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- ItemAdvancing 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, RobertThe 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.”
- Item- Entwurf - Datenpublikation – Workflows für die Archivierung und Publikation wissenschaftlicher Forschungsdaten in RADAR(RADAR-Projektteam, 2014) Engel, Thomas; Furtado, Filipe; Hahn, Matthias; Kraft, Angelina; Martens, Jörn; Neumann, Janna; Porzel, Andrea; Potthoff, Jan; Ziedorn, FraukeUm die Schritte zu einer nachhaltigen, zitierfähigen Datenpublikation in RADAR darzulegen wurden drei exemplarische Workflows entwickelt: • Workflow (A) - Wahl zwischen Angeboten: Archivierung oder Publikation • Workflow (B) - Varianten der Datenpublikation (direkt, mit Embargo, Verlagsanbindung mit Artikel-Review) • Workflow (C) - Übergang Archivierung - Datenpublikation (optionale Ausbaustufe für 2015/16) Workflows A und B stellen in kompakter, graphischer Form die Grundfunktionen von RADAR dem zweistufigen Dienstleistungsmodell dar und sollen die Kunden bei der Wahl der passenden Angebotsstufe, Archivierung oder Archivierung mit integrierter Datenpublikation, unterstützen. Workflow C stellt den Übergang zwischen beiden Angebotsstufen dar, bei denen der Kunde bereits archivierte Daten in wenigen Arbeitsschritten unverändert auf die Ebene der Publikation überführen kann. Die Implementierung dieses Übergangs ist im Anschluss an den Aufbau des RADAR-Grundfunktionen im dritten Projektjahr vorgesehen.
- Item- 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, FraukeUm 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.
- ItemAtMoDat: Improving the reusability of ATmospheric MOdel DATa with DataCite DOIs paving the path towards FAIR data(München : European Geosciences Union, 2020) Neumann, Daniel; Ganske, Anette; Voss, Vivien; Kraft, Angelina; Höck, Heinke; Peters, Karsten; Quaas, Johannes; Schluenzen, Heinke; Thiemann, HannesThe generation of high quality research data is expensive. The FAIR principles were established to foster the reuse of such data for the benefit of the scientific community and beyond. Publishing research data with metadata and DataCite DOIs in public repositories makes them findable and accessible (FA of FAIR). However, DOIs and basic metadata do not guarantee the data are actually reusable without discipline-specific knowledge: if data are saved in proprietary or undocumented file formats, if detailed discipline-specific metadata are missing and if quality information on the data and metadata are not provided. In this contribution, we present ongoing work in the AtMoDat project, -a consortium of atmospheric scientists and infrastructure providers, which aims on improving the reusability of atmospheric model data. Consistent standards are necessary to simplify the reuse of research data. Although standardization of file structure and metadata is well established for some subdomains of the earth system modeling community – e.g. CMIP –, several other subdomains are lacking such standardization. Hence, scientists from the Universities of Hamburg and Leipzig and infrastructure operators cooperate in the AtMoDat project in order to advance standardization for model output files in specific subdomains of the atmospheric modeling community. Starting from the demanding CMIP6 standard, the aim is to establish an easy-to-use standard that is at least compliant with the Climate and Forecast (CF) conventions. In parallel, an existing netCDF file convention checker is extended to check for the new standards. This enhanced checker is designed to support the creation of compliant files and thus lower the hurdle for data producers to comply with the new standard. The transfer of this approach to further sub-disciplines of the earth system modeling community will be supported by a best-practice guide and other documentation. A showcase of a standard for the urban atmospheric modeling community will be presented in this session. The standard is based on CF Conventions and adapts several global attributes and controlled vocabularies from the well-established CMIP6 standard. Additionally, the AtMoDat project aims on introducing a generic quality indicator into the DataCite metadata schema to foster further reuse of data. This quality indicator should require a discipline-specific implementation of a quality standard linked to the indicator. We will present the concept of the generic quality indicator in general and in the context of urban atmospheric modeling data.
