Women in Tech at NIH: Togetherness Enables Transformation

Guest post by Susan Gregurick, PhD, associate director for data science and director of the Office of Data Science Strategy, National Institutes of Health.

There is an African proverb that says, “If you want to go fast, go alone. If you want to go far, go together.”

As I approach my first anniversary as the associate director for data science at NIH, this statement could not ring truer for me. By going together, NIH has made astonishing progress during this past year to enable more advanced data science, impressive data and computational infrastructure advances, and better FAIR data sharing.

Togetherness means collaboration that harnesses the power and strength of a diverse team. At NIH, women are using their expertise in data science and their teamwork skills to rapidly enable transformative programs.

Andrea Norris, director of the Center for Information Technology, said it well last year:

“This is such an exciting time for innovation at the intersection of biomedical, medical, and technology domains. It’s dynamic and fast moving. Whether you have scientific skills, business expertise or know technology, there’s a role — an important role — for you in this space, especially here at NIH.”

I spoke with 11 women who are significantly impacting data science activities at NIH about how they enable data science; their advice for young, aspiring women data scientists; and the data science accomplishments that make them proud.

Collaboration and the role that NIH has played in responding to the COVID-19 pandemic were common themes in our discussions. These women also spoke about the importance of having a mentor, the four antidotes to challenging times, and the necessity of diverse perspectives.

To get to know these women even better, read their full responses on the Women in Data Science  page.

Jessica Mazerik, PhD, Data Science Workforce Director, Office of Data Science Strategy (ODSS)

Leads the Coding it Forward Civic Digital Fellows at NIH and NIH DATA Scholars programs

Bringing diverse talent to NIH.

I lead central fellowship programs to bring talented computer and data scientists to NIH. Our external outreach efforts encourage women and other minorities to apply for the programs we support. And, internally, we support engagement across NIH to place students in diverse positions.

Breaking down silos to advance data science.

Talented and driven staff across NIH have mobilized to lead implementation tactics under the strategic plan for data science, and we’ve built a forum for discussion in monthly town hall meetings. Most importantly, teams across NIH are working together and communicating widely to break down silos to continue advancing data science. 

Teresa Zayas Cabán, PhD, Coordinator, Fast Healthcare Interoperability Resources (FHIR) Acceleration, National Library of Medicine (NLM)

Co-leads the NIH FHIR Working Group

Advancing data standards within and beyond NIH. 

I’m leading efforts to enable the use of standardized clinical and research data sharing to advance discovery. We’re not only working collaboratively within NIH to advance data science, but also across departments, government offices, and the field itself. Together, we are leading the field in a new direction with the use in research, as appropriate, of the same standards used in health care. 

Be confident in what you know.

Don’t sell yourself short — speak up about what you know. Find good mentors who can advise you and be in your corner throughout your career. Find a good cohort of colleagues to collaborate and commiserate with. 

Belinda Seto, PhD, Deputy Director, ODSS

Co-leads the NIH FHIR Working Group

Women leading data science communities.

We all have varying perspectives and visions for data science. Nonetheless, we have become nuclei of the NIH data science community. Through our collaborations, we are emissaries for data science to extramural grantee communities. I see this as a concentric circle of expanding national and even global communities of data science.

Technical and sociocultural accomplishments in data science.

A sociocultural accomplishment is that many silos have been dismantled, and the willingness and readiness to collaborate are demonstrably strong. On the technical front, there are successful examples of progress toward an NIH data ecosystem, both at the foundational level and at the leading edge.

Lisa Federer, PhD, Data Science and Open Science Librarian, Office of Strategic Initiatives, NLM

Leads the NIH Data Science Training Committee

Be a lifelong learner.

Embrace lifelong learning — there’s always something new to learn! I’ve made it a priority to learn new things that I can bring to my work, including going back to school to get a PhD in information science with a focus on data science.

Open science practices advancing our understanding of COVID-19.

NIH has been doing impressive work in advancing our understanding of COVID-19 and has been a leader in making data related to SARS-CoV-2 widely available so that researchers around the world can help tackle this important issue. In the face of this global problem, open science practices will help us make progress toward therapies and vaccines more quickly.

Jennie Larkin, PhD, Deputy Director, Division of Neuroscience, National Institute on Aging

Co-leads the FAIR Data Repositories Team, which ran the one-year NIH Figshare instance pilot

Engage and embed data science in different programs.

