TY - JOUR
T1 - Semantics in support of biodiversity knowledge discovery
T2 - An introduction to the biological collections ontology and related ontologies
AU - Walls, Ramona L.
AU - Deck, John
AU - Guralnick, Robert
AU - Baskauf, Steve
AU - Beaman, Reed
AU - Blum, Stanley
AU - Bowers, Shawn
AU - Buttigieg, Pier Luigi
AU - Davies, Neil
AU - Endresen, Dag
AU - Gandolfo, Maria Alejandra
AU - Hanner, Robert
AU - Janning, Alyssa
AU - Krishtalka, Leonard
AU - Matsunaga, Andréa
AU - Midford, Peter
AU - Morrison, Norman
AU - Tuama, Éamonn Ó
AU - Schildhauer, Mark
AU - Smith, Barry
AU - Stucky, Brian J.
AU - Thomer, Andrea
AU - Wieczorek, John
AU - Whitacre, Jamie
AU - Wooley, John
PY - 2014/3/3
Y1 - 2014/3/3
N2 - The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, traitmeasurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape ofmetadata 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.
AB - The study of biodiversity spans many disciplines and includes data pertaining to species distributions and abundances, genetic sequences, traitmeasurements, and ecological niches, complemented by information on collection and measurement protocols. A review of the current landscape ofmetadata 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.
UR - https://www.scopus.com/pages/publications/84897063495
UR - https://www.scopus.com/pages/publications/84897063495#tab=citedBy
U2 - 10.1371/journal.pone.0089606
DO - 10.1371/journal.pone.0089606
M3 - Article
C2 - 24595056
AN - SCOPUS:84897063495
SN - 1932-6203
VL - 9
JO - PloS one
JF - PloS one
IS - 3
M1 - e89606
ER -