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Promoting coherent minimum reporting guidelines for biological and biomedical investigations: the MIBBI project
Chris F. Taylor, Dawn Field, Susanna-Assunta Sansone, Jan Aerts, Rolf Apweiler, Michael Ashburner, Catherine A. Ball, Pierre-Alain Binz, Molly Bogue, Tim Booth, Alvis Brazma, Ryan R. Brinkman, Adam Michael Clark, Eric W. Deutsch, Oliver Fiehn, Jennifer Fostel, Peter Ghazal, Frank Gibson, Tanya Gray, Graeme Grimes, John M. Hancock, Nigel W. Hardy, Henning Hermjakob, Randall K. Julian, Matthew Kane, Carsten Kettner, Christopher Kinsinger, Eugene Kolker, Martin Kuiper, Nicolas Le Novere, Jim Leebens-Mack, Suzanna E. Lewis, Phillip Lord, Ann-Marie Mallon, Nishanth Marthandan, Hiroshi Masuya, Ruth McNally, Alexander Mehrle, Norman Morrison, Sandra Orchard, John Quackenbush, James M. Reecy, Donald G. Robertson, Philippe Rocca-Serra, Henry Rodriguez, Heiko Rosenfelder, Javier Santoyo-Lopez, Richard H. Scheuermann, Daniel Schober, Barry Smith & Jason Snape
Nature Biotechnology 26 (8):889-896 (2008)
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The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to (...) |
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Ontology is one strategy for promoting interoperability of heterogeneous data through consistent tagging. An ontology is a controlled structured vocabulary consisting of general terms (such as “cell” or “image” or “tissue” or “microscope”) that form the basis for such tagging. These terms are designed to represent the types of entities in the domain of reality that the ontology has been devised to capture; the terms are provided with logical defi nitions thereby also supporting reasoning over the tagged data. Aim: This (...) |
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Community databases have become crucial to the collection, ordering and retrieval of data gathered on model organisms, as well as to the ways in which these data are interpreted and used across a range of research contexts. This paper analyses the impact of community databases on research practices in model organism biology by focusing on the history and current use of four community databases: FlyBase, Mouse Genome Informatics, WormBase and The Arabidopsis Information Resource. We discuss the standards used by the (...) |
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This paper discusses what it means and what it takes to integrate data in order to acquire new knowledge about biological entities and processes. Maureen O’Malley and Orkun Soyer have pointed to the scientific work involved in data integration as important and distinct from the work required by other forms of integration, such as methodological and explanatory integration, which have been more successful in captivating the attention of philosophers of science. Here I explore what data integration involves in more detail (...) |