%0 journal article
%@ 2296-665X
%A Koedel, U., Schuetze, C., Fischer, P., Bussmann, I., Sauer, P., Nixdorf, E., Kalbacher, T., Wichert, V., Rechid, D., Bouwer, L., Dietrich, P.

%D 2022
%J Frontiers in Environmental Science

%P 772666 
%R doi:10.3389/fenvs.2021.772666
%T Challenges in the Evaluation of Observational Data Trustworthiness From a Data Producers Viewpoint (FAIR+)
%U https://doi.org/10.3389/fenvs.2021.772666
 
%X Recent discussions in many scientific disciplines stress the necessity of “FAIR” data. FAIR data, however, does not necessarily include information on data trustworthiness, where trustworthiness comprises reliability, validity and provenience/provenance. This opens up the risk of misinterpreting scientific data, even though all criteria of “FAIR” are fulfilled. Especially applications such as secondary data processing, data blending, and joint interpretation or visualization efforts are affected. This paper intends to start a discussion in the scientific community about how to evaluate, describe, and implement trustworthiness in a standardized data evaluation approach and in its metadata description following the FAIR principles. It discusses exemplarily different assessment tools regarding soil moisture measurements, data processing and visualization and elaborates on which additional (metadata) information is required to increase the trustworthiness of data for secondary usage. Taking into account the perspectives of data collectors, providers and users, the authors identify three aspects of data trustworthiness that promote efficient data sharing: 1) trustworthiness of the measurement 2) trustworthiness of the data processing and 3) trustworthiness of the data integration and visualization. The paper should be seen as the basis for a community discussion on data trustworthiness for a scientifically correct secondary use of the data. We do not have the intention to replace existing procedures and do not claim completeness of reliable tools and approaches described. Our intention is to discuss several important aspects to assess data trustworthiness based on the data life cycle of soil moisture data as an example.