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677 results for “Replication package”
Replication package for "An Exploratory Study on Default Class Openness in Java and Kotlin"
<p>This dataset includes scripts, text files, and cached CSV/Parquet or raw TXT data files used to generate all analysis and results from the paper. A <strong>README.md</strong> file is included in <strong>replication-pkg.zip</strong> for details on using the scripts.</p> <p>If you only want to inspect the figures, you do not need a data ZIP.</p> <p>If you want to simply re-generate the figures without changes, download <strong>data-cached.zip</strong>. If you want to make any sort of change to the analyses, you will want to download <strong>data-csv.zip</strong> and/or <strong>data-raw.zip</strong>. The CSV files are converted versions of the raw TXT files.</p>
Replication package for "An Empirical Study of Q&A Websites for Game Developers"
<p><strong>Replication package for the paper "An Empirical Study of Q&A Websites for Game Developers"</strong></p> <p>This repository contains the datasets and scripts used to replicate the results from the paper "An Empirical Study of Q&A Websites for Game Developers".</p> <p>This is an exact copy of the repository on GitHub: https://github.com/asgaardlab/done-21-arthur-gamedev_qa_websites-code</p> <p><strong>Replication data</strong></p> <p>The datasets used to replicate the results for the paper can be found in the data directory (data/). These are the datasets we obtained after running all of the notebooks in this repository.</p> <p>Two of the studied websites are owned by companies (Epic and Unity) and we are not legally allowed to share the textual contents of the questions and answers as they are considered intellectual property. Therefore, instead of sharing the content of those posts, we included the URLs to all of the pages where the information used in the paper can be found, so that they can be crawled by future researchers.</p> <p>This is not an issue for Stack Overflow and the Game Development Stack Exchange, since that data is provided by Stack Exchange in the Stack Exchange Data Dump (https://archive.org/details/stackexchange).</p> <p><strong>Survey data:</strong> Unfortunately, our University's ethics board only allows us to share the survey responses in aggregated format, which is done in the paper. In this repository, we added the list of communities in which we shared the survey (data/surveyed_communities.csv).</p> <p><strong>Using this repository</strong></p> <p>If you are using the datasets provided in this repository, you just need to run the analysis notebook (code/analysis/paper_results.ipynb) to obtain the results as shown in the paper.</p> <p>Otherwise, if you want to run the whole pipeline from scratch, follow these steps:</p> <p>1. Download the data from Unity Answers and the UE4 AnswerHub from their websites (you can use the URLs provided in our datasets). Parse the HTML pages and extract the required information.</p> <p>2. Download the data from Stack Overflow and the Game Development Stack Exchange from the Stack Exchange Data Dump (https://archive.org/details/stackexchange). Run the notebooks to process the XML files from the Stack Exchange data dump (code/process_xml). For Stack Overflow, run the select_gamedev_posts.ipynb (code/process_xml/stackoverflow/select_gamedev_posts.ipynb) first.</p> <p>3. Run the text processing notebook (code/text_processing.ipynb).</p> <p>4. Run the topic modelling notebook (code/topic_modelling.ipynb).</p> <p>5. Run the topic comparisons notebook (code/topic_comparisons.ipynb).</p> <p>6. Finally, run the analysis notebook (code/analysis/paper_results.ipynb) to get the results as shown on the paper.</p>
Replication Package for "Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis"
