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81 results for “research data sharing”
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>
Research Data Management and Sharing for images: beautiful fountains require ugly piping!
<p>The consensus is clear: research data funded by public resources should be shared. Globally, the advantages of sharing research data are widely recognized. It promotes transparency and validation, reduces redundant efforts, accelerates discovery, enhances equity, and increases the impact of research through collaboration and efficient use of resources.</p> <p>Image data, however, presents unique challenges. Advanced technologies produce large, multimodal, and multiplexed datasets that span multiple targets across various spatiotemporal scales.</p> <p>This image data comes from a range of sources—such as optical, electron microscopy, and medical imaging—each with specific technical requirements. Managing this complexity is a daunting task without global metadata standardization as well as robust Research Data Management and Sharing (RDMS) cyberinfrastructure to bring it all together.</p> <p>The figure illustrates a common issue: while the importance of the <strong>“beautiful fountains”</strong> of scientific discoveries and medical treatments is widely understood, fewer people recognize the <strong>need to invest in building the often ignored “ugly plumbing” </strong>required to build a strong RDMS cyberinfrastructure.</p> <p> </p>
Questionnaire data to research small-scale farmers' information sharing for adapting to climate change in Mozambique (2019-2020)
<p>Data collected from individual questionnaires with local communities of 4 districts of Mozambique in November 2019 and July 2020. It contains as well data from nine individual questionnaires to institutions (government and NGOs) working with local communities for their development.</p> <p>Data are replies from interviews containing open and closed questions about a) climate change adaptation options necessary for Mozambican small scale farmers, about b) the most used and preferred information sources of farmers, about c) the main barriers for a better exchange of information, and about d) proposals for improving it. The questionnaire can be consulted in Appendix A (in English and Portuguese). The open questions had the purpose to understand the causes and explanations about the themes presented. The closed questions followed a 0-5 likert scale approach, where 5 meant a very important factor and 0 non important one. This format was pursued for developing statistical analysis and comparison between the different types of participants. We used the same questions and format for interviewing farmers and stakeholders, although the questionnaire for farmers included also personal aspects like gender, age, and education.</p>
Jisc Research Data Shared Service metadata focus group use cases
<p>Dataset of use cases collected between July and October 2016 during a series of metadata focus groups conducted with a number of the <strong>Research Data Shared Service</strong> pilots who volunteered for the process.</p> <p>This dataset is available in two formats (including an open format) with the same content: 180 use cases in the following user story structure: </p> <ul> <li><strong>As a</strong></li> <li><strong>Theme</strong></li> <li><strong>I want </strong></li> <li><strong>So that</strong></li> <li><strong>Comments</strong></li> </ul> <p>The .xlsx file contains additional formatting grouping the use cases by theme, role, data and community.</p>
Music Data Sharing Platform for Computational Musicology Research (CCMUSIC DATASET)
<p>This platform is a multi-functional music data sharing platform for Computational Musicology research. It contains many music datas such as the sound information of Chinese traditional musical instruments and the labeling information of Chinese pop music, which is available for free use by computational musicology researchers.</p> <p>This platform is also a large-scale music data sharing platform specially used for Computational Musicology research in China, including 3 music databases: Chinese Traditional Instrument Sound Database (CTIS), Midi-wav Bi-directional Database of Pop Music and Multi-functional Music Database for MIR Research (CCMusic). All 3 databases are available for free use by computational musicology researchers. For the contents contained in the database, we will provide audio files recorded by the professional team of the conservatory of music, as well as corresponding labelled files, which have no commodity copyright problem and facilitate large-scale promotion. We hope that this music data sharing platform can meet the one-stop data needs of users and contribute to the research in the field of Computational Musicology.