>
Non-wood forest products- collection and usage in Europe
<p>This is a data-set generated within WP1 of FP7 StarTree project (grant agreement No. 311919). Data is generated through a survey distributed to NWFP experts from 14 regions within 12 European 12 countries with a wide geographical spread. They are: Alentejo Region (Portugal); Šumadija and Western Serbia (Serbia); Bursa Province (Turkey); Catalonia and Valladolid (Spain); Latvia, North-east Romania, North Karelia (Finland), Osrednjeslovenska Region (Slovenia), Eastern Scotland and Wales (UK); Styria (Austria); Trentino-Alto Adige (Italy); Waldemärker (Germany). Data set has 39 variables and 1962 entries. It was collected in 2014-2015 period. </p>
Replication Package for "Type Inference in Kotlin: An Exploratory Study of Developer Usage Patterns"
<p>This dataset includes scripts and data files used to generate all analysis and results from the paper. A <strong>README.md</strong> file is included for details on using the scripts.</p> <p>The dataset is quite large. It is broken down into three archives. All scripts are in <strong>replication-pkg.zip</strong> and the other 2 files only contain data. So if you want to just inspect the analysis, you only need that single zip.</p> <p>If you grab the <strong>data-cache.zip</strong> file and extract it, it will need around 1GB of space. This is the processed dataset stored in Parquet files. Use this if you want to just recreate the tables/figures from the paper.</p> <p>If you want to make changes to the analyses, you will need the raw data in <strong>data.zip</strong>. This will need around 13GB of space once extracted. If you then generate the CSV files from those TXT files (which you will need to do for any custom analysis), you will need an additional 12GB of space.</p>
Understanding API Usage and Testing: An Empricial Study of C Libraries (Artifact)
<h1>LibProbe Artifact</h1> <p>For the sake of the evaluation we preprocessed the CCScanner data to identify all clients of the libraries used in our evaluation and included those in our <code>MongoDB</code> database. This artifact will start by importing this pre-processed dependency information into a docker image which is then used for the evaluation.</p> <h2>Running the docker image</h2> <ul> <li> <p>First load the docker image by running</p> <ul> <li> <div> <div><code>gunzip -c libprobe_v2.2.tar.gz | sudo docker import - libprobe:latest </code></div> </div> </li> </ul> <p>this will load the image in your local docker images.</p> </li> <li> <p>Run a container from the image: <code>sudo docker run -it --device /dev/snd --privileged libprobe:latest /bin/bash</code> This will run a docker container which maps the pulseaudio and alsa configurations from your local host to the docker image. This is necessary to get some clients for some target libraries to build correctly. You will need an ubuntu host machine which has pulseaudio and alsa installed.</p> </li> <li>Run <code>mongod --fork --logpath /var/log/mongodb/mongod.log</code> followed by <code>mongorestore --drop --db apiusage /tmp/libprobe/database/apiusage</code> to import the results saved in the artifact. </li> </ul> <h2>Validating analysis results</h2> <ul> <li>The docker image provided does not contain any clients due to size limitations on sharing. The Mongo database contains all the results of running this evaluation.</li> <li>We provide a script <code>clone_clients.py</code> with a <code>client_repos.json </code>file in <code>/tmp/libprobe/extra</code> which can be used to clone the clients in the clients directory. </li> <li>To get the results it's possible to run <code>python3 libprobe.py analyse all -n</code> from <code>/tmp/libprobe</code>. This will overwrite the JSON files in the <code>json_files</code> directory and overwrite the graphs in the <code>graphs</code> directory.