The Library Data Services Program recently posted about the National Institutes of Health (NIH) Data Management and Sharing Policy and how it will affect UC Berkeley researchers. Please read more about the new policy on this post.
Here are a list of FAQs about the new policy. Please contact the UC Berkeley Library Data Services Program (firstname.lastname@example.org) with questions.
How is UC Berkeley responding to this policy?
The Library Data Services Program is collaborating with the Research Data Management Program to provide guidance and documentation to ensure compliance with the NIH policy.
What is considered “scientific data” for the purposes of this plan?
The final NIH Policy defines Scientific Data as: “The recorded factual material commonly accepted in the scientific community as of sufficient quality to validate and replicate research findings, regardless of whether the data are used to support scholarly publications. Scientific data do not include laboratory notebooks, preliminary analyses, completed case report forms, drafts of scientific papers, plans for future research, peer reviews, communications with colleagues, or physical objects, such as laboratory specimens.” The NIH states that “the final DMS Policy is designed to increase the sharing of scientific data, regardless of whether a publication is produced…Data that do not form the basis of a publication produced during the award period should be shared by the end of the award period.”
What is included in a Data Management and Sharing Plan?
In these max two-page documents, researchers will describe their:
- Data type(s)
- Related tools, software, and/or code
- Data preservation, access, and associated timelines
- Access, distribution, or reuse considerations
- Oversight of data management and sharing
Read more about Data Management Plans and see sample language
Can I make my data available upon request?
NIH strongly prefers that scientific data be shared and preserved through repositories or, for datasets up to 2GB, through PubMed Central-deposited supplemental data files, rather than kept by a researcher and provided upon request.
How will the plans be assessed?
NIH program staff will assess the DMS plans but peer reviewers may comment on the proposed budget for data management and sharing.
What data repository should I use?
NIH encourages the use of established repositories. To select the best repository for your data consider the following:
- Is there a specific NIH repository named in the funding announcement?
- Is there a data repository specific to your discipline?
- If not, is there a general data repository you can use?
- For UC Berkeley researchers, consider Dryad data repository
To learn more, read the NIH guidance on selecting a data repository.
What is a standard? What standards are relevant to my research?
A standard specifies how exactly data and related materials should be stored, organized, and described. In the context of research data, the term typically refers to the use of specific and well-defined formats, schemas, vocabularies, and ontologies in the description and organization of data. However, for researchers within a community where more formal standards have not been well established, it can also be interpreted more broadly to refer to the adoption of the same (or similar) data management-related activities, conventions, or strategies by different researchers and across different projects.
When do I need to make my data available?
NIH encourages scientific data to be shared as soon as possible, and no later than time of an associated publication or end of the performance period, whichever comes first.
What data management and sharing costs can I include in my grant?
Allowable costs can include:
- data curation and developing documentation (formatting data, de-identifying data, preparing metadata, curating data for a data repository)
- data management considerations (unique and specialized information infrastructure necessary to provide local management and preservation before depositing in a repository)
- preserving data in data repositories (data deposit fees)
Read more about allowable costs.
Guidance for data management and sharing costs on NIH budget requests
What happens if I do not comply with the NIH policy or make my data available as described in the DMS policy?
NIH Program Staff will be monitoring compliance with the policy during the funding period. “Noncompliance with Plans may result in the NIH ICO adding special Terms and Conditions of Award or terminating the award. If award recipients are not compliant with Plans at the end of the award, noncompliance may be factored into future funding decisions.”
I work with sensitive topics/populations – how do I protect my participants’ privacy?
NIH strongly encourages researchers who work with sensitive topics and/or populations to address data sharing in the Informed Consent process. See the UC Berkeley Human Research Protection Program’s Informed Consent page, which includes guidelines and appropriate form templates.
Researchers should pay special attention to their de-identification process to ensure that all identifying information has been fully removed. Researchers should consider depositing their data in restricted access repositories that require data use agreements and research plans in order to access the data. Contact email@example.com if you would like guidance on selecting restricted access repositories.
Please view the UCSF’s resources on data de-identification and sharing de-identified data for additional guidance.
Do specific NIH Institutes and Centers (ICs) have additional policies or recommendations?
Yes, NIH ICs may have additional requirements or recommendations. Please identify the institute or center using this table to learn more about requirements.
Supplemental information from the NIH:
Responsible Management and Sharing of American Indian/Alaska Native Participant Data
Protecting Privacy When Sharing Human Research Participant Data
Many thanks to Ariel Deardorff at the UCSF Library for allowing us to adapt their list of Frequently Asked Questions and thank you to UC Berkeley’s Elliott Smith, Michael Sholinbeck, and Erin Foster for all of their expertise and contributions.