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To share or not to share: publishing research data
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  1. To share or not to share: publishing research data Alma Swan Key Perspectives Ltd Truro, UK

  2. Study for RINwww.rin.ac.uk/data-publication • Methodology: interviews with 100+ researchers, data managers and data experts • Eight areas: • Astronomy • Chemical crystallography • Classics • Climate science • Genomics • Social and public health science • Plus two interdisciplinary areas: systems biology and the Rural Economy & Land Use (RELU) programme Key Perspectives Ltd

  3. Topics of focus • Data creation and care • Motivations and constraints • Discovery, access and usability • Quality assurance Key Perspectives Ltd

  4. Data creation and care: researchers • Variations and commonalities between disciplines • Attitudes and needs • Available infrastructure • Policies • Raw / derived / reduced data provision • Haphazard storage by researchers • Funder policies may not match disciplinary norms • Metadata quality is very variable Key Perspectives Ltd

  5. Data creation and care: external • Data centres in some disciplines • Public databanks in others • Metadata quality is high • Standard of curation and preservation is high • Cleaning, checking, verifying, annotating, access tools, etc Key Perspectives Ltd

  6. Motivations • Motivations • Altruism • Peer norms and expectations • Hope of collaborations • Hope of publications • BUT: lack of explicit career rewards is a major disincentive Key Perspectives Ltd

  7. Constraints • Need to fully exploit data • Lack of time and resources • Lack of expertise • Legal and ethical constraints • Lack of appropriate archiving service • Fear of inappropriate use of the data Key Perspectives Ltd

  8. Discoverability, access, usability • Publishers require datasets, or provide persistent links • PDF is a common sharing format (unsuitable for re-use in the majority of cases) • Inadequate metadata • Need for sophisticated technologies (including specialised software and programming skills) Key Perspectives Ltd

  9. Quality assurance • Data centres apply rigorous processes that ensure data quality • Researchers largely take each others’ datasets on trust • Peer review mechanisms are not generally established (nor realistically can be) Key Perspectives Ltd

  10. Conclusions and recommendations: funders and institutions • Policies should be clear about the data they wish to see shared and preserved • Policies should take disciplinary norms and behaviours into account • Should cooperate on seeking long-term sustainable solutions for data preservation • Should actively encourage data sharing and publication… Key Perspectives Ltd

  11. Encourage data sharing by… • Promoting the benefits and value to research • Providing support – advice, help with data management plans, etc • Providing grant support for data management • Offer career-related rewards for data sharing • Develop strategies to address the skills gap • Provide advice on mechanisms for access control • Promote best practice for curation and preservation of dynamic datasets (‘freeze- and build’) • Seek clarification from publishers on text-mining Key Perspectives Ltd

  12. Conclusions and recommendations: Access, usability and QA • Funders should promote improved access through better discovery tools and metadata standards • Learned societies, researchers and funders should work together to develop and promote standard methods for citing datasets • Publishers should require links to datasets, or the datasets themselves (and make efforts to provide in native format) • Stakeholders should work together to develop approaches to formal assessment of datasets (scholarly and technical qualities) Key Perspectives Ltd

  13. Thank you for listening aswan@keyperspectives.co.uk www.keyperspectives.co.uk www.keyperspectives.com Key Perspectives Ltd