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1,916 results for “software,”
FunTaxDB database for uBin software
<p>This dataset consists of the FunTaxDB database used for the uBin software. The uBin software is designed to facilitate the curation of metagenome-asssembled genomes (MAGs). Please see a preprint on the uBin software on https://www.biorxiv.org/content/10.1101/2020.07.15.204776v2 . The FunTaxDB is based on the UniRef100 database with additional taxonomic strings in the FASTA headers. Those entries that did not have a taxonomic affiliation, were additionally BLASTed vs the ncbi-nr database. Entries that had a 100% similarity match to NCBI-nr recieved the NCBI-nr taxonomic affiliation of the matched record. Special characters in the taxonomic levels, separated by ';', were replaced by underscores to make working with regex less error-prone.</p>
Software Similarity Dataset
<p>This dataset contains the post-processed data for software similarity learning. More information is given: <a href="https://github.com/SoftwareUnderstanding/softsim">SoftwareSim_Github</a></p> <p> </p> <p>post_process: All embedded software with autoencoder to make sure each function is the same length (1024 bits), each final is the embedded graph representation of software.</p> <p>final_data: All information obtained by <a href="https://github.com/KnowledgeCaptureAndDiscovery/somef">Somef</a> & <a href="https://github.com/SoftwareUnderstanding/inspect4py">Inspect4py</a> as well as cleaning. Each file represents software in the format given --> Function_Name: [[Called Function], [Function Tokens]]</p> <p>lean_simscore.csv: This file contains software pairs as well as the similarity metrics, format is given:</p> <table> <tbody> <tr> <td>Property</td> <td>Example</td> </tr> <tr> <td>Graph_1</td> <td>kakaobrain_helo_word</td> </tr> <tr> <td>Graph_2</td> <td>mblondel_soft-dtw</td> </tr> <tr> <td>miniLM</td> <td>0.4503</td> </tr> <tr> <td>Sbert</td> <td>0.7204</td> </tr> <tr> <td>TSDAE</td> <td>0.5714</td> </tr> </tbody> </table> <p> </p>
Appendices of the work "On the perceived relevance of critical internal quality attributes when evolving software features"
<p>Several refactorings performed while evolving software features aim to improve internal quality attributes like cohesion and complexity. Studies show that non-assisted refactorings might worsen, not improve, internal attributes. Current knowledge is scarce on how developers perceive the relevance of critical internal attributes while evolving features. Internal attributes are critical if their measurement assumes anomalous values. This qualitative study investigates the developer's perception on the relevance of critical internal attributes when evolving features. We target six class-level critical attributes: low cohesion, high complexity, high coupling, large hierarchy depth, large hierarchy breadth, and large size. We performed two industry case studies based on online focus group sessions. Developers discussed how much (and why) critical attributes are relevant for adding or enhancing features. We assessed the relevance of critical attributes individually and relatively, reasons behind the relevance of each critical attribute, and interrelations of critical attributes. Low cohesion and high complexity were perceived as very relevant because they often make evolving features hard while tracking failures and adding features. The other critical attributes were perceived as less relevant when reusing code or adopting design patterns. An example of perceived interrelation is high complexity leading to high coupling.</p>
Appendices of the work "On the perceived relevance of critical internal quality attributes when evolving software features"
