Facilitating NCAR Data Discovery by Connecting Related Resources

08/01/2012 - 6:00pm
FL-1001, Small Seminar Room
Antonia Rosati


Linking datasets, creators, and users by employing the proper standards helps to increase the impact of funded research. In order for users to find a dataset, it must first be named. Data citations play the important role of giving datasets a persistent presence by assigning a formal “name” and location. This project focuses on the next step of the “name-find-use” sequence: enhancing discoverability of NCAR data by connecting related resources on the web. By examining metadata schemas that document datasets, I examined how Semantic Web approaches can help to ensure the widest possible range of data users. The focus was to move from search engine optimization (SEO) to information connectivity.

Two main markup types are very visible in the Semantic Web and applicable to scientific dataset discovery: The Open Archives Initiative-Object Reuse and Exchange (OAI-ORE - www.openarchives.org) and Microdata (HTML5 and www.schema.org). My project creates pilot aggregations of related resources using both markup types for three case studies: The North American Regional Climate Change Assessment Program (NARCCAP) dataset and related publications, the Palmer Drought Severity Index (PSDI) animation and image files from NCAR’s Visualization Lab (VisLab), and the multidisciplinary data types and formats from the Advanced Cooperative Arctic Data and Information Service  (ACADIS).

This project documents the differences between these markups and how each creates connectedness on the web. My recommendations point toward the most efficient and effective markup schema for aggregating resources within the three case studies based on the following assessment criteria: ease of use, current state of support and adoption of technology, integration with typical web tools, available vocabularies and geoinformatic standards, interoperability with current repositories and access portals (e.g. ESG, Java), and relation to data citation tools and methods.