Data publishing is a cornerstone of open, reproducible science and modern scholarly communication. Data publications enable researchers to share their research outputs via dedicated workflows, services and infrastructures. These workflows, services and infrastructures help ensure that data are well documented, curated, persistent, interoperable, citable, quality assured, findable/discoverable, and reusable. Data publishing workflows have an enormous impact on researchers, research practices and publishing paradigms, as well as career and research evaluations - and are critical to making data sharing and Open Science work.
It is crucial for all stakeholders to understand the options and standards for data publishing workflows and for both newcomers and more established players to learn from best practices. To that end, the RDA-WDS Data Publishing Workflows group surveyed the current data publishing workflow landscape across disciplines and multiple publishing models in order to surface common components and standard practices. Using such case studies allowed us to identify, analyze and categorize the main building key components of data publishing workflows, to identify an initial reference model for data publishing, and to present definitions for key data publishing products.
In the session we will present our findings of this survey, i.e. the reference model and how others might reuse it, as well as our work on the follow-up survey, addressing how the intent to publish research data influences the research workflow. In keeping with our experience of working through the results of the first survey, we expect to identify opportunities and challenges that will spur discussion and discovery of solutions and next steps for the community. These discussions will consider solutions for the current data publication phase as well as what may be possible in the active research phase to create better opportunities for improved data publication.