Variations in Scientific Data Production: What Can We Learn from #Overlyhonestmethods?
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In recent months months the hashtag #overlyhonestmethods has steadily been gaining popularity. Posts under this hashtag—presumably by scientists—detail aspects of daily scientific research that differ considerably from the idealized interpretation of scientific experimentation as standardized, objective and reproducible. Over and above its entertainment value, the popularity of this hashtag raises two important points for those who study both science and scientists. Firstly, the posts highlight that the generation of data through experimentation is often far less standardized than is commonly assumed. Secondly, the popularity of the hashtag together with its relatively blasé reception by the scientific community reveal that the actions reported in the tweets are far from shocking and indeed may be considered just “part of scientific research”. Such observations give considerable pause for thought, and suggest that current conceptions of data might be limited by failing to recognize this “inherent variability” within the actions of generation—and thus within data themselves. Is it possible, we must ask, that epistemic virtues such as standardization, consistency, reportability and reproducibility need to be reevaluated? Such considerations are, of course, of particular importance to data sharing discussions and the Open Data movement. This paper suggests that the notion of a “moral professionalism” for data generation and sharing needs to be considered in more detail if the inherent variability of data are to be addressed in any meaningful manner.
Keywords#Overlyhonestmethods Research methods Tacit knowledge Moral professionalism Data sharing Scientific data Open data
Many thanks to Prof Brian Rappert, Dr Ann-Sophie Barwich and Ms Helena van der Vegt for their invaluable comments on this manuscript and subject. I also thank the two anonymous reviewers for their helpful contributions to the manuscript.
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