DataSHIELD – New Directions and Dimensions

Abstract

In disciplines such as biomedicine and social sciences, sharing and combining sensitive individual-level data is often prohibited by ethical-legal or governance constraints and other barriers such as the control of intellectual property or the huge sample sizes. DataSHIELD (Data Aggregation Through Anonymous Summary-statistics from Harmonised Individual-levEL Databases) is a distributed approach that allows the analysis of sensitive individual-level data from one study, and the co-analysis of such data from several studies simultaneously without physically pooling them or disclosing any data.Following initial proof of principle, a stable DataSHIELD platform has now been implemented in a number of epidemiological consortia. This paper reports three new applications of DataSHIELD including application to post-publication sensitive data analysis, text data analysis and privacy protected data visualisation. Expansion of DataSHIELD analytic functionality and application to additional data types demonstrate the broad applications of the software beyond biomedical sciences.

Cohort Harmonization and Integrative Analysis From a Biomedical Engineering Perspective

“…However, each individual dataset remains on local servers. The DataSHIELD (Data aggregation through anonymous summary-statistics from harmonized individual level databases) method provides powerful tools and functions that permit the federated statistical analyses of pooled datasets of several collaborating studies [55,56]. It enables a fully efficient integrated analysis of biomedical data even if ethical and/or legal considerations do not allow the spread of individual-level data to third parties.…”

“…The GDPR (http://www.eugdpr.org/eugdpr.org.html) mainly overhauls Figure 4. The basic IT infrastructure underlying the DataSHIELD distributed approach (adapted from [56]). the EU Directive 95/46/EC with respect to rights of the data subject by introducing or strengthening the rights to: (i) access to data, (ii) rectification and erasure ('right to be forgotten'), (iii) data portability, and (iv) notification for a personal data breach.…”

Best practice data life cycle approaches for the life sciences

“…the GA4GH Security Technology Infrastructure document ) and conversion to non-identifiable data if data is to be shared (see Sharing data section). For data where privacy is a concern, one approach is separating the data storage from the analysis location and limiting the analysis outputs to ‘nondisclosive’ results 83 . An example is DataShield 83 , which is mostly used for public health rather than ‘omics’ data.…”

“…For data where privacy is a concern, one approach is separating the data storage from the analysis location and limiting the analysis outputs to ‘nondisclosive’ results 83 . An example is DataShield 83 , which is mostly used for public health rather than ‘omics’ data. Subdomain-specific practice should be considered when choosing appropriate formats and linking metadata, as outlined in 84 .…”

Synthetic ALSPAC longitudinal datasets for the Big Data VR project

“…The value of this emerging technology and its potential applications to e-health and wider use in medicine was recognised by the winning collaboration who continue the development of a VR proof-of-concept biomedical data exploration and visualisation tool under the Big Data VR project using the ALSPAC cohort study as a use case. This project has additionally explored a variety of VR visual analytic methodologies, investigated VR analytics applied to different scales of data and scoped the integration of privacy protecting analytical methods via DataSHIELD 1 . Findings will be reported in a forthcoming paper.…”