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Data Management Plans for Grant Funded Projects (NSF, NIH)

Writing a Data Management Plan
Sample Data Management Plans
Templates for Data Management Plans
Further Information
Data Management Workshops
Contact Us

Since 1996 US federal granting agencies have required that funded research data be made available to the public [US Office of Management and Budget (OMB) Circular A-110 ]. However most agencies do not have established requirements for the creation, sharing and storage of data.


The National Institutes of Health have required data sharing since 2003. Their Data Sharing Policy page provides the policy, guidance, a data sharing workbook and an FAQ.

Starting in January, 2011, the National Science Foundation will require the submission of a two page data management plan as a part of all proposals for NSF grant funding. This is considered a first step in what will become a comprehensive approach to data policy. Guidance from the NSF is general, leaving the specifics to be determined by the “community of interest”, essentially the discipline to which the proposed project is relevant.  NSF initially announced the change in May, 2010. The NSF has updated their Grant.gov Application Guide to include information about the data management plan requirements, in Chapter V, Section 4.12.


Some subject areas, such as engineering or mathematical and physical sciences, may have specific guidelines addressing unique data management issues for that respective community. Be sure to look at proposal details and main directorate or division website for additional guidelines. Existing disciplinary requirements are posted at the Data Sharing and Dissemination of Results, which also includes the general data policy.


NSF also provides an FAQ on data sharing which will answer some questions about the requirements and process.


Funding for Data Sharing 


The NIH guidelines state that:
NIH recognizes that it takes time and money to prepare data for sharing. Thus, applicants can request funds for data sharing and archiving in their grant application. (See also the section on What to Include in an NIH Application.) Investigators who incorporate data sharing in the initial design of the study may more readily and economically establish adequate procedures for protecting the identities of participants and share a useful dataset with appropriate documentation.


The NSF Social Sciences Directorate says about funding: Any costs should be explained in the Budget Justification pages.


Proprietary Data
The NSF Engineering Directorate  document states that:

Some proposals may involve proprietary or other restricted data. For example, projects having proprietary information that will eventually lead to commercialization, such as Engineering Research Center (ERC), Nanoscale Science and Technology Center (NSEC), Industry/University Cooperative Research Center (I/UCRC), Small Business Innovative Research (SBIR), Small Business Technology Transfer (STTR), and Grant Opportunities for Academic Liaison with Industry (GOALI) awards. In addition, membership agreements, contracts, involvement with other agencies, and similar obligations may place some restrictions on data sharing.

Any such data-management issues should be discussed as well as the conditions that might prevent or delay the sharing of data. The proposal’s DMP would address the distinction between released and restricted data and how they would be managed. Exceptions to the basic data-management policy should be discussed with the cognizant program officer before submission of such proposals.

(Highlighting is ours)


Writing a Data Management Plan

What does this mean for UConn researchers?  In general, a data management plan may have the following parts:


Let’s look at each step of this outline.

1. Types of Data

  • What do you expect to generate during the course of your research?
  • Is it observational or experimental?
  • Is it simulated from test models or compiled from other data sources?
  • Is it real-time or reproducible?
  • Is it live or ready to be archived?
  • Describe what kind of data you expect to produce.

2. Standards and Formats

  • What standards will you use for your data and how will you describe it (metadata)? 
  • What file formats will you use?
  • What metadata standards will you use?
  • How will you create or capture metadata details?

  Examples of Data Standards:

Information about Metadata:

Examples of Metadata for science and social science:

3. Provisions for Archiving and Preservation

  • How long should your data be kept?
  • Have you identified a repository or archive in which to deposit your data?
  • How will the data be prepared for long term preservation (if needed.)
  • Will funding or other institutional commitments be required for preservation?
  • What are the procedures for your intended long term data location for preservation and backup?
  • How will documentation and curation responsibilities be transferred from one entity to another?

Information about archiving and preservation:

Data-Pass - Data preservation Alliance for the Social Sciences
Backups and Security (MIT)
Stewardship and Archiving  (University of Minnesota)

4. Access and Re-Use Policies and Provisions

  • Is the data shared with other researchers?
  • Are there ethical issues or privacy concerns?
  • Are there confidentiality issues?
  • Should the data be restricted or embargoed for intellectual property reasons?
  • Do you have the right to share the data if it is not produced by you?
  • Who will you share it with?
  • Do any regulations apply to the data (for example HIPAA)?
  • Is any of the data covered by copyright? Copyright can be waived under CCO declaration http://creativecommons.org/choose/zero)
  • Will the data be licensed?
  • Who may be interested in your data in the future and what might it be used for?
  • Are there any reasons not to allow re-use?

Ethical and Legal Issues (MIT)
http://libraries.mit.edu/guides/subjects/data-management/ethical.html

5. Plans for Transition or Termination of the Data Collection

  • How long will your data be active?
  • Who will manage it?
  • Do you have a retention schedule or a schedule to destroy your data at some point?
  • Is there a need to migrate or transition your data to another media or structure in the future?
  • Will data be destroyed after a specific time period? If so:
    • Do you have the right to do this? Is it your data (copyrighted, etc.)?
    • Are there ethical or legal obligations for the secure removal of data after a specific time period?
    • How do you plan to destroy the data?

Lists of Data Repositories and Archives

List of Data Repositories from Open Access Directory
Archives and Repositories for Data  from the University of Minnesota
D2C2 Distributed Data Curation Center  from Purdue University

 

Sample NSF Data Management Plans


The following links are to examples of data management plans from 2011 NSF grant applications.  You can use these examples as  guides when  you write your own plan. Each link provides examples for a variety of disciplines.

Rice University  written by members of their Faculty Senate’s Research Committee
University of New Mexico (links on the left)
University of California San Diego
University of Minnesota


We will post UConn examples of plans from funded applications once they become e available. Please  share  plans from your funded application with us and consider allowing us to post it on this site.

Templates for Data Management Plans

DMP Tool: Guidance and Resources for your Data Management Plan

Further Information on Data Management Plans


 

Data Management Workshops

Data Management for Grant Sponsored Programs
May 10, 2011 workshop  -   powerpoint slides


Contacts for further information:

  • UConn Libaries:
  • Office of Sponsored Programs:
    • Antje Harnisch, Assistant Director, Pre-Award and Contract Services
  • University ITS:

Revised 9 May 2011 (CM)

 

 

 

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Last Updated: Friday, 02-Mar-2012 13:58:09 EST