Expanding research impact through better data and code practices

Data cleaning and management are often viewed as the less glamorous part of a scientific endeavor, despite consuming a significant amount of time. As water data collection methods have expanded and scientists need to integrate more and more data into their analyses, some traditional scientific approaches have failed to scale. For many scientists, wrangling data and conducting scientific analyses now requires new skills and techniques, including learning to code in languages such as R and Python. Mastery of these skills can significantly improve efficiency, veracity, and flexibility of scientific research, enabling reproducible results and reusable methods. In this session, we will cover some of the essential concepts and approaches that researchers of any technical ability can adopt to improve their data analysis workflows, including best practices for code organization and development, cultural shifts necessary to support and encourage development of these skills, and a brief introduction to programmatic solutions for automating workflows. Researchers will leave this session feeling empowered to improve their data-handling techniques in order to build more robust, trustworthy, and innovative scientific workflows that can support the growing demands for scientific vigor in a “big data” world.

Presentation Preference: Oral

Primary Presenter: Lindsay Platt, Consortium of Universities for the Advancement of Hydrologic Science, Inc (CUAHSI) (lplatt@cuahsi.org)

Authors:

Abner Bogan, Consortium of Universities for the Advancement of Hydrologic Science, Inc (CUAHSI)  (abogan@cuahsi.org)

Jordan Read, Consortium of Universities for the Advancement of Hydrologic Science, Inc (CUAHSI)  (jread@cuahsi.org)