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208 results for “research and development”
MiRoR15-P2-Development of ARCADIA: a tool for assessing the quality of peer-review reports in biomedical research
<p>Survey questionnaire, anonymised survey data, and codebook related to: Superchi C, Hren D, Blanco D, Rius R, Recchioni A, Boutron I, González JA. Development of ARCADIA: a tool for assessing the quality of peer-review reports in biomedical research. BMJ Open 2020;0:e035604. doi:10.1136/bmjopen-2019-035604</p>
Beyond the Digital Divide: Sharing Research Data across Developing and Developed Countries
<p>The primary data collection element of this project related to observational based fieldwork at four universities in Kenya and South Africa undertaken by Louise Bezuidenhout (hereafter ‘LB’) as the award researcher. The award team selected fieldsites through a series of strategic decisions. First, it was decided that all fieldsites would be in Africa, as this continent is largely missing from discussions about Open Science. Second, two countries were selected – one in southern (South Africa) and one in eastern Africa (Kenya) – based on the existence of the robust national research programs in these countries compared to elsewhere on the continent. As country background, Kenya has 22 public universities, many of whom conduct research. It also has a robust history of international research collaboration – a prime example being the long-standing KEMRI-Wellcome Trust partnership. While the government encourages research, financial support for it remains limited and the focus of national universities is primarily on undergraduate teaching. South Africa has 25 public universities, all of whom conduct research. As a country, South Africa has a long history of academic research, one which continues to be actively supported by the government. </p> <p>Third, in order to speak to conditions of research in Africa, we sought examples of vibrant, “homegrown” research. While some of the researchers at the sites visited collaborated with others in Europe and North America, by design none of the fieldsites were formally affiliated to large internationally funded research consortia or networks. Fourth, within these two countries four departments or research groups in academic institutions were selected for inclusion based on their common discipline (chemistry/biochemistry) and research interests (medicinal chemistry). These decisions were to ensure that the differences in data sharing practices and perceptions between disciplines noted in previous studies would be minimized. </p> <p>Within Kenya, site 1 (KY1) and Site 2 (KY2) were both chemistry departments of well-established universities. Both departments had over 15 full time faculty members, however faculty to student ratios were high and the teaching loads considerable. KY1 had a large number of MSc and PhD candidates, the majority of whom were full-time and a number of whom had financial assistance. In contrast, KY2 had a very high number of MSc students, the majority of whom were self-funded and part-time (and thus conducted their laboratory work during holidays). In both departments space in laboratories was at a premium and students shared space and equipment. Neither department had any postdoctoral researchers. </p> <p>Within South Africa, site 1 (SA1) was a research group within the large chemistry department of a well-established and comparatively well-resourced university with a tradition of research. Site 2 (SA2) was the chemistry/biochemistry department of a university that had previously been designated a university for marginalized population groups under the Apartheid system. Both sites were the recipients of numerous national and international grants. SA2 had one postdoctoral researcher at the time, while SA1 had none.</p> <p>Empirical data was gathered using a combination of qualitative methods including embedded laboratory observations and semi-structured interviews. Each site visit took between three and six weeks, during which time LB participated in departmental activities, interviewed faculty and postgraduate students, and observed social and physical working environments in the departments and laboratories. Data collection was undertaken over a period of five months between November 2014 and March 2015, with 56 semi-structured interviews in total conducted with faculty and graduate students. Follow-on visits to each site were made in late 2015 by LB and Brian Rappert to solicit feedback on our analysis. </p>
Open Access in developing countries – attitudes and experiences of researchers Dataset