</li> </ul> <h2>Running the evaluation for one library (vorbis)</h2> <ul> <li>Download clients: Go to <code>/tmp/libprobe</code> and run <code>python3 libprobe.py download vorbis</code></li> <li>Prepare the library: <ul> <li>In /tmp/data/libraries/xiph@@vorbis run make clean then make and make check and make install.</li> <li>Copy all C files from /tmp/data/libraries/xiph@@vorbis/lib to /tmp/data/libraries/xiph@@vorbis/lib/.libs to collect accurate API coverage information.</li> </ul> </li> <li>Process the library to get the APIs and the coverage information : <code>python3 libprobe.py processlib vorbis</code></li> <li>Prepare clients for excluding sub directories that might contain vorbis library code: <code>python3 libprobe.py prepclients vorbis</code></li> <li>Get client usages: <code>python3 libprobe.py fetchusages vorbis</code></li> <li>Analyse: <code>python3 libprobe.py analyse vorbis -n</code></li> <li>(optional) Measure differential coverage for improved coverage libs: <code>python3 libprobe.py coverage vorbis</code></li> </ul> <h2>Running the evaluation for all libraries (this requires at least 300GB of disk space)</h2> <ul> <li>Download clients: Go to <code>\tmp\libprobe</code> and run <code>python3 libprobe.py download all</code></li> <li>Process the libraries: <code>python3 libprobe.py processlib all</code></li> <li>Prepare clients: <code>python3 libprobe.py prepclients all</code></li> <li>Get usages: <code>python3 libprobe.py fetchusages all</code></li> <li>Analyse: <code>python3 libprobe.py analyse all -n</code></li> <li>(optional) Measure differential coverage for improved coverage libs: <code>python3 libprobe.py coverage <library></code></li> </ul> <h2>Getting baseline coverage for libraries</h2> <p>All libraries are cloned in <code>/tmp/data/libraries</code> and clients are cloned in <code>/tmp/data/clients</code>.</p> <div> </div> <ul> <li>MBedtls: Copy the script <code>coverage_mbedtls.sh</code> from <code>/tmp/libprobe/extra</code> into the the build directory of Mbedtls <code>/tmp/data/libraries/Mbed-TLS@@mbedtls/build</code> and run <code>./coverage_mbedtls.sh</code> followed by <code>genhtml baseline.info --output-directory</code> out this will calculate the baseline coverage for mbedtls.</li> <li>FFTW: Copy the script coverage.sh from /tmp/libprobe/extra into the the root dir of FFTW and run ./coverage.sh baseline this will calculate the baseline coverage for fftw</li> <li>HDF5: Copy the script coverage_hdf.sh from /tmp/libprobe/extra into the the root dir of HDF and run <code>./coverage_hdf.sh baseline</code> this will calculate the baseline coverage for HDF.</li> <li>LMDB: Copy the script coverage_lmdb.sh from /tmp/libprobe/extra into the <code>/tmp/data/libraries/LMDB@@lmdb/libraries/liblmdb</code> and run <code>./coverage_lmdb.sh baseline</code> this will calculate the baseline coverage for LMDB.</li> <li>Zip: Copy the script coverage_zip.sh from /tmp/libprobe/extra into <code>/tmp/data/libraries/kuba--@@zip/build/CMakeFiles/zip.dir/sr</code>c and run <code>./coverage_zip.sh baseline</code> this will calculate the baseline coverage for zip.</li> <li>Vorbis: Copy the script cal_cov.py from /tmp/libprobe/extra to <code>/tmp/data/libraries/xiph@@vorbis/lib/.libs</code> and then copy all source files in the <code>.libs</code> folder by running <code>cp ../*.c .</code> from the <code>.libs</code> folder. Finally run <code>python3 cal_cov.py .</code>.</li> <li>XXhash: Copy the script coverage.sh from /tmp/libprobe/extra into /tmp/data/libraries/Cyan4973@@xxHash and run ./coverage.sh baseline this will calculate the baseline coverage for xxhash</li> </ul> <h2>Reproducing increased coverage using clients</h2> <ul> <li> <p>LMDB: The client we will use is Knot DNS.</p> <ul> <li>Change directory to <code>/tmp/data/clients/CZ-NIC@@knot</code> and run <code>autogen.sh</code>.</li> <li>Run <code>./configure --with-lmdb=/usr/local</code>.</li> <li>Then <code>make && make check</code>.</li> </ul> <p>Now go back to the LMDB directory and run</p> <ul> <li> <p>Run <code>./coverage_lmdb.sh after_knot</code>.