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22 results for “Software Framework”
Robust framework and software implementation for fast speciation mapping
<p>R script and raw data to test the sparse excitation energy XAS procedure.</p>
DASP: A Framework for Driving the Adoption of Software Security Practices
<p>Online appendix for the publication:</p> <p>DASP: A Framework for Driving the Adoption of Software Security Practices.</p>
Supplemental Material for "Make Your Tools Sparkle with Trust: The PICSE Framework for Trust in Software Tools"
<p>This package contains the materials that were used for the paper "Make Your Tools Sparkle with Trust: The PICSE Framework for Trust in Software Tools".</p> <ul> <li><strong>Interview Script.docx</strong> contains the script that was used for the semi-structured interviews.</li> <li><strong>Codebook.xlsx </strong>contains the final codebook of the study.</li> </ul>
Are Game Engines Software Frameworks? A Three-perspective Study
<p>Dataset for the paper: "Are Game Engines Software Frameworks? A Three-perspective Study"</p>
Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review (Article Pool)
<p>This pdf includes all of the articles that analyzed in the study: "Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review".</p>
Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review (Matching articles with categories)
<p>This document includes which primary study falls into which category with respect to the RQs in the following study: “Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review”</p>
Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review (Matching articles with categories)
<p>This document includes which primary study falls into which category with respect to the RQs in the following study: “Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review”</p>
Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review (Matching articles with categories)
<p>This document includes which primary study falls into which category with respect to the RQs in the following study: “Quality Evaluation Models or Frameworks for Open Source Software: A Systematic Literature Review”</p>
REIP: a Reconfigurable Environmental Intelligence Platform and Software Framework for Fast Sensor Network Prototyping - use case dataset
<p>Sensor networks have dynamically expanded our ability to monitor and study the world. Their presence and need keep increasing, and new hardware configurations expand the range of physical stimuli that can be accurately recorded. Sensors are also no longer simply recording the data, they process it and transform into something useful before uploading to the cloud. However, building sensor networks is costly and very time consuming. It is difficult to build upon other people’s work and there are only a few open-source solutions for integrating different devices and sensing modalities. We introduce REIP, a Reconfigurable Environmental Intelligence Platform for fast sensor network prototyping. REIP’s first and most central tool, implemented in this work, is an open-source software framework, an SDK, with a flexible modular API for data collection and analysis using multiple sensing modalities. REIP is developed with the aim of being user-friendly, device-agnostic, and easily extensible, allowing for fast prototyping of heterogeneous sensor networks. Furthermore, our software framework is implemented in Python to reduce the entrance barrier for future contributions. We show the potential and versatility of REIP in real world applications, along with performance studies and benchmark REIP SDK against similar systems.</p> <p>This dataset was created for the case study in Section 5 of the paper.</p>
Productivity simulation framework in software engineering - Bibliometric data
<p>Bibliographic data from the Web of Science and SCOPUS relate to this search string: <em>framework AND simulation AND productivity AND (software development OR software engineering)</em>. The authors also include the Bibliometrix application report.</p>
A stochastic framework for predicting epidemiological risk areas using the Ornstein-Uhlenbeck process: Software and supplementary material
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CGRE Framework Dataset - A Dataset automatically generated to evaluate OCR Software on Webdocuments
