standards > terminology artifact > DOI:10.25504/FAIRsharing.5970hq
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ready OBOE: The Extensible Observation Ontology

Abbreviation: OBOE


General Information
OBOE is a suite of OWL-DL ontologies for modeling and representing scientific observations. The OBOE model is designed as a generic data model with a number of constructs for defining observational data. Key features of OBOE include its ability to represent a wide range of measurement types, a mechanism for specifying measurement context, and the ability to associate the type of entity (e.g., sample, organism, etc.) being measured. OBOE is being used and developed within the Semtools project for describing a wide variety of ecological data stored within the Knowledge Network for Biodiversity (KNB) as well as extensions for ontology-based data annotation and discovery within the MetaCat software infrastructure.


This record replaces or incorporates the following deprecated resources:

Homepage https://github.com/NCEAS/oboe/

Countries that developed this resource United States

Created in 2006

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How to cite this record FAIRsharing.org: OBOE; OBOE: The Extensible Observation Ontology; DOI: https://doi.org/10.25504/FAIRsharing.5970hq; Last edited: Jan. 29, 2020, 11:07 a.m.; Last accessed: Aug 11 2020 1:37 p.m.

Publication for citation  An ontology for describing and synthesizing ecological observation data. Madin, J., S. Bowers, M. Schildhauer, S. Krivov, D. Pennington, and F. Villa.; Ecological Informatics 2:279–296. ; 2007; 10.5063/F11C1TTM;


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Record updated: Jan. 29, 2020, 11:06 a.m. by The FAIRsharing Team.

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Publications

An ontology for describing and synthesizing ecological observation data.

Madin, J., S. Bowers, M. Schildhauer, S. Krivov, D. Pennington, and F. Villa.
Ecological Informatics 2:279–296. 2007

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Related Databases (1)
The Knowledge Network for Biocomplexity
An international repository intended to facilitate ecological and environmental research. For scientists, the KNB is an efficient way to share, discover, access and interpret complex ecological data. Due to rich contextual information provided with KNB data, scientists are able to integrate and analyze data with less effort. The data originate from a highly-distributed set of field stations, laboratories, research sites, and individual researchers. The foundation of the KNB is the rich, detailed metadata provided by researchers that collect data, which promotes both automated and manual integration of data into new projects. As part of the KNB effort, data management software is developed in a free and open source manner, so other groups can build upon the tools. The KNB is powered by the Metacat data management system, and is optimized for handling data sets described using the Ecological Metadata Language, but can store any XML-based metadata document.

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