How Figshare meets the NIH ‘Desirable Characteristics for Data Repositories’ – a help article for using figshare

“The new NIH Policy for Data Management and Sharing (effective January 25, 2023) includes supplemental information on Selecting a Data Repository (NOT-OD-21-016), which outlines the data repositories characteristics that researchers should seek out to share their NIH-funded research data and materials. 

Figshare.com is an appropriate and well-established generalist repository for researchers to permanently store the datasets and other materials produced from their NIH-funded research and to include in their NIH Data Management and Sharing Plans. Figshare+ uses the same repository infrastructure to offer support for sharing large datasets including transparent costs that can be included in funding proposal budgets. Note that Figshare may also be included in Data Management and Sharing Plans in combination with discipline-specific repositories for sharing any types of research outputs that may not be accepted in more specific repositories. Figshare is currently working with NIH as part of their Generalist Repository Ecosystem Initiative to continue enhancing our support for NIH-funded researcher needs. 

Figshare repositories offer established repository infrastructure including adherence to community best practices and standards for persistence, provenance, and discoverability with the flexibility to share any file type and any type of research material and documentation. Figshare makes it easy to share your data in a way that is citable and reusable and to get credit for all of your work. 

Figshare is listed as a recommended data sharing resource in the following: 

NIH Scientific Data Sharing: Generalist Repositories
NIH National Library of Medicine (NLM): Generalist Repositories
NIH HEAL Initiative Recommended Repositories
Nature’s Data Repository Guidance …”

Statement on NIH plans to speed access to federally funded research results | National Institutes of Health (NIH)

“Today, the White House Office of Science and Technology Policy (OSTP) issued updated policy guidance

(link is external) directing federal agencies to expedite access to results of federally funded research. NIH has long championed principles of transparency and accessibility in NIH-funded research and supports this important step by the Biden Administration.

Over the coming months, NIH will work expeditiously to develop and share its plans for implementing the OSTP policy guidance. NIH intends to work with interagency partners and stakeholders to revise its current Public Access Policy to enable researchers, clinicians, students, and the public to access NIH research results immediately upon publication. I am pleased to report that NIH’s efforts to maximize access to scientific data are already underway through implementation of the new NIH Policy for Data Management and Sharing (DMS Policy), which takes effect on January 25, 2023. Through the DMS Policy, NIH clearly articulates that sharing scientific data is fundamental to accelerating biomedical research discovery. Our DMS Policy implementation efforts continue, and I encourage you to visit sharing.nih.gov for the latest updates and resources that NIH has developed to support our community of researchers and institutions. 

We are enthusiastic to move forward on these important efforts to make research results more accessible and look forward to working together to strengthen our shared responsibility in making federally funded research results accessible to the public.”

NLM Leverages Its Information Resources to Improve Access to Monkeypox-related Literature and Research

“The National Library of Medicine (NLM) is working to accelerate the global monkeypox response through initiatives that expand access to scientific literature, sequence data, clinical trial information, and consumer health information related to monkeypox.

NLM’s efforts follow on declarations by the World Health Organization and U.S. Department of Health and Human Services Secretary of the ongoing spread of monkeypox virus as a public health emergency. NLM is responding to the call to action by the White House Office Science and Technology Policy (OSTP) and science and technology leaders from more than a dozen other nations to make monkeypox-related research and data immediately available to the public.

NLM will leverage its existing relationships with publishers that submit to PubMed Central (PMC), its digital archive of peer-reviewed biomedical and life sciences literature, to make the wide range of journal articles that can inform the monkeypox response freely available to the public. Depositing appropriate articles into the PMC Open Access collection will ensure that monkeypox-related research is readily available in both human- and machine-readable formats. Readers will be able to discover articles via PubMed and access the full text in PMC without delay. Artificial intelligence researchers can continue to develop and apply novel approaches to text mining to help answer questions about monkeypox.