<p>Several refactorings performed while evolving software features aim to improve internal quality attributes like cohesion and complexity. Studies show that non-assisted refactorings might worsen, not improve, internal attributes. Current knowledge is scarce on how developers perceive the relevance of critical internal attributes while evolving features. Internal attributes are critical if their measurement assumes anomalous values. This qualitative study investigates the developer's perception on the relevance of critical internal attributes when evolving features. We target six class-level critical attributes: low cohesion, high complexity, high coupling, large hierarchy depth, large hierarchy breadth, and large size. We performed two industry case studies based on online focus group sessions. Developers discussed how much (and why) critical attributes are relevant for adding or enhancing features. We assessed the relevance of critical attributes individually and relatively, reasons behind the relevance of each critical attribute, and interrelations of critical attributes. Low cohesion and high complexity were perceived as very relevant because they often make evolving features hard while tracking failures and adding features. The other critical attributes were perceived as less relevant when reusing code or adopting design patterns. An example of perceived interrelation is high complexity leading to high coupling.</p>
ePol: Impacts of Diversity on Software Teams Dataset
<p>Data set that has all the records of the activities carried out in the ePol Project, with their respective attributes. Additionally, it also includes code that does a cleanup according to some criteria. After the cleaning performed by the aforementioned code, the results are also available in the "dataset_epol_result.csv" file.</p> <p>For more information read: SOUZA, NATAN. MASSONI, TIAGO. SARMENTO, CAMILLA. <strong>Impactos da Diversidade em Equipes de Software</strong>, 2023.</p>
Diversity Awareness in Software Engineering Participant Research
<p>This dataset contains the result of a classification of three ICSE venues namely, ICSE 2019, 2020, and 2021 technical tracks, as stated in the methodology of the paper “Diversity awareness in software engineering participant studies” by Dutta et al. (2023).</p>
A census of research software in 171 academic institutional repositories.
<p> A dataset of metadata for 171 UK academic institutional repositories, including a census of research software contained.</p> <table> <tbody> <tr> <td><strong>URL</strong></td> <td>The OAI url</td> </tr> <tr> <td><strong>id</strong></td> <td>CORE Identifier</td> </tr> <tr> <td><strong>openDoarId</strong></td> <td>Open DOAR identifier</td> </tr> <tr> <td><strong>name</strong></td> <td>Name of repository</td> </tr> <tr> <td><strong>Russell_member</strong></td> <td>If the university is a member of the Russell Group of research intensive universities</td> </tr> <tr> <td><strong>RSE_group</strong></td> <td>If an RSE group is present (based on Soc of RSE data)</td> </tr> <tr> <td><strong>email</strong></td> <td>Redacted</td> </tr> <tr> <td><strong>uri</strong></td> <td>Not used</td> </tr> <tr> <td><strong>uni_sld</strong></td> <td>Second level domain (the part of the url between . And .ac.uk</td> </tr> <tr> <td><strong>homepageUrl</strong></td> <td>University website</td> </tr> <tr> <td><strong>source</strong></td> <td>Not used</td> </tr> <tr> <td><strong>ris_software</strong></td> <td>the Research Information System software used</td> </tr> <tr> <td><strong>ris_software_enum</strong></td> <td>Resolve ris_software into similar types (e.g. Eprints 3, EPrints3.3.16 both equal eprints)</td> </tr> <tr> <td><strong>metadataFormat</strong></td> <td>the protocol used for metadata</td> </tr> <tr> <td><strong>createdDate</strong></td> <td>Repository creation date</td> </tr> <tr> <td><strong>location</strong></td> <td>location of university</td> </tr> <tr> <td><strong>logo</strong></td> <td>University logo (resolves in error)</td> </tr> <tr> <td><strong>type</strong></td> <td>Only = Repository for this dataset. Can be = journal etc.</td> </tr> <tr> <td><strong>stats</strong></td> <td>Not used</td> </tr> <tr> <td><strong>contains_software_set</strong></td> <td>Whether the OAI-PMH software set is present in the repository.</td> </tr> <tr> <td><strong>Num_sw_records</strong></td> <td>The response of the OAI-PMH query for software (erroneous as discussed in paper)</td> </tr> <tr> <td><strong>Error</strong></td> <td>The category of error returned by the experiment’s OAI-PMH queries (see paper)</td> </tr> <tr> <td><strong>Manual_Num_sw_records</strong></td> <td>The true amount of software contained in the repository as found by a manual exhaustive search of each university website</td> </tr> <tr> <td><strong>Category</strong></td> <td>Whether the repository (a) contains software; (b) can contain software, but doesn’t yet; (c) has no separate type of research output called software or similar</td> </tr> </tbody> </table>
