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32 results for “Software Process”
Software for processing data from a fast-responding RINKO EC oxygen/temperature sensor (JFE Advantech Co, Ltd)
This dataset describes how data from a fast-responding JFE Advantech RINKO EC ARO-EC-CM sensor connected to a Nortek Vector is processed to obtain accurate aquatic eddy covariance measurements. The code and documentation are stored in a .zip file. It consists of a manual, Fortran source code, a definition file and a complied executable suitable for running on Microsoft Windows. The software development was supported by NSF funding to PI Berg (OCE-1824144, OCE-2223204).
[Dataset] Software Process Line as an Approach to Support Software Process Reuse: a Systematic Literature Review
<p>Dataset of a Systematic Literature Review on Software Process Line as an Approach to Support Software Process Reuse</p> <p>Please, read README.txt file before go through dataset.</p>
How Software Organizations are using the ISO/IEC 29110 Standard's Processes: A Survey of the State of the Art and Practice
<p>ISO/IEC 29110 was developed containing a set of industrially validated practices that can potentially be adopted by software Very Small Entities (VSE). VSEs usually have characteristics that differentiate them from organizations of different sizes, such as extremely limited resources and informal project management processes, tending to adopt Agile methods and having an historical resistance to the adoption of standards, that are in general developed for large organizations. In this sense, our research question arises: "How are software organizations using the ISO/IEC 29110 practices?". To answer this question, a Systematic Mapping Study (SM), and a Survey with software organizations were carried out in order to identify the state of the art and the state of the practice in relation to the use of the standard’s practices. The SM identified 21 primary studies reporting the use of the standard in hundreds of software organizations with positive results such as organizational learning, process improvement, improved communication, and also some negative results, such as deployment difficulties in technical areas and the need for additional time and resources. The Survey carried out with 23 software companies identified that, in general, companies do not explicitly know the content of the standard, but partially carry out, in accordance with the standard, practices related to planning, monitoring, control and execution of a project plan, and do not carry out requirements analysis or architecture and detailed design as defined in the standard. </p>
Impact of Antipatterns on Software systems- Snowballing Process
<p> This study presents a systematic literature review that accumulates, summarizes, and reports the results of 97 relevant Primary Studies (PSs) concerning the impact of APs on Object Oriented (OO), Service Oriented (SO), and mobile Oriented (MO) software applications from 2005 to 2024 while considering several internal and external quality attributes. The PSs are classified based on the techniques used to find the impact of APs, type of datasets, evaluation measures, and tool support. </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>
Figure 1. Visual and synthetic representation of the modelling process in the software industry-The Fundamentals Regarding the Usage of the Concept of Interface for the Modeling of the Software Artefacts
<p>The experience that is accumulated regarding the modelling paradigms in the software engineering is impressive. Thus, the software engineering recognizes modelling paradigms like object orientation, aspect orientation, component orientation, service orientation, agent orientation. In one form or another, these paradigms prove their ex- cellence in certain types of IT projects. At the same time, these paradigms reveal their objective limits when they are used to engineer the real world software systems. Every modelling paradigm represents, in fact, a modality to represent the real world using a specific formal framework. The specificity of the formal framework is defined from both a syntactic and semantic perspective. The formal syntactic framework of a paradigm refers to the concepts that are used by the paradigm in order to represent the real world, but also to the recommended principles that allow for these concepts to interact in a correct and efficient manner. Both the concepts and the principles benefit from a formal representation that ultimately favours communication as a secondary modelling lever inside the IT projects. Every syntactic artefact of a paradigm can be associated with a certain real world semantics, which it abstracts. As a consequence, considering that the real world continuously enhances its semantic potential, the syntactic constructs that are favoured by the paradigm may become problematic.</p>
Low-Range FMCW Insect Radar - Lab Experiments Data, Results, and Data Processing Software
<p>This research was funded by the Hessian State Ministry for Higher Education, Research and the Arts, Germany, as part of the LOEWE priority project Nature 4.0—Sensing Biodiversity.</p>
UnCoVar: Benchmarking dataset for SARS-CoV-2 sequence processing software pipelines, Sanger sequences
Open the record for dataset details and reuse information.
Does the social distancing measures impact collaboration in software processes?
