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492 results for “Agile”
Integrating Co-design activities in agile development
<p>Within the Cos4Cloud project the agile methodologies recommended the instrument of co-design digests. This instrument was intended to form a bridge between co-design and the actual agile development process. During the project, the final process was elaborated closely with the co-design team, and was documented in the project's wiki page.</p> <p>The process includes the following steps:</p> <ul> <li>Set up a co-design digest page in the wiki</li> <li>co-design session is taking place</li> <li>results are collected in a co-design report (as it used to be)</li> <li>essential feedback/requirement will be added to the digest page</li> <li>Note to the developer team to review and update the digest table</li> <li>Developer team refines and decides on each entry in the digest table and links to user</li> <li>stories in their backlog (does not have to be accessible, though)</li> <li>Developer team updates the digest table during development of related user stories</li> <li>(decisions, implementation status, changes to the story, ...)</li> <li>Occasionally development team is reminded to update the digests</li> </ul> <p>Partners were invited to adopt this intermediary step to densify, and evaluate the outcome of co-design sessions before further integrate them into the actual agile development process.</p> <p>For one co-design session 52°North and Science for Change prepared a session where participants were asked to think in user stories. The outcome was a list of user stories which only had to be reviewed and refined. For such sessions the development teams chose to make evaluation and integration into the backlog an ad-hoc task and skipped the extra creation of a digest to avoid duplicated work. A proximity of co-design output and agile input naturally accelerates the intermediary step of a Co-design digest and its integration work or (in the extreme) even makes it superfluous.</p>
Agile detail levels
<p>Following <a href="https://en.wikipedia.org/wiki/INVEST_(mnemonic)">INVEST principles (by Bill Wake)</a> allows you to write user stories at different levels of detail. User stories should be scaleable with a certain scope. However, it may helps to group stories under a bigger meaning (Epic, Theme, or Activity). Scalable content means to be negotiable over time by all parties involved. The most detailed level describes the decisions made, all tasks a user story is considered to be done, and the specific acceptance criteria the realized story have to meet. </p>
Whole-genome capture and sequencing of Mycobacterium tuberculosis directly from clinical samples - Design of RNA oligonucleotide baits for Agilent Technologies' SureSelect target enrichment
<p>This dataset comprises the sequence of <strong>44 278 RNA oligonucleotide "baits" (120 bp each) </strong>designed to perform <strong>whole-genome capture and sequencing of <em>Mycobacterium tuberculosis</em> directly from clinical samples</strong> (DNA) using Agilent Technologies’ SureSelect target enrichment system following the Illumina paired-end multiplexed sequencing library protocol. </p> <p>RNA oligonucleotide “baits” were designed to span the ∼4.5 Mb of the <em>M. tuberculosis</em> genome. In brief, the reference genome sequence of the MTBC H37Rv strain (Genbank #AL123456) was <em>in silico</em> fragmented into 120 bp sequences twice, to ensure an overlap of 60 bp between sequences. Due to their rich GC content, which could interfere with DNA capture, all MTBC genes of the PE, PPE and PE-PGRS family were also independently fragmented into 120 bp sequences, in order to increase capture sensitivity. All resulting sequences were BLASTn searched against the Human Genomic + Transcript database to excluded homologous sequences to the human genome. Overall, a total of 42,278 RNA probes were generated and this custom bait library was then uploaded to the SureDesign software (https://earray.chem.agilent.com/suredesign) and synthesized by Agilent Technologies. During synthesis, the 2198 sequences complementary to the PE, PPE and PE-PGRS family were unbalanced 8:1 to potentiate capture.</p> <p>More details can be found in the following publication:</p> <p>- Macedo, R., Isidro, J., Ferreira, R., Pinto, M., Borges, V., Duarte, S., Vieira, L., & Gomes, J. P. (2023). Molecular Capture of <em>Mycobacterium tuberculosis</em> Genomes Directly from Clinical Samples: A Potential Backup Approach for Epidemiological and Drug Susceptibility Inferences. <em>International journal of molecular sciences</em>, <em>24</em>(3), 2912. https://doi.org/10.3390/ijms24032912</p>
Agile Accelerator Program: From Industry-Academia Collaboration to Effective Agile Training
<p>The agile accelerator program takes place in a Brazilian technology park, as a collaboration between a university and a world-renowned technology company, specialized in agile development and consulting. This partnership has 8-year long with the main goal of preparing undergraduate students to work in high-performance agile teams. This partnership created a culturally rich environment for student learning while influencing other companies to follow the same initiative within this technology park. We conducted a Case Study aiming to characterize this partnership (explaining how it works) and the resulting program, understanding the benefits to the program students. Our results point out the importance of the kind of partnership that provides an immersive learning environment to students, where students can learn empirically, with real projects and real stakeholders and how important it was for the program's former students to enter the job market. This successful enhanced students' training program on agile software development through the blending of culture between institutions can be of inspiration to those interested in aiming to bridge the gap between academia and industry.</p>
The Contradiction of Agile Measures: Customer as Focus, but Process as Measured?
