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184 results for “Replication Study”
Replication data for: "The hapax / type ratio: an indicator of minimally required sample size in productivity studies?"
<p>The dataset accompanies the scientific article "The hapax / type ratio: an indicator of minimally required sample size in productivity studies?" and can be used to reproduce the findings presented in this article. This dataset consists of two components, namely (i) the corpus data involving the Dutch semi-copular verb "raken" and (ii) an R analysis script to reproduce the computational steps.</p>
Replication data for: Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study
<p>This is the replication data for the scientific paper titled "Online Media Use and COVID-19 Vaccination in Real-World Personal Networks: Quantitative Study" accepted for publication in the Journal of Medical Internet Research (JMIR). For details on how to use the data files, please consider the "supplementary_material.R" file or the "supplementary_material.pdf" where the variables of interest and R code are presented.</p> <p>For the code to run correctly, have the files "multilevel_labels.R" and "glm_labels.R" in the same working directory as the .R or .Rmd script. They are executed in the background, applying modifications to labels inside the regression tables. </p> <p> </p>
Dataset and replication information for It's About Time: How to Study Intertemporal Choice in Systems Design
<p>Dataset and replication package for the paper <em>It's About Time: How to Study Intertemporal Choice in Systems Design</em> (Fagerholm, F., De los Ríos, A., Cárdenas Castro, C., Gil, J., Chatzigeorgiou, A., Ampatzoglou, A., Becker, C. (2023). It’s About Time: How To Study Intertemporal Choice in Systems Design. Information and Software Technology.). The dataset consists of answers to a scenario-based questionnaire that collects data on intertemporal choice in the context of software development. An analysis script is provided to show the details of the calculations and analyses performed for the paper. The replication package includes the protocol for data collection sessions and different versions of the task scenario and questionnaire. More information is given in the description file.</p>
Synchronous transmissions on Bluetooth 5 and IEEE 802.15.4 - A replication study
<p>Archive of the following GitHub repository: <a href="https://github.com/romain-jacob/ST_data_and_viz">https://github.com/romain-jacob/ST_data_and_viz</a></p> <p>This repository contains the raw data and analysis scripts of a replication study of synchronous transmissions using the <a href="https://www.nordicsemi.com/en/Software%20and%20tools/Development%20Kits/nRF52840%20Dongle">nRF52840 Dongle</a> where we compare the success rate of synchronous transmission attempts using two transmitters while varying a number of parameters. The repository also contains the source code of a data visualization application, which you can either</p> <ul> <li>run locally or</li> <li><a href="https://github.com/romain-jacob/ST_data_and_viz/blob/master/URL">online in your web browser</a> at <a href="http://explore-st-data.ethz.ch/">http://explore-st-data.ethz.ch/</a></li> </ul> <p>The study is described in more details in the following publication.</p> <blockquote> <p><strong>Synchronous transmissions on Bluetooth 5 and IEEE 802.15.4 - A replication study</strong><br> Romain Jacob, Anna-Brit Schaper, Andreas Biri, Reto da Forno, Lothar Thiele<br> <a href="https://cpsbench20.ethz.ch/">CPS-IoTBench'20</a><br> [ <a href="https://openreview.net/forum?id=BSZPNEUHiS2">Direct link</a> ]</p> </blockquote> <p>The dataset has been collected during the <a href="https://doi.org/10.3929/ethz-b-000375332">Master thesis of Anna-Brit Schaper.</a></p>
Replication material for 'The impact of local identities on voting behaviour: A Scouse case study'
<p>This holds the replication material for the paper 'The impact of local identities on voting behaviour: A Scouse case study'</p>
Dataset and replication package for Temporal Discounting in Software Engineering: A Replication Study
<p>Dataset and replication package for the paper Temporal Discounting in Software Engineering: A Replication Study (Fagerholm, F., Becker, C., Chatzigeorgiou, A., Betz, S., Duboc, L., Penzenstadler, B., Mohanani, R., Venters, C. (2019). Temporal Discounting in Software Engineering: A Replication Study. 13th ACM/IEEE International Symposium of Empirical Software Engineering and Measurement (ESEM 2019)). The dataset consists of answers to a questionnaire on temporal discounting in a technical debt context. Two questionnaire templates illustrate how to gather the data for professional and student participants. An analysis script is provided which shows the details of the calculations and analyses performed for the paper. More information is given in the description file.</p>
Studying Bug-Fixing Commits in the WoC Dataset: Replication Package
<p>A replication package for the MSR 2023 Challenge submission titled "Studying Bug-Fixing Commits in the WoC Dataset".</p>
Investigating the visual number form area: A replication study
Open the record for dataset details and reuse information.
