Sociometric methods for relevancy analysis of Long Tail Science Data
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As the push towards electronic storage, publication, curation, and discoverability of research data collected in multiple research domains has grown, so too have the massive numbers of small to medium datasets that are highly distributed and not easily discoverable - a region of data that is sometimes referred to as the long tail of science. The rapidly increasing, sheer volume of these long tail data present one aspect of the Big Data problem: how does one more easily access, discover, use, and reuse long tail data to lead to new multidisciplinary collaborative research and scientific advancement? In this paper, we describe DataBridge, a new e-science collaboration environment that will realize the potential of long tail data by implementing algorithms and tools to more easily enable data discoverability and reuse. DataBridge will define different types of semantic bridges that link diverse datasets by applying a set of sociometric network analysis (SNA) and relevance algorithms. We will measure relevancy by examining different ways datasets can be related to each other: data to data, user to data, and method to data connections. Through analysis of metadata and ontology, by pattern analysis and feature extraction, through usage tools and models, and via human connections, DataBridge will create an environment for long tail data that is greater than the sum of its parts. In the project's initial phase, we will test and validate the new tools with real-world data contained in the Dataverse Network, the largest social science data repository. In this short paper, we discuss the background and vision for the DataBridge project, and present an introduction to the proposed SNA algorithms and analytical tools that are relevant for discoverability of long tail science data. 2013 IEEE.
