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We explore concepts of data identification, organization, equivalence and reproducibility for such data intensive scientific processing. We present a conceptual model useful for describing and representing data provenance suitable for very precise data and processing identification. We present a scheme for creating and maintaining identifiers for precise dataset membership and provenance equivalence at various degrees of granularity and data aggregation.
Application of this model will allow more specific data citations in scientific literature based on large datasets and data provenance equivalence. Our provenance representations will enable independent reproducibility required by scientific transparency. Increasing transparency will contribute to understanding, and ultimately, credibility of scientific results.
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