NIDDK-CR is in the process of updating the Data and Resources Use Agreement (DUA) template and Material Transfer Agreement (MTA) template language in alignment with NIH’s security practices in addition to clarifying users’ commonly asked questions. Users can expect an updated Security and Non-transferability section to address NIH security policy updates; the MTA has updated the Terms of Agreement and codified standardized use limitations. Check back soon to find links to the updated templates.

NIDDK-CR staff compiled several commonly asked DUA questions and updated the FAQs with a new DUA subsection. The FAQs page can be found here.

Starting in February, NIDDK-CR will be hosting the monthly Data Science and Meet the Expert Webinar Series that aims to accelerate data science and AI-driven biomedical research by fostering collaboration between biomedical researchers and experts in the field. The goals of this webinar series are:

• Goal 1: Enhance understanding of the data science lifecycle, including advanced data use methodologies and AI methods, in support of the NIH data science ecosystem
• Goal 2: Encourage the generation of new research questions and projects that apply the data science lifecycle and advanced data use methodologies, leveraging data and cloud resources available to the research community from NIH/NIDDK

Check out the NIDDK-CR Webinar Series page for information on the webinars in the series and how to join.

We’re thrilled to announce exciting system upgrades to the NIDDK-CR platform! Get ready for dynamic new features and functionalities, including interactive graphs, comprehensive study information pages, advanced file exploration tools, and next-level search experience. These enhancements are designed with users in mind to make locating and accessing studies and resources faster and easier than ever. Stay tuned for the release date announcement!—this is one update you won’t want to miss!

The S-index challenge, led by the National Eye Institute (NEI), aims to incentivize and reward data sharing by promoting a new metric, the S-index, that will assess how effectively researchers share data. This metric will incorporate factors such as adherence to Findable, Accessible, Interoperable, and Reusable (FAIR) standards, data timeliness, completeness of annotation, frequency of utilization in other studies, and related publications and patents. Phase 1 for this challenge is to generate a proof of concept proposal and will open on April 21, 2025 at 8:00 a.m. EDT. Check out Challenge.gov to learn more about the challenge, how to enter, eligibility, and cash prizes.

The Federation of American Societies for Experimental Biology (FASEB) and the National Institutes of Health (NIH) partnered together to host the 2024 DataWorks! Prize. This challenge is a collaboration with the seven generalist repositories participating in the NIH-funded Generalist Repositories Ecosystem Initiative (GREI) and will focus on best practices in data reuse and secondary analysis that advance human health. NIDDK-CR data may be used for this challenge in combination with data from a participating GREI repository. Phase 1 for this challenge closes on 11:59 p.m. EDT on October 23, 2024. Check out Challenge.gov to learn more about the challenge, how to enter, eligibility, and prizes.

Winners of the Intermediate/Advanced-level Data Centric Challenge, Dr. Ali Loveys and Fiona Meng from FI Consulting, presented Laying the Foundation for AI-Ready Data at the ODSS Data Sharing and Reuse Seminar on May 10, 2024. The FI Consulting team successfully consolidated and unified multiple TrialNet data sets and identified data outliers, then enhanced raw data to ensure consistent variable representation and identified numeric and categorical “missingness” to prevent modeling bias, thus enhancing TrialNet data for AI-readiness. View the recording from the past webinar .

NIDDK Central Repository (NIDDK-CR) has released a revised policy that aligns with the NIH Data Management and Sharing (DMS) policy and NIDDK DMS Guidance: https://grants.nih.gov/grants/guide/notice-files/NOT-DK-24-003.html. More details and complete NIDDK-CR Resource Archival and Sharing policy are available under “Helpful Information”- “General Information”: https://repository.niddk.nih.gov/pages/general_information

In September 2023, the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) Central Repository announced the Data Centric Challenge aimed at enhancing NIDDK datasets for future artificial intelligence (AI) applications. Challenge participants were tasked to generate an AI-ready dataset that can be used for future data challenges and produce methods to enhance the AI-readiness of NIDDK data. Participation in the Challenge was tiered (i.e., beginner-level and intermediate/advanced-level) and utilized data from two longitudinal studies focused on type 1 diabetes (TEDDY and TrialNet). NIDDK is excited to announce ICF Inc. and FI Consulting as the Data Centric Challenge winners. Visit Challenge.gov to read more about the winning solutions: https://www.challenge.gov/?challenge=niddk-central-repository-data-centric-challenge&tab=winners

DOIs are now available for every NIDDK-CR R4R study! DOIs increase resource visibility and accessibility and enable appropriate credit citation and interoperability. Researchers are encouraged to use them when making public releases using NIDDK-CR R4R resources. A study’s DOI can be found at the top of the study’s overview page along with citation guidance. Additionally, a data availability statement, with data package version and DOI, is available to requestors who are submitting publications.

As of January 25, 2023, the NIH Policy for Data Management and Sharing (DMS) is in effect. All research that is funded in whole or in part by NIH is required to submit a DMS plan outlining how scientific data and accompanying metadata will be managed and shared. NIH DMS plans should address the elements described in the Supplemental Information to the NIH DMS Policy. NIH has provided a DMS plan format on the Writing a Data Management and Sharing Plan page on the NIH Scientific Data Sharing website. NIDDK is offering Institute-specific guidance for Writing a DMS Plan and provides NIDDK DMS Tools and Examples.

This revamped, streamlined process for requesting biospecimens incorporates user feedback to better facilitate access to biospecimens. Certain requests will still require an X01 Resource Access Award; however, many will need only an administrative review or will leverage existing grant applications peer review if one was submitted. The new request process is designed to share NIDDK-CR resources with more researchers faster and easier to continue fostering innovative research. Researchers can visit the Material Counts page to explore the available resources and start a request.

Vivli and the National Institute of Diabetes and Digestive and Kidney Disease (NIDDK), part of the National Institutes of Health (NIH), will be working together to broaden exposure and strengthen NIDDK data ecosystem. As part of Vivli’s award to serve as one of six generalist repositories that will work with the NIH Office of Data Science Strategy under its Generalist Repository Ecosystem Initiative (GREI), NIDDK will include NIDDK Central Repository (NIDDK-CR) resources in the Vivli global clinical research data sharing platform. NIDDK-CR’s resources inclusion within Vivli as part of GREI improves discoverability and reuse of NIDDK-CR hosted resources making data more Findable, Accessible, Interoperable, and Reusable (FAIR), increases the scientific value by providing additional opportunities to the NIDDK scientific community, and maximizes the contributions of research participants. To learn more: contact Dr. Rebecca Rodriguez or visit the Vivli member page.

The NIDDK Central Repository (CR) plays a crucial role in making data Findable, Accessible, Interoperable, and Reusable (FAIR). To enhance FAIRness of studies in the Repository, NIDDK and Booz Allen Hamilton, the current Data Repository contractor, have piloted a natural language processing (NLP) pipeline project for harmonizing study variables with NIH CDEs and for identifying potential new CDEs from dataset variables mapped to ontology concepts. Initial results show highly specific mapping of variables to CDEs as well as successful identification of relevant concepts for new CDEs. The pipeline can be refined and applied to other studies to potentially improve FAIRness of the NIDDK CR. Contact Dr. Rebecca Rodriguez with any questions.