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dc.contributor.authorTilmes, Curt
dc.contributor.authorFox, Peter
dc.contributor.authorMa, Xiaogang
dc.contributor.authorMcGuinness, Deborah
dc.contributor.authorPrivette, Ana
dc.contributor.authorSmith, Aaron
dc.contributor.authorWaple, Anne
dc.contributor.authorZednik, Stephan
dc.contributor.authorZheng, Jin
dc.date.accessioned2022-02-18T02:36:54Z
dc.date.available2022-02-18T02:36:54Z
dc.date.issued2013-07-12
dc.identifier.other129
dc.identifier.urihttps://ieeexplore.ieee.org/document/6558476
dc.identifier.urihttps://hdl.handle.net/20.500.13015/4531
dc.description.abstractThe important topic of global climate change builds on a huge collection of scientific research. It is common for agencies releasing climate change information to be served with requests for all supporting materials resulting in a particular conclusion. Capturing and presenting global change provenance, linking to the research papers, data sets, models, analyses, observations, satellites, etc., that support the key research findings in this domain can increase understanding and aid in reproducibility of results and conclusions. The U.S. Global Change Research Program is now coordinating the production of a national climate assessment (NCA) that presents our best understanding of global change. We are now developing a global change information system that will present the content of that report and its provenance, including the scientific support for the findings of the assessment. We are using an approach that will present this information both through a human accessible Web site as well as a machine-readable interface for automated mining of the provenance graph. We plan to use the developing World Wide Web Consortium (W3C) PROV data model and ontology for this system. This paper will describe an overview of the process of developing the NCA and how the provenance trail of the report and each of the technical inputs can be captured and represented using the W3C PROV ontology. This will improve the visibility into the assessment process, increase understanding and possibility of reproducibility, and ultimately increase the credibility and trust of the resulting report.
dc.publisherIEEE Transactions on Geoscience and Remote Sensing
dc.relation.urihttps://tw.rpi.edu/project/gcis-imsap
dc.subjectGlobal Change Information System: Information Model and Semantic Application Prototypes
dc.titleProvenance Representation for the National Climate Assessment in the Global Change Information System


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