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281 results for “source code”
LaMEM source code and input files corresponding to Present‐day upper‐mantle architecture of the Alps: Insights from data‐driven dynamic modelling
<p>This repository contains LaMEM source code and input files for the models presented in Kumar, A., Cacace, M., Scheck-Wenderoth, M., Götze, H.-J., & Kaus, B. J. P. (2022). Present-day upper-mantle architecture of the Alps: Insights from data-driven dynamic modeling. Geophysical Research Letters, 49, e2022GL099476. https://doi. org/10.1029/2022GL099476</p>
What really changes when developers intend to improve their source code: A commit-level study of static metric value and static analysis warning changes
<p>This is the dataset for the publication "What really changes when developers intend to improve their source code: A commit-level study of static metric value and static analysis warning changes".</p> <p>It contains a random sample of 2533 commits from 54 Java Apache open source projects classified by two researchers into perfective, corrective and other changes (manual_labels.csv). Moreover, we include static source code metrics and static analysis warnings for the 2533 changes in al_changes_gt.csv.gz.</p> <p>In addition, we include the full dataset of 125482 commits in all_changes_sebert.csv.gz with all metrics and automatic labels for every commit that was not manually labeled. The automatic labels were provided by a fine-tuned transformer model (BERT) pre-trained exclusively on software engineering data.</p> <p>We also provide the fine tuned version of the pre-trained model in seBERT_fine_tuned_commit_intent.tar.gz as well as a Snapshot of the SmartSHARK MongoDB database used in gathering the raw data in smartshark_emse.agz.</p> <p>The model can be tested live on the <a href="https://user.informatik.uni-goettingen.de/~trautsch2/emse_2021/commit_intent.html">website</a> accompanying the publication.</p>
Dataset for A catalog of source code metrics – a tertiary study
<p>The dataset is divided into two excel files. </p> <p>The excel file "Characteristics quality assessment search results Of Secondary Studies" contains the meta data related to the included secondary studies, quality assessment score, and quality criteria used.</p> <p>The excel file "sourceCodeMetrics" contains the unique source code metrics.</p> <p>-Within this file, "Catalog of Metrics" contains the final list of metrics that are reported in the 52 secondary studies.</p> <p>- "CodeUnit Definition" provides the description of the code units used by the source code metrics. </p> <p>- "Scope Definition" provides the description of the scope or hierarchy level at which these source code metrics report their values.</p> <p>- "Attribute definition" describes the internal quality attributes.</p> <p> </p>
Changes in holopelagic Sargassum spp. biomass composition across an unusual year - supporting particle data and analysis source code
<p>As specified in 'Data, Materials, and Software Availability' of Tonon et al. (2024), PNAS, Vol. 121, e2312173121, https://doi.org/10.1073/pnas.2312173121, the following data and software are provided:</p> <p>Particle forward tracking (statistical data – for Fig. 2)</p> <p>Particle backward tracking (primary data – for Fig. 3)</p> <p>Analysis of primary data for gridded particle fractional coverage and mean age (Fortran source code)</p>
Polyconvex inelastic Constitutive Artificial Neural Networks: Source code and data
<p>This dataset contains the source code of the polyconvex extension of the inelastic Constitutive Artificial Neural Network (iCANN) as well as the data for the examples from the publication:</p> <p>Holthusen, H., Lamm, L., Brepols, T., Reese, S., & E. Kuhl.<em> Polyconvex inelastic Constitutive Artificial Neural Networks.</em></p> <p> </p> <p><strong>Results:</strong> Discovering a model for the polymer VHB 4910 subjected to cyclic loading</p> <p>Here, we investigate the ability of the polyconvex iCANN to discover and learn a model for the material response of VHB 4910 polymer subjected to cyclic loading at different stretch rates.</p> <p>The experimental data are taken from the literature:</p> <p>Hossain, M., Vu, D. K., & Steinmann, P. (2012). Experimental study and numerical modelling of VHB 4910 polymer. <em>Computational Materials Science</em>, <em>59</em>, 65-74.</p> <p><a href="https://doi.org/10.1016/j.commatsci.2012.02.027">https://doi.org/10.1016/j.commatsci.2012.02.027</a></p>
Polytopic autoencoders for very low-dimensional parametrizations of fluid flow models [source code]
<p>J. Heiland & Y. Kim, 'Polytopic autoencoders for very low-dimensional parametrizations of fluid flow models', GAMM 2024</p>
Enhancing Climate Model Performance through Improving Volcanic Aerosol Representation Dataset and model source codes
<p>Enhancing Climate Model Performance through Improving Volcanic Aerosol Representation: Dataset and codes used in the study</p> <p> </p>
Datasets and source code for case study on Pomerini, Tanzania
Open the record for dataset details and reuse information.