Ask questions, learn, and engage. We need more bright people who can bring new perspectives, expertise, and energy to data science and help embed data science in different research programs.

Working with the community to address the COVID-19 pandemic.

The increasing breadth and depth of data science expertise across NIH and the larger biomedical enterprise has allowed us to rapidly accomplish much more than was possible just a few years ago. We have seen the best of our community, in the willingness to come together to meet the challenge of the COVID-19 pandemic.

Rebecca Rosen, PhD, Program Lead, NIMH Data Archive and Senior Advisor, Office of Technology Development and Coordination, National Institute of Mental Health

Leads the Researcher Auth Service Initiative

Learn from traditional and nontraditional resources.

I encourage young women in all biomedical science fields to incorporate data science into their career development plans. Look for data science educational resources from both traditional and nontraditional sources and network within those sources.

Collaboration to realize a data ecosystem.

The NIH data ecosystem has an increasingly tangible presence. We have growing numbers of researchers analyzing data across NIH cloud-based platforms, thanks in part to the new Office of Data Science Strategy, the NIH STRIDES Initiative, and a greater level of collaboration across NIH Institutes and Centers.

Heidi Sofia, PhD, Program Director, National Human Genome Research Institute (NHGRI)

Co-leads the Biomedical Information Science and Technology Initiative consortium and organized supplements to enhance software tools for open science (NOT-OD-20-073)

Beauty, awe, love, and humor.

I am never happier than when some brilliant young or established scientist in the community brings forward innovative, transformative science which I can endeavor to foster. In these instances, I find the first two of the four antidotes to our challenging times (beauty, awe, love, and humor). And my colleagues often provide the last one.

Use your power for good.

Among the first “computers” were women who performed the mathematical calculations needed to advance science, starting in 1757 in the search for Halley’s comet. Today, data science is a superpower for women in fields ranging from medicine to the natural sciences to business. So empower yourself, and use your power for good!

Maryam Zaringhalam, PhD, Data Science and Open Science Officer, Office of Strategic Initiatives, NLM

Organized the Webinar on Sharing, Discovering, and Citing COVID-19 Data and Code in Generalist Repositories

Women make data science better.

The lived experiences and perspectives of women — particularly women who are Black, Indigenous and People of Color (BIPOC); members of the LGBTQIA+ community; or members of the disability community — are critically important in ensuring that the products of data science have the greatest benefit for us all. Every chance I get, I tell women that they not only belong in data science, but that data science is better because of them.

Enabling researchers to make COVID-19 data available.

I was proud to be involved in quickly planning and organizing a joint NLM-ODSS webinar on sharing, discovering, and citing COVID-19 data and code using generalist repositories. It’s been inspiring to see the research community so eager to share the data and tools they’ve been generating, so this workshop felt like a timely and impactful contribution in support of researchers.

Valentina Di Francesco, MS, Lead Program Director, Computational Genomics and Data Science Program, NHGRI

Co-lead for the NIH Cloud Platform Interoperability Effort

Realizing a trans-NIH federated data ecosystem.

Among the variety of projects I am involved in, I am particularly enthusiastic about the NIH Cloud Platform Interoperability Effort, which aims to establish and implement guidelines and technical standards to empower end-user analyses across participating cloud-based platforms established across NIH in order to facilitate the realization of a trans-NIH federated data ecosystem.

Data science is a science at NIH.

After many years at NIH, only recently have I noticed a solid appreciation of the essential contributions of the statistical, mathematical, and computer science approaches to better understand biological systems. Finally, data science is respected as a field at NIH! I can’t think of a better time to join the ranks of women data scientists in biomedical research.

Kim Pruitt, PhD, Chief, Information Engineering Branch, National Center for Biotechnology Information, NLM

Co-leads the Lifecycle Metrics Working Group, which hosted the NIH Virtual Workshop on Data Metrics

Persevere, find a mentor, understand expectations, persevere.

My advice to someone entering this field is to persevere, to find an excellent mentor, to go into collaborations with a clear understanding of each member’s role and publication expectations, and to continually look for lessons learned when an analysis strategy fails (that is, cycle back to persevere).

Providing data access in the cloud

Providing access to data on the NIH STRIDES Initiative cloud-based platform is a prerequisite to supporting and growing the biomedical data science field. Most notable to me is the significant achievement of providing the complete Sequence Read Archive data (roughly 40 PB and growing) in two formats and ahead of the planned schedule.

