NIH Data Management and Sharing FAQs for MSU

1. Read through the DMS Resources for MSU webpage to familiarize yourself with the resources and information available to researchers at MSU.  Also carefully read through the policy itself (including the supplements).
2. Familiarize yourself with the FAIR principles for data management. The FAIR (findable, accessible, interoperable, reusable) data principles are the guiding principles the NIH has used in creating the new policy. 
3. Assess your project’s data management needs and practices relative to the policy, especially around documenting existing practices and developing new ones to address the increased emphasis on data sharing and oversight.
4. Determine services needed and consider costs.  Familiarize yourself with the resources and services available at MSU and assess whether they will meet your needs. Also consider costs you may need to budget for, such as the labor required to clean and format the data to meet repository requirements.

Your Data Management and Sharing Plan should be two pages or fewer and must include the following elements:

• Data Type
• Related Tools, Software and/or Code
• Standards
• Data Preservation, Access, and Associated Timelines
• Access, Distribution, or Reuse Considerations
• Oversight of Data Management and Sharing.

Please see the NIH’s Draft DMS Plan Format Page for more information.

• Underestimating the amount of time and effort involved - One of the biggest pitfalls researchers make is underestimating the amount of time and effort involved in complying with data management and sharing requirements.  It is challenging to work with the NIH repositories and related data and information, and researchers often find that the actual time spent on data management and sharing far exceeds their initial estimates.  When estimating the amount of time needed, researchers should not assume that everything will go smoothly, especially if it is their first time working with a particular data repository.
• Not adequately budgeting for data management and sharing costs – Costs for complying with data management and sharing requirements are real costs to the project and should be adequately budgeted for.  Data management and sharing (DMS) costs should be identified in the proposal budget and directly charged to the sponsored program award during the project period.  Reasonable costs should be identified and allocated and should be consistently treated as direct costs to the specific project, program or activity.   As mentioned above, researchers often underestimate the amount of time involved in complying with DMS requirements, which can result in salaries being inadequately budgeted for on the proposal.  Researchers should be aware that agency-specific cost calculators may underestimate costs or fail to capture all associated costs.  Researchers should also be aware that there may be limitations in finding data managers available.  As a result, projects may need to use contractors, which will likely need to be budgeted for at a higher rate.  For more information, please refer to the Budgeting for Data Management webpage.
• Not following the NIH Data Management and Sharing Plan template – We strongly recommend that the NIH template be used to ensure that your plan includes all the required information. Please be sure to include all six Elements specified in the NIH template and make sure your plan complies with the NIH’s 2-page limit. 
• The Data Management Plan not aligning with the aims of the project – The scientific aims of your project should guide the development of the DMP.  If there are any elements of the data management plan that do not align with your project’s aims, they should be revisited and updated.
• If your research includes human subjects data, additional steps may be required to protect the privacy, rights, and confidentiality of prospective participants (i.e., through de-identification, Certificates of Confidentiality, and other protective measures).  Please be sure to review the resources linked on our Human Research Considerations webpage, including NIH's Protecting Participant Privacy when Sharing Scientific Data webpage if you are planning to conduct research that involves human subjects data.

No. The DMS Policy does not apply to awards for Training Programs (e.g., Ts, D43) and Fellowship Programs (Fs); however, Research Career Programs (Ks) are subject to the DMS Policy.  While Training and Fellowship Programs are not subject to the DMS Policy, NIH encourages trainees and fellows to consider integrating data management and sharing practices into their activities if appropriate.  Please see a complete list of NIH activity codes subject to the DMS Policy.

For other activity codes subject to the DMS Policy that also include a training or infrastructure development component, applicants are only expected to address in Plans the sharing of scientific data generated in the course of their research.  Applicants are not expected to address in their Plans the sharing of scientific data generated during training or infrastructure development.

No. Researchers are expected to share appropriate scientific data, which is defined as data commonly accepted in the scientific community as being of sufficient quality to validate and replicate the research findings.

Researchers are not expected to share the following:

• Data that are not necessary for or of sufficient quality to validate and replicate the research findings,
• Laboratory notebooks,
• Preliminary analyses that are not necessary for or of sufficient quality to validate and replicate the research findings,
• Completed case report forms,
• Drafts of scientific papers,
• Plans for future research,
• Peer reviews,
• Communications with colleagues, or
• Physical objects, such as laboratory specimens.

It is important to note that not all scientific data may be appropriate to share, and that there may be justifiable factors to limit sharing of scientific data.

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.” Even the scientific data that are not used to support a publication are considered scientific data and within the final DMS Policy’s scope. 

Researchers are not expected to share the following:

• Data that are not necessary for or of sufficient quality to validate and replicate the research findings,
• Laboratory notebooks,
• Preliminary analyses that are not necessary for or of sufficient quality to validate and replicate the research findings,
• Completed case report forms,
• Drafts of scientific papers,
• Plans for future research,
• Peer reviews,
• Communications with colleagues, or
• Physical objects, such as laboratory specimens.

