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.
113
datasets available to search
ShareScore release 0.9.0
Dataset results
113 results for “research software”
BEE-STEWARD: a research and decision support software for effective land management to promote bumblebee populations
Open the record for dataset details and reuse information.
Experimental Data for: Research Perspective on Supporting Software Engineering via Physical 3D Models
<p>Experimental data for the experiment presented in the technical report 1507: "Research Perspective on Supporting Software Engineering via Physical 3D Models"</p>
Understanding Software in Research: Examining Nature Articles
<p>The dataset contains a review of 40 <em>Nature </em>articles, consisting of an assessment of all of the research articles from January, February, and March of 2016. The articles were scored and metrics were collected on each individual and distinct mention of software in the articles. The mentions were characterized on how they were cited. Values pertaining to the overall collection of articles were also calculated. </p>
Dataset for "Beyond Self-Promotion: How Software Engineering Research Is Discussed on LinkedIn"
<p>This repository contains the artifacts of our study on how software engineering research papers are shared and interacted with on LinkedIn, a professional social network. This includes:</p> <ul> <li><em>included-papers.csv</em>: the list of the 79 ICSE and FSE papers we found on LinkedIn</li> <li><em>linkedin-post-data.csv</em>: the final data of the 98 LinkedIn posts we collected and synthesized</li> <li><em>linkedin-post-scraping.zip</em>: the scripts used to automatically collect several attributes of the LinkedIn posts</li> <li><em>analysis.zip</em>: the Jupyter notebook used to analyze and visualize <em>linkedin-post-data.csv</em></li> </ul>
Data from: Research and exploratory analysis driven - time-data visualization (read-tv) software
<strong><em>read-tv</em></strong> <p>The main paper is about, <em>read-tv</em>, open-source software for longitudinal data visualization. We uploaded sample use case surgical flow disruption data to highlight <em>read-tv</em>'s capabilities. We scrubbed the data of protected health information, and uploaded it as a single CSV file. A description of the original data is described below.</p> Data source <p>Surgical workflow disruptions, defined as "<i>deviations from the natural progression of an operation thereby potentially compromising the efficiency or safety of care", </i>provide a window on the systems of work through which it is possible to analyze <u>mismatches between the work demands and the ability of the people to deliver the work</u>. They have been shown to be sensitive to different intraoperative technologies, surgical errors, surgical experience, room layout, checklist implementation and the effectiveness of the supporting team. The significance of flow disruptions lies in their ability to provide a hitherto unavailable perspective on the quality and efficiency of the system. This allows for a systematic, quantitative and replicable assessment of risks in surgical systems, evaluation of interventions to address them, and assessment of the role that technology plays in exacerbation or mitigation.</p> <p>In 2014, Drs Catchpole and Anger were awarded NIBIB R03 EB017447 to investigate flow disruptions in Robotic Surgery which has resulted in the detailed, multi-level analysis of over 4,000 flow disruptions. Direct observation of 89 RAS (robitic assisted surgery) cases, found a mean of 9.62 flow disruptions per hour, which varies across different surgical phases, predominantly caused by coordination, communication, equipment, and training problems.</p>
Terms4FAIRskills - an overview for the Research Data Alliance 'Birds of a Feather' session, Skills and training curriculums to support FAIR for Research Software
<p>Short talk (7m) on the terms4FAIRskills initiative for the Research Data Alliance 'Birds of a Feather' session, Skills and training curriculums to support FAIR for Research Software, at RDA plenary 18 on 3 Nov 2021.</p> <p>Please see https://www.rd-alliance.org/skills-and-training-curriculums-support-fair-research-software for more information on the BoF session. Please see https://terms4fairskills.github.io/ for more information on the terms4FAIRskills initiative.</p>
Social Science Theories in Software Engineering Research - Replication Package
<p>Replication package for the article "Social Science Theories in Software Engineering Research".</p> <p>See README file for additional details.</p>
How have views on Software Quality differed over time? Research and practice viewpoints (Replication package)
<p>Theoretical and practical viewpoints on the quality of code snippets.</p>
Euro PM2021: Use of research software
