Computational Ecology Group

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Ecological Informatics Research in ecology increasingly relies on the integration of traditionally small-scale, focused studies to produce larger data sets that allow for more powerful, synthetic analyses. Synthetic analyses are critical to guiding decisions about how to sustainably manage our natural environment. It is therefore important for researchers to be able to effectively discover and integrate relevant data. This is a major challenge however, as ecological data encompass an extremely broad range of data types, structures, and semantic concepts. Furthermore, ecological data is widely distributed, with few well-established repositories or standard protocols for their archiving and retrieval. These factors presently make the discovery and integration of ecological data sets a highly labour-intensive task. The CE Group’s informatics research looks for mechanisms of improving our ability to discover and integrate ecological data for broader, more synthetic and informative analysis of the constantly changing natural world. CE Group Publications J. Madin, S. Bowers, M. Schildhauer and M. Jones. 2008. Advancing ecological research with ontologies. Trends in Ecology and Evolution. 23:159-168. [link] Ecology is inherently cross-disciplinary, drawing together many types of information to address questions about the natural world. Therefore, finding and integrating relevant data to assist in these analyses is crucial, but is difficult owing to ambiguous terminology and the lack of sufficient information about data sets. Ontologies provide a formal mechanism for defining terms and their relationships. In the paper we review ontology efforts in ecology, and describe how these can benefit research by enhancing the location and interpretation of relevant data for confronting crucial ecological questions J. Madin, S. Bowers, M. Schildhauer, S. Krivov, D. Pennington and F. Villa. 2007. An ontology for describing and synthesizing ecological observational data. Ecological Informatics 2:279-296. [link] We present a formal ontology for capturing the semantics of generic scientific observation and measurement. The ontology provides a convenient basis for adding detailed semantic annotations to scientific data, which crystallise the inherent “meaning”. The ontology can be used to characterise the context of an observation (e.g., space and time), and clarify inter-observational relationships such as dependency hierarchies (e.g., nested experimental observations) and meaningful dimensions within the data (e.g., axes for cross-classified categorical summarisation). The ontology can be easily extended with specialised domain vocabularies, making it both broadly applicable and highly customisable. Finally, we describe the utility of the ontology for enriching the capabilities of data discovery and integration processes . Other Relevant Publications M. Jones, M. Schildhauer, O. Reichman and S. Bowers (2006) The new bioinformatics: integrating ecological data from the gene to the biosphere. Annu. Rev. Ecol. Evol. Syst. 37, 519–544. [link] Links Ecoinformatics Program at NCEAS Kepler Project Knowledge Network for Biocomplexity Science Environment for Ecological Knowledge http://dx.doi.org/10.1016/j.tree.2007.11.007http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B7W63-4PHSFS4-1&_user=10&_coverDate=10%2F31%2F2007&_rdoc=10&_fmt=summary&_orig=browse&_srch=doc-info(%23toc%2328539%232007%23999979996%23671182%23FLA%23display%23Volume)&_cdi=28539&_sort=d&_docanchor=&_ct=11&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=0525afab5171854382b8b34f57b73db5http://arjournals.annualreviews.org/doi/abs/10.1146/annurev.ecolsys.37.091305.110031http://www.nceas.ucsb.edu/ecoinfohttp://www.nceas.ucsb.edu/http://kepler-project.org/http://knb.ecoinformatics.org/http://seek.ecoinformatics.org/shapeimage_8_link_0shapeimage_8_link_1shapeimage_8_link_2shapeimage_8_link_3shapeimage_8_link_4shapeimage_8_link_5shapeimage_8_link_6shapeimage_8_link_7