Open Science for Synthesis

Course Description

Our Open Science for Synthesis training focuses on teaching how to collaborate with peers successfully while carrying out a synthesis research project. It mixes technical programming skills with data management best practices, as well as facilitation and collaboration tools.

Whether you’re just starting out in your research career or you’re looking to deepen your expertise, this hands-on course offers valuable data science skills. This course was designed for both early-career researchers and established professionals seeking to enhance their skills in synthesis science and team science, reproducible science, and data management.

Learning Objectives

OpenS focuses on scientific computing and software tools for reproducible research. Instructors emphasize integrating statistical analysis into well-documented workflows, using open-source, community-supported programming languages such as R and Python. Participants gain practical skills for quickly and reliably implementing open-source scientific software with applications to ecological, environmental, and evolutionary Earth, and marine science synthesis.

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.

Throughout the course, participants work on group synthesis projects following these core themes:

• Collaboration modes and technologies, including virtual collaboration
• Data management, preservation, and sharing
• Data manipulation, integration, and exploration
• Scientific workflows and reproducible research
• Sustainable software practices
• Data analysis and modeling
• Communicating results to diverse audiences

The course provides a solid foundation in the computing skills needed for synthetic research in today’s computationally- and data-intensive era. These topics include:

• Instruction in programming languages like R and Python for data manipulation, analysis, and visualization
• Analytical methods for synthesis research, such as meta-analysis and systematic reviews
• An overview of general programming principles, paradigms, and best practices
• Exposure to the Linux/UNIX command line environment and essential tools
• An introduction to the underlying technology of modern computing and its relevance to scientific research
• Discussions on cyberinfrastructure trends supporting open, 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.