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81 results for “research data sharing”
Data Management and Sharing: Practices and Perceptions of Psychology Researchers
<p class="CxSpFirst">Research data is increasingly viewed as an important scholarly output. While a growing body of studies have investigated researcher practices and perceptions related to data sharing, information about data-related practices throughout the research process (including data collection and analysis) remains largely anecdotal. Building on our previous study of data practices in neuroimaging research, we conducted a survey of data management practices in the field of psychology. Our survey included questions about the type(s) of data collected, the tools used for data analysis, practices related to data organization, maintaining documentation, backup procedures, and long-term archiving of research materials. Our results demonstrate the complexity of managing and sharing data in psychology. Data is collected in multifarious forms from human participants, analyzed using a range of software tools, and archived in formats that may become obsolete. As individuals, our participants demonstrated relatively good data management practices, however they also indicated that there was little standardization within their research group. Participants generally indicated that they were willing to change their current practices in light of new technologies, opportunities, or requirements. </p>
Supplementary material 3: List of tested and analyzed data sharing tools (non-exhaustive) from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
List of tested and analyzed data sharing tools (non-exhaustive)
Supplementary material 2: Definitions and Concepts from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Definitions and concepts in the context of the main paper.
Supplementary material 1: List of selected tools. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
List of selected tools.
Figure 7. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 7. - Mobile app for sporadic observations reporting.
Figure 2. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 2. - The Plazi workflow (green) within EU BON.
Data underlying figures in Beyond Data: sharing related research outputs to make data reusable
<p>Data that underlies figures in forthcoming article Beyond Data: sharing related research outputs to make data reusable, to be published in the journal Learned Publishing. Data in these files was retrieved from the DataCite REST API and the DataCite metadata schema. Zipped file contains identical data in 2 formats: individual CSV files (one per figure, plus a file with the overall count of DataCite DOIs by resource type and year) and single XLSX file with multiple tabs. </p>
Data of "Using current research information systems to investigate data acquisition and data sharing practices of computer scientists"
<p>This study describes a methodology where departmental academic publications are used to analyse the ways in which computer scientists share research data.</p> <p>Without sufficient information about researchers’ data sharing, there is a risk of mismatching FAIR data service efforts with the needs of researchers. This study describes a methodology where departmental academic publications are used to analyse the ways in which computer scientists share research data. The advancement of FAIR data would benefit from novel methodologies that reliably examine data sharing at the level of multidisciplinary research organisations. Studies that use CRIS publication data to elicit insight into researchers’ data sharing may therefore be a valuable addition to the current interview and questionnaire methodologies.</p> <p><strong>Data was collected from the following sources:</strong></p> <p>All journal articles published by researchers in the computer science department of the case study’s university during 2019 were extracted for scrutiny from the current research information system. For these 193 articles, a coding framework was developed to capture the key elements of acquiring and sharing research data. Article DOIs are included in the research data.</p> <p>The scientific journal articles and theirs DOIs are used in this study for the purpose of academic expression.</p> <p>The raw data is compiled into a single CSV file. Rows represent specific articles and columns are the values of the data points described below. Author names and affiliations were not collected and are not included in the data set. </p> <p> </p> <p>The following data points were used in the analysis:</p> <p><strong>Data points</strong></p> <ul> <li><strong>Main study types</strong></li> <li>Literature-based study (e.g. literature reviews, archive studies, studies of social media)</li> <li>yes/no</li> <li>Novel computational methods (e.g. algorithms, simulations, software)</li> <li>yes/no</li> <li>Interaction studies (e.g, interviews, surveys, tasks, ethnography)</li> <li>yes/no</li> <li>Intervention studies (e.g., EEG, MRI, clinical trials)</li> <li>yes/no</li> <li>Measurement studies (e.g. astronomy, weather, acoustics, chemistry)</li> <li>yes/no</li> <li>Life sciences (e.g. “omics”, ecology)</li> <li>yes/no</li> <li><strong>Data acquisition</strong></li> <li>Article presents a data availability statement</li> <li>yes/no</li> <li>Article does not utilise data</li> <li>yes/no</li> <li>Original data was collected</li> <li>yes/no</li> <li>Open data from prior studies were used</li> <li>yes/no</li> <li>Open data from public authorities, companies, universities and associations</li> <li>yes/no</li> <li><strong>Data sharing</strong></li> <li>Article does not use original data</li> <li>yes/no</li> <li>Data of the article is not available for reuse</li> <li>yes/no</li> <li>Article used openly available data</li> <li>yes/no</li> <li>Authors agree to share their data to interested readers</li> <li>yes/no</li> <li>Article shared data (or part of) as supplementary material</li> <li>yes/no</li> <li>Article shared data (or part of) via open deposition</li> <li>yes/no</li> <li>Article deposited code or used open code</li> <li>yes/no</li> </ul>