<p>A survey was conducted of 507 researchers from the developing world and connected to INASP’s AuthorAID project to ascertain experiences and attitudes to Open Access publishing. This file is the raw output from the survey, with names and email addresses removed to preserve anonymity. </p>
Research data related to switchable contact model (SCM) development
<p>Research data of the linked journal article, is composed of input and output files of the LIGGGHTS simulations (Project folders) and an Excel data sheet (Results_Excel) which includes calculated data.</p> <p>The contents are the simulation data and results of cake formation in centrifugal filtration using conventional (mesh) method and novel Switchable contact model (SCM, primitive) method. (For more information see the linked article and the source code of SCM: <a href="https://github.com/DamlaSerper/SCM">https://github.com/DamlaSerper/SCM</a>)</p>
DOI's with SDG labels on Target level | 1.4M research articles (2009-2020) related to Sustainable Development Goals
<p>Table content: This data set contains 1.4 million publication DOI's related to the <a href="http://metadata.un.org/sdg/">Targets of the Sustainable Development Goals</a> in the period 2009 - 2020.</p> <p>Table dimensions: rows: 1.4 million, columns: 4 / rows: 1.4 million, columns: 180</p> <p>Table columns: <a href="https://en.wikipedia.org/wiki/Digital_object_identifier">doi</a> | date | <a href="http://metadata.un.org/sdg/ontology#Target">sdg_target</a> | <a href="http://metadata.un.org/sdg/ontology#Goal">sdg_goal</a> / <a href="https://en.wikipedia.org/wiki/Digital_object_identifier">doi</a> | date | <a href="http://metadata.un.org/sdg/ontology#Target">169 sdg_targets</a> | <a href="http://metadata.un.org/sdg/ontology#Goal">17 sdg_goalsl</a></p> <p>Table formats: <a href="https://en.wikipedia.org/wiki/Comma-separated_values">.csv</a> | <a href="https://en.wikipedia.org/wiki/Microsoft_Excel">.xlsx</a> | <a href="https://en.wikipedia.org/wiki/Apache_Parquet">.parquet</a></p> <p><em>How we made this data:</em></p> <p>We have made a search on <a href="https://scopus.com">Scopus </a>using the <a href="https://aurora-network-global.github.io/sdg-queries/">Aurora SDG queries version 5</a> for each of the targets, with a limited year range from 2009 till 2020.</p> <p>Good to know: don't be alarmed if you can find a doi that is labeled with more than one target (~16%). This is not a bug, this is a feature... We used 169 queries, one for each target, a publication can appear in more han one result set.</p> <p>Read this <a href="https://zenodo.org/record/4964606/files/Evaluation_on_accuracy_of_mapping_science_to_the_United_Nations__Sustainable_Development_Goals__SDGs__of_the_Aurora_SDG_queries.pdf?download=1">report to learn more about the accuracy</a> of the queries and the data result sets.</p> <p><em>How can you use this data:</em></p> <p>You can use this data to 1. quickly match your existing publication lists to this list to see how that your publications are related to the targets of the SDG's. 2. use these as a basis / seed set / gold set to train more advanced text / graph classifiers (after you have extracted title, abstract or even full-text using crossref.org, unpaywall.org, etc)</p> <p><em>How can you help:</em></p> <p><a href="https://sites.google.com/vu.nl/aurora-sdg-research-dashboard/sdg-knowledge-base#h.d2pd3c39k276">Let us know</a> how you use this data. We'll put your project on the list in our <a href="https://sites.google.com/vu.nl/aurora-sdg-research-dashboard/sdg-knowledge-base">SDG matching knowledge base.</a></p>
Survey data of "Mapping Research Output to the Sustainable Development Goals (SDGs)"
<p><strong>This dataset contains information on what papers and concepts researchers find relevant to map domain specific research output to the 17 Sustainable Development Goals (SDGs).</strong></p> <p><a href="https://sustainabledevelopment.un.org/sdgs">Sustainable Development Goals</a> are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">Aurora SDG dashboard</a> is the initial outcome as proof of practice.)</p> <p>In order to validate our current classification model (on soundness/precision and completeness/recall), and receive input for improvement, a survey has been conducted to<strong> capture expert knowledge from senior researchers in their research domain related to the SDG</strong>. The survey was open to the world, but mainly distributed to researchers from the <a href="https://aurora-network.global/">Aurora Universities Network</a>. <strong>The survey was open from October 2019 till January 2020, and captured data from 244 respondents in Europe and North America.