Format

Send to

Choose Destination
PLoS One. 2014 Mar 3;9(3):e89606. doi: 10.1371/journal.pone.0089606. eCollection 2014.

Semantics in support of biodiversity knowledge discovery: an introduction to the biological collections ontology and related ontologies.

Author information

1
The iPlant Collaborative, University of Arizona, Tucson, Arizona, United States of America.
2
University of California, Berkeley, Berkeley, California, United States of America.
3
Department of Ecology and Evolutionary Biology and the CU Museum of Natural History, University of Colorado at Boulder, Boulder, Colorado, United States of America.
4
Department of Biological Sciences, Vanderbilt University, Nashville, Tennessee, United States of America.
5
University of Florida, Florida Museum of Natural History, Gainesville, Florida, United States of America.
6
Research Informatics, California Academy of Sciences, San Francisco, California, United States of America.
7
Gonzaga University, Computer Science, Spokane, Washington, United States of America.
8
Alfred Wegener Institute, Helmholtz Centre for Polar and Marine Research, Bremerhaven, Germany.
9
University of California, Berkeley, Gump South Pacific Research Station, Moorea, French Polynesia.
10
GBIF Norway, Natural History Museum, University in Oslo, Oslo, Norway.
11
LH Bailey Hortorium, Department of Plant Biology, Cornell University, Ithaca, New York, United States of America.
12
Biodiversity Institute of Ontario, University of Guelph, Guelph, ON, Canada.
13
School of Information Resources and Library Science, University of Arizona, Tucson, Arizona, United States of America.
14
Biodiversity Institute and Ecology & Evolutionary Biology, The University of Kansas, Lawrence, Kansas, United States of America.
15
University of Florida, Gainesville, Florida, United States of America.
16
Ecology and Evolutionary Biology, University of Kansas, Lawrence, Kansas, United States of America.
17
The BioVeL Project, School of Computer Science, The University of Manchester, Manchester, United Kingdom.
18
GBIF Secretariat, Copenhagen, Denmark.
19
National Center for Ecological Analysis and Synthesis, Santa Barbara, California, United States of America.
20
Department of Philosophy, University at Buffalo, Buffalo, New York, United States of America.
21
Department of Ecology and Evolutionary Biology, University of Colorado, Boulder, Colorado, United States of America.
22
Graduate School of Library and Information Science, University of Illinois at Urbana-Champaign, Urbana-Champaign, Illinois, United States of America.
23
3101 VLSB, Museum of Vertebrate Zoology, University of California, Berkeley, Berkeley, California, United States of America.
24
Informatics Branch, Information Technology Office, National Museum of Natural History, Smithsonian Institution, Washington, DC, United States of America.
25
University of California San Diego, La Jolla, California, United States of America.

Abstract

The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, trait measurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape of metadata standards and ontologies in biodiversity science suggests that existing standards such as the Darwin Core terminology are inadequate for describing biodiversity data in a semantically meaningful and computationally useful way. Existing ontologies, such as the Gene Ontology and others in the Open Biological and Biomedical Ontologies (OBO) Foundry library, provide a semantic structure but lack many of the necessary terms to describe biodiversity data in all its dimensions. In this paper, we describe the motivation for and ongoing development of a new Biological Collections Ontology, the Environment Ontology, and the Population and Community Ontology. These ontologies share the aim of improving data aggregation and integration across the biodiversity domain and can be used to describe physical samples and sampling processes (for example, collection, extraction, and preservation techniques), as well as biodiversity observations that involve no physical sampling. Together they encompass studies of: 1) individual organisms, including voucher specimens from ecological studies and museum specimens, 2) bulk or environmental samples (e.g., gut contents, soil, water) that include DNA, other molecules, and potentially many organisms, especially microbes, and 3) survey-based ecological observations. We discuss how these ontologies can be applied to biodiversity use cases that span genetic, organismal, and ecosystem levels of organization. We argue that if adopted as a standard and rigorously applied and enriched by the biodiversity community, these ontologies would significantly reduce barriers to data discovery, integration, and exchange among biodiversity resources and researchers.

PMID:
24595056
PMCID:
PMC3940615
DOI:
10.1371/journal.pone.0089606
[Indexed for MEDLINE]
Free PMC Article

Supplemental Content

Full text links

Icon for Public Library of Science Icon for PubMed Central
Loading ...
Support Center