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University of Graz Research data management Re-using data Daten finden
  • Planning research
  • Collecting data
  • Analyzing data
  • Archiving data
  • Sharing data
  • Re-using data
  • Data Stewardship Training
  • AI in Research Data Management
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  • Planning research
  • Collecting data
  • Analyzing data
  • Archiving data
  • Sharing data
  • Re-using data
  • Data Stewardship Training
  • AI in Research Data Management
  • Events

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Help and training

The availability of research data varies across disciplines. However, the ways to find archived data are similar. Research data can, for example, be found here:

  • Websites of research institutes or scientists in the field.
  • Journals that archive research data on articles or data journals; link collections to data journals:
    • Forschungsdaten.org
    • FOSTER (EU project, 2017-19)
    • Kindling & Strecker (2022). List of data journals. https://doi.org/10.5281/zenodo.7082126.
    • Uni Würzburg (FDM pages)
    • or with peer-review University of Edinburgh
  • Subject-specific, institution-affiliated or general repositories
    • Subject- or discipline-specific: e.g.,
      • AUSSDA (social sciences)
      • GAMS (humanities)
      • ICSPR (Inter-university Consortium for Political and Social Research)
      • Search by research discipline ("subject") on re3data.org
    • institutional, e.g. Phaidra at the University of Vienna
    • general, e.g.,
      • Zenodo (Cern)
      • Figshare (MacMillan Group, Nature)
      • Dryad (Nonprofit Organization)
      • EUDAT B2SHARE (EU)
  • Public data portals of public authorities and other organizations: e.g.,
    • Austria Micro Data Center (Statistik Austria)
    • GovData (Germany)
  • Data catalogs and aggregators:
    • data.gov (USA)
    • Kaggle (Google)
    • Open Aire Explore (EU)
    • EOSC Portal (EU)
    • CESSDA Data Catalogue (ERIC)
    • Google Dataset Search
  • Repository Catalogs:
    • re3data.org
    • openDOAR
    • RIsources (Focus: Germany)

When searching for research data, it is of course important to be able to assess the quality of the data one finds. Parameters for this are, on the one hand, the subject-specific assessment of the data. On the other hand, a comprehensive description of the data and how they were created or processed (summarized, for example, in a data management plan) indicate at least a good curation of the data, as does whether a license for subsequent use is available. The quality of the archived data can also be inferred to a certain extent from the repository guidelines.

RDM Cycle Reuse Cite {f:if(condition: 'CC-BY-SA, Helmut W. Klug - Remix of image by Gaelen Pinnock.', then: '©CC-BY-SA, Helmut W. Klug - Remix of image by Gaelen Pinnock.')}
©CC-BY-SA, Helmut W. Klug - Remix of image by Gaelen Pinnock.

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