Generalized representation and mapping for social-ecological data: Freeing data from the database

Abstract

Scientific discovery increasingly requires collaboration between scientific sub-domains that often have different representations for their data. To bridge gaps between varying domain representations, researchers are developing metadata and semantic representations meaningful to broader communities. Through exploiting these representations we propose a logical model and architecture by which cross-domain researchers can more easily discover, use, and eventually archive, data. In this paper we present an architecture, intermediate data model, and methodology for mapping diverse social-ecological data sources stored in relational databases to a common representation, and for classifying textual data using machine learning. The results are visualized through client views that are built against the general logical model, and applied against a longitudinal database from social-ecological research.