<p>For the presentation „<a href="https://zenodo.org/deposit/7127468">Wissenschaftliche Forschungssoftware nachhaltig entwickeln und nutzen</a>“ I examined the extent to which the used software is mentioned in the <a href="https://europm2021.com/">Euro PM2021</a> conference proceedings.</p> <p>The three sheets of the „Software at Euro PM2021.xlsx“ file contain the following information:</p> <ul> <li>„Euro PM2021 Suchbegriffe“: the search strings used for finding software. Searching was done with <a href="https://github.com/phiresky/ripgrep-all">ripgrep-all</a> v0.9.6 on the pdf files from the official <a href="https://www.epma.com/publications/euro-pm-proceedings/category/euro-pm2021-congress-proceedings">Euro PM2021 Congress Proceedings</a> available from EPMA.</li> <li>„Euro PM2021 Paper mit Software“: all the papers that yielded some results.</li> <li>„Abouaf-Implementierung“: This has nothing to do with Euro PM2021, but is another part of the mentioned presentation. This is a (albeit most likely not complete) list of scientific literature that (re-)implement the Abouaf densification model for Hot Isostating Pressing.</li> </ul>
The Research Software Community Landscape in the Global South [Video]
<p>The Research Software Community Landscape in the Global South</p> <p><strong>Presentation and Video DOI: 10.5281/zenodo.7192692</strong></p> <p><strong>Watch the Video: <a href="https://youtu.be/pxmYroTxz-A">https://youtu.be/pxmYroTxz-A</a></strong></p> <p>Description:</p> <p>The Research Software Alliance's (ReSA) mission is to bring research software communities together to collaborate on the advancement of research software. Given the ReSA mission, it is important to understand the landscape of communities involved with research software. In 2020, ReSA completed an initial exercise to scope the international research software community landscape. This work was reported by ReSA's Software Landscape Analysis task force via a blog post. The majority of the communities in the previous analysis represented the global north. To improve the extent of this landscape analysis, ReSA announced a paid opportunity for short-term contractors located in the global south to collect data on communities and funders in their region in early 2022. This document describes how the work was undertaken, a summary of findings, the gaps and opportunities perceived by the data collectors and some highlights. This work identified 126 organisations and communities and 62 funder bodies that support research software in the global south. Their main activities are connecting people, training, and networking, and support through research grants.</p> <p>Blog post: <a href="https://www.researchsoft.org/blog/2022-10/">https://www.researchsoft.org/blog/2022-10/</a></p> <p>Please cite this work as: Martinez, Paula Andrea. (2022). The Research Software Community Landscape in the Global South. Zenodo. <a href="https://doi.org/10.5281/zenodo.7179892">https://doi.org/10.5281/zenodo.7179892</a></p> <p>Martinez, Paula Andrea. (2022). Research Software Communities Global South [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7179807">https://doi.org/10.5281/zenodo.7179807</a></p> <p>Martinez, Paula Andrea. (2022). Research Software Funders Global South [Data set]. Zenodo. <a href="https://doi.org/10.5281/zenodo.7179867">https://doi.org/10.5281/zenodo.7179867</a></p> <p> </p> <p>To add to the funders list please fill in the following form: <a href="https://forms.gle/CJWo24MUCjhWKh9U8">https://forms.gle/CJWo24MUCjhWKh9U8</a></p> <p>To add to the communities list please fill in the following form <a href="https://forms.gle/KJE9vkBnM6vhh7cEA">https://forms.gle/KJE9vkBnM6vhh7cEA </a></p>
On a Microservice System Benchmark with Multiple Architected Variants for Software Engineering Research
Open the record for dataset details and reuse information.
Dataset about Reproducibility in Software Engineering Research: A Systematic Mapping Study
<p>This artifact contains the results collected in one Systematic Mapping Study (SMS), about Reproducibility in Software Engineering Research. The results are associated with the selected studies and with the research questions considered.</p>
Comparing the Use of Research Resource Identifiers and Natural Language Processing for Citation of Databases, Software and Other Digital Artifacts
<p><strong>The Research Resource Identifier was introduced in biomedicine in 2014 to more precisely identify the reagents and tools used in published biomedical research and to track use of tools across the breadth of the biomedical literature. The current RRID specification covers key biological and digital resources. Authors are instructed to include an RRID after the first mention of any resource used. RRIDs are designed to be easy to find using a full text search search engine. </strong></p> <p><strong>The published data sets were used in our comparative study where comparing the output of our RRID curation workflow with the outputs of automated text mining systems that have been used to identify mentions of resources in the text of publications. All files in tab-separated format (tsv). </strong></p> <p><strong>Scibot.tsv: Records of the RRID curation workflow using SciBot. </strong></p> <p>Each record shows that a resource RRID was identified in paper PMID with curator tags (Tag1, Tag2, both optional)</p> <p><strong> </strong>PMID: Pubmed ID</p> <p> RRID: Research Resource Identifier</p> <p> Tag1: Curator tags (optional)</p> <p> Tag2: Additional curator tags (optional)</p> <p><strong>rdwsorted.tsv: Records of the output from RDW, a text mining software. </strong></p> <p>RDW identifies mentions of research resources in papers. Each record shows that a resource RRID was identified in paper PMID.