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Better Research Trainee Data through Streamlined Reporting Processes

Training of the next generation of biomedical scientists is a core responsibility of NIH and its partner grantee institutions.The NIH Advisory Council to the Director‘s Biomedical Research Workforce Working Group recommended that NIH should “develop a simple and comprehensive tracking system for trainees” as part of the broader challenge of gathering better biomedical workforce data. Unambiguous identification of NIH trainees was an absolutely critical first step in establishing the ability to examine contributing factors that lead to various post-training careers. Accordingly we started collecting information in this way by requiring that all post-doctorates and all graduate and undergraduate students listed in a grantee’s progress report have an eRA Commons ID (as of 2009 and 2013, respectively). The next important step is developing a system to automate the capture of trainee data that has long been provided by extramural institutions in their training grant applications.

In 2013, we supported approximately 13,000 trainees using Ruth L. Kirschstein National Research Service Award (NRSA) institutional training grants (T32, T34, T35). For these grants, faculty at the awardee institution identify who would most benefit from training grant support; most of those trainees are graduate students or postdocs, but NIH also supports undergraduates through the Maximizing Access to Research Careers program.

Since the beginning of the NRSA programs in 1974, program directors have been collecting and reporting information on trainees’ subsequent employment and their contributions to science. These data have been used to inform peer review of training grant applications, but the information was not reported in a way that could be used for workforce analysis. Therefore, in 2013, we began developing the Extramural Trainee Career Tracking system (xTRACT), a web-based system that will reduce the burden associated with creating training tables in training grant applications, while simultaneously improving data integrity and availability to grantees and the NIH.

The xTRACT system will allow NIH institutional training grant applicants and grantees to more easily generate the training tables included in their applications and progress reports. xTRACT will utilize existing data to help pre-populate the system with information on trainees, faculty, and their other awards that NIH already has captured electronically. As more trainees and investigators obtain eRA Commons accounts and the system will have access to more data for pre-population and manual data entry will decrease further. The xTRACT system will provide institutions with access to training table information for their own analyses and will eventually link to publication records and SciENcv.

NIH is currently testing the design of xTRACT with a small group of institutions and gathering their feedback before we phase the system into production. The goal is to have it fully operational by the end of fiscal year 2016. While xTRACT will initially be developed for creating the tables associated with T32 institutional training grants, eventually it will be extended to other institutional training and career development awards.

In addition to easing data collection and submission for our training grant applicants and awardees, xTRACT complements NIH’s other workforce initiatives, allowing us to more fully determine trends in biomedical researchers’ careers, identify factors associated with successful training experiences, and support data-driven decisions about various NIH programs and priorities — all of which are extremely important to sustain and support a vibrant research enterprise.

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3 thoughts on “Better Research Trainee Data through Streamlined Reporting Processes

  1. Is there movement afoot to simplify the RPPR process for all NCs? In particular, changing the mode of entry for section D.1 Participants to allow for upload, for instance, as opposed to “adding” names (which can get quite onerous for multi-project or training mechanisms)?

  2. This is indeed exciting news and I am extremely optimistic that the test process will show success. I understand that this is still in the testing stage but wonder if NIH can tell us which tables and sections will still need to be manually populated. We have a couple of training grants and would like to update our table data as much as possible now without duplicating what will be automated.

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