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1,916 results for “software,”
MACREL software benchmark data set: Simulated metagenomes with sequencing quality, errors profile and abundance distributions derived from real samples
<p>These metagenomes were used in the benchmarking of FACS pipeline, and were designed after NGLess benchmark dataset (doi.org/10.5281/zenodo.2560288). Metagenomes were simulated with <a href="https://www.niehs.nih.gov/research/resources/software/biostatistics/art/index.cfm">ART-bin-MountRainier-2016.06.05</a> using real abundance profiles (.abund files) available <a href="https://doi.org/10.5281/zenodo.2560288">elsewhere</a>, and <a href="http://progenomes1.embl.de/data/repGenomes/representatives.contigs.fasta.gz">proGenomes' representative contigs</a> as reference genomes. There are available metagenomes with 40, 60 and 80 M (million of reads) based in the reference genomes and abundances of the following samples:</p> <pre><code>SAMEA2466916 SAMEA2466953 SAMEA2466965 SAMEA2621107 SAMEA2621229 SAMEA2621247</code></pre> <p>To convert them from the CRAM format back to fastq files:</p> <pre><code> ## 1. converting from cram to bam format: samtools view -b -T refgenome.fa -o file.bam file.cram ## 2. sorting the bam file: samtools sort -n file.bam -o input_sorted.bam # sort reads by identifier-name (-n) ## 3. converting from bam to fastq format: bedtools bamtofastq -i input_sorted.bam -fq output_r1.fastq -fq2 output_r2.fastq </code></pre> <p> </p>
The Research Software Alliance (ReSA) and the community landscape
<p>The Research Software Alliance (ReSA)’s mission is to bring research software communities together to collaborate on the advancement of research software. ReSA has formed taskforces and one of them revolves around a <strong>software landscape analysis</strong> aiming to answer the question "How can we identify the different communities and topics of interest for the research software community (e.g., preservation, RSEs, citation, productivity, sustainability)?"</p> <p>Here we include text describing the work of the taskforce to date (see <a href="https://zenodo.org/api/files/5a1e0c32-cbf7-4b9a-9c02-139b2ce66e85/2020-03-11-ReSA-landscape.md?versionId=ffedcfe9-33f4-4aba-9d6b-9975fc1c548d">2020-03-11-ReSA-landscape.md</a>), as well as plans for the future, and an invitation to readers to contribute to the ReSA list of research software communities. We are a;sp including the current version of the list in a CSV file (see <a href="https://zenodo.org/api/files/5a1e0c32-cbf7-4b9a-9c02-139b2ce66e85/2020-03-11-ReSA-landscape.csv?versionId=be4bbcdd-79a8-4444-a764-98bf0d548922">2020-03-11-ReSA-landscape.csv </a>), and we welcome contributions on the live spreadsheet that can be found via this <a href="https://docs.google.com/spreadsheets/d/15JHqOxR4HIKHYe821IPvbxIuXP1zMjXKGEIJwB-GPqE/edit#gid=0">link</a>.</p>
DATASET - AVALIAÇÃO EMPÍRICA DA GERAÇÃO AUTOMATIZADA DE TESTES DE SOFTWARE SOB A PERSPECTIVA DE TEST SMELLS
<p>A constante busca pela qualidade sempre está em destaque na área de Engenharia de Software. Dentre as diversas disciplinas dedicadas a essa temática, o teste de software tem se estabelecido como uma das mais importantes, dado sua eficácia na identificação de defeitos, em momento prévio à liberação de sistemas de software para o mercado. O teste de software é atividade-chave para o desenvolvimento de software de qualidade. Entretanto, desenvolver testes é tão ou mais custoso do que desenvolver o código de produção. Uma alternativa para a redução dos custos associados ao teste de software se dá pelo uso intensivo de ferramentas de automação de testes. A proposta dessas ferramentas é reduzir o tempo de produção sem afetar a qualidade do código. Apesar dessa premissa, não é comum encontrar abordagens que incluam uma camada de verificação de qualidade dos testes gerados automaticamente, o que pode reduzir a confiabilidade da eficácia desses testes. Neste cenário, a proposta dessa dissertação é analisar empiricamente massas de dados de teste, sob a perspectiva de test smells, no sentido de avaliar a qualidade dos testes produzidos por ferramentas de geração automatizada de testes de software. Test smells são más escolhas no design dos testes e tem características sintomáticas e podem acarretar diminuição na qualidade dos sistemas. Considerando os test smells em código de teste, o estudo analisa os testes gerados por duas ferramentas amplamente aceitas pela comunidade de testes: Evosuite e Randoop. Um conjunto de vinte e um projetos de software de código aberto, disponíveis na plataforma Github foram considerados no estudo. A análise considerou a dispersão de test smells no código de teste desses projetos, bem como a existência de potenciais correlações entre test smells e as relações com as métricas estruturais. Como principais resultados, encontramos fortes correlações entre os test smells e as métricas de cobertura do código, diferenças significativas entre os dados encontrados nas suítes de testes geradas automaticamente e com os testes pré-existentes nos projetos avaliados.</p>
