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Author: Kraft, Angelina × Has files: Yes × WGL Institute: TIB ×

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    ATMODAT Standard v3.0
    (Hamburg : DKRZ, 2020) Gasnke, Anette; Kraft, Angelina; Kaiser, Amandine; Heydebreck, Daniel; Lammert, Andrea; Höck, Heinke; Thiemann, Hannes; Voss, Vivien; Grawe, David; Leitl, Bernd; Schlünzen, K. Heinke; Kretzschmar, Jan; Quaas, Johannes
    Within the AtMoDat project (Atmospheric Model Data), a standard has been developed which is meant for improving the FAIRness of atmospheric model data published in repositories. The ATMODAT standard includes concrete recommendations related to the maturity, publication and enhanced FAIRness of atmospheric model data. The suggestions include requirements for rich metadata with controlled vocabularies, structured landing pages, file formats (netCDF) and the structure within files. Human- and machine readable landing pages are a core element of this standard, and should hold and present discipline-specific metadata on simulation and variable level. This standard is an updated and translated version of "Bericht über initialen Kernstandard und Kurationskriterien des AtMoDat Projektes (v2.4)
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    Informationsbeschaffungs- und Publikationsverhalten von Wissenschaftlerinnen und Wissenschaftlern der natur- und ingenieurwissenschaftlichen Fächern : Auswertung einer Umfrage mit Schwerpunkt auf nicht-textuellen Materialien
    (Hannover : Technische Informationsbibliothek (TIB), 2017) Einbock, Joanna; Dreyer, Britta; Heller, Lambert; Kraft, Angelina; Niemeyer, Sandra; Plank, Margret; Schrenk, Philip; Sens, Irina; Struß, Julia; Tullney, Marco; Bernhofer, Carolin; Häfner, Peter
    [no abstract available]
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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)!
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    Moving towards FAIRness in Research Data and Software Management
    (Meyrin : CERN, 2020-07-03) Kraft, Angelina
    Presentation during the Thüringer FDM Tage 2020 within the workshop "FAIR Research Software and Beyond: How to make the most of your code".
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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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    Access and preservation of digital research content: Linked open data services - A research library perspective
    (München : European Geosciences Union, 2016) Kraft, Angelina; Sens, Irina; Löwe, Peter; Dreyer, Britta
    [no abstract available]
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    Advancing Research Data Management in Universities of Science and Technology
    (Meyrin : CERN, 2020-02-13) Björnemalm, Matthias; Cappellutti, Federica; Dunning, Alastair; Gheorghe, Dana; Goraczek, Malgorzata Zofia; Hausen, Daniela; Hermann, Sibylle; Kraft, Angelina; Martinez Lavanchy, Paula; Prisecaru, Tudor; Sànchez, Barbara; Strötgen, Robert
    The white paper ‘Advancing Research Data Management in Universities of Science and Technology’ shares insights on the state-of-the-art in research data management, and recommendations for advancement. A core part of the paper are the results of a survey, which was distributed to our member institutions in 2019 and addressed the following aspects of research data management (RDM): (i) the establishment of a RDM policy at the university; (ii) the provision of suitable RDM infrastructure and tools; and (iii) the establishment of RDM support services and trainings tailored to the requirements of science and technology disciplines. The paper reveals that while substantial progress has been made, there is still a long way to go when it comes to establishing “advanced-degree programmes at our major universities for the emerging field of data scientist”, as recommended in the seminal 2010 report ‘Riding the Wave’, and our white paper offers concrete recommendations and best practices for university leaders, researchers, operational staff, and policy makers. The topic of RDM has become a focal point in many scientific disciplines, in Europe and globally. The management and full utilisation of research data are now also at the top of the European agenda, as exemplified by Ursula von der Leyen addressat this year’s World Economic Forum.However, the implementation of RDM remains divergent across Europe. The white paper was written by a diverse team of RDM specialists, including data scientists and data stewards, with the work led by the RDM subgroup of our Task Force Open Science. The writing team included Angelina Kraft (Head of Lab Research Data Services at TIB, Leibniz University Hannover) who said: “The launch of RDM courses and teaching materials at universities of science and technology is a first important step to motivate people to manage their data. Furthermore, professors and PIs of all disciplines should actively support data management and motivate PhD students to publish their data in recognised digital repositories.” Another part of the writing team was Barbara Sanchez (Head of Centre for Research Data Management, TU Wien) and Malgorzata Goraczek (International Research Support / Data Management Support, TU Wien) who added:“A reliable research data infrastructure is a central component of any RDM service. In addition to the infrastructure, proper RDM is all about communication and cooperation. This includes bringing tools, infrastructures, staff and units together.” Alastair Dunning (Head of 4TU.ResearchData, Delft University of Technology), also one of the writers, added: “There is a popular misconception that better research data management only means faster and more efficient computers. In this white paper, we emphasise the role that training and a culture of good research data management must play.”