<p>This repository provides the data sets and scripts used in the paper "Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis" by Sebastian Nielebock, Robert Heumüller, Kevin Michael Schott, and Frank Ortmeier from the Faculty of Computer Science of the Otto-von-Guericke University Magdeburg, Germany. This paper is published in Springer's "Automated Software Engineering - An International Journal" in August 2021. The article is available as open access at <a href="https://dx.doi.org/10.1007/s10515-021-00294-x">https://dx.doi.org/10.1007/s10515-021-00294-x</a>. A preprint is available under <a href="https://arxiv.org/abs/2008.00277">https://arxiv.org/abs/2008.00277</a>.</p> <p>All scripts and data sets are provided by the authors and come without any guarantee. For any issues regarding replication do not hesitate to contact us ({sebastian.nielebock,robert.heumueller, kevin.schott, frank.ortmeier} <at> ovgu.de)</p> <p>If you use or refer to these datasets, please cite our paper using the following BibTex entry.</p> <pre>@article{NielebockAPIFilterSearch2021, title = {Guided Pattern Mining for API Misuse Detection by Change-Based Code Analysis}, author = {Sebastian Nielebock and Robert Heum\"{u}ller and Kevin Michael Schott and Frank Ortmeier}, editor = {Springer}, journal = {Springer Automated Software Engineering - An International Journal}, number = {15}, pages = {1-48}, volume = {28}, url = {https://arxiv.org/abs/2008.00277}, doi = {10.1007/s10515-021-00294-x}, year = {2021}, } </pre>
Replication package for: "Concentration Bias in Intertemporal Choice"
<p>This is the replication package for Dertwinkel-Kalt, Gerhardt, Riener, Schwerter, and Strang, “Concentration Bias in Intertemporal Choice,” <em>Review of Economic Studies</em> 89, no. 3 (2022): 1314–1334, <a href="https://doi.org/10.1093/restud/rdab043" target="_blank" rel="noopener">https://doi.org/10.1093/restud/rdab043</a>.</p> <p>The package includes the <a href="https://www.otree.org">oTree</a> code for running the consumption experiment and the <a href="https://www.ztree.uzh.ch/en.html">z-Tree</a> code for running the money experiment described in the article.</p> <p>It also includes the <a href="https://www.stata.com">Stata</a> code of all hypothesis tests and statistics reported in the article (figures, tables, numbers reported in the text) as well as the data on which the code runs. Moreover, it includes the <a href="https://www.r-project.org">R</a> code of the calibration exercise mentioned in Section 4.</p>
Replication package for: Measuring the Incentive to Collude: The Vitamin Cartels, 1990–1999 (version 3)
<p>This replication package contains the data and the code to generate the results reported in "Measuring the Incentive to Collude: The Vitamin Cartels, 1990–1999" by Mitsuru Igami and Takuo Sugaya, to be published in The Review of Economic Studies.</p>
Replication Package - Moderator Factors of Software Security and Performance Verification
<p>Replication package for the paper "Moderator Factors of Software Security and Performance Verification"</p>
Replication package for "How does Migrating to Kotlin Impact the Run-time Efficiency of Android Apps?"
<p><strong>Replication Package</strong></p> <p>This section briefly describes the contents of the replication package, which makes it possible to replicate this study.</p> <p><strong>Dataset construction:</strong> The recreation of the GitLab AndroidTimeMachine instance is done using the snapshot related code found in the Kotlin program. This program uses a running instance of the AndroidTimeMachine’s Neo4j database and uses its data to make API calls to a running GitLab instance which imports the GitHub projects found. This step can be skipped, and a GitLab instance can be instantiated directly using our created Docker volumes. The is also capable of performing the Kotlin filtering steps. For this, it uses the results of the previous steps as input and clones each repository for performing the filtering procedure.</p> <p><strong>SLOC counting:</strong> SLOC counting related scripts are found in and implement our methods by cloning the project and running CLOC. It also plots the line graphs that we manually categorized into evolution trends. The raw SLOC data is found in , and all of the plotted evolution trends are found in.</p> <p><strong>Kotlin migration mining:</strong> The implementation for mining is located in , and implements our methods by cloning a repository from the running GitLab instance and executing described methods. All commits we found are located in.</p> <p><strong>Run-time efficiency:</strong> As mentioned in the thesis, we used AndroidRunner. A fork of AndroidRunner we used for conducting our experiments is located in . In the folder , each configuration combined with raw results is found per test subject. The final data we used after removing corrupt frame times is located in the folder, which also contains . This python script implements our statistical analysis methods.</p>
