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As developers acknowledge that provenance is essential, more and more datasets are attempting to keep provenance records describing how they were created. Some of these datasets are constructed using workflows, others cobble together processes and applications to manipulate the data. While the provenance needs are the same, the inputs and set of processes used must be kept, the identity needs are very different. The authors outline several identification strategies that can be used for data manipulation outside of workflows. They evaluate these strategies in terms of time to create and store identity, and the space needed to keep this information. Additionally, the authors discuss the strengths and weaknesses of each strategy.
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