</p> </li> <li> <p>Now go /tmp/libprobe and run <code>python3 libprobe.py coverage lmdb</code></p> </li> </ul> </li> <li> <p>VORBIS: The client we will use in SFML.</p> <p>Go to the vorbis library dir <code>/tmp/data/libraries/xiph@@vorbis</code> and run <code>make clean</code>.</p> <ul> <li>Run <code>make && make check && make install</code>.</li> <li>Go to the <code>.libs</code> folder and copy all c files there by doing <code>cp ../*.c .</code>.</li> <li>Copy <code>/tmp/libprobe/extra/cal_cov.py</code> into the <code>.libs</code> folder and run <code>python3 cal_cov.py .</code>. This will show the baseline coverage.</li> </ul> <p>Go to the SFML directory <code>/tmp/data/clients/SFML@@SFML</code>.</p> <ul> <li>Create build directory <code>mkdir build && cd build</code>.</li> <li>Run <code>cmake -DSFML_BUILD_TEST_SUITE=TRUE -GNinja ..</code>.</li> <li>Run <code>ninja</code>.</li> <li>Run <code>ctest</code>. You will see some failing tests. Thats normal as we are only interested in the Audio tests for vorbis. All Audio tests should pass.</li> </ul> <p>Go back to the <code>.libs</code> folder in vorbis and re-run the <code>cal_cov.py</code> script.</p> <ul> <li>Now go /tmp/libprobe and run <code>python3 libprobe.py coverage vorbis</code></li> </ul> </li> </ul> <ul> <li>SDL: The client we will use in UFOAI.<br> <ul> <li> <p>Go to the SDL library dir <code>/tmp/data/libraries/libsdl-org\@\@SDL</code> and then the <code>build2</code> directory where the built library is. .</p> </li> <li>Run <code>make clean && make && make install && make test</code>.</li> <li>Copy <code>/tmp/libprobe/extra/coverage_sdl.sh</code> into the <code>build2</code> folder and run <code>./coverage_sdl.sh baseline</code>. Run <code>genhtml baseline.info --output-directory out</code></li> </ul> </li> </ul> <p> Go to the UFOAI directory <code>/tmp/data/clients/ufoaiorg\@\@ufoai</code></p> <ul> <li> <ul> <li>Run <code>./configure --target-os=linux --disable-uforadiant</code> && <code>make</code>.</li> <li>Run .<code>/testall</code></li> </ul> </li> </ul> <p> Now go /tmp/libprobe and run <code>python3 libprobe.py coverage sdl</code></p> <p> </p> <ul> <li>FFTW: The client we will use in CAVA.</li> </ul> <p> Go to the FFTW3 library dir <code>/tmp/data/libraries/FFTW@@fftw3</code> and run <code>reset_cov.sh</code> then <code>make clean</code></p> <ul> <li> <ul> <li>Run <code>make && make install && make check</code>.</li> <li>Copy <code>/tmp/libprobe/extra/coverage.sh</code> into root directory of the library and run the <code>.libs</code> folder and run <code>./coverage.sh baseline</code> This will show the baseline coverage.</li> </ul> </li> </ul> <p> Go to the CAVA directory <code>/tmp/data/clients/karlstav@@cava</code>.</p> <ul> <li> <ul> <li>Run <code>./autogen.sh</code> followed by <code>./configure</code> then <code>make</code></li> <li>Run the script <code>./run_all_tests.sh</code> . </li> </ul> </li> </ul> <p> Go back to the FFTW3 library and run <code>./coverage.sh after_cava</code></p> <ul> <li> <ul> <li>Now go /tmp/libprobe and run <code>python3 libprobe.py coverage fftw3</code></li> </ul> </li> </ul>
ERA5 atmospheric stability and Geostrophic wind shear for usage in WAsP
<p>The dataset "ERA5-meso.nc" is obtained by loading the variables needed for calculating the temperature scale from hourly ERA5 files. These files are available in grib format that have been obtained from the Copernicus Data Store (CDS). They are opened using xarray and cfgrib and processed using the functions stability_histogram from the python package PyWAsP. Because a conditional mean based on the 50% highest wind speeds must be calculated, all values are binned according to wind speed at 100 m and this histogram is then used to calculate the mean and root-mean-square of the temperature scale. The boundary layer height scale is calculated in a similar fashion. For more documentation see the accompanying paper. A validation of the WAsP model using these data is available in the references.