Replication Package for the paper: "Caracterizando a evolução de software de contratos inteligentes: Um estudo exploratório-descritivo utilizando GitHub e Etherscan"
<p>This is the replication package for the paper "Caracterizando a evolução de software de contratos inteligentes: Um estudo exploratório-descritivo utilizando GitHub e Etherscan". The paper was originally published in the VEM workshop, the prime Brazilian workshop for software Visualisation, Evolution and Maintance.</p> <p> </p> <p>The source code we used to perform the study is available at: https://github.com/gesid/smart-contracts-software-evolution. Please note that the current version of the code on Github may have evolved from the time we first ran the study. For the exact version of the code used to run the study, please refer to the code provided in this replication package.</p> <p> </p> <p>We provide a single .zip file with a few folders inside.</p> <p>- etherscan_contracts: the source code files for the smart contracts as obtained from Etherscan. We provde both the original versions and the ones without comments;</p> <p>- github_data: the source code files as extracted from the smart contracts' repositories on github;</p> <p>- repositories_insights: our data analysis and main results;</p> <p>- research_source_code: the code we used to run our mining, preprocessing, analyses etc;</p> <p>- results_combinations: the similarity values for the combination of .sol files compared to the Etherscan files.</p>
APIzation: Generating Reusable APIs from StackOverflow Code Snippets - Replication Package
<p>This repository represents the replication package for the paper <em>APIzation: Generating Reusable APIs from StackOverflow Code Snippets</em>.</p> <p>The paper is published in the proceeding of the <em>36th IEEE/ACM International Conference on Automated Software Engineering (ASE)</em>.</p> <p>In this replication package, we provide all the <em>APIzations</em> we produced with our tool. Also, we include the data we used for our evaluation.</p>
Replication package for: The real effects of monetary expansions: evidence from a large-scale historical experiment
<p>The replication materials contain a README file, STATA datasets and do-files. This replication package for Palma (2021) constructs the entire analysis from the data sources described in the published paper, using STATA. The replicator should expect the code to run for less than 10 minutes.</p> <p>Palma, N. (2021). The real effects of monetary expansions: evidence from a large-scale historical experiment. Review of Economic Studies, forthcoming</p> <p> </p>
To Automatically Map Source Code Entities to Architectural Modules with Naive Bayes: Replication Package
<p>This is the replication package for the JSS article To Automatically Map Source Code Entities to Architectural Modules with Naive Bayes. It provides the source data files and the r-script to produce analysis and images.</p>
Replication Package for the Paper: Understanding Software Architecture Erosion: A Systematic Mapping Study
<p>This is the replication package for the paper: "Understanding Software Architecture Erosion: A Systematic Mapping Study". It contains five files as described below:</p> <p><strong>1. List of the Selected Studies_SMS.xlsx</strong><br> includes the detailed information of the 73 selected studies.</p> <p><strong>2. Data Extraction.xlsx</strong><br> includes the extracted data based on the data items (i.e., D1-D16).</p> <p><strong>3. Sample of Study Selection.xlsx</strong><br> includes the 100 randomly selected papers from the search results and the results of each round selection.</p> <p><strong>4. Pilot Data Extraction.xlsx</strong><br> includes the pilot data extraction results from five papers.</p> <p><strong>5. Sample of Data Extraction.xlsx</strong><br> includes the data extraction results from another randomly selected five papers.</p>
Replication Package for: Caught between Cultures: Unintended Consequences of Improving Opportunity for Immigrant Girls
<p>The package contains all the data and code necessary to reproduce the figures and tables in Dahl, Felfe, Frijters, and Rainer (forthcoming). "Caught between Cultures: Unintended Consequences of Improving Opportunity for Immigrant Girls", Review of Economic Studies.</p>
Replication Package for: Patience and Comparative Development
<p>This file contains the replication material for "Patience and Comparative Development" (forthcoming in Review of Economic Studies).</p>
