@inproceedings{
 title = {Modeling heterogeneous data resources for social-ecological research: A data-centric perspective},
 type = {inproceedings},
 year = {2013},
 id = {dec3ce77-1f77-302a-bdb9-83542ef078ac},
 created = {2019-10-01T17:20:44.656Z},
 file_attached = {false},
 profile_id = {42d295c0-0737-38d6-8b43-508cab6ea85d},
 last_modified = {2019-10-01T17:23:10.038Z},
 read = {false},
 starred = {false},
 authored = {true},
 confirmed = {true},
 hidden = {false},
 citation_key = {Chen2013},
 folder_uuids = {73f994b4-a3be-4035-a6dd-3802077ce863},
 private_publication = {false},
 abstract = {Digital repositories are grappling with an influx of scientific data brought about by the well publicized "data deluge" in science, business, and society. One particularly perplexing problem is the long-term archival and reuse of complex data sets. This paper presents an integrated approach to data discovery over heterogeneous data resources in social-ecological systems research. Social-ecological systems data is complex because the research draws from both social and natural sciences. Using a sample set of data resources from the domain, we explore an approach to discovery and representation of this data. Specifically, we develop an ontology-based process of organization and visualization from a data-centric perspective. We define data resources broadly and identify six key categories of resources that include data collected from site visits to shared ecological resources, the structure of research instruments, domain concepts, research designs, publications, theories and models. We identify the underlying relationships and construct an ontology that captures these relationships using semantic web languages. The ontology and a NoSQL data store at the back end store the data resource instances. These are integrated into a portal architecture we refer to as the Integrated Visualization of Social-Ecological Resources (IViSER) that allows users to both browse the relationships captured in the ontology and easily visualize the granular details of data resources. Copyright © 2013 by the Association for Computing Machinery, Inc. (ACM).},
 bibtype = {inproceedings},
 author = {Chen, M. and Pavalanathan, U. and Jensen, S. and Plale, B.},
 doi = {10.1145/2467696.2467737},
 booktitle = {Proceedings of the ACM/IEEE Joint Conference on Digital Libraries}
}

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