Data and codes for the work: "Quasiparticle dynamics in a superconducting qubit irradiated by a localized infrared source"
<p>All data and codes used for the work can be found here. </p> <p> </p> <ul> <li>For figures 2 and SM6, one must unzip the files and change the directory in the codes accordingly.</li> <li>For figures 3, SM7 and SM8, one should use the file "Figure3_data.h5", already containing the analysis of the raw data of the pulsed experiment, which is also contained inside the zip file.</li> <li>Comsol 6.2 was used to create the simulation file for the sample temperature.</li> </ul>
Coding data of manuscript "How Do Developers Utilize Source Code from Stack Overflow?"
<p>This is the coding data for the manuscript "How Do Developers Utilize Source Code from Stack Overflow?".</p>
Linking Sketches and Diagrams to Source Code Artifacts — Supplementary Material
<p>Sketches and diagrams play an important role in the daily work of software developers. If they are archived, they are often detached from the source code they document, because there is no adequate tool support to assist developers in capturing, archiving, and retrieving sketches related to certain source code artifacts. We implemented <em>SketchLink</em> to increasing the value of sketches and diagrams created during software development by supporting developers in these tasks. Our prototype implementation provides a web application that employs the camera of smartphones and tablets to capture analog sketches, but can also be used on desktop computers to upload, for instance, computer-generated diagrams. We also implemented a plugin that embeds the links in Javadoc comments and visualizes them in situ in the source code editor as graphical icons for the IntelliJ Java IDE. Besides being a useful software documentation tool, SketchLink also enables developers to navigate through their source code using the linked sketches and diagrams.</p> <p>This dataset contains:</p> <ul> <li>The source code of the <em>SketchLink</em> server, web application, and IntelliJ plugin.</li> <li>A demo video.</li> <li>Recordings of the user study sessions (audio removed due to confidentiality).</li> <li>The questionnaire and task list used for the plugin study sessions.</li> </ul>
Data and Source Codes used in "Development of a Global Quasi-3-D Multiscale Modeling Framework: I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"
<p>Data and Source Codes used in the paper "Development of a Global Quasi-3-D Multiscale Modeling Framework: I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"</p> <p>Advection Test (ADV): East-West A_TST (100km, Cube), C_TST (25km, Cube), E_TST (5km, Cube),</p> <p> North-South K_TST (100km, Cube), M_TST (25km, Cube), O_TST (5km, Cube) </p> <p>Barotropic Test (BAR): A_TST5 (100km, Cube), Y_TST4 (100km, RLL), C_TST3 (5km, Cube), C_TST1 (5km, RLL)</p> <p>Baroclinic Test (BCL): J_TST30 (100km, Cube), J_TST20 (100km, RLL)</p>
359,569 commits with source code density; 1149 commits of which have software maintenance activity labels (adaptive, corrective, perfective)
<p>This dataset comes as SQL-importable file and is compatible with the widely available MariaDB- and MySQL-databases.</p> <p>It is based on (and incorporates/extends) the dataset "<em>1151 commits with software maintenance activity labels (corrective,perfective,adaptive)</em>" by Levin and Yehudai (<a href="https://doi.org/10.5281/zenodo.835534">https://doi.org/10.5281/zenodo.835534</a>).</p> <p>The extensions to this dataset were obtained using <em>Git-Tools</em>, a tool that is included in the <strong>Git-Density</strong> (<a href="https://doi.org/10.5281/zenodo.2565238">https://doi.org/10.5281/zenodo.2565238</a>) suite. For each of the projects in the original dataset, Git-Tools was run in <em>extended</em> mode.</p> <p>The dataset contains these tables:</p> <ul> <li><strong>x1151</strong>: The original dataset from Levin and Yehudai. <ul> <li>despite its name, this dataset has only 1,149 commits, as two commits were duplicates in the original dataset.</li> <li>This dataset spanned 11 projects, each of which had between 99 and 114 commits</li> <li>This dataset has <strong>71</strong> features and spans the projects <em>RxJava, hbase, elasticsearch, intellij-community, hadoop, drools, Kotlin, restlet-framework-java, orientdb, camel</em> and <em>spring-framework</em>.