Our path to better science in less time using open data science tools

Curated on Posted on by Stefaan Verhulst

Julia S. Stewart Lowndes et al in Nature: “Reproducibility has long been a tenet of science but has been challenging to achieve—we learned this the hard way when our old approaches proved inadequate to efficiently reproduce our own work. Here we describe how several free software tools have fundamentally upgraded our approach to collaborative research, making our entire workflow more transparent and streamlined. By describing specific tools and how we incrementally began using them for the Ocean Health Index project, we hope to encourage others in the scientific community to do the same—so we can all produce better science in less time.

Figure 1: Better science in less time, illustrated by the Ocean Health Index project.
Figure 1

Every year since 2012 we have repeated Ocean Health Index (OHI) methods to track change in global ocean health36,37. Increased reproducibility and collaboration has reduced the amount of time required to repeat methods (size of bubbles) with updated data annually, allowing us to focus on improving methods each year (text labels show the biggest innovations). The original assessment in 2012 focused solely on scientific methods (for example, obtaining and analysing data, developing models, calculating, and presenting results; dark shading). In 2013, by necessity we gave more focus to data science (for example, data organization and wrangling, coding, versioning, and documentation; light shading), using open data science tools. We established R as the main language for all data preparation and modelling (using RStudio), which drastically decreased the time involved to complete the assessment. In 2014, we adopted Git and GitHub for version control, project management, and collaboration. This further decreased the time required to repeat the assessment. We also created the OHI Toolbox, which includes our R package ohicore for core analytical operations used in all OHI assessments. In subsequent years we have continued (and plan to continue) this trajectory towards better science in less time by improving code with principles of tidy data33; standardizing file and data structure; and focusing more on communication, in part by creating websites with the same open data science tools and workflow. See text and Table 1 for more details….(More)”