</p> <p>The file "ERA5-baro.nc" contains the geostrophic wind shear. The mean magnitude and direction is obtained sector-wise in similar fashion as described above. The geostrophic wind shear can be calculated from the pressure level geopotential height. The way to do this is described here:</p> <p><a href="https://orbit.dtu.dk/en/publications/implementation-of-large-scale-average-geostrophic-wind-shear-in-w" target="_blank" rel="noopener">https://orbit.dtu.dk/en/publications/implementation-of-large-scale-average-geostrophic-wind-shear-in-w</a></p> <p>These data are for estimating atmospheric stability conditions, if you are looking for data to estimate air density, please refer to the item "ERA5 data for air density calculations in WAsP" (related materials item 5). The methods for this are described in related materials item 7.</p> <p>v1-v2: Version corresponding to paper before review (related materials 3), do not use these.</p> <p>v3: Final version that corresponds to the published version of the paper (related materials 6):<br>https://doi.org/10.1007/s10546-023-00803-3<br>This is slightly different then the first version due to Eq. 9</p> <p>v4: Updates to load the files using PyWAsP versions specifically suited for use in pywasp with the variable names adopted in PyWAsP. For ERA5-baro.nc NaNs are filled with 0.0, i.e. assuming a barotropic atmosphere.</p> <p>Mirror of: https://data.dtu.dk/articles/dataset/ERA5_atmospheric_stability_for_usage_in_WAsP_12_8/19576042</p>
Social Media Usage According to Different Locations
<p>This ai-generated dataset provides detailed information on how individuals allocate their time across various social media platforms, including Facebook, Twitter, Instagram, YouTube, Snapchat, TikTok, LinkedIn, WhatsApp, and Pinterest. Each entry represents the number of hours spent on each platform and includes location data to explore geographic trends in social media consumption.</p> <p>The dataset is ideal for analyzing:</p> <ul> <li>Time distribution across social platforms.</li> <li>Location-based patterns in social media usage.</li> <li>Comparative studies on platform preferences.</li> </ul> <p>Perfect for social behavior analysis and data-driven marketing insights!</p>
DWUG EN: Diachronic Word Usage Graphs for English
<p>This data collection contains diachronic Word Usage Graphs (WUGs) for English. Find a description of the data format, code to process the data and further datasets on the <a href="https://www.ims.uni-stuttgart.de/data/wugs">WUGsite</a>.</p> <p>See previous versions for additional testsets.</p> <p>Please find more information on the provided data in the papers referenced below.</p> <h3>Reference</h3> <p>Dominik Schlechtweg, Nina Tahmasebi, Simon Hengchen, Haim Dubossarsky, Barbara McGillivray. 2021. <a href="https://aclanthology.org/2021.emnlp-main.567/">DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages</a>. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.</p> <p>Dominik Schlechtweg, Pierluigi Cassotti, Bill Noble, David Alfter, Sabine Schulte im Walde, Nina Tahmasebi. <a href="https://aclanthology.org/2024.emnlp-main.796/">More DWUGs: Extending and Evaluating Word Usage Graph Datasets in Multiple Languages</a>. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.</p>
DWUG DE: Diachronic Word Usage Graphs for German