</p> <p> </p> <p>If you want to know more information or obtain complete files, please go to the official website of this platform:</p> <p><a href="https://ccmusic-database.github.io/en/">Music Data Sharing Platform for Academic Research</a></p> <p> </p> <ul> <li> <p><strong>Chinese Traditional Instrument Sound Database (CTIS)</strong></p> </li> </ul> <p>This database is developed by Prof. Han Baoqiang's team for many years, which collects sound information about Chinese traditional musical instruments. The database includes 287 Chinese national musical instruments, including traditional musical instruments, improved musical instruments and ethnic minority musical instruments.</p> <ul> <li> <p><strong>Multi-functional Music Database for MIR Research</strong></p> </li> </ul> <p>This database collects sound materials of pop music, folk music and hundreds of national musical instruments, and makes comprehensive annotation to form a multi-purpose music database for MIR researchers.</p> <ul> <li><strong>Midi-wav Bi-directional Database of Pop Music</strong></li> </ul> <p>This database contains hundreds of Chinese pop songs, and each song contains the corresponding midi-audio-lyric information. Among them, recording the vocal part and accompaniment part of audio independently is helpful to study the MIR task under the ideal situation. In addition, the information of singing techniques consistent with vocal part (such as breath sound, falsetto, breathing, vibrato, mute, slide, etc.) is marked in MuseScore, which constitutes a Midi-Wav bi-direction corresponding pop music database.</p>
Sharing research data and findings relevant to the novel coronavirus (COVID-19) outbreak - Literature sources
<p>The spreadsheet in the present dataset (CSV format) includes the sources considered during the literature review stage for the report: From intent to impact: Investigating the effects of open sharing commitments. Please note that not all sources in this deposit have been referenced in the above-mentioned report and that the report may include additional sources</p>
Sharing research data and findings relevant to the novel coronavirus (COVID-19) outbreak - Survey responses
<p>The spreadsheets in the present dataset (CSV format) include the anonymised responses to our online survey of signatories of the Joint Statement on open research and data sharing. Responses have been split into quantitative responses (i.e., closed survey questions) and qualitative responses (i.e., free text survey questions).</p> <p>This data has been used to inform our final report, which is available in our <a href="https://zenodo.org/communities/data-sharing-in-public-health-emergencies">Zenodo Project Community</a>.</p>
Sharing research data and findings relevant to the novel coronavirus (COVID-19) outbreak - Thematic coding of qualitative research findings
<p>The spreadsheet in the present dataset (CSV format) includes the anonymised thematic coding that has been applied to our interview and literature review findings to inform the preparation of the report: From intent to impact: Investigating the effects of open sharing commitments.</p> <p>The thematic coding has been applied by using <a href="https://www.qsrinternational.com/nvivo-qualitative-data-analysis-software/home">NVivo</a>, a professional qualitative analysis software, and then exported in spreadsheet form for public sharing.</p> <p>Find out more about this project in our dedicated <a href="https://zenodo.org/communities/data-sharing-in-public-health-emergencies">Zenodo project community</a>.</p>
How to Ensure Researchers Share Their FAIR Data: Practical Tips and Tools [Online Workshop, Recording]
<p>The online hands-on workshop was aimed at trainers and support staff covering critical elements of data sharing and available tools and resources for supporting Open Science including:<br> • Open Science resources and Data Management Planning<br> • Consent and Ethical considerations<br> • Legislation and Licence frameworks<br> The objectives of the workshop were i) to raise awareness of key tools and resources available for Open Science training ii) to enable a platform to exchange ideas regarding key training topics and iii)n to provide training materials and worksheets for future reuse.<br> The workshop consisted of presentations, demos, a roundtable discussion on ethical considerations, a showcase of licence frameworks at different European archives and an exercise with all participants fostering an exchange of experiences focused on learnt lessons.</p> <p>The video is available on<a href="https://www.youtube.com/watch?v=uztTCRFRZHg"> the CESSDA Training YouTube channel</a>.</p>
Figure 9. 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 9. - Information flows between EU BON and LTER Europe, as envisaged on the 3rd EU BON Stakeholder Roundtable in Granada on 9-11 December 2015.
Figure 8. 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 8. - The patchiness of survey coverage in Europe illustrated by the distribution map of Plantago lanceolata taken from GBIF in 2016. This species is one of the commonest and most widespread in Europe, it should occur in almost all areas of this map, but in fact the data traces out the borders of countries and area who have published data on GBIF.