In addition, NLM is prioritizing the review of monkeypox sequence submissions through its genetic sequence database, GenBank, as well as submissions to ClinicalTrials.gov, the world’s largest publicly accessible database of privately and publicly funded clinical studies….”

NOT-OD-22-189: Implementation Details for the NIH Data Management and Sharing Policy

“The purpose of this notice is to inform the extramural research community of implementation details for the NIH Policy for Data Management and Sharing (DMS Policy) affecting grant and cooperative agreement applications submitted for receipt dates on or after January 25, 2023. The specific changes to competing grant and cooperative agreement application instructions clarified below will be implemented with application form packages identified with a Competition ID of “FORMS-H” and incorporated into the forthcoming FORMS-H application guides.

Although the DMS Policy will apply also to Research and Development (R&D) contracts, NIH intramural research projects, and other funding agreements (e.g., Other Transactions), the forms changes and other implementation details provided in this Notice apply only to NIH extramural grant and cooperative agreement activities. Details applicable to R&D contracts will be incorporated into the appropriate Requests for Proposals, and details applicable to Other Transactions will be incorporated into the appropriate Research Opportunity Announcement….”

Ten simple rules for maximizing the recommendations of the NIH data management and sharing plan | PLOS Computational Biology

Abstract:  The National Institutes of Health (NIH) Policy for Data Management and Sharing (DMS Policy) recognizes the NIH’s role as a key steward of United States biomedical research and information and seeks to enhance that stewardship through systematic recommendations for the preservation and sharing of research data generated by funded projects. The policy is effective as of January 2023. The recommendations include a requirement for the submission of a Data Management and Sharing Plan (DMSP) with funding applications, and while no strict template was provided, the NIH has released supplemental draft guidance on elements to consider when developing a plan. This article provides 10 key recommendations for creating a DMSP that is both maximally compliant and effective.

 

Recognizing Our Collective Responsibility in the Prioritization of Open Data Metrics · Issue 4.3, Summer 2022

Abstract:  With the rise in data-sharing policies, development of supportive infrastructure, and the amount of data published over the last decades, evaluation and assessment are increasingly necessary to understand the reach, impact, and return on investment of data-sharing practices. As biomedical research stakeholders prepare for the implementation of the updated National Institutes of Health (NIH) Data Management and Sharing Policy in 2023, it is essential that the development of responsible, evidence-based open data metrics are prioritized. If the community is not mindful of our responsibility in building for assessment upfront, there are prominent risks to the advancement of open data-sharing practices: failing to live up to the policy’s goals, losing community ownership of the open data landscape, and creating disparate incentive systems that do not allow for researcher reward. These risks can be mitigated if the community recognizes data as its own scholarly output, resources and leverages open infrastructure, and builds broad community agreement around approaches for open data metrics, including using existing standards and resources. In preparation for the NIH policy, the community has an opportune moment to build for researchers’ best interests and support the advancement of biomedical sciences, including assessment, reward, and mechanisms for improving policy resources and supportive infrastructure as the space evolves.

DataWorks! Prize – Incentives for building a culture of data sharing and reuse – NIH Extramural Nexus

“A $500,000 prize purse, rewarding data sharing and reuse in biomedical research, is a new, innovative strategy for supporting the research community. The DataWorks! Prize highlights the role of data sharing and reuse in scientific discovery while recognizing and rewarding researchers who engage in these practices. This prize, which launched on May 11, 2022, is a partnership between the NIH Office of Data Science Strategy and the Federation of American Societies for Experimental Biology (FASEB)….”

Gearing Up for 2023 Part II: Implementing the NIH Data Management and Sharing Policy – NIH Extramural Nexus

“NIH has a long history of developing consent language and, as such, our team worked across the agency – and with you! – to develop a new resource that shares best practices for developing informed consents to facilitate data/biospecimen storage and sharing for future use.  It also provides modifiable sample language that investigators and IRBs can use to assist in the clear communication of potential risks and benefits associated with data/biospecimen storage and sharing.  In developing this resource, we engaged with key federal partners, as well as scientific societies and associations.  Importantly, we also considered the 102 comments from stakeholders in response to a RFI that we issued in 2021.