Measuring Software Testability Modulo Test Quality - Replication Package
<p>This repository represents the replication package for the paper <em>Measuring Software Testability Modulo Test Quality</em>.</p> <p>It includes the dataset and the Jupyter Notebook we used for the analysis in our paper.</p>
Enterprise-Driven Open Source Software
<p>We present a dataset of open source software developed mainly by enterprises rather than volunteers. This can be used to address known generalizability concerns, and, also, to perform research on open source business software development. Based on the premise that an enterprise's employees are likely to contribute to a project developed by their organization using the email account provided by it, we mine domain names associated with enterprises from open data sources as well as through white- and blacklisting, and use them through three heuristics to identify 17,264 enterprise GitHub projects. We provide these as a dataset detailing their provenance and properties. A manual evaluation of a dataset sample shows an identification accuracy of 89%. Through an exploratory data analysis we found that projects are staffed by a plurality of enterprise insiders, who appear to be pulling more than their weight, and that in a small percentage of relatively large projects development happens exclusively through enterprise insiders.</p> <p>The main dataset is provided as a 17,264 record tab-separated file named <code>enterprise_projects.txt</code> with the following 29 fields.</p> <ul> <li><strong>url</strong>: the project's GitHub URL</li> <li><strong>project_id</strong>: the project's GHTorrent identifier</li> <li><strong>sdtc</strong>: true if selected using the same domain top committers heuristic (9,016 records)</li> <li><strong>mcpc</strong>: true if selected using the multiple committers from a valid enterprise heuristic (8,314 records)</li> <li><strong>mcve</strong>: true if selected using the multiple committers from a probable company heuristic (8,015 records),</li> <li><strong>star_number</strong>: number of GitHub watchers</li> <li><strong>commit_count</strong>: number of commits</li> <li><strong>files</strong>: number of files in current main branch</li> <li><strong>lines</strong>: corresponding number of lines in text files</li> <li><strong>pull_requests</strong>: number of pull requests</li> <li><strong>github_repo_creation</strong>: timestamp of the GitHub repository creation</li> <li><strong>earliest_commit</strong>: timestamp of the earliest commit</li> <li><strong>most_recent_commit</strong>: date of the most recent commit</li> <li><strong>committer_count</strong>: number of different committers</li> <li><strong>author_count</strong>: number of different authors</li> <li><strong>dominant_domain</strong>: the projects dominant email domain</li> <li><strong>dominant_domain_committer_commits</strong>: number of commits made by committers whose email matches the project's dominant domain</li> <li><strong>dominant_domain_author_commits</strong>: corresponding number for commit authors</li> <li><strong>dominant_domain_committers</strong>: number of committers whose email matches the project's dominant domain</li> <li><strong>dominant_domain_authors</strong>: corresponding number for commit authors</li> <li><strong>cik</strong>: SEC's EDGAR "central index key"</li> <li><strong>fg500</strong>: true if this is a Fortune Global 500 company (2,233 records)</li> <li><strong>sec10k</strong>: true if the company files SEC 10-K forms (4,180 records)</li> <li><strong>sec20f</strong>: true if the company files SEC 20-F forms (429 records)</li> <li><strong>project_name</strong>: GitHub project name</li> <li><strong>owner_login</strong>: GitHub project's owner login</li> <li><strong>company_name</strong>: company name as derived from the SEC and Fortune 500 data</li> <li><strong>owner_company</strong>: GitHub project's owner company name</li> <li><strong>license</strong>: SPDX license identifier</li> </ul> <p>The file <code>cohost_project_details.txt</code> provides the full set of 311,223 cohort projects that are not part of the enterprise data set, but have comparable quality attributes.</p> <ul> <li><strong>url</strong>: the project's GitHub URL</li> <li><strong>project_id</strong>: the project's GHTorrent identifier</li> <li><strong>stars</strong>: number of GitHub watchers</li> <li><strong>commit_count</strong>: number of commits</li> </ul>
ASA³P Software & Database volume
