EnvironmentalDataAnalytics

Course repository from July 2016 held at NCAR Mesa Lab, Boulder, CO

This workshop series ( starting in 2014) is designed to help prepare the next generation of researchers and practitioners to work within, and contribute to, the data-rich era. Each workshop will bring together graduate students and senior scientists in environmental statistics and related fields to explore contemporary topics in applied environmental data modeling.

Across scientific fields, researchers face challenges coupling data with imperfect models to better understand variability in their system of interest. Inference garnered through these analyses support decisions with important economic, ecological, and social implications. Increasingly, the bottleneck for researchers is not access to data; rather, it is the need to identify and apply appropriate statistical methods using efficient software.

#Workshop program and objectives

The workshop will consist of hands-on computing and modeling tutorials, presentations from graduate student participants, and invited talks from early career and established leaders in environmental data modeling. Tutorials and invited talks will address useful ideas and tools directly applicable to student participants' current and future research.

#Workshop participants will:

Develop new modeling and computing skills through hands-on analyses and lectures led by quantitative scientists

Share research findings and explore open questions within and at the interface of environmental, ecological, climatic, and statistical sciences

Learn about the National Center for Atmospheric Research (NCAR) and National Ecological Observatory Network (NEON) data resources that can facilitate scientific discovery

Workshop participants will also have ample time to enjoy the mountains and downtown Boulder.

#Workshop tutorials:

• Climate data analytics, Doug Nychka, Institute for Mathematics Applied to Geosciences, NCAR

• Introduction to Bayesian statistics and modeling for environmental and ecological data, Alix Gitelman, Department of Statistics, Oregon State University

• Hierarchical models for spatio-temporal data analysis, Andrew Finley, Department of Forestry, Michigan State University

• NIMBLE: R software for Bayesian modeling* , Chris Paciorek, Department of Statistics, University of California - Berkeley.