<p>These Datasets contain the articles used in the research "The Contradiction of Agile Measures: Customer as Focus, but Process as Measured?" and a tabulation of the full set of measures found in articles chosen.</p> <p>This research study adopts a systematic mapping process to understand which agile approach is related to performance measures, what kind of measures are being used, and if the papers are focused on product measures or process measures.</p>
Anchoring pan-scalar map Agile Dataset
<p>This dataset contains data used for the design of anchored pan-scalar maps in Madison and Chambery. Anchoring pan-scalar maps means improving the saliency and memorability of some cartographic elements, i.e. the pan-scalar anchors, to enhance the navigational cues that can be used for self-localization during or after a zoom in a map.</p> <ul> <li>Madison :</li> </ul> <ol> <li>VECTORSQUELETON : This folder contains the digitized data used to create map skeletons at different zoom levels in Madison.(12,13,14,15,16)</li> <li>ANCHOR TRAINLINE : This folder contains the map anchor generalized train line zoom 12,13,14,15,16<br>(12,13,14,15,16) pou madison</li> <li>ANCHOR PENINSULA :This folder contains the peninsula map anchor generalized to zoom 12,13,14,15,16 for Madison.</li> <li>...</li> </ol> <ul> <li>QGIS Project : Qgis project to make the squeleton map</li> <li>Illustration : </li> <li>Web : Anchoring map from madison</li> <li>Scalemaster : Scaleline of madison for anchoring</li> <li>Globalscaline : Scaleline on the global aspect of pan-scalar map</li> </ul>
Supplementary Material for Disruptive Solutions on Requirement Engineering for Agile Software Development: A tertiary study
<p>This repository delivers the supplementary material for the paper: <em>Disruptive Solutions on Requirement Engineering for Agile Software Development: A tertiary study.</em></p> <p>In the following, we present the abstract of the study:</p> <p><strong>Context:</strong> Agile Software Development (ASD) is a disruptive process compared to traditional software development. Therefore, traditional Requirements Engineering (RE) forms may not be the best way to do RE for ASD (RE-ASD). <strong>Objective:</strong> Working with ASD using traditional RE ways could limit ASD's potential. Thus, it is necessary to investigate what academia and industry have done in RE to take full advantage of all of the capabilities of ASD beyond traditional RE. <strong>Method: </strong>We conducted a Tertiary Study looking for solutions for RE-ASD using the Systematic Literature Review (SLR) protocol described by Kitchenham and Charters. We then categorized the solutions into families using Targeted Coding and Constant Comparison, tools from Socio-Technical Grounded Theory (STGT). Afterward, we classified the solutions as disruptive using our model based on the Hype Level Curve concept, assessing their hype (popularity) in the software engineering community using Google Trends and Google Colab tools. <strong>Results:</strong> After executing the SLR protocol, we accepted 37 studies and encountered 136 solutions used by academia and industry for RE-ASD. We categorized these solutions into 21 solution families, six of which we classified as disruptive. Design Thinking (DT) and Artificial Intelligence (AI) were the two families of solutions that stood out the most. We also identified the type of solution (e.g., process, method, technique, tool, model, framework) and domain (academia or industry). Furthermore, we cataloged the challenges presented by the solutions. <strong>Conclusion:</strong> We concluded that only a few solutions that have been used for RE-ASD have the power to successfully challenge the mainstream Agile Software Development process by using innovation (26 out of 106). There is a gap between academia and industry regarding these disruptive solutions, and some challenges still need to be addressed in using these solutions.