Filters

2014 - 202024

More filters

2019 - 2019152020 - 20249
Reset filters

Settings

search.filters.applied.f.access: openAccess × Author: Kraft, Angelina × Type: Text ×

Search Results

Now showing 1 - 10 of 24
  • Thumbnail Image
    Item
    Archivierung 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, Karina
    Disziplinübergreifendes 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.
  • Thumbnail Image
    Item
    Making the Maturity of Data and Metadata Visible with Datacite DOIs
    (Washington, DC : ESSOAr, 2020) Kaiser, Amandine; Heydebreck, Daniel; Ganske, Anette; Kraft, Angelina
    Data 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
  • Thumbnail Image
    Item
    14 Years of PID services at the German National Library of Science and Technology (TIB): Connected frameworks, research data and lessons learned from a National Research Library perspective
    (Paris : CODATA, 2017) Kraft, Angelina; Dreyer, Britta; Löwe, Peter; Ziedorn, Frauke
    In an ideal research world, any scientific content should be citable and the coherent content, as well as the citation itself, should be persistent. However, today’s scientists do not only produce traditional research papers – they produce comprehensive digital resources and collections. TIB’s mission is to develop a supportive framework for a sustainable access to such digital content – focusing on areas of engineering as well as architecture, chemistry, information technology, mathematics and physics. The term digital content comprises all digitally available resources such as audiovisual media, databases, texts, images, spreadsheets, digital lab journals, multimedia, 3D objects, statistics and software code. In executing this mission, TIB provides services for the management of digital content during ongoing and for finished research. This includes: • a technical and administrative infrastructure for indexing, cataloguing, DOI registration and licensing for text and digital objects, namely the TIB DOI registration which is active since 2005, • the administration of the ORCID DE consortium, an institutional network fostering the adoption of ORCID across academic institutions in Germany, • training and consultancy for data management, complemented with a digital repository for the deposition and provision of accessible, traceable and citable research data (RADAR), • a Research and Development Department where innovative projects focus on the visualization and the sustainable access to digital information, and • the development of a supportive framework within the German research data community which accompanies the life cycle of scientific knowledge generation and transfer. Its goal is to harmonize (meta)data display and exchange primarily on a national level (LEIBNIZ DATA project).
  • Thumbnail Image
    Item
    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.
  • Thumbnail Image
    Item
    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
  • Thumbnail Image
    Item
    The RADAR Project - A Service for Research Data Archival and Publication
    (Basel : MDPI, 2016) Kraft, Angelina; Razum, Matthias; Potthoff, Jan; Porzel, Andrea; Engel, Thomas; Lange, Frank; van den Broek, Karina; Furtado, Filipe
    The aim of the RADAR (Research Data Repository) project is to set up and establish an infrastructure that facilitates research data management: the infrastructure will allow researchers to store, manage, annotate, cite, curate, search and find scientific data in a digital platform available at any time that can be used by multiple (specialized) disciplines. While appropriate and innovative preservation strategies and systems are in place for the big data communities (e.g., environmental sciences, space, and climate), the stewardship for many other disciplines, often called the “long tail research domains”, is uncertain. Funded by the German Research Foundation (DFG), the RADAR collaboration project develops a service oriented infrastructure for the preservation, publication and traceability of (independent) research data. The key aspect of RADAR is the implementation of a two-stage business model for data preservation and publication: clients may preserve research results for up to 15 years and assign well-graded access rights, or to publish data with a DOI assignment for an unlimited period of time. Potential clients include libraries, research institutions, publishers and open platforms that desire an adaptable digital infrastructure to archive and publish data according to their institutional requirements and workflows.
  • Thumbnail Image
    Item
    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]
  • Thumbnail Image
    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, Frauke
    Um 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.
  • Thumbnail Image
    Item
    Reproducible research through persistently linked and visualized data
    (Berlin : Weierstraß-Institut für Angewandte Analysis und Stochastik, 2017) Drees, Bastian; Kraft, Angelina; Koprucki, Thomas
    The demand of reproducible results in the numerical simulation of opto-electronic devices or more general in mathematical modeling and simulation requires the (long-term) accessibility of data and software that were used to generate those results. Moreover, to present those results in a comprehensible manner data visualizations such as videos are useful. Persistent identifier can be used to ensure the permanent connection of these different digital objects thereby preserving all information in the right context. Here we give an overview over the state-of-the art of data preservation, data and software citation and illustrate the benefits and opportunities of enhancing publications with visual simulation data by showing a use case from opto-electronics.
  • Thumbnail Image
    Item
    RADAR 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, Frauke
    A 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.