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677 results for “Replication package”
Replication Package for "Evaluating the layout quality of UML class diagrams using machine learning"
<p>Open Science material including dataset and replication instructions accompanying the article "Evaluating the layout quality of UML class diagrams using machine learning."</p>
Replication Package for "TriggerBench: A Performance Benchmark for Serverless Function Triggers"
<p>This replication package contains the code (`aws-triggers` and `azure-trigger`), data analysis scripts (`data-analysis`), and dataset (`data`) of the TriggerBench cross-provider serverless benchmark.</p> <p>It also bundles a customized extension of the `serverless-benchmarker` tool to automate and analyze serverless performance experiments.</p> <p><strong>TriggerBench</strong></p> <p>The Github repository <a href="https://github.com/joe4dev/trigger-bench/">joe4dev/trigger-bench</a> contains the last version of TriggerBench. This replication package describes the version for the paper "TriggerBench: A Performance Benchmark for Serverless Function Triggers".</p> <p>TriggerBench currently supports three triggers on AWS and eight triggers on Microsoft Azure.</p> <p><strong>Dataset</strong></p> <p>The `data/aws` and `data/azure` directories contain data from benchmark executions from April 2022.</p> <p>Each execution is a separate directory with a timestamp in the format `yyyy-mm-dd-HH-MM-SS` (e.g., `2022-04-15_21-58-52`) and contains the following files:</p> <ul> <li>`k6_metrics.csv`: Load generator HTTP client logs in CSV format (see [K6 docs](https://k6.io/docs/results-visualization/csv/))</li> <li>`sb_config.yml`: serverless benchmarker execution configuration including experiment label.</li> <li>`trigger.csv`: analyzer output CSV per trace. <ul> <li>`root_trace_id`: The trace id created by k6 and adopted by the invoker function</li> <li>`child_trace_id`: The trace id newly created by the receiver function if trace propagation is not supported (this is the case for most asynchronous triggers)</li> <li>`t1`-`t4`: Timestamps following the trace model (see paper)</li> <li>`t5`-`t9`: Additional timestamps for measuring timestamping overhead</li> <li>`coldstart_f1=True|False`: coldstart status for invoker (f1) and receiver (f2) functions</li> <li>`trace_ids.txt`: text file with each pair of `root_trace_id` and `child_trace_id` on a new line.</li> </ul> </li> <li>`traces.json`: raw trace JSON representation as retrieved from the provider tracing service. For AWS, see [X-Ray segment docs](https://docs.aws.amazon.com/xray/latest/devguide/xray-api-segmentdocuments.html). For Azure, see [Application Insights telemetry data model](https://docs.microsoft.com/en-us/azure/azure-monitor/app/data-model).</li> <li>`workload_options.json`: [K6 load scenario](https://k6.io/docs/using-k6/scenarios/) configuration.</li> </ul> <p><strong>Replicate Data Analysis</strong></p> <p><strong>Installation</strong></p> <p>1. Install [Python](https://www.python.org/downloads/) 3.10+</p> <p>2. Install Python dependencies `pip install -r requirements.txt`</p> <p><strong>Create Plots</strong></p> <p>1. Run `python plots.py` generates the plots and the statistical summaries presented in the paper.</p> <p>By default, the plots will be saved into a `plots` sub-directory.<br> An alternative output directory can be configured through the environment variable `PLOTS_PATH`.</p> <p>> Hint: For interactive development, we recommend the VSCode [Python extension](https://marketplace.visualstudio.com/items?itemName=ms-python.python) in [interactive mode](https://youtu.be/lwN4-W1WR84?t=107).</p> <p> </p> <p><strong>Replicate Cloud Experiments</strong></p> <p>The following experiment plan automates benchmarking experiments with different types workloads (constant and bursty).</p> <p>This generates a new dataset in the same format as described above.</p> <p> </p> <ol> <li> Set up a load generator as vantage point following the description in [LOADGENERATOR](./serverless-benchmarker/docs/LOADGENERATOR.md).