An Exploratory Study of Documentation Strategies for Product Features in Popular GitHub Projects [Replication Package]
<h2>Artefact Summary</h2> <p>This repository contains the replication package for the paper 'An Exploratory Study of Documentation Strategies for Product Features in Popular GitHub Projects,' presented at the <em><a href="https://cyprusconferences.org/icsme2022/" target="_blank" rel="noopener">38th IEEE International Conference on Software Maintenance and Evolution (ICSME'22)</a></em>.</p> <p>The purpose of the package is to facilitate the verification and reproduction of the study results.<br>It provides all computational notebooks used to collect and analyse data, as well as the slides of the conference presentation.</p> <h2>Paper Abstract</h2> <p>[Background] In large open-source software projects, development knowledge is often fragmented across multiple artefacts and contributors such that individual stakeholders are generally unaware of the full breadth of the product features. However, users want to know what the software is capable of, while contributors need to know where to fix, update, and add features. [Objective] This work aims at understanding how feature knowledge is documented in GitHub projects and how it is linked (if at all) to the source code. [Method] We conducted an in-depth qualitative exploratory content analysis of 25 popular GitHub repositories that provided the documentation artefacts recommended by GitHub’s Community Standards indicator. We extracted strategies used to document software features in textual artefacts and which strategies were used to link the feature documentation with source code. [Results] We observed feature documentation in all studied projects in artefacts such as READMEs, wikis, and website resource files. However, the features were often described in an unstructured way. Additionally, tracing techniques to connect feature documentation and source code were rarely used. [Conclusions] Our results suggest a lacking (or a low-prioritised) feature documentation in open-source projects, little use of normalised structures, and a rare explicit referencing to source code. As a result, product feature traceability is likely to be very limited, and maintainability to suffer over time.</p> <h2>References</h2> <p>The published paper is available on <a href="https://doi.org/10.1109/ICSME55016.2022.00043" target="_blank" rel="noopener">IEEE Xplore</a> and the preprint on <a href="https://doi.org/10.48550/arXiv.2208.01317" target="_blank" rel="noopener">arXiv</a>.</p>
Replication package for the paper: "A Study on the Pythonic Functional Constructs' Understandability"
<h1>Replication Package for "<em>A Study on the Pythonic Functional Constructs' Understandability</em>" to appear at ICSE 2024</h1> <ul> <li><strong>Authors</strong>: Cyrine Zid, Fiorella Zampetti, Giuliano Antoniol, Massimiliano Di penta</li> <li><strong>Article Preprint:</strong> <a href="https://mdipenta.github.io/files/ICSE24_funcExperiment.pdf">https://mdipenta.github.io/files/ICSE24_funcExperiment.pdf</a></li> <li><strong>Artifacts:</strong> <a href="https://doi.org/10.5281/zenodo.8191782">https://doi.org/10.5281/zenodo.8191782</a></li> <li><strong>License</strong>: <a>GPL V3.0</a></li> </ul> <p>This package contains folders and files with code and data used in the study described in the paper. In the following, we first provide all fields required for the submission, and then report a detailed description of all repository folders.</p> <h2>Artifact Description</h2> <h3>Purpose</h3> <p>The artifact is about a controlled experiment aimed at investigating the extent to which Pythonic functional constructs have an impact on source code understandability. The artifact archive contains:</p> <ol> <li>The material to allow replicating the study (see Section <a>Experimental-Material</a>)</li> <li>Raw quantitative results, working datasets, and scripts to replicate the statistical analyses reported in the paper. Specifically, the executable part of the replication package reproduces figures and tables of the quantitative analysis (RQ1 and RQ2) of the paper starting from the working datasets.</li> <li>Spreadsheets used for the qualitative analysis (RQ3).