<p>Context: Social distancing measures to reduce the impacts of the COVID-19 pandemic bring challenges for several organizations<br> and their Information Systems (IS). Problem: Social distancing were suddenly implemented, with no adequate planning. Furthermore, there is not a clear understanding of the impacts of social distancing measures on the software development teams’ work. Solution: This study aims to identify the presence and impacts of social distancing on collaboration between people, as well as<br> on activities and technologies used in software development, from the perspective of professionals who work in software development teams. IS Theory: The research was developed under the aegis of the Team Resilience Theory. Method: A field study was conducted based on structured interviews and qualitative analysis. Results: In general, it was observed no impact on the software processes activities, although their performance was affected. It was also observed that the impact on the collaboration dynamics in software development was perceived in different ways, by several team members. This study identified the most common tools used by organizations, as well as the benefits and challenges of collaboration in software development after social distancing measures. Contribution: The impacts of social distancing on collaboration in software development were identified, reinforcing the Team Resilience Theory and similar study conducted in the beginning of the COVID-19 pandemic. This study contributes to the Grand Research Challenge "Systemic and Socially Aware Perspective for Information Systems".</p>
Codes and data for Software lock-in processing
<p>These codes and data sets are going to be published in:</p> <p>Oppermann, F., Günther, T.: A remote-control datalogger for large-scale resistivity surveys and robust processing of its signals using a software lock-in approach; Geosci. Instrum. Method. Data Syst.</p>
The Decision-Making Process Behind Software Library Adoption
<p>Dataset supporting journal publication "The Decision-Making Process Behind Software Library Adoption".</p>
Comparing the Use of Research Resource Identifiers and Natural Language Processing for Citation of Databases, Software and Other Digital Artifacts
<p><strong>The Research Resource Identifier was introduced in biomedicine in 2014 to more precisely identify the reagents and tools used in published biomedical research and to track use of tools across the breadth of the biomedical literature. The current RRID specification covers key biological and digital resources. Authors are instructed to include an RRID after the first mention of any resource used. RRIDs are designed to be easy to find using a full text search search engine. </strong></p> <p><strong>The published data sets were used in our comparative study where comparing the output of our RRID curation workflow with the outputs of automated text mining systems that have been used to identify mentions of resources in the text of publications. All files in tab-separated format (tsv). </strong></p> <p><strong>Scibot.tsv: Records of the RRID curation workflow using SciBot. </strong></p> <p>Each record shows that a resource RRID was identified in paper PMID with curator tags (Tag1, Tag2, both optional)</p> <p><strong> </strong>PMID: Pubmed ID</p> <p> RRID: Research Resource Identifier</p> <p> Tag1: Curator tags (optional)</p> <p> Tag2: Additional curator tags (optional)</p> <p><strong>rdwsorted.tsv: Records of the output from RDW, a text mining software. </strong></p> <p>RDW identifies mentions of research resources in papers. Each record shows that a resource RRID was identified in paper PMID.</p> <p><strong> </strong>PMID: Pubmed ID</p> <p> RRID: Research Resource Identifier</p> <p><strong>rridbyrdw05282019.tsv: Records of the output of the RRID-by-RDW in RDW. </strong></p> <p>RRID-by-RDW is a component in RDW that identifies mentions of research resources in papers by matching patterns of RRID specifications. Each record shows that a resource RRID was identified in paper PMID.</p> <p><strong> </strong>PMID: Pubmed ID</p> <p> RRID: Research Resource Identifier</p> <p> Context: Snippet where the RRID was found</p> <p><strong>resource_metadata20190418.tsv: Metadata of RRIDs</strong></p> <p>This file contains metadata of resources and their RRIDs. See file header for column definitions.</p> <p><strong>RRIDCUR-definitions.tsv: Definitions of curator tags used in Scibot.tsv.</strong></p> <p><strong> </strong>tag: Tag name</p> <p> definition: Definition of the tag</p>
Data set and data processing software of: Bacterial cell size modulation along the growth curve across nutrient conditions
<div>In Repository.zip it is possible to find the following folders:</div> <div> </div> <div>ImageProcess: Shows an example of the studied phtos, the segmentation mask obtained using Ilastik and the scripts used to estimate the cell dimensions.</div> <div> </div> <div>DataProcessing: Includes the raw data for cells size in all the studied conditions, a script showing the filtering and the data processing for plotting most of the figures of the article.