View Request for Information (RFI): Best Practices for Sharing NIH Supported Research Software

In celebration of the history, culture, and contributions of Hispanics and Latinos, the NIH Office of Data Science Strategy (ODSS) is highlighting one of its flagship initiatives. The AIM-AHEAD program, launched in 2021, is increasing diversity in the AI/ML workforce and building a more inclusive research community to address health disparities and advance health equity.

AIM-AHEAD’s overall mission is to bring the benefit of AI/ML to all people of diverse backgrounds, especially those who may have been left out in the AI/ML research enterprise.  Many historically underserved communities, including Hispanic and Latino communities, have not been well represented in the AI/ML workforce, datasets, research, and infrastructure development. The lack of representation can contribute to AI bias, leading to inaccurate clinical outcomes that may not reflect these underserved communities' health conditions or lived experiences.

ODSS recognizes that achieving diversity in the AI/ML workforce is critical to addressing the sources of AI bias contributing to health disparities and inequities. The AIM-AHEAD initiative provides a range of training opportunities across the academic continuum to increase the representation of Hispanic, Latino, and other underrepresented researchers in the AI/ML and data science space, including:

• Professional Development Program: training underrepresented healthcare professionals in AI/ML and health equity.
• PRIME Training Practicum: Enhancing technical competencies in AI/ML for graduate students from minority serving institutions.
• Research Fellowship Cohort 1: Engaging early-career researchers from under-represented populations in biomedical research that uses AI/ML methodologies on Electronic Health Record Data.
• Leadership Fellowship Cohort 1: preparing diverse leaders to champion the use of AI/ML in addressing persistent health disparities.

These training and fellowship programs include 15 Hispanic individuals. In recognition of Hispanic Heritage Month 2023, ODSS is pleased to share some of their perspectives in the video below.

Zhiyong Lu, Ph.D. will present AI in Medicine: Improving Access to Literature Data for Knowledge Discovery at the monthly Data Sharing and Reuse Seminar on Friday, October 13, 2023, at 12 p.m. EDT.

About the Seminar

AI in Medicine: Improving Access to Literature Data for Knowledge Discovery

The explosion of biomedical big data and information in the past decade or so has created new opportunities for discoveries to improve the treatment and prevention of human diseases. But the large body of knowledge—mostly exists as free text in journal articles for humans to read—presents a grand new challenge: individual scientists around the world are increasingly finding themselves overwhelmed by the sheer volume of research literature and are struggling to keep up to date and to make sense of this wealth of textual information. Our research aims to break down this barrier and to empower scientists towards accelerated knowledge discovery. This seminar will discuss the development of large-scale, AI-based solutions for better understanding scientific text in the biomedical literature. Moreover, I will demonstrate their uses in some real-world applications such as improving PubMed searches (Fiorini et al., Nature Biotechnology 2018), supporting precision medicine with LitVar (Allot et al., Nature Genetics 2023), and taming COVID-19 pandemic paper tsunami in LitCovid (Chen et al., Nature 2000).

About the Speaker

Dr. Zhiyong Lu is a (tenured) Senior Investigator at the National Library of Medicine Intramural Research Program, leading research in biomedical text and image processing, information retrieval, and AI/machine learning. In his role as Deputy Director for Literature Search at National Center of Biotechnology Information (NCBI), Dr. Lu oversees the overall R&D efforts to improve literature search and information access in resources like PubMed and LitCovid, which are used by millions worldwide each day. Dr. Lu also serves as an Associate Editor of Bioinformatics, and Organizer of the BioCreative NLP challenge. Over the last 15 years, Dr. Lu has mentored over 60 trainees, many of whom have gone on to become independent faculty members/researchers at academic institutions in the US, Europe, and Asia. With over 300 peer-reviewed publications, Dr. Lu is a highly cited author, and a Fellow of the American College of Medical Informatics (ACMI) and the International Academy of Health Sciences Informatics (IAHSI).

About the Seminar Series

The seminar is open to the public and registration is required each month. Individuals who need interpreting services and/or other reasonable accommodations to participate in this event should contact Janiya Peters at 301-670-4990. Requests should be made at least five days in advance of the event.

The National Institutes of Health (NIH) Office of Data Science Strategy hosts this seminar series to highlight exemplars of data sharing and reuse on the second Friday of each month at noon ET. The monthly series highlights researchers who have taken existing data and found clever ways to reuse the data or generate new findings. A different NIH institute or center will also share its data science activities each month.

Professor Steven Kleinstein, Ph.D. will present Leveraging Shared Data for Systems Immunology: Signatures of Vaccination and Infection at the monthly Data Sharing and Reuse Seminar on Friday, September 8, 2023, at 12 p.m. EDT.

About the Seminar

This seminar will discuss how we can leverage shared data to discover signatures of human vaccination and infection responses. A key example will be work done as part of the NIH Human Immunology Project Consortium (HIPC) where data from ImmPort was compiled and reanalyzed to identify pre-vaccination and temporal signatures of antibody responses that were shared across multiple vaccines.

