From Field to Cloud - streamlining the data preperation phase in joint research projects

Between the acquisition of raw data and its publication lies considerable work and time. Data must be checked, filtered, calibrated, and analysed. Experience from various collaborative projects and consulting practice as a research data steward at Justus Liebig University shows that this work is often poorly documented, and data is stored in an unstructured manner. For many measurements in the agricultural and environmental sectors, contextual information is also crucial for accurate evaluation: measurement instruments require maintenance, study areas are cultivated or devastated by storms. ODMF is a web application with a data model for storing time series data at geographically locatable points. Data can be transparently maintained: erroneous measurements are marked rather than deleted; calibrations are calculated on-the-fly; derived quantities are computed directly. The web application is used for visualization and data export. Sensor networks with telemetry, such as LoRa, ADCON, and Campbell systems, can serve as data sources. Other data sources, like the Meteosol climate station network, are integrated via the ODMF API. Output formats from offline loggers, laboratory analysis devices, or legacy templates for field management can be described with configuration files and imported into the database annotated with metadata.