<p>ASA³P is an automatic and highly scalable assembly, annotation and higher-level analyses pipeline for closely related bacterial isolates. <a href="https://github.com/oschwengers/asap">https://github.com/oschwengers/asap</a></p> <p>ASA³P is a fully automatic, locally executable and scalable assembly, annotation and higher-level analysis pipeline creating results in standard bioinformatics file formats as well as sophisticated HTML5 documents. Its main purpose is the automatic processing of NGS WGS data of multiple closely related isolates, thus transforming raw reads into assembled and annotated genomes and finally gathering as much information on every single bacterial genome as possible. Per-isolate analyses are complemented by comparative insights. Therefore, the pipeline incorporates many best-in-class open source bioinformatics tools and thus minimizes the burden of ever-repeating tasks. Envisaged as a preprocessing tool it provides comprehensive insights as well as a general overview and comparison of analysed genomes along with all necessary result files for subsequent deeper analyses. All results are presented via modern HTML5 documents comprising interactive visualizations.</p> <p>Schwengers et al, 2020 PLOS Comp Bio DOI:10.1371/journal.pcbi.1007134</p>
Dataset do VII EBBC [Análise métrica do "Regime de Informação" com software IRaMuTeQ]
<p>Planilha de dados recuperados das bases de dados - <strong>Scopus, Brapci e LISA</strong> - para análise e apresentação da pesquisa empírica intitulada - <strong>Análise métrica do "Regime de Informação" com software IRaMuTeQ</strong> - no <strong>VII EBBC – Encontro Brasileiro de Bibliometria e Cientometria</strong>: <a href="http://www.ebbc2020.ici.ufba.br/">http://www.ebbc2020.ici.ufba.br/</a></p> <p> </p>
The Daily Life of Software Engineers during the COVID-19 Pandemic -- Replication Package
<p>Following the onset of the COVID-19 pandemic and subsequent lockdowns, software engineers' daily life was disrupted and abruptly forced into remote working from home. This change deeply impacted typical working routines, affecting both well-being and productivity. Moreover, this pandemic will have long-lasting effects in the software industry, with several tech companies allowing their employees to work from home indefinitely if they wish to do so. Therefore, it is crucial to analyze and understand how a typical working day looks like when working from home and how individual activities affect software developers' well-being and productivity. We performed a two-wave longitudinal study involving almost 200 globally carefully selected software professionals, inferring daily activities with perceived well-being, productivity, and other relevant psychological and social variables. Results suggest that the time software engineers spent doing specific activities from home was similar when working in the office. (e.g., coding > emails > code review > networking). However, we also found some meaningful mean differences. The amount of time developers spent on each activity was unrelated to their well-being, perceived productivity, and other variables. We conclude that working remotely is not per se a challenge for organizations or developers.</p>
Testing 3D modelling software. Modelling charging pads for WPT of electric vehicles for EM emissions simulation.
<p>Even for the experienced 3D FEM modelers it may not be obvious which geometry discretization is the most appropriate and suitable for this type of problem. It may be a conservative approach to test the computation tool on a simplified geometry, on which the magnetic field distribution is known. As part of the “Metrology for inductive charging of electric vehicles” (MICEV) project (www.micev.eu), an axisymmetric geometry was used, with the results reported.</p>
Detection of Functionally Similar Code Clones: Data, Analysis Software, Benchmark
<p>We analysed 2,800 programs in Java and C for which we knew they are functionally similar. We checked if existing clone detection tools are able to find these functional similarities and classified the non-detected differences. We make all used data, the analysis software as well as the resulting benchmark available here.</p>
The Debsources Dataset: Two Decades of Free and Open Source Software
<p>This is the Debsources Dataset: source code and related metadata spanning two decades of Free and Open Source Software (FOSS) history, seen through the lens of the Debian distribution.</p> <p>The dataset spans more than 3 billion lines of source code as well as metadata about them such as: size metrics (lines of code, disk usage), developer-defined symbols (ctags), file-level checksums (SHA1, SHA256, TLSH), file media types (MIME), release information (which version of which package containing which source code files has been released when), and license informa-<br> tion (GPL, BSD, etc).</p> <p>The Debsources Dataset comes as a set of tarballs containing deduplicated unique source code files organized by their SHA1 checksums (the source code), plus a portable PostgreSQL database dump (the metadata).</p> <p>The Debsources Dataset is described in full in the paper The Debsources Dataset: Two Decades of Free and Open Source Software, published on the Empirical Software Engineering journal with DOI 10.1007/s10664-016-9461-5 . A preprint of the paper is available at https://upsilon.cc/~zack/research/publications/debsources-ese-2016.pdf .</p>
Circularity3 DDOMP - Project tracksheets: scholarly publications, non-peer-reviewed digital outputs, dataset log, and software log.