A Systematic Literature Review of Machine Learning for Uncovering Software Faults and Failures
<p>This data set contains the results of an extensive, systematic literature review on the use of machine learning (ML) for uncovering software faults and failures. Covering the period of 2019 to 2022, this literature review identifies 874 relevant publications, classified into six distinct quality assurance tasks. Results show a compound annual growth rate (CAGR) of relevant publications of 38% over the last five years.</p> <p>This literature review particularly analyzed in how far these relevant papers leverage synergies between different quality assurance tasks. Results show that only 3% of all relevant papers leverage such synergies, indicating ample opportunities for future research. For example, a single type of quality assurance activity may not suffice to deliver the expected software quality. Ideally, one would use a suitable combination of different types of activities – such as combining dynamic testing with static code analysis. Also, leveraging the synergies between different quality assurance activities can increase the effectiveness of the individual activities. For example, having a good estimate of the fault density of a software component (e.g., using deep learning-driven fault prediction techniques) could help optimize and prioritize testing effort and budget.</p>
A StackExchange Dataset of Developer Questions Related to Checked-in Secrets in Software Artifacts
<p>Throughout 2021, GitGuardian's monitoring of public GitHub repositories revealed a two-fold increase in the number of secrets (database credentials, API keys, and other credentials) exposed compared to 2020, accumulating more than six million secrets. To our knowledge, the challenges developers face to avoid checked-in secrets are not yet characterized. In our artifact, we provide a dataset containing 779 questions mined from three StackExchange sites asked by developers related to checked-in secrets from three StackExchange sites. In addition, we provide 434 accepted answers provided by the other users of StackExchange to mitigate the challenge of checked-in secrets.</p> <p> </p> <table> <caption>An overview of StackExchange artifact</caption> <thead> <tr> <th scope="col">Field Name</th> <th scope="col">Description</th> </tr> </thead> <tbody> <tr> <td>Id</td> <td>An unique identifier of the question.</td> </tr> <tr> <td>Title</td> <td>The title of the question.</td> </tr> <tr> <td>Body</td> <td>The description of the question.</td> </tr> <tr> <td>Tags</td> <td>The tags related to the question such as "security", "git" and "key-management".</td> </tr> <tr> <td>CreationDate</td> <td>The date when the question is posted.</td> </tr> <tr> <td>Score</td> <td>The count of upvotes in the question.</td> </tr> <tr> <td>ViewCount</td> <td>The number of users who viewed the question.</td> </tr> <tr> <td>AnswerCount</td> <td>The total number of answers posted in the question.</td> </tr> <tr> <td>CommentCount</td> <td>The total number of comments posted in the question.</td> </tr> <tr> <td>FavouriteCount</td> <td>The total number of users who marked the question as favourite.</td> </tr> <tr> <td>ClosedDate</td> <td>The date when the community marked the question as closed. </td> </tr> <tr> <td>URL</td> <td>The url of the question.</td> </tr> <tr> <td>AcceptedAnswerId</td> <td>The unique identifier of the accepted answer for the question.</td> </tr> <tr> <td>Answer</td> <td>The accepted answer of the question.</td> </tr> </tbody> </table>
Break the Code? Breaking Changes and Their Impact on Software Evolution (Artefacts)