</p> <p><strong> </strong>PMID: Pubmed ID</p> <p> RRID: Research Resource Identifier</p> <p><strong>rridbyrdw05282019.tsv: Records of the output of the RRID-by-RDW in RDW. </strong></p> <p>RRID-by-RDW is a component in RDW that identifies mentions of research resources in papers by matching patterns of RRID specifications. Each record shows that a resource RRID was identified in paper PMID.</p> <p><strong> </strong>PMID: Pubmed ID</p> <p> RRID: Research Resource Identifier</p> <p> Context: Snippet where the RRID was found</p> <p><strong>resource_metadata20190418.tsv: Metadata of RRIDs</strong></p> <p>This file contains metadata of resources and their RRIDs. See file header for column definitions.</p> <p><strong>RRIDCUR-definitions.tsv: Definitions of curator tags used in Scibot.tsv.</strong></p> <p><strong> </strong>tag: Tag name</p> <p> definition: Definition of the tag</p>
Survey on how do Brazilian Software Engineering Researchers Perceive and Practice Open Science
<p>This document provides our survey questionnaire and the responses of 31 researchers.</p>
Topic modeling in software engineering research
<p>Raw data collected from 111 papers applying topic modeling techniques in software engineering studies.</p>
(re)Use Indications of High Energy Physics related Research Data and Software in Zenodo
<p>This dataset contains High Energy Physics related research data and software (re)use indications (formal citations, informal mentions) in scholarly works. All research data and software resources were identified and extracted from Zenodo. The (re)use indications were identified by a mix of approaches: use of citation discovery services and multiple search approaches in Google Scholar. All identified research data and software (re)use indications were classified according to their purpose, location, and elements.</p> <p>The data was collected in 2018 for a PhD thesis on research data and software (re)use indications in scholarly works.</p>
Comparing the Intensity of Variability Changes in Software Product Line Evolution - Related Research Artifacts
<p>This archived open repository contains open science material related to the following submission to the <a href="https://www.journals.elsevier.com/journal-of-systems-and-software/call-for-papers/software-reuse-for-the-next-generation">Journal of Systems and Software (JSS) special issue <em>Software Reuse for the Next Generation</em></a>:</p> <p>C. Kröher, L. Gerling, K. Schmid, <em>Comparing the intensity of variability changes in software product line evolution</em>, Journal of Systems and Software. Submitted November 2022.</p> <p>The paper presents the application of a fine-grained, variability-centric analysis approach to four different software product lines: <a href="https://github.com/torvalds/linux">Linux kernel</a>, <a href="https://github.com/coreboot/coreboot">coreboot firmware</a>, <a href="https://github.com/mirror/busybox">BusyBox UNIX utilities</a>, and <a href="https://sourceforge.net/projects/axtls/">axTLS embedded SSL</a>. The approach is based on the differentiation between artifact-specific and variability information in code, build, and variability model artifacts to identify the intensity (the frequency and the amount) with which developers change variability information in practice.</p> <p>In order to complement the results presented in the submission and support reproducibility as well as reuse, the following artifacts are available:</p> <ul> <li><strong>JSS-VM_2022-11-01.zip</strong>: a compressed archive containing the virtual machine in which the analysis was executed. This virtual machine was created with <a href="https://www.vmware.com/content/vmware/vmware-published-sites/us/products/workstation-player/workstation-player-evaluation.html.html">VMware Workstation 16 Player</a> based on <a href="https://ubuntu.com/download/desktop">Ubuntu Desktop 22.04.1</a> (username and password: jss). It provides all installed software, configuration files, data sets, and results as described in the submission. Download, extract, and start the virtual machine to access the detailed description of its content and usage on the desktop.</li> <li><strong>ComAnI-Applications.zip</strong>: a compressed archive containing the technical realization of the analysis approach and the configuration files used to apply it to the individual software product lines. This includes: <ul> <li><em>ComAnI_Guide.pdf</em>: the guide explaining the application and its usage in general</li> <li><em>ComAnI.jar</em>: the Java executable archive file representing the main application for starting an analysis</li> <li><em>ComAnI-PS.jar</em>: a modified version of the previous Java executable archive file, which provides the number of code, build, and variability model artifacts as specified by the regular expressions of a given configuration file as well as their total number of lines based on the current state of a repository only (no history)</li> <li><em>DeadCodeChangeAnalyzer.jar</em>: a commit analyzer plug-in for detecting changes relevant to dead code detection (not used in the submission)</li> <li><em>GitCommitExtractor.jar</em>: a commit extraction plug-in for extracting commits from Git repositories</li> <li><em>SvnCommitExtractor.jar</em>: a commit extraction plug-in for extracting commits from Subversion (SVN) repositories</li> <li><em>VariabilityChangeAnalyzer.jar</em>: a commit