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18,621 results for “development;”
FIGURE 3 in Recent developments on the morphology of the brachyuran foregut ossicles and gastric teeth
FIGURE 3. Foregut ossicles of Dynomene filholi Bouvier, 1894, A. dorsal view, B. lateral view; gastric teeth of Paradynomene tuberculata Sakai, 1963, C. lateral tooth, D. accessory teeth, E. dorso-median tooth, F. cardio-pyloric valve; scales: A. & B. 1mm, C. 200µm, D. 100µm, E. 200µm, F. 500µm.
TUT Acoustic scenes 2017, Development dataset
<p>TUT Acoustic Scenes 2017, development dataset consists of 10-seconds audio segments from 15 acoustic scenes:</p> <ul> <li>Bus - traveling by bus in the city (vehicle)</li> <li>Cafe / Restaurant - small cafe/restaurant (indoor)</li> <li>Car - driving or traveling as a passenger, in the city (vehicle)</li> <li>City center (outdoor)</li> <li>Forest path (outdoor)</li> <li>Grocery store - medium size grocery store (indoor)</li> <li>Home (indoor)</li> <li>Lakeside beach (outdoor)</li> <li>Library (indoor)</li> <li>Metro station (indoor)</li> <li>Office - multiple persons, typical work day (indoor)</li> <li>Residential area (outdoor)</li> <li>Train (traveling, vehicle)</li> <li>Tram (traveling, vehicle)</li> <li>Urban park (outdoor)</li> </ul> <p>Each acoustic scene has 312 segments totaling 52 minutes of audio.</p>
TUT Sound events 2017, Development dataset
<p>TUT Sound events 2017, development dataset consists of 24 audio recordings from a single acoustic scene:</p> <ul> <li> Street (outdoor), totaling 1:32:08</li> </ul>
Dataset for the Paper: "How Do OSS Developers Reuse Architectural Solutions from Q&A Sites: An Empirical Study"
<p>This package contains the dataset of the paper "How Do OSS Developers Reuse Architectural Solutions from Q&A Sites: An Empirical Study":</p> <p><strong>(1) Commits and Issues.xlsx</strong></p> <p>This file includes 984 commits and issues for the mining study.</p> <p><strong>(2) Survey Questionnaire (Customized Invitation Email).docx</strong></p> <p>This file provides the customized email used to invite each survey participant.</p> <p><strong>(3) Survey Questionnaire.xlsx</strong></p> <p>This file includes 12 survey questions used to collect the responses from the participants.</p> <p><strong>(4) Survey Responses.</strong><strong>xlsx</strong></p> <p>This file includes 227 valid responses and the results of data extraction and analysis for the survey study.</p> <p><strong>(5) Data Extraction and Analysis.mx20</strong></p> <p>This file contains the results of data extraction and analysis for the mining study, which were analyzed by the MAXQDA tool. The file can be opened by MAXQDA 2020 or higher versions, which are available at https://www.maxqda.com/. You may also use the free 14 days trial version of MAXQDA 2020, which is available at https://www.maxqda.com/trial for download.</p>
Artifact of the paper "An Empirical Investigation on the Challenges in Scientific Workflow Systems Development"
<p><strong><span>Scientific Workflow Systems (SWSs)</span></strong><span> play a critical role in the contemporary scientific landscape, significantly enriching research endeavors by augmenting productivity and fostering collaboration</span><span>, SWSs</span><span> elevate the standard of scholarly inquiry, fortifying its pillars of reproducibility and ethical adherence. Essentially, they </span><span>serve as</span><span> the bedrock upon which efficient, transparent, and impactful research </span><span>is built</span><span>, propelling knowledge and innovation across diverse fields. SWSs accomplish mundane yet essential tasks intrinsic to scientific inquiry—ranging from data acquisition to analysis and reporting. By liberating researchers from the shackles of manual labor, SWSs enable them to channel their energies toward more intellectually demanding pursuits, thereby enhancing the pace and quality of research outcomes. Moreover, SWSs wield a formidable influence in standardizing workflows across research cohorts, instilling a sense of uniformity in experimental methodologies and data-handling practices. This standardization not only cultivates a culture of rigor and coherence but also fosters cross-disciplinary dialogue and collaboration.</span></p> <p>Integral to the operation of SWSs is their capacity to integrate diverse tools, software, and data sources, effectively functioning as centralized hubs for research management. This integration expedites the research process and facilitates seamless data exchange and interoperability—a pivotal asset in an era characterized by the deluge of data and the imperative of interdisciplinary collaboration. Furthermore, SWSs afford researchers and project managers real-time insights into the progress of research endeavors, empowering them to identify bottlenecks, allocate resources judiciously, and optimize workflow execution. This granular oversight enhances project transparency and accountability and serves as a catalyst for informed decision-making.</p> <p>Crucially, SWSs are engineered to accommodate the complexities inherent in scientific inquiry, adeptly handling vast volumes of data and supporting parallel processing to meet the evolving demands of research projects. This scalability underscores their adaptability to diverse research paradigms, ensuring their relevance across a spectrum of scientific disciplines. Facilitating collaboration across geographic and temporal divides, SWSs offer a suite of collaborative features—including version control, shared workspaces, and communication tools—that transcend the constraints of physical proximity. By fostering a culture of inclusivity and knowledge exchange, SWSs catalyze innovation and synergy among distributed research teams.