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Library resources and services for grant development


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Library resources and services for grant development

  2. 2. Contents • Introduction • Scope of presentation • Data management / data sharing mandates • Library online resources for planning • Case study – NIH data sharing consultation • Conclusion, Q&A
  3. 3. Scope Research Lifecycle (top row) and Library Data Services (bottom row) Identify research topic • Identify existing datasets for reuse (subject and institutional repositories Develop grant proposal; Secure funding • Guidance for data management plans (DMP) Collect, condition and analyze data • Assistance with data description and deposit Develop conclusions; Identify future research • Data sharing and preservation per DMP Disseminate results • Guidance for data citation Focus: data planning for external funders
  4. 4. U.S. Research Agencies and their Data Management/Sharing Mandates • NIH Data Sharing Plans: NIH requests that all extramural applicants seeking $500,000 or more in direct costs in any one year provide a data-sharing plan in their applications. • Some NIH solicitations will ask for data sharing plans (regardless of grant amount), particularly if online archives and public access are significant components.
  5. 5. U.S. Research Agencies and their Data Management/Sharing Mandates (cont’d) • NSF Data Management Plans: Proposals must include a supplementary document of no more than two pages labeled “Data Management Plan”. • Supplement should describe how the proposal will conform to NSF policy on the dissemination and sharing of research results. • Data Management Plan will be reviewed as an integral part of the proposal, coming under Intellectual Merit or Broader Impacts criteria.
  6. 6. U.S. Research Agencies and their Data Management/Sharing Mandates (cont’d) • White House OSTP: February 22, 2013 memorandum directs each Federal agency with over $100 million in annual research and development expenditures to develop a plan to support increased public access to the results of research funded by the Federal Government • Plan should include public access to scientific publications and to scientific data in digital formats.
  7. 7. WSU Library System – Online guide for Research Data Services • • NSF Data Management (policies, tools, templates) • NIH Data Sharing (policies, tools) • Data Repositories (directories and evaluation criteria) • Provides background information, FAQs, suggestions for dealing with unique situations (examples: sensitive data, proprietary data)
  8. 8. Case Study – NIH Data Sharing consultation • Medical researcher considering submission of R24 (resource) proposal to NIH • Due to the nature of the resource (proteomics dataset), PI felt that a strong data sharing plan would make the proposal more competitive • PI asked about feasibility of using WSU institutional repository for archival of his data
  9. 9. Case Study – NIH Data Sharing consultation (cont’d) • Step 1: Data “interview” with researchers • Objective: basic understanding of the dataset(s) • Nature/formats/amount of data • Restrictions on dissemination • Privacy or PHI content • Step 2: Review solicitation for unique requirements
  10. 10. Case Study – NIH Data Sharing consultation (cont’d) • Step 3: Identify suitable data repository and metadata • Prefer existing/mature subject repositories where available • Trusted Repositories Audit & Certification (TRAC) evaluation criteria for repositories include: • organizational infrastructure • digital object management • technologies, technical infrastructure, & security • Step 3a: (If no suitable repository exists), estimate costs for infrastructure to be included in the funding request
  11. 11. Case Study – NIH Data Sharing consultation (cont’d) • Step 4: Draft language for data sharing plan for integration into the submission • Mature disciplinary data repository used for the large datasets; repository requires use of standard (HUPO) metadata for description • Publications and smaller “interpreted” datasets to be deposited in WSU institutional repository for increased discovery and impact • Example 1 (what we provided) • Example 2 (final version with edits by PI)
  12. 12. Conclusion – Q&A • Future work: “checklist” for researchers considering data sharing / data publication • Thanks! • Questions?
  13. 13. Backup • Data sharing achieves many important goals for the scientific community, such as • reinforcing open scientific inquiry, • encouraging diversity of analysis and opinion, • promoting new research, testing of new or alternative hypotheses and methods of analysis, • supporting studies on data collection methods and measurement, • facilitating education of new researchers, • enabling the exploration of topics not envisioned by the initial investigators, and • permitting the creation of new datasets by combining data from multiple sources. (from
  14. 14. Backup • Costello, M. J. (2009). Motivating online publication of data. BioScience, 59(5), 418-427. doi: 10.1525/bio.2009.59.5.9 • Piwowar, H. A., Day, R. S., & Fridsma, D. B. (2007). Sharing detailed research data is associated with increased citation rate. PLoS ONE, 2(3). doi: 10.1371/journal.pone.0000308 • Piwowar, H. A., Vision, T. J., & Whitlock, M. C. (2011). Data archiving is a good investment. Nature, 473(7347), 285. doi: 10.1038/473285a • Wicherts, J. M., Bakker, M., & Molenaar, D. (2011). Willingness to share research data is related to the strength of the evidence and the quality of reporting of statistical results. PLoS ONE, 6(11). doi: 10.1371/journal.pone.0026828