analyzer plug-in for detecting changes to artifact-specific and variability information in code, build, and variability model artifacts (realization of the commit analysis process described in the submission)</li> <li><em>busybox-commit-list</em>: a plain text file containing a subset of the BusyBox commits as required by the application to extract and analyze only those parts of its entire history relevant for the submission (quote from the submission: <em>"For BusyBox, we had to further exclude commits before the complete migration to Kbuild and after the introduction of a script for extracting variability model information from code artifacts, which initiated defining variability information of the variability model as part of comments in code artifacts. This mixing of information and artifact types is not supported by our tooling"</em>)</li> <li><em>axtls.properties</em>: the configuration (file) defining the required properties for extracting and analyzing commits of the axTLS history</li> <li><em>busybox.properties</em>: the configuration (file) defining the required properties for extracting and analyzing all commits of the BusyBox history</li> <li><em>busybox-subset.properties</em>: the configuration (file) defining the required properties for extracting and analyzing the commits specified in the busybox-commit-list file only, resulting in the respective subset of the BusyBox history</li> <li><em>coreboot.properties</em>: the configuration (file) defining the required properties for extracting and analyzing all commits of the coreboot history</li> <li><em>linux.properties</em>: the configuration (file) defining the required properties for extracting and analyzing all commits of the Linux kernel history</li> <li><em>template.properties</em>: the configuration (file) template including descriptions of each property and its valid values</li> </ul> </li> <li><strong>ComAnI-Results.zip</strong>: a compressed archive containing the (raw) analysis results from applying the approach via its realization as provided by the previous archive to each of the software product lines. Hence, for each subject, a directory with the respective name exist, which in turn contains the following artifacts: <ul> <li><em>VariabilityChangeAnalyzer-Results_2022-09-[…]</em>: the directory containing the respective analysis results and some automated visualizations. Please note that some files are not correctly labeled, .e.g. some files for axTLS and BusyBox are prefixed with "coreboot", while containing the correct data for the respective subjects. Further, for BusyBox, two directories exist, which the additional readme-file explains</li> <li><em>project-size-trace.txt</em>: the complete trace from starting ComAnI-PS.jar (see description above) with a specific configuration file to its final output</li> </ul> </li> <li><strong>JSS23_Extended-Evolution-Analysis_Statistics.ods</strong>: a <a href="https://www.libreoffice.org/discover/calc/">LibreOffice Calc</a> spreadsheet containing data derived from the raw ones of the ComAnI-Results.zip and the respective visualizations as presented in the submission. Further, some sheets include additional data preparations used to write certain parts of the result and discussion sections.</li> </ul> <p>The additional <strong>LICENSE</strong> file defines <a href="https://www.apache.org/licenses/LICENSE-2.0">Apache License Version 2.0, January 2004</a> to apply for all artifacts in this repository.</p> <p>This work is partially supported by the Evoline project, funded by the DFG (German Research Foundation) under Priority Programme SPP 1593 and by the ITEA3 project REVaMP², funded by the BMBF (German Ministry of Research and Education) under grant 01IS16042H. Any opinions expressed herein are solely by the authors and not of the DFG or BMBF.</p>
Investigating the Adoption of Research Software: A Survey with Brazilian Academic Researchers
<p>Artifacts used for data collection and analysis.</p>
Psychometric Instruments in Software Engineering Research on Personality: Status Quo After Fifty Years
<p>Step files.</p>
How to choose a research data repository software? Experience report. Table of requirements.
<p>In the age of digital transformation, scientific and social interest for data and data products is constantly on the rise. The quantity as well as the variety of digital research data is increasing significantly. This raises the question about the governance of this data. For example, how to store the data so that it is presented transparently, freely accessible and subsequently available for re-use in the context of good scientific practice. Research data repositories provide solutions to these issues.</p> <p>Considering the variety of repository software, it is sometimes difficult to identify a fitting solution for a specific use case. For this purpose a detailed analysis of existing software is needed. Presented table of requirements can serve as a starting point and decision-making guide for choosing the most suitable for your purposes repository software. This table is dealing as a supplementary material for the paper "How to choose a research data repository software? Experience report." (persistent identifier to the paper will be added as soon as paper is published).</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.