</p> <p>The repository contains the following:</p> <ul> <li>Dataset from the Tertiary Study: <ul> <li>Data of the retrieved studies. It presents the classifications of the documents as 'Accepted,' 'Rejected' (with the indication of the step of the protocol the authors rejected the study), or 'Duplicated.'</li> <li>Data of all solutions retrieved from the accepted studies</li> </ul> </li> <li>Socio-Technical Grounded Theory (STGT) tools <ul> <li>Result of the use of Targeted Coding and Constant Comparison</li> </ul> </li> <li>The Google Colab Notebook <ul> <li>Code in python</li> <li>Results</li> </ul> </li> </ul> <p> </p>
Datasets for the Next Release Problem (agile and classic costs)
<p>Datasets synthetically sampled for experimentation for the (multi-objective) Next Release Problem. </p> <p>aX datasets have cost values sampled from fibonacci scale.</p> <p>cX and dX datasets have cost values sampled from Function Points size of real datasets from the ISBSG 2015 dataset.</p> <p> </p> <table> <tbody> <tr> <td><strong>Dataset</strong></td> <td><strong>#Stakeholders</strong></td> <td><strong>|R|</strong></td> <td><strong>#(ri⇒D⊆R)</strong></td> <td><strong>%(r_i⇒D⊆R)</strong></td> <td><strong>Avg|D|</strong></td> </tr> <tr> <td>a1</td> <td>5</td> <td>50</td> <td>18</td> <td>0.360</td> <td>2.222</td> </tr> <tr> <td>a2</td> <td>15</td> <td>50</td> <td>18</td> <td>0.360</td> <td>2.722</td> </tr> <tr> <td>a3</td> <td>5</td> <td>200</td> <td>74</td> <td>0.370</td> <td>1.946</td> </tr> <tr> <td>a4</td> <td>15</td> <td>200</td> <td>75</td> <td>0.375</td> <td>2.253</td> </tr> <tr> <td>c1</td> <td>15</td> <td>50</td> <td>20</td> <td>0.400</td> <td>2.400</td> </tr> <tr> <td>c2</td> <td>100</td> <td>50</td> <td>17</td> <td>0.340</td> <td>3.529</td> </tr> <tr> <td>c3</td> <td>15</td> <td>200</td> <td>69</td> <td>0.345</td> <td>1.942</td> </tr> <tr> <td>c4</td> <td>100</td> <td>200</td> <td>75</td> <td>0.375</td> <td>2.093</td> </tr> <tr> <td>d1</td> <td>15</td> <td>200</td> <td>88</td> <td>0.440</td> <td>3.352</td> </tr> <tr> <td>d2</td> <td>50</td> <td>200</td> <td>88</td> <td>0.440</td> <td>4.852</td> </tr> <tr> <td>d3</td> <td>15</td> <td>300</td> <td>131</td> <td>0.437</td> <td>3.771</td> </tr> <tr> <td>d4</td> <td>50</td> <td>300</td> <td>145</td> <td>0.483</td> <td>3.697</td> </tr> </tbody> </table>
Fig. 5 in A retrospective study of Babesia macropus associated with morbidity and mortality in eastern grey kangaroos (Macropus giganteus) and agile wallabies (Macropus agilis)
Fig. 5. Phylogenetic tree of heat shock protein 70 (hsp70) gene sequences of eastern grey kangaroo and agile wallaby Babesia and other piroplasm hsp70 sequences in the GenBank nucleotide database. For each sequence, the GenBank GI number is followed by the species name. The representative Babesia isolates from eastern grey kangaroos and an agile wallaby in this study are shown with a - and a ♦ respectively. The evolutionary history was inferred using the Maximum Likelihood method based on the TamuraNei model (Tamura and Nei, 1993). The tree with the highest log likelihood (−6290.3774) is shown. Initial tree for the heuristic search was obtained automatically as follows. When the number of common sites was <100 or less than one fourth of the total number of sites, the maximum parsimony method was used; otherwise, BIONJ method with MCL distance matrix was used. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The scale bar represents the number of substitutions per nucleotide. All positions containing gaps and missing data were eliminated. There are limited data available on this locus within the public data repositories and as such there is some lack of consistency with the 18S ribosomal RNA tree.
