Pilot projects (“pilots”) are important for testing hypotheses in advance of investing more funds for full research studies. For some programs, such as Clinical and Translational Science Awards (CTSAs) supported by the National Center for Translational Sciences, pilots also make up a significant proportion of the research projects conducted with direct CTSA support. Unfortunately, administrative data on pilots are not typically captured in accessible databases. Though data on pilots are included in Research Performance Progress Reports, it is often difficult to extract, especially for large programs like the CTSAs where more than 600 pilots may be reported across all awardees annually. Data extraction challenges preclude analyses that could provide valuable information about pilots to researchers and administrators.

To address those challenges, we describe a script that partially automates extraction of pilot data from CTSA research progress reports. After extraction of the pilot data, we use an established machine learning (ML) model to determine the scientific content of pilots for subsequent analysis. Analysis of ML-assigned scientific categories reveals the scientific diversity of the CTSA pilot portfolio and relationships among individual pilots and institutions.

The CTSA pilots are widely distributed across a number of scientific areas. Content analysis identifies similar projects and the degree of overlap for scientific interests among hubs.

Our results demonstrate that pilot data remain challenging to extract but can provide useful information for communicating with stakeholders, administering pilot portfolios, and facilitating collaboration among researchers and hubs.

Identifying the most effective ways to support career development of early stage investigators in clinical and translational science should yield benefits for the biomedical research community. Institutions with Clinical and Translational Science Awards (CTSA) offer KL2 programs to facilitate career development; however, the sustained impact has not been widely assessed.

A survey comprised of quantitative and qualitative questions was sent to 2144 individuals that had previously received support through CTSA KL2 mechanisms. The 547 responses were analyzed with identifying information redacted.

Respondents held MD (47%), PhD (36%), and MD/PhD (13%) degrees. After KL2 support was completed, physicians’ time was divided 50% to research and 30% to patient care, whereas PhD respondents devoted 70% time to research. Funded research effort averaged 60% for the cohort. Respondents were satisfied with their career progression. More than 95% thought their current job was meaningful. Two-thirds felt confident or very confident in their ability to sustain a career in clinical and translational research. Factors cited as contributing to career success included protected time, mentoring, and collaborations.

This first large systematic survey of KL2 alumni provides valuable insight into the group’s perceptions of the program and outcome information. Former scholars are largely satisfied with their career choice and direction, national recognition of their expertise, and impact of their work. Importantly, they identified training activities that contributed to success. Our results and future analysis of the survey data should inform the framework for developing platforms to launch sustaining careers of translational scientists.