As for our second resource, we are requesting public comment on protecting the privacy of research participants when data is shared. I think I need to be upfront and acknowledge that we have issued many of these types of requests over the last several months and NIH understands the effort that folks take to thoughtfully respond.  With that said, we think the research community will greatly benefit from this resource and we want to hear your thoughts on whether it hits the mark or needs adjustment….”

DataWorks! Challenge | HeroX

“Share your story of how you reused or shared data to further your biological and/or biomedical research effort and get recognized!…

The Federation of American Societies for Experimental Biology (FASEB) and the National Institutes of Health (NIH) are championing a bold vision of data sharing and reuse. The DataWorks! Prize fuels this vision with an annual challenge that showcases the benefits of research data management while recognizing and rewarding teams whose research demonstrates the power of data sharing or reuse practices to advance scientific discovery and human health. We are seeking new and innovative approaches to data sharing and reuse in the fields of biological and biomedical research. 

To incentivize effective practices and increase community engagement around data sharing and reuse, the 2022 DataWorks! Prize will distribute up to 12 monetary team awards. Submissions will undergo a two-stage review process, with final awards selected by a judging panel of NIH officials. The NIH will recognize winning teams with a cash prize, and winners will share their stories in a DataWorks! Prize symposium.”

Using the State of Open Data survey to put the NIH Policy on Data Management and Sharing into practice

“Join us for a webinar on how the State of Open Data survey — the annual survey on researchers’ attitudes toward open data and data sharing — can help your institution put the NIH Policy on Data Management and Sharing into practice. …”

NIH issues a seismic mandate: share data publicly

“In January 2023, the US National Institutes of Health (NIH) will begin requiring most of the 300,000 researchers and 2,500 institutions it funds annually to include a data-management plan in their grant applications — and to eventually make their data publicly available.

Researchers who spoke to Nature largely applaud the open-science principles underlying the policy — and the global example it sets. But some have concerns about the logistical challenges that researchers and their institutions will face in complying with it. Namely, they worry that the policy might exacerbate existing inequities in the science-funding landscape and could be a burden for early-career scientists, who do the lion’s share of data collection and are already stretched thin….

Such a seismic shift in practice has left some researchers worried about the amount of work that the mandate will require when it becomes effective….

Others worry that data-management activities will further sap funds from under-resourced labs. Although the policy outlines certain fees that researchers can add to their proposed budgets to offset the costs of compliance with the mandate, it doesn’t specify what criteria the NIH will use to grant these requests….

Despite its potential pitfalls, Ross thinks that the policy will have a ripple effect that will persuade smaller funding agencies and industry to adopt similar changes. “This policy establishes what people expect from clinical research,” he says. “It’s essentially saying the culture of research needs to change.” ”

NOT-OD-22-029: Request for Information on Proposed Updates and Long-Term Considerations for the NIH Genomic Data Sharing Policy

“Respect for and protection of the interests of research participants are central tenets of the NIH GDS Policy and are fundamental to NIH’s stewardship of large-scale genomic data. Data derived from human research participants under the GDS Policy must be de-identified and provided with a random, unique code, the key to which is held by the submitting institution. NIH acknowledges that the concept of “identifiability” is a matter of ongoing deliberation within the scientific and bioethics communities. NIH relies on robust protections beyond de-identification, such as Institutional Review Board (IRB) consideration of risks associated with data submission, designating controlled access for certain data types, use of Data Access Committees to review requests, data use agreements to prohibit data disclosure and participant re-identification, and Certificates of Confidentiality[ii] to prohibit disclosure. As outlined in the NIH GDS Policy, the criteria for establishing de-identification are:

Identities of research participants cannot be readily ascertained or otherwise associated with the data by the repository staff or secondary data users (45 CFR 46.102(e) (Federal Policy for the Protection of Human Subjects); and
18 identifiers enumerated at 45 CFR 164.514(b)(2)(the HIPAA Privacy Rule) are removed.

