Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
30
datasets available to search
ShareScore release 0.9.0
Dataset results
30 results for “reproducibility package”
Reproduction package for paper "How far are we from reproducible research on code smell detection? A systematic literature review"
<p>Checklist and data extracted from publications analyzed for "How far are we from reproducible research on code smell detection? A systematic literature review" paper, together with processing scripts and calculations of Cohen's Kappa.</p> <p>Paper that describes details of the data is available here: https://doi.org/10.1016/j.infsof.2021.106783</p>
Reproducibility package for Using root economics traits to predict biotic plant soil-feedbacks
<p>Using root economics space to predict biotic plant soil-feedbacks presents a novel framework linking below ground ecological theory to plant soil feedback effects. We show how to calculate root functional distance and location of two plant species in root economics space and how these measures can help to predict the strength and direction of the plant soil feedback between them. </p> <p>Contains data and scripts to reproduce analysis and figures for the manuscript (https://github.com/ggpmrutten/linkingRES-PSF)</p>
Reproducibility Package: Automata-Driven Partial Order Reduction and Guided Search for LTL
<p>Reproducibility package for the paper <strong>Automata-Driven Partial Order Reduction and Guided Search for LTL Model Checking</strong> accepted for VMCAI'22.</p> <p>Contains scripts, 64-bit Linux binaries, and the dataset used to run the experiments detailed in the paper, a source code snapshot used to compile those binaries, as well as scripts for processing the results into the tables seen in the paper. The file README.md contains details on running the various parts of the artifacts, assuming a Linux environment.</p>
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>
Solutions for Reproducibility in Empirical Research: Virtual Machines, Containers, Environment Management Packages, and Cloud Platforms
<p>This image provides a comprehensive overview of various technologies and platforms used to enhance the reproducibility of empirical research. It is divided into several sections:</p> <ol> <li><strong>Virtual Machines (VMs): </strong>the left section of the image illustrates the architecture of VMs with Type 1 and Type 2 hypervisors. <br> - <em>Type 1 Hypervisor </em>runs directly on the hardware, providing high efficiency and performance. Examples include VMware ESXi, <strong>Microsoft Hyper-v</strong>, and Xen Project.<br> - <em>Type 2 Hypervisor</em> runs on an existing operating system, offering flexibility at the cost of some performance. Examples include <strong>Oracle VirtualBox</strong>, VMware Workstation, and Parallels.</li> <li><strong>Containers: </strong>the middle section of the image explains the containerization concept, which shares the host operating system's kernel, making containers more lightweight than VMs. Technologies like <strong>Docker</strong> and <strong>Kubernetes</strong> are shown as popular solutions for container orchestration.</li> <li><strong>Environment Management Packages: </strong>the top right section focuses on tools for managing software dependencies and environments. <strong>renv</strong> (for R) and <strong>Conda</strong> (for Python and other languages) are highlighted as key tools for creating reproducible research environments.</li> <li>Cloud Platforms: the bottom right section features various cloud-based platforms that facilitate reproducible research by providing scalable and shareable computational environments. Platforms include <strong>Google Colab</strong>, <strong>Posit Cloud</strong>, JupyterHub, <strong>Binder</strong>, Nextjournal, OpenShift, and <strong>Code Ocean</strong>.</li> </ol> <p>Together, these solutions provide a robust framework for ensuring that empirical research can be reliably reproduced and validated by others, addressing the challenges of dependency management, environment consistency, and computational resource availability.</p>
Reproducibility package for: Badges for Geoscience Containers
<p>This repository contains demo data and software for research in using badges to improve discovery for scholarly publications in the geosciences. The web service "badger" provides an HTTP API for retrieving several types of badges based on a publications digital object identifier (DOI). The "extender" client integrates with the web browser and enhances existing platforms for discovery of publications with badges independent of the platforms code.</p> <p>Included in the archive are the source code of the required web services and client as well as ready to use Docker containers (including database and required web service with test data) to reproduce the results with minimal system requirements.</p> <p>Background on the project is provided in a peer-reviewed short paper from the AGILE conference 2019, <a href="https://doi.org/10.31223/osf.io/xtsqh">https://doi.org/10.31223/osf.io/xtsqh</a> , and in the blog post <a href="https://o2r.info/2017/09/12/reproducible-research-badges/">https://o2r.info/2017/09/12/reproducible-research-badges/</a>.</p> <p>For reproduction, follow the instructions in README.md and Makefile.and means to download the required software to enhance research websites with badges for reproducible research papers.</p>
Reproducibility Package for "Reproducible research and GIScience: an evaluation using AGILE conference papers"
