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Data management plans (DMPs) are required from researchers seeking funding from federal agencies in the United States. Ideally, DMPs disclose how research outputs will be managed and shared. How well DMPs communicate those plans is less understood. Evaluation tools such as the DART rubric and the Belmont scorecard assess the completeness of DMPs and offer one view into what DMPs communicate. This paper compares the evaluation criteria of the two tools by applying them to the same corpus of 150 DMPs from five different NSF programs. Findings suggest that the DART rubric and the Belmont score overlap significantly, but the Belmont scorecard provides a better method to assess completeness. We find that most DMPs fail to address many of the best practices that are articulated by librarians and information professionals in the different evaluation tools. However, the evaluation methodology of both tools relies on a rating scale that does not account for the interaction of key areas of data management. This work contributes to the improvement of evaluation tools for data management planning. 

Citizen scientists make valuable contributions to science but need to learn about the data they are working with to be able to perform more advanced tasks. We present a set of design principles for identifying the kinds of background knowledge that are important to support learning at different stages of engagement, drawn from a study of how free/libre open source software developers are guided to create and use documents. Specifically, we suggest that newcomers require help understanding the purpose, form and content of the documents they engage with, while more advanced developers add understanding of information provenance and the boundaries, relevant participants and work processes. We apply those principles in two separate but related studies. In study 1, we analyze the background knowledge presented to volunteers in the Gravity Spy citizen-science project, mapping the resources to the framework and identifying kinds of knowledge that were not initially provided. In study 2, we use the principles proactively to develop design suggestions for Gravity Spy 2.0, which will involve volunteers in analyzing more diverse sources of data. This new project extends the application of the principles by seeking to use them to support understanding of the relationships between documents, not just the documents individually. We conclude by discussing future work, including a planned evaluation of Gravity Spy 2.0 that will provide a further test of the design principles.