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<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Phanerozoic Biodiversity.png|240px]]</div>
<div style="float: left; margin: 0.5em 0.9em 0.4em 0em;">[[File:Fig2 Berezin PLoSCompBio23 19-12.png|240px]]</div>
'''[[Biodiversity informatics]]''' is the application of informatics techniques to biodiversity [[information]] for improved management, presentation, discovery, exploration, and analysis. It typically builds on a foundation of taxonomic, biogeographic, and synecologic information stored in digital form, which, with the application of modern computer techniques, can yield new ways to view and analyze existing information, as well as predictive models for information that does not yet exist. Biodiversity informatics has also been described by others as "the creation, integration, analysis, and understanding of information regarding biological diversity" and a field of science "that brings information science and technologies to bear on the data and information generated by the study of organisms, their genes, and their interactions."
'''"[[Journal:Ten simple rules for managing laboratory information|Ten simple rules for managing laboratory information]]"'''


According to correspondence reproduced by Walter Berendsohn, the term "biodiversity informatics" was coined by John Whiting in 1992 to cover the activities of an entity known as the Canadian Biodiversity Informatics Consortium (CBIC), a group involved with fusing basic biodiversity information with environmental economics and geospatial information. Subsequently it appears to have lost at least some connection with the geospatial world, becoming more closely associated with the computerized management of biodiversity information. ('''[[Biodiversity informatics|Full article...]]''')<br />
[[Information]] is the cornerstone of [[research]], from experimental data/[[metadata]] and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging [[laboratory information management system]]s (LIMS) to transform this large information load into useful scientific findings. The development of [[mathematical model]]s that can predict the properties of biological systems is the holy grail of [[computational biology]]. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... ('''[[Journal:Ten simple rules for managing laboratory information|Full article...]]''')<br />


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''Recently featured'':
''Recently featured'': [[American Society of Crime Laboratory Directors/Laboratory Accreditation Board]], [[Environmental informatics]], [[Application programming interface]]
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Revision as of 18:03, 10 June 2024

Fig2 Berezin PLoSCompBio23 19-12.png

"Ten simple rules for managing laboratory information"

Information is the cornerstone of research, from experimental data/metadata and computational processes to complex inventories of reagents and equipment. These 10 simple rules discuss best practices for leveraging laboratory information management systems (LIMS) to transform this large information load into useful scientific findings. The development of mathematical models that can predict the properties of biological systems is the holy grail of computational biology. Such models can be used to test biological hypotheses, guide the development of biomanufactured products, engineer new systems meeting user-defined specifications, and much more ... (Full article...)

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