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379 results for “data sharing”
Visual and auditory brain areas share a representational structure that supports emotion perception: fMRI data
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IPBES Data Management Tutorials - Session 5.6: Publishing and sharing
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The<em> Tools for data management </em>chapter provides IPBES authors with an overview of open source tools used frequently by the scientific community to help it implement data management for the entire data life cycle.</p> <p>This session on <em>publishing and sharing</em> introduces GitHub and Zenodo as two important open access tools for sharing and publishing information.</p>
IPBES Data Management Tutorials - Session 3.6: Data management report details: Data sharing and access considerations
<p>The <em>IPBES data management tutorials</em> are short videos to help experts implement the IPBES data management Policy. They cover topics ranging from data management policy, reports, active research data, tools, and examples.</p> <p>The<em> Tools for data management c</em>hapter provides an overview and discussion of specific elements of IPBES data management reports.</p> <p>This session <em>Data sharing and access considerations </em>covers details on licenses, exceptions to data sharing, and intellectual property considerations. </p>
[Supplementary Information] Can LCA be FAIR? – Assessing the status quo and opportunities for FAIR data sharing
<p>This is the supplementary information related to a the manuscript - 'Can LCA be FAIR?' - Assessing the status quo and opportunities for FAIR data sharing. The purpose of this study is to assess the status quo of data sharing in LCA in relation to the FAIR data principles (Findability, Accessibility, Interoperability and Re-use).</p><p>The supplementary information consists of three files:</p><p><strong>SI 1</strong> - How the life cycle inventory is shared in relation to the FAIR data principles in 25 peer reviewed LCA journal articles between 2018 -2022.</p><p><strong>SI 2</strong> - Review of ten data management plans of EU Horizon Europe projects in relation to LCA to assess the recommendations on the implementation of FAIR principles.</p>
Supplementary Materials for "Accelerating data sharing and re-use in volume electron microscopy"
<p>The deposition contains supporting materials for "Accelerating data sharing and re-use in volume electron microscopy" Comment</p> <ul> <li>Sample preparation protocol for cell monolayers optimized for serial block face scanning electron microscopy</li> <li>Supporting movies showing models of biological specimens imaged using volume electron microscopy</li> </ul>
DIPROMATS 2024 - Shared Task 2: testing data for narrative identification
<p>Narratives are causally connected sequences of events that are selected and evaluated as meaningful for a particular audience. They make sense of the world by identifying the significance of people, places, objects, and events in time. In international relations, international actors create strategic narratives to “construct a shared meaning of the past, present, and future of international politics to shape the behavior of domestic and international actors”</p> <p>DIPROMATS 2024 Task 2 is a multiclass multilabel classification problem. Given a series of predefined narratives of each international actor, systems must determine which narrative the tweets belong to. Systems will receive the description of each narrative and a few examples of tweets in both languages (English and Spanish) that belong to each of them (few-shot learning). A tweet may be associated with one, several or none of the narratives.</p> <p>The few-shot training data can be found here: <a href="https://doi.org/10.5281/zenodo.10820961" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.10820961</a></p> <p>These are the testing datasets for Englsih and Spanish. They are provided without the keys so the large language models can't be contaminated. If you are interested on testing your system, write anselmo@lsi.uned.es for details on submission and leaderboards.</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>
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>
Data sharing and processing infrastructures
<p>These illustrations depict a variety of options for sharing and processing large (research) datasets typically containing human (sometimes personal) data. They were inspired in part by the research article "Bringing Code to Data: Do Not Forget Governance"*, and make use of source material available openly at <a href="https://undraw.co/">https://undraw.co/</a>.</p> <p>When using any of these images, please credit it with:</p> <p>"This image was created by Stephan Heunis and is used under a CC-BY licence."</p> <p>The use and re-use of these images are encouraged, including remixing the images for example changing the colours or merging them together with additional (openly licensed) images.</p> <p><em>*Suver C, Thorogood A, Doerr M, Wilbanks J, Knoppers B. Bringing Code to Data: Do Not Forget Governance. J Med Internet Res 2020;22(7):e18087. DOI: <a href="https://www.jmir.org/2020/7/e18087">10.2196/18087</a></em></p>
Epidemiology and Disease Burden of Neurocritical Disorders: A Cohort Study - Data Sharing
<p>Dataset of a Neurocritical Brazil cohort study whose summary is described below.</p> <p><strong>Abstract</strong></p> <p><strong>Objective:</strong> To describe a cohort of neurocritical patients and their differences based on primary neurological diagnoses and identify predictors of mortality and unfavorable outcome along with the disease burden of each neurological condition on intensive care unit (ICU) admission. <strong>Methods:</strong> Prospective cohort study including patients admitted to 36 ICUs in Brazil and followed up for 30 days. <strong>Results:</strong> Of 4245 patients admitted to the participating ICUs during the study period, 1194 (28.1%) were neurocritical patients and were included in the study. Neurocritical patients had a mean mortality rate 1.7 times higher than non-neurocritical patients admitted to the same ICUs (17.21% versus 10.1%, respectively). The most frequent primary neurological diagnoses on ICU admission were postoperative care of elective neurosurgery, traumatic brain injury, ischemic stroke, and encephalopathy. The estimated total disability-adjusted life-years (DALYs) were 4482.94 in the overall cohort, and the diagnosis with the highest DALYs was traumatic brain injury (1634.42). DALYs were significantly impacted by the patients’ primary neurological diagnosis, sex, age group, and number of secondary neurological injuries. <strong>Conclusion:</strong> We accurately described the epidemiology of neurocritical patients and estimated their overall and relative disease burden. The findings of this study are important to direct policies regarding education, prevention, and treatment of severe neurocritical diseases.</p> <p> </p>
Data for: Patterns of shared signatures of recent positive selection across human populations
<p>Genome-wide summary stats for modified iHS scan in 1KG as reported in:</p> <p><a href="https://pubmed.ncbi.nlm.nih.gov/29459708/">Patterns of shared signatures of recent positive selection across human populations.</a></p> <p>Johnson KE, Voight BF.Nat Ecol Evol. 2018 Apr;2(4):713-720. doi: 10.1038/s41559-018-0478-6. Epub 2018 Feb 19.</p> <p>PMID: 29459708</p> <p>Code available at: https://github.com/bvoightlab/iHS_calc</p>
Anonymisation for data sharing in practice [Online Workshop. Recording]
<p>The goal of this event was to show trainers the tools they need to teach the fundamentals of data anonymisation and disclosure control in training sessions while also giving them hands-on experience with current open source technologies (sdcMicro). Some of the concepts and techniques presented, included k-anonymity, top/bottom coding and aggregation with practical examples and recommendations on incorporating anonymisation into research designs.</p> <p> </p> <p>The video is available on<a href="https://www.youtube.com/watch?v=JeJ6OOxXZwo&t=328s"> the CESSDA Training YouTube channel</a>.</p> <p> </p> <p> </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>
Data_share
<p>This dataset contains the subsets of the high-resolution orthophoto-maps that were used for an accuracy assessment study described at the manuscript entitled: "The Effect of Environmental Conditions on the Quality of UAS Orthophoto-Maps in the Coastal Environment". The manuscript is submitted for publication at the <em>ISPRS International Journal of Geo-Information.</em></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.