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1,916 results for “software,”
Data and Software for "Determining the orientation of a magnetic reconnection X line and implications for a 2D coordinate system"
<p>Supporting information for "Determining the orientation of a magnetic reconnection X line and implications for a 2D coordinate system", by Denton et al. Includes a copy of the paper and previous relevant papers, the simulation data used in the paper, and the reconstruction code used in the paper. See the readme files.</p>
Supplementary Material for "Aiding the Design of Critical Software Systems by Iterative Exploration of Distinct Requirement Violation Scenarios"
<p>This dataset provides artifacts about an industrial case study of a Steer-by-Wire system. It collects models of the system modeled in the open-source Gamma Statechart Composition Framework. You can find more information about the framework here: <a href="https://github.com/ftsrg/gamma">https://github.com/ftsrg/gamma</a>.</p>
Dataset and Analysis Scripts for Survey "Understanding Security Tactics in Microservice APIs using Annotated Software Architecture Decomposition Models -- A Controlled Experiment"
<pre>Dataset, R-Scripts and questionnaire templates for our survey <em>Understanding Security Tactics in Microservice APIs using Annotated Software Architecture Decomposition Models -- A Controlled Experiment.</em></pre>
Dataset for: Multi-mode Heterodyne Laser Interferometry Realized via Software Defined Radio
<p>Repository of data plotted in figures for the journal publication "Multi-mode Heterodyne Laser Interferometry Realized via Software Defined Radio" (doi: 10.1364/OE.500077 ).</p> <p>Please see metadata file for details on individual data files.</p>
Data from: Cellects, a software to quantify cell expansion and motion
<p>Automated quantification offers unique opportunities to study biological phenomena, increasing reproducibility, replicability, accuracy, and throughput, while reducing observer biases. We present Cellects, a tool to quantify growth and motion in 2D. This software operates with image sequences containing specimens growing and moving on an immobile flat surface. Its user-friendly interface makes it easy to adjust the quantification parameters to cover a wide range of species and conditions, and includes tools to validate the results and correct mistakes if necessary. The software provides the region covered by the specimens at each point of time, as well as many geometrical descriptors that characterize it. We validated Cellects with <em>Physarum polycephalum</em>, which is particularly difficult to detect because of its complex shape and internal heterogeneity. This validation covered five different conditions with different background and lighting, and found Cellects to be highly accurate in all cases. Cellects' main strengths are its broad scope of action, automated computation of a variety of geometrical descriptors, easy installation and user-friendly interface.</p>
Unveiling Hurdles in Software Engineering Education: The Role of Learning Management Systems
<p>Learning management systems (LMSs) are established tools in higher education, especially in the field of software engineering (SE). The onset of the COVID-19 pandemic further amplified the utilization of these systems, which necessitated their integration into educational curricula for both lecturers and students. However, adopting LMSs within SE education has presented distinctive challenges impeding their seamless incorporation into the courses. This paper aims to scrutinize the challenges and requirements encountered by professors, lecturers, and students in the domain of SE education when using LMSs. We conducted an empirical study that included (i) a survey with 47 professors/lecturers and 133 students, (ii) an analysis of the ensuing data, and (iii) 18 additional interviews conducted with professors and lecturers to delve into nuanced variations in viewpoints. The findings derived from our study reveal that the challenges and requirements pertaining to LMSs are rather specific depending on the scope and size of the respective courses. Nevertheless, many participants have a consensus on numerous challenges and requirements for improving certain features of LMSs in order to improve their usage in SE education. The findings are valuable for advancing research and development in the field of LMSs and provide guidance for lecturers in SE education.</p>