</strong></p> <p>17 surveys were created from a single template, where the content was made specific for each SDG. Content, like a random set of publications, of each survey was ingested by a data provisioning server. That collected research output metadata for each SDG in an earlier stage. It took on average 1 hour for a respondent to complete the survey.<strong> The outcome of the survey data can be used for validating current and optimizing future SDG classification models for mapping research output to the SDGs</strong>.</p> <p><strong>The survey contains the following questions (see inside dataset for exact wording):</strong></p> <ul> <li><strong>Are you familiar with this SDG?</strong> <ul> <li>Respondents could only proceed if they were familiar with the targets and indicators of this SDG. Goal of this question was to weed out un knowledgeable respondents and to increase the quality of the survey data.</li> </ul> </li> <li><strong>Suggest research papers that are relevant for this SDG (upload list)</strong> <ul> <li>This question, to provide a list, was put first to reduce influenced by the other questions. Goal of this question was to measure the completeness/recall of the papers in the result set of our current classification model. (To lower the bar, these lists could be provided by either uploading a file from a reference manager (preferred) in .ris of bibtex format, or by a list of titles. This heterogenous input was processed further on by hand into a uniform format.)</li> </ul> </li> <li><strong>Select research papers that are relevant for this SDG (radio buttons: accept, reject)</strong> <ul> <li>A randomly selected set of 100 papers was injected in the survey, out of the full list of thousands of papers in the result set of our current classification model. Goal of this question was to measure the soundness/precision of our current classification model.</li> </ul> </li> <li><strong>Select and Suggest Keywords related to SDG (checkboxes: accept | text field: suggestions)</strong> <ul> <li>The survey was injected with the top 100 most frequent keywords that appeared in the metadata of the papers in the result set of the current classification model. respondents could select relevant keywords we found, and add ones in a blank text field. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li><strong>Suggest SDG related glossaries with relevant keywords (text fields: url)</strong> <ul> <li>Open text field to add URL to lists with hundreds of relevant keywords related to this SDG. Goal of this question was to get suggestions for keywords we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li><strong>Select and Suggest Journals fully related to SDG (checkboxes: accept | text field: suggestions)</strong> <ul> <li>The survey was injected with the top 100 most frequent journals that appeared in the metadata of the papers in the result set of the current classification model. Respondents could select relevant journals we found, and add ones in a blank text field. Goal of this question was to get suggestions for complete journals we can use to increase the recall of relevant papers in a new classification model.</li> </ul> </li> <li><strong>Suggest improvements for the current queries (text field: suggestions per target)</strong> <ul> <li>We showed respondents the queries we used in our current classification model next to each of the targets within the goal. Open text fields were presented to change, add, re-order, delete something (keywords, boolean operators, etc. ) in the query to improve it in their opinion. Goal of this question was to get suggestions we can use to increase the recall and precision of relevant papers in a new classification model.</li> </ul> </li> </ul> <p><strong>In the dataset root you'll find the following folders and files:</strong></p> <ul> <li><strong>/00-survey-input/</strong> <ul> <li>This contains the survey questions for all the individual SDGs. It also contains lists of EIDs categorised to the SDGs we used to make randomized selections from to present to the respondents.</li> </ul> </li> <li><strong>/01-raw-data/</strong> <ul> <li>This contains the raw survey output. (Excluding privacy sensitive information for public release.) This data needs to be combined with the data on the provisioning server to make sense.</li> </ul> </li> <li><strong>/02-aggregated-data/</strong> <ul> <li>This data is where individual responses are aggregated. Also the survey data is combined with the provisioning server, of all sdg surveys combined, responses are aggregated, and split per question type.