</li> </ul> </li> <li><strong>gtools_ex</strong> (short for <em>Git-Tools, extended</em>) <ul> <li>Contains <strong>359,569</strong> commits, analyzed using Git-Tools in extended mode</li> <li>It spans all commits and projects from the x1151 dataset as well.</li> <li>All 11 projects were analyzed, from the initial commit until the end of January 2019. For the projects <em>Intellij</em> and <em>Kotlin</em>, the first 35,000 resp. 30,000 commits were analyzed.</li> <li>This dataset introduces <strong>35 new</strong> features (see list below), 22 of which are <em><strong>size</strong></em>- or <em><strong>density</strong></em>-related.</li> </ul> </li> </ul> <p>The dataset contains these views:</p> <ul> <li><strong>geX_L</strong> (short for Git-<em>tools, extended, with labels</em>) <ul> <li>Joins the commits' labels from <em>x1151</em> with the extended attributes from <em>gtools_ex</em>, using the commits' hashes.</li> </ul> </li> <li><strong>jeX_L</strong> (short for <em>joined, extended, with labels</em>) <ul> <li>Joins the datasets <em>x1151</em> and <em>gtools_ex</em> entirely, based on the commits' hashes.</li> </ul> </li> </ul> <p> </p> <p>Features of the <strong>gtools_ex</strong> dataset:</p> <ul> <li><strong>SHA1</strong></li> <li><strong>RepoPathOrUrl</strong></li> <li><strong>AuthorName</strong></li> <li><strong>CommitterName</strong></li> <li><strong>AuthorTime </strong>(UTC)</li> <li><strong>CommitterTime </strong>(UTC)</li> <li><strong>MinutesSincePreviousCommit</strong>: Double, describing the amount of minutes that passed since the previous commit. Previous refers to the <strong>parent</strong> commit, not the previous in time.</li> <li><strong>Message</strong>: The commit's message/comment</li> <li><strong>AuthorEmail</strong></li> <li><strong>CommitterEmail</strong></li> <li><strong>AuthorNominalLabel</strong>: All authors of a repository are analyzed and merged by Git-Density using some heuristic, even if they do not always use the same email address or name. This label is a unique string that helps identifying the same author across commits, even if the author did not always use the exact same identity.</li> <li><strong>CommitterNominalLabel</strong>: The same as <em>AuthorNominalLabel</em>, but for the committer this time.</li> <li><strong>IsInitialCommit</strong>: A boolean indicating, whether a commit is preceded by a parent or not.</li> <li><strong>IsMergeCommit</strong>: A boolean indicating whether a commit has more than one parent.</li> <li><strong>NumberOfParentCommits</strong></li> <li><strong>ParentCommitSHA1s</strong>: A comma-concatenated string of the parents' SHA1 IDs</li> <li><strong>NumberOfFilesAdded</strong></li> <li><strong>NumberOfFilesAddedNet</strong>: Like the previous property, but if the net-size of all changes of an added file is zero (i.e. when adding a file that is empty/whitespace or does not contain code), then this property does not count the file.</li> <li><strong>NumberOfLinesAddedByAddedFiles</strong></li> <li><strong>NumberOfLinesAddedByAddedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfFilesDeleted</strong></li> <li><strong>NumberOfFilesDeletedNet</strong>: Like the previous property, but considers only files that had net-changes</li> <li><strong>NumberOfLinesDeletedByDeletedFiles</strong></li> <li><strong>NumberOfLinesDeletedByDeletedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfFilesModified</strong></li> <li><strong>NumberOfFilesModifiedNet</strong>: Like the previous property, but considers only files that had net-changes</li> <li><strong>NumberOfFilesRenamed</strong></li> <li><strong>NumberOfFilesRenamedNet</strong>: Like the previous property, but considers only files that had net-changes</li> <li><strong>NumberOfLinesAddedByModifiedFiles</strong></li> <li><strong>NumberOfLinesAddedByModifiedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfLinesDeletedByModifiedFiles</strong></li> <li><strong>NumberOfLinesDeletedByModifiedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfLinesAddedByRenamedFiles</strong></li> <li><strong>NumberOfLinesAddedByRenamedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>NumberOfLinesDeletedByRenamedFiles</strong></li> <li><strong>NumberOfLinesDeletedByRenamedFilesNet</strong>: Like the previous property, but counts the net-lines</li> <li><strong>Density</strong>: The ratio between the two sums of all lines added+deleted+modified+renamed and their resp. gross-version. A density of zero means that the sum of net-lines is zero (i.e. all lines changes were just whitespace, comments etc.). A density of of 1 means that all changed net-lines contribute to the gross-size of the commit (i.e. no useless lines with e.g. only comments or whitespace).