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Data-Driven Basic Science: Disseminating Data, Tools, and Expertise

This initiative aims to disseminate data, tools, and expertise in data-driven basic science through a data platform and integration with existing data facilities on campus. It also focuses on adopting data standards and practices, implementing data policy and economics-related projects, and incorporating data in the curriculum.

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Data-Driven Basic Science: Disseminating Data, Tools, and Expertise

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  1. Data Driven Basic Science (interim 20130916)

  2. Data Driven Basic Science • Problem sets (see next) • Short-term • Disseminate; data, tools, expertise. • Data platform • Data in the curriculum • Long-term • Discovery, cognitive tools, policy/ ethics, abduction! • Intelligent Data Infrastructure: all campuses http://www.apsnet.org/edcenter/instcomm/TeachingArticles/Article%20Images/TPDD_fig01.jpg

  3. Themes and … Key challenges/approaches Data Computing (addition to Academic Computing and Research Computing) Opening up the data culture at RPI Integrating with existing data facilities on campus … • Enable inter-disciplinary science discovery in key research areas • Getting data really into the loop - the loop is based on a variety of models and is iterative (must be convergent) • Rensselaer Data Platform • Pull concepts from Digital Rensselaer, data.rpi.edu, others • Adopt RDA ratified standards, practices (and others) • Initiate robust data policy and data economics-related projects • Place data in the curriculum (DATUM, CDS, others) • Digital Society and Digital meets Reality • Advance data-intensive computing (Blue Gene/Q++, software applications, visual platforms, others) • Inverse problems (using multi-modal data), scale variation, dealing with sample bias as a result • State of complexity as we traverse scales (perhaps gene/cell/tissue/organ/organism) • Complex systems with incomplete data (sparse) • Adjoint/ variational data-assimilation approaches in highly non-linear, heterogeneous, stiff systems • Big parameter spaces (~100 dim) change the game in Bayesian analysis. Huge computational undertaking • Strategies for putting together data (adapting structure) • … • Open-world uncertainty quantification • Visual analytics (new tools) • New ways of organizing data (for “Data Discovery”) RDA = http://rd-alliance.org

  4. BHAPs – next step…

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