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    The ATMODAT Standard enhances FAIRness of Atmospheric Model data
    (Washington, DC : ESSOAr, 2020) Heydebreck, Daniel; Kaiser, Amandine; Ganske, Anette; Kraft, Angelina; Schluenzen, Heinke; Voss, Vivien
    Within the AtMoDat project (Atmospheric Model Data, www.atmodat.de), a standard has been developed which is meant for improving the FAIRness of atmospheric model data published in repositories. Atmospheric model data form the basis to understand and predict natural events, including atmospheric circulation, local air quality patterns, and the planetary energy budget. Such data should be made available for evaluation and reuse by scientists, the public sector, and relevant stakeholders. Atmospheric modeling is ahead of other fields in many regards towards FAIR (Findable, Accessible, Interoperable, Reusable, see e.g. Wilkinson et al. (2016, doi:10.1101/418376)) data: many models write their output directly into netCDF or file formats that can be converted into netCDF. NetCDF is a non-proprietary, binary, and self-describing format, ensuring interoperability and facilitating reusability. Nevertheless, consistent human- and machine-readable standards for discipline-specific metadata are also necessary. While standardisation of file structure and metadata (e.g. the Climate and Forecast Conventions) is well established for some subdomains of the earth system modeling community (e.g. the Coupled Model Intercomparison Project, Juckes et al. (2020, https:doi.org/10.5194/gmd-13-201-2020)), other subdomains are still lacking such standardisation. For example, standardisation is not well advanced for obstacle-resolving atmospheric models (e.g. for urban-scale modeling). The ATMODAT standard, which will be presented here, includes concrete recommendations related to the maturity, publication, and enhanced FAIRness of atmospheric model data. The suggestions include requirements for rich metadata with controlled vocabularies, structured landing pages, file formats (netCDF), and the structure within files. Human- and machine-readable landing pages are a core element of this standard and should hold and present discipline-specific metadata on simulation and variable level.
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    Thesenpapier Nationale Forschungsdateninfrastruktur für die Chemie (NFDI4Chem)
    (Zenodo, 2018) Koepler, Oliver; Jung, Nicole; Kraft, Angelina; Neumann, Janna; Auer, Sören; Bach, Felix; Bähr, Thomas; Engel, Thomas; Kettner, Carsten; Kowol-Santen, Johanna; Liermann, Johannes; Lipp, Anne; Porzel, Andrea; Razum, Matthias; Schlörer, Niels; Solle, Dörte; Winkler, Torsten
    “Der stufenweise Aufbau einer Nationalen Forschungsdateninfrastruktur in Netzwerkform hat das Ziel, ein verlässliches und nachhaltiges Dienste-Portfolio zu schaffen, welches generische und fachspezifische Bedarfe des Forschungsdatenmanagements in Deutschland abdeckt.” Für das Fachgebiet Chemie ermöglicht eine solche nationale Forschungsdateninfrastruktur, öffentlich-finanzierte Forschungsdaten effizient zu erheben, standardisiert zu beschreiben, dauerhaft zu speichern und durch Persistent Identifier (PID) eindeutig referenzierbar und auffindbar zu machen. Sie unterstützt gemäß den Vorgaben des RfII die Reproduzierbarkeit und Nachnutzbarkeit der Daten zum Zwecke einer perpetuierten Wissensgenerierung. Mit der Reproduzierbarkeit von Forschungsergebnissen unterstützt eine solche Forschungsdateninfrastruktur den Peer Reviewing Prozess zur Förderung der wissenschaftlichen Selbstkontrolle und erhöht die Datenqualität, insbesondere in wissenschaftlichen Publikationen. Die NFDI4Chem ist ein gemeinschaftlicher Ansatz von Wissenschaftlern aus der Chemie, der Fachgesellschaft Gesellschaft Deutscher Chemiker und deren Fachgruppen, Einrichtungen aus der Forschungsförderung und Infrastruktureinrichtungen (Technische Informationsbibliothek). Eine Gruppe von Vertretern dieser Stakeholder hat sich Ende April 2018 zu einem Auftakttreffen “Fachgespräch NFDI4Chem” in Hannover getroffen. Dieses Papier fasst die Erkenntnisse des Fachgespräches zusammen. Weitere Stakeholder wie Verlage oder Datenbankanbieter sind im folgenden Diskurs willkommen.
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    - Entwurf - Autorenrichtlinien für RADAR ; Research Data Repositorium
    (RADAR-Projektteam, 2014) Engel, Thomas; Furtado, Filipe; Hahn, Matthias; Kraft, Angelina; Martens, Jörn; Neumann, Janna; Porzel, Andrea; Potthoff, Jan; Ziedorn, Frauke
    Um die Autoren und Nutzer auf dem Gebiet des nachhaltigen wissenschaftlichen Forschungsdatenmanagements zu unterstützen bietet RADAR die Möglichkeit zur disziplinübergreifenden Archivierung und (optionalen) Publikation digitaler Forschungsdaten. Dieser Leitfaden soll RADAR-Kunden einen Überblick zu den Zielsetzungen und Funktionen des Datenarchivs geben und gleichzeitig eine Entscheidungshilfe zu den angebotenen Dienstleistungen anbieten. Darüber hinaus werden Empfehlungen zur Qualitätssicherung von wissenschaftlichen Daten auf der Basis aktueller informationstechnischer Standards dokumentiert. Die hier vorgestellten Standards umfassen Empfehlungen zur Datenauswahl, zur Wahl eines geeigneten Dateiformats und zur Wahl einer Lizenz für publizierte Daten. Dazu werden u.a. folgende Fragen für Autoren und Datengeber erörtert: - Datenauswahl - Welche Daten sollten archiviert bzw. veröffentlicht werden? - Dateiformat - Welche Software ist zum Lesen der Daten notwendig? - Lizenz - Welche Bedingungen und Auflagen sind bei der Nachnutzung der Daten zu beachten? Die Leitlinien sollen dazu beitragen, über einen langen Zeitraum eine möglichst nachhaltige Datenspeicherung zu garantieren und, im Fall von publizierten Daten, deren effektive Nachnutzbarkeit und Zitierfähigkeit sicherzustellen.