</li> </ul> </li> <li><strong>/03-scripts/</strong> <ul> <li>This contains scripts to split data, and to add descriptive metadata for text analysis in a later stage.</li> </ul> </li> <li><strong>/04-processed-data/</strong> <ul> <li>This is the main final result that can be used for further analysis. Data is split by SDG into subdirectories, in there you'll find files per question type containing the aggregated data of the respondents.</li> </ul> </li> <li><strong>/images/</strong> <ul> <li>images of the results used in this README.md.</li> </ul> </li> <li><strong>LICENSE.md</strong> <ul> <li>terms and conditions for reusing this data.</li> </ul> </li> <li><strong>README.md</strong> <ul> <li>description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.</li> </ul> </li> </ul> <p><strong>In the /04-processed-data/ you'll find in each SDG sub-folder the following files.:</strong></p> <ul> <li><strong>SDG-survey-questions.pdf</strong> <ul> <li>This file contains the survey questions</li> </ul> </li> <li><strong>SDG-survey-questions.doc</strong> <ul> <li>This file contains the survey questions</li> </ul> </li> <li><strong>SDG-survey-respondents-per-sdg.csv</strong> <ul> <li>Basic information about the survey and responses</li> </ul> </li> <li><strong>SDG-survey-city-heatmap.csv</strong> <ul> <li>Origin of the respondents per SDG survey</li> </ul> </li> <li><strong>SDG-survey-suggested-publications.txt</strong> <ul> <li>Formatted list of research papers researchers have uploaded or listed they want to see back in the result-set for this SDG.</li> </ul> </li> <li><strong>SDG-survey-suggested-publications-with-eid-match.csv</strong> <ul> <li>same as above, only matched with an EID. EIDs are matched my Elsevier's internal fuzzy matching algorithm. Only papers with high confidence are show with a match of an EID, referring to a record in Scopus.</li> </ul> </li> <li><strong>SDG-survey-selected-publications-accepted.csv</strong> <ul> <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe represent this SDG. (TRUE=accepted)</li> </ul> </li> <li><strong>SDG-survey-selected-publications-rejected.csv</strong> <ul> <li>Based on our previous result set of papers, researchers were presented random samples, they selected papers they believe not to represent this SDG. (FALSE=rejected)</li> </ul> </li> <li><strong>SDG-survey-selected-keywords.csv</strong> <ul> <li>Based on our previous result set of papers, we presented researchers the keywords that are in the metadata of those papers, they selected keywords they believe represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-unselected-keywords.csv</strong> <ul> <li>As "selected-keywords", this is the list of keywords that respondents have not selected to represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-suggested-keywords.csv</strong> <ul> <li>List of keywords researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li><strong>SDG-survey-glossaries.csv</strong> <ul> <li>List of glossaries, containing keywords, researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li><strong>SDG-survey-selected-journals.csv</strong> <ul> <li>Based on our previous result set of papers, we presented researchers the journals that are in the metadata of those papers, they selected journals they believe represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-unselected-journals.csv</strong> <ul> <li>As "selected-journals", this is the list of journals that respondents have not selected to represent this SDG.</li> </ul> </li> <li><strong>SDG-survey-suggested-journals.csv</strong> <ul> <li>List of journals researchers suggest to use to find papers related to this SDG</li> </ul> </li> <li><strong>SDG-survey-suggested-query.csv</strong> <ul> <li>List of query improvements researchers suggest to use to find papers related to this SDG</li> </ul> </li> </ul> <p><strong>Cite as:</strong></p> <blockquote> <p><em>Survey data of "Mapping Research output to the SDGs"</em> by Aurora Universities Network (AUR) <a href="http://doi.org/10.5281/zenodo.3798385">doi:10.5281/zenodo.3798385</a></p> </blockquote> <p><strong>Attribute as:</strong></p> <blockquote> <p><em><strong>Survey data of "Mapping Research output to the SDGs</strong>"</em> by Aurora Universities Network (AUR); Alessandro Arienzo (UNA); Roberto Delle Donne (UNA); Ignasi Salvadó Estivill (URV); José Luis González Ugarte (URV); Didier Vercueil (UGA); Nykohla Strong (UAB); Eike Spielberg (UDE); Felix Schmidt (UDE); Linda Hasse (UDE); Ane Sesma (UEA); Baldvin Zarioh (UIC); Friedrich Gaigg (UIN); René Otten (VUA); Nicolien van der Grijp (VUA); Yasin Gunes (VUA); Peter van den Besselaar (VUA); Joeri Both (VUA); Maurice Vanderfeesten (VUA);<strong> is licensed under a Creative Commons Attribution 4.0 International License.</strong> <a href="https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/">https://aurora-network.global/project/sdg-analysis-bibliometrics-relevance/</a></p> </blockquote>