Jennifer Couch, PhD, Chief, Structural Biology and Molecular Applications Branch, National Cancer Institute

NIH Citizen Science Coordinator

Bringing new approaches to biomedical research.

My focus is on bringing new, diverse, and often outsider perspectives, tools, approaches, and methods into the biomedical research space. Together with many talented colleagues and collaborators, I look for ways to bring new approaches to biomedical research. Sometimes that involves creating opportunities for different research communities to come together and find ways to collaborate.

On finding the right collaborators.

Hone your skills, don’t be afraid to try out new methods, and find collaborators with interesting questions who will know the answer when they see it. Find those collaborators who appreciate that your skills and insights are critical to your joint project’s success.

Dr. Gregurick leads the implementation of the NIH Strategic Plan for Data Science through scientific, technical, and operational collaborations with the Institutes, Centers, and offices that make up NIH. She has substantial expertise in computational biology, high-performance computing, and bioinformatics.

Some Insights on the Roles and Uses of Generalist Repositories

Guest post by Susan Gregurick, PhD, Associate Director for Data Science and Director, Office of Data Science Strategy, NIH

Data repositories are a useful way for researchers to both share data and make their data more findable, accessible, interoperable, and reusable (that is, aligned with the FAIR Data Principles).

Generalist repositories can house a vast array of data. This kind of repository does not restrict data by type, format, content, or topic. NIH has been exploring the roles and uses of generalist repositories in our data repository landscape through three activities, which I describe below, garnering valuable insights over the last year.

A pilot project with a generalist repository

NIH Figshare archive

Last September, I introduced Musings readers to the one-year Figshare pilot project, which was recently completed. Information about the NIH Figshare instance — and the outcomes of the project — is available on the Office of Data Science Strategy’s website. This project gave us an opportunity to uncover how NIH-funded researchers might utilize a generalist repository’s existing features. It also allowed us to test some specific options, such as a direct link to grant information, expert guidance, and metadata improvements.

There are three key takeaways from the project:

  • Generalist repositories are growing. More researchers are depositing data in, and more publications are linking to, generalist repositories.
  • Researchers need more education and guidance on where to publish data and how to effectively describe datasets using detailed metadata.
  • Better metadata enables greater discoverability. Expert metadata review proved to be one of the most impactful and unique features of the pilot instance, which we determined through two key metrics. When compared to data uploaded to the main Figshare repository by NIH-funded investigators, the NIH Figshare instance had files with more descriptive titles (e.g., twice as long) and metadata descriptions that were more than three times longer.
Illustrating how professionals can identify opportunities for collaboration and competition.

The NIH Figshare instance is now an archive, but the data are still discoverable and reusable. Although this specific pilot has concluded, we encourage NIH-funded researchers to use a generalist repository that meets the White House Office of Science and Technology Policy criteria when a domain-specific or institutional repository is not available.

A community workshop on the role of generalist repositories

In February, the Office of Data Science Strategy hosted the NIH Workshop on the Role of Generalist and Institutional Repositories to Enhance Data Discoverability and Reuse, bringing together representatives of generalist and institutional repositories for a day and a half of rich discussion. The conversations centered around the concept of “coopetition,” the importance of people in the broader data ecosystem, and the importance of code. A full workshop summary is available, and our co-chairs and the workshop’s participating generalist repositories recently published a generalist repository comparison chart as one of the outcomes of this event.

We plan to keep engaging with this community to better enable coopetition among repositories while working collaboratively with repositories to ensure that researchers can share data effectively.

An independent assessment of the generalist repository landscape

We completed an independent assessment to understand the generalist repository landscape, discover where we were in tune with the community, and identify our blind spots. Key findings include the following:

  • There is a clear need for the services that generalist repositories provide.
  • Many researchers currently view generalist repository platforms as a place to deposit their own data, rather than a place to find and reuse other people’s data.
  • Repositories and researchers alike are looking to NIH to define its data sharing requirements, so each group knows what is expected of them.
  • The current lack of recognition and rewards for data sharing helps reinforce the focus on publications as the key metric of scientific output and therefore may be a disincentive to data sharing.

The pilot, workshop, and assessment provided us with a deeper understanding of the repository landscape.

