SOM NIH Data Management and Sharing Policy: Writing a DMSP

If you plan to generate scientific data, you must submit a Data Management and Sharing Plan to the funding NIH ICO as part of the Budget Justification section of your application for extramural awards. Also, be aware that the NIH may decide to make some parts of the DMS Plans public at a later date.

Your plan should be two pages or fewer and must include:

Element 1: Data Type

Briefly describe the scientific data to be managed and shared:

• Summarize the types (for example, 256-channel EEG data and fMRI images) and amount (for example, from 50 research participants) of scientific data to be generated and/or used in the research. Descriptions may include the data modality (e.g., imaging, genomic, mobile, survey), level of aggregation (e.g., individual, aggregated, summarized), and/or the degree of data processing.
• Describe which scientific data from the project will be preserved and shared. NIH does not anticipate that researchers will preserve and share all scientific data generated in a study. Researchers should decide which scientific data to preserve and share based on ethical, legal, and technical factors. The plan should provide the reasoning for these decisions.
• A brief listing of the metadata, other relevant data, and any associated documentation (e.g., study protocols and data collection instruments) that will be made accessible to facilitate interpretation of the scientific data

Element 2: Related Tools, Software and/or Code

Indicate whether specialized tools are needed to access or manipulate shared scientific data to support replication or reuse, and name(s) of the needed tool(s) and software. If applicable, specify how needed tools can be accessed.

Element 3: Standards

For data management, standards usually address how data should be collected, measured, recorded, or formatted. Using standards can save time and reduce common errors.  You should include information on standards that  you will use in a DMP as using data standards will strengthen the data's value.

Examples of Standards:

For data, standards such as:

• Data file formats that enable reuse, preservation and access
• Common data elements (CDEs), standard survey instruments (eg, USCDI), or other systematic data collection tools

For metadata:

• Standard terminologies, e.g., from thesauri, taxonomies, ontologies.
• Minimum information standards (e.g., MIAME, MINSEQE)
• Chemical substance identifiers
• Persistent Identifiers (eg, ORCID, accession numbers, DOI)

Registries of standards:

• NCBO BioPortal
• FAIRSharing

If there are no established standards, how will you facilitate access and reuse of the data and metadata?

Element 4: Data Preservation, Access, and Associated Timelines

Give plans and timelines for data preservation and access, including:

• The name of the repository(ies) where scientific data and metadata arising from the project will be archived.
• How the scientific data will be findable and identifiable, i.e., via a persistent unique identifier or other standard indexing tools.
• When the scientific data will be made available to other users and for how long. Identify any differences in timelines for different subsets of scientific data to be shared.

Element 5: Access, Distribution, or Reuse Considerations

Describe any applicable factors affecting subsequent access, distribution, or reuse of scientific data related to:

• Informed consent
• Privacy and confidentiality protections consistent with applicable federal, Tribal, state, and local laws, regulations, and policies
• Whether access to scientific data derived from humans will be controlled 
• Any restrictions imposed by federal, Tribal, or state laws, regulations, or policies, or existing or anticipated agreements
• Any other considerations that may limit the extent of data sharing.

Element 6: Oversight of Data Management and Sharing

Indicate how compliance with the DMS Plan will be monitored and managed.

Oversight will usually be the responsibility of the PI. You may consider listing a backup person as well, or any other research team members with particular data responsibilities (eg, a database administrator in your lab). Oversight includes revising the DMSP and adhering to submission deadlines for sharing data. All members of the team should have training on the DMSP. Please do NOT list any Northwell offices as responsible for oversight without explicit permission.

From the NIH, Iowa State

The DMPTool is a free, open-source, online application that helps researchers create data management plans. ZSOM/Feinstein is a member of DMPTool. When you sign up for an account, please choose Northwell Health, Feinstein Institutes for Medical Research, Zucker School of Medicine as your organization.

If you need help with data management, choosing a data repository, or writing a DMSP, please contact:

Lena Bohman, Data Services and Research Impact Librarian, lena.g.bohman@hofstra.edu