Now more than ever, science demands reproducibility, collaboration, and effective communication. Since environmental scientists use vast amounts of data spanning multiple disciplines, they face unique challenges in achieving goals of transparency and reproducibility.
In a recent Nature Ecology & Evolution publication, the Ocean Health Index (OHI) team shares their challenges in reproducibility and the path they took to achieve a more transparent and streamlined workflow, resulting in better science in less time.
The OHI story begins in 2012 when they produced their first assessment, which required synthesizing heterogeneous data from nearly 100 different sources. When they went to produce their second assessment in 2013, the team struggled to efficiently reproduce their own work. Once the OHI team identified this issue, they began adopting philosophies, tools, and workflows to improve reproducibility of their work.
The OHI team decided to base their work in R and RStudio for coding and visualization, Git for version control, GitHub for collaboration, and a combination of GitHub and RStudio for organization, documentation, project management, online publishing, distribution, and communication. They also developed the OHI Toolbox which is shared online for free and provides a file structure, data, code, and instruction for developing an OHI assessment.
By describing specific tools and how they incrementally began using them for the Ocean Health Index project, the team hopes to encourage others in the scientific community to do the same—so we can all produce better science in less time.
For more info, check out this Q&A with primary author Julia Stewart Lowndes, and their paper below,
Our path to better science in less time using open data science tools
Lowndes, J.S.S., Best, B.D., Scarborough, C., Afflerbach, J.C., Frazier, M.R., O'Hara, C.C., Jiang, N., Halpern, B.S.
Nature Ecology & Evolution, May 2017, doi: 10.1038/s41559-017-0160