Figure 6. 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 6. - Individual Metacat instances can be connected to DataOne which replicates public files. Thus the data is still available if a single instance goes offline.https://search.dataone.org/#data/page/0
Figure 5. 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 5. - PPBio has installed a Metacat instance for their researchers to upload and make publicly available the results of work related to biodiversity in the Western Amazon.https://ppbiodata.inpa.gov.br/metacatui/
Figure 4. 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 4. - The public data repository provided by the Knowledge Network for Biocomplexity (KNB).https://knb.ecoinformatics.org/#data/page/0
Figure 3. 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 3. - The implementation of Darwin Core Archive in Plazi to transfer treatment data. Observation data described with Darwin Core terms.
Figure 1. 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 1. - ARPHA consists of two integrated workflows: in ARPHA-XML, the manuscript is written and processed via the ARPHA Writing Tool, and in ARPHA-DOC, the manuscript is submitted and processed as document file(s).
Open Research Data in Medicine - Polish scientists' attitudes towards data sharing
<p>The survey on the attitudes and beliefs of research staff has been carried out at selected Polish medical universities. The purpose of the questionnaire was to collect respondents' opinions on opening research data created during their scientific work. The research was aimed at preparing the necessary educational, technical and legal support for scientists after launching the Polish Medical Platform, when Polish scientists will be asked to deposit their data in local repositories.</p>
Data sharing: an integral part of research practice? Codebook
<p>List of thematic codes for qualitative review of studies focusing on data sharing motives and barriers.</p>
Results of the poll in the study "Information Scientists' Motivations for Research Data Sharing and Reuse"
<p>This is a dataset with results of the poll conducted in the study “Information Scientists’ Motivations for Research Data Sharing and Reuse”.</p> <p>In terms of the Uses and Gratifications Theory (Questions 1 and 2), the most popular uses relate to the categories of research support and information. Researchers share, or would share, their research data in general for any reusability purposes and especially for combination of different datasets to produce new evidence. Also, the vast majority of study participants associate research data sharing with possibilities to accelerate scientific progress and to increase research efficiency. In case of research data reuse, all the researchers indicated that they use, or would use, others’ data first of all for inspiration. Interestingly, study participants put relatively high the category of recognition in case of sharing, but at the same time they do not associate increased recognition among colleagues and other researchers with research data reuse. The remaining categories belonging to the categories of self-esteem and social interaction, i.e. increased citation level and visibility of the research as well as enhanced scientific reputation, possible cooperations and co-authorship, were selected only by few respondents. Also remarkably, data reuse is more frequently linked to entertainment then data sharing. </p> <p>In terms of the Self-Determination Theory (Questions 3 and 4), all but one of the interviewees indicated that they have shared or would share their research data because it can accelerate scientific progress which they consider important and would like to contribute to it (i.e., identified regulation). The second most popular motivation turned out to be the obligation by employer, project funder and/or journals (i.e., external regulation). The third most popular option was social influence, i.e. because many other researchers participate in data sharing and they feel obligated to do the same (i.e., external regulation).This way, the participants demonstrate a mixture of identified motivation and external regulation, both material and social. In the case of data reuse, the participants demonstrate more homogeneous results with identification and intrinsic motivation having most of the votes. The role of external regulation seems to be much less important as in the case with data sharing. So, researchers reuse, or would reuse, research data because it can accelerate scientific progress which is important for them. Additionally, researchers enjoy exploring and using third party research data. Thus, interviewees participate or would participate in data sharing because they consider it important, but also feel or are obliged to do so. At the same time, study participants do not feel pressure from outside when deciding whether to reuse data or not.</p> <p>For more information about the study and its results, please read the article “Information Scientists’ Motivations for Research Data Sharing and Reuse” by Shutsko and Stock (2023).</p>
Supplementary Material: Patterns in research and data sharing for the study of form and function in caviomorph rodents
<p>Supplementary Material for the publication <em>Patterns in research and data sharing for the study of form and function in caviomorph rodents</em> by Luis D. Verde Arregoitia*, Pablo Teta, and Guillermo D'Elía. <em>Journal of Mammalogy</em></p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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International Brain Laboratory public data
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OpenNeuro
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