<p>Data and code for analysis and plots used in the manuscript "Reproducible research and GIScience: an evaluation using AGILE conference papers": <a href="https://doi.org/10.7287/peerj.preprints.26561v1">https://doi.org/10.7287/peerj.preprints.26561v1</a></p> <p>The deposited archived includes a <a href="https://en.wikipedia.org/wiki/Docker_(software)">Dockerfile</a> and an <a href="http://rmarkdown.rstudio.com/">R Markdown</a> document suitable for use with <a href="http://mybinder.org/">Binder</a>: <a href="https://mybinder.org/v2/gh/nuest/reproducible-research-and-giscience/6">https://mybinder.org/v2/gh/nuest/reproducible-research-and-giscience/6</a></p> <p>The version tag of this repository matches the <a href="https://git-scm.com/book/en/v2/Git-Basics-Tagging">git tag</a> on the code repository at <a href="https://github.com/nuest/reproducible-research-and-giscience">https://github.com/nuest/reproducible-research-and-giscience</a>, except version <code>6-fixed</code> which matches the tag <code>6</code>.</p> <p> </p>
Reproducibility package for the KDD 2019 paper "Pairwise Comparisons with Flexible Time-Dynamics"
<p>This archive contains the data necessary to reproduce most experiments and all plots presented in the following paper:</p> <p>Lucas Maystre, Victor Kristof, Matthias Grossglauser, <a href="https://arxiv.org/abs/1903.07746">Pairwise Comparisons with Flexible Time-dynamics</a>, KDD 2019.</p>
Design and Development of a Smartphone-Based Geolocalized Exposure Therapy Software for Anxiety Disorders: SyMptOMS-ET -- Reproducibility Package
<p>R Notebook and datasets for the submitted paper "<em>Towards a self-applied, mobile-based geolocated exposure therapy software for anxiety disorders: SyMptOMS-ET app</em>"</p> <blockquote> <p>Alberto González-Pérez, Laura Diaz-Sanahuja, Miguel Matey-Sanz, Jorge Osma, Carlos Granell, Juana Bretón-López, Sven Casteleyn. Towards a self-applied, mobile-based geolocated exposure therapy software for anxiety disorders: SyMptOMS-ET app. <a href="https://journals.sagepub.com/home/dhj">Digital Health Journal</a> [Submitted]</p> </blockquote> <p>Experiments were conducted using the v1.2.0 version of the SyMptOMS-ET open-source app, which can be found <a href="https://github.com/GeoTecINIT/symptoms-mobile-app/releases/tag/v1.2.0">here</a>.</p>
Reproducibility Package for TACAS'21 Paper Resilient Capacity-Aware Routing
<p>This package contains all the necessary information for the reproduction of the experimental results in the paper "Resilient Capacity-Aware Routing" accepted for TACAS'21. In particular we provide all the python scripts that we used, their dependencies and the shell scripts for running the experiments or its subset.</p>
"Constructing Temporal Networks of OSS Programming Language Ecosystems" reproducibility package
<p>Reproducibility package for the paper submission "Constructing Temporal Networks of OSS Programming Language Ecosystems". Contains the dataset, extracted and external metrics, and all scripts used for the construction and the analysis done in the paper.</p> <p> </p> <p>Anonymised for the double-blind review process.</p>
Reproducibility package for: AllSynth: Transiently Correct Network Update Synthesis Accounting for Operator Preferences
<p>This is a reproducibility package for the paper "AllSynth: Transiently Correct Network Update Synthesis Accounting for Operator Preferences" to appear in TASE'22.</p>
Adaptive Caching for Operation-based Versioning of Models (Reproducibility Package)
Open the record for dataset details and reuse information.
How to Drawjectory? - Trajectory Planning using Programming by Demonstration (Reproducibility Package)
<p>The experiments were conducted following the scenario description from <em>scenario_description.pdf. </em>The scripts containing the DSL commands used for programming the trajectories can be found under <em>/dsl_scripts.</em></p> <p>The evaluation was conducted using the R (v3.6.3 and tested on 4.4.1 too) programming language. The main script is <em>experiments/evaluation.R</em> which includes and sources all other necessary scripts. To re-run this script, make sure to open the R project (<em>experiments/xperiment.Rproj</em>) and have the following packages installed (note: other versions may also work): </p> <ul> <li>jsonlite (1.8.8)</li> <li>magrittr (2.0.3)</li> <li>dplyr (1.1.4)</li> <li>ggplot2 (3.5.1)</li> <li>wesanderson (0.3.7)</li> <li>pracma (2.4.4)</li> <li>SimilarityMeasures (1.4)</li> <li>progress (1.2.3)</li> </ul>
Reproducibility Package for Predictive Limitations of Physics-Informed Neural Networks in Vortex Shedding
<p>This archive contains the repro-pack for the paper at <a href="http://github.com/barbagroup/jcs_paper_pinn">https://github.com/barbagroup/jcs_paper_pinn</a>.</p>
Reproducibility Package (Raw Data) for Predictive Limitations of Physics-Informed Neural Networks in Vortex Shedding
<p>Raw data results from the simulations using PINN methods and PetIBM for the paper at: https://github.com/barbagroup/jcs_paper_pinn</p>
Reproduction package for "Revisiting the reproducibility of empirical software engineering studies based on data retrieved from development repositories"
<p>Reproduction package for "Revisiting the reproducibility of empirical software engineering studies based on data retrieved from development repositories", published in Information and Software Technology, Volume 164, December 2023. DOI: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.infsof.2023.107318" target="_blank" rel="noreferrer noopener"><span><span>https://doi.org/10.1016/j.infsof.2023.107318</span></span></a></p>
Investigating the Reproducibility of NPM Packages
<p>Open-source Dataset</p> <p><br> This dataset contains the NPM packages that we built using our tool-chain. It consists of the diffoscope outputs, the versions built by our tool-chain, and the pre-built packages present on the npmjs registry.</p>
Reproducibility package for "Super-Tonks-Girardeau Quench in the Extended Bose-Hubbard Model"
Open the record for dataset details and reuse information.
Reproducibility Package for SyRep: Efficient Synthesis and Repair of Fast Re-Route Forwarding Tables for Resilient Networks
<p>This is a reproducibility package for the paper SyRep: Efficient Synthesis and Repaird of Fast Re-Route Forwarding Tables for Resilient Networks that is going to appear at DSN'24 conference.</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.