<p>This data collection contains diachronic Word Usage Graphs (WUGs) for German. Find a description of the data format, code to process the data and further datasets on the <a href="https://www.ims.uni-stuttgart.de/data/wugs">WUGsite</a>.</p> <p>See previous versions for additional testsets. Find a version of this dataset annotated with classical word sense definitions at <a href="https://zenodo.org/doi/10.5281/zenodo.8197552">DWUG DE Sense</a>.</p> <p>Please find more information on the provided data in the papers referenced below.</p> <h3>Reference</h3> <p>Dominik Schlechtweg, Nina Tahmasebi, Simon Hengchen, Haim Dubossarsky, Barbara McGillivray. 2021. <a href="https://aclanthology.org/2021.emnlp-main.567/">DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages</a>. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.</p> <p>Dominik Schlechtweg, Pierluigi Cassotti, Bill Noble, David Alfter, Sabine Schulte im Walde, Nina Tahmasebi. <a href="https://aclanthology.org/2024.emnlp-main.796/">More DWUGs: Extending and Evaluating Word Usage Graph Datasets in Multiple Languages</a>. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.</p>
DWUG SV: Diachronic Word Usage Graphs for Swedish
<p>This data collection contains diachronic Word Usage Graphs (WUGs) for Swedish. Find a description of the data format, code to process the data and further datasets on the <a href="https://www.ims.uni-stuttgart.de/data/wugs">WUGsite</a>.</p> <p>See previous versions for additional testsets.</p> <p>Please find more information on the provided data in the papers referenced below.</p> <h3>Reference</h3> <p>Dominik Schlechtweg, Nina Tahmasebi, Simon Hengchen, Haim Dubossarsky, Barbara McGillivray. 2021. <a href="https://aclanthology.org/2021.emnlp-main.567/">DWUG: A large Resource of Diachronic Word Usage Graphs in Four Languages</a>. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.</p> <p>Dominik Schlechtweg, Pierluigi Cassotti, Bill Noble, David Alfter, Sabine Schulte im Walde, Nina Tahmasebi. More DWUGs: Extending and Evaluating Word Usage Graph Datasets in Multiple Languages. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing.</p>
Usage and Impact Vocabularies Peer Review Dataset
<p>This dataset reflects responses received via the Qualtrics data collection instrument provided to usage and impact vocabulary stakeholders to solicit peer review on a draft version of the glossary and crosswalk spreadsheet developed for the "EAGER: Secure Research Impact Metric Data Exchange: Data Supply Chain" project funded by The National Science Foundation (Award # 2335827).</p>
DWUG LA: Diachronic Word Usage Graphs for Latin
<p>This data collection contains diachronic Word Usage Graphs (WUGs) for Latin. Find a description of the data format, code to process the data and further datasets on the <a href="https://www.ims.uni-stuttgart.de/data/wugs">WUGsite</a>.</p> <p>The annotation was coordinated by Barbara McGillivray, and done by Annie Burman, Daria Kondakova, Francesca Dell'Oro, Helena Bermudez Sabel, Hugo Burgess, Paola Marongiu, Rozalia Dobos and Tomaz Potocnik. The pre-annotation was coordinated and designed by Barbara McGillivray and done by Manuel Márquez Cruz.</p> <p>Please find more information on the provided data in the paper referenced below.</p>
Figs. 32–36 in Nests, Petal Usage, Floral Preferences, and Immatures of Osmia (Ozbekosmia) avosetta (Megachilidae: Megachilinae: Osmiini), Including Biological Comparisons with Other Osmiine Bees
Figs. 32–36. Diagram of fifth larval instar of Osmia avosetta. 32. Entire larva, lateral view; rectangle identifies approximate area on abdominal segment 8 pictured in fig. 41. 33, 34. Head, frontal and lateral views, respectively; ptp 5 position of posterior tentorial pit. 35, 36. Right mandible, dorsal and outer views, respectively.
Fig. 22 in Nests, Petal Usage, Floral Preferences, and Immatures of Osmia (Ozbekosmia) avosetta (Megachilidae: Megachilinae: Osmiini), Including Biological Comparisons with Other Osmiine Bees
Fig. 22. Two brood cells of Osmia (Tergosmia) rhodoensis in rock cavity; overlying stone removed to make cells visible (Jordan, Jerash, 23.4.2007; photo C. Sedivy). Figures 23, 24. Brood cells of O. (T.) tergestensis in cavities between stones; in both cases, overlying stone removed to make the cell visible. Petals used for brood cell construction were from Geranium and Helianthemum (fig. 23) and Geranium only (fig. 24) (Switzerland, Zeneggen, 10.7.1990; Photos A. Müller).