</p> <p>Moreover, SWSs serve as custodians of reproducibility, meticulously documenting each facet of the research workflow—from data sources to analysis methods—thus safeguarding the integrity of scientific inquiry. This commitment to transparency and methodological rigor underpins the credibility of research findings, engendering trust within the scientific community and beyond. The customizable nature of SWSs empowers research teams to tailor their workflows to suit their unique needs and preferences, further amplifying their utility and versatility. In essence, SWSs emerge not merely as tools of convenience but as indispensable allies in the relentless pursuit of scientific excellence.</p> <p>Numerous developers actively participate in the advancement of SWSs through diverse roles, including designing system architectures to ensure flexibility and performance, developing algorithms for data processing and analysis, crafting user-friendly interfaces, handling backend logic, integrating with external tools, and ensuring quality, security, and compliance. They address challenges such as optimizing performance and scalability by leveraging parallel processing and distributed computing techniques. To tackle these diverse tasks, developers encounter numerous challenges, often turning to crowd-sourced platforms like Stack Overflow and GitHub to discuss and address them. Stack Overflow serves as a vital resource for developers to seek solutions, learn new technologies, validate best practices, and engage with the programming community. Similarly, GitHub facilitates collaborative development by allowing developers to report problems, propose enhancements, and contribute to open-source projects. Our research draws insights from Stack Overflow discussions, GitHub issues, and pull request reports related to SWSs, reflecting the dynamic and collaborative nature of software development in this domain.</p>
Products developed through the "What About Model Data?, Determining Best Practices for Preservation and Replicability, EarthCube Research Coordination Network" project
This dataset includes products developed through the "What About Model Data? Determining Best Practices for Preservation and Replicability, EarthCube Research Coordination Network (RCN)" project. Products include: 1) a rubric worksheet to assist researchers in deciding what simulation output needs to be preserved in a trusted, community repository to communicate knowledge and satisfy publisher and funder requirements, 2) instructions on how to use the rubric worksheet, which include reference use cases, and 3) outputs and presentations from the three project workshops.
Does Co-Development with AI Assistants Lead to More Maintainable Code? Replication Package
<p>This is the replication package for the study "Echoes of AI: Investigating the Downstream Effects of AI Assistants on Software Maintainability" preregistered aat ICSME 2024 as “Does Co-Development with AI Assistants Lead to More Maintainable Code?”</p> <p>Abstract from the registered report:</p> <p>[Background/Context] AI assistants like GitHub Copilot are transforming software engineering, with several studies highlighting productivity improvements. However, their impact on code quality, particularly in terms of maintainability, requires further investigation.<br>[Objective/Aim] This study aims to examine the influence of AI assistants on software maintainability, specifically assessing how these tools affect the ability of developers to evolve code.<br>[Method] We will conduct a two-phased controlled experiment involving professional developers. In Phase 1, developers will add a new feature to a Java project, with or without the aid of an AI assistant. Phase 2, a randomized controlled trial, will involve a different set of developers evolving random Phase 1 projects - working without AI assistants. We will employ Bayesian analysis to evaluate differences in completion time, perceived productivity, code quality, and test coverage.</p> <p>Note: To maintain the integrity of the study, i.e., preventing any leakage to AI assistants' training data, we choose not to host the code in a public git repository. Instead, all relevant documents and code are shared through a replication package on Zenodo, available as PDF documents generated by repo2pdf (https://github.com/BankkRoll/repo2pdf). We have deliberately used settings to obfuscate the code (e.g., line numbers) to ensure it will not be scraped by any large language models before the study has been completed.</p> <p>Contents:</p> <ul> <li>Task 1 instructions.</li> <li>Task 2 instructions.</li> <li>The source code that the participants received.</li> <li>A causal graph with analysis details.</li> <li>Archives containing anonymized experimental data and analysis scripts (in .zip and .tar.gz for convenience). </li> </ul>
DevGPT: Studying Developer-ChatGPT Conversations
<p>DevGPT is a curated dataset which encompasses 17,913 prompts and ChatGPT's responses including 11,751 code snippets, coupled with the corresponding software development artifacts—ranging from source code, commits, issues, pull requests, to discussions and Hacker News threads—to enable the analysis of the context and implications of these developer interactions with ChatGPT.</p><p><strong>Important</strong><br>Version 9 (2023-11-09) resolves the empty list of conversations attribute: https://github.com/NAIST-SE/DevGPT/issues/8</p>
Supplementary Material for The Impact of COVID-19 on Open Source Development Activities: A Multi-Method Study
