Data governance in predictive toxicology: A review.

Background Due to recent advances in data storage and sharing for further data processing in predictive toxicology, there is an increasing need for flexible data representations, secure and consistent data curation and automated data quality checking. Toxicity prediction involves multidisciplinary data. There are hundreds of collections of chemical, biological and toxicological data that are widely dispersed, mostly in the open literature, professional research bodies and commercial companies. In order to better manage and make full use of such large amount of toxicity data, there is a trend to develop functionalities aiming towards data governance in predictive toxicology to formalise a set of processes to guarantee high data quality and better data management. In this paper, data quality mainly refers in a data storage sense (e.g. accuracy, completeness and integrity) and not in a toxicological sense (e.g. the quality of experimental results).