New scientific discoveries and knowledge emerge when the existing corpus of data, information and knowledge is archived, discoverable and interpretable. Data sharing perceptions and practices have evolved over the last several decades in response to the emergence of large research programs and networks, as well as requirements from research sponsors, institutions and publishers. Five practices that facilitate effective data sharing are described herein: (1) creating and following a data management plan; (2) adhering to a reasonable data sharing and attribution license; (3) comprehensively documenting the data following community standards and best practices; (4) protecting and making available data, metadata, and algorithms and workflows via a trusted community data repository; and (5) disseminating and advertising the existence of the data. Preparing a preservation-ready data product for submission to a repository requires that the data contributor logically, consistently and clearly name and describe the data package, including the variables, files and algorithms and workflows. Furthermore, the data should be quality assured, completely and comprehensively documented, and protected throughout the research. Data repositories often have specific data organization and submission guidelines and play an important role in preserving and disseminating data that may represent a valuable scientific resource for decades to come.
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