<p>Project tracking sheets for the Circularity3 project. </p> <p>The following tracking sheets are provided:</p> <ol> <li>Scholarly Publications </li> <li>Non-peer-reviewed digital outputs</li> <li>Dataset log </li> <li>Software log </li> </ol> <p>For further specifications and explanations of the tracking sheets refer to the Circularity3 DDOMP here: https://doi.org/10.5281/zenodo.11047951</p> <p> </p> <p>This tracking sheets reference widely the PARSEC research teams tracking sheets, to whom we are very grateful for their transparent and insightful documentation.</p> <p>Stall, Shelley, Specht, Alison, Corrêa, Pedro Luiz Pizzigatti, David, Romain, Edmunds, Rorie, Mabile, Laurence, Machicao, Jeaneth, Miyairi, Nobuko, Murayama, Yasuhiro, O'Brien, Margaret, Wyborn, Lesley, & Vellenich, Danton Ferreira. (2023). PARSEC Data and Digital Output Management Plan and Workbook. Zenodo. <a href="https://doi.org/10.5281/zenodo.3891426">https://doi.org/10.5281/zenodo.3891426</a></p>
RRID Gold Set of annotations for software tools in the biomedical literature
<p>This data is a subset of a larger human curated, machine assisted gold standard data set of RRID citations within the text of the scientific literature. The full set is accessible via Hypothes.is at https://hypothes.is/users/scibot?q=group%3A__world__ and via individual RRID records such as RRID:SCR_016250 https://scicrunch.org/resolver/SCR_016250/mentions?q=&i=rrid:scr_016250 </p><p>The data is based on authors who added RRIDs into their manuscripts. The data was then extracted by SciBot (RRID:SCR_016250), into Hypothes.is (RRID:SCR_000430) and then manually checked by a curator to determine if the author and the database agreed. The list of annotators is available in Hypothes.is user group: SciBotCurationGroup.</p><p>There are 78,140 rows and each row contains an annotation (annotation id, URI), linked to a paper (paper identifiers: PMID, DOI, PMC) and linked to the RRID (scr_id, exact, text_quote_selector). </p><p>A second spreadsheet contains a list of 8,322 software tools from the SciCrunch Registry (available here https://scicrunch.org/resources/data/source/nlx_144509-1/search ), enhanced by additions by thousands of individual authors, and curated over 10 years (Ozyurt et al., 2016). </p><p>The third spreadsheet contains a data dictionary and links to related ontologies, and tagging sets. </p><p> </p><p> </p>
Dataset and software for support of the article "To Chaos or Not To Chaos: going into the resilience of the ecosystem".
