The Palgrave Handbook of Biology and Society 2018
DOI: 10.1057/978-1-137-52879-7_14
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Assembling Biomedical Big Data

Abstract: This chapter examines the challenges involved in disseminating, integrating and analyzing large datasets collected on both human subjects and non-human experimental organisms, and within both clinical and research settings. I highlight some of the technical, ethical and epistemic concerns underlying current attempts to portray and use Big Data as revolutionary tools for producing biomedical knowledge and related interventions. When bringing together data collected on human subjects with data collected from oth… Show more

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Cited by 4 publications
(5 citation statements)
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“…Finally, recent work at the junction of empirical philosophy of science and STS is highly relevant for exploring uncertainty in precision medicine from a research‐oriented viewpoint. In particular, work that investigates challenges in big data‐driven life science (including data curation and processing) and the entanglement of theoretical assumptions, material practices and infrastructure will hold theoretical tools for further analysis of the uncertainty paradox in precision medicine 38–41 …”
Section: Resultsmentioning
confidence: 99%
See 1 more Smart Citation
“…Finally, recent work at the junction of empirical philosophy of science and STS is highly relevant for exploring uncertainty in precision medicine from a research‐oriented viewpoint. In particular, work that investigates challenges in big data‐driven life science (including data curation and processing) and the entanglement of theoretical assumptions, material practices and infrastructure will hold theoretical tools for further analysis of the uncertainty paradox in precision medicine 38–41 …”
Section: Resultsmentioning
confidence: 99%
“…In particular, work that investigates challenges in big datadriven life science (including data curation and processing) and the entanglement of theoretical assumptions, material practices and infrastructure will hold theoretical tools for further analysis of the uncertainty paradox in precision medicine. [38][39][40][41]…”
Section: Lohse | 561mentioning
confidence: 99%
“…Data coming from animal research, clinical trials, administrative sources, and patients records have been kept in distinct silos: They are stored in data infrastructures financed by different organizations, utilizing different standards and responding to different systems of amalgamation, resulting in little if any interoperability across. The emphasis on RCT data over all others has taken pressure off attempts to link these data to other sources of relevant evidence, resulting in ever-increasing trouble with sharing and integrating data beyond specified and highly contained environments (Leonelli 2017;Fleming et al 2017). A direct consequence of these practices and governance model is that data analysis has been largely confined within specific methodological traditions, with modeling and inferential reasoning typically applied to homogenous data of the same type, rather than bringing together data of diverse provenance, formats, and representational power.…”
Section: Evidence Rankings and The Contemporary Health Data Ecosystemmentioning
confidence: 99%
“…The transformation of biology into the information science of bioinformatics, and the transcoding of organic biological samples into bioinformational data, raises a host of issues regarding the ways biodata are accessed, circulated or capitalised on. The assembling and analysis of big data in computational biology entails radical new divisions of research labour and training for biological scientists in computer programming, data analytics and information infrastructures, as biology has moved from in vivo experimentation to in silico databases and pattern detection (Leonelli 2018). Bioinformatics does not only signify the physical transformation of human tissue and DNA into machine-readable digital data, but is also implicated in bodily commodification, the emergence of biocapital markets, the expansion of the biotech industry, and questions of ethics, inclusion, exclusion and inequality (Reardon 2017).…”
Section: Bioinformatics and Sociogenomicsmentioning
confidence: 99%