Roadmap for Developing a Dynamic and Reproducible Research Article with ARTE workflow
<p>The figures illustrates a roadmap for developing a dynamic and reproducible research article using <strong>ARTE (Article Reproducibility Template & Environment) </strong>workflow. The process is categorized into three levels of reproducibility: <strong>Minimal, Proper, and Full</strong>. Each level integrates specific tools and practices to enhance the reproducibility of the research.</p> <p>This proposal is published in the following <strong>OSF project</strong>: <a title="OSF" href="https://osf.io/njdq5/" target="_blank" rel="noopener">https://osf.io/njdq5/</a><br>Shared in the following <strong>GitHub repository</strong>: <a title="GitHub" href="https://github.com/phdpablo/article-template" target="_blank" rel="noopener">https://github.com/phdpablo/article-template</a><br>Exemplified in the following <strong>URL address</strong>: <a title="Article Example" href="https://phdpablo.github.io/article-template/" target="_blank" rel="noopener">https://phdpablo.github.io/article-template/</a></p> <h1>Minimal Reproducibility</h1> <p><strong>1. Use this template</strong>: Start by utilizing the provided template, which is pre-configured with the <strong>TIER Protocol 4.0</strong>. This protocol helps organize research projects in a systematic manner.</p> <p><strong>2. Edit READMEs</strong>: Customize the README files to reflect the details and conclusions of your research. These README files help document the project structure and contents.</p> <p><strong>3. Share on OSF</strong>: Share the project on the <strong>Open Science Framework (OSF)</strong> to ensure accessibility and transparency. This can be done at the beginning, during, or at the end of the research process.</p> <h1>Proper Reproducibility</h1> <p>In addition to the steps mentioned above, the following steps are added:</p> <p><strong>4. Quarto settings:</strong> Adjust the Quarto configuration to fit the needs of your project. This includes modifying the <em>_quarto.yml</em> file for different themes and output formats.</p> <p><strong>5. Develop your narrative</strong>: Write the research narrative using <em>Quarto’s .qmd files</em> within RStudio. This narrative forms the main body of your article and integrates text, code, and outputs seamlessly.</p> <p><strong>6. Environment control:</strong> Implement environment control using the <em>renv package</em>. This ensures that the R environment is consistent and reproducible. The <em>renv.lock</em> file captures the exact versions of R packages used in the project.</p> <p><strong>7. Share dynamic article:</strong> Render and share the dynamic document via GitHub Pages. The Quarto-generated HTML files (docs folders) are hosted on GitHub Pages, making the research accessible and interactive.</p> <h1>Full Reproducibility</h1> <p>Building on the proper reproducibility steps, full reproducibility adds:</p> <p><strong>8. Use Docker:</strong> Employ Docker for operating system-level environment control. A Docker container encapsulates the entire project environment, ensuring that the research can be replicated exactly, regardless of the local machine setup.</p> <h2>Tools Utilized</h2> <ul> <li><strong>TIER Protocol 4.0</strong>: Provides a framework for organizing and documenting research projects.</li> <li><strong>OSF:</strong> A platform for sharing research outputs and ensuring open science practices.</li> <li><strong>Quarto:</strong> A tool for creating dynamic documents that integrate text, code, and outputs.</li> <li><strong>RStudio:</strong> An integrated development environment (IDE) for R, facilitating data analysis and reproducible research.</li> <li><strong>Git/GitHub:</strong> Version control systems that track changes and manage project versions.</li> <li><strong>renv: </strong>An R package for managing and reproducing consistent R environments.</li> <li><strong>GitHub Pages:</strong> A service for hosting static websites directly from a GitHub repository.</li> <li><strong>Docker:</strong> A platform for containerizing applications to ensure consistent environments across different systems.</li> </ul> <h2>Summary</h2> <p>This template guides researchers through creating a reproducible and dynamic article using ARTE (Article Reproducibility Template & Environment) workflow. It starts with basic project setup and documentation, progresses through developing the research narrative with environment control, and culminates in full reproducibility with Docker. This structured approach ensures that research is well-documented, versioned, and easily shareable, promoting open science practices.</p>
