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Data Safe Havens in health research and healthcare.

Burton PR, Murtagh MJ, Boyd A, Williams JB, Dove ES, Wallace SE, Tassé AM, Little J, Chisholm RL, Gaye A, Hveem K, Brookes AJ, Goodwin P, Fistein J, Bobrow M, Knoppers BM - Bioinformatics (2015)

Bottom Line: A fundamental challenge is to site such data in repositories that can easily be accessed under appropriate technical and governance controls which are effectively audited and are viewed as trustworthy by diverse stakeholders.This review explores a fundamental question: 'what are the specific criteria that ought reasonably to be met by a data repository if it is to be seen as consistent with this interpretation and viewed as worthy of being accorded the status of 'Data Safe Haven' by key stakeholders'?We propose 12 such criteria. paul.burton@bristol.ac.uk.

View Article: PubMed Central - PubMed

Affiliation: Data to Knowledge (D2K) Research Group, University of Bristol, Oakfield House, Oakfield Grove, Clifton, Bristol BS8 2BN, UK, Public Population Project in Genomics and Society (PG), Montreal, QC H3A 0G1, Canada.

No MeSH data available.


The data pipeline
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btv279-F1: The data pipeline

Mentions: The term ‘data pipeline’ is used widely; in the computing setting it typically refers to ‘a chain of data-processing stages…’ (e.g. (Dilger et al., 2013). Here, we use it—with this same basic interpretation—to refer to a simplified conceptual representation of the life-course of individual-level data in a biomedical data repository (a location for the secure storage of biomedical research and/or healthcare-related data) from the moment of collection or generation, through their utilization and conversion into useful knowledge, and ultimately their archiving or destruction. At its simplest, this data pipeline may be viewed as comprising two primary components that are temporally and sometimes spatially distinct: acquisition and exploitation (Fig. 1). Acquisition subsumes the processes of data generation, capture, storage and archiving whereby data originating in human and social contexts are amassed in a data repository. Exploitation refers to the means by which these data are processed and managed to be readied for access for health service provision, audit and evaluation and/or by research users for analysis and interpretation which can itself generate individual-level data which may be returned to the repository. Analysis and interpretation may span a broad spectrum involving hypothesis- or model-driven methods and/or data mining and hypothesis-free approaches to knowledge discovery.Fig. 1.


Data Safe Havens in health research and healthcare.

Burton PR, Murtagh MJ, Boyd A, Williams JB, Dove ES, Wallace SE, Tassé AM, Little J, Chisholm RL, Gaye A, Hveem K, Brookes AJ, Goodwin P, Fistein J, Bobrow M, Knoppers BM - Bioinformatics (2015)

The data pipeline
© Copyright Policy - creative-commons
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4595892&req=5

btv279-F1: The data pipeline
Mentions: The term ‘data pipeline’ is used widely; in the computing setting it typically refers to ‘a chain of data-processing stages…’ (e.g. (Dilger et al., 2013). Here, we use it—with this same basic interpretation—to refer to a simplified conceptual representation of the life-course of individual-level data in a biomedical data repository (a location for the secure storage of biomedical research and/or healthcare-related data) from the moment of collection or generation, through their utilization and conversion into useful knowledge, and ultimately their archiving or destruction. At its simplest, this data pipeline may be viewed as comprising two primary components that are temporally and sometimes spatially distinct: acquisition and exploitation (Fig. 1). Acquisition subsumes the processes of data generation, capture, storage and archiving whereby data originating in human and social contexts are amassed in a data repository. Exploitation refers to the means by which these data are processed and managed to be readied for access for health service provision, audit and evaluation and/or by research users for analysis and interpretation which can itself generate individual-level data which may be returned to the repository. Analysis and interpretation may span a broad spectrum involving hypothesis- or model-driven methods and/or data mining and hypothesis-free approaches to knowledge discovery.Fig. 1.

Bottom Line: A fundamental challenge is to site such data in repositories that can easily be accessed under appropriate technical and governance controls which are effectively audited and are viewed as trustworthy by diverse stakeholders.This review explores a fundamental question: 'what are the specific criteria that ought reasonably to be met by a data repository if it is to be seen as consistent with this interpretation and viewed as worthy of being accorded the status of 'Data Safe Haven' by key stakeholders'?We propose 12 such criteria. paul.burton@bristol.ac.uk.

View Article: PubMed Central - PubMed

Affiliation: Data to Knowledge (D2K) Research Group, University of Bristol, Oakfield House, Oakfield Grove, Clifton, Bristol BS8 2BN, UK, Public Population Project in Genomics and Society (PG), Montreal, QC H3A 0G1, Canada.

No MeSH data available.