Software and data underlying the article 'A serious game approach for lake modeling and management: the EscapeBLOOM'
<p>Here we share the player version of the EscapeBLOOM, a dummy version showcasing the techniques to create a similar digital escape room, and the anonymized data of the quantitative survey as presented in the publication 'A serious game approach for lake modeling and management: the EscapeBLOOM'.</p> <p>Anyone is free to play or adjust the game for their own educational purposes. The dummy and supplementary material of the publication 'A serious game approach for lake modeling and management: The EscapeBLOOM' <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.envsoft.2024.105941" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.envsoft.2024.105941</a> together provide guides on how to create a new game from the start and may help to adjust the existing game.</p> <p>The data of the survey was used for the analysis of perceived learning in the publication 'A serious game approach for lake modeling and management: the EscapeBLOOM'.</p>
ChatGPT's Aptitude in Utilizing UML Diagrams for Software Engineering Exercise Generation
<p>The integration of Artificial Intelligence (AI) technologies into educational settings has paved the way for innovative teaching and learning approaches. In Software Engineering (SE) education, using Unified Modeling Language (UML) diagrams is a fundamental teaching element for understanding complex software systems. This research addresses the ability of ChatGPT to utilize UML class and sequence diagrams for creating SE modeling exercises. We use ChatGPT to generate exercises based on the information from uploaded UML diagrams by analyzing textual UML representations such as Mermaid and graphical diagrams. The research explores ChatGPT's ability to synthesize UML-specific information from class and sequence diagrams, enabling the generation of various exercises tailored to strengthen conceptual understanding and practical application. Furthermore, we investigate generating graphical UML class and sequence diagrams based on natural language as input. By bridging the gap between AI-driven natural language understanding and the comprehension of UML diagrams, this study highlights the potential of ChatGPT to improve SE education. Our concise findings address educators, practitioners, and other researchers engaged in the field of SE education with a special focus on UML.</p>
Experimental package for "Live Software Documentation of Design Pattern Instances"
Experimental package containing the materials and data for an empirical study conducted with the DesignPatterDoc plugin for IntelliJ IDEA.
Mapping of swMATH ids and Software Heritage ids
<p>We present the mapping from swMATH identifiers and software heritage identifiers for selected git repositories, with the following fields:</p> <p><strong>swmathid</strong>: Unique identifier from swMATH. Prefix with https://swmath.org/software/ to visit resolve. For example, 4503 is associated with https://swmath.org/software/4503 <br><strong>cvs</strong>: The link to the git repository<br><strong>swhid</strong>: Unique software heritage identifer. Prefix with <code>https://archive.softwareheritage.org/</code> to resolve. For example, swh:1:snp:e9e31cc35f3677801b896fc4d84f1c26fd3df3d2 is assocated with <code>https://archive.softwareheritage.org/</code>swh:1:snp:e9e31cc35f3677801b896fc4d84f1c26fd3df3d2 </p> <p><span>The following software versions were used</span></p> <ol> <li><span><a href="https://archive.softwareheritage.org/swh:1:cnt:3c525f7ef4feb933692f2d33d8186f69481d248b;origin=https:/github.com/MaRDI4NFDI/swMATH4EOSC;visit=swh:1:snp:ef1fa467e1a15ad613cf328b4b9bfe6db5be7f9d;anchor=swh:1:rev:8d7031d00664ca66e9b50ea4631cf297d1f16dc6;path=/swMATH_analysis/swMATH_no_empty_source_code.csv"><span>swh:1:cnt:3c525f7ef4feb933692f2d33d8186f69481d248b</span></a> to get the swmath and git repositories</span></li> <li><span><a href="https://archive.softwareheritage.org/swh:1:cnt:3aea0d0f97d9276282ee1de5ebc5945cc66e77b5;origin=https:/phabricator.wikimedia.org/diffusion/EMAS/extension-mathsearch.git;visit=swh:1:snp:2ba33a0d4adb14c00a63ab4cfb595412c3e47cbc;anchor=swh:1:rev:4f2f4b12f4763a817e8533e2997ece9407cd0b04;path=/maintenance/AddSwhids.php" target="_blank" rel="noopener"><span>swh:1:cnt:3aea0d0f97d9276282ee1de5ebc5945cc66e77b5</span></a> to get the corresponding software heritage identifiers</span></li> </ol> <p> </p> <p>Then the following Query was used to retrieve the uploaded file.