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@ARTICLE{Witte:282752,
      author       = {M.-L. Witte and A. Schoneberg and S. Hanss and M.
                      Lablans$^*$ and J. Vehreschild and D. Krefting},
      title        = {{A}daptability of {E}xisting {F}easibility {T}ools for
                      {C}linical {S}tudy {R}esearch {D}ata {P}latforms.},
      journal      = {Studies in health technology and informatics},
      volume       = {307},
      issn         = {0926-9630},
      address      = {Amsterdam},
      publisher    = {IOS Press},
      reportid     = {DKFZ-2023-01871},
      isbn         = {9781643684284 (print)},
      pages        = {39-48},
      year         = {2023},
      abstract     = {The increasing need for secondary use of clinical study
                      data requires FAIR infrastructures, i.e. provide findable,
                      accessible, interoperable and reusable data. It is crucial
                      for data scientists to assess the number and distribution of
                      cohorts that meet complex combinations of criteria defined
                      by the research question. This so-called feasibility test is
                      increasingly offered as a self-service, where scientists can
                      filter the available data according to specific parameters.
                      Early feasibility tools have been developed for biosamples
                      or image collections. They are of high interest for clinical
                      study platforms that federate multiple studies and data
                      types, but they pose specific requirements on the
                      integration of data sources and data protection.Mandatory
                      and desired requirements for such tools were acquired from
                      two user groups - primary users and staff managing a
                      platform's transfer office. Open Source feasibility tools
                      were sought by different literature search strategies and
                      evaluated on their adaptability to the requirements.We
                      identified seven feasibility tools that we evaluated based
                      on six mandatory properties.We determined five feasibility
                      tools to be most promising candidates for adaption to a
                      clinical study research data platform, the Clinical
                      Communication Platform, the German Portal for Medical
                      Research Data, the Feasibility Explorer, Medical
                      Controlling, and the Sample Locator.},
      keywords     = {Humans / Feasibility Studies / Biomedical Research /
                      Physicians / FAIR (Other) / clinical study (Other) / data
                      provision (Other) / data reuse (Other) / feasibility (Other)
                      / findability (Other) / software tools (Other) / user
                      interface (Other)},
      cin          = {E260},
      ddc          = {300},
      cid          = {I:(DE-He78)E260-20160331},
      pnm          = {315 - Bildgebung und Radioonkologie (POF4-315)},
      pid          = {G:(DE-HGF)POF4-315},
      typ          = {PUB:(DE-HGF)3 / PUB:(DE-HGF)16},
      pubmed       = {pmid:37697836},
      doi          = {DOI: 10.3233/SHTI230691},
      url          = {https://inrepo02.dkfz.de/record/282752},
}