</li> <li> Choose the `PROVIDER` (aws or azure) in the [constant.py](./experiment-plans/constant.py) experiment plan</li> <li> Run the [constant.py](./experiment-plans/constant.py) experiment plan <ol> <li> Open tmux</li> <li> Activate virtualenv `source sb-env/bin/activate`</li> <li> Run `./constant.py 2>&1 | tee -a constant.log`</li> </ol> </li> </ol> <p><strong>Contributors</strong></p> <p>The initial trigger implementations for AWS and Azure are based on two master thesis projects at Chalmers University of Technology in Sweden supervised by Joel:</p> <ul> <li>AWS + Azure: <a href="https://odr.chalmers.se/handle/20.500.12380/302822">Performance Comparison of Function-as- a-Service Triggers: A Cross-Platform Performance Study of Function Triggers in Function-as-a-Service</a> by Marcus Bertilsson and Oskar Grönqvist, 2021.</li> <li>Azure Extension: <a href="https://odr.chalmers.se/handle/20.500.12380/305138">Serverless Function Triggers in Azure: An Analysis of Latency and Reliability</a> by Henrik Lagergren and Henrik Tao, 2022.</li> </ul> <p>Joel contributed many improvements to their original source code as documented in the import commits <a href="https://github.com/joe4dev/trigger-bench/commit/a00b67a1dd8476ca77d026e59adf2674c7807e68">a00b67a</a> and <a href="https://github.com/joe4dev/trigger-bench/commit/6d2f5ef8bda0596b3f295cb6c6cbeba212c6ef43">6d2f5ef</a> and developed TriggerBench as an integrated benchmark suite (see commit history for detailed changelog).</p>
Replication package for "Teams and Individuals in Standard Auction Formats: Decision and Emotions"
<p>This is the replication package for the paper "Teams and Individuals in Standard Auction Formats: Decision and Emotions" by Maria Karmeliuk, Martin Kocher and Georg Schmidt, forthcoming in <em>Experimental Economics</em>.</p> <p>The package contains instructions and zTree code for running the laboratory experiment, as well as raw data from the experiment including the data from face recognition software FaceReader. It also contains Stata and R code for data cleaning, cleaned datasets and Stata code for replicating data analysis and statistics reported in the paper.</p>
Replication package for "An Exploratory Study on the Predominant Programming Paradigms in Python Code"
<p>This dataset includes scripts and data files used to generate all analysis and results from the paper. A README.md file is included for details on using the scripts - though all of the data the scripts generate should already be cached and none of the scripts actually need run.</p> <p>It also includes a spreadsheet containing the human judgements from Table 4 of the paper.</p> <p>Always current source for the scripts is available on GitHub: <a href="https://github.com/psybers/python-paradigms">https://github.com/psybers/python-paradigms</a></p>
Replication Package of the study "Automated Identification and Qualitative Characterization of Safety Concerns Reported in UAV Software Platforms"
<p><strong>Description of the Dataset of the work "Automated Identification and Qualitative Characterization of Safety<br> Concerns Reported in UAV Software Platforms"</strong></p> <p><strong><em>"1_Safety-Dataset" folder: </em></strong>This folder contains the bugs data and row data of all analyzed projects.<br> Specifically, this folder contains the following relevant entries<br> <br> - "bugs" folder: It contains the bugs of all analyzed projects (PX4-merged.json.gz, dDronin-merged.json.gz, ardupilot-merged.json.gz)<br> of all sentences extracted from the project issues<br> - "Dataset-safety-bugs.csv": For all projects, it contains the raw data of the set of sentences classified as safety and non-safety related.<br> </p> <p><em><strong>"2_Scripts-and-generated-data (RQ1)" folder:</strong> </em>This folder contains the scripts and code used to preprocess and analyze the issue data in <br> the context of RQ1<br> Specifically, this folder contains the following relevant entries<br> <br> - "main-program.py" file: Main program executing all subscripts generating the data required for RQ1 (detailed in the following line)<br> - "utilities.R" file: (Utility) R script containing relevant functions for pre-processing/indexing text and issue data<br> - "1_Script-to-create-test-dataset.r" file: R script containing simple code for analyzing issue data<br> - "2_MainScript.r" file: Main R program orchestrating the scripts "utilities.R" and "1_Script-to-create-test-dataset.r" execution<br> - "files-setDirectory" folder: Folder where data are generated and stored from the "main-program.py"<br> - "fasttext" folder: Folder where data used as input from fastText (by "main-program.py") are reported<br> - "cross-project-analysis" folder: Folder with data used for the cross-project analysis</p> <p> - "main-program-grid-search.py" file: Main program executing all experiments for the grid search analysis</p> <p><em><strong>"3_Results" folder: </strong></em>This folder contains the results, scripts and figures used to discuss results of the study.<br> Specifically, this folder contains the following relevant entries<br> <br> - "RQ1" folder: This folder contains the results, scripts and figures used to discuss results of RQ1.<br> - "RQ2" folder: This folder contains the results, scripts and Tables used to discuss results of RQ2.</p>