</p> <p>SPARQL Query (<a href="https://query.portal.mardi4nfdi.de/#PREFIX%20wdt%3A%20%3Chttps%3A%2F%2Fportal.mardi4nfdi.de%2Fprop%2Fdirect%2F%3E%0A%0ASELECT%20%3Fswmathid%20%20%28str%28%3Frepo%29%20as%20%3Fcvs%29%20%3Fswhid%0AWHERE%20%7B%0A%20%20%3Fitem%20wdt%3AP13%20%3Fswmathid.%0A%20%20%3Fitem%20wdt%3AP339%20%3Frepo.%0A%20%20%3Fitem%20wdt%3AP1454%20%3Fswhid%0A%7D" target="_blank" rel="noopener">try it out</a>)</p> <pre><code>PREFIX wdt: <https://portal.mardi4nfdi.de/prop/direct/> SELECT ?swmathid (str(?repo) as ?cvs) ?swhid WHERE { ?item wdt:P13 ?swmathid. ?item wdt:P339 ?repo. ?item wdt:P1454 ?swhid }<br><br></code>For more details see our upcoming paper.</pre>
Supplementary Material - "Explanations in Everyday Software Systems: Towards a Taxonomy for Explainability Needs" (RE'24)
<p>This artifact contains the questionnaire, coding guidelines and resulting coded data set for the research paper "Explanations in Everyday Software Systems: Towards a Taxonomy for Explainability Needs" submitted to and accepted at the 32nd IEEE International Requirements Engineering 2024 conference. This artifact does not contain any automated analyses or tools.</p>
Software for optimizing treatment to slow the spatial propagation of invasive species: Code and results
<p>Slowing the spread of invasive species is a major challenge. How can we achieve this goal in the most cost-effective manner? This package includes the complete code and simulation results that help finding the optimal, most cost-effective treatment to slow the spread of a propagating species. This package accompanies the paper "Optimizing strategies for slowing the spread of invasive species" by Adam Lampert (PLOS Computational Biology, DOI: 10.1371/journal.pcbi.1011996). The file general_model_code.zip contains the code for the general model; the file spongy_moth_model_code.zip contains the code for the spongy moth model; and the file general_model_simulation_results.zip contains the results for the general model; and the file spongy_moth_model_simulation_results.zip contains the results for the spongy moth model.</p>
Replication Package for "Software Quality Assurance Analytics: Enabling Software Engineers to Reflect on QA Practices" Paper (SCAM 2024)
<p>Welcome to our artifact!<br>In here we provide additional information for you to retrace our steps in the interview analysis.<br>It has the following contents:</p> <ul> <li><code>codebook.xlsx</code>: Our full codebook with our open codes, structured after the axial codes that emerged. <code>codebook-statistics.xlsx</code> lists for each code in which participant's interview it can be found.</li> <li><code>generate-figures</code>: The plain data and scripts used to generate the figures in the paper.</li> <li><code>survey.pdf</code>: An printout of our whole online questionnaire that guided the participants through the pretest-posttest study and the interview.</li> <li><code>survey-answers.xlsx</code>: The complete data for our participants answers in the online survey during the interviews.</li> <li><code>repoinsights-dashboard-software</code>: The code of our prototype repoinsights. As it is under active development, this is not yet documented for replicating the study setup or extending it. Still, we are providing the source code for transparency and will publish a version with comprehensive setup instructions later.</li> </ul>
A software benchmark for cardiac elastodynamics
<p>Data used in the article:<br>"A software benchmark for cardiac elastodynamics" by Arostica et al, Computer Methods in Applied Mechanics and Engineering. The DOI of the article was not available at the moment of publishing this data set.</p>
Dataset and software for processing of hyperspectral images of different CDW materials