PRISMA-P (Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols) of the research entitled "Development of Competences for the Fashion Designer: a Scope Review
<p>PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) 2015 checklist: recommended items to address in a systematic review protocol and Check list CAPSI - Critical analysis of the articles related to the specific objective: map the current themes that permeate the competencies of fashion design professionals through a scoping review.</p>
DATA SUPPORTING RESEARCH ON THE DEVELOPMENT OF PALEONTOLOGY IN BRAZIL (NETWORK VISUALIZATIONS)
<p>The documents made available present the data set that was processed in Lucas George Wendt's dissertation, presented in 2024 in the Postgraduate Program in Information Science (PPGCIN) of the Federal University of Rio Grande do Sul (UFRGS). The study is entitled: Brazilian Paleontology: a scientometric analysis based on the Lattes Curriculum. The abstract is as follows. This research sought to carry out a scientometric analysis of Paleontology in Brazil based on data collected in the Lattes Curriculum. The general objective of this dissertation is to analyze the scientific field of Paleontology diachronically and through a scientometric study - which will be explained based on the personal information of the researchers collected in their profiles and the scientific literature produced and registered in the Lattes Curriculum of the Lattes Platform. The literature review presented the concepts of Information Science, the area that, in this study, seeks to understand Paleontology through its research instruments; Scientific Communication, the main subject analyzed in this study; Metric Information Studies, the theoretical-methodological framework used in this research; Scientometrics, the theoretical scope used to understand in greater depth the constitution of the field of national Paleontology. Finally, references were also presented that help in the understanding of Paleontology in its national, South American, North American and European contexts. The research used a mixed approach of qualitative and quantitative elements. The data were generated from the CVs of researchers registered on the Lattes Platform, collected using the Brapci Bibliometric Tools tool and analyzed in specific software for metric analysis. To achieve the research objectives, data from 1,465 researcher profiles were analyzed. Regarding the full articles published in journals, 43,333 articles were considered valid. Regarding the keywords of the articles, 91,922 keywords were analyzed for word clouds and 84,771 for relationship networks. Of the academic orientations, 1,182 profiles generated 51,400 valid orientations. The aspect of the current employment relationship had 1,256 profiles considered. Regarding academic backgrounds, 1,465 profiles generated 4,556 academic backgrounds analyzed. The main contribution of this study is the realization of an unprecedented mapping of the panorama of Paleontology in Brazil, since there are no other studies that establish the same relationships that this research sought to establish. Regarding the results, based on the data collected and analyzed, the general metric indicators linked to the scientific production associated with Brazilian Paleontology were presented based on the information collected in the Lattes Curriculum; the directions of research in Paleontology that currently constitute this field in Brazil were mapped, as well as their thematic associations with other fields of knowledge; the training of PhD researchers who work with Paleontology or who have their production associated with Paleontology in terms of their academic training was characterized; and where the scientific knowledge in Paleontology or associated with Paleontology is produced was identified. The results of this study are relevant to understanding Brazilian Paleontology, highlighting its national orientation in fossil studies, doctoral training in local institutions and predominant activity in national organizations. These elements are important to consolidate Brazilian paleontological science globally. Regarding interdisciplinary relations, a clear proximity between Paleontology and Geosciences is observed, influenced by the history and current dynamics of the field. The study is available in full at this link: https://lume.ufrgs.br/handle/10183/278682.</p>