</li> </ol> <p>We apply for the following badges:</p> <ul> <li><strong>Available and reusable</strong>: because we provide all the material that can be used to replicate the experiment, but also to perform the statistical analyses and the qualitative analyses (spreadsheets, in this case)</li> </ul> <h3>Provenance</h3> <ul> <li><strong>Paper preprint link:</strong> <a href="https://mdipenta.github.io/files/ICSE24_funcExperiment.pdf">https://mdipenta.github.io/files/ICSE24_funcExperiment.pdf</a></li> <li><strong>Artifacts:</strong> <a href="https://doi.org/10.5281/zenodo.8191782">https://doi.org/10.5281/zenodo.8191782</a></li> </ul> <h3>Data</h3> <p>Results have been obtained by conducting the controlled experiment involving <a href="https://www.prolific.com/">Prolific</a>workers as participants. Data collection and processing followed a protocol approved by the University ethical board. Note that all data enclosed in the artifact is completely anonymized and does not contain sensible information.</p> <p>Further details about the provided dataset can be found in the Section <a>Results' directory and files</a></p> <h3>Setup and Usage (for executable artifacts):</h3> <p>See the Section <a>Scripts to reproduce the results, and instructions for running them</a></p> <h2><a>Experiment-Material/</a></h2> <p>Contains the material used for the experiment, and, specifically, the following subdirectories:</p> <h3><a>Google-Forms/</a></h3> <p>Contains (as PDF documents) the questionnaires submitted to the ten experimental groups.</p> <h3><a>Task-Sources/</a></h3> <p>Contains, for each experimental group (G-1...G-10), the sources used to produce the Google Forms, and, specifically: - The cover letter (Letter.docx). - A directory for each experimental task (Lambda 1, Lambda 2, Comp 1, Comp 2, MRF 1, MRF 2, Lambda Comparison, Comp Comparison, MRF Comparison). Each directory contains: (i) the exercise text (in both Word and .txt format), the source code snippet, and its .png image to be used in the form. <strong>Note:</strong> the "Comparison" tasks do not have any exercise as the purpose is always the same, i.e., to compare the (perceived) understandability of the snippets and return the results of the comparison.</p> <h3><a>Code-Examples-Table1/</a></h3> <p>Contains the source code snippets used as objects of the study (the same you can find under "Task-Sources/"), named as reported in Table 1.</p> <h2>Results' directory and files</h2> <p> </p> <h3><a>raw-responses/</a></h3> <p>Contains, as spreadsheets, the raw responses provided by the study participants through Google forms.</p> <h3><a>raw-results-RQ1/</a></h3> <p>Contains the raw results for RQ1. Specifically, the directory contains a subdirectory for each group (G1-G10). Each subdirectory contains: - For each user (named using their Prolific IDs, a directory containing, for each question (Q1-Q6) the produced python code (Qn.py) its output (QnR.txt) and its StdErr output (QnErr.txt). - "expected-outputs/": A directory containing the expected outputs for each task (Qn.txt).</p> <h4><a>working-results/RQ1-RQ2-files-for-statistical-analysis/</a></h4> <p>Contains three .csv files used as input for conducting the statistical analysis and drawing the graphs for addressing the first two research questions of the study. Specifically:</p> <ul> <li> <p><a>ConstructUsage.csv</a> contains the declared frequency usage of the three functional constructs object of the study. This file is used to draw Figure 4. The file contains an entry for each participant, reporting the (text-coded) frequency of construct usage for Comprehension, Lambda, and MRF.</p> </li> <li> <p><a>RQ1.csv</a> contains the collected data used for the mixed-effect logistic regression relating the use of functional constructs with the correctness of the change task, as well as the logistic regression relating the use of map/reduce/filter functions with the correctness of the change task. The csv file contains an entry for each answer provided by each subject, and features the following columns:</p> <ul> <li><em>Group</em>: experimental group to which the participant is assigned</li> <li><em>User</em>: user ID</li> <li><em>Time</em>: task time in seconds</li> <li><em>Approvals</em>: number of approvals on previous tasks performed on Prolific</li> <li><em>Student</em>: whether the participant declared themselves as a student</li> <li><em>Section</em>: section of the questionnaire (lambda, comp, or mrf)</li> <li><em>Construct</em>: specific construct being presented (same as "Section" for lambda and comp, for mrf it says whether it is a map, reduce, or filter)</li> <li><em>Question</em>: question id, from Q1 to Q6, indicate the ordering of the question</li> <li><em>MainFactor</em>: main factor treatment for the given question - "f" for functional, "p" for procedural counterpart</li> <li><em>Outcome</em>: TRUE if the task was correctly performed, FALSE otherwise</li> <li><em>Complexity</em>: cyclomatic complexity of the construct (empty for mrf)</li> <li><em>UsageFrequency</em>: usage frequency of the given construct</li> </ul> </li> <li> <p><a>RQ1Paired-RQ2.csv</a> contains