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The first step in the preservation of digital scientific data is gathering enough information “about” a scientific outcome or data collection so that it can be discovered and used a decade later as easily as it is used at the time. Data provenance, or lineage of a collection, can capture the way in which a particular scientific collection was created, when, and by whom. Tools that automate the collection of provenance can reduce the burden on the researcher, and provenance data can be stored in ways that make the data more amenable to long-term preservation. We discuss the various dimensions of data provenance in data-driven geospatial science with the goal of conveying a good grasp of provenance collection, representation, and use. Our research in data cyberinfrastructure utilizes real-time observational data in on-demand weather forecasts, and we discuss this aspect as well.

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