</li> <li><strong>AffectedFilesRatioNet</strong>: The ratio between the sums of <em>NumberOfFilesXXX</em> and <em>NumberOfFilesXXXNet</em></li> </ul> <p> </p> <p>This dataset is supporting the paper <strong>"<em>Importance and Aptitude of Source code Density for Commit Classification into Maintenance Activities</em></strong><strong>"</strong>, as submitted to the <em>QRS2019</em> conference (The 19th IEEE International Conference on Software Quality, Reliability, and Security). Citation: Hönel, S., Ericsson, M., Löwe, W. and Wingkvist, A., 2019. Importance and Aptitude of Source code Density for Commit Classification into Maintenance Activities. In <em>The 19th IEEE International Conference on Software Quality, Reliability, and Security</em>.</p>
Preprocessed C# Source Codes for Machine Learning
<p>The dataset comes from the HackerRank site, 329,937 C# source codes of 22 tasks were collected and all verified by unit tests.</p> <p>During the download process, source codes received only a unique serial number instead of the user name who solved the task and stored inside the 'task_name/origin' folder. After collecting the data, a new database was created, which included cleaned-up versions of the source codes ('task_name/cleaned' folders contains). Finally, a third set of data was extracted from this cleaned-up version, where a delimiter was inserted before and after each elementary expression to support easy processing and analysis processes ('task_name/reduced' folders contains). Inside the 'task_name' folder three csv files, which contain the equality checking result. The compressed folder also contains a vector space (and related files) made from the reduced data set. These four files are directly in the main folder.</p>
Simulation data and source code for Hausdorff dimension measurements in two-dimensional quantum gravity
<p>This entry contains the source code and simulation data used as basis for the paper</p> <p>J. Barkley, T. Budd, "Precision measurements of Hausdorff dimensions in two-dimensional quantum gravity." Preprint <a href="https://arxiv.org/abs/1908.09469">arXiv:1908.09469</a> (2019)</p> <p>Both the source code and the data consist of two parts:</p> <ul> <li>Measurements of (dual) graph distances in various models of random planar maps.</li> <li>Measurements of discrete Liouville first passage percolation distances on a regular lattice with periodic boundary conditions.</li> </ul> <p>Instructions on compiling and running the simulation software are included with the source code (see README files). Descriptions of the simulation data formats accompany the data files (see README files again). For background on the simulation and data analysis we refer to the publication mentioned above.</p>
The codrep machine learning on source code competition, the raw diff
<p>CodRep is a machine learning competition on source code data. It is carefully designed so that anybody can enter the competition, whether professional researchers, students or independent scholars, without specific knowledge in machine learning or program analysis. In particular, it aims at being a common playground on which the machine learning and the software engineering research communities can interact.</p> <p> </p> <p>This dataset provides the raw diffs that we collected, that are used for generating the prediction tasks. See more info at https://github.com/KTH/CodRep-competition.</p>
Model source code, modified model code, and all scripts of the paper "A comprehensive estimate of the anthropogenic aerosol radiative effects using the GAMIL model with reduced complexity".
