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1 – 10 of 24Johann Eder and Vladimir A. Shekhovtsov
Medical research requires biological material and data collected through biobanks in reliable processes with quality assurance. Medical studies based on data with unknown or…
Abstract
Purpose
Medical 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based on data with unknown or questionable quality are useless or even dangerous, as evidenced by recent examples of withdrawn studies.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/approach
Following a design science approach, this paper develops a conceptual model for capturing and relating metadata of medical data in biobanks to support medical research.
Findings
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, 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/value
This 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.
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Keywords
Iryna Prus, Raoul C.D. Nacamulli and Alessandra Lazazzara
The purpose of this paper is to consolidate the state of extant academic research on workplace innovation (WI) by proposing a comprehensive conceptual framework and outlining…
Abstract
Purpose
The purpose of this paper is to consolidate the state of extant academic research on workplace innovation (WI) by proposing a comprehensive conceptual framework and outlining research traditions on the phenomenon.
Design/methodology/approach
This paper systematically reviewed the literature published over the past 20 years, basing on a predefined research protocol. The dimensions of WI were explored with the help of thematic synthesis, while the research perspectives were studied by means of textual narrative synthesis.
Findings
The analysis suggests that there exist four research traditions on WI – built container, humanized landscape, socio-material macro-actor, and polyadic network – and each of them comprises its own set of assumptions, foci of study, and ontological bases. The findings suggest that WI is a heterogeneous process of renovation occurring in eight different dimensions, namely work system, workplace democracy, high-tech application, workplace boundaries, workspaces, people practices, workplace experience, and workplace culture. The analysis showed that over years the meaning of innovation within these dimensions changed, therefore it is argued that research should account for the variability of these categories.
Practical implications
The paper includes implications for developing and implementing WI programs. Moreover, it discusses the role of HR in the WI process.
Originality/value
This paper for the first time systematically reviews literature on the topic of WI, clarifies the concept and discusses directions and implications for the future research.
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