- ItemDo researchers need to care about PID systems?(Zenodo, 2018) Kraft, Angelina; Dreyer, BrittaA 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)!
- ItemRADAR Metadata Kernel with attribute values and controlled vocabularies(RADAR-Projektteam, 2014) Engel, Thomas; Furtado, Filipe; Hahn, Matthias; Kraft, Angelina; Martens, Jörn; Neumann, Janna; Porzel, Andrea; Potthoff, Jan; Ziedorn, FraukeA central feature of the RADAR project is a Metadata Kernel, which manages and characterizes all archived and published research data. The kernel aims to enhance the traceability and usability of research data by maintaining a discipline-agnostic character and simultaneously allowing a description of discipline-specific data. For this purpose, a set of generic parameters were chosen, which allow an accurate and consistent identification of a resource for citation and retrieval purposes and also meet the requirements of more discipline-specific datasets. Furthermore, the Kernel provides recommended use instructions along with appropriate examples on how to correctly describe research data. The following metadata profile includes 9 mandatory fields which represent the general core of the scheme. These contain the main requirements for the DOI registration, in accordance with the DataCite Metadata Schema (v 3.1)1 and must be supplied when submitting metadata to RADAR. Additionally, 12 optional metadata parameters serve the purpose of describing discipline-specific data. These were implemented with a combination of controlledvocabularies and free-text entries, thereby covering heterogeneous data produced by a multitude of disciplines. The controlled-vocabulary entries were defined in accordance with established regulations in mind (for example, ISO standards for language and country of origin of the data). RADAR clients who wish to enhance the prospects of their metadata being found, cited and linked to original research are strongly encouraged to submit the optional as along with the mandatory set of properties.
- ItemNFDI4Ing - the National Research Data Infrastructure for Engineering Sciences(Meyrin : CERN, 2020-09-25) Schmitt, Robert H.; Anthofer, Verena; Auer, Sören; Başkaya, Sait; Bischof, Christian; Bronger, Torsten; Claus, Florian; Cordes, Florian; Demandt, Évariste; Eifert, Thomas; Flemisch, Bernd; Fuchs, Matthias; Fuhrmans, Marc; Gerike, Regine; Gerstner, Eva-Maria; Hanke, Vanessa; Heine, Ina; Huebser, Louis; Iglezakis, Dorothea; Jagusch, Gerald; Klinger, Axel; Krafczyk, Manfred; Kraft, Angelina; Kuckertz, Patrick; Küsters, Ulrike; Lachmayer, Roland; Langenbach, Christian; Mozgova, Iryna; Müller, Matthias S.; Nestler, Britta; Pelz, Peter; Politze, Marius; Preuß, Nils; Przybylski-Freund, Marie-Dominique; Rißler-Pipka, Nanette; Robinius, Martin; Schachtner, Joachim; Schlenz, Hartmut; Schwarz, Annett; Schwibs, Jürgen; Selzer, Michael; Sens, Irina; Stäcker, Thomas; Stemmer, Christian; Stille, Wolfgang; Stolten, Detlef; Stotzka, Rainer; Streit, Achim; Strötgen, Robert; Wang, Wei MinNFDI4Ing brings together the engineering communities and fosters the management of engineering research data. The consortium represents engineers from all walks of the profession. It offers a unique method-oriented and user-centred approach in order to make engineering research data FAIR – findable, accessible, interoperable, and re-usable. NFDI4Ing has been founded in 2017. The consortium has actively engaged engineers across all five engineering research areas of the DFG classification. Leading figures have teamed up with experienced infrastructure providers. As one important step, NFDI4Ing has taken on the task of structuring the wealth of concrete needs in research data management. A broad consensus on typical methods and workflows in engineering research has been established: The archetypes. So far, seven archetypes are harmonising the methodological needs: Alex: bespoke experiments with high variability of setups, Betty: engineering research software, Caden: provenance tracking of physical samples & data samples, Doris: high performance measurement & computation, Ellen: extensive and heterogeneous data requirements, Frank: many participants & simultaneous devices, Golo: field data & distributed systems. A survey of the entire engineering research landscape in Germany confirms that the concept of engineering archetypes has been very well received. 