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Harmonizing, annotating and sharing data in biodiversityecosystem functioning research

MPG-Autoren
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Bönisch,  Gerhard
Interdepartmental Max Planck Fellow Group Functional Biogeography, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Kattge,  Jens
Interdepartmental Max Planck Fellow Group Functional Biogeography, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Zitation

Nadrowski, K., Ratcliffe, S., Bönisch, G., Bruelheide, H., Kattge, J., Liu, X., et al. (2013). Harmonizing, annotating and sharing data in biodiversityecosystem functioning research. Methods in Ecology and Evolution, 4(2), 201-205. doi:10.1111/2041-210x.12009.


Zitierlink: https://hdl.handle.net/11858/00-001M-0000-000E-E3C4-9
Zusammenfassung
The integrative research field of biodiversity–ecosystem functioning (BEF) requires close collaboration between researchers from different disciplines working on different scales in time, space as well as taxon resolution. Data can describe anything from abiotic ecosystem components, to organisms, parts of organisms, genetic information or element stocks and flows. Researchers prefer the convenience of spreadsheets for data preparation, which can lead to isolated data sets that are diverse in structure and follow diverging naming conventions. 2. BEFdata (https://github.com/befdata/befdata) is a new, open source web platform for the upload, validation and storage of data from a formatted Excel workbook. Metadata can be downloaded in Ecological Metadata Language (EML). BEFdata allows the harmonization of naming conventions by generating category lists from the primary data, which can be reviewed and managed via the Excel workbook or directly on the platform. BEFdata provides a secure environment during ongoing analysis; projectmembers can only access primary data from other researchers after the acceptance of a data request. 3. Due to its generic database schema, BEFdata platforms can be used for any research domain working with tabular data. It supports the compilation of coherent data sets at the level of the primary data, allowing researchers to explicitly model correlation structures across data sets for synthesis. The EML export enables efficient publishing of data in global repositories.