A navigation model for exploring scientific workflow provenance graphs
Proceedings of the 4th Workshop on Workflows in Support of Large-Scale Science
Modeling and querying provenance by extending CIDOC CRM
Distributed and Parallel Databases
From artifacts to aggregations: Modeling scientific life cycles on the semantic Web
Journal of the American Society for Information Science and Technology
The Foundations for Provenance on the Web
Foundations and Trends in Web Science
The conundrum of sharing research data
Journal of the American Society for Information Science and Technology
Database support for exploring scientific workflow provenance graphs
SSDBM'12 Proceedings of the 24th international conference on Scientific and Statistical Database Management
Toward the modeling of data provenance in scientific publications
Computer Standards & Interfaces
A Web-based resource model for scholarship 2.0: object reuse & exchange
Concurrency and Computation: Practice & Experience
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Scientific communities are under increasing pressure from funding organizations to publish their raw data, in addition to their traditional publications, in open archives. Many scientists would be willing to do this if they had tools that streamlined the process and exposed simple provenance information, i.e., enough to explain the methodology and validate the results without compromising the author’s intellectual property or competitive advantage. This paper presents Provenance Explorer, a tool that enables the provenance trail associated with a scientific discovery process to be visualized and explored through a graphical user interface (GUI). Based on RDF graphs, it displays the sequence of data, states and events associated with a scientific workflow, illustrating the methodology that led to the published results. The GUI also allows permitted users to expand selected links between nodes to reveal more fine-grained information and sub-workflows. But more importantly, the system enables scientists to selectively construct “scientific publication packages” by choosing particular nodes from the visual provenance trail and dragging-and-dropping them into an RDF package which can be uploaded to an archive or repository for publication or e-learning. The provenance relationships between the individual components in the package are automatically inferred using a rules-based inferencing engine.