ArticleBig data & society2014
What Difference Does Quantity Make? On the Epistemology of Big Data in Biology.
Article in Big data & society, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 41 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
41 citing papers in PubMed.
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- Measurement under uncertainty: theory-measurement relations in early