<p>The artefacts included in this repository accompany the thesis "Break the Code? Breaking Changes and Their Impact on Software Evolution" authored by Lina María Ochoa Venegas and supervised by prof.dr. Jurgen Vinju, prof.dr. Mark van den Brand, and dr.Thomas Degueule. The thesis was developed at Eindhoven University of Technology (TU/e) in Eindhoven, The Netherlands and Centrum Wiskunde & Informatica (CWI) in Amsterdam, The Netherlands. It was submitted to revision in 2022 and defended in 2023.</p> <p> </p> <p><strong>Relevant Links</strong></p> <ul> <li><strong>Maracas:</strong> https://github.com/alien-tools/maracas</li> <li><strong>BreakBot: </strong>https://github.com/alien-tools/breakbot</li> </ul>
Data, code and software to reproduce the article entitled "Modeling soil-plant functioning of intercrops using comprehensive and generic formalisms implemented in the STICS model"
<p>This is the data, code and software to reproduce the article entitled " Modeling soil-plant functioning of intercrops using comprehensive and generic formalisms implemented in the STICS model". Here is a summary of the paper:</p> <p>The growing demand for sustainable agriculture is raising interest in intercropping for its multiple potential benefits to avoid or limit the use of chemical inputs or increase the production per surface unit. Predicting the existence and magnitude of those benefits remains a challenge given the numerous interactions between interspecific plant-plant relationships, their environment and the agricultural practices. Soil-crop models are critical in understanding these interactions in dynamics during the whole growing season, but few models are capable of accurately simulating intercropping systems.</p> <p>In this study, we propose a set of simple and generic formalisms for simulating key interactions in intercropping systems that can be readily included into existing dynamic crop models. This requires simulating important processes such as development, light interception, plant growth, N and water balance, and yield formation in response to management practices, soil conditions, and climate. These formalisms were integrated into the STICS soil-crop model and evaluated using observed data of intercropping systems of cereal and legumes mixtures, including Faba bean-Wheat, Pea-Barley, Sunflower-Soybean, and Wheat-Pea mixtures. We demonstrate that the proposed formalisms provide a comprehensive simulation of soil-plant interactions in various types of bispecific intercrops. The model was found consistent and generic under a range of spring and winter intercrops (nRMSE = 25% for maximum leaf area index, 23% for shoot biomass at harvest, and 18% for yield).</p> <p>This is the first time a complete set of formalisms has been developed and published for simulating intercropping systems and integrated into a soil-crop model. With its emphasis on being generic, sufficiently accurate, simple, and easy to parameterize, STICS is well-suited to help researchers designing <em>in silico</em> the agroecological transition by virtually pre-screening sustainable, manageable intercrop systems adapted to local conditions.</p> <p> </p> <p> </p> <p> </p>
Data and software: Heat flux for semi-local machine-learning potentials
<p><br> This repository contains data, code, and related artefacts supporting the following publication:</p> <p>"Heat flux for semi-local machine-learning potentials"<br> by Marcel F. Langer, Florian Knoop, Christian Carbogno, Matthias Scheffler, and Matthias Rupp<br> arXiv: TBD<br> doi: TBD<br> </p> <p>More details can be found in the main README.md file, and the README.md files in the subfolders.</p> <p><br> For any further questions, feel free to contact mail@marcel.science, @marceldotsci on Twitter, or @marcel@sigmoid.social.</p> <p> </p>
Data and Software for "Gullies on Mars could have formed by melting of water ice during periods of high obliquity"
<p>Code, movies and climate model outputs for "Gullies on Mars could have formed by melting of water ice during periods of high obliquity" by Dickson et al. Science, 2023.</p>
A dataset of metadata for UK academic institutional repositories, including a census of research software contained.