<p><strong>Description</strong><br> The provided dataset was generated by the <a href="https://github.com/Drizzy3D/CGRE">CGRE Framework.</a><br> It was generated as a part of a bachelor thesis and used to evaluate the Tesseract OCR Software on webdocuments.</p> <p><strong>CGRE_dataset.zip:</strong><br> <em>1. crawl.json</em><br> This file contains crawling results from the alexa.com Top 50 most used webpages in the US from the 7th June 2020.<br> The crawling was done specifically for styling information only.</p> <p><em>2. html</em><br> The generated webdocuments can be found in this directory.<br> They are based on the crawled styling information.<br> The levels of the directory are used to store the different styling attributes.<br> Every directory is named by the used value for a specific styling attribute.<br> Every word is placed in a span html element.</p> <p><em>3. dataset</em><br> The rendered webdocuments can be found in this directory as png files.<br> They were rendered using the Chromium Embedded Framework (CEF) and contain corresponding labels.<br> The labels are in the same directory with the same name as the corresponding rendered webdocument, just as txt files.<br> The labels contain "word\t(left,top,width,height)\n" lines.<br> "(left,top,width,height)" is the bounding box of a span element containing a word.<br> "word" is the word in the bounding box.</p> <p><em>4. dataset_tesseract_complete</em><br> This directory contains the Tesseract results on the dataset as txt files.<br> The structure is analogue to the dataset.<br> The txt files contain analogue to the dataset "word\t(left,top,width,height)\n" lines.</p> <p><em>5. evaluation</em><br> The results of the evaluation of Tesseract on the dataset.<br> To evaluate the localisation of words by Tesseract, the Intersection Over Union metric was used, with different threshold values (0.5, 0.6, 0.7, 0.8, 0.9).<br> To evaluate the determination of words by Tesseract, a normalized Levenshtein distance metric was used, with different threshold values (0.5, 0.6, 0.7, 0.8, 0.9).<br> The times were measured by using this system:<br> Ubuntu 20.04, AMD Ryzen 5 1600 CPU, AMD Radeon RX Vega 56 GPU, 16 GB DDR4 RAM with 2400 MHz<br> The different threshold values are stored in the filenames.<br> You can find the results in the csv files.<br> Every line contains the results for a specific webdocument.<br> The txt files contain calculated precision and recall values.</p>
Open Data Package: Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science.
<p>Open Data Package for the paper: Stefanie Betz, Birgit Penzenstadler, Leticia Duboc, Ruzanna Chitchyan, Sedef Akinli Kocak, Ian Brooks, Shola Oyedeji, Jari Porras, Norbert Seyff, and Colin C. Venters. 2024. Lessons Learned from Developing a Sustainability Awareness Framework for Software Engineering Using Design Science. ACM Trans. Softw. Eng. Methodol. 24 00, JA, Article 00 (March 2024), 39 pages. https://doi.org/10.1145/3649597 25</p>
The KISS principle in Software-Defined Networking: a framework for secure communications - dkreutz data
<p>Scripts, summarized data, plots</p> <p>type of data: raw data and processed data</p>
Inclusion and Exclusion Criteria in Software Engineering Tertiary Studies: A Systematic Mapping and Emerging Framework - Replication Package
<p>Replication Package for the paper:</p> <p>D. Costal, C. Farré, X. Franch, C. Quer. 2021. Inclusion and Exclusion Criteria in Software Engineering Tertiary<br> Studies: A Systematic Mapping and Emerging Framework. ESEM '21, <a href="https://doi.org/10.1145/3475716.3484190">https://doi.org/10.1145/3475716.3484190</a></p> <p>Please refer to the above paper if you want to cite/use this data.</p>
Relevant Datasets and Software Used for Paper "KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description"
<p>This repository contains relevant datasets and software used in a paper "KGML-xDTD: A Knowledge Graph-based Machine Learning Framework for Drug Treatment Prediction and Mechanism Description". They are used to run the code of <em>KGML-xDTD </em>stored on <a href="https://github.com/chunyuma/KGML-xDTD">Github</a> and support the results of this paper.</p> <p><strong>About the datasets</strong></p> <p>1. <em>bkg_rtxkg2c_v2.7.3.tar.gz</em></p> <p>This tar.gz file contains three sub-folders: tsv_files, scripts, and relevant_dbs. The "tsv_files" sub-folder has the input files that the neo4j software uses. The "scripts" sub-folder contains a shell script with a relevant python script to construct the biomedical knowledge graph. The "relevant_dbs" sub-folder stores two auxiliary databases that <em>KGML-xDTD</em> needs to use. </p> <p>2. <em>indication_paths.yaml</em></p> <p>This file contains the <a href="https://sulab.github.io/DrugMechDB">DrugMechDB</a> MOA paths that we used to evaluate the predicted MOA paths by <em>KGML-xDTD. </em>It is downloaded from the official <a href="https://github.com/SuLab/DrugMechDB">GitHub repository</a> of DrugMechDB.