</div> <div> </div> <div>CFUod: Includes the dataset of CFU and OD measurements studied in the article. The inered trends over different biological replica and the data processing for plotting the Figures in the main text. </div> <div> </div> <div> </div> <div>_______________________________________________________________</div> <div> </div> <div>ImageProces:</div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* IMAGES folder: Contains a 10 arbitrary folders of images, one for different OD conditions for the experiment of M9 + 0.25% CAS. Each image is a .tif file. The pixel size is 0.07 micrometers per pixel and they were obtained using bright field microscopy imaging. </div> <div> </div> <div>* SEG folder: Contains the masks for the same number of folders and photos equivalent photos in the IMAGES folder. Masks are also in .tif format.</div> <div> </div> <div>* "Dataset.csv": Is a typical dataset obtained from the images using the script of image processing. The data consists on the following columns:</div> <div>a. OD: Label of the OD measurement. Following experimental arbitrary notation, this number was the time in hours times 10. </div> <div>b. Photo: The label of the segmented photo.</div> <div>c. Area: Area of the segmenteated contour (squared micrometers).</div> <div>d. Len: Cell size length (Micrometers).</div> <div> </div> <div>* "ImageProcesing.ipynb": Jupyter notebook for procesing the images and their masks. The output is "Dataset.csv"</div> <div> </div> <div>____________________________________________________________________________________________________</div> <div> </div> <div> </div> <div>DataProcessing:</div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* RawData.csv: comma separated values file with the dimensions of different cells in for the studied conditions. The data consists on the following columns:</div> <div>a. Strain: Represents the experimental condition. It has the following values:</div> <div>M9= E.coli Growth in minimal M9</div> <div>M9cas25= E.coli in M9 + 0.25% Casaminoacids</div> <div>LBSS= E.coli in LB in steady growth</div> <div>SalLB= S. enterica in LB.</div> <div>SalM9=S. enterica in M9</div> <div>M9cas50= E.coli in M9 + 0.5% Casaminoacids</div> <div>LB2= E. coli in LB</div> <div>b. Photo: label for the studied photo.</div> <div>c. Time: Time in hours after resuspension.</div> <div>d. OD: Optical density of the studied population.</div> <div>e. Len: Cell length of the situdied contour (micrometers).</div> <div>f. Area: Projected area of the cell contour (squared micrometers).</div> <div>g. Area: Volume of the cell (cubic micrometers).</div> <div>h. SAV surface/volume ratio.</div> <div>i. Width: Cell width </div> <div>j. Aspect; Aspect ratio length/width</div> <div> </div> <div>*Stats.csv: Results of the statistical moments of cell size dimensions calculated from "Rawdata.csv" using "Plotter.ipynb". These data consists on the following columns:</div> <div> </div> <div>a. Time: Time (hours)</div> <div>b. OD: Optical density </div> <div>c. MnVol: Mean cell volume (cubic micrometers)</div> <div>d. MnVolErr: 95% confidence interval of the mean volume.</div> <div>e. CV2Vol: squared coefficient of variation of the volume.</div> <div>f. CV2VolErr: 95% confidence interval squared coefficient of variation of the volume.</div> <div>g. Mnw: Mean cell width (micrometers)</div> <div>h. MnwErr: 95% confidence interval of the mean width.</div> <div>i. CV2w: squared coefficient of variation of the cell width.</div> <div>j. CV2wErr: 95% confidence interval squared coefficient of variation of the width.</div> <div>k. MnLen: Mean cell length (micrometers)</div> <div>l. MnLenErr: 95% confidence interval of the mean length.</div> <div>m. CV2Len: squared coefficient of variation of the cell length.</div> <div>n. CV2LenErr: 95% confidence interval of the squared coefficient of variation of the cell length.</div> <div>o. Strain: Nutrient conditions</div> <div> </div> <div>*Ploter.ipnyb: Jupyter notebook which using "RawData.csv" calculates the moments in "Stats.csv" and plots most of the figures of the main article. </div> <div> </div> <div> </div> <div>__________________________________________________________________________________ </div> <div> </div> <div>CFUod: </div> <div> </div> <div>This folder contains:</div> <div> </div> <div>* resultsOD.csv: OD values for different biology replicas. The columns are as follows:</div> <div>a. t: Time (hours)</div> <div>b. log(OD): Natural logarithm of the bets fit for the optical density</div> <div>c. log(OD) error: 95% confidence interval for the best fit of the natural logarithm of the optical density.</div> <div>d. gr: best fit growth rate in units of 1/hours.</div> <div>e. gr error: 95% confidence interval of the growth rate.</div> <div>f. three columns called "od": each represents the optical density for each experimental replica.</div> <div> </div> <div> </div> <div>* resultscfu.csv: cfu values for different biology replicas. The columns are as follows:</div> <div>a. t: Time (hours)</div> <div>b. log(OD): Natural logarithm of the bets fit for the cfu</div> <div>c. log(OD) error: 95% confidence interval for the best fit of the natural logarithm of the cfu.</div> <div>d. gr: best fit growth rate in units of 1/hours.</div> <div>e. gr error: 95% confidence interval of the growth rate.</div> <div>f. three columns called "od": each represents the cfu for each experimental replica.</div> <div> </div> <div> </div> <div>*ODGrowthRate.ipynb: jupyter notebook that uses "resultsOD.csv" and "resultscfu.csv" for plotting the ratio OD/cfu.</div> <div> </div> <div> </div> <div>Any question please ask cnieto@udel.edu</div> <div> </div> <div>Cesar Augusto Nieto Acuna</div> <div> </div> <div>Newark, Delaware, USA</div> <div> </div> <div>08/05/2024</div>
Open-Source Software Product Line Extraction Processes: the ArgoUML-SPL and Phaser Cases