the collected data used for the ordinal logistic regression of the relationship between the perceived ease of understanding of the functional constructs and (i) participants' usage frequency, and (ii) constructs' complexity (except for map/reduce/filter). The file features a row for each participant, and the columns are the following:</p> <ul> <li><em>Group</em>: experimental group to which the participant is assigned</li> <li><em>User</em>: user ID</li> <li><em>Time</em>: task time in seconds</li> <li><em>Approvals</em>: number of approvals on previous tasks performed on Prolific</li> <li><em>Student</em>: whether the participant declared themselves as a student</li> <li><em>LambdaF</em>: result for the change task related to a lambda construct</li> <li><em>LambdaP</em>: result for the change task related to the procedural counterpart of a lambda construct</li> <li><em>CompF</em>: result for the change task related to a comprehension construct</li> <li><em>CompP</em>: result for the change task related to the procedural counterpart of a comprehension construct</li> <li><em>MrfF</em>: result for the change task related to an MRF construct</li> <li><em>MrfP</em>: result for the change task related to the procedural counterpart of a MRF construct</li> <li><em>LambdaComp</em>: perceived understandability level for the comparison task (RQ2) between a lambda and its procedural counterpart</li> <li><em>CompComp</em>: perceived understandability level for the comparison task (RQ2) between a comprehension and its procedural counterpart</li> <li><em>MrfComp</em>: perceived understandability level for the comparison task (RQ2) between a MRF and its procedural counterpart</li> <li><em>LambdaCompCplx</em>: cyclomatic complexity of the lambda construct involved in the comparison task (RQ2)</li> <li><em>CompCompCplx</em>: cyclomatic complexity of the comprehension construct involved in the comparison task (RQ2)</li> <li><em>MrfCompType</em>: type of MRF construct (map, reduce, or filter) used in the comparison task (RQ2)</li> <li><em>LambdaUsageFrequency</em>: self-declared usage frequency on lambda constructs</li> <li><em>CompUsageFrequency</em>: self-declared usage frequency on comprehension constructs</li> <li><em>MrfUsageFrequency</em>: self-declared usage frequency on MRF constructs</li> <li><em>LambdaComparisonAssessment</em>: outcome of the manual assessment of the answer to the "check question" required for the lambda comparison ("yes" means valid, "no" means wrong, "moderate<em>chatgpt" and "extreme</em>chatgpt" are the results of GPTZero)</li> <li><em>CompComparisonAssessment</em>: as above, but for comprehension</li> <li><em>MrfComparisonAssessment</em>: as above, but for MRF</li> </ul> </li> </ul> <h3><a>working-results/inter-rater-RQ3-files/</a></h3> <p>This directory contains four .csv files used as input for computing the inter-rater agreement for the manual labeling used for addressing RQ3. Specifically, you will find one file for each functional construct, i.e., comprehension.csv, lambda.csv, and mrf.csv, and a different file used for highlighting the reasons why participants prefer to use the procedural paradigm, i.e., procedural.csv.</p> <h3><a>working-results/RQ2ManualValidation.csv</a></h3> <p>This file contains the results of the manual validation being done to sanitize the answers provided by our participants used for addressing RQ2. Specifically, we coded the behaviour description using four different levels: (i) correct ("yes"), (ii) somewhat correct ("partial"), (iii) wrong ("no"), and (iv) automatically generated. The file features a row for each participant, and the columns are the following:</p> <ul> <li><em>ID</em>: ID we used to refer the participant in the paper's qualitative analysis</li> <li><em>Group</em>: experimental group to which the participant is assigned</li> <li><em>ProlificID</em>: user ID</li> <li><em>Comparison for lambda construct description</em>: answer provided by the user for the lambda comparison task</li> <li><em>Final Classification</em>: our assessment of the lambda comparison answer</li> <li><em>Comparison for comprehension description</em>: answer provided by the user for the comprehension comparison task</li> <li><em>Final Classification</em>: our assessment of the comprehension comparison answer</li> <li><em>Comparison for MRF description</em>: answer provided by the user for the MRF comparison task</li> <li><em>Final Classification</em>: our assessment of the MRF comparison answer</li> </ul> <h3><a>working-results/RQ3ManualValidation.xlsx</a></h3> <p>This file contains the results of the open coding applied to address our third research question. Specifically, you will find four sheets, one for each functional construct and one for the procedural paradigm. Each sheet reports the provided answers together with the categories assigned to them. Each sheet contains the following columns:</p> <ul> <li><em>ID</em>: ID we used to refer the participant in the paper's qualitative analysis</li> <li><em>Group</em>: experimental group to which the participant is assigned</li> <li><em>ProlificID</em>: user ID (as in the tables from the quantitative analysis)</li> <li>: question asked to the user</li> <li><em>Final Classification</em>: The outcome of our categorization according to the taxonomy shown in Table 10.