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National Center for Ecological Analysis and Synthesis

This hands-on data science course was designed for both early career and established researchers to gain skills in data science, including scientific synthesis, reproducible science, and data management.

Participants came to NCEAS for three weeks of intensive training in scientific computing and scientific software for reproducible science. We held this course three times from 2013-2017.

This page archives the descriptions and materials for those courses.

Course Format

The training revolves around scientific computing and software for reproducible science. Our instructors emphasize integrating statistical analysis into well-documented workflows with the use of open-source, community-supported programming languages. Participants learn skills for rapid and robust implementation of open source scientific software. These approaches are explored and applied to ecological, environmental, evolutionary, Earth, and marine science synthesis.

The course focuses on techniques for data management, scientific programming, synthetic analysis, and collaboration techniques through the use of open-source, community-supported tools. Participants learn skills for rapid and robust use of open source scientific software.

The course weaves together several core themes which are reinforced through daily work on group synthesis projects. Core training themes address:

  • Collaboration modes and technologies, virtual collaboration
  • Data management, preservation, and sharing
  • Data manipulation, integration, and exploration
  • Scientific workflows and reproducible research
  • Agile and sustainable software practices
  • Data analysis and modeling
  • Communicating results to broad communities

Throughout the course participants will receive a solid foundation in computing fundamentals for doing synthetic research in today’s computational- and data-intensive era. This includes:

  • Instruction on languages like R and Python for data manipulation, analysis, and visualization
  • Analytical techniques for synthesis research, including meta-analysis and systematic reviews
  • Survey of general programming constructs, paradigms, and best practices
  • Exposure to the Linux/UNIX command line environment and useful tools
  • Demystification of modern computers that have bearing on effective science
  • Discussion of cyberinfrastructure trends supporting open, networked, reproducible science

Group Synthesis Projects

Participants form small synthesis teams that focus on utilizing the software skills they learn each day in the context of cross-cutting science research projects. Using an open community engagement process, participants maximize their success in collaborative research, which can lead to publishable results.

Previous Courses