<p>A dataset of metadata for UK academic institutional repositories, including a census of research software contained.</p> <table> <tbody> <tr> <td><strong>URL</strong></td> <td>The OAI url</td> </tr> <tr> <td><strong>id</strong></td> <td>CORE Identifier</td> </tr> <tr> <td><strong>openDoarId</strong></td> <td>Open DOAR identifier</td> </tr> <tr> <td><strong>name</strong></td> <td>Name of repository</td> </tr> <tr> <td><strong>Russell_member</strong></td> <td>If the university is a member of the Russell Group of research intensive universities</td> </tr> <tr> <td><strong>RSE_group</strong></td> <td>If an RSE group is present (based on Soc of RSE data)</td> </tr> <tr> <td><strong>email</strong></td> <td>Redacted</td> </tr> <tr> <td><strong>uri</strong></td> <td>Not used</td> </tr> <tr> <td><strong>uni_sld</strong></td> <td>Second level domain (the part of the url between . And .ac.uk</td> </tr> <tr> <td><strong>homepageUrl</strong></td> <td>University website</td> </tr> <tr> <td><strong>source</strong></td> <td>Not used</td> </tr> <tr> <td><strong>ris_software</strong></td> <td>the Research Information System software used</td> </tr> <tr> <td><strong>ris_software_enum</strong></td> <td>Resolve ris_software into similar types (e.g. Eprints 3, EPrints3.3.16 both equal eprints)</td> </tr> <tr> <td><strong>metadataFormat</strong></td> <td>the protocol used for metadata</td> </tr> <tr> <td><strong>createdDate</strong></td> <td>Repository creation date</td> </tr> <tr> <td><strong>location</strong></td> <td>location of university</td> </tr> <tr> <td><strong>logo</strong></td> <td>University logo (resolves in error)</td> </tr> <tr> <td><strong>type</strong></td> <td>Only = Repository for this dataset. Can be = journal etc.</td> </tr> <tr> <td><strong>stats</strong></td> <td>Not used</td> </tr> <tr> <td><strong>contains_software_set</strong></td> <td>Whether the OAI-PMH software set is present in the repository.</td> </tr> <tr> <td><strong>Num_sw_records</strong></td> <td>The response of the OAI-PMH query for software (erroneous as discussed in paper)</td> </tr> <tr> <td><strong>Error</strong></td> <td>The category of error returned by the experiment’s OAI-PMH queries (see paper)</td> </tr> <tr> <td><strong>Manual_Num_sw_records</strong></td> <td>The true amount of software contained in the repository as found by a manual exhaustive search of each university website</td> </tr> <tr> <td><strong>Category</strong></td> <td>Whether the repository (a) contains software; (b) can contain software, but doesn’t yet; (c) has no separate type of research output called software or similar</td> </tr> </tbody> </table> <p> </p>
A software for automatic identification of oyster species
<p>The files includes all data and final analysis of the work done in CS8 - oysters, task 8.2, CSTP8.2.2_A new software for automatic identification of oyster species. This includes the data management descriptor document (DataSheet_oyster_image_classification.docx), images used (oyster_classification_images.zip), the code developed (oyster_classification_code_package.zip) and different models evaluated (oyster_classification_models.zip), the genetics data produced (oyster_classification_biometrics and PCR.xlsx) and the project report (C639_ostronklassificering.pdf). The content of the files is described briefely below. The data is used in deliverables D1.2, D1.4, D1.5 and D1.6 in the AquaVitae project.</p> <p>oyster_classification_images.zip</p> <p>The data set contains the images used for training the classification models that are capable of classifying images of oysters as either Ostrea edulis or Magallana gigas. The images are sorted in folders named “train” (training data) and “validation” (validation data) with both folders containing sub-folders called “mg” (images of Magallana gigas) and “oe” (images of Ostrea edulis).</p> <p>oyster_classification_code_package.zip</p> <p>The data set contains the code for training a neural network for classifying oyster species based on images. The code also includes localization of oyster within an image and inference of the classification along with the trained models.</p> <p>oyster_classification_models.zip</p> <p>The data set contains the trained classification models that are capable of classifying images of oysters as either Ostrea edulis or Magallana gigas.</p> <p>oyster_classification_biometrics and PCR.xlsx</p> <p>The data set contains biometric information for a subset of 240 Ostrea edulis, 240 Magallana gigas and 204 oysters of unsure species denotation sampled as a start pool for the image analysis project and for genetic evaluation of species belonging.</p>
GitHub Top 25 Software Project Analysis