</li> </ul> <h2>Scripts to reproduce the results and instructions for running them</h2> <p> </p> <h3><a>FuncConstructs-Statistics.r</a></h3> <p>This file contains an R script that you can reuse to re-run all the analyses conducted and discussed in the paper.</p> <h3><a>FuncConstructs-Statistics.ipynb</a></h3> <p>This file contains the code to re-execute all the analysis conducted in the paper as a Jupyter Notebook (using the R Kernel).</p> <h3><a>run-analysis.sh</a></h3> <p>This script can be used to run the R script <code>FuncConstructs-Statistics.r</code> using a Docker container (see Option 1 below) in Unix operating systems.</p> <h3><a>run-analysis.bat</a></h3> <p>This script can be used to run the R script <code>FuncConstructs-Statistics.r</code> using a Docker container (see Option 1 below) in Windows operating systems (power shell recommended).</p> <h3><a>run-jupyter-container.sh</a></h3> <p>This script can be used to run a local Jupyter server (with R kernel and all required packages) from a Docker container (see Option 3 below) in Unix operating system.</p> <h3><a>run-jupyter-container.bat</a></h3> <p>This script can be used to run a local Jupyter server (with R kernel and all required packages) from a Docker container (see Option 3 below) in Windows operating systems (power shell recommended).</p> <h3>How to Run the scripts</h3> <p>There are four options to run the scripts. In all cases, one has first to open a shell terminal window (e.g., bash or sh in Unixes) in the replication package directory. For Windows, we suggest to use a Power Shell.</p> <ol> <li> <p><strong>Running the R script using Dockerized R installation</strong>: this is the simplest option, and it simply requires a running Docker engine. In <strong>Unix (MacOS, Linux)</strong>, to produce the results, one has to run the shell script "run-analysis.sh" (e.g., by typing <code>sh run-analysis.sh</code> or simply <code>./run-analysis.sh</code> after making it executable). In <strong>Windows</strong>, one has to run the script "run-analysis.bat" instead (by typing <code>.\run-analysis.bat</code>). This script (either .sh or .bat) will:</p> <ul> <li>Pull a docker image named <code>mdipenta/rexp</code> which contains an R installation with all required packages.</li> <li> <p>Run R from the container created from the image and produce the paper's results under a directory named <code>results/</code>.</p> </li> <li> <p><strong>Note:</strong> An alternative would be to run everything from inside the container, after running it in interactive mode. To this aim, please execute the following commands:</p> <ol> <li>In <strong>Unix</strong>: <code>docker run -v${PWD}:/data --rm -ti --name shell mdipenta/rexp:latest bash</code>in <strong>Windows</strong>: <code>docker run -v %cd%:/data --rm -ti --name shell mdipenta/rexp:latest bash</code></li> <li><code>cd data</code></li> <li><code>R --no-save < FuncConstructs-Statistics.r</code> After exiting the container, the "results" directory will be again populated with the study results.</li> </ol> </li> </ul> </li> <li> <p><strong>Running the R script from own R installation</strong>: this option works if one has an R installation already (or wants to use an R installation) without relying on the Docker image. The steps to be followed are:</p> <ul> <li>Uncomment the <code>install.packages(..)</code> instruction in the first lines of the script. This will allow for the installation of the required packages.</li> <li>Just run, from the current directory, the script <code>FuncConstructs-Statistics.r</code>, using the command <code>Rscript FuncConstructs-Statistics.r</code> (making sure the directory containing Rscript is in your PATH, this should work fine in Unixes, it might require to modify the PATH environment variable in Windows). Should you experience problems with the first part of the script (installations), try to execute the <code>install.packages(..)</code> statement from your R GUI, and then run the script again.