95% of the research groups identify themselves with at least one of the NFDI4Ing archetypes. NFDI4Ing plans to further coordinate its engagement along the gateways provided by the DFG classification of engineering research areas. Consequently, NFDI4Ing will support five community clusters. In addition, an overarching task area will provide seven base services to be accessed by both the community clusters and the archetype task areas. Base services address quality assurance & metrics, research software development, terminologies & metadata, repositories & storage, data security & sovereignty, training, and data & knowledge discovery. With the archetype approach, NFDI4Ing’s work programme is modular and distinctly method-oriented. With the community clusters and base services, NFDI4Ing’s work programme remains firmly user-centred and highly integrated. NFDI4Ing has set in place an internal organisational structure that ensures viability, operational efficiency, and openness to new partners during the course of the consortium’s development. NFDI4Ing’s management team brings in the experience from two applicant institutions and from two years of actively engaging with the engineering communities. Eleven applicant institutions and over fifty participants have committed to carrying out NFDI4Ing’s work programme. Moreover, NFDI4Ing’s connectedness with consortia from nearby disciplinary fields is strong. Collaboration on cross-cutting topics is well prepared and foreseen. As a result, NFDI4Ing is ready to join the National Research Data Infrastructure.
- ItemATMODAT 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, JohannesWithin 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)
- ItemDeskriptives Metadatenprofil für RADAR(RADAR-Projektteam, 2014) Engel, Thomas; Furtado, Filipe; Hahn, Matthias; Kraft, Angelina; Martens, Jörn; Neumann, Janna; Porzel, Andrea; Potthoff, Jan; Ziedorn, FraukeEine zentrale Aufgabe im RADAR-Projekt ist die Erstellung eines Metadatenprofils, das sowohl einen interdisziplinären, zentralen Nachweis der in RADAR archivierten und publizierten Forschungsdaten erlaubt als auch die fachspezifischen Anforderungen zur Suche und zur Nachnutzung dieser Daten erfüllt. Dazu wurden geeignete generische Metadatenparameter, die disziplinspezifisch angepasst werden können identifiziert und ausgewählt. Das nachfolgende Metadatenprofil umfasst 9 Pflichtfelder, welche zusammen den allgemeinen, deskriptiven Teil des Profils darstellen, sowie 12 optionale Felder, welche auch die fachspezifischen Beschreibungen der Datensätze abbilden. Die Pflichtfelder des entwickelten Metadatenprofils enthalten die Grundanforderungen für eine DOI-Registrierung nach dem DataCite-Metadatenschema v 3.11. Um die heterogenen Ansprüche verschiedener wissenschaftlicher Fachgebiete und die generische Ausrichtung von RADAR zusammenzubringen wurde bei der Definition der 12 optionalen Metadatenfelder mit einer Kombination von kontrollierten Listen und Freitextfeldern gearbeitet. Bei der Definition der kontrollierten Listen wurde auf weltweit anerkannte, verständliche Standards (z.B. ISONormen für die Sprache und das Entstehungsland der Forschungsdaten) zurückgegriffen. Dieses Metadatenprofil soll, in Verbindung mit exemplarischen Beispielen den Wissenschaftlern der jeweiligen Fachgebiete darlegen, wie eine detaillierte Beschreibung eines Datensatzes aussehen kann.
- ItemRADAR – Repositorium und Publikations-Service für Forschungsdaten(Hannover : Technische Informationsbibliothek, 2016) Kraft, Angelina; Razum, Matthias; Lange, Frank[no abstract available]