<p>Model source code, modified model code, and all scripts of the paper "A comprehensive estimate of the anthropogenic aerosol radiative effects using the GAMIL model with reduced complexity".</p>
Model source code, simulation results, and all scripts for the paper "A comprehensive estimate of the anthropogenic aerosol radiative effects using the GAMIL model with reduced complexity".
<p>FigureTable_Scripts.rar is NCL scripts used for figures and tables. <br> Model_Scripts.rar is the modified model code and scripts used for running model.<br> models.rar is the GAMIL model source code.<br> ModelResults.rar is the model results. Note that only the variables used for making plots.</p>
source code of the ANN model for pH estimation
<p>The source code of paper: Retrieving monthly and interannual pHT on the East China Sea Shelf using an artificial neural network: ANN-pHT-v1<br> </p>
Online Appendix of "Toward Interactive Optimization of Source Code Differences: An Empirical Study of Its Performance"
<div> <div><strong>Abstract</strong></div> <div>This is the dataset of the paper entitled "Toward Interactive Optimization of Source Code Differences: An Empirical Study of Its Performance", presented at SCAM 2024. It contains information related to the target commits and their attributes, as well as simulation results pertaining to the research questions in the paper.</div> <div> </div> <div>The target projects and commits in the dataset are based on a prior study: Nugroho, et al.: "How different are different diff algorithms in Git?: Use --histogram for code changes", Empirical Software Engineering, 2020, https://doi.org/10.1007/s10664-019-09772-z</div> <div> </div> </div> <div> <div><strong>Survey Overview</strong></div> <div> <div><em>1. Filtration</em></div> <div> <div>We collected attributes related to the changes for filtering and RQ purposes.</div> <ul> <li>Number of lines</li> <li>Number of changed lines</li> <li>Similarity distance</li> <li>Number of mismatch diff area</li> </ul> <div><em>2. RQ1</em></div> <div> <div>We investigate the minimum number of feedback actions needed to correct the initial diffs to the target diffs. Regarding the simulation, two types of heuristic functions (non-admissible and admissible functions) have been used to reduce costs. When the search with the non-admissible heuristic function of the initial state does not match the ideal optimal result, i.e., when there is room for improvement in the number of feedback actions, we applied another A* search with the admissible heuristic function.</div> <div> </div> <div> <div>We obtained the following results through search:</div> <ul> <li>Number of feedback actions (A* search with non-admissible heuristic)</li> <li>Number of feedback actions (A* search with admissible heuristic)</li> </ul> <div> </div> <div><em>3. RQ2</em></div> <div> <div> <div>We investigated the various effects that feedbacks have on the diffs by examining the diffs at depth 1 of the search tree. The dataset records the maximum, minimum, median, mean, and standard deviation for each search problem.</div> <br> <div>The study yielded the following results:</div> <ul> <li>Similarity distance</li> <li>Number of mismatch diff area</li> </ul> </div> </div> </div> </div> </div> </div> <div> <div> </div> <div> <div><strong>Dataset Columns</strong></div> <div>The following are the contents represented by the columns in the CSV file and their descriptions.</div> <div> </div> <div> <table> <tbody> <tr> <td>Column name</td> <td>Description</td> </tr> <tr> <td>project_name</td> <td>Name of the project associated with the data.</td> </tr> <tr> <td>filename</td> <td>Name of the file being analyzed.</td> </tr> <tr> <td>filepath</td> <td>Path to the file within the project.</td> </tr> <tr> <td>commit_id</td> <td>Commit hash representing the new version of the file.</td> </tr> <tr> <td>parent_commit</td> <td>Commit hash representing the old version of the file.</td> </tr> <tr> <td>error_commit</td> <td>An error occurred when retrieving the commit from the repository.