A METHOD FOR CREATING NIH DATA TRAINING TABLES WITH REDCAP AND NIH XTRACT

Area of Science:

• Biomedical Research Administration
• Grant Management
• Scientific Workforce Development

Background:

• NIH training grants require specific data tables for submission.
• Universities face administrative challenges in generating these tables efficiently.
• Ad hoc or manual data collection methods are common but time-consuming.

Purpose of the Study:

• To describe an efficient federated method for constructing NIH Data Training Tables.
• To leverage REDCap and NIH xTRACT for improved data collection and table generation.
• To address the pre-award administrative challenge of timely, high-quality data table creation.

Main Methods:

• Implemented a federated data collection approach.
• Combined the use of REDCap for data entry and NIH xTRACT for table construction.
• Developed a streamlined process for generating multiple NIH Data Training Tables (2, 4, 5A/B, 6A/B, 8A/8C Part III).

Main Results:

• The federated method proved efficient for data collection and table construction.
• Leveraging REDCap and NIH xTRACT strengths facilitated the process.
• This approach supports both new and renewal training grant applications.

Conclusions:

• The described federated method offers an efficient solution for generating NIH Data Training Tables.
• This strategy can reduce administrative burden and improve the quality of training grant submissions.
• Optimizing data management is crucial for successful pre-award processes in research institutions.