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greasing the wheels of biological big data analysis
Pete E. Pascuzzi Assistant Professor, Libraries Assistant Professor of Biochemistry (by courtesy)
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background B.A. in Biology and Chemistry Ph.D. in Biochemistry
Services Research Education Data B.A. in Biology and Chemistry Ph.D. in Biochemistry Postdoctoral training in genomics and bioinformatics Joined Purdue Libraries in 2013 as part of a cluster hire in Systems Biology
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Problems with Big data Nature Neuroscience 17, 1442–1447 (2014) doi: /nn.3838
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Problems with Big data gapingvoid.com
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Problems with Big data
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biological Big data
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biological Big data
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biological Big data
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PURDUE BBD Network ITaP D2C2 PU Library MCMP Statistics CTSI
Statistical Bioinformatics Center PURR Agronomy ITaP Research Computing D2C2 Computer Science Community Clusters PU Library Engineering Bindley Bioscience Center Bioinformatics Core Health and Human Sciences Biological Sciences Veterinary Medicine MCMP Horticulture Botany & Plant Pathology Statistics
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World BBD Network Cloud Computing Dryad GitHub Libraries NIH Industry
NCBI NSF Coursera Dryad GitHub Bioconductor Open Source Libraries USDA Software Carpentry EMBL - EBI Genome projects CRAN PyPi NIH Scientific Societies Stack Overflow Industry
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BBD EFforts credit courses
Introduction to R and Bioconductor Data Management at the Bench disciplinary faculty research collaborations Guide students through analyses Acquire published data Advise on tools and workflows D-Velop lab Data Visualization Experience Lab Experience Lab of Purdue 3D printing Data viz wall Specialized software
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credit courses Research data management DATA SHARING MANDATES
DATA MANAGEMENT PLANS DATA ARCHIVING METADATA STANDARDS
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credit courses Introduction to R and Bioconductor
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credit courses Data management at the bench
First credit course offered by Purdue Libraries Targeted to students in science and engineering Lecture with associated lab Data management practices, computing (Unix and R), Excel, and workflow documentation. Big data addressed using Purdue Research Computing resources such as Scholar and Data Depot.
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credit courses Data management at the bench
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research Disciplinary faculty research collaborations Acquisition
Reformatting Visualization Analysis Proposal Development Consultation Training Commitment Low High
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wilmeth active learning center
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D-Velop space
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conclusions “Academics, employers and funders in particular should actively encourage functional specialists, especially data scientists, and ensure that it becomes no more unusual for a researcher to become an expert in data science than to specialize in dark matter or fullerenes.” Dr. Timmo Hannay, Digital Science, Wired, August 26, 2014
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