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#CODATA2020: Maturity of Data and Metadata

Nov 26, 2020

ELightning presentation of AtMoDat project partners at the virtual International FAIR Convergence Symposium 2020.





Data of scientific projects should be published in a way that the results are reusable and comprehensible. The sharing of data with other scientists is of immense importance, as the produced amount of data from simulations is often so huge, that the whole amount of data cannot be analysed in detail by the data producers within the funding period. Therefore, it is reasonable to publish the simulation output in a formalized/standardized way to foster reuse.

Data maturity describes the degree of the formalization/standardization of a data object with respect to FAIRness and quality of the (meta-)data. It is an important topic in data management, which is reflected by a growing number of tools and theories trying to measure it, e.g. the FAIR testing tools assessed by RDA [1] or the NOAA maturity matrix [2].

If the results of stewardship and curation tasks cannot be shown directly in the metadata, reusers of data cannot easily recognise which data are easy to reuse. For example, the DataCite Metadata Schema [3] does not provide an explicit property to link/store information on data maturity (e.g. FAIRness or quality of data/metadata). The AtMoDat project (Atmospheric Model Data) [4] aims to improve the reusability of published atmospheric model data by scientists, the public sector, companies and other stakeholders. These data are valuable because they form the basis for understanding and predicting natural events, including the atmospheric circulation and ultimately the atmospheric and planetary energy budget. As most atmospheric data has been published with DataCite [5] DOIs, it is of high importance that the maturity of the datasets can be easily found in the DOI’s Metadata. Published data of other fields of research would also benefit from easily findable maturity information. 

Within the context of the AtMoDat project, we have developed a Maturity Indicator concept, which proposes to introduce a new property “Maturity Indicator” in the DataCite Metadata Schema. This indicator is generic and independent of any scientific discipline and data stewardship tool. Hence, it can be used in a variety of research fields. The goal of the Maturity Indicator is to enable the scientific community to highlight high-quality datasets. The presentation will introduce the Maturity Indicator concept, which was recently submitted to DataCite.



[2] Peng et al., 2015:




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BMBF Gross Crop

within the framework of "Forschungsvorhaben zur Entwicklung und Erpro-bung von Kurationskrite-rien und Qualitätsstan-dards von Forschungs-daten".

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