Analysis of shared research data in Spanish scientific papers about COVID-19: a first approach
<p><strong>Introduction:</strong> During the coronavirus pandemic, changes in the way science is done and shared occurred, which motivates meta-research to help understand science communication in crises and improve its effectiveness. <strong>Objective: </strong>To study how many Spanish scientific papers on COVID-19 published during 2020 share their research data. <strong>Methodology:</strong> Qualitative and descriptive study applying nine attributes: (1) availability, (2) accessibility, (3) format, (4) licensing, (5) linkage, (6) funding, (7) editorial policy, (8) content and (9) statistics. <strong>Results:</strong> We analyzed 1340 papers, 1173 (87.5%) did not have research data. 12.5% share their research data of which 2.1% share their data in repositories, 5% share their data through a simple request, 0.2% do not have permission to share their data and 5.2% share their data as supplementary material. <strong>Conclusions:</strong> There is a small percentage that shares their research data, however it demonstrates the researchers' poor knowledge on how to properly share their research data and their lack of knowledge on what is research data.</p>
The sharing of research raw data in journals indexed in the Cell & Tissue Engineering JCR category (2011-2015)
<p>The availability of research data sets is an important milestone since it can enhance the dynamics of research. This study aims to analyze the PubMed Central repository to determine the availability and type of raw data sets in Cell & Tissue Engineering journals indexed in the Journal Citation Reports. The number and types of files were registered. A search of the 21 journals from the Cell & Tissue Engineering category of the 2015 Journal Citation Reports was conducted. Information was collected from October to December 2016. A study of the supplementary material of the original articles published between 2011-2015 was performed through a search in the PubMed Central repository, which is the most used free full-text repository in biomedicine. Only articles with supplementary material were retrieved. The number and types of files were registered. In cases where a compressed file, such as a .zip or .rar file, was found, it was opened to check what kinds of files it contained.</p>
Data sharing in the life sciences : a study of researchers at the Norwegian University of Life Sciences
<p>Survey data collected in 2012 as basis for master thesis in library and information sciences titled "Data sharing in the life sciences : a study of researchers at the Norwegian University of Life Sciences" </p>
Research data sharing in Spain: exploring determinants, practices and perceptions
<p>Responses of the Spanish survey for research data, which was carried out within the framework of the project Datasea at the beginning of 2015. The purpose was to identify the habits and current experiences of Spanish researchers in health sciences in relation to the management and sharing of raw research data. The electronic questionnaire composed of 40 questions divided into three blocks that contained questions on the following aspects: A) Personal information; B) Creation and reuse of data; C) Preservation of data.</p>
Data underlying the manuscript: "Analysis of Research Data Sharing in Scientific Articles on Climate Change in the Covid-19 Year. The Spanish case 2020".
<p>This is the research data for the manuscript "Analysis of Research Data Sharing in Scientific Articles on Climate Change in the Covid-19 Year. The Spanish case 2020".<br>The following is the original abstract: Introduction: Sharing research data on climate change would facilitate the development of solutions to curb its impact, for this, data needs to be shared in an optimal way. General objective: To identify how many Spanish scientific articles on climate change published during 2020 share their research data in some way. Specific objectives: a) Identify the attributes of shared research data b) Describe the characteristics of the case studies found on how research data are shared. Methodology: Qualitative and descriptive study analyzing nine attributes: availability (1), accessibility (2), format (3), license (4), linkage (5), funding (6), editorial policy (7), content (8), statistics (9). Results: We analyzed 2212 articles were analyzed, 1867 (84%) articles had no associated research data. The remaining 16% have associated research data: 152 (7%) articles deposited their data in repositories, 42 (2%) submitted their data as supplementary material, 136 (6%) will share their data upon request to the author and 15 (1%) do not have publication permissions. Conclusions: Researchers are willing to share their research data, but under different conditions. Researchers who reused research data did not share the new data they generated. There is a lack of training among researchers on how to manage their research data. There is information on the web on this topic, but it is not just a matter of publishing manuals, but also of creating training spaces within universities, institutes and research centers to build a community of researchers committed to Open Science.</p>
Dataset for "Initial insight of three modes of data sharing: Prevalence of primary reuse, data integration and dataset release in research articles"
