Abstract
The use of computers and complex software is pervasive in archaeology, yet their role in the analytical pipeline is rarely exposed for other researchers to inspect or reuse. This limits the progress of archaeology because researchers cannot easily reproduce each other’s work to verify or extend it. Four general principles of reproducible research that have emerged in other fields are presented. An archaeological case study is described that shows how each principle can be implemented using freely available software. The costs and benefits of implementing reproducible research are assessed. The primary benefit, of sharing data in particular, is increased impact via an increased number of citations. The primary cost is the additional time required to enhance reproducibility, although the exact amount is difficult to quantify.
Highlights
Archaeology, like all scientific fields, advances through rigorous tests of previously published studies
Many of the decisions made in cleaning, tidying, analyzing and visualizing the data are unrecorded and unreported. This is a problem because as computational results become increasingly common and complex in archaeology, and we are increasingly dependent on software to generate our results, we risk deviating from the scientific method if we are unable to reproduce the computational results of our peers (Dafoe, 2014)
A further problem is that when the methods are underspecified, it limits the ease with which they can be reused by the original author, and extended by others (Buckheit & Donoho, 1995; Donoho, Maleki, Rahman, Shahram, & Stodden, 2009; Schwab, Karrenbach, & Claerbout, 2000). This means that when a new methods paper in archaeology is published as a stand-alone account, it is challenging and time-consuming for others to benefit from this new method
Summary
Archaeology, like all scientific fields, advances through rigorous tests of previously published studies. The four principles are: make data and code openly available and archive it in a suitable location, use a programming language to write scripts for data analysis and visualizations, use version control to manage multiple versions of files and contributions from collaborators, and document and share the computational environment of the analysis. Researchers following these principles will benefit from an increase in the transparency and efficiency of their research pipeline (Markowetz, 2015). We treated our Docker image as a disposable and isolated component, deleting and recreating it regularly to be sure that the computational environment documented in the Dockerfile could run our analyses
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