This document discusses library resources and services to support grant development, specifically regarding data management and sharing requirements of major funders like NIH and NSF. It provides an overview of mandates from these agencies requiring data management plans and sharing of research data. The WSU Library System online guide for research data services is introduced, which provides tools, templates and guidance on data management policies and repositories. A case study example is presented of a consultation provided to a researcher on developing a strong data sharing plan for an NIH proposal.

1. LIBRARY RESOURCES AND SERVICES
FOR GRANT DEVELOPMENT
Jim Van Loon, MSME/MLIS
Science Librarian

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. 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. 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. 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. 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. WSU Library System –
Online guide for Research Data Services
• http://guides.lib.wayne.edu/datamanagement
• 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. 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. 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. 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. 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. Conclusion – Q&A
• Future work: “checklist” for researchers considering data
sharing / data publication
• Thanks!
• Questions?

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 http://grants.nih.gov/grants/policy/data_sharing/data_sharing_faqs.htm)

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