Clear and findable publishing policies are important for authors to choose appropriate journals for publication. We investigated the clarity of policies of 171 major academic journals across disciplines regarding peer review and preprinting. 31.6% of journals surveyed do not provide information on the type of peer review they use. Information on whether preprints can be posted or not is unclear in 39.2% of journals. 58.5% of journals offer no clear information on whether reviewer identities are revealed to authors. Around 75% of journals have no clear policy on co-reviewing, citation of preprints, and publication of reviewer identities. Information regarding practices of open peer review is even more scarce, with <20% of journals providing clear information. Having found a lack of clear information, we conclude by examining the implications this has for researchers (especially early career) and the spread of open research practices.

Part of the current enthusiasm about open science stems from its promises to reform scientific practice in service of the common good, to ensure that scientific outputs will be found and reused more easily, and to enhance scientific impact on policy and society. With this article, we question this optimism by analysing the potential for open science practices to enhance research uptake at the science–policy interface. Science advice is critical to help policy-makers make informed decisions. Likewise, some interpretations of open science hold that making research processes and outputs more transparent and accessible will also enhance the uptake of results by policy and society at large. However, we argue that this hope is based on an unjustifiably simplistic understanding of the science–policy interface that leaves key terms (“impact”, “uptake”) undefined. We show that this understanding—based upon linear models of research uptake—likewise grounds the influential “evidence–policy gap” diagnosis which holds that to improve research uptake, communication and interaction between researchers and policy-makers need to be improved. The overall normative stance of both discussions has sidelined empirical description of the science–policy interface, ignoring questions about the underlying differences between the policy domain and academia. Importantly, both open science and literature on closing the evidence–policy gap recommend improving communication (in terms of either the content or the means) as a viable strategy. To correct some of these views, we combine insights from policy theory with a narrative review of the literature on the evidence–policy gap in the health domain and find that removing barriers to access by itself will not be enough to foster research uptake.

Research Data Management (RDM) promises to make research outputs more transparent, findable, and reproducible. Strategies to streamline data management across disciplines are of key importance. This paper presents results of an institutional survey (N = 258) at a medium‐sized Austrian university with a STEM focus, supplemented with interviews (N = 18), to give an overview of the state‐of‐play of RDM practices across faculties and disciplinary contexts. RDM services are on the rise but remain somewhat behind leading countries like the Netherlands and UK, showing only the beginnings of a culture attuned to RDM. There is considerable variation between faculties and institutes with respect to data amounts, complexity of data sets, data collection and analysis, and data archiving. Data sharing practices within fields tend to be inconsistent. RDM is predominantly regarded as an administrative task, to the detriment of considerations of good research practice. Problems with RDM fall in two categories: Generic problems transcend specific research interests, infrastructures, and departments while discipline‐specific problems need a more targeted approach. The paper extends the state‐of‐the‐art on RDM practices by combining in‐depth qualitative material with quantified, detailed data about RDM practices and needs. The findings should be of interest to any comparable research institution with a similar agenda.

Clear and findable publishing policies are important for authors to choose appropriate journals for publication. We investigated the clarity of policies of 171 major academic journals across disciplines regarding peer review and preprinting. 31.6% of journals surveyed do not provide information on the type of peer review they use. Information on whether preprints can be posted or not is unclear in 39.2% of journals. 58.5% of journals offer no clear information on whether reviewer identities are revealed to authors. Around 75% of journals have no clear policy on co-reviewing, citation of preprints, and publication of reviewer identities. Information regarding practices of Open Peer Review is even more scarce, with <20% of journals providing clear information. Having found a lack of clear information, we conclude by examining the implications this has for researchers (especially early career) and the spread of open research practices.

Scientific institutions have increasingly embraced formalized research data management strategies, which involve complex social practices of codifying the tacit dimensions of data practices. Several guidelines to facilitate these practices have been introduced in recent years, for example, the FAIR guiding principles. The aim of these practices is to foster transparency and reproducibility through ‘data sharing,’ the public release of data for unbounded reuse. However, a closer look suggests that many scientists’ practices of data release might be better described as what I call data handovers. These practices are not rooted in the lofty ideals of good scientific practice and global data reuse but in the more mundane necessities of research continuity, which have become more urgent in light of increasing academic mobility. The Austrian scientists interviewed for this study reinterpreted defining features of research data management – such as ensuring findability – as techniques for managing the effects of researcher mobility. This suggests that the adoption of Open Science practices might be dissociated from its stated epistemic goals, and explains why many Open Science initiatives at present are administratively strong but normatively weak.

This contribution has originally been conceived as the introduction to my dissertation, to be submitted in February 2022 to the University of Graz (Department of Sociology). In the dissertation, I take up the vast discourse on data sharing to argue that until now, the most influential positions (exemplified in the works of philosopher Sabina Leonelli and information scientists Christine L. Borgman) have construed their subject matter rather narrowly, both in terms of the dominant case studies as well as in focusing on the narrowly stated epistemic goals of the Open Science movement (of ensuring reproducibility, transparency, and data reuse, say). I take issue with both of these claims based on a series of empirical case studies, to show that data sharing is broader than has hitherto been assumed, especially in terms of its aims which have, by and large, been modelled along the lines of enabling unspecified, global data reuse. With this preprint, I explain what this means based on an analysis of three discourses, all pertaining to data sharing: Discussions predominantly found within information science documenting a tremendous variability of data types, formats, practices, and sharing behaviour, and an empirical literature that has frequently overlooked the difficulties in accounting for data practices at the level of faculties, research groups, departments, or universities, as well as a more critical discourse that has recently emerged around the factors that inhibit data sharing and reuse, such as the situatedness and contextuality of data practices. The contribution documents a growing awareness of the situated nature of data practices, in particular with respect to research activities in the global south. It further documents mounting evidence for what has been called data contextuality, discussions that ensued from the observation, already a decade old, that “data” are impossible to define, leading to a view now known as the relational view of data (data as potential evidence for knowledge claims).