Evaluation of Climate Services: Enabling Users to Assess the Quality of Multi-model Climate Projections and Derived Products

Zahid, Muhammad; Zohbi, Juliane El; Viktor, Elisabeth; Rechid, Diana; Schuck-Zöller, Susanne; Keup-Thiel, Elke; Jacob, Daniela

doi:10.1007/978-3-030-36875-3_10

Cited by 4 publications

(1 citation statement)

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“…Data quality evaluates the condition of data and especially information on data accuracy, data completeness, data consistency, data reliability, and data currency as key attributes of high-quality data. There are already many tools available to evaluate some of these key attributes, such as among others the conformity to the defined data standards [e.g., Infrastructure for Spatial Information in the European Community (INSPIRE)], or quality assurance/quality control tools [e.g., INSPIRE or Integrated Carbon Observation System (ICOS)] to identify inaccurate or incomplete data within a given data set, or the QUACK tool developed by Zahid et al (2020) in the frame of Copernicus Climate Change Service (C3S) contracts.…”

Section: Introductionmentioning

confidence: 99%

Challenges in the Evaluation of Observational Data Trustworthiness From a Data Producers Viewpoint (FAIR+)

Ködel

Schuetze

Fischer

et al. 2022

Front. Environ. Sci.

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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.

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Section: Introductionmentioning

confidence: 99%