Stephanie E. Hampton|Matthew B. Jones|Leah Wasser|Mark Schildhauer|Sarah R. Supp|Julien Brun|Rebecca R. Hernandez|Carl Boettiger|Scott L. Collins|Louis J. Gross|Diego Fernández|Amber E Budden|Ethan P. White|Tracy Teal|Stephanie Labou|Juliann E. Aukema
Skills and Knowledge for Data-Intensive Environmental Research
National Center for Ecological Analysis and Synthesis|University of New Mexico|National Center for Ecological Analysis and Synthesis|University of New Mexico|National Center for Ecological Analysis and Synthesis|University of New Mexico|National Center for Ecological Analysis and Synthesis|University of New Mexico|National Center for Ecological Analysis and Synthesis|University of New Mexico|National Center for Ecological Analysis and Synthesis|University of New Mexico|National Center for Ecological Analysis and Synthesis|University of New Mexico|National Center for Ecological Analysis and Synthesis|University of New Mexico|National Center for Ecological Analysis and Synthesis|University of New Mexico|National Center for Ecological Analysis and Synthesis|University of New Mexico|National Center for Ecological Analysis and Synthesis|University of New Mexico|National Center for Ecological Analysis and Synthesis|University of New Mexico|National Center for Ecological Analysis and Synthesis|University of New Mexico|National Center for Ecological Analysis and Synthesis|University of New Mexico|National Center for Ecological Analysis and Synthesis|University of New Mexico|National Center for Ecological Analysis and Synthesis|University of New Mexico
scale magnitude complex pressing environmental issues lend urgency need integrative reproducible analysis synthesis facilitated dataintensive research approaches however recent pace technological change appropriate skills accomplish dataintensive research lacking among environmental scientists ever need greater access training mentorship computational skills provide roadmap raising data competencies current nextgeneration environmental researchers describing concepts skills needed effectively engaging heterogeneous distributed rapidly growing volumes available data articulate five key skills 1 data management processing 2 analysis 3 software skills science 4 visualization 5 communication methods collaboration dissemination provide overview current suite training initiatives available environmental scientists models closing skilltransfer gap