The reliance on the 18 identifiers enumerated at 45 CFR 164.514(b)(2) (the HIPAA Privacy Rule) as the only acceptable method under the GDS Policy for de-identification has recently presented several challenges. Certain data elements considered potentially identifiable, such as date ranges shorter than a year, may have scientific utility, especially when studying disease progression (e.g., with COVID-19) or higher resolution location data than the regulatory standard (e.g., full ZIP codes or mobile location data), which may be valuable for studying the social determinants of health or environmental risk.

Challenges have also arisen recently around data linkage. It is difficult to know in advance which data sources may add scientific value when combined, so it is not always possible to tell participants about data linkage during their initial consent. Linking data refers to connecting two or more data sources (often multiple studies) to bring together information about a person, enabling researchers to learn more about a participant or small group of participants. For example, a participant might enroll in a study that uses their electronic health record as well as a separate study that uses a sample of their blood, and the data about them from those studies could later be linked in new research for more powerful analyses. This challenge in prospectively informing participants about data linkage raises questions about respecting individuals’ autonomy and what participants understand about how their data will be used. Furthermore, data from multiple sources may not have been obtained under the same consent and de-identification expectations as the GDS Policy….”

NOT-OD-22-029: Request for Information on Proposed Updates and Long-Term Considerations for the NIH Genomic Data Sharing Policy

“Respect for and protection of the interests of research participants are central tenets of the NIH GDS Policy and are fundamental to NIH’s stewardship of large-scale genomic data. Data derived from human research participants under the GDS Policy must be de-identified and provided with a random, unique code, the key to which is held by the submitting institution. NIH acknowledges that the concept of “identifiability” is a matter of ongoing deliberation within the scientific and bioethics communities. NIH relies on robust protections beyond de-identification, such as Institutional Review Board (IRB) consideration of risks associated with data submission, designating controlled access for certain data types, use of Data Access Committees to review requests, data use agreements to prohibit data disclosure and participant re-identification, and Certificates of Confidentiality[ii] to prohibit disclosure. As outlined in the NIH GDS Policy, the criteria for establishing de-identification are:

Identities of research participants cannot be readily ascertained or otherwise associated with the data by the repository staff or secondary data users (45 CFR 46.102(e) (Federal Policy for the Protection of Human Subjects); and
18 identifiers enumerated at 45 CFR 164.514(b)(2)(the HIPAA Privacy Rule) are removed.

The reliance on the 18 identifiers enumerated at 45 CFR 164.514(b)(2) (the HIPAA Privacy Rule) as the only acceptable method under the GDS Policy for de-identification has recently presented several challenges. Certain data elements considered potentially identifiable, such as date ranges shorter than a year, may have scientific utility, especially when studying disease progression (e.g., with COVID-19) or higher resolution location data than the regulatory standard (e.g., full ZIP codes or mobile location data), which may be valuable for studying the social determinants of health or environmental risk.

Challenges have also arisen recently around data linkage. It is difficult to know in advance which data sources may add scientific value when combined, so it is not always possible to tell participants about data linkage during their initial consent. Linking data refers to connecting two or more data sources (often multiple studies) to bring together information about a person, enabling researchers to learn more about a participant or small group of participants. For example, a participant might enroll in a study that uses their electronic health record as well as a separate study that uses a sample of their blood, and the data about them from those studies could later be linked in new research for more powerful analyses. This challenge in prospectively informing participants about data linkage raises questions about respecting individuals’ autonomy and what participants understand about how their data will be used. Furthermore, data from multiple sources may not have been obtained under the same consent and de-identification expectations as the GDS Policy….”