Survey on Developer and Researcher Views on the Ethics of Experiments on Open-Source Projects
<p>Results of a survey of 180 GitHub developers and 44 authors of research papers concerning the ethics of performing experiments on open source projects.</p>
Figure 2. Some screenshots from the software system-Design and Development of a Software System for Swarm Intelligence Based Research Studies
<p>All of the mentioned operations can be performed easily by using the provided controls over<br> the related interfaces – windows of each algorithm. It is also important that each algorithm interface<br> is supported by visual controls to view obtained results with typical iteration-based graphics or<br> problem oriented visual elements. For instance, resulting graph structures are automatically shown<br> by the algorithm interfaces after solving some specific, popular problems like Travelling Salesman<br> Problem (TSP), Vehicle Routing Problem (VCP)…etc. Visually improved using features and<br> functions of the software system are critical aspects to provide more effective and useful platform to<br> perform SI based research studies better.<br> Related to the designed and developed software system, some screenshots from the software<br> system [interfaces of two algorithms (IWDs and ABC)] are represented in Fig. 2.</p>
Results of a research software programming and development survey at the University of Reading
<p>In 2017 an online survey of University of Reading staff active in or supporting research and registered PhD students was undertaken to assess the nature and extent of research programming and software development activities in the University, and to understand how the University might provide guidance, training and support. The survey was a administered by the Research Data Manager on behalf of the University's Research Data Management Steering Group. The survey ran from 1st November to 15th December 2017 and collected a total of 170 responses.</p> <p>The survey sought responses from anyone in the University who was involved in any of the following activities:</p> <ul> <li>writing code and using software for numerical and statistical analysis;</li> <li>creating and contributing to computational models or simulations;</li> <li>conducting Text and Data Mining (TDM) and content analysis;</li> <li>creating and contributing to software distributed as a product or implemented as a service;</li> <li>creating data visualisations;</li> <li>using markup languages to structure and render content.</li> </ul> <p>The survey was distributed using the Bristol Online Survey. A dataset of anonymised survey responses and a PDF of the survey questions are here included.</p>
Dataset for: Developing research data management services and support for researchers: a mixed methods study
<p><strong>Overview</strong></p> <p>This dataset contains the raw data for the manuscript: <br> Perrier L, Barnes L. Developing research data management services and support for researchers: a mixed methods study. Partnership. 2018;13(1). doi: doi.org/10.21083/partnership.v13i1.4115.</p> <p>Full-text available at: <a href="https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202">https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202</a></p> <p><strong>Data and Documentation Files</strong></p> <p>Five files make up the dataset: </p> <ol> <li>Coding Scheme: RDMServicesSupport_Codes.txt</li> <li>Transcript, Focus Group 01 (anonymized): RDMServicesSupport_FocusGroup01.pdf</li> <li>Transcript, Focus Group 02 (anonymized): RDMServicesSupport_FocusGroup02.pdf</li> <li>Transcript, Focus Group 03 (anonymized): RDMServicesSupport_FocusGroup03.pdf</li> <li>Transcript, Focus Group 04 (anonymized): RDMServicesSupport_FocusGroup04.pdf</li> </ol> <p>Contact: Laure Perrier: <a href="https://journal.lib.uoguelph.ca/index.php/perj/article/view/4115/4202">orcid.org/0000-0001-9941-7129</a></p>
PERCEIVE Education, Research Development and Innovation in the South-East Development Region A Case Study 20191006
<p>The data set include secondary data extracted from EUROSTAT database. The data set referring to the education and research infrastructure on Sud-Est NUTS II region from Romania for the period 2007-2013. </p>
Figure 1 in Development of experimental mesocosms for cicada nymphs Graptopsaltria nigrofuscata: methodology and research recommendations
Figure 1. Photographs of the mesocosm experiment. (A) a final instar nymph of Graptopsaltria nigrofuscata cicada in a mesocosm cage. (B) An empty burrow made by a cicada nymph. The nymph might feed on larch root at the interior of burrow. Photographs were taken at the end of mesocosm experiment (7 July).
