A relevancy algorithm for curating earth science data around phenomenon. (September 2017)

• Record Type: Journal Article
• Authors: Maskey, Manil; Ramachandran, Rahul; Li, Xiang; Weigel, Amanda; Bugbee, Kaylin; Gatlin, Patrick; Miller, J.J.
• Is Part Of: Computers & geosciences. Volume 106(2017)
• Page Start: 164
• Page End: 170
• Publication Date: 2017-09
• Subjects: Data curation -- Search paradigms -- Information retrieval -- Earth science phenomena -- Relevancy algorithm; Environmental policy -- Periodicals; 550.5
• DOI: 10.1016/j.cageo.2017.06.007
• Languages: English
• ISSNs: 0098-3004

Abstract

Abstract: Earth science data are being collected for various science needs and applications, processed using different algorithms at multiple resolutions and coverages, and then archived at different archiving centers for distribution and stewardship causing difficulty in data discovery. Curation, which typically occurs in museums, art galleries, and libraries, is traditionally defined as the process of collecting and organizing information around a common subject matter or a topic of interest. Curating data sets around topics or areas of interest addresses some of the data discovery needs in the field of Earth science, especially for unanticipated users of data. This paper describes a methodology to automate search and selection of data around specific phenomena. Different components of the methodology including the assumptions, the process, and the relevancy ranking algorithm are described. The paper makes two unique contributions to improving data search and discovery capabilities. First, the paper describes a novel methodology developed for automatically curating data around a topic using Earth science metadata records. Second, the methodology has been implemented as a stand-alone web service that is utilized to augment search and usability of data in a variety of tools. Highlights: A new method that ranks Earth science data sets around phenomena is proposed. The method considers curation aspects of datasets using domain experts. Application of the method is demonstrated for several phenomena. Valuable lessons learned in curation of Earth science data is presented.