</td> </tr> <tr> <td>error_setup</td> <td>Any error when generating the new and old versions of the file.</td> </tr> <tr> <td>error_analyze</td> <td>An error when collecting information for filtering.</td> </tr> <tr> <td>new_loc</td> <td>Lines of the new version of the source code.</td> </tr> <tr> <td>old_loc</td> <td>Lines of the old version of the source code.</td> </tr> <tr> <td>histogram_len</td> <td>Path length of the diff when using the Histogram algorithm.</td> </tr> <tr> <td>myers_len</td> <td>Path length of the diff when using the Myers algorithm.</td> </tr> <tr> <td>histogram-myers#edge</td> <td>Number of difference edges between Histogram and Myers diff.</td> </tr> <tr> <td>histogram-dp#edge</td> <td>Number of difference edges between Histogram and initial diff.</td> </tr> <tr> <td>myers-dp#edge</td> <td>Number of difference edges between Myers and initial diff.</td> </tr> <tr> <td>histogram-myers#area</td> <td>Number of mismatch diff areas between Histogram and Myers diff.</td> </tr> <tr> <td>histogram-dp#area</td> <td>Number of mismatch diff areas between Histogram and initial diff.</td> </tr> <tr> <td>myers-dp#area</td> <td>Number of mismatch diff areas between Myers and initial diff.</td> </tr> <tr> <td>dp#candidate</td> <td>Number of feedback candidates of initial diff (similarity distance).</td> </tr> <tr> <td>#insert</td> <td>Number of lines added in the change.</td> </tr> <tr> <td>#delete</td> <td>Number of lines deleted in the change.</td> </tr> <tr> <td>#change</td> <td>#insert + #delete.</td> </tr> <tr> <td>error_Asearch</td> <td>An error occurring during A* search with a non-admissible heuristic.</td> </tr> <tr> <td>#feedback_A</td> <td>Number of feedback actions for A* search with a non-admissible heuristic.</td> </tr> <tr> <td>time_A</td> <td>Time taken for A* search with a non-admissible heuristic (ms).</td> </tr> <tr> <td>RQ1_error_iteration</td> <td>An error when iterations exceed the limit (10,000,000) during A* search with an admissible heuristic.</td> </tr> <tr> <td>RQ1_error_timeout</td> <td>An error when the search time exceeds the limit (1,800 seconds).</td> </tr> <tr> <td>RQ1_error_other</td> <td>Other errors encountered during A* search with an admissible heuristic.</td> </tr> <tr> <td>RQ1#feedback</td> <td>Number of feedback actions for A* search with an admissible heuristic.</td> </tr> <tr> <td>RQ1#iter</td> <td>Number of iterations for A* search with an admissible heuristic.</td> </tr> <tr> <td>RQ1_time</td> <td>Time taken for A* search with an admissible heuristic.</td> </tr> <tr> <td>RQ2_error_exceed</td> <td>An error due to exceeding time or iteration limits in RQ2.</td> </tr> <tr> <td>RQ2_error_other</td> <td>Other errors encountered during RQ2.</td> </tr> <tr> <td>RQ2#children</td> <td>Number of children nodes of the initial state (= similarity distance).</td> </tr> <tr> <td>RQ2#candidate_min</td> <td>Minimum similarity distance among the generated diffs.</td> </tr> <tr> <td>RQ2#candidate_max</td> <td>Maximum similarity distance among the generated diffs.</td> </tr> <tr> <td>RQ2#candidate_ave</td> <td>Average of similarity distance among the generated diffs.</td> </tr> <tr> <td>RQ2#candidate_median</td> <td>Median of similarity distance among the generated diffs.</td> </tr> <tr> <td>RQ2#candidate_sd</td> <td>Standard deviation of similarity distance among the generated diffs.</td> </tr> <tr> <td>RQ2#area_min</td> <td>Minimum number of mismatch diff areas among the generated diffs.</td> </tr> <tr> <td>RQ2#area_max</td> <td>Maximum number of mismatch diff areas among the generated diffs.</td> </tr> <tr> <td>RQ2#area_ave</td> <td>Average number of mismatch diff areas among the generated diffs.</td> </tr> <tr> <td>RQ2#area_median</td> <td>Median number of mismatch diff areas among the generated diffs.</td> </tr> <tr> <td>RQ2#area_sd</td> <td>Standard deviation of number of mismatch diff areas among the generated diffs.</td> </tr> <tr> <td>is_used</td> <td>Indicates whether this data is used in the results of RQ1 and RQ2.</td> </tr> </tbody> </table> </div> </div> </div> </div>
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.