Dataset and Replication Package for the View-Based Retriever Approach To Reverse Engineering Software Architecture Models
<div> <div><span>Dataset and replication package for the view-based Retriever approach to reverse engineering software architecture models. Each Dataset project is structured as follows:</span></div> <ul> <li><span>The .ruleengine.yml file contains the configuration for running the Retriever approach.</span> <ul> <li><span>The repository value is the ID of a GitHub repository.</span></li> <li><span>The current_version value is the latest version of the retriever approach used to build the architectural models.</span></li> <li><span>The rules values are the rules used to build the architectural models.</span></li> </ul> </li> <li><span>The model_re folder contains the architectural model of the system automatically generated by the Retriever approach.</span> <ul> <li><span>The pcm folder contains the Palladio Component Model (PCM) of the system.</span></li> <li><span>The uml folder contains the PlantUML model.</span></li> </ul> </li> <li><span>The model_gs folder contains our manual gold standards for the system.</span></li> </ul> <div><span>The easiest way to use our approach is to use the CLI application with the given parameters: ./eclipse -i /path/to/input/directory -o /path/to/output/directory -r supported_rules</span></div> </div>
Source Code Archiving to the Rescue of Reproducible Deployment — Replication Package
<p>Replication package for the paper:</p> <p>Ludovic Courtès, Timothy Sample, Simon Tournier, Stefano Zacchiroli.<br><em>Source Code Archiving to the Rescue of Reproducible Deployment</em><br><a href="https://acm-rep.github.io/2024/">ACM REP'24</a>, June 18-20, 2024, Rennes, France<br><a href="https://doi.org/10.1145/3641525.3663622">https://doi.org/10.1145/3641525.3663622</a></p> <h2>Generating the paper</h2> <p>The paper can be generated using the following command:</p> <pre><code>guix time-machine -C channels.scm \ -- shell -C -m manifest.scm \ -- make </code></pre> <p>This uses GNU Guix to run <code>make</code> in the exact same computational environment used when preparing the paper. The computational environment is described by two files. The <code>channels.scm</code> file specifies the exact version of the Guix package collection to use. The <code>manifest.scm</code> file selects a subset of those packages to include in the environment.</p> <p>It may be possible to generate the paper without Guix. To do so, you will need the following software (on top of a Unix-like environment):</p> <ul> <li>GNU Make</li> <li>SQLite 3</li> <li>GNU AWK</li> <li>Rubber</li> <li>Graphviz</li> <li>TeXLive</li> </ul> <h2>Structure</h2> <ul> <li><code>data/</code> contains the data examined in the paper</li> <li><code>scripts/</code> contains dedicated code for the paper</li> <li><code>logs/</code> contains logs generated during certain computations</li> </ul> <h2>Preservation of Guix</h2> <p>Some of the claims in the paper come from analyzing the Preservation of Guix (PoG) database as published on January 26, 2024. This database is the result of years of monitoring the extent to which the source code referenced by Guix packages is archived. This monitoring has been carried out by Timothy Sample who occasionally publishes reports on his personal website: <a href="https://ngyro.com/pog-reports/latest/">https://ngyro.com/pog-reports/latest/</a>. The database included in this package (<code>data/pog.sql</code>) was downloaded from <a href="https://ngyro.com/pog-reports/2024-01-26/pog.db">https://ngyro.com/pog-reports/2024-01-26/pog.db</a> and then exported to SQL format. In addition to the SQL file, the database schema is also included in this package as <code>data/schema.sql</code>.