Data Management and Sharing
U.S. Federal funders, and many private funders, require making data associated with grants available for further research. Data are shared through public online data repositories when possible or with restricted access. Grant proposals may require data management plans (or Data Management and Sharing plans) that describe how the proposal will meet those requirements. JHU Data Services provides resources and consultation on writing data management plans using the DMPTool. This section provides an overview of plan components and resources for most funder requirements.
NIH DMSP guide
Find Funder Requirements for Data Sharing
Funders Data-related Mandates and Public Access Plans
Most US public funders and many private funders require data management and sharing plans for funded projects. These links provide databases of data-related requirements and public access policies for U.S. and many international and private funders. Also listed are direct links to a few major funders.
- DMPTool: up-to-date requirements with funder-specific templates. (See Guidelines within templates for links to policies)
- FAIRsharing.org: searchable database includes many international and private funder requirements, in addition to U.S. funders.
- Sherpa Juliet: database with U.S., international and private funder requirements. May be less frequently updated.
- National Institutes of Health (NIH) Policy for Data Management and Sharing
- National Science Foundation (NSF) Data Management Plan requirements
- United States Agency for International Development (USAID) Data Policy
- Gates Foundation Open Data Policy
Write a Data Management and Sharing Plan
We recommend the DMPTool for writing plans from funder-specific templates, with associated guidance and examples. JHU users can log in with their JHU emails and credentials. JHU Data Service will provide direct feedback on drafts that can be sent within the DMPTool or directly to email@example.com for feedback.
Data Management Plan Components
Here is an overview of typical components of a proposal data management plan for U.S. funders. The elements described in this section includes links within this guide and external resources for more details and guidance.
Funders, generally, look for the following in a DMP:
- What type of data will be produced?
- What are the standards of organization and metadata for documenting data?
- How will privacy, security, confidentiality and intellectual property be protected?
- How will data be accessed and shared to allow others to use it?
- How will data be archived and preserved and for how long?
Consider listing all the products of research, both "raw" and processed data used to support results. All types require management during the project. The list could include sources, file types, format and size. Also indicate which data will be made accessible.
Indicate which research products will be shared, ideally indicating the value to a range of research communities. Sharing policies prefer unmediated distribution through an online repository or database. Include where the data will be accessed and when it is available, such as accompanying publications. More guidance on data sharing
Shared data should be accompanied by sufficient documentation to be understood and ideally reused. Guidelines prefer use of metadata standards of one's research community, such as accepted descriptors of common data elements. Formatting that facilitates machine readability is ideal. More guidance on documentation
Plans should indicate any requirements for those accessing data, such as citing datasets, and restrictions on use such as intellectual property or proprietary data that might limit what is shared. Plans should also indicate privacy conditions, whether data will be de-identified or require restricted access through an approval process such as IRB reviews. More guidance on usage conditions
Many plans ask for brief details on how data will be stored, especially data requiring high-capacity storage, special collaborative access, or security such as JHU's SAFE Desktop secure data enclave. Also indicate which data will be preserved and for how long after the grant period. Some plans ask who will be responsible for preservation and long-term access to shared data. More guidance on storage and preservation
What are Research Data?
Researchers often ask what constitutes their data. Johns Hopkins University defines research data “records that would be used for the reconstruction and evaluation of reported or otherwise published results” in the policy on access and retention of research data and materials. Examples include laboratory notebooks, numerical raw experimental results and instrumental outputs.
The FAIR Guiding Principles for scientific data management and stewardship, published in 2016, outlined methods for broadening access to shared data, focusing particularly on better discovery and open access through data repositories, and better reuse through documentation and machine-readable metadata standards. FAIR Principles fit within the wider promotion of Open Science and reproducible research. Data sharing policies by funders often cite these principles as a goal for making publicly funded data more widely available.
- FAIR Principles: overview provided by the GO FAIR Initiative
- FAIRsharing.org: provides resources and database collections supporting FAIR principles for various stakeholders including:
- FAIR Sharing Standards: A registry of terminology artefacts, models/formats, reporting guidelines, and identifier schemas.
- FAIR Data Repositories & Knowledgebases: A registry of knowledgebases and repositories of data and other digital assets
- FAIRsharing.org Data Policies database: A registry of data preservation, management and sharing policies from international funding agencies, regulators, journals, and other organisations.
- CARE Principles for Indigenous Data Governance: discussing special considerations for sharing data from indigenous populations
- FASEB Science Policy and Advocacy: Federation of American Societies for Experimental Biology's collection of policy statements and best practices regarding data management and sharing, including the DataWorks! initiative promoting data sharing and exemplary data management plans.
Allowable Costs for Data Management and Sharing
Most US funders allow certain costs for data management and sharing to be included in grant budgets. It can be challenging to estimate costs at the time of proposal. For example, a plan might requires annual funding of repository fees for 10 years on a 5 year grant. Anonymizing data for public access might require hiring a statistician. JHU's Research Administration offices can advise on some of these costs. Funder program officers should also be aware of allowable costs. JHU Data Services can help investigate costs associated with data repositories. Here are additional resources from funders and others:
National Science Foundation (NSF):
Proposal & Award Policies & Procedures Guide Policy of allowable costs. NSF allows certain data management costs, such as covering fees deposits of data (see FAQ) but program officers may need to advise on applicable categories for budgeting.
National Institutes of Health (NIH):
- Summary on NIH's Data Sharing site: Budgeting for Data Management and Sharing
- Supplemental Information to the NIH Policy for Data Management and Sharing: Allowable Costs for Data Management and Sharing:
- NIH Grants Policy Statement on Allowable and Unallowable costs
- Costing guidance from COGR's NIH Data Sharing Readiness Guide (coming soon)
- NIMH NDA cost estimator
An infographic from USC Department of Budget and Grants listing a range of costs for data management and sharing.
More Resources for Writing Data Management Plans
Examples of Data Management Plans
- DMPTool's ongoing collection of publicly shared data management plans that can be filtered by funder, institution and subject
- DMP examples from University of Arizona
- Sample NIH Data Management and Sharing plans for Clinical, Secondary, and Genomic research from NIHM
- Examples DMPs on GitHub
NIH's website for the policy, guidance, and resources for data management and sharing.
A user-friendly page of tables and checklists that reviewers or writers of plans can use to quickly evaluate data management plans. More information about the worksheet can be found here.
JHU Data Services Online Training: Preparing a Data Management Plan
This one-hour online training course contains 10 mini-modules, created by JHU Data Services.