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Bags of sand and dirt : exploiting the British Geological Survey's environmental samples and database

Johnson, Christopher; Hobbs, Susan; MacKenzie, Alan. 2008 Bags of sand and dirt : exploiting the British Geological Survey's environmental samples and database. In: Exploiting Geoscience Collections, London, 12-13 May 2008. unknown, 20-21.

Abstract
Ask any member of the public or even the science community what samples they would expect to find in a geological survey organisation and they would undoubtedly list fossils, rocks and minerals, the traditional view of a geological collection. Few would think about the tons of sediment, soil, and water samples – environmental samples less glamorously referred to as bags of sand and dirt. This is hardly moon or gold dust yet collectively these samples are a valuable asset for the British Geological Survey (BGS) that require collating in a systematic and secure manner. The corporate BGS Geochemical Database contains some half-a-million samples and eight million analyte determinations. This Oracle database contains numerous tables describing the samples, the sites from where they were collected, details about the laboratories that have carried out analyses and the methods used, in addition to chemical analyses. It is an under-used environmental resource that contains BGS geochemical data gathered over a period of nearly 40 years. Bringing this collection of digital environmental data from disparate projects together into one database has required procedures to be developed to ensure consistent coding for the description of samples. The chemical data has to be verified as being fit for purpose through data conditioning to ensure a consistency of quality. The environmental data is increasingly being used for legislative purposes, for example the identification of contaminated land under the Environment Protection Act Part IIa. The data therefore has to be presented in a way that non-scientists can understand what it shows and, equally as important, what are the quality issues and limits associated with the data.
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