Workshop: Data Science with Gen3
Thursday, April 11, 2019
The workshop on Data Science with Gen3 recently brought together research teams from across the country to learn about an open-source solution for accelerating and democratizing the process of scientific discovery over large complex datasets. This technical workshop taught participants the basics of using Gen3 to manage their research data and get easy access to customizable analysis tools. The University of Chicago hosted the event, where an engineering team from the Center for Translational Data Science led a combination of lectures and hands-on lab sessions.
Sessions started with data modeling and curation and walked participants through the steps needed to use apps like jupyter notebooks for their custom analyses. The group collaborated on use cases, technical setup, and 3rd party integrations. The workshop was highly interactive, and spawned many ideas around how Gen3 might enable the research community to better collaborate on the sharing of their data and analysis tools.
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The workshop is designed for technical users who want to learn how to use the Gen3 open source platform to build data commons and Gen3's implementation of the Data Commons Framework Services to build data ecosystems.
The workshop is held at the Knapp Center for Biomedical Discovery on the campus of the University of Chicago. Tours of the Center for Translational Data Science will be available to workshop participants.
The workshop offers lectures, discussions, and lab sessions where participants can bring their own datasets to get technical guidance and practical data science experience using Gen3.
Gen3 is an open source (Apache 2.0) data platform for building data commons and data ecosystems. Gen3 data commons can be built over AWS, GCP and OpenStack. For more information, see Gen3.org.
The Center for Translational Data Science is a research center at the University of Chicago pioneering translational data science to advance biology, medicine, healthcare, and environmental research.
This project has been funded in whole or in part with Federal funds from the National Cancer Institute, National Institutes of Health, Task Order No. 17X053 under Contract No. HHSN261200800001E.