We are committed to advancing progress in this important area of the data ecosystem of which we are all a part. We are currently developing ways to continue fostering coopetition among generalist repositories; strategies for increasing engagement with researchers, institutional repositories, and data librarians; and opportunities to better educate the biomedical research community on the value of effective data management and sharing.

The Office of Data Science Strategy will announce specific next steps in the near future. In the meantime, we invite you to share your ideas with us at datascience@nih.gov.

Dr. Gregurick leads the implementation of the NIH Strategic Plan for Data Science through scientific, technical, and operational collaboration with the institutes, centers, and offices that make up NIH. She has substantial expertise in computational biology, high performance computing, and bioinformatics.

Bridging the Gap: From Research to Policy

Guest post by Ellen T. Kurtzman, PhD, MPH, RN, FAAN, associate professor, School of Nursing, The George Washington University

As a health services researcher, I have always been interested in how to bridge the divide between research and policy. I constantly ask myself, “Which of my research questions will inform today’s most pressing policy debates?” and “How can I teach the next generation of nurse scientists to conduct policy-relevant research?” I recently left my academic position and spent a year working on Capitol Hill as one of eight 2018 –2019 Robert Wood Johnson Foundation Health Policy Fellows. In this blog, I offer a few key lessons from my time as a fellow that influenced my scholarship.

Lessons from my fellowship year

  • Right place, right time. The policymaking environment is fast paced. New issues emerge quickly, moving others lower on the priority list. The deck is constantly being reshuffled. Perhaps there is no better example of this than COVID-19. Who knew a year ago that a pandemic would draw decision makers’ attention away from other pressing policy issues? When a policy issue like this emerges unexpectedly, the need for evidence is virtually instantaneous. But the research process is methodical and cannot easily be accelerated. Randomized studies and clinical trials take time. Which implies that the scientific process and policymaking timelines do not naturally mesh. Recognizing that available evidence needs to be ready at precisely the moment that a policy issue is being contemplated suggests that the relationship between science and policymaking should be reframed.  
  • Positioning researchers to contribute. Because there are so many policy issues being contemplated simultaneously, deep subject matter expertise from authoritative and independent sources is highly valued. Scientists and academics are ideally situated to be honest brokers, yet it is not always easy for policy staff to find expertise on short notice. Researchers need to better position themselves and their science during a noncrisis period so that they are ‘top-of-mind’ when urgent needs emerge.
  • All about trade-offs. Harold Lasswell, an influential political scientist and theorist, helped define “politics” by asking, “Who gets what, when, and how?” Public policy is the art of allocating scarce resources to competing parties. I have always been interested in research questions about health care quality and value, but many of the secondary data sources I rely on lack the variables that would enable me to examine price or cost outcomes. In the short time I spent on Capitol Hill, it became abundantly clear to me why research that examines quality in the absence of cost considerations is insufficient.

Possible solutions

  • Policy in all things. Nursing, medical, and health sciences programs typically include a single health policy course and/or rotation. Rather than relegating policy to just one course, why not see “policy in all things”? During OB-GYN grand rounds, why not discuss policy solutions that address maternal mortality? What keeps us from asking our psychiatric nursing students to debate mental health parity issues or veteran suicide rates? If we incorporate policy into every course, our students will leave their programs better prepared to bridge the divide between science and policy.
  • New definitions of scholarship. Historically, academia has viewed scholarship in narrow terms. For example, criteria for appointments, promotion, and tenure (APT) reward refereed journal articles and colloquia, yet these materials are not generally accessible or readily available outside of academic circles. To bridge the divide between science and policy, academics might consider adopting a broader definition of scholarship and creating incentives for deliverables that appeal to decision makers. Could we, for example, adjust APT criteria so that the process rewards policy papers, issue briefs, and congressional testimony equally? By encouraging scholarship that reaches decision makers, we would be optimizing the policy impact of our science.
  • Enhanced dissemination and outreach. Policymakers need the deep expertise that scientists and academics possess, but we are often siloed from one another. With rare exceptions, we tend not to attend the same meetings or conferences, read the same journals or books, or consume the same news or other media. I now realize that, for my work to inform policy, I need to reconsider how I package and disseminate my findings as well as how I position myself as a subject matter expert. By understanding and following key policy issues, learning how to communicate with policymakers, and investing time and energy in building relationships during times of calm, I will be facilitating swifter adoption of my science and more meaningful dialogue with policy staff when there is a critical need for information.

