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SERONTO: a Socio-Ecological Research and Observation oNTOlogy.

By D.C. (Bert) van der werf, Mihai Adamescu, Minu Ayromlou, Nicolas Bertrand, Jakub Borovec, Hugues Boussard, Constatin Cazacu, Toon van Daele, Sabina Datcu, Mark Frenzel, Volker Hammen, Helena Karasti, Miklos Kertesz, Pirjo Kuitunen, Mandy Lane, Juraj Lieskovsky, Barbara Magagna, Johannes Peterseil, Sue Rennie, Herbert Schentz, Katharina Schleidt and Liisa Tuominen


SERONTO is an ontology developed within ALTER-Net, a Long Term Biodiversity, Ecosystem, and Awareness Research Network funded by the European Union. ALTER-Net addresses major biodiversity issues at a European scale. Within this framework SERONTO has been developed to solve the problem of integrating and managing data stored and collected at different locations within the European Union. SERONTO is a product of a group of people with diverse scientific backgrounds.\ud The ontology is a formal description of the concepts and relationships for the most important aspects of biodiversity data derived from monitoring, experiments and investigations. SERONTO is an ontology that enables seamless presentation of data from different origins in a similar conceptual manner. \ud With SERONTO, meta-analysis, data mining, and data presentation should be possible across datasets collected for different purposes. SERONTO consists of a core ontology and a separate unit and dimensions ontology. The core ontology is designed to be the basis for domain specific ontologies (e.g. species, geography, water, vegetation), which extend the concepts and relationships of the core for their specific needs and requirements. The concepts of the core are derived from scientific principles and lean heavily on statistical methodology. Important considerations in designing SERONTO were\ud 1. Repeatability: The ontology should be capable of holding enough meta-data that another person can repeat the experiment or observation at another place and time. It is not obligatory, however, to provide all information for all datasets; for instance, some information may be missing for old datasets.\ud 2. Transparency: It must be possible to record and retrieve meta-data describing what actually happened. SERONTO includes concepts of things going wrong and documenting data collection under less than ideal conditions. If data and meta-data are available in this way, it will be clear what assumptions must be made to combine data and correctly interpret analyses.\ud Important concepts in the SERONTO core are:\ud 1. Investigation item – the research object or experimental unit;\ud 2. Parameters – the measurement, classification and treatment of the investigation item;\ud 3. Value sets – placeholders for time series and other complex data;\ud 4. Reference lists – nominal values, such as species lists;\ud 5. Methods – used for each parameter, including units, scale, and dimensions;\ud 6. Sampling structure – the origin of the research object or population, and the way it was chosen;\ud 7. Groupings of objects, such as experimental blocks, on which observer, time or other aspects are assigned or related to;\ud 8. Additional information, such as actors (observer, observer groups and institutions), project information, etc., can be attached to several different concepts.\ud Each subsequent analysis has to make assumptions. The assumptions of any particular analysis can be found in the deviation between how the data were obtained and the requirements of the analytical method. \ud The presentation will go deeper into the design considerations and the core concepts. Explanations of the concepts, their interrelationships, and their use in subsequent analysis will be given along with examples from different domains.\u

Topics: Data and Information
Year: 2008
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