Infrastructure for Data Intensive Biology – a statement of work

Written by: C. Titus Brown

Primary Source: Living in an Ivory Basement

Since being chosen as a Moore Foundation Data Driven Discovery Investigator, I’ve been putting together the paperwork at UC Davis to actually receive the money. Part of that is putting together a budget and a Statement of Work to help guide the conversation between me, Davis, and the Moore Foundation. Here’s what I sent Chris Mentzel at Moore:


Title: Infrastructure for Data Intensive Biology

OUTCOME: In support of demonstrating the high level of scientific impact that data scientists deliver through their focus on interdisciplinary data-driven research, funds from this award will be used to better understand gene function in non-model organisms through the development of new workflows and better data sharing technology for large-scale data analysis.

Research direction 1: Develop and extend protocols for non-model genomic and transcriptomic analysis.

Research direction 2: Integrate and extend existing workflow and data analysis software into a cloud-enabled deployment system with a Web interface for executing protocols.

Research direction 3: Investigate and develop a distributed graph database system and distributed query functionality to support distributed data-driven discovery. (DDDD :)


For more of the background, see my full award submissionmy presentation, and a science fiction story that I would like to enable.

Comments and pointers welcome!

–titus

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C. Titus Brown
C. Titus Brown is an assistant professor in the Department of Computer Science and Engineering and the Department of Microbiology and Molecular Genetics. He earned his PhD ('06) in developmental molecular biology from the California Institute of Technology. Brown is director of the laboratory for Genomics, Evolution, and Development (GED) at Michigan State University. He is a member of the Python Software Foundation and an active contributor to the open source software community. His research interests include computational biology, bioinformatics, open source software development, and software engineering.