<p>The dataset for "Initial insight of three modes of data sharing: Prevalence of primary reuse, data integration and dataset release in research articles" is coded as follows:</p> <p>01 DOI: DOI<br> 02 article number: the accession number in Web of Science<br> 03 article title: title of the articles<br> 04 exclude: if the article was excluded from the sample, assign 1.<br> 05 research_field: the categories of research fields are described in the Appendix (Table S1)<br> 06 target_of_study: the categories of the target of studies are described in the Appendix (Table S1)<br> 29 release_location_nameofpublicarchive: the names of the deposited public archives (comma separated)</p> <p>The following items, if they occur, are assigned a value of 1:<br> 07 No_datause: The article did not use data<br> 08 primary_reuse: primary reuse<br> 09 primary_data_specificresarchdata: primary reuse of specific research data<br> 10 primary_data_resource: primary reuse of resource<br> 11 primary_source_self: primary reuse from self-constructed data<br> 12 primary_source_citation: primary reuse from citation<br> 13 primary_source_archive: primary reuse from an archive<br> 14 primary_source_others: primary reuse from the other source<br> 15 primary_souce_na: primary reuse source is not available<br> 16 data_integration: data integration<br> 17 integration_type_empirical: data integration as empirical type<br> 18 integration_type_Introductionmaterialresearchmethod: data integration as introduction/material/research methods type<br> 19 integration_type_combinedanalysis: data integration as introduction/material/research methods type<br> 20 integration_source_self: data integration from self-constructed data<br> 21 integration_source_citation: data integration from citation<br> 22 integration_source_archive: data integration from an archive<br> 23 integration_source_others: data integration from the other source<br> 24 integration_source_na: data integration source is not available<br> 25 dataset_release: dataset release<br> 26 release_location_publicarchive: dataset deposit to a public archive <br> 27 release_location_supporting: dataset release in Supporting Information<br> 28 release_location_onrequest: dataset release through personal contacts </p> <p> </p> <p>The appendix includes following tables:<br> Table S1. Coding schema for analysis<br> Table S2. Primary reuse by research field and reused data<br> Table S3. Primary reuse by target of study and reused data<br> Table S4. Data integration by research field and reuse type<br> Table S5. Data integration by target of study and reuse type<br> Table S6. Dataset release by research field<br> Table S7. Dataset release by target of study and methods<br> Table S8. List of names of public data archives for dataset release</p>
Data Management and Sharing: Practices and Perceptions of Psychology Researchers
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Data from: A framework for sharing power in research teams and promoting justice in scientific publication
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Data: Researcher Perspectives on the Use and Sharing of Software
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Data from: Research data sharing: practices and attitudes of geophysicists
Open data policies have been introduced by governments, funders, and publishers over the past decade. Previous research showed a growing recognition by scientists of the benefits of data-sharing and reuse, but actual practices lag and are not always compliant with new regulations. The goal of this study is to investigate motives, attitudes, and data practices of the community of earth and planetary geophysicists, a discipline believed to have accepting attitudes towards data sharing and reuse. A better understanding of the attitudes and current data-sharing practices of this scientific community could enable funders, publishers, data managers, and librarians to design systems and services that help scientists understand and adhere to mandates and to create practices, tools, and services that are scientist-focused. An online survey was distributed to the members of the American Geophysical Union (AGU), producing 1372 responses from 116 countries. The attitudes of researchers to data sharing and reuse were generally positive, but in practice scientists had concerns about sharing their own research data. These concerns include the possibility of potential data misuse and the need for assurance of proper citation and acknowledgement. Training and assistance in good data management practices are lacking in many scientific fields and might help to alleviate these doubts.
Overview of Research Data Sharing Policies Across Various Scientific Publishers
<p><strong>Context </strong></p><p>With the aim of advancing trust in published research, (scientific) publishers are developing Research Data Sharing Policies for their portfolio. Some publishers have the same policy across all journals. For example for the publishers Frontiers and Springer-Nature, a Data Availability statement is mandatory for all journals in their portfolio. While other publishers have different policy levels depending on the journal (e.g. Elsevier, American Chemical Society). In this list, one will find a short overview of Research Data Sharing Policies across publishers (mostly relevant to the Faculty of Science at Utrecht University).</p><p><strong>Limitations</strong></p><p>The list of publishers does not encompass all scientific publishers. Some Research Data Sharing Policies might be subjected to change.</p>
Dataset of the study "Open Access and Data Sharing in Cancer Stem Cells research"
<p>Dataset of the study "Open Access and Data Sharing in Cancer Stem Cells research"</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)
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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.