Zisis, K; (2016) Biobanking with Big Data: A Need for Developing “Big Data Metrics”. Biopreservation and Biobanking , 14 (5) pp. 450-451. 10.1089/bio.2015.0106.

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Abstract

The term “big data” has often been used as an all-encompassing phrase for research that involves the use of large-scale data sets. However, the use of the term does little to signify the underlying complexity of definitions, of data sets, and of the requirements that need to be taken into consideration for sustainable research and the estimation of downstream impact. In particular, “big data” is frequently connected with biobanks and biobank networks as the institutions involved in tissue preservation are increasingly and perhaps unavoidably linked to the de facto preservation of information. “Big data” is commonly defined as collections of data sets so large and complex that its manipulation and management present significant logistical challenges (Oxford English Dictionary, 2013). Within the sphere of clinically oriented research, the term is often synonymous to electronic patient records from large hospitals, clinical trials, and/or -omics-based consortia and their associated banked samples. These data can be structured or unstructured, generated from diverse sources (sometimes in real time), and in very large volumes.

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