<h2>Overview</h2> <p>The provided scripts are designed to process hyperspectral images of construction and demolition waste (CDW) materials, extract relevant features, and train a machine-learning model for material classification. The scripts perform the following tasks:</p> <ol> <li><strong>Feature Extraction</strong>: Extract spectral features from hyperspectral data.</li> <li><strong>Background Removal and Subset Extraction</strong>: Remove backgrounds from images and extract subsets for analysis.</li> <li><strong>Data Visualization</strong>: Generate plots to visualize the extracted features and reflectance curves.</li> <li><strong>Machine Learning Model Training</strong>: Using the extracted features, train and evaluate a multilayer perceptron (MLP) classifier.</li> </ol> <h2>Prerequisites</h2> <p>Before running the scripts, ensure that you have the following:</p> <ul> <li><strong>Python 3.x</strong> installed on your system.</li> <li>Required Python packages: <ul> <li><code>numpy</code></li> <li><code>matplotlib</code></li> <li><code>scipy</code></li> <li><code>pandas</code></li> <li><code>scikit-learn</code></li> <li><code>seaborn</code></li> <li><code>rembg</code> (for background removal)</li> <li><code>Pillow</code> (PIL)</li> </ul> </li> <li><strong>Hyperspectral data files</strong> in <code>.mat</code> format containing calibrated hyperspectral cubes and wavelength information.</li> <li>A directory structure to organize input and output files as described in each script.</li> </ul> <h2>Scripts Description</h2> <h3>1. <code>hyperspectral_features_v2.py</code></h3> <h4><strong>Purpose</strong></h4> <p>This script processes individual hyperspectral image files to extract spectral features from a central subset of the image. It generates RGB images from the hyperspectral data, plots the mean reflectance spectra, and outputs a LaTeX-formatted table containing the extracted features.</p> <h4><strong>Functionality</strong></h4> <ul> <li><strong>Loading Data</strong>: Reads <code>.mat</code> files containing hyperspectral data from a specified input directory.</li> <li><strong>Feature Calculation</strong>: <ul> <li>Calculates mean reflectance within a central window of the image.</li> <li>Extracts spectral features such as peak wavelength and area under the reflectance curve.</li> <li>Records reflectance values at selected wavelengths, including standard RGB channels and additional wavelengths.</li> </ul> </li> <li><strong>RGB Image Generation</strong>: Creates RGB images using specific wavelengths corresponding to the red, green, and blue channels.</li> <li><strong>Spectra Plotting</strong>: Plots the mean reflectance spectra for each sample.</li> <li><strong>LaTeX Table Generation</strong>: Produces a LaTeX-formatted table of the extracted features for inclusion in a report or paper.</li> </ul> <h4><strong>Usage Instructions</strong></h4> <ol> <li> <p><strong>Prepare Input Data</strong>:</p> <ul> <li>Place your <code>.mat</code> files containing the hyperspectral data in the appropriate input directory (e.g., <code>input/mortar</code>).</li> </ul> </li> <li> <p><strong>Run the Script</strong>:</p> <ul> <li>Modify the <code>materials</code> list at the end of the script to include the materials you want to process (e.g., <code>materials = ['mortar']</code>).</li> <li>Execute the script: <div> <div>bash</div> <div> <div> <div> </div> </div> </div> </div> </li> </ul> </li> </ol>
Awareness of FAIR and FAIR4RS among international research software funders (Dataset)
<p><span>This research employed a mixed methods online survey to investigate research software funders’ perspectives. </span></p> <p><span>All participants gave informed consent at the start of the online survey. The University of Illinois Urbana-Champaign Institutional Review Board (no. 24374) reviewed the study and determined it exempt.