Dr. Kurtzman is a health services researcher and a tenured associate professor of nursing with secondary appointments in the university’s Milken Institute School of Public Health and Trachtenberg School of Public Policy & Public Administration. Her investigator-initiated research explores the impact of federal, state, and institutional policies on health care quality and the role of the health care workforce in achieving higher value care. She is currently exploring the impact of states’ cannabis policies on health outcomes including the consequences for pregnant women and their infants. 

Biomedical Informatics and Health Equity: Using One to Improve the Other

Guest post by Kevin B. Johnson, MD, MS, Cornelius Vanderbilt Professor and Chair of Biomedical Informatics, and Professor of Pediatrics at Vanderbilt University Medical Center.

I started Informatics in the Round for a lay audience seeking to understand the world of biomedical informatics. There are other podcasts out there about the latest and the greatest in research, but I really wanted to create a space where people could learn more about the field and hear from leaders who continue to inspire me.

Earlier this month, #ShutDownSTEM gave me the opportunity to bring together academicians and biomedical informatics leaders to outline what our role as informaticians should be in the fight against racism in all its forms. One of the leaders who joined me in that discussion and encouraged me to share this with all of you was NLM Director Patricia Flatley Brennan, RN, PhD.

I often refer to Dr. Brennan as the newly minted matriarch of our field. Her contributions to this recent discussion were, not surprisingly, incredibly insightful. The entire conversation can be found here.

As chair of the Department of Biomedical Informatics at Vanderbilt and an informatics evangelist, I want — no, I need — to share some highlights from this conversation in the hope of broadening the discussion. There is much we haven’t done that is clearly within both our skill set and reach.

Click on the image above to listen to the full episode.

How Bioinformatics Can Support an Equitable Future

I started the conversation by setting the stage for a discussion of how biomedical informatics can play a role in the fight against racism. As a medical specialty, the field is about aggregating and transforming health data to create knowledge, improve lives, and build a world with better health outcomes.

We’ve been an equal opportunity field that thinks of technology as the great equalizer when it comes to health care. But we’ve missed a key opportunity to embrace equity along with equality. In an equitable world, the benefits of informatics would not simply be made available to all equally; rather, we would recognize, embrace, and adapt solutions to the unique needs of the many in our society who cannot type, who don’t speak English, who fear giving out private information to the government because of historical missteps, or who have jobs that challenge our traditional care models.

To respond to the realities of our built environment, our field needs to understand where the people are and what they need. From a technology standpoint, this requires designing models that reflect more than just the average care needs. We need to build technologies based on an understanding of how diverse people are, as opposed to how similar we are. We need to think about the presumptions that we make, often unconsciously, and how our presumptions get built into technologies.

A challenge that we often face in the field of informatics is that we spend a lot of time on the technology side and don’t pay enough attention to the people side. Some call this the “softer side,” and it’s often pushed to the side with the intention to address it later. Unconscious bias can show up in very subtle ways, such as sending out a confirmation email and presuming that there will be a response or assuming that a patient even has a personal email address. And we often picture the nuclear family when we think about relationships.

We need to immerse ourselves in diverse stories and relationships so that we can see how technology fits into people’s lives and how to create tools to meet their needs. For example, how do we help young-adult graduates of the foster care system collect and summarize their past medical histories from what could be a dozen different parents? 

As we think about how health care systems are built and experienced, let’s take into consideration factors that impact who is represented at all levels of those systems. Our actions are rarely nefarious and are quickly corrected when called out as biased, in most cases. But, unfortunately, groups that lack diversity don’t have stakeholders who easily notice when actions leave underrepresented groups behind. Where is our diversity in science?

During our conversation, we heard difficult messages from two younger Black faculty. One expressed fear that the issues that might propel them into a career in data science and informatics were not issues that would be rewarded through extramural funding and promotion. Another was convinced that the perception of academia as slow to embrace change, skeptical of new ideas, and mired in red tape was off-putting to people of color who historically have seen those behaviors lead to discriminatory actions. 

Those are tough pills to swallow, but things we need to confront directly.

Throughout the conversation, we examined our privilege and how we can break down barriers and eliminate anti-Black racism with the goal of equity in mind. There is no single answer, but the best thing we can give to young people and to our colleagues is the capacity to be brave. We also discussed how people who experience privilege must be willing to use that privilege to build bridges and close the gap. We need to continue to be voices that say these issues matter.