</p> <p>The database itself is largely the result of scripts, but also of manual adjustments (where necessary or convenient). The scripts are available at <a href="https://git.ngyro.com/preservation-of-guix/">https://git.ngyro.com/preservation-of-guix/</a>, which is preserved in the Software Heritage archive as well: <a href="https://archive.softwareheritage.org/swh:1:snp:efba3456a4aff0bc25b271e128aa8340ae2bc816;origin=https://git.ngyro.com/preservation-of-guix">https://archive.softwareheritage.org/swh:1:snp:efba3456a4aff0bc25b271e128aa8340ae2bc816;origin=https://git.ngyro.com/preservation-of-guix</a>. These scripts rely on the availability of source code in certain locations on the Internet, and therefore will not yield exactly the same result when run again.</p> <h3>Analysis</h3> <p>Here is an overview of how we use the PoG database in the paper. The exact way it is queried to produce graphs and tables for the paper is laid out in the Makefile.</p> <p>The <code>pog-types.sql</code> query gives the counts of each source type (e.g. “git” or “tar-gz”) for each commit covered by the database.</p> <p>The <code>pog-status.sql</code> query gives the archival status of the sources by commit. For each commit, it produces a count of how many sources are <em>stored</em> in the Software Heritage archive, <em>missing</em> from it, or <em>unknown</em> if stored or missing. The <code>pog-status-total.sql</code> query does the same thing but over all sources without sorting them into individual commits.</p> <p>The <code>disarchive-ratio.sql</code> query estimates the success rate of Disarchive disassembly.</p> <p>Finally, the <code>swhid-ratio.sql</code> query gives the proportion of sources for which the PoG database has an SWHID.</p> <h3>Estimating missing sources</h3> <p>The Preservation of Guix database only covers sources from a sample of commits to the Guix repository. This greatly simplifies the process of collecting the sources at the risk of missing a few. We estimate how many are missed by searching Guix’s Git history for Nix-style base-32 hashes. The result of this search is compared to the hashes in the PoG database.</p> <p>A naïve search of Git history results in an over estimate due to Guix’s branch development model. We find hashes that were never exposed to users of ‘guix pull’. To work around this, we also approximate the history of commits available to ‘guix pull’. We do this by scraping push events from the guix-commits mailing list archives (<code>data/guix-commits.mbox</code>). Unfortunately, those archives are not quite complete. Missing history is reconstructed in the <code>data/missing-links.txt</code> file.</p> <p>This estimate requires a copy of the Guix Git repository (not included in this package). The repository can be obtained from GNU at <a href="https://git.savannah.gnu.org/git/guix.git">https://git.savannah.gnu.org/git/guix.git</a> or from the Software Heritage archive: <a href="https://archive.softwareheritage.org/swh:1:snp:9d7b8dcf5625c17e42d51357848baa226b70e4bb;origin=https://git.savannah.gnu.org/git/guix.git">https://archive.softwareheritage.org/swh:1:snp:9d7b8dcf5625c17e42d51357848baa226b70e4bb;origin=https://git.savannah.gnu.org/git/guix.git</a>. Once obtained, its location must be specified in the Makefile.</p> <p>To generate the estimate, use:</p> <pre><code>guix time-machine -C channels.scm \ -- shell -C -m manifest.scm \ -- make data/missing-sources.txt </code></pre> <p>If not using Guix, you will need additional software beyond what is used to generate the paper:</p> <ul> <li>GNU Guile</li> <li>GNU Bash</li> <li>GNU Mailutils</li> <li>GNU Parallel</li> </ul> <h2>Measuring link rot</h2> <p>In order to measure link rot, we ran Guix Scheme scripts, i.e., scripts that exploit Guix as a Scheme library. The scripts depend on the state of world at the very specific moment when they ran. Hence, it is not possible to reproduce the exact same outputs. However, their tendency over the passing of time should be very similar. For running them, you need an installation of <a href="https://guix.gnu.org/manual/deve/en/html_node/Installation.html">Guix</a>. For instance,</p> <pre><code>guix repl -q scripts/table-per-origin.scm </code></pre> <p>When running these scripts for the paper, we tracked their output and saved it inside the <code>logs</code> directory.</p>
