The Ohio State University created the Translational Data Analytics Institute to enable collaboration and innovation in translational data analytics, co-develop externally responsive translational data analytics solutions with industry and community partners, expand the workforce capable of delivering translational data analytics solutions, and build a sustainable core of data science and analytics scholarship.
We are doing this by:
Meet our TDAI faculty affiliates and their areas of expertise.
Meet our program team.
The use of the term “translational” reflects a fundamental shift toward utilizing data science and analytics in solving issues of global importance.
Beginning in 2014, TDAI has defined “translational data analytics” as:
The application of data analytics theories and methods to generate solutions for real world problems, or use cases, derived from consultation with impacted stakeholders, and the subsequent delivery and dissemination of those solutions in a manner that enables stakeholders to use them in a tangible and quantifiable way.
Since then, the National Science Foundation has applied the “translational” concept to data science:
“Translational data science” is a new term that is being used for an emerging field that applies data science principles, techniques, and technologies to challenging scientific problems that hold the promise of having an important impact on human or societal welfare. The term is also used when data science principles, techniques and technologies are applied to problems in different domains in general, including—but not restricted to—science and engineering research.
In June 2017, TDAI co-chaired the NSF’s inaugural Workshop on Translational Data Science, an important step towards developing a community around translational data science and analytics.