How Do Scientists Define Openness? Exploring the Relationship Between Open Science Policies and Research Practice

Abstract

This article documents how biomedical researchers in the United Kingdom understand and enact the idea of “openness.” This is of particular interest to researchers and science policy worldwide in view of the recent adoption of pioneering policies on Open Science and Open Access by the U.K. government—policies whose impact on and implications for research practice are in need of urgent evaluation, so as to decide on their eventual implementation elsewhere. This study is based on 22 in-depth interviews with U.K. researchers in systems biology, synthetic biology, and bioinformatics, which were conducted between September 2013 and February 2014. Through an analysis of the interview transcripts, we identify seven core themes that characterize researchers’ understanding of openness in science and nine factors that shape the practice of openness in research. Our findings highlight the implications that Open Science policies can have for research processes and outcomes and provide recommendations for enhancing their content, effectiveness, and implementation.

From intent to implementation: Factors affecting public involvement in life science research

“…In order to continue sustained investment into life science research, we must build more comprehensive, interactive and mutually beneficial opportunities for dialogue and exchange with our largest funders-the public [65]. Openness in science does not solely relate to article processing fees [66]. Responsibility and openness in science also relates to increasing the accessibility and use of scientific knowledge.…”

A tale of two 'opens': intersections between Free and Open Source Software and Open Scholarship

“…These all flew in the face of the subscription model, which, even in digital environments, relied on resource-based constraints by creating the illusion of scarcity in production and dissemination. Fundamental in the origins of OA were the unconditional free public availability for reading, as well as unconstrained re-use so long as original sources were attributed; often equated with the Creative Commons Attribution (CC-BY) license (Suber 2007b;Tennant et al 2016 research practices, and more efficient or rigorous research workflows (Watson 2015;Levin et al 2016;Crick, Hall, and Ishtiaq 2017;Masuzzo and Martens 2017;McKiernan 2017;Bowman and Keene 2018;Fraser et al 2018). This includes a diverse range of practices such as pre-registration and registered reports (Nosek and Lakens 2014;Nosek et al 2018), sharing of code and data (Barnes 2010;Levin 2015;Mons 2018), and opening up the peer review process in different ways (Morey et al 2016;Ross-Hellauer 2017;Tennant et al 2017).…”

“…For example, Ryan and Deci suggest that both types of motivation can be traced to basic human needs for autonomy, competence and relatedness (Ryan and Deci 2000). Such tensions between motivations and practices can manifest themselves between selective communication of knowledge, an inherent willingness to share, and the strange duality of scholarly research articles often being simultaneously public and not publicly accessible (Hilgartner 2012;Levin et al 2016).…”

“…Any renewed understanding of 'impact' for Open Scholarship should encourage recognition of an increased diversity of research practices and outputs rather than those confined to peer reviewed research papers (Fecher and Friesike 2014). However, relevant and necessary changes in reward or 'incentive' structures are typically lagging behind this understanding at virtually all levels, including policy, institutional, and practical levels (Levin et al 2016). The continued use of inappropriate metrics and proxies for value has contributed to a hypercompetitive culture widely known as 'publish or perish', and represents a vastly unscientific approach towards research evaluation.…”

Global Data Quality Assessment and the Situated Nature of “Best” Research Practices in Biology

“…This trend is most evident and best documented in the case of genomic sequencing, a technology whose development required a high level of investment by governmental agencies -an investment on which funders expect to see returns, thus pushing researchers to capitalise on the resulting genomic data (e.g. Hilgartner 2017); • The fast-moving technological developments in the relevant field, which makes even very wellestablished and visible research groups fearful of being left behind or unaware of the latest instruments and techniques on offer (see also Levin et al 2016).…”