Figs. 30, 31 in Nests, Petal Usage, Floral Preferences, and Immatures of Osmia (Ozbekosmia) avosetta (Megachilidae: Megachilinae: Osmiini), Including Biological Comparisons with Other Osmiine Bees
Figs. 30, 31. Diagrams of eggs of Osmia avosetta and of parasite thought to be Sapyga pulcherrima, lateral views, respectively, to same scale; anterior ends at right.
Figs. 6–9 in Nests, Petal Usage, Floral Preferences, and Immatures of Osmia (Ozbekosmia) avosetta (Megachilidae: Megachilinae: Osmiini), Including Biological Comparisons with Other Osmiine Bees
Figs. 6–9. Nesting of Osmia avosetta at 10 km northeast of Sepidan, Yasouj region, Iran. 6. The site. 7. Pollen plant, Hedysarum elymaiticum. 8, 9. Two brood cells. (Photos C. Sedivy).
Fig. 1 in Nests, Petal Usage, Floral Preferences, and Immatures of Osmia (Ozbekosmia) avosetta (Megachilidae: Megachilinae: Osmiini), Including Biological Comparisons with Other Osmiine Bees
Fig. 1. Nesting site of Osmia avosetta in Seklik Mevkii, Turkey, to the left of observers in fore- and midground; note pinkish-red flowers of pollen plant, Onobrychis viciifolia. Figures 2, 3. Closed brood cells of O. avosetta, side views, showing shape and variation in coloration of outer envelopes. Figure 4. Open cell of same, side view. Figure 5. Closed cell of same containing intermediate stage larva. (Photos J.G. Rozen.)
Fig. 27 in Nests, Petal Usage, Floral Preferences, and Immatures of Osmia (Ozbekosmia) avosetta (Megachilidae: Megachilinae: Osmiini), Including Biological Comparisons with Other Osmiine Bees
Fig. 27. Open brood cell of Osmia lunata in a short cavity excavated in rather hard ground; twig concealing nest entrance removed to make cell opening visible. Figure 28. Flower of Helianthemum sp. with one petal bitten off by female of O. (T.) lunata (Morocco, Tafraoute, 20.4.2009; photo A. Müller). Figure 29. Nest of O. (T.) lunata consisting of 10 brood cells at base of small shrub. Uppermost part of cell still under construction (top right) was the only visible sign of nest before excavation; the nine already closed cells were all hidden under thin layer of soil (Morocco, Tafraoute, 20.4.2009; photos A. Müller).
Fig. 25 in Nests, Petal Usage, Floral Preferences, and Immatures of Osmia (Ozbekosmia) avosetta (Megachilidae: Megachilinae: Osmiini), Including Biological Comparisons with Other Osmiine Bees
Fig. 25. Four closed brood cells and one cell still being provisioned of Osmia (Tergosmia) tergestensis between blades of dense grass tussock; grass blades removed to make cells visible (Italy, Gimillan (Aosta), 15.7.1996; photo A. Müller). Figure 26. Female of O. (T.) tergestensis biting off petal from flower of Geranium pyrenaicum Burm. fil. Petal is tightly folded before being transported to nest. On each visit, female removed one petal (Italy, Gimillan (Aosta), 15.7.1996; photo A. Krebs).
Figs. 41–44 in Nests, Petal Usage, Floral Preferences, and Immatures of Osmia (Ozbekosmia) avosetta (Megachilidae: Megachilinae: Osmiini), Including Biological Comparisons with Other Osmiine Bees
Figs. 41–44. Microphotographs of fifth larval instar of Osmia avosetta. 41. Setae of pleural swelling, left side of abdominal segment 8, as identified by rectangle in figure 32. 42. Spiracle showing narrow peritreme relative to large atrial opening and wrinkled atrial wall. 43, 44. Short and long subatria, respectively. (Photos J.G. Rozen).
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.