It is important to note that not all scientific data may be appropriate to share, and that there may be justifiable factors to limit sharing of scientific data.

Scientific data should be made accessible as soon as possible, and in general, should be shared by the earlier of two timepoints:

• The time of an associated publication:  Scientific data underlying peer-reviewed journal articles should be made accessible no later than the date on which the article is first made available in print or electronic format.

OR:

• The end of the performance period: Scientific data underlying findings not disseminated through peer-reviewed journal articles should be shared by the end of the performance period unless the grant enters into a no-cost extension. If a no cost extension is permitted, then the recipient should share the data by the end of the extended performance period. These scientific data may underlie unpublished key findings, developments, and conclusions; or findings documented within preprints, conference proceedings, or book chapters. For example, scientific data underlying null and negative findings are important to share even though these key findings are not always published.  Researchers should be aware that some preprint servers may require the sharing of data upon preprint posting, and repositories storing data may similarly require public release of data upon preprint posting.

Scientific data can result from secondary research, but researchers are not expected to share the existing, shared primary data used to conduct the secondary research. Researchers are, however, expected to maximize appropriate sharing of any new, derived data generated as a result of their research. Note that use of data obtained from repositories or other sources and derived data may be subject to limitations on sharing as a condition of access, which is a justifiable reason for limiting sharing.

Yes, social and behavioral scientific research that results in the generation of scientific data are subject to the DMS Policy. Qualitative data may constitute scientific data if it meets the definition in the DMS Policy. 

Costs to execute the Data Management and Sharing Plan should be identified in the proposal budget and directly charged to the sponsored program award during the project period.  Requirements to manage and share data are identified by some sponsors and/or on specific awards, which allows associated costs to be charged as direct costs to maintain compliance with terms and conditions.  Reasonable costs should be identified and allocated and should be consistently treated as direct costs to the specific project, program or activity.   Project budgets should include expected costs when applicable.  Allowable costs include labor for data curation, preservation, de-identification, and more. The NIH has a provided a list of allowable and unallowable costs and has extensive information on the NIH DMSP FAQ page under "F. Budget/Costs". 

It is very important that researchers adequately budget for data management and sharing costs at the proposal stage.  Researchers often underestimate the amount of time involved in complying with DMS requirements, which can result in inadequate budgets for salaries.  Researchers should avoid relying on agency-specific cost calculators, as these often underestimate costs or fail to capture all associated costs.  Some researchers may also have trouble finding data managers who are available to work on their projects.  As a result, projects may need to use contractors, which will likely need to be budgeted for at a higher rate.

For more information about budgeting for data management and sharing costs, please see MSU’s Budgeting for Data Management webpage.

Direct costs budgeted for activities within the Data Management Plan should be included as follows:

• Detailed Budgets – as a single line item in section F. Other Direct Costs, labeled “Data Management and Sharing Costs”.  Costs should also be explained in the budget justification.  Please note, effort by MSU employees to perform data management and sharing activities should continue to be budgeted in the Personnel/Salaries & Wages category of the budget.
• Modular Budgets – Add an Additional Narrative Justification describing the costs budgeted for the DMP.

Please note that any DMS costs incurred by subawards are to be included in the subawards Research & Related (R&R) budget as a single line item.

For assistance with budgeting costs for Data Management Plans, see the OSP Budget Guidelines webpage.

Any costs related to complying with the policy must be paid for up-front during the performance period. For example, costs for long-term data preservation must be budgeted for in the proposal and paid before the end of the grant.  For more information about budgeting for data management and sharing costs, please see MSU’s Budgeting for Data Management webpage.

In response to the release of the NIH’s optional DMS Plan format page , we have received questions related to Element 6: Oversight of Data Management and Sharing, which states:  “Describe how compliance with this Plan will be monitored and managed, frequency of oversight, and by whom at your institution (e.g., titles, roles).”

The PI will want to consider what is a reasonable oversight methodology for their proposed project.  Building on one of the samples from NIH as a template, we have developed an option for language below that investigators may choose to use as a starting point in drafting their response for Element 6.  Please note that the paragraph as written may need adjustments or may not work for the specific project/staffing and/or NIH specific direction in a Request for Applications (RFA). The PI may need to modify or create their own wording to communicate planned oversight. 

Data will be submitted according to this DMSP by a project data manager from the PI’s project team. The data manager will oversee data collection, analysis, storage, and sharing. The PI will conduct periodic meetings with key study personnel to support compliance with the DMSP and the timeliness and quality of data submissions. The PI is aware of MSU resources at:  Data Management Plans-Resources for MSU.