Replication Package for the Paper: "Code Reviewer Recommendation for Architecture Violations: An Exploratory Study"
<p>This is the replication package for the paper: "Code Reviewer Recommendation for Architecture Violations: An Exploratory Study".</p> <p><strong>1) scripts.zip </strong>includes the Python scripts used to run the experiments in this work. Experimental details (e.g., parameters) are described in the Python files. Choose the relevant experimental settings and run "Experiment.py" to start the experiments.</p> <p><strong>2) dataset.xlsx </strong>is the dataset used in the experiments on code reviewer recommendation, which includes the code review comments (from the four OSS projects) related to architecture violations and the file paths of code changes.</p>
Replication package of "Creativity and corporate culture", ECONOMIC JOURNAL
<p>Data files in .csv format for replication for the paper "Creativity and corporate culture", Economic Journal, forthcoming.</p>
ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search – Replication Package
<p>This is the replication package associated with the paper "<em>ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search</em>" accepted at the 45th IEEE/ACM International Conference on Software Engineering (ICSE 2023) – Technical Track. Cite this paper using the following:</p> <p><em>@inproceedings{pan2023atm,<br> title={ATM: Black-box Test Case Minimization based on Test Code Similarity and Evolutionary Search},<br> author={Pan, Rongqi and Ghaleb, Taher A. and Briand, Lionel},<br> booktitle={Proceedings of the 45th IEEE/ACM International Conference on Software Engineering},<br> year={2023},<br> pages={1--12}<br> }</em></p> <p><strong>Replication Package Contents:</strong><br> The replication package contains all the necessary data and code required to reproduce the results reported in the paper. We also provide the results for other minimization budgets, and detailed <em>FDR,</em> execution time, and statistical test results. In addition, we provide the data and code required to reproduce the results of baselines techniques: FAST-R and random minimization.</p> <p><strong>Data:</strong><br> We provide in the <em><strong>Data</strong></em> directory the data used in our experiments, which is based on 16 projects from <a href="https://github.com/rjust/defects4j">Defects4J</a>, whose characteristics can be found in <em><strong>Data/subject_projects.csv</strong></em><em>.</em></p> <p><strong>Code:</strong><br> We provide in the <em><strong>Code</strong></em> directory the code and scripts (Java, Python, and Bash) required to run the experiments and reproduce the results.</p> <p><strong>Results:</strong><br> We provide in the <em><strong>Results</strong></em> directory the results for each technique independently, and also a summary of all results together for comparison purposes. The source code for this step is in the <em><strong>Code/ATM/CodeToAST</strong></em> directory. The source code for this step is in the <em><strong>Code/ATM/Similarity</strong></em> directory.</p> <p><strong>_________________________________</strong></p> <p><strong>ATM - Code to AST transformation:</strong></p> <p><strong>Requirements:</strong><br> * Eclipse IDE (we used 2021-12)<br> * The libraries (the <em><strong>.jar</strong></em> files in the <em><strong>Code/ATM/CodeToAST/lib</strong></em> directory)</p> <p><strong>Input:</strong><br> All zipped data files should be unzipped before running each step.<br> * Data/test_suites/all_test_cases.zip → Data/test_suites/all_test_cases<br> * Data/test_suites/changed_test_cases.zip → Data/test_suites/changed_test_cases<br> * Data/test_suites/relevant_test_cases.zip → Data/test_suites/relevant_test_cases</p> <p><strong>Output:</strong><br> * Data/ATM/ASTs/all_test_cases<br> * Data/ATM/ASTs/changed_test_cases</p> <p><strong>Running the experiment:</strong><br> To generate ASTS for all test cases in the project test suites, the <em><strong>Code/ATM/CodeToAST/src/CodeToAST.java</strong></em> file should be compiled and run using the Eclipse IDE by including all the required <em><strong>.jar</strong></em> files in the <em><strong>Code/ATM/CodeToAST/lib</strong></em> directory as part of the classpath. A bash script is provided along with a pre-generated <em><strong>.jar</strong></em> file in the <em><strong>Code/ATM/CodeToAST/bin</strong></em> directory to run this step, as