The “FAIR in practice” task force of the EOSC FAIR working group has shown that improvements in FAIR practices are necessary, recommending that research communities must define how the FAIR principles and related concepts apply in their context [EU 2020].

Although a well-known concept within the Earth and Environmental sciences communities, the application of FAIR principles remains quite poor, and the FAIRness of existing resources is rarely measured adequately or not measured at all. In the FAIR-EASE project, we aim to ensure that FAIR principles are fully endorsed to optimise the implementation of application and services for the observation and modelling of the Earth system and environment. To this end, an evaluation of the FAIRness of research objects used by our communities (data, software, VRE, Semantic artefacts) is ongoing, and guidelines to improve their FAIRness will be produced.

The presentation will address the evaluation of data and software as well as the methodology and sources used to carry out it.

For data, to avoid the risk mentioned by the FAIR Metrics and Data Quality Task Force: “the same Digital Object (DO) assessment by different tools often exhibits widely different results because of independent interpretations of the Metrics, metadata publishing paradigms, and even the intent of FAIR itself” [Wilkinson et al. 2022], the evaluation of the dataset will be performed using two different approaches and the results will be compared: 1) the F-UJI tool is used to obtain a first evaluation based on the metrics produced by the FAIRsFair project [Devaraju el al. 2022], 2) the Fair Data Maturity Model (FDMM) is a “fill-in-the-spreadsheet” and used to conduct a second analysis of the dataset [FDMM2020].

For softwares, the FAIR4RS principles [Chue 2022], which aims to guide creators and owners on how to make their software FAIR, are more appropriate. However, FAIR4RS principles are too abstract to be applied and the definition of specific indicators makes them more concrete and appreciable. An example of transforming these principles into measurable indicators has been conducted by the Elixir Infrastructure [FAIRSoft 2022]. We propose a set of research software indicators for Earth and Environmental sciences communities as a declination of FAIR4RS principles combined with an adaptation of FAIRSoft indicators.

Feedback from the community about the methods used, the limitations and difficulties of understanding them will be provided. . Some preliminary conclusions about the indicators used (e.g. are they applicable? are any missing? ) and propositions for improving the different approaches will be discussed together with the next evaluation of other research objects commonly handled by our community, such as semantic artefacts and VRE.