<p>Collection of datasets and analysis scripts supporting the information provided in the text.</p> <p>There are two compressed files, one for the ArgoUML data and one for the Phaser data. Each compressed file contains a README describing important information.</p>
Modeling software processes from different domains using SPEM and BPMN notations: An experience report of teaching software processes
<p>In a current application development scenario in different environments, technologies and contexts, such as IoT, Blockchain, Machine Learning and Cloud Computing, there is a need for particular solutions for domain-specific software development processes. The proper definition of software processes requires understanding the involved teams and organization’s particularities and specialized technical knowledge in Software Engineering. Although it is an essential part of Software Engineering, many university curricula do not dedicate as much effort to teach software processes, focusing more on the basic principles of Software Engineering, such as requirements, architecture and programming languages. Another important aspect of software processes is modeling. The modeling of a software process provides a basis for managing, automating and supporting the software process improvement. In this context, teaching software process modeling becomes challenging, mainly due to the great emphasis on theory and few practices. This work presents an experience report teaching the definition and modeling of software processes in different domains. We applied in the discipline of software processes a practice for defining and modeling processes in various application domains, such as: IoT, cloud, mobile, critical systems, self-adaptive systems, machine learning, blockchain and games. The processes were modeled in the Software & Systems Process Engineering Metamodel (SPEM) and Business Process Model and Notation (BPMN) notations based on references from the literature for each domain. We evaluated the process modeling practice with the SPEM and BPMN in 3 classes of the software processes discipline and compared the use of the two notations applied to the different domains. We concluded that the modeling tool and the maturity in the domain are essential for the excellent performance of the process.</p>
SatTerm experience: vocabulary control and facet analysis help improve the software requirement solicitation process
<p>One of the most difficult steps in the software development process is moving from requirements written in natural, uncontrolled language, to the formalisms required by the design modelling languages. To solve this issue, practitioners should pay attention to the possibility of applying vocabulary control and knowledge representation techniques to produce better specifications. The use of controlled vocabularies and the modelling of the conceptual relationships between concepts in a specific domain are expected to improve the quality of the specifications.<br> Vocabulary control and semantic modelling are promising tools to avoid the most frequent problems in the requirements specification process: lack of consistency and ambiguity. This paper provides a detailed description of the development process of an ontology used for requirements modelling in the area of satellite control systems. The process applied is based on well-established practices and guidelines applied for the construction of controlled vocabularies and faceted classifications schemas. Engineers can use the ontology when writing system specifications using predefined templates. The use of this ontology ensures the consistency of the specifications written by different engineers improves the communication with other parties involved in the system construction activities and sets the foundations for a semi-automated generation of models for subsequent design activities.</p>
A stochastic framework for predicting epidemiological risk areas using the Ornstein-Uhlenbeck process: Software and supplementary material
Open the record for dataset details and reuse information.
Dataset and Code for "Mining and Predicting Micro-Process Patterns of Issue Resolution for Open Source Software Projects"
<p>Dataset and Code for "Mining and Predicting Micro-Process Patterns of Issue Resolution for Open Source Software Projects" with README included</p>
A Process for Evaluating the Energy Efficiency of Software - Case Study A
<p>This laboratory package includes the data of the case study carried out in this work.</p> <p> </p> <p> </p>
Use of Gamification as Implementation for Software Process Improvement
<p>Studies carried out in the context of software process improvement (SPI) report problems and difficulties that organizations face to implement process models and standards, mainly related to the inability to overcome some critical factors. Thus, properly conducting SPI can facilitate the development of more efficient processes to overcome such difficulties, using gamification elements. Therefore, this article aims to identify problems in the implementation phase of an SPI effort and use gamification as a tool to mitigate barriers during SPI implementation in a company that opted for an SPI program such as CMMI Dev 2.0 and /or MR-MPS-SW and create the applicability guide using gamification in SPI that can be used by software organizations. The search string returned 499 (Four hundred and Ninety-Nine) works, of which 7 (Seven) were selected according to the Systematic Review of Literature criteria.</p>
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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.