The central offices at MSU, e.g., OSP, CGA, SPA, ICER, ITS, IRB, will not be responsible for monitoring DMS plans and should not be indicated in Element 6.

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.” See more under Section VIII of the Final NIH Policy.

To avoid possible issues when reporting progress, ensure that your submitted plan contains enough detail for the program officer to be able to evaluate compliance.

If you make changes to your submitted plan, your new plan must be re-approved by NIH. the process varies depending on if the change is made pre-award or post-award

Topic-specific questions can be directed to the following areas:

• MSU Libraries – DMP Support (researchdata@lib.msu.edu)
• MSU Research IT Support (researchIT@msu.edu)
• Pre-award Support – OSP Contacts by College

No.  MSU recommends the use of existing repositories for data sharing whenever practicable and discourages the development of new data repositories at MSU to fulfill data sharing requirements.

Yes. If the total direct costs, including data management and sharing costs, exceed $250,000 in any budget period then a modular budget may not be used.

Yes. Data management and sharing costs are included as direct costs. Applicants must seek approval  from an Institute/Center program officer  at least 6 weeks prior to the anticipated submission of any application when the total direct costs (excluding consortium F&A costs) are $500,000 or more in any budget period.

See NIH Grants Policy Statement, Section 2.3.7.2 Acceptance for Review of Unsolicited Applications Requesting $500,000 or More in Direct Costs for additional policy information.

MSU has several resources available to assist researchers with their data management and sharing needs.  Please note that some of these services are not free.  Project specific services that would incur costs during the project period should be appropriately budgeted for.  For more information, please see the DMS Resources for MSU webpage.  Some commonly used tools and services are linked below.  Please note that this list is not exhaustive.

• DMP Tool - A click-through wizard for creating a DMP that complies with funder requirements; provides examples and template language for answering questions.  Sign in using your MSU NetID.
• MSU Libraries – provide guidance to faculty in the development and execution of research data management plans, connect faculty to research data support services throughout the university, and educate students, faculty, and staff about the importance of data management.
• Research Data Services Catalog - a guide to research data services available to MSU-affiliated researchers. It provides a unified presence for the distributed network of research data resources that exist across campus.
• Biomedical Research Informatics Core (BRIC) – services include preaward planning, databases, data management, data storage and security, custom application development, and co-investigation.
• Institute for Cyber-Enabled Research (ICER) - provides the cyberinfrastructure for researchers to perform their computational research and supports multidisciplinary research in all facets of computational and data science by providing access to training, consulting, hardware and software infrastructure solutions. 
• Center for Statistical Training and Consulting (CSTAT) - provides expertise and guidance on study design, statistical methods, interpretation of results, and conducting statistical analyses.
• Clinical Research Support Core (CRSC) - assists researchers with development, implementation, management, and completion of government and industry-funded clinical research (i.e., clinical trials, investigator-initiated research, etc.) conducted through MSU and its community partners, to expedite the research administration process, and to facilitate research compliance.
• Humanities Commons - a nonprofit network that enables scholars, researchers, practitioners, teachers, and students to create a professional profile, discuss common interests, develop new publications, and share their work. The Commons network is open to anyone, regardless of field, language, institutional affiliation, or form of employment.
• Information Technology Services (ITS) – research data support services include short-term data storage & file sharing, web access/hosting, database development, database management, database hosting, and assistance with all aspects of administering an Oracle database for software applications and systems.
• Office of Research Regulatory Support (ORRS) – research data planning and design services are provided, including guidance to ensure research meets regulatory and MSU policies and ensure human subjects protections are adequate (e.g., HIPAA, FERPA compliant).
• Proposal Services, Office of Research and Innovation - provides grant writing, editing, and consulting services to assist MSU researchers with proposal preparation support.
• Office of Sponsored Programs (OSP) - help with proposal submissions for external funding, award negotiation, and the various stages in financial and administrative management. 
• Office for Survey Research (OSR) - a comprehensive survey data collection and analysis facility whose mission is to serve the information needs of MSU faculty, MSU administrative offices and staff, the Federal government, the State of Michigan, smaller units of government, non-profit organizations.
• Research Data Management Guidance (RDMG) – The MSU Libraries in conjunction with the University Archives offers a Research Data Management Guidance service to respond to the emerging concern surrounding research data management.
• Contract and Grant Administration (CGA) – review and submission of NIH Research Performance Progress Report.

NIH encourages data management and sharing practices to be consistent with the FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and reflective of practices within specific research communities. In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. The principles emphasize machine-actionability because humans increasingly rely on computational support to deal with data resulting from the increase in volume, complexity, and creation speed of data.

