There is a concerted global effort to digitize biodiversity occurrence data from herbarium and museum collections that together offer an unparalleled archive of life on Earth over the past few centuries. The Global Biodiversity Information Facility provides the largest single gateway to these data. Since 2004 it has provided a single point of access to specimen data from databases of biological surveys and collections. Biologists now have rapid access to more than 120 million observations, for use in many biological analyses. We investigate the quality and coverage of data digitally available, from the perspective of a biologist seeking distribution data for spatial analysis on a global scale. We present an example of automatic verification of geographic data using distributions from the International Legume Database and Information Service to test empirically, issues of geographic coverage and accuracy. There are over 1/2 million records covering 31% of all Legume species, and 84% of these records pass geographic validation. These data are not yet a global biodiversity resource for all species, or all countries. A user will encounter many biases and gaps in these data which should be understood before data are used or analyzed. The data are notably deficient in many of the world's biodiversity hotspots. The deficiencies in data coverage can be resolved by an increased application of resources to digitize and publish data throughout these most diverse regions. But in the push to provide ever more data online, we should not forget that consistent data quality is of paramount importance if the data are to be useful in capturing a meaningful picture of life on Earth.

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Manual assembly of large morphometric data sets for investigations in population biology and biosystematics is extremely time consuming. Automated image handling allows rapid and accurate data collection and in addition makes it practicable to uncouple image acquisition and shape description. Variation can be characterized flexibly when these two phases of "scoring" are separated. Different descriptor systems can be used to describe a set of stored outlines, without having to return to the original objects. This paper introduces an integrated automated outline handling system and compares the relative performance of shape descriptor systems based on (i) landmark and perimeter measurements, (ii) chain codes, (iii) elliptic Fourier coefficients, and (iv) moment invariants. These methods were assessed by their ability to characterize the pattern of variation in a set of Betula leaf silhouettes with a hierarchical sample structure that allowed variation to be partitioned into within-tree, between-tree and between-species components. The descriptor systems using landmark and perimeter distances, elliptic Fourier coefficients, and moment invariants performed well, not only reflecting differences between species and between individual trees but also recovering the two-dimensional geometry of the within-tree sample positions. The elliptic Fourier method is judged to have performed best; the chain-code descriptor system performed poorly.

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Summary Berendsohn, W. G., Anagnostopoulos, A., Hagedorn, G., Jakupovic, J., Nimis, P. L., Valdés, B., Güntsch, A., Pankhurst, R. J. & White, R. J.: A comprehensive reference model for biological collections and surveys. – Taxon 48: 511‐562. 1999. ISSN 0040‐0262. The article describes an extended entity‐relationship model covering biological collections, i.e. natural history collections of biotic origin; data collections used in floristic or faunistic mapping, survey, and monitoring projects; live collections such as botanical or zoological gardens, seed banks, microbial strain collections and gene banks; as well as novel collection kinds such as of secondary metabolites or DNA samples. The central element in the model is the unit, which stands for any object containing, being or being part of a living, fossilised, or conserved organism. The unit may be gathered (observed or collected) in the field and derived units may recursively emerge from it through specimen processing, breeding or cultivation. In addition, units may form associations (e.g. host/parasite), ensembles (lichen on a rock with fossils), and assemblages (herd, artificial grouping). Gathering events, specimen management (acquisition, accession, storage, preservation, exchange, ownership), and taxonomic or other identifications relate to the unit and are treated in detail. Geographic and geo‐ecological data have not been fully modelled; taxonomic (name) data and descriptive information are treated by reference to other published models.