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- ItemArchivierung und Publikation von Forschungsdaten: Die Rolle von digitalen Repositorien am Beispiel des RADAR-Projekts(Berlin : de Gruyter, 2016) Kraft, Angelina; Razum, Matthias; Potthoff, Jan; Porzel, Andrea; Engel, Thomas; Lange, Frank; van den Broek, KarinaDisziplinübergreifendes Forschungsdatenmanagement für Hochschulbibliotheken und Projekte zu vereinfachen und zu etablieren – das ist das Ziel von RADAR. Im Sommer 2016 geht mit ‚RADAR – Research Data Repository‘ ein Service an den Start, der Forschenden, Institutionen verschiedener Fachdisziplinen und Verlagen eine generische Infrastruktur für die Archivierung und Publikation von Forschungsdaten anbietet. Zu den Dienstleistungen gehören u. a. die Langzeitverfügbarkeit der Daten mit Handle oder Digital Object Identifier (DOI), ein anpassbares Rollen- und Zugriffsrechtemanagement, eine optionale Peer-Review-Funktion und Zugriffsstatistiken. Das Geschäftsmodell ermutigt Forschende, die anfallenden Nutzungsgebühren des Repositoriums in Drittmittelanträge und Datenmanagementpläne zu integrieren. Publizierte Daten stehen als Open Data zur Nachnutzung wie etwa Data Mining, Metadaten-Harvesting und Verknüpfung mit Suchportalen zur Verfügung. Diese Vernetzung ermöglicht ein nachhaltiges Forschungsdatenmanagement und die Etablierung von Dateninfrastrukturen wie RADAR.
- ItemMaking the Maturity of Data and Metadata Visible with Datacite DOIs(Washington, DC : ESSOAr, 2020) Kaiser, Amandine; Heydebreck, Daniel; Ganske, Anette; Kraft, AngelinaData maturity describes the degree of the formalisation/standardisation of a data object with respect to FAIRness and quality of the (meta-) data. Therefore, a high (meta-) data maturity increases the reusability of data. Moreover, 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 tasks cannot be shown directly in the metadata, reusers of data cannot easily recognise which data is easy to reuse. For example, the DataCite Metadata Schema does not provide an explicit property to link/store information on data maturity (e.g. FAIRness or quality of data/metadata). The AtMoDat project (3, Atmospheric Model Data) 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 to understand and predict natural events, including the atmospheric circulation and ultimately the atmospheric and planetary energy budget. As most atmospheric data has been published with DataCite DOIs, it is of high importance that the maturity of the datasets can be easily found in the DOI’s Metadata. Published data from other fields of research would also benefit from easily findable maturity information. Therefore, we developed a Maturity Indicator concept and propose to introduce it as a new property 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. 1 https://doi.org/10.15497/RDA00034 2 Peng et al., 2015: https://doi.org/10.2481/dsj.14-049 3 www.atmodat.de
- ItemThe ATMODAT Standard enhances FAIRness of Atmospheric Model data(Washington, DC : ESSOAr, 2020) Heydebreck, Daniel; Kaiser, Amandine; Ganske, Anette; Kraft, Angelina; Schluenzen, Heinke; Voss, VivienWithin 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.
- 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.
- 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.
- 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 - A repository for long tail data(2015) Kraft, Angelina; Neumann, JannaThe way knowledge is shared is experiencing a paradigm shift: Digital networks allow new degrees of openness for research and its resources, accompanied by a huge potential for scientists, inventors, industry and the general public. Accessible data will allow all groups to participate in innovation and value creation regardless of their geographical location or individual background. However, for researchers who are evaluated by their academic performance and scientific excellence, there is a fine balance between benefits and concerns regarding the openness of resources such as knowledge and data. With the Research Data Repository (RADAR) project we provide solutions to maintain this balance: In RADAR, an interdisciplinary infrastructure for the preservation, publication, creditability and traceability of research data from the fields of the 'long tail of science' is developed. Here we present the first RADAR prototype: A robust, generic end-point data repository which enables clients to preserve research results up to 15 years and assign well-graded access rights, or to publish and preserve data with a DOI assignment for an unlimited period of time. Potential clients include libraries, research institutions, publishers and open platforms which require an adaptable digital infrastructure to archive and publish data according to their institutional needs and workflows. In a nutshell, RADAR can help clients to handle following issues: - Secure storage of research data. - Preservation of information after a project is completed, a grant ends or employees leave. - Traceable and citable data publication across communities via a discipline-agnostic metadata scheme. - Ensuring that data are ‘stable’ after publication e.g. to allow accurate comparisons later. - Provision of data management services for their customers up front while using RADAR as a back-end system.
- 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.
- ItemRADAR – Repositorium und Publikations-Service für Forschungsdaten(Hannover : Technische Informationsbibliothek, 2016) Kraft, Angelina; Razum, Matthias; Lange, Frank[no abstract available]
- ItemMoving towards FAIRness in Research Data and Software Management(Meyrin : CERN, 2020-07-03) Kraft, AngelinaPresentation during the Thüringer FDM Tage 2020 within the workshop "FAIR Research Software and Beyond: How to make the most of your code".