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Author: Kraft, Angelina × Subject: DOI ×

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    A short guide to increase FAIRness of atmospheric model data
    (Stuttgart : E. Schweizerbart Science Publishers, 2020) Ganske, Anette; Heydebreck, Daniel; Höck, Daniel; Kraft, Angelina; Quaas, Johannes; Kaiser, Amandine
    The generation, processing and analysis of atmospheric model data are expensive, as atmospheric model runs are often computationally intensive and the costs of ‘fast’ disk space are rising. Moreover, atmospheric models are mostly developed by groups of scientists over many years and therefore only few appropriate models exist for specific analyses, e.g. for urban climate. Hence, atmospheric model data should be made available for reuse by scientists, the public sector, companies and other stakeholders. Thereby, this leads to an increasing need for swift, user-friendly adaptation of standards.The FAIR data principles (Findable, Accessible, Interoperable, Reusable) were established to foster the reuse of data. Research data become findable and accessible if they are published in public repositories with general metadata and Persistent Identifiers (PIDs), e.g. DataCite DOIs. The use of PIDs should ensure that describing metadata is persistently available. Nevertheless, PIDs and basic metadata do not guarantee that the data are indeed interoperable and reusable without project-specific knowledge. Additionally, the lack of standardised machine-readable metadata reduces the FAIRness of data. Unfortunately, there are no common standards for non-climate models, e.g. for mesoscale models, available. This paper proposes a concept to improve the FAIRness of archived atmospheric model data. This concept was developed within the AtMoDat project (Atmospheric Model Data). The approach consists of several aspects, each of which is easy to implement: requirements for rich metadata with controlled vocabulary, the landing pages, file formats (netCDF) and the structure within the files. The landing pages are a core element of this concept as they should be human- and machine readable, hold discipline-specific metadata and present metadata on simulation and variable level. This guide is meant to help data producers and curators to prepare data for publication. Furthermore, this guide provides information for the choice of keywords, which supports data reusers in their search for data with search engines. © 2020 The authors
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    Do researchers need to care about PID systems?
    (Zenodo, 2018) Kraft, Angelina; Dreyer, Britta
    A survey across 1400 scientists in the natural sciences and engineering across Germany conducted in 2016 revealed that although more than 70 % of the researchers are using DOIs for journal publications, less than 10% use DOIs for research data. To the question of why they are not using DOIs more than half (56%) answered that they don’t know about the option to use DOIs for other publications (datasets, conference papers etc.) Therefore it is not surprising that the majority (57 %) stated that they had no need for DOI counselling services. 40% of the questioned researchers need more information and almost 30% cannot see a benefit. Publishers have been using PID systems for articles for years, and the DOI registration and citation are a natural part of the standard publication workflow. With the new digital age, the possibilities to publishing digital research objects beyond articles are bigger than ever – but the respective infrastructure providers are still struggling to provide integrated PID services. Infrastructure providers need to learn from publishers and offer integrated PID services, complementing existing workflows, using researcher’s vocabulary to support usability and promotion. Sell the benefit and enable researchers to focus on what they are best at: Do research (and not worry about the rest)!