<p>Companion dataset for the paper "For a More Transparent Governance of Open Source" published in the Communications of the ACM, 66, 8, 28-30, 2023.</p> <p>Data collected on May, 13th, 2022.</p> <p><strong>Note:</strong> cell annotations are only visible in the Excel version of the dataset.</p>
Rapid Review Dataset for Seeking Enlightenment: Incorporating Evidence-Based Practice Techniques in a Research Software Engineering Team
<p>A collection of evidence briefings produced through a rapid literature review protocol the Department of Software Engineering and Research at Sandia National Laboratories. These briefings are described in our research paper, "Seeking Enlightenment: Incorporating Evidence-Based Practice Techniques in a Research Software Engineering Team", which was accepted for publication at the 1st Annual Conference of the United States Research Software Engineer Association (US-RSE'23).</p> <p>Sandia National Laboratories is a multimission laboratory managed and operated by National Technology & Engineering Solutions of Sandia, LLC, a wholly owned subsidiary of Honeywell International Inc., for the U.S. Department of Energy's National Nuclear Security Administration under contract DE-NA0003525. SAND2023-06549O.</p>
A Thematic Synthesis on Empathy in Software Engineering based on the Practitioners' Perspective - Supplementary Material
<p>This repository contains the supplementary material of the paper "A Thematic Synthesis on Empathy in Software Engineering based on the Practitioners' Perspective" <br> (DOI https://doi.org/10.1145/3613372.3613407) accepted at the Research Track of the <br> XXXVII Brazilian Symposium on Software Engineering (SBES 2023).</p> <p>The artifacts are a result of a thematic synthesis of grey literature <br> performed to investigate the meaning, importance, practices, and effects of empathy <br> from the perspective of software practitioners. <br> The analysis was based on web articles from DEV, an online community used by software developers. <br> The data were collected and stored in the repository to preserve the evidence and ensure the study’s replicability.<br> <br> The repository contains the following material:</p> <p>1- <all codes.ods> and <all codes.xlsx><br> All codes generated in the data extraction process, considering research questions RQ1-RQ5:<br> The two files have the same content in different formats - ODS and XLSX.</p> <p>2 - <dataset.csv> <br> The list of web articles collected from the DEV in CSV format with all inclusion and <br> exclusion information, plus demographic data.</p> <p>3 - <empathy-framework.jpg><br> Figure 3 of the paper: A conceptual map of the meaning (boxes in orange) and <br> the value (boxes in blue) of empathy according to the software practitioners</p> <p>4 - <empathy-model.jpg> <br> Figure 4 of the paper: A conceptual framework for communication and collaboration (A), <br> management and leadership (B), coding (C), and code review (D).</p> <p>5 - <extraction.ods> and <extraction.xlsx>. The data extracted from the web articles, <br> including codes and quotes for each research question. <br> The two files have the same content in different formats - ODS and XLSX.</p>
OpenAIRE Covid-19 publications, datasets, software and projects metadata.
<p>This dataset provides access to the metadata records of publications, research data, software and projects that may be relevant to the Corona Virus Disease (COVID-19) fight. The dataset contains the OpenAIRE COVID-19 Gateway records, identified via full-text mining and inference techniques applied to the <a href="https://explore.openaire.eu">OpenAIRE Graph</a>. The OpenAIRE Graph is one of the largest Open Access collections of metadata records and links between publications, datasets, software, projects, funders, and organizations, aggregating 12,000+ scientific data sources world-wide, among which the Covid-19 data sources Zenodo COVID-19 Community, WHO (World Health Organization), BIP! FInder for COVID-19, Protein Data Bank, Dimensions, scienceOpen, and RSNA.</p> <p>The dataset consists of a tar archive containing gzip files with one json per line. Each json is compliant to the schema available at <a href="https://doi.org/10.5281/zenodo.3974226">https://doi.org/</a><a href="https://doi.org/10.5281/zenodo.8238913">10.5281/zenodo.8238913</a>.</p> <p> </p>
Phloem anatomy constraints root system architecture development: theoretical clues from in silico experiments [software and dataset]
<p>Simulation software and results for "<strong>Phloem anatomy constraints root system architecture development: theoretical clues from in silico experiments</strong>"</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.