Synthetic Derivative
The Synthetic Derivative (SD) is Vanderbilt’s de-identified research data repository which contains longitudinal data from over 15 years. Researchers most commonly access the SD through either the SD Discover or Record Counter tools, which are maintained by VICTR. Both SD Discover and Record Counter are similar in functionality, each utilizing a drag-and-drop graphical interface to quickly query data. The Record Counter tool is primarily used to explore cohorts for future studies and yields numeric counts of patients according to specified selection or exclusion criteria. IRB approval is not required to use the Record Counter. In contrast, the SD Discover tool offers similar functionality, but yields de-identified patient records for the respective selection criteria which can be used for research studies. The SD also links to BioVU – Vanderbilt’s de-identified DNA biorepostory. Access to the SD is free for all Vanderbilt researchers, but access to the SD Discover tool requires a data use agreement and IRB approval. Highly complex queries that require multiple linkages to different data sources may benefit from the SD Custom Extract service, available from the IDASC core.
Research Derivative
The Research Derivative (RD) is similar in functionality to the SD, but it contains fully identifiable longitudinal data from over 15 years. Researchers most commonly access the RD through the RD Discover tool, which is maintained by VICTR. RD Discover utilizes a drag-and-drop interface to quickly query data, yielding fully identified patient records for the respective selection criteria. Access to RD Discover is free for all Vanderbilt researchers, but it requires a data use agreement and IRB approval prior to use. Highly complex queries that require multiple linkages from different data sources may benefit from the RD Custom Extract service, available from the IDASC core.
Record Counter
In the video below, Dr. Steitz demonstrates how to use VUMC’s Record Counter tool to quickly assess record numbers available after applying inclusion and exclusion criteria. This is helpful when planning a research study and needing to assess if there’s a large enough sample for the work to be feasible.