Ten Simple Rules for Creating a Good Data Management Plan.

Abstract

Research papers and data products are key outcomes of the science enterprise. Governmental, nongovernmental, and private foundation sponsors of research are increasingly recognizing the value of research data. As a result, most funders now require that sufficiently detailed data management plans be submitted as part of a research proposal. A data management plan (DMP) is a document that describes how you will treat your data during a project and what happens with the data after the project ends. Such plans typically cover all or portions of the data life cycle—from data discovery, collection, and organization (e.g., spreadsheets, databases), through quality assurance/quality control, documentation (e.g., data types, laboratory methods) and use of the data, to data preservation and sharing with others (e.g., data policies and dissemination approaches). Fig 1 illustrates the relationship between hypothetical research and data life cycles and highlights the links to the rules presented in this paper. The DMP undergoes peer review and is used in part to evaluate a project’s merit. Plans also document the data management activities associated with funded projects and may be revisited during performance reviews. Open in a separate window Fig 1 Relationship of the research life cycle (A) to the data life cycle (B); note: highlighted circles refer to the rules that are most closely linked to the steps of the data life cycle. As part of the research life cycle (A), many researchers (1) test ideas and hypotheses by (2) acquiring data that are (3) incorporated into various analyses and visualizations, leading to interpretations that are then (4) published in the literature and disseminated via other mechanisms (e.g., conference presentations, blogs, tweets), and that often lead back to (1) new ideas and hypotheses. During the data life cycle (B), researchers typically (1) develop a plan for how data will be managed during and after the project; (2) discover and acquire existing data and (3) collect and organize new data; (4) assure the quality of the data; (5) describe the data (i.e., ascribe metadata); (6) use the data in analyses, models, visualizations, etc.; and (7) preserve and (8) share the data with others (e.g., researchers, students, decision makers), possibly leading to new ideas and hypotheses.