</span></p> <p><span>Data collection took place from December 2023 to May 2024. The mean completion time for the detailed survey was 28 minutes and 13 seconds. The data were cleaned and prepared for analysis by removing any identifiable respondent details. </span></p> <h2><span>Survey design</span></h2> <p><span>The survey began by collecting profile information, including institutional affiliation and job title. The survey primarily gathered detailed information about initiatives, policies, or programs to support research software but also included a much smaller set of questions about additional topics, such as strategic funding priorities and awareness of key concepts. The data generated from this survey are too extensive to report in a single manuscript. Here, we focus on the results generated via the set of questions asking about FAIR and FAIR4RS, specifically, the following survey items: </span></p> <table> <tbody> <tr> <td> <p><strong><span>Variable</span></strong></p> </td> <td> <p><strong><span>Survey item</span></strong></p> </td> <td> <p><strong><span>Response options</span></strong></p> </td> </tr> <tr> <td> <p><span>Awareness of FAIR principles</span></p> </td> <td> <p><span>“Have you ever heard of the FAIR (findable, accessible, interoperable, and reusable) principles for data?”</span></p> </td> <td> <p><span>Yes, No, Unsure</span></p> <p><span>(If ‘Yes’, then the next question was asked)</span></p> </td> </tr> <tr> <td> <p><span>“How familiar are you with the FAIR principles for data?”</span></p> </td> <td> <p><span>Not at all Familiar, Slightly Familiar, Somewhat Familiar, Moderately Familiar, Extremely Familiar</span></p> </td> </tr> <tr> <td> <p><span>Awareness of FAIR4RS principles</span></p> </td> <td> <p><span>“Have you ever heard of the FAIR4RS principles for research software?”</span></p> </td> <td> <p><span>Yes, No, Unsure</span></p> <p><span>(If ‘Yes’, then the next question was asked)</span></p> </td> </tr> <tr> <td> <p><span>“How familiar are you with the FAIR4RS principles for research software?”</span></p> </td> <td> <p><span>Not at all Familiar, Slightly Familiar, Somewhat Familiar, Moderately Familiar, Extremely Familiar</span></p> </td> </tr> </tbody> </table> <p><span> </span></p> <p><span>In addition, an open-ended question asked for further detail about the respondents’ assessments of FAIR4RS’s relevance to their work.</span></p> <h2><span>Sampling</span></h2> <p><span>The survey targeted international research funders, including governmental and non-governmental (e.g., philanthropic) organizations. An initial contact list was created based on participation in the Research Software Association (ReSA) and known responsibilities for research software funding among the authors' networks. This list was refined by removing individuals who had moved to unrelated professional roles or were unavailable long-term due to personal issues.</span></p> <p><span>The final contact list comprised 71 people at 37 funding organizations. After excluding individuals when a member of their organization had already provided a complete response or when the person was no longer working on a relevant topic or was otherwise unavailable (total of n=30), 41 people remained. Of these, five did not complete the survey, while 36 individuals (representing 30 research funding organizations) did, yielding a response rate of 87.8% (and representing 81% of the original organizations). Fully completed survey responses were not required for inclusion in the sample, resulting in varied sample sizes across different survey questions.</span></p> <p><span>The respondents represented governmental (n=26), philanthropic (n=6), and corporate (n=1) research funders.</span></p> <p><span>Respondents’ job titles spanned the following categories: Senior Leadership and Executive (e.g., Vice President of Strategy); Program and Project Management (e.g., Senior Program Manager); Planning and Business Development; and Scientific, Technical, and IT roles (e.g., Scientific Information Lead).</span></p> <p><span>Most respondents, 72.7% (n=24), answered “Yes” to the question, “Has your organization established any policies, initiatives, or programs aimed at supporting research software?” Meanwhile, 18.2% (n=6) said “No,” and 9.1% (n=3) were “Unsure.”</span></p> <p><span>Regarding geographic distribution in the achieved sample, most survey respondents were from North America and Europe, with 15 and 12 participants, respectively. The sample also comprised 4 participants from South America, 3 from Oceania, and 1 from Asia, reflecting a global but uneven representation across continents. Some participating funders covered a broad spectrum of disciplines, while others focused on specific domains such as social sciences, health, environment, physical sciences, or humanities.</span></p>