Dataset for "Radiation environment at the surface and subsurface of the Moon: Model development and validation" publication in Journal of Geophysical Research: Planets
<p>data set used for plots in manuscript "Radiation environment at the surface and subsurface of the Moon: Model development and validation" submitted to GRL</p>
Structure of the complete review matrix developed in the context of the "Reviewing Computational Thinking in Compulsory Education: State of Play and Practices from the Field" research study
<p>This is the structure of the complete review matrix used to analyse in-depth <strong>98 selected publications from between 2016 and 2021 </strong>in the context of the "<a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC128347">Reviewing Computational Thinking in Compulsory Education: State of Play and</a> <a href="https://publications.jrc.ec.europa.eu/repository/handle/JRC128347"> Practices from the Field</a>" research study. The <a href="https://computhink2study.eu/">study </a>was designed, funded, and followed by the European Commission’s Joint Research Centre (JRC) to investigate <strong>how Computational Thinking (CT) is currently positioned within compulsory school education in Europe’s various Member States, as well as outside the EU</strong>. The study was carried out from April to December 2021 by the Institute for Educational Technology of the Italian National Research Council (CNR-ITD), together with European Schoolnet (EUN) and Vilnius University (VU).</p>
Dataset of article: Investigating Developers' Perception on Success Factors for Research Software Development
<p>This dataset is an addendum to the article "Investigating Developers' Perception on Success Factors for Research Software Development" to provide information regarding the anonymously collected data.</p> <p> </p> <p> </p> <p> </p>
Data supporting research on the development of Paleontology in Brazil
<p>The documents made available present the data set that was processed in Lucas George Wendt's dissertation, presented in 2024 in the Postgraduate Program in Information Science (PPGCIN) of the Federal University of Rio Grande do Sul (UFRGS). The study is entitled: Brazilian Paleontology: a scientometric analysis based on the Lattes Curriculum. The abstract is as follows. This research sought to carry out a scientometric analysis of Paleontology in Brazil based on data collected in the Lattes Curriculum. The general objective of this dissertation is to analyze the scientific field of Paleontology diachronically and through a scientometric study - which will be explained based on the personal information of the researchers collected in their profiles and the scientific literature produced and registered in the Lattes Curriculum of the Lattes Platform. The literature review presented the concepts of Information Science, the area that, in this study, seeks to understand Paleontology through its research instruments; Scientific Communication, the main subject analyzed in this study; Metric Information Studies, the theoretical-methodological framework used in this research; Scientometrics, the theoretical scope used to understand in greater depth the constitution of the field of national Paleontology. Finally, references were also presented that help in the understanding of Paleontology in its national, South American, North American and European contexts. The research used a mixed approach of qualitative and quantitative elements. The data were generated from the CVs of researchers registered on the Lattes Platform, collected using the Brapci Bibliometric Tools tool and analyzed in specific software for metric analysis. To achieve the research objectives, data from 1,465 researcher profiles were analyzed. Regarding the full articles published in journals, 43,333 articles were considered valid. Regarding the keywords of the articles, 91,922 keywords were analyzed for word clouds and 84,771 for relationship networks. Of the academic orientations, 1,182 profiles generated 51,400 valid orientations. The aspect of the current employment relationship had 1,256 profiles considered. Regarding academic backgrounds, 1,465 profiles generated 4,556 academic backgrounds analyzed. The main contribution of this study is the realization of an unprecedented mapping of the panorama of Paleontology in Brazil, since there are no other studies that establish the same relationships that this research sought to establish. Regarding the results, based on the data collected and analyzed, the general metric indicators linked to the scientific production associated with Brazilian Paleontology were presented based on the information collected in the Lattes Curriculum; the directions of research in Paleontology that currently constitute this field in Brazil were mapped, as well as their thematic associations with other fields of knowledge; the training of PhD researchers who work with Paleontology or who have their production associated with Paleontology in terms of their academic training was characterized; and where the scientific knowledge in Paleontology or associated with Paleontology is produced was identified. The results of this study are relevant to understanding Brazilian Paleontology, highlighting its national orientation in fossil studies, doctoral training in local institutions and predominant activity in national organizations. These elements are important to consolidate Brazilian paleontological science globally. Regarding interdisciplinary relations, a clear proximity between Paleontology and Geosciences is observed, influenced by the history and current dynamics of the field. The study is available in full at this link: https://lume.ufrgs.br/handle/10183/278682.</p>
Supplemental Material for Genome Editing in Crop Plant Research - Alignment of expectations and current developments
<p>Supplemental Material for Paper "Genome Editing in Crop Plant Research - Alignment of expectations and current developments" as submitted to Plants</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.