Replication Package for "Catching Smells in the Act: A GitHub Actions Workflow Investigation" (SCAM 2024)
<p>Welcome to our artifact! In here we provide additional information on how to retrace our steps performed during the research. We have split up our content into four sections based on the RQ's we have answered. Below you can find a quick summary of the contents of each folder, each folder also contains additional information regarding any data and scripts present.</p> <ul> <li>RQ1 + 2: Contains excel files with the commits we have analyzed and the scripts we have used to automate this process.</li> <li>RQ3: Contains our smell detector and evaluation of the detector</li> <li>RQ4: Contains the data on our contribution study</li> </ul>
carjgil/teacher-bias: Teacher Bias - Replication Package
<p>*Project: Teacher's Bias in Assessments</p> <p>*Code: Replication of data cleaning and preparation and analyses</p> <p>*Authors: Carlos J. Gil-Hernández, Irene Pañeda-Fernández, Leire Salazar, and Jonatan Castaño-Muñoz</p> <p>*Last Update: 04/07/2024</p> <p>*Software: STATA/MP 17</p> <p>Here you can find the replication dofile in STATA format in the "code" folder and the raw and working datasets (including the codebook) of the teacher's bias in assessment experiment in the "data" folder:</p> <p>1. "/replication files/code/datacleaning.do" contains all the data cleaning and preparation procedures from the raw anonymized Qualtrics data where we applied the survey experiment (see "data" folder .dta or .csv files named "raw_dataset_anonymized") to set a working dataset ready to be analyzed.</p> <p>2. The folder "/replication files/data" contains the data files named "raw_dataset_anonymized" and "cleandataset" in .dta (data/STATA) or .csv (data/CSV) format on the raw and working data, respectively, to replicate the findings of the teacher's bias in assessments project or run your own analyses. If you do not have access to STATA software, you can check the variables labels of the "cleandataset" in the "data/codebook_cleandataset" Excel file.</p> <blockquote> <p>Data Citation: Gil-Hernández, Carlos J., Leire Salazar, Jonatan Castaño Muñoz, and Irene Pañeda-Fernandez. 2023. "Teacher's Bias Dataset: A Factorial Survey Experiment." European Commission, Joint Research Centre (JRC) [Dataset] PID: http://data.europa.eu/89h/f14f5209-f032-4218-a89a-4643143809af</p> </blockquote> <p>3. "datanalysis.do" reproduces all the tables and figures presented in the article and online appendix (if you want to reproduce the analyses from the pre-test pilot data, please get in contact with the corresponding author) using the data file named "cleandataset" in the "data" folder (in .dta or .csv format). The output from "datanalysis.do" will be printed in the "/replication files/output" subfolders for tables (main or appendix) or figures (main or appendix).</p>
Replication package for: "The Rise of Fiscal Capacity"
<div> <div> <div>The code in this replication package constructs the analysis and output files for the paper "The Rise of Fiscal Capacity: Administration and State Consolidation in the Holy Roman Empire" from various data sources using Stata and R. The provided code reproduces all tables and figures in the paper and in the appendix.</div> </div> </div>
Assessing the sustainability performance of sustainability management software - replication package
<p>This is the replication package for the article "Assessing the sustainability performance of sustainability management software", published in Technologies – SI „Advanced Green Information and Communication Technology“</p> <p><strong>Contents</strong><br> The repository contains the following files:</p> <ul> <li><em>Data_-_Scenario_Hardware_Measurement.csv:</em><br> measurement data acquired during the measurement of the standard usage scenario</li> <li><em>Data_-_Scenario_Log.txt:</em><br> timestamp log file for the measurements of the standard usage scenario</li> <li><em>Data_-_Baseline_Hardware_Measurement.csv:</em><br> baseline meaurements</li> <li><em>Data_-_Baseline_Log.txt:</em><br> baseline timestamp log file</li> <li><em>R_image.Rdata:</em><br> data is also available in an R image dump</li> <li><em>R_Analysis_Script.R:</em><br> analysis script, written in R</li> <li><em>Results_SCSS_WeSustain_ESM.pdf:</em><br> results of the analysis</li> <li><em>Results_and_calculation_for_Indicators_1.2.b_and_1.2.c.ods:</em><br> calculation for indicators 1.1.4.d) 1.2.b) and 1.2.c)</li> <li><em>Usage-scenario_WeSustain_ESM.pdf:</em><br> description of the standard usage scenario</li> </ul> <p><strong>Usage</strong><br> To recreate the analysis, run the R script and, if necessary, modify <em>lines 103 to 115</em> to fit the filenames where to find the data.