Abstract
PurposeMedical research requires biological material and data collected through biobanks in reliable processes with quality assurance. Medical studies based on data with unknown or questionable quality are useless or even dangerous, as evidenced by recent examples of withdrawn studies. Medical data sets consist of highly sensitive personal data, which has to be protected carefully and is available for research only after the approval of ethics committees. The purpose of this research is to propose an architecture to support researchers to efficiently and effectively identify relevant collections of material and data with documented quality for their research projects while observing strict privacy rules.Design/methodology/approachFollowing a design science approach, this paper develops a conceptual model for capturing and relating metadata of medical data in biobanks to support medical research.FindingsThis study describes the landscape of biobanks as federated medical data lakes such as the collections of samples and their annotations in the European federation of biobanks (Biobanking and Biomolecular Resources Research Infrastructure – European Research Infrastructure Consortium, BBMRI-ERIC) and develops a conceptual model capturing schema information with quality annotation. This paper discusses the quality dimensions for data sets for medical research in-depth and proposes representations of both the metadata and data quality documentation with the aim to support researchers to effectively and efficiently identify suitable data sets for medical studies.Originality/valueThis novel conceptual model for metadata for medical data lakes has a unique focus on the high privacy requirements of the data sets contained in medical data lakes and also stands out in the detailed representation of data quality and metadata quality of medical data sets.
Highlights
Data lakes are architectures for the storage of data for further use (Inmon, 2016; Giebler et al, 2019; Sawadogo and Darmont, 2020)
This study describes the landscape of biobanks as federated medical data lakes such as the collections of samples and their annotations in the European federation of biobanks (Biobanking and Biomolecular Resources Research Infrastructure – European Research Infrastructure Consortium, BBMRIERIC) and develops a conceptual model capturing schema information with quality annotation
Based on all these research efforts, we propose a generic architecture and a conceptual model based on the exchange of ontological metadata and metadata quality characteristics to support the location of data sets, which are potentially useful for specified needs
Summary
Data lakes are architectures for the storage of data for further use (Inmon, 2016; Giebler et al, 2019; Sawadogo and Darmont, 2020). The data lake concept arose with the advent of big data as organizations were not able to keep up with the ever-increasing possibilities for collecting and storing data and to integrate all these data in structured data repositories. Data warehouses (Golfarelli and Rizzi, 2018; Vaisman and Zimanyi, 2014) require that data, which should be stored in a data warehouse or a data mart, is structured, cleaned, harmonized and integrated, before it is entered into the data warehouse – usually through a carefully designed process of extracting data from the sources, transforming the data into. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode
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