follows:</p> <pre><code class="language-bash">cd Code/ATM/CodeToAST bash transform_code_to_ast.sh</code></pre> <p>Each test file in the <em><strong>Data/test_suites/all_test_cases</strong></em> and <em><strong>Data/test_suites/changed_test_cases</strong></em> directories is parsed to generate a corresponding AST for each test case method (saved in an XML format in <strong>Data/ATM/ASTs/all_test_cases</strong> and <em><strong>Data/ATM/ASTs/changed_test_cases</strong></em> for each project version)<br> <strong>_________________________________</strong></p> <p><strong>ATM - Similarity Measurement:</strong></p> <p><strong>Requirements:</strong><br> * Eclipse IDE (we used 2021-12)<br> * The libraries (the <em><strong>.jar</strong></em><strong> </strong>files in the <em><strong>Code/ATM/Similarity/lib</strong></em> directory)<br> <br> <strong>Input:</strong><br> * Data/test_suites/all_test_cases<br> * Data/test_suites/changed_test_cases<br> <br> <strong>Output:</strong><br> * Data/ATM/similarity_measurements<br> <br> <strong>Running the experiment:</strong><br> To measure the similarity between each pair of test cases, the <em><strong>Code/ATM/Similarity/src/SimilarityMeasurement.java</strong></em> file should be compiled and run using the Eclipse IDE by including all the required <em><strong>.jar</strong></em> files in the <em><strong>Code/ATM/Similarity/lib</strong></em> directory as part of the classpath. A bash script is provided along with a pre-generated <em><strong>.jar</strong></em> file in the <em><strong>Code/ATM/Similarity/bin</strong></em> directory to run this step, as follows:</p> <pre><code class="language-bash">cd Code/ATM/Similarity bash measure_similarity.sh</code></pre> <p>ASTs of each project in the <em><strong>Data/ATM/ASTs/all_test_cases</strong></em> and <em><strong>Data/ATM/ASTs/changed_test_cases</strong></em> directories are parsed to create pairs of ASTs containing one test case from the <em><strong>Data/ATM/ASTs/all_test_cases</strong></em> directory with another test case from the <em><strong>Data/ATM/ASTs/changed_test_cases</strong></em> directory (redundant pairs are discarded). Then, all similarity measurements are saved in the <em><strong>Data/ATM/similarity_measurements.zip</strong></em> file.<br> __________________________________________</p> <p><strong>Search-based Minimization Algorithms:</strong><br> The source code for this step is in the <em><strong>Code/ATM/Search</strong></em> directory.<br> <br> <strong>Requirements:</strong><br> To run this step, Python 3 is required (we used <em><strong>Python 3.10</strong></em>). Also, the libraries in the <strong>Code/AMT/Search/requirements.txt</strong> file should be installed, as follows:</p> <pre><code class="language-bash">cd Code/ATM/Search pip install -r requirements.txt</code></pre> <p><strong>Input:</strong><br> * Data/ATM/similarity_measurements<br> <br> <strong>Output:</strong><br> * Results/ATM/minimization_results<br> <br> <strong>Running the experiment:</strong><br> To minimize the test suites in our dataset, the following bash script should be executed:</p> <pre><code class="language-bash">bash minimize.sh</code></pre> <p>All similarity measurements are parsed for each version of the projects, independently. Each version is run 10 times using three minimization budgets (25%, 50%, and 75%). Genetic Algorithm (GA) is run using four similarity measures, namely top-down, bottom-up, combined, and tree edit distance. NSGA-II is run using two combinations of similarity measures: top-down & bottom-up and combined & tree edit distance. The minimization results are generated in the <em><strong>Results/ATM/minimization_results</strong></em> directory.<br> __________________</p> <p><strong>Evaluate results:</strong><br> To evaluate and summarize the minimization results, run the following:</p> <pre><code class="language-bash">cd Code/ATM/Evaluation bash evaluate.sh</code></pre> <p>This will generate summarized <em>FDR</em> and execution time results (per-project and per-version) for each minimization budget, which can all be found in <strong>Results/ATM</strong>. In this replication package, we provide the final, merged <em>FDR</em> with execution time results.</p> <p><strong>_________________________________</strong></p> <p><strong>Running FAST-R experiments</strong><br> ATM was compared to <a href="https://github.com/ICSE19-FAST-R/FAST-R">FAST-R</a>, a state-of-the-art baseline, which is a set of test case minimization techniques called: <em>FAST++, FAST-CS, FAST-pw, and FAST-all</em>, which we adapted to our data and experimental setup.