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Making sense of big data

My work explores how big data is shaped by and also shapes biomedicine.  I am currently finishing a book titled Metabolizing Data, which analyzes the challenges that post-genomic researchers encounter as they make and make sense of “big” biomedical data. As society invests in data as a way to unlock the secrets of health, my book draws on the everyday experiences of biomedical researchers—and their struggles surrounding data—to question the widespread assumption that “more data is better,” or that data is more objective than other forms of expertise. My book introduces the notion of an “anthropology of data,” setting out an agenda for examining the norms and politics embedded within data, which are immaterial and transient, and yet affect society in profound ways. 


My research focuses on the field of metabolomics, the post-genomic study of the molecules and processes that make up metabolism.  Metabolism is an incredibly complex process and is also central to the modern “epidemic” of chronic conditions like obesity and diabetes, and therefore makes an excellent case study to explore how data’s practices, questions, and effects on society are far from settled.


For this research, I have collaborated with researchers from the Computational and Systems Medicine laboratory at Imperial College London, the Steinbeck Group at the European Bioinformatics Institute, and the Center for Metabolomics and Mass Spectrometry at The Scripps Research Institute.


Interrogating "openness" in research


My work explores the challenges of sharing and disseminating data in databases, software, and publications, and their relationship to science policy. Examining the scientific practices associated with the Open Science movement, in which researchers in North America and Europe are encouraged to be “open” with their results, I argue that openness is not inherently good or bad, but rather is a mode of valuing the research process and its outputs. This research draws on my postdoctoral research at the University of Exeter, and is part of an active collaboration with Dr Sabina Leonelli, who leads the Data Studies group at Exeter Unversity.  


To explore how these issues affect the metabolomics community, I am currently carrying out research at the European Bioinformatics Institute and the Scripps Research Institute, two institutions which are at the forefront of metabolomics Open Science initiatives. Drawing on interviews and participant observation, I am investigating how metabolomics researchers struggle to negotiate the standardization and value of complex metabolic data, which are generated by proprietary scientific instruments, and which are not governed by centralized norms or guidelines.

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