Introduction

Open science/open scholarship/open research – whichever term is preferred – refers to a set of perspectives, techniques and tools that seek to enhance the transparency, reproducibility and overall robustness of research. While open access (OA) focuses on unrestricted access to the research article, conversations around open scholarship reach beyond this, considering how the whole research lifecycle can be opened up,1 with the ultimate aim of improving the quality and integrity of research.

Open research practices are gaining in traction, with increasing examples and discussion about the opportunities that openness brings for individual researchers,2 academic institutions3 and for other players in this ecosystem, such as research councils, funders and international bodies.

One of the key strands in the expanding discourse around open scholarship centres on open research data. Recent research from figshare indicates that ‘open data is becoming more embedded in the research community’4 with 64% of respondents to their State of Open Data survey claiming that they made their data openly available in 2018 (a 4% increase on 2017). This rise in a positive attitude towards sharing data corresponds with a general industry development, with more institutions and funders recognizing the value of data sets.

In recent years research data policies have been used by funding agencies, publishers and research institutions to drive research data sharing.5 Requirements across these stakeholders vary, but tend to include common aspects, such as the preparation of data management plans, data archiving, citation of data sets and inclusion of data availability statements in published research papers; and in some cases, the peer review of research data. In October 2018 the Belmont Forum, which represents 26 funding agencies internationally, released a draft position paper6 outlining requirements for data availability statements to be made available outside any paywalls applied to articles, and the minimum features they should include, such as persistent identifiers for the data set and licensing and access information. These developments suggest a growing consensus among funding agencies and publishers on key features of data-sharing policies.

In journal publishing, which is our focus in this article, adherence to a data-sharing policy supports authors in making the data associated with a publication available as an open data set so that the conclusions reached in the publication can be checked and verified. As scholarly journals and publishers find themselves at the heart of the shift towards openness, it is not surprising that 2017–2018 saw an increase in data-sharing policies from major publishers, resulting in a significant number of scholarly journals with policies aiming to increase transparency.7

In this article we present two case studies which examine the experiences that two different publishers have had in rolling out data-sharing policies. Both are leading academic publishers, publishing scholarly journals, books and educational reference material. Taylor & Francis Group is a publisher of scholarly journals, books, e-books, text books and reference works in all areas of the humanities, social sciences, behavioural sciences, science, technology and medicine sectors.8 Springer Nature is a research, educational and professional publisher and is home to brands including Springer, Nature Research, BioMed Central (BMC), Palgrave Macmillan and Scientific American. In the first case study, we reflect on Taylor & Francis’ experience as we approach the first anniversary of the launch of the publisher’s data-sharing policies. We focus on how the policies have been devised with consideration of very different research subject areas, on the response to the policies from the communities Taylor & Francis work with, and how this is shaping future work. In the second case study, the more ‘mature’ Springer Nature policies, which have been available since 2016, are examined, describing initiatives that are being undertaken to enhance compliance with these policies, and to raise the profile of data availability statements which the policies either recommend or require.

The intention in presenting these two case studies is to illustrate some of the considerations involved in providing consistent policies across journals of many disciplines, and how publishers inform, support and encourage authors to share the data underpinning their research. Following the two case studies, the work of other stakeholders, including the Research Data Alliance and Center for Open Science, is also outlined and future plans for aligning data policies and supporting data sharing are described.