Towards better traceability of field sampling data

Ensuring traceability of both field experimental data and laboratory sampling data for a reproducible research remains a challenge nowadays. Between the time when geolocalized specimens are taken, and the time the resulting data ends up in analysis published within a study, many manual operations ta...

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Veröffentlicht in:Computers & geosciences 2019-08, Vol.129, p.82-91
Hauptverfasser: Plumejeaud-Perreau, Quinton, Eric, Pignol, Cécile, Linyer, Hector, Ancelin, Julin, Cipière, Sébastien, Heintz, Wilfried, Rouan, Mathias, Damy, Sylvie, Bretagnolle, Vincent
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Sprache:eng
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Zusammenfassung:Ensuring traceability of both field experimental data and laboratory sampling data for a reproducible research remains a challenge nowadays. Between the time when geolocalized specimens are taken, and the time the resulting data ends up in analysis published within a study, many manual operations take place that are prone to generate errors. The French nodes of the European Long-Term Socio-Ecological Research Infrastructure called ”Zones Ateliers” propose a solution as generic as possible to this problem of monitoring of the samples and the data associated with them. Compared to existing solutions such as Laboratory Information Management Systems, we target a robust solution for labelling adapted to outdoor working conditions, with the management of storages and movements of samples. We designed and realized a software package tested from end to end, using open source licenses and cheap hardware, including small printers (mobile or not) and Raspberry Pis. This system provides sufficient flexibility so that it can facilitate working with a wide variety of existing protocols. One of the most interesting feature consists to record all contextual data associated with the samples, which constitute important parameters of the subsequent analyses. Furthermore, not only traceability is thus guaranteed, but also we can expect a reduced handling times and an increased streamlining of the storage of samples that will improve the return on investment.
ISSN:0098-3004
1873-7803
DOI:10.1016/j.cageo.2019.04.009