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Published on: April 4, 2018
The emerging complexity of Open Science: assessing Intelligent Data Openness in Genomic Anthropology and Human
Paolo Anagnostou1, Marco Capocasa2, Francesca Brisighelli3
1Department of Environmental Biology, Sapienza University of Rome, Piazzale Aldo Moro 5, Rome, Italy; Istituto Italiano di Antropologia, Piazzale Aldo Moro 5, Rome, Italy, paolo.anagnostou@uniroma1.it.
Abstract:
In recent decades, the scientific community has become aware of the importance of science being effectively open in order to speed up scientific and technological progress. In this context, the achievement of a robust, effective and responsible form of data sharing is now widely acknowledged as a fundamental part of the research process. The production and resolution of human genomic data has steadily increased in recent years, mainly due to technological advances and decreasing costs of DNA genotyping and sequencing. There is, however, a downside to this process due to the huge increase in the complexity of the data and related metadata. This means it is advisable to go beyond traditional forms of sharing analysis, which have focused on data availability only. Here we present a pilot study that aims to complement a survey on the availability of data related to peer-reviewed publications with an analysis of their findability, accessibility, useability and assessability (according to the "intelligent data openness" scheme). Sharing rates in genomic anthropology (73.0%) were found to be higher than human genomics (32.4%), but lower than closely related research fields (from 96.8% to 79.2% for paleogenetics and evolutionary genetics, respectively). We discuss the privacy and methodological issues that could be linked to this finding. Comparisons of sharing rates across a wide range of disciplines has suggested that the idea of human genomics as a forerunner for the open data movement should be questioned. Finally, both in genomic anthropology and human genomics, findability and useability were found to be compliant with the expectations of an intelligent data openness, whereas only a minor part of studies met the need to make the data completely assessable.
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