No. The policy only applies to new competing grant applications, it does not retroactively apply. The effective date of the DMS Policy is January 25, 2023, including for:

• Competing grant applications that are submitted to NIH for the January 25, 2023, and subsequent receipt dates
• Proposals for contracts that are submitted to NIH on or after January 25, 2023
• NIH Intramural Research Projects conducted on or after January 25, 2023
• Other funding agreements (e.g., Other Transactions) that are executed on or after January 25, 2023, unless otherwise stipulated by NIH

For guidance on applications for receipt dates BEFORE January 25, 2023, refer to the 2003 NIH Data Sharing Policy.

NIH program staff will assess the Data Management and Sharing Plans.  The Plans will not be peer reviewed.  Costs budgeted for Data Management and Sharing will be reviewed and could receive comments, but those comments will not impact the overall score.  See more under Section VI of the Final NIH Policy.

The following service providers offer short-term storage and/or file sharing services:

• Biomedical Research Informatics Core (BRIC)
• Institute for Cyber-Enabled Research (iCER)
• Institute for Research on Teaching and Learning College of Education (IRTL)
• Information Technology Services (ITS)
• Office for Survey Research (OSR)
• Visual Resource Library (VRL)

Long term data storage is considered to be permanent and persistent post-project data archiving. While permanency cannot be guaranteed, long term data storage providers will have a clear business model, supporting policy, and the authority to sign contractual agreements to support long term data storage. Most long term data storage providers are equipped to provide an enterprise level networked storage environment with a high degree of redundancy and geographically distributed backup(s).

In general, NIH does not endorse or require sharing data in any particular repository, although some initiatives and funding opportunities will have individual requirements.  Overall, NIH encourages researchers to select the repository that is most appropriate for their data type and discipline. See Selecting a Data Repository.

The following providers also offer long-term storage services to campus:

• Biomedical Research Informatics Core (BRIC)
• Research Data Management Guidance (RDMG)

Metadata is a set of data that provides information about, or describes, other data.  Scientific metadata may include:

• Variable-related information such as labels, plausibility limits, codes for missing data, measurement units, or expectations about distributional properties and associations,
• Study and process level information related to the design characteristics of a data collection, recruitment of participants, measurement methods,
• Methods of analysis

Data standards specify how 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.  For more information, please refer to our Data Management and Sharing Best Practices webpage.

Some programs, types of data, ICOs, or Funding Opportunity Announcements (FOAs) may require data deposition in particular data repositories, and “primary consideration should be given to data repositories that are discipline or data-type specific to support effective data discovery and reuse.” NIH encourages the use of established repositories. To select a repository relevant to your data, consider the following:

• Is there a specific NIH repository named in the FOA?
• Is there a data repository specific to the data type(s) relevant to your research and your scientific discipline?
• Is there a data repository specified by the journal in which you are publishing or hope to publish?
• If there are no relevant discipline-specific repositories, is there a generalist data repository you can use?

For data generated from research for which no data repository is specified by NIH, researchers are encouraged to select a data repository that is appropriate for the data generated from the research project and is in accordance with the NIH Desirable Characteristics for All Data Repositories. To learn more, check out the NIH guidance on selecting a data repository.

Most data repositories are committed to holding data indefinitely. When selecting a repository, the following should be considered, per NIH's guidance on selecting a data repository:

• Unique Persistent Identifiers: Assigns datasets a citable, unique persistent identifier, such as a digital object identifier (DOI) or accession number, to support data discovery, reporting, and research assessment. The identifier points to a persistent landing page that remains accessible even if the dataset is de-accessioned or no longer available.
• Long-Term Sustainability: Has a plan for long-term management of data, including maintaining integrity, authenticity, and availability of datasets; building on a stable technical infrastructure and funding plans; and having contingency plans to ensure data are available and maintained during and after unforeseen events.

According to the MSU patent policy, the university has the right to own any intellectual property, including original data, created using university facilities, equipment, or funds controlled or administered by the university.  This enables the University to respond to inquiries from funders and third parties, as well as appropriately protect the data, data subjects, and researchers. Principal Investigators (PIs) and other researchers are stewards and custodians of research data. Therefore, the PI’s responsibilities with respect to research data include:

• Ensuring proper management and retention of research data
• Establishing and maintaining appropriate procedures for the protection of research data
• Ensuring compliance with program requirements
• Maintaining confidentiality of research data
• Maintaining appropriate data use agreements for the sharing of research data
• Complying with applicable federal, state, and local laws and regulations

Pre-Award Changes to DMS Plans:

• If NIH requires changes, applicants will be notified via Just-in-Time (JIT)
• Revisions should be submitted as part of the JIT response (e.g. via OSP to Grants Management Specialist (GMS) or in eRA Commons)
• Defer to the instructions from NIH GMS

Post-Award Changes to DMS Plans:

• When PIs identify they need to change their DMS Plan, these should be submitted to CGA as a formal request to NIH for changing their plan. NIH also expects DMS Plan changes to be addressed in the Research Performance Progress Reports.