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  4. Curating, Collecting, and Cataloguing Global COVID-19 Datasets for the Aim of Predicting Personalized Risk
 
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2024
Journal Article
Title

Curating, Collecting, and Cataloguing Global COVID-19 Datasets for the Aim of Predicting Personalized Risk

Abstract
Although hundreds of datasets have been published since the beginning of the coronavirus pandemic, there is a lack of centralized resources where these datasets are listed and harmonized to facilitate their applicability and uptake by predictive modeling approaches. Firstly, such a centralized resource provides information about data owners to researchers who are searching datasets to develop their predictive models. Secondly, the harmonization of the datasets supports simultaneously taking advantage of several similar datasets. This, in turn, does not only ease the imperative external validation of data-driven models but can also be used for virtual cohort generation, which helps to overcome data sharing impediments. Here, we present that 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 related to COVID-19 in a comprehensive repository. We believe that our work will facilitate future research and development of personalized predictive risk models for COVID-19.
Author(s)
Golriz Khatami, Sepehr  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Sargsyan, Astghik  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Russo, Maria Francesca
Domingo Fernández, Daniel  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Zaliani, Andrea  
Fraunhofer-Institut für Translationale Medizin und Pharmakologie ITMP  
Kaladharan, Abish
Sethumadhavan, Priya
Mubeen, Sarah  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Gadiya, Yojana  
Fraunhofer-Institut für Translationale Medizin und Pharmakologie ITMP  
Karki, Reagon  
Fraunhofer-Institut für Translationale Medizin und Pharmakologie ITMP  
Mingrone, Geltrude
Claussen, Carsten
Lage-Rupprecht, Vanessa
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Archipovas, Saulius  
Fraunhofer-Institut für Digitale Medizin MEVIS  
Gebel, Stephan
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Surulinathan, Ram Kumar Ruppa
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Jacobs, Marc  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Hofmann-Apitius, Martin  
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Tom Kodamullil, Alpha
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Journal
Data  
Open Access
DOI
10.3390/data9020025
Language
English
Fraunhofer-Institut für Algorithmen und Wissenschaftliches Rechnen SCAI  
Fraunhofer-Institut für Translationale Medizin und Pharmakologie ITMP  
Fraunhofer-Institut für Digitale Medizin MEVIS  
Keyword(s)
  • COVID-19

  • data catalogue

  • personalized risk model

  • predictive models

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