</li> </ul> </li> <li> <p><strong>Using the Jupyter Notebook using a Dockerized Jupyter lab with R kernel</strong>: this option allows for opening the Jupyter Notebook with all results without having to install Jupyter with the R kernel, nor all the required R packages. The steps required are:</p> <ul> <li>Run the <code>./run-jupyter-container.sh</code> (<strong>Unix</strong>) or <code>.\run-jupyter-container.bat</code> (<strong>Windows</strong>). It will download the <code>mdipenta/myjupyter</code> image and run Jupyter lab from it.</li> <li>Open a browser on <a href="http://localhost:8888/">localhost:8888</a> (or if it does not work, <a href="http://127.0.0.1:8888/">127.0.0.1:8888)</a> and, when being asked for a password, type <code>docker</code>.</li> <li>From the Jupyter lab page, open the "FuncConstruct-Statistics.ipynb" notebook, and (if you wish) re-run it, or simply browse its results. Note: differently from options 1 and 2, results are not saved, but just displayed in the notebook.</li> </ul> </li> <li> <p><strong>Using the Jupyter Notebook from your installation</strong>: this is similar to Option 3, but it can work if you have already Jupyter lab installed, with the R kernel enabled (for details see: <a href="https://github.com/IRkernel/IRkernel">https://github.com/IRkernel/IRkernel</a>). The steps to follow are:</p> <ul> <li>Run jupyter lab (e.g., jupyter lab from the command line) and open it on a webpage.</li> <li>Open the <code>FuncConstruct-Statistics.ipynb</code> notebook.</li> <li>If you want to re-execute it, uncomment the <code>install.packages()</code> line.</li> <li>Re-run it (if you wish).</li> </ul> </li> </ol> <h3>The output</h3> <p>If using Option 1 or 2, the <code>results</code> directory will contain the following files:</p> <ul> <li><strong>Figures 4 and 5</strong> as in the paper.</li> <li><strong>Tables 2-9</strong> as in the paper in various formats (csv, tex, and for Tables 3-5 also .txt). Some notes: The diagnostics (top part, up to "Fixed effects") for Tables 3-5 are shown in the .txt files only. However, these files do not report the "OR" columns that correspond to exp(Estimate). This is because the .txt file contains the statistics dump which does not include the ORs. The .csv and .tex tables report the Fixed effects as shown in the paper, including the ORs.</li> <li><strong>rq1-rq2-correlation</strong> (.tex and .csv) contains the correlation analysis between RQ1 and RQ2 results as discussed in the "Threats to construct validity" (Section 6).</li> <li><strong>rq3-inter-rater</strong> (.tex and .csv) contains the results of the inter-rater agreements analysis discussed in Section 3.6.</li> </ul>
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>
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>
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 An Extended Study of Syntactic Breaking Changes in the Wild
<p>This is the replication package associated with the paper titled 'An Extended Study of Syntactic Breaking Changes in the Wild' published under the Empirical Software Engineering journal.<br>Modern software applications rely heavily on the usage of libraries, which provide reusable functionality, to accelerate the development process. As libraries evolve and release new versions, the software systems that depend on those libraries (the clients) should update their dependencies to use these new versions as the new release could, for example, include critical fixes for security vulnerabilities. However, updating is not always a smooth process, as it can result in software failures in the clients if the new version<br>includes breaking changes. Yet, there is little research on how these breaking changes impact the client projects in the wild. <br>To identify if changes between two library versions cause breaking changes at the client end, we perform an empirical study on Java projects built using Maven. For the analysis, we used 18,415 Maven artifacts, which declared 142,355 direct dependencies, of which 71.60% were not up-to-date. We updated these dependencies and found<br>that 11.58% of the dependency updates contain breaking changes that impact the client. We further analyzed these changes in the library which impact the client projects and examine if libraries have adhered to the semantic versioning scheme when introducing breaking changes in their releases. Our results show that changes in transitive dependencies were a major factor in introducing breaking changes during dependency updates and almost half of the detected client impacting breaking changes violate the semantic versioning scheme by introducing breaking changes in non-Major upda</p>