As we strive for justice and meaningful change, we need to better inform ourselves about the perceptions of underrepresented groups that negatively affect their career choices. We need to narrow the research funding gap and examine the peer-driven study process, including biases in the review process and in publishing.

The National Library of Medicine uses grant mechanisms (G08, R01) to develop informatics to reduce health disparities and supports research that examines how information can be presented in culturally relevant ways. NLM’s data science work, including COVID-19 research, encourages researchers to develop techniques that illustrate the causes of and solutions to the health disparities that exist in the world today.

I encourage you to listen to the full discussion to hear more from our thoughtful panelists.  

And I leave you with this challenge: What will you do today, or this year, to build bridges and close the gap on your team, in your workplace, and in your community?

Dr. Johnson received his MD from The Johns Hopkins Hospital in Baltimore and his MS in Medical Informatics from Stanford University. He is an internationally known scholar and educator in clinical informatics, having served as a board-certified pediatrician and consistently funded researcher as well as chief information officer during his tenure at Vanderbilt.  

In addition to leadership roles in the American Academy of Pediatrics, the American Board of Pediatrics, the American Medical Informatics Association, and the National Academy of Medicine, Dr. Johnson serves as chair of the 
NLM Board of Scientific Counselors and sits on the NIH Council of Councils.

NIH One Step Closer to Speeding Delivery of COVID-19 Testing Technologies to Those Who Need It Most Through RADx-UP

This piece was authored in collaboration with the leadership across NIH and represents a unified effort to meet the challenges presented by the COVID-19 pandemic with excellence and innovation.

Before the nation can safely return to business as usual, it will be essential to develop and deliver effective and reliable COVID-19 testing and then implement it widely so that it is available to everyone. The NIH is rising to this challenge through the NIH’s Rapid Acceleration of Diagnostics (RADx) initiative — a national call for scientists and organizations to advance their innovative ideas for new COVID-19 testing approaches and strategies.

To speed innovation in the development, commercialization, and implementation of technologies for COVID-19 testing, NIH will use a variety of mechanisms, including extramural grants, contracts, and cooperative agreements, to move more advanced diagnostic technologies swiftly through the development pipeline toward commercialization and widespread availability — with the goal of making millions of tests available to Americans each week, especially those most vulnerable to and disproportionately affected by COVID-19.

To achieve this goal, NIH is partnering with other government organizations including the Biomedical Advanced Research and Development Authority (BARDA), the Centers for Disease Control and Prevention (CDC), the Defense Advanced Research Projects Agency (DARPA), the Health Resources and Services Administration (HRSA), and the U.S. Food and Drug Administration (FDA). 

RADx Underserved Populations (RADx-UP)

One of the four RADx components, RADx Underserved Populations (RADx-UP) will establish a network of community-engaged projects to improve access to and acceptance of COVID-19 testing for underserved and vulnerable populations who are disproportionately affected by COVID-19. This includes populations most affected by health disparities, particularly African Americans, Hispanics or Latinos, and American Indians/Alaska Natives; those in nursing homes, jails, rural areas, or underserved urban areas; pregnant women; and the homeless.

The overarching goal of RADx-UP is to understand the factors associated with disparities in COVID-19 morbidity and mortality and, ultimately, to mitigate them through enhanced access to or acceptance of testing. RADx-UP will utilize implementation science projects to learn how to increase uptake of viral testing and engagement with care in these populations, who are disproportionately affected by, have the highest infection rates of, or are most at risk for complications or poor outcomes from the COVID-19 pandemic.

Specific activities of RADx-UP may include establishing multiple clinical research sites across the country to conduct real-time evaluations of a variety of testing methods in specific populations, areas, and settings, as well as encouraging collaboration between the program sites and the community — such as tribal health centers, places of worship, homeless shelters, and prison systems — to identify and address their unique needs.

This initiative will also develop testing strategies to apply the technological advances emerging from the various RADx efforts in real-world settings.

The RADx-UP program includes four associated funding opportunity announcements.

The first funding opportunity is a limited solicitation targeting networks and consortia with established research infrastructures and community partnerships with underserved and vulnerable communities. The goal of this funding opportunity is to better understand COVID-19 testing patterns and implement strategies or interventions with the potential to rapidly increase reach, access, acceptance, uptake, and sustainment of FDA-authorized and approved diagnostics among vulnerable populations in underserved geographic locations. Proposals are due August 7, 2020. 

