Dynamically generating T32 training documents using structured data.

Area of Science:

• Biomedical research administration
• Grant management technologies

Background:

• National Institutes of Health (NIH) training grants (T32) require extensive documentation.
• Manual creation of T32 grant tables is a significant administrative burden, often resulting in lengthy documents.

Purpose of the Study:

• To develop a method for dynamically generating tables for T32 grant submissions.
• To reduce the administrative time and effort required for T32 grant preparation.

Main Methods:

• Collected identity and publication metadata from institutional systems.
• Utilized the ReCiter author disambiguation engine for data processing.
• Stored structured bibliographic data in a MySQL database.
• Developed a database query to generate Word extensible markup (XML) documents conforming to NIH specifications.

Main Results:

• Successfully generated T32 training grant documents by linking faculty and their mentees' publications.
• The process requires only a single grant identifier as input due to well-structured source data.
• The open-source code for the T32 Table Generator is publicly available.

Conclusions:

• The T32 Table Generator significantly alleviates the administrative burden of creating grant tables.
• Institutions with access to structured identity and publication data can readily implement this solution.