This research develops advanced classifiers to automatically identify available individual participant data (IPD) from clinical trials by analyzing data sharing statements.

Key Findings:

• Pre-trained language models effectively interpret textual data-sharing statements in clinical trial databases
• Automated identification of available IPD improves accessibility for scientific reuse
• The approach helps researchers navigate large clinical trial repositories to find shareable medical data

Why It Matters: This technology streamlines the discovery of valuable medical research data, accelerating secondary analyses and potentially speeding up medical innovations by making it easier to find and use existing clinical datasets.

Classifiers of Data Sharing Statements in Clinical Trial Records