Reproducibility and Open GIScience

One of the primary threats to reproducibility in GIS is a lack of transparency. Primary examples of scientific opacity are data, tools, and software getting locked up behind paywalls. For researchers to “check” the work of those who came before them, they need to be able to understand the methods that have been used in the past. When researchers keep their code private or fail to adequately document their work, it leaves the community, as a whole, less able to build upon their findings. Therefore, the advent of open-source software provides an opportunity for improved reproducibility. Open-source software makes it easier for GIScientists to understand the methods that others have used in the past because the underlying tools and code can be inspected by anyone. If a particular study is carried out using commercial software, the steps that are taken are much more opaque, difficult to document, and difficult to replicate than if a researcher uses an open source software and those who wish to replicate the study can dig into the exact code used. In my own experience, open-source tools such as Google Earth Engine or Whitebox Tools are an example of software wherein work is fairly easy to document and share with others. This may be because they are script-based tools and one can simply share a script to share their work. However, even with QGIS, someone looking to replicate a study or project can look at a workflow and find the exact code that causes a specific tool to work the way it does.

While open-source software increases transparency in spatial science, it cannot solve some of the other issues in the current scientific community. For example, pressure to publish and produce new, dramatic findings appears to be a major issue in the scientific space. While open-source software may help foster a spirit of collaboration, it cannot necessarily help mediate the pressure on scientists to produce findings potentially resulting from questionable research practices. As long as powerful journals continue to control much of the release of scientific literature, it will be difficult for scientists to be able to escape the lure of getting published in a major journal. The “boring” science of checking others’ work and making sure methods are truly accurate and precise will need to be continually championed.

References:
NASEM. 2019. Reproducibility and Replicability in Science. Washington, D.C.: National Academies Press. DOI: 10.17226/25303

Rey, S. J. 2009. Show me the code: Spatial analysis and open source. Journal of Geographical Systems 11 (2):191–207. DOI: 10.1007/s10109-009-0086-8