The second funding opportunity has a similar focus, but shifts the pool of grants eligible for supplements to individual research awards that include community-collaborations or partnerships to support COVID-19 testing, or that have the capacity to ramp up quickly, to reach underserved or vulnerable populations. Proposals are due August 7, 2020 and September 8, 2020.

The third funding opportunity addresses the urgent need to understand the social, ethical, and behavioral implications  of COVID-19 testing among underserved and/or vulnerable populations across the United States. The overarching goal is to understand factors that have led to disproportionate burden of the pandemic on these underserved populations so that interventions can be implemented to decrease these disparities. Proposals are due August 7, 2020 and September 8, 2020.

The final funding opportunity will fund a single organization to create a Coordination and Data Collection Center that will serve as a national resource, working with NIH scientific staff, and consortium members to coordinate and facilitate research activities across the programs supported by the funding opportunities identified above. Proposals are due August 7, 2020.

The other elements of RADx are:

  • RADx Tech (RADx-tech) to speed the development, validation, and commercialization of innovative point-of-care and home-based tests, as well as improve clinical laboratory tests that can directly detect SARS CoV-2, the virus that causes COVID-19. Led by the National Institute of Biomedical Imaging and Bioengineering, this fast track program leverages the Point of Care Technologies Research Network (POCTRN) to stimulate the development and commercialization of innovative technologies to significantly increase the nation’s testing capacity for SARS CoV-2.. 
  • RADx Radical (RADx-rad) to support new, non-traditional approaches, including the development of rapid detection devices and home-based testing technologies, that address gaps in current COVID-19 testing mechanisms. The program will also support new or non-traditional applications of existing approaches to make them more usable, accessible, or accurate. These may lead to new ways to identify the  SARS-CoV-2 virus as well as potential future viruses. Watch for new funding announcements from this program later this summer.
  • RADx Advanced Technology Platforms (RADx-ATP) to increase testing capacity and output by identifying existing and late-stage testing platforms for COVID-19 that are advanced enough to achieve rapid scale-up or expanded geographical placement in a short amount of time. These efforts will focus on scaling up technologies, including improving existing high-throughput platforms, to increase overall performance.

Want to Learn More?

If you are interested in learning more, NIH will hold two pre-application webinars. Registration is required

  • The first webinar will be held on Friday, June 26, 2020, from 2:00pm – 4:00pm EDT. This webinar will provide an overview of the RADx-UP initiative, followed by presentations on each funding opportunity (NOT-OD-20-119, NOT-OD-20-120, NOT-OD-20-121, and RFA-OD-20-013).
  • The second webinar will be held on Wednesday, July 1, 2020, from 3:00pm – 5:00pm EDT. This webinar will focus on questions for applications for the Coordinating and Data Collection Center in response to RFA-OD-20-013. Questions related to the other three FOAs will also be addressed.

During this period of heightened awareness about the ways social injustices contribute to ongoing health disparities, it is essential that agencies use their mission-focused efforts to understand and, where possible, ameliorate health disparities.

Please let us know how you believe we can better serve science and society.

Top Row (left to right):
Diana W. Bianchi, M.D., Director, Eunice Kennedy Shriver National Institute of Child Health and Human Development 
Patricia Flatley Brennan, R.N., Ph.D., Director, National Library of Medicine
Gary H. Gibbons, M.D., Director, National Heart, Lung, and Blood Institute
Joshua Gordon, M.D., Ph.D., Director, National Institute of Mental Health
Middle Row (left to right):
Richard J. Hodes, M.D., Director, National Institute on Aging
Jon R. Lorsch, Ph.D., Director, National Institute of General Medical Sciences
George A. Mensah, M.D., Division Director, National Heart, Lung, and Blood Institute
Eliseo J. Pérez-Stable, M.D., Director, National Institute on Minority Health and Health Disparities
Bottom Row (left to right):
William Riley, Ph.D., Director, NIH Office of Behavioral and Social Sciences Research
Tara A. Schwetz, Ph.D., Associate Deputy Director, National Institutes of Health and Acting Director, National Institute of Nursing Research
Nora D. Volkow, M.D., Director, National Institute on Drug