While the arguments for data sharing are compelling and the rhetoric often exciting ("researchers are creating, gathering and using data in hitherto unimagined-volumes" \cite{force}) reservations have been expressed that reflect researchers' concerns, including the absence of infrastructure and incentives, and the presence of disincentives like the fear of getting scooped and concerns about misinterpretation or misuse of shared data \cite{Bezuidenhout_2018} \cite{question}. Whatever your position in those arguments, it is fair to say that the open data challenge is a big challenge, and it is important to recognise that communities of researchers are ready to meet it in different ways and to different degrees \cite{sharing}. For example, life scientists have been reported as the most ready to share the data they create \cite{Jones_2019} \cite{Grant_2018}, and early career researchers may be particularly well-prepared to do so \cite{Sholler_2019} \cite{Campbell_2019}. In fact, researchers in most data-oriented disciplines have embraced the challenge to an extent, and even where research data are associated with complex ethical issues like consent and privacy, the obligation to share data is recognised \cite{Taichman_2017}. It helps that researchers can look forward to greater impact \cite{data} \cite{mark}. It also helps that understanding of what 'open' data really means has become quite sophisticated, aided by the FAIR Data Principles \cite{Wilkinson_2016} and promoted with the much-used soundbite that research data should be "as open as possible, as closed as necessary" \cite{2020}.

Looking at this through a publishing-focused lens, when researchers are ready to share data, publishers and journals can play a useful role in enabling and realising the benefits. They help communicate and explain standards and expectations \citep{Wu_2019} \cite{publishers}. They help researchers meet the data sharing requirements including those set by their funders \cite{easier}. They increase the discoverability of shared data, perhaps 1000-fold (although there may be more correlation in that number than simply causation) \cite{Vines_2013}. They prompt researchers to "plan for the longevity, reusability, and stability of the data" \cite{samors2018a}.