</p> <p><strong>Requirements:</strong><br> To run this step, Python 3.7 is required. Also, the libraries in the <em><strong>Code/FAST-R/requirements.txt</strong></em> file should be installed, as follows:</p> <pre><code class="language-bash">cd Code/FAST-R pip install -r requirements.txt</code></pre> <p><strong>Input:</strong><br> * Data/FAST-R/test_methods<br> * Data/FAST-R/test_classes</p> <p><strong>Output:</strong><br> * Results/FAST-R/test_methods/FDR_and_Exec_Time_Results_[budget]%_budget.csv<br> * Results/FAST-R/test_classes/FDR_and_Exec_Time_Results_[budget]%_budget.csv<br> <br> To run FAST-R experiments, the following bash script should be executed:</p> <pre><code class="language-bash">bash fast_r.sh test_methods #method level bash fast_r.sh test_classes #class level</code></pre> <p>Results are generated in <em><strong>.csv</strong></em> files for each budget. For example, for the 50% budget, results are saved in <strong>FDR_and_Exec_Time_Results_50%_budget.csv</strong> in the <em><strong>Results/FAST-R/test_methods</strong></em> and <em><strong>Results/FAST-R/test_classes</strong></em> directories.</p> <p><strong>_________________________________</strong></p> <p><strong>Running the random minimization experiments</strong><br> ATM was also compared to random minimization as a standard baseline.</p> <p><strong>Requirements:</strong> To run this step, Python 3 is required (we used <em><strong>Python 3.10</strong></em>). Also, the libraries in the <em><strong>Code/RandomMinimization/requirements.txt</strong></em> file should be installed, as follows:</p> <pre><code class="language-bash">cd Code/RandomMinimization pip install -r requirements.txt</code></pre> <p><strong>Input:</strong><br> <em>N/A</em></p> <p><strong>Output:</strong><br> * Results/RandomMinimization/FDR_and_Exec_Time_Results_[budget]%_budget.csv<br> <br> To run the random selection experiments, the following bash script should be executed:</p> <pre><code class="language-bash">bash random_minimization.sh</code></pre> <p>Results are generated in <em><strong>.csv</strong></em> files for each budget. For example, for the 50% budget, results are saved in <em><strong>FDR_and_Exec_Time_Results_50%_budget.csv</strong></em> in the <em><strong>Results/RandomMinimization</strong></em> directory.</p>
Replication package for: How the Other Half Died: Immigration and Mortality in US Cities
<p>The code in this replication package replicates *How the Other Half Died: Immigration and Mortality in US Cities*, RESTUD forthcoming. All code is in R. It first builds the analysis data from several raw data sources. It then produces all the figures and tables in the paper. One master file (`master.R`) runs all of the code to generate the input data and then the figures and tables in the paper (and then the figures and tables in the appendix).</p>
Replication package for the Helm charts empirical study
<p><strong>Helm Charts for Kubernetes Applications: Evolution, Outdatedness and Security Risks</strong></p> <p>This repository represents a replication package for our MSR study on Helm charts.</p> <p>This replication package requires Python 3.5+ to be installed, and all the dependencies listed in ``requirements.txt``.</p> <p>They can be automatically installed using ``pip install -r requirements.txt``. <br> These experiments were executed on a Linux Ubuntu OS.</p> <p>This replication package contains three folders:<br> - notebooks: contains notebooks where we analyze data. <br> - figures: contains figures saved from the notebooks<br> - datasets: contains all datasets required</p> <p>To obtain the analysis used in the paper, one should execute ``jupyter notebook`` at the root of this replication package, and open the notebook contained in ``notebooks``.</p> <p>The data is under the Creative Commons Attribution Share-Alike 4.0 license. The source code is under the GNU General Public License.</p>
[Replication Package] Impact of Architectural Smells on Software Performance
<p>Replication Package of the paper "Impact of Architectural Smells on Software Performance: an Exploratory Study" accepted for presentation to the 27th International Conference on Evaluation and Assessment in Software Engineering (EASE) 2023.</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.