<p>The CAVeg model, as well as its dependencies, and the CAVeg data files with the configurations for the two and three ecophysiological types, including the data needed to perform the Lyapunov exponents calculations with the CRAN-R DChaos package are available in this dataset.</p>
Data products and software for `X-ray diagnostics of Cassiopeia A's "Green Monster": evidence for dense shocked circumstellar plasma`
<div> <h2>Data Reproduction Package for the publication ‘X-ray diagnostics of Cassiopeia A’s “Green Monster”: evidence for dense shocked circumstellar plasma’</h2> </div> <div> <h3>Authors: Jacco Vink, Manan Agarwal, Patrick Slane, Ilse De Looze, Dan Milisavljevic, Daniel Patnaude, and Tea Temim.</h3> </div> <div> <h3>Link to paper: <a href="https://doi.org/10.3847/2041-8213/ad2fc5">https://doi.org/10.3847/2041-8213/ad2fc5</a> </h3> <p> </p> </div> <div> <h4>This package was prepared by Jacco Vink and Manan Agarwal (University of Amsterdam)</h4> </div> <div> <h3>Summary</h3> </div> <div> <p>This data reproduction package contains the data files in FITS format used to<br>generate the figures in the paper. The data files concern the revised manuscript, which incorporates changes made in response to the journal’s referee report.</p> </div> <div> <p>The paper is based on Chandra X-ray Observatory (CXO) data of Cassiopeia A taken in 2004. The raw archival data used, maintained by the Chandra Data Archive, can be retrieved using the following DOI link: <a href="https://doi.org/10.25574/cdc.209">https://doi.org/10.25574/cdc.209</a>.</p> </div> <div> <p>Additional James Webb Space Telescope (JWST) data are stored at the Mikulski Archive for Space Telescopes (MAST) at the Space Telescope Science Institute. The data used in the paper can be downloaded through DOI link <a href="https://doi.org/10.17909/szf2-bg42">https://doi.org/10.17909/szf2-bg42</a>.</p> </div> <div> <p>The data produced from the above raw data are stored in the files:</p> </div> <div> <ul> <li>green_monster_image_data.tar.gz</li> <li>spectral_files_and_models.tar.gz</li> <li>imaging_and_pca_code.tar.gz</li> <li>green_monster_pca_input_output.tar.gz</li> </ul> <p>The repository contains JWST/MIRI mosaics of Cassiopeia A which are described in detail in the paper "A JWST Survey of the Supernova Remnant Cassiopeia A", by D. Milisavljevic, T. Temim, I. De Looze, et al.; see https://arxiv.org/abs/2401.02477, to be published in ApJ letters.<br> </p> </div>
Credit for software creators: An adapted illustration from The Turing Way: Shared under CC-BY 4.0 for reuse
<p>Illustration adapted from <strong>The Turing Way Community, & Scriberia. (2023). Illustrations from The Turing Way: Shared under CC-BY 4.0 for reuse. Zenodo. <a href="https://doi.org/10.5281/zenodo.8169292">https://doi.org/10.5281/zenodo.8169292</a></strong>.</p> <p>The illustration has been adapted by Stephan Druskat (subsumed as co-author in <em>The Turing Way Community</em>):</p> <ul> <li>The speech bubble has been changed to read "More credit" instead of "More credit<strong>s</strong>". (One character removed and alignment adapted.)</li> <li>The inscription on the purse has been changed to read "Credit" instead of "Credit<strong>s</strong>". (One character removed and alignment adapted.)</li> </ul>
Software-based decision support tools used in the sanitation sector
<p>This dataset includes data used in a scoping review on how decision support tools used in the sanitation sector address resource recovery considerations. The dataset is an accompaniment to the publication "A review of how decision support tools address resource recovery in sanitation systems", which was submitted to the Journal of Cleaner Production.</p>
Cartolabe Software Heritage
<p>The Software Heritage dataset shows projects that are collected by the <a href="https://www.softwareheritage.org/">Software Heritage</a> archive initiative. Text data for each software comes from the project's description on github.</p>
Dataset: Systematic Mapping Study on the Development and Application of Sentiment Analysis Tools in Software Engineering
<p>Update: We updated the data set in March 2022 by adding newly published papers and by providing more insights on how we analyzed them. Details can be found in the file " SEnti-SMS.xlsx".</p> <p>----------</p> <p>Update: The updated version (-v2) contains the results of one more snowballing iteration and extracted information on the accuracy of the used methods.</p> <p>----------</p> <p>In 2020, we conducted a systematic literature review to explore the development and application of sentiment analysis tools in software engineering.</p> <p>Information on the execution of the SLR, its scope, the search string, etc. are presented in the paper linked below.</p> <p> </p> <p> </p>
Dataset for Openseize Software Release v1.0
<p><strong>A dataset record containing demonstration data for openseize, an open-source python software package for analyzing large EEG datasets from a variety of formats. Specifically, this record contains the following datasets:</strong></p> <p><em><strong>recording_001.edf</strong></em></p> <p>A European data format+ file containing four channels of EEG data sampled at 5 KHz for 3775 seconds (~1.05 Hrs). The header of this file contains all the necessary metadata to interpret the data.</p> <p><em><strong>annotations_001.txt</strong></em></p> <p>A tab separated value file containing annotations for the file <em>recording_001.edf</em>. The first 6 lines of this file containing metadata concerning the experiment and can be ignored. The column data consists of the annotation number, the start date-stamp of the annotation, the stop date-stamp of the annotation, the time from the start of the recording in seconds, the channel the annotation was identified on, and the user defined annotation label. The annotation labels in this file denote three different behavioral states identified by a video recording. They are grooming, rest and exploring. Each state was measured for approximately 15 secs.</p> <p> </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.