</p> <p>3. <em>training_data.tar.gz</em></p> <p>This tar.gz file contains the processed training data of four data sources (e.g., <a href="https://mychem.info">MyChem</a>, <a href="https://lhncbc.nlm.nih.gov/ii/tools/SemRep_SemMedDB_SKR/SemMedDB_download.html">SemMedDB</a>, <a href="https://bioportal.bioontology.org/ontologies/NDFRT">NDF-RT</a>, <a href="https://unmtid-shinyapps.net/shiny/repodb/">RepoDB</a>) mentioned in the paper. These processed drug-disease pairs have been matched to the identifiers of biological entities used in our biomedical knowledge graph and respectively split into true positive (tp) sets and true negative (tn) sets. We also provide the names of these drug identifiers and disease identifiers under a sub-folder "translated _to_name".</p> <p><strong>About the software</strong></p> <p><em>neo4j-community-3.5.26.tar.gz</em></p> <p>This tar.gz is the Neo4j community version 3.5.26 downloaded from <a href="https://neo4j.com/download-center/#community">Neo4j Download Center</a>. Although the newer versions are available, due to their big changes in the Neo4j setting that are not compatible with our scripts on Github, we provide the version that we used in our research. If you would like to use the newer version, modifications to our script will be required to import the biomedical knowledge graph into your local Neo4j database with the new setting.</p>
PSAS: A Framework for Peer Assessment of an Individuals Skills in a Software Projects Team
<p>Competency assessment in a software project team allows you to identify how each individual can collaborate to achieve the success of a project. The peer assessment approach can bring better results by having the vision of more than one team member. This article proposes a framework to automate the peer assessment of people’s competences in software project teams, developed and evaluated using the interaction design approach. The framework allows to create and apply peer reviews with co-assessment, 360-degree and self-assessment strategies and graphically analyze the performance of people, teams, compare the results of the assessment, identify highlights and follow trends through history.</p>
Machine Learning-Enabled Software and System Architecture Frameworks
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Datasets of "FraSSD: a framework to help teachers in the challenge of developing hard and soft skills in Software Engineering students"
<p>Hard and soft skills are essential for software professionals to perform their daily activities successfully, considering the specificities of the area, which requires a combination of different skills during the software development cycle. Thus, professionals must develop such skills from undergraduate onwards. In this context, although there are related works in the literature, no results were found that were aimed at supporting teachers in developing students' soft skills in the classroom through active education methodologies, focusing on student motivation. Therefore, to provide a model to help teachers in the challenge of contemplating hard and soft skills in a more meaningful teaching-learning process for students enrolled in Software Engineering subjects, this paper presents a framework based on the active methodologies PBL - Project Based Learning and role-play. Emphasis was placed on the six main soft skills required of software engineers: communication, teamwork, organization, leadership, learning, and creativity. To complement the motivational elements, the framework, called FraSSD – Framework for Soft Skills Development, uses some gamification elements, namely competition, scoring, and scoreboard. Finally, the activities established for students are based on Bloom's Taxonomy, which provides progressive knowledge development. As a proof of concept, FraSSD was applied in two Software Engineering classes, and a self-evaluation was conducted on the part of the students before and after the development of the project proposed by the framework, in which students evaluated their level of development in each of the six soft skills worked on during the course. The results were compared through statistical testing, showing an increase in the level of development of soft skills worked on, emphasizing organization, creativity, and teamwork in Class 1 and teamwork, creativity, and leadership in Class 2. Furthermore, a phenomenological analysis of an open question in the self-evaluation enabled FraSSD's positive points to be observed, as well as suggestions for improvements. From the teachers' point of view who applied the framework, although they suggested some modifications, they considered the FraSSD model easy to implement, which helped motivate students as opposed to traditional teaching methods.</p>
Models, scripts, simulated data, and results from the article "Evaluation and comparison of methods for neuronal parameter optimization using the Neuroptimus software framework."
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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.