Taking On the Challenge of Better Biomedical Workforce Data


The primary goal of the NIH Advisory Committee to the Director (ACD) biomedical research workforce working group was the creation of pathways through undergraduate, graduate and postdoctoral training that provide excellent preparation for biomedical research careers in a timely fashion, and that ensure future US competitiveness and innovation in biomedical research. In their report, the working group members described how they were “frustrated and sometimes stymied” by the quality of the data available on the biomedical research workforce, e.g., major gaps in information on the total number of individuals working as postdocs, inadequate information on postdocs who obtained degrees in other countries, and lack of systematic data on graduate students trained in labs supported by NIH research grants.

So to this end, we’ve been working on a number of plans to try and fill these gaps in biomedical workforce information. Here’s a quick overview of the directions we are headed.

Identification of all NIH-supported students and postdocs: NIH has considerable experience tracking students and postdocs supported by the Ruth L. Kirschstein National Research Service Awards (NRSA). This information enables us to carry out studies on the careers of former NRSA recipients. Unfortunately, we have far less information about the students and postdocs that are associated with research grants even though there are two times more students and four times more postdocs supported on research grants than NRSAs. So, in 2009 we began requiring eRA Commons accounts for all postdocs listed in the grantees’ annual progress report. We are exploring the idea of extending that requirement to graduate students to provide a more complete picture of all the trainees supported by NIH funding.

Automated NRSA training tables: For decades, training grants have required elaborate data tables on the trainees and faculty associated with the training program, along with the career outcomes for trainees who left the program within the most recent ten year period. This rich source of data on the subsequent employment and productivity of trainees provides reviewers with considerable information about the success of training programs, but the tables themselves are cumbersome to complete, and the information is not easily aggregated into information on the work environment faced by those leaving the training program. We are working to turn those tables into an automated, pre-populated, digital archive of information using an NIH-wide approach similar to that employed by CareerTrac (currently used by three NIH institutes and centers) and a similar trainee tracking system used by NINDS.

Develop a Fed-wide researcher profile system: We are engaged in a project with five other federal agencies to develop an online curriculum vitae called the Science Experts Network (SciENcv). This system is being built by the NIH’s National Library of Medicine with input from the agency and university representatives that make up the Federal Demonstration Partnership. SciENcv will permit researchers to use existing data sources to easily assemble and validate the information necessary for building a biosketch in the format required by federal research agencies. SciENcv will reduce the burden associated with preparing applications for federal grants and at the same time provide a rich source of information on researchers, their grants and their scientific output. A beta version of SciENcv is expected sometime this summer.

Encourage adoption of unique persistent researcher IDs: Identifying the output of individuals with commonly occurring names is difficult. Reducing name ambiguity within and across data systems is always expensive and time consuming. It appears that an international, non-profit organization called the Open Researcher and Contributor ID (ORCID) is gaining traction. ORCID is a persistent digital identifier that can be associated with author names in publications. The ORCID system also will allow individuals to identify their research output and create a registry of IDs. SciENcv will include a utility that make it easy for users to obtain an ORCID and to link it to their publications and grants. A broadly used researcher ID also will facilitate the identification of scientific output from those who work outside federally funded research programs.

These are just a few measures starting to take shape, and we’ll likely have more to come, thanks to input from you – your feedback on how to collect better workforce data is one of the areas we’ve asked you to comment on in the request for information (RFI) that opened in February this year (as published in the NIH Guide and described in previous posts).

True to our nature as scientists, the prospect of collecting new, solid data is really exciting to me and my colleagues, as I think it will definitely enhance how NIH – and educational institutions – approach training. Once these new systems and tools are in place, I hope our improved understanding of how NIH trainees — past and present — become biomedical researchers will be able to improve our programs and support of these individuals.

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