Simulation data for benchmarking de novo long read transcriptome assembly software
<p>Method of simulation of differentially expressed biological replicates</p> <p>We first obtained a subset of transcripts that are widely expressed in the GTEx v9 dataset (92 samples) using Gencode comprehensive annotation (v44). We kept transcripts with more than 5 reads in at least 15 samples after Salmon quantification (18145 genes, 40509 transcripts), and stored their mean count per million (CPM) values as the control group’s baseline expression. We then generated a perturbed set of CPM values where transcript expression was changed by: (1) randomly selecting 1000 genes and changing all transcripts belonging to that gene concordantly (500 genes 2 fold up and 500 genes 2 fold down), (2) selected another 1000 genes randomly, and then select 2 random transcripts from the gene and swap their expression, (3) selected another 1000 genes randomly, and then select 1 random transcript to change its expression (500 transcripts 2 fold up and 500 transcripts 2 fold down). The updated CPM were stored as the perturbed group baseline expression. We then generated a count matrix and CPM matrix for 3 control replicates and 3 perturbed replicates with gamma distribution, followed by a Poisson distribution <a href="https://www.zotero.org/google-docs/?cUP4ui">(Baldoni et al., 2024)</a>. Both long-read and short-read FASTQ files were simulated using SQANTI-SIM with default settings and ONT R9.4 cDNA error profile (v 0.2.1) <a href="https://www.zotero.org/google-docs/?Qyopst">(Mestre-Tomás et al., 2023)</a>. The long read data contained 6 million reads in total, and an average read length of 1085 bp, and short read data was 100 bp paired-end. We then subsampled the short-read data to match the total number of base pairs in the long read data (6.5 billion bases). The simulated data was non-stranded, and contains 2000 DE genes, 2000 genes with DTU, 5927 transcripts with DTU and 6933 DE transcripts.</p> <p> </p>
JSON files containing parameters of training gene models for ab-initio prediction software
<p>These are the JSON files containing parameters of training gene models for ab-initio prediction software. These training datasets are Phytophthora specific and can be further utilized for the gene prediction and annotation of other related Phytophthora strains.</p>
Reference data and analysis software for "Four-color single-molecule imaging with engineered tags resolves the molecular architecture of signaling complexes in the plasma membrane"
<p>Reference data set for the single molecule co-tracking analysis presented in "Four-color single-molecule imaging with engineered tags resolves the molecular architecture of signaling complexes in the plasma membrane". Corresponding author for further inquiries:</p> <p>Prof. Dr. Jacob Piehler</p> <p>University of Osnabrück, Department of Biology/Chemistry, Division of Biophysics, Barbarastr. 11, 49076 Osnabrück, Germany</p> <p>https://www.biophysik.uni-osnabrueck.de/</p>
Segurança Informação - LGPD - Aplicado no Desenvolvimento de Software - ERES
<p>Este artigo tem como principal objetivo descrever as práticas adotadas em uma empresa de desenvolvimento de software, relacionadas à Segurança da Informação e LGPD (Lei Geral de Proteção de Dados), na construção e manutenção de aplicações seguras. Para um melhor entendimento sobre o contexto, uma revisão literária foi realizada. A partir da observação das práticas de segurança, um modelo temático foi elaborado tendo como eixos: Técnico, Cultural/Pessoal e Jurídico, subdivididos nas seguintes áreas: Desenvolvimento, Produto e TIC (no eixo Técnico), Interno e Externo (no eixo Cultural/Pessoal). O eixo Jurídico não foi subdividido. Foram identificadas 42 práticas, sendo algumas adotadas exclusivamente para segurança com foco na LGPD e outras já existiam, sendo modificadas como necessário. Por fim, um resumo das melhores práticas fora elaborado.</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.