About the Speaker

Professor Steven Kleinstein is a computational immunologist with a combination of big data analysis and immunology domain expertise. His research interests include both developing new computational methods and applying these methods to study human immune responses. His lab develops the widely used Immcantation framework, which provides a start-to-finish analytical ecosystem for high-throughput adaptive immune receptor repertoire sequencing (AIRR-seq) datasets. He currently co-leads the data coordinating center for the NIH Human Immunology Project Consortium (HIPC).

Prof. Kleinstein is Anthony N. Brady Professor of Pathology at the Yale School of Medicine where he co-directs the Program in Computational Biology & Bioinformatics. He received a B.A.S. in Computer Science from the University of Pennsylvania and a Ph.D. in Computer Science from Princeton University.

About the Seminar Series

The seminar is open to the public and registration is required each month. Individuals who need interpreting services and/or other reasonable accommodations to participate in this event should contact Janiya Peters at 301-670-4990. Requests should be made at least five days in advance of the event.

The National Institutes of Health (NIH) Office of Data Science Strategy hosts this seminar series to highlight exemplars of data sharing and reuse on the second Friday of each month at noon ET. The monthly series highlights researchers who have taken existing data and found clever ways to reuse the data or generate new findings. A different NIH institute or center will also share its data science activities each month.

Dr. Ana Navas-Acien will present Environment, justice, and health: consortia and data sharing needs through a community lens at the monthly Data Sharing and Reuse Seminar on Friday, August 11, 2023, at 12 p.m. EDT.

About the Seminar

This session will discuss the growing evidence and the critical need to further study the role of the environment in human health and the importance of environmental justice to address existent inequalities in environmental exposures and related disease, with a focus on metal exposures, related molecular pathways and gene-environment interactions, and relevant interventions. Lessons learned from Indigenous communities and successful experiences in data sharing and reuse will be presented.

About the Speaker

Ana Navas-Acien, M.D., Ph.D. is a Professor of Environmental Health Sciences at Columbia University Mailman School of Public Health. Her research investigates the health effects of environmental exposures (metals, tobacco smoke, e-cigarettes, air pollution, water pollution), molecular pathways and gene-environment interactions, and effective interventions for reducing involuntary exposures and their health effects, with the goal of improving people’s health and advance environmental justice. She trained in Medicine obtaining her MD from the University of Granada, Spain, and completed her residency training in Preventive Medicine and Public Health at the Hospital La Paz, Madrid and her PhD in Epidemiology at Johns Hopkins University, Baltimore, MD. She is recognized for bridging medical and environmental health sciences using a participatory approach. She directs the Columbia University Northern Plains Superfund Research Program, a center that integrates science, technology, and traditional knowledge to protect the Northern Plains water resources and Indigenous communities from hazardous metal exposures.

About the Seminar Series

The seminar is open to the public and registration is required each month. Individuals who need interpreting services and/or other reasonable accommodations to participate in this event should contact Janiya Peters at 301-670-4990. Requests should be made at least five days in advance of the event.

The National Institutes of Health (NIH) Office of Data Science Strategy hosts this seminar series to highlight exemplars of data sharing and reuse on the second Friday of each month at noon ET. The monthly series highlights researchers who have taken existing data and found clever ways to reuse the data or generate new findings. A different NIH institute or center will also share its data science activities each month.

The 2023 DataWorks! Prize Challenge is underway, building off the successes of its first year. The challenge is sponsored by the NIH Office of Data Science Strategy, in partnership with the Federation of American Societies for Experimental Biology (FASEB).

The 2022 DataWorks! Prize saw over 100 teams, consisting of over 500 individuals, register to compete for the most innovative approaches to data sharing and reuse. It wasn’t just researchers who were excited about this challenge: Over 2,100 members of the data science community voted for their favorite projects, two of which were awarded the Federation of American Societies for Experimental Biology (FASEB) People’s Choice award.

This year’s challenge builds on the successes and insights from the 2022 prize. This challenge has the potential to make an enduring impact on the field of data science. Instead of novel data management techniques, this year’s prize will focus on best-practice “recipes” that advance biological and biomedical research activities by prioritizing practices that enable robust data management during the research process. This will enable the creation of an ongoing archive of best practices and resources that can be used by researchers to facilitate better data storage, sharing, and reuse. 

This year’s prize offers up to 16 NIH-funded monetary awards, totaling up to $500,000, and up to two People’s Choice Awards, as determined by FASEB. Submissions will be evaluated based on:

• Excellence in Data Sharing and Reuse
• Innovative Impact on Human Health
• Excellence in Communication and Adoption of Practices Outside of Original Context

I’m very excited about the possibilities this challenge offers. A compilation of data management best practices has the potential for wide-ranging impact in the fields of biological and biomedical research.

The 2023 DataWorks! Prize is accepting submissions now through August 15. We truly hope that you’ll help others enhance their data management practices by sharing your wisdom and recipes.