<br> <em>Lines 193 to 217</em> need to be executed manually, to generate the desired plots and calculations.</p> <p>It is also possible to load the <em>R_image.RData</em> data dump into an R session, import the library `psych` (<em>line 2</em> in the script) and manually execute <em>lines 193 to 217</em> to generate the desired plots and calculations.</p>
[Replication package] INFSOF-D-18-00533
<p>Data and scripts associated with the following publications:</p> <p>Kelly Blincoe, Francis Harrison and Daniela Damian. "Ecosystems in GitHub and a Method for Ecosystem Identification using Reference Coupling." In Proceedings of the 12th Working Conference on Mining Software Repositories (MSR '15), pp. 202-211. IEEE, 2015.</p> <p>Kelly Blincoe, Francis Harrison, Navpreet Kaur, and Daniela Damian. "Reference Coupling: An Exploration of Inter-project Technical Dependencies and their Characteristics within Large Software Ecosystems." <em>Information and Software Technology</em>. In Press, 2019. </p>
Replication Package for Article "CoVeriTest: Cooperative Verifier-Based Testing" in Proc. FASE '19
<p>Replication package for the article<br> "Dirk Beyer and Marie-Christine Jakobs. CoVeriTest: Cooperative Verifier-Based Testing. In Proc. FASE, 2019. Springer."</p> <p>It contains all tools and data that are necessary to reproduce the results in our article.<br> The included README contains detailed instructions.</p> <p>Abstract:<br> Testing is a widely used method to assess software quality. Coverage criteria and coverage measurements are used to ensure that the constructed test suites adequately test the given software. Since manually developing such test suites is too expensive in practice, various automatic test-generation approaches were proposed. Since all approaches come with different strengths, combinations are necessary in order to achieve stronger tools. We study cooperative combinations of verification approaches for test generation, with high-level information exchange.<br> We present CoVeriTest, a hybrid approach for test-case generation, which iteratively applies different conditional model checkers. Thereby, it allows to adjust the level of cooperation and to assign individual time budgets per verifier. In our experiments, we combine explicit-state model checking and predicate abstraction (from CPAchecker) to systematically study different CoVeriTest configurations. Moreover, CoVeriTest achieves higher coverage than state-of-the-art test-generation tools for some programs.</p> <p> </p>
Replication package: assessing the sustainability of software products - a method comparison
<p><strong>Assessing the Sustainability of Software Products - A Method Comparison - Replication Package</strong></p> <p>This is a replication package for the paper entitled "Assessing the Sustainability of Software Products - A Method Comparison". The paper was submitted to the 33. EnviroInfo conference "Environmental Informatics – Computational sustainability: ICT methods to achieve the UN Sustainable Development Goals", 23th – 26th September 2019 at the University of Kassel, Germany.</p> <p>For further information, please refer to the <a href="https://zenodo.org/record/3257517/files/README.md?download=1">README.md</a></p> <p>This replication package is licensed under <a href="https://creativecommons.org/licenses/by-nc/4.0/">Creative Commons CC BY-NC 4.0</a>.</p>
Replication package for "Evolution of statistical analysis in empirical software engineering research: Current state and steps forward"
<p>This is the replication package for the analysis done in the paper "Evolution of statistical analysis in empirical software engineering research: Current state and steps forward" (DOI: <a href="https://doi.org/10.1016/j.jss.2019.07.002">https://doi.org/10.1016/j.jss.2019.07.002</a>, preprint: <a href="https://arxiv.org/abs/1706.00933">https://arxiv.org/abs/1706.00933</a>).</p> <p>The package includes CSV files with data on statistical usage extracted from 5 journals in SE (EMSE, IST, JSS, TOSEM, TSE). The data was extracted from papers between 2001 - 2015. The package also contains forms, scripts and figures (generated using the scripts) used in the paper.</p> <p>The extraction tool mentioned in the paper is available in dockerhub via: <a href="https://hub.docker.com/r/robertfeldt/sept">https://hub.docker.com/r/robertfeldt/sept</a></p>
Replication package for "Evolution of statistical analysis in ESE research"
<p>This is the replication package for the analysis done in the paper "Evolution of statistical analysis in empirical software engineering research: Current state and steps forward" (DOI: <a href="https://doi.org/10.1016/j.jss.2019.07.002">https://doi.org/10.1016/j.jss.2019.07.002</a>, preprint: <a href="https://arxiv.org/abs/1706.00933">https://arxiv.org/abs/1706.00933</a>).</p> <p>The package includes CSV files with data on statistical usage extracted from 5 journals in SE (EMSE, IST, JSS, TOSEM, TSE). The data was extracted from papers between 2001 - 2015. The package also contains forms, scripts and figures (generated using the scripts) used in the paper.</p> <p>The extraction tool mentioned in the paper is available in dockerhub via: <a href="https://hub.docker.com/r/robertfeldt/sept">https://hub.docker.com/r/robertfeldt/sept</a></p>
Replication package for the paper: "Technical Debt's State of Practice on Stack Overflow: a Preliminary Study"
<p>This is the replication package for the paper "Technical Debt’s State of Practice on Stack Overflow: a Preliminary Study", published (in Portuguese) in the preliminary results track of SBQS, the Brazilian Symposium on Software Quality.</p> <p> </p> <p>We provide the data for all steps of our methodology and final analysis. Each file is numbered, indicating the order in which they were produced in our study. </p>
On the Role of Font Formats in Building Efficient Web Applications - Replication Package
<p>Replication Package for the paper - On the Role of Font Formats in Building Efficient Web Applications. It is the final version of the replication package for the PROFES 2023 conference (<a href="https://conf.researchr.org/details/profes-2023/profes-2023-papers/18/On-the-Role-of-Font-Formats-in-Building-Efficient-Web-Applications">https://conf.researchr.org/details/profes-2023/profes-2023-papers/18/On-the-Role-of-Font-Formats-in-Building-Efficient-Web-Applications</a>). </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>
Replication Package for a Systematic Literature Mapping of Agility in Safety-Critical Software Development within the Aerospace Industry
<p>This file collection package facilitates the replication of a Systematic Literature Mapping (SLM) focused on Agility in Safety-Critical Software Development within the Aerospace Industry. Authored by J. Eduardo Ferreira Ribeiro, João Gabriel Silva, and Ademar Aguiar, this dataset is dedicated to improving transparency and reproducibility in this field of study and future research.</p> <p>Specifically, the package includes:</p> <ul> <li><a href="https://github.com/zemacedo99/Replication-Package-Builder/releases/tag/v1.0.2">Replication Package Builder Version 1.0.2</a></li> <li>A list of terms (both inclusion and exclusion) used to construct the research string.</li> <li>A list of venues unrelated to the research topic, to be excluded from the results.</li> <li>The inclusion and exclusion criteria applied during the study.</li> <li>Lists of publication results from indexing services like Scopus, IEEE Xplore, Science Direct, HAL Open Science, Springer Nature, and the ACM Digital Library are all provided in CSV file format.</li> <li>A list of all publications in CSV format, compiled after the automated exclusion phase using the established inclusion and exclusion criteria.</li> <li>Finally, a complete list of all publications, including those from Snowball sampling, in XLSX format was compiled after the manual exclusion phase using the established inclusion and exclusion criteria.</li> </ul> <p>Compiled and published on Saturday, September 14, 2024, this dataset is crucial for researchers seeking to replicate or extend the SLM's findings.</p> <p>Lastly, we thank J. Antonio Dantas Macedo for contributing to developing and providing this <a href="https://github.com/zemacedo99/Replication-Package-Builder">replication package builder</a>.</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.