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Curating, collecting, and cataloguing global COVID-19 datasets for the aim of predicting personalized risk
Sepehr Golriz Khatami; Maria Francesca Russo; Daniel Domingo-Fernandez; Andrea Zaliani; Sarah Mubeen; Yojana Gadiya; Astghik Sargsyan; Reagon Karki; Stephan Gebel; Ram Kumar Ruppa Surulinathan; Vanessa Lage-Rupprech; Saulius Archipovas; Geltrude Mingrone; Marc Jacobs; Carsten Claussen; Martin Hofmann-apitius; Alpha Tom Kodamullil.
Affiliation
  • Sepehr Golriz Khatami; Fraunhofer Institute
  • Maria Francesca Russo; Fondazione Policlinico Universitario Agostino Gemelli IRCCS
  • Daniel Domingo-Fernandez; Fraunhofer SCAI
  • Andrea Zaliani; Fraunhofer ITMP
  • Sarah Mubeen; Fraunhofer SCAI
  • Yojana Gadiya; Fraunhofer ITMP
  • Astghik Sargsyan; Fraunhofer Institute for Algorithms and Scientific Computing SCAI
  • Reagon Karki; Fraunhofer SCAI
  • Stephan Gebel; Fraunhofer SCAI
  • Ram Kumar Ruppa Surulinathan; Fraunhofer SCAI
  • Vanessa Lage-Rupprech; Fraunhofer SCAI
  • Saulius Archipovas; Fraunhofer MEVIS
  • Geltrude Mingrone; University of Cattolica del Sacro Cuore
  • Marc Jacobs; Fraunhofer SCAI
  • Carsten Claussen; Fraunhofer ITMP
  • Martin Hofmann-apitius; Fraunhofer SCAI
  • Alpha Tom Kodamullil; Fraunhofer Institute for Algorithms and Scientific Computing SCAI
Preprint in English | medRxiv | ID: ppmedrxiv-21265797
ABSTRACT
The COVID-19 data catalogue is a repository that provides a landscape view of COVID-19 studies and datasets as a putative source to enable researchers to develop personalized COVID-19 predictive risk models. The COVID-19 data catalogue currently contains over 400 studies and their relevant information collected from a wide range of global sources such as global initiatives, clinical trial repositories, publications and data repositories. Further, the curated content stored in this data catalogue is complemented by a web application, providing visualizations of these studies, including their references, relevant information such as measured variables, and the geographical locations of where these studies were performed. This resource is one of the first to capture, organize and store studies, datasets and metadata in the area of COVID-19 in a comprehensive repository. We are convinced that our work will facilitate future research and development of personalized predictive risk models of COVID-19.
License
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Full text: Available Collection: Preprints Database: medRxiv Type of study: Prognostic study / Rct Language: English Year: 2021 Document type: Preprint
Full text: Available Collection: Preprints Database: medRxiv Type of study: Prognostic study / Rct Language: English Year: 2021 Document type: Preprint
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