Fig. 4 in A retrospective study of Babesia macropus associated with morbidity and mortality in eastern grey kangaroos (Macropus giganteus) and agile wallabies (Macropus agilis)
Fig. 4. Phylogenetic tree of 18S ribosomal RNA (18S rRNA) gene sequences of eastern grey kangaroo and agile wallaby Babesia and other piroplasms that are in the GenBank nucleotide database. For each sequence, the GenBank GI number is followed by the species name. The representative Babesia isolates from eastern grey kangaroos and an agile wallaby in this study are shown with a - and a ♦ respectively. Evolutionary history was inferred using the Maximum Likelihood method based on the Tamura 3-parameter model. The tree with the highest log likelihood (−1820.5242) is shown. Initial tree for the heuristic search was obtained automatically as follows. When the number of common sites was <100 or less than one fourth of the total number of sites, the maximum parsimony method was used; otherwise, BIONJ method with MCL distance matrix was used. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The scale bar represents the number of substitutions per nucleotide. All positions containing gaps and missing data were eliminated. Evolutionary analyses were conducted in MEGA6 (Tamura et al., 2011).
Fig. 1 in A retrospective study of Babesia macropus associated with morbidity and mortality in eastern grey kangaroos (Macropus giganteus) and agile wallabies (Macropus agilis)
Fig. 1. Map showing the distribution of the 38 cases of Babesia infection in eastern grey kangaroos in coastal New South Wales and southeastern Queensland over the period 1995–2013. Insert also shows the two locations of the three cases identified in agile wallabies in northern Queensland in 2009 and 2013. The locations of cases were converted to GPS coordinates and mapped using GPS Visualizer on 21/05/2014 (www.gpsvisualizer.com).
Fig. 3 in A retrospective study of Babesia macropus associated with morbidity and mortality in eastern grey kangaroos (Macropus giganteus) and agile wallabies (Macropus agilis)
Fig. 3. Transmission electron micrographs showing the intravascular location and structure of Babesia organisms in the kidney and brain of eastern grey kangaroos. (A) Kidney, the cytoplasm of two adjacent erythrocytes contains Babesia merozoites (arrows) with a membrane-bound nucleus (N) and cytoplasm containing polymorphic vacuoles and some electron dense particles (C) (scale bar = 1.0 μm). (B) Brain, adjacent to an intact erythrocyte and the nucleus of an endothelial cell is a cluster of extraerythrocytic Babesia organisms containing electron dense micronemes and developing pellicles (arrows) (scale bar = 2.0 μm). (C) Brain, within the capillary lumen is a cluster of eight or nine extraerythrocytic organisms (thick arrow) and a distorted erythrocyte (thin arrow) containing four intracytoplasmic parasites (scale bar = 5.0 μm).
Fig. 2 in A retrospective study of Babesia macropus associated with morbidity and mortality in eastern grey kangaroos (Macropus giganteus) and agile wallabies (Macropus agilis)
Fig. 2. Photomicrographs showing the forms of Babesia seen in cytological preparations and tissue sections in macropods. (A) Agile wallaby. Giemsa stained peripheral blood smear showing extraerythrocytic zoites (thin arrow) and merozoites (thick arrow) within an intact erythrocyte. (B) Eastern grey kangaroo. Diff-Quik-stained renal impression smear demonstrating 2 or 4 merozoites within intact erythrocytes (thick arrows) and clusters of extraerythrocytic zoites (thin arrows). (C) Eastern grey kangaroo. DiffQuik-stained brain squash preparation showing large clusters of intravascular zoites (arrows). (D) Eastern grey kangaroo. H&E stained section of kidney glomerulus showing merozoites within intact erythrocytes (thick arrows) and as large extraerythrocytic clusters of zoites (thin arrows). All scale bars = 20 μm.
Reproducibility Package for "Reproducible research and GIScience: an evaluation using AGILE conference papers"
<p>Data and code for analysis and plots used in the manuscript "Reproducible research and GIScience: an evaluation using AGILE conference papers": <a href="https://doi.org/10.7287/peerj.preprints.26561v1">https://doi.org/10.7287/peerj.preprints.26561v1</a></p> <p>The deposited archived includes a <a href="https://en.wikipedia.org/wiki/Docker_(software)">Dockerfile</a> and an <a href="http://rmarkdown.rstudio.com/">R Markdown</a> document suitable for use with <a href="http://mybinder.org/">Binder</a>: <a href="https://mybinder.org/v2/gh/nuest/reproducible-research-and-giscience/6">https://mybinder.org/v2/gh/nuest/reproducible-research-and-giscience/6</a></p> <p>The version tag of this repository matches the <a href="https://git-scm.com/book/en/v2/Git-Basics-Tagging">git tag</a> on the code repository at <a href="https://github.com/nuest/reproducible-research-and-giscience">https://github.com/nuest/reproducible-research-and-giscience</a>, except version <code>6-fixed</code> which matches the tag <code>6</code>.</p> <p> </p>
Replication Package for a Systematic Literature Mapping of Agility in Safety-Critical Software Development within the Aerospace Industry
<p>This file collection package facilitates the replication of a Systematic Literature Mapping (SLM) focused on Agility in Safety-Critical Software Development within the Aerospace Industry. Authored by J. Eduardo Ferreira Ribeiro, João Gabriel Silva, and Ademar Aguiar, this dataset is dedicated to improving transparency and reproducibility in this field of study and future research.</p> <p>Specifically, the package includes:</p> <ul> <li><a href="https://github.com/zemacedo99/Replication-Package-Builder/releases/tag/v1.0.2">Replication Package Builder Version 1.0.2</a></li> <li>A list of terms (both inclusion and exclusion) used to construct the research string.</li> <li>A list of venues unrelated to the research topic, to be excluded from the results.</li> <li>The inclusion and exclusion criteria applied during the study.</li> <li>Lists of publication results from indexing services like Scopus, IEEE Xplore, Science Direct, HAL Open Science, Springer Nature, and the ACM Digital Library are all provided in CSV file format.</li> <li>A list of all publications in CSV format, compiled after the automated exclusion phase using the established inclusion and exclusion criteria.</li> <li>Finally, a complete list of all publications, including those from Snowball sampling, in XLSX format was compiled after the manual exclusion phase using the established inclusion and exclusion criteria.</li> </ul> <p>Compiled and published on Saturday, September 14, 2024, this dataset is crucial for researchers seeking to replicate or extend the SLM's findings.</p> <p>Lastly, we thank J. Antonio Dantas Macedo for contributing to developing and providing this <a href="https://github.com/zemacedo99/Replication-Package-Builder">replication package builder</a>.</p>
Enhancing Motivation in Software Engineering Education through Gamified Agile Project-based Learning
<p>Project-based learning (PBL), e.g., student software development projects, is an essential part of today's Software Engineering (SE) education. They allow students to work on real-world projects and gain practical experience as a team. However, several challenges arise in such projects, including learning new technologies and dealing with communication and coordination issues within the team. These factors can lead to a lack of motivation to contribute to the project and a decrease in productivity, potentially resulting in an insufficient project outcome. This paper aims to promote student motivation in PBL and increase team productivity by applying gamification. We conducted a user and requirements analysis to identify the needs of students and supervisors of such projects. Based on the insights, we designed and implemented DinoDev, a gamified project management tool that combines project management features with gamification elements. The DinoDev concept was evaluated in a student project, indicating increased motivation and team productivity. The findings are valuable for advancing research on using gamification in PBL and for lecturers to improve their students' motivation and team productivity in SE education.</p>
Fig, 2. Example sonograms of rehabilitant, released and wild agile female gibbons: a, rehabilitant; b, released captive-raised; c, wildraised. in Covarvariation In The Great Calls Of Rehabilitant And Wild Gibbons (Hylobates Albibarbis)
Fig, 2. Example sonograms of rehabilitant, released and wild agile female gibbons: a, rehabilitant; b, released captive-raised; c, wildraised.
Dataset for Agile 5G Network Measurements: Operator Benefits of Employing Aerial Mobility
<p>Dataset of paper "Agile 5G Network Measurements: Operator Benefits of Employing Aerial Mobility".</p> <p>The measurement data contains two different scenarios: Scenario 1 and Scenario 2, according to the paper. These two datasets include two different kinds of files: those related to measurements taken with a phone and those taken with a drone (UAV).</p>
Dataset for the study "Agile Change Approach for Collaborative Software Development Contexts" - Umbrella Review
<p>Dataset for the study "Agile Change Approach for Collaborative Software Development Contexts"</p> <p>Umbrella review - First review</p> <p>The objective of this umbrella review is to check that there are no reviews in the defined period from 2000 to 2024 that respond to the objective of this research</p> <p> </p>
Dataset for the study "Agile Change Approach for Collaborative Software Development Contexts" - Systematic Literature Review
<p>Dataset for the study "Agile Change Approach for Collaborative Software Development Contexts" </p> <p>Second review</p> <p> </p> <p>This is the dataset for the full systematic literature review</p>
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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)
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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.