<p><strong>Context:</strong> The social isolation measures resulting from the COVID-19 outbreak changed work practices in various sectors, especially with the shift to working from home. However, it is still unclear the implications of the pandemic on the maintenance and evolution of open-source software (OSS). Objective: In this study, we analyze the effects of COVID-19 on the development activity of OSS and how social isolation changed the productivity and emotional state of OSS contributors. <strong>Method:</strong><br>To investigate this issue, we have conducted a multi-method study. We first quantitatively investigated how the COVID-19 outbreak impacted the development activity of open-source projects by mining the development history of 155 open-source projects. We then qualitatively investigated the perceptions of open-source core developers about the impact of COVID-19 on their activities, using a survey as an instrument. <strong>Results:</strong> Our results indicate that the pandemic led to some effect on the development<br>activity of OSS repositories, especially in the early days of the outbreak. For instance although we observe an increase in the number of pull requests accepted, our results also indicate a sudden increase in the turnover rate of core developers. However, a few months after the declaration of the current pandemic, our results suggest stabilization in these metrics. Other effects were more far-reaching, such as a decrease in newcomers throughout the observed period after the pandemic was declared. Some findings are supported by the results of our qualitative study, whose results indicate that most of the respondents of our survey consider that COVID-19 did not change their productivity substantially. <strong>Conclusions:</strong> Our findings can help practitioners and researchers to better understand the effect of COVID-19 on the software engineering field.</p>
On the stepped-leader development of negative cloud-to-ground lightning associated with the evolution of thunderstorm
<p>The files contain the complete radar and lightning datasets of the 12 thunderstorms used in the analysis presented in “On the stepped-leader development of negative cloud-to-ground lightning associated with the evolution of thunderstorms.”</p>
ICITS'24 - A Systematic Mapping Study on the Use and Development of Research Software
<p>Artifacts used for data collection and analysis of the article accepted for publication in ICITS'24.</p><p>Mourão, E., Trevisan, D., Viterbo, J. and Pantoja, C.E. (2024). A Systematic Mapping Study on the Use and Development of Research Software. In: ICITS'24 - 7th International Conference on Information Technology & Systems. Lecture Notes in Networks and Systems. Springer, Cham.</p>
Freshly-developed Low-latitude Postmidnight-to-Dawn F-region Ionospheric Irregularities over China on November 13, 2015
<p>The dataset reports the calculated slant Total Electron Content (STEC) from 4 GPS receivers came from the Crustal Movement Observation Network of China on 13 November 2015. The temporal resolution of data is thirty seconds.</p>
Structural Data for the Antibody Developability Manuscript: Cartography of Developability Landscapes in Native and Human-Engineered Antibodies
<p>Here is the additional structural data (predicted models and MD trajectories) for the "Cartography of the Developability Landscapes of Native and Human-Engineered Antibodies" manuscript. Please cite our paper when referring to and using this data. If you have any questions, please contact Eva Smorodina at ribes.ev@gmail.com. Thank you!</p>
example healpix dataset for developping xarray-DGGS
<p>example healpix dataset for developping xarray-DGGS</p>
FIGURE 1 in Larvae of the sand stargazer Sindoscopus australis and notes on the development of Dactyloscopidae (Perciformes: Blennioidei)
FIGURE 1. Larvae of Sindoscopus australis, a) 4.4 mm, preflexion, b) 7.3 mm, preflexion, c) 8.6 mm, flexion, d) 13.2 mm, postflexion, e) 16.0 mm, postflexion.
Dataset for Citizen Science as a Relevant Approach to the Challenges of Complex Thinking Development in Higher Education
<p>Dataset with questions and analysis for the article Citizen Science as a Relevant Approach to the Challenges of Complex Thinking Development in Higher Education: Mapping and Bibliometric Analysis</p>
FIG. 3 in Development of a Geographical Information System for the marine resources of Rodrigues
FIG. 3. GIS frames illustrating how the survey sites (pink diamonds) in the Rodrigues lagoon are linked to a photograph of the site, a description of the biotope, a species list and photographs of the species found at that site.
FIG. 5. GIS frames illustrating a in Development of a Geographical Information System for the marine resources of Rodrigues
FIG. 5. GIS frames illustrating a section of the biotope map from the southern lagoon to show how polygons are labelled and hyperlinked to illustrated descriptions of the constituent biotopes.
FIG. 10 in Development of a Geographical Information System for the marine resources of Rodrigues
FIG. 10. Thematic map from the GIS to show all proposed Marine Protected Areas, including those shown in figure 9 and the approximate positions of the new MPAs (orange boundaries) proposed by Shoals Rodrigues in 2003 and the Terrestrial–Marine Protected Area (green boundary) proposed by the United Nations Development Programme in 2004. Underlying the MPA boundaries layer is a biotope layer showing the Coral (red and pink infill) and Lagoon mud (brown infill) biotopes only, for clarity.
FIG. 4 in Development of a Geographical Information System for the marine resources of Rodrigues
FIG. 4. The biotope map of the Rodrigues lagoon as a vector image with the Landsat 7 ETMz satellite image of the land area as a backdrop for orientation and interpretation purposes. The biotopes are illustrated using colour schemes according to habitat groups and biotopes, as described in figure 2.
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.