Replication Data for the retroharmonize R Package Case Study: Working With Arab Barometer Surveys
<p>Replication datasets for the <a href="https://retroharmonize.dataobservatory.eu/articles/arabbarometer.html">retroharmonize Case Study: Working With Arab Barometer Surveys</a></p>
Escherichia coli DNA replication study: processed alignment data
<p>Genomes are replicated by large protein complexes called replisomes. In bacterial DNA replication, two replisomes replicate the DNA starting from the same origin site and proceeding in opposite directions. Understanding their movement in vivo has been challenging. We used quantitative genome sequencing to characterize the dynamics of bacterial replisomes at 5 different temperatures (17, 22, 27, 32 and 37 °C) in exponential growth (3 replicates) or in stationary phase (one experiment at 17, 27 and 37 °C).</p> <p>The data deposited here give the coordinates of the sequence reads (deposited under the BioProject PRJNA772106) covering the Escherichia coli str. K-12 substr. MG1655 complete genome (accession number U00096.3).</p> <p>The file archive contains data files for each sample, at nucleotide resolution and binned in intervals of 10,000 base pairs. It also contains a C program to perform the binning and a README summarising how the alignment was done. <em>Please note that once uncompressed, the data will take 5 Gb of disks space in total.</em></p>
Replication Package: Model-Driven Engineering for the Interoperability of Simulation Modeling Languages: a Case Study in the Space Industry
<p>Replication package "Architectural Support for Software Performance in Continuous Software Engineering: a Systematic Mapping Study".</p>
Replication package for the study "Digital Sufficiency in Flexible Work"
<p>This dataset is published for transparency and open data purposes, as part of the work "<em>'We are always on, is that really necessary?' Exploring the Path to Digital Sufficiency in Flexible Work</em>" published at <a href="https://conf.researchr.org/home/ict4s-2023">ICT4S 2023</a> conference.</p> <p><strong>The study:</strong></p> <p>We conducted three focus groups with a total of 11 participants, inside two different companies. Our aim was to investigate the notion of digital sufficiency in the context of flexible work.</p> <p><strong>Content:</strong></p> <ul> <li><em>Preliminary interview guide.pdf</em>: the interview guide containing the questions for the preliminary interview</li> <li><em>Focus group slides.pdf</em>: the slides presented to the participants of the three focus groups</li> <li><em>Focus group participant print-outs.pdf</em>: the sheets distributed to the participants of the three focus groups, for individual note-taking</li> <li><em>Codebook.xlsx</em>: the codes extracted (with the help of the software <a href="http://www.saturateapp.com">Saturate</a>) from the transcripts of the three focus groups: <ul> <li>tab <em>CODEBOOK</em>: the codes, sorted by three levels and by research question, along with their definition and a description on when the code is applicable,</li> <li>tab <em>Code Count</em>: the number of occurrence of each code in the transcripts,</li> <li>tab <em>Clustering Tactics</em>: the thematic analysis performed on the codes with level 1 "tactic". Corresponds to the Table III in the paper</li> </ul> </li> </ul> <p>For privacy reason, all names are removed and the transcript and audio recording are not part of this replication package.</p>
Dataset to "Loneliness is associated with retrospective self-reports of adverse childhood experiences – A replication study in Ethiopia"
<p>Find our SPSS Dataset belonging to the yet to be published work <strong>“Loneliness is associated with retrospective self-reports of adverse childhood experiences – A replication study in Ethiopia”</strong>. This cross-sectional study is investigating the interplay between adverse childhood experiences, loneliness, social network size, and mental health problems in Ethiopia in a clinical and non-clinical group. The study sample consists of 125 psychiatric outpatients at Jimma University Medical Center, South-West of Ethiopia, suffering from major depressive disorder, bipolar disorder, or psychotic disorders, and 131 non-clinical study participants. Led by an interviewer, participants completed the UCLA Loneliness Scale, the Childhood Trauma Questionnaire, the Social Network Index, and the WHO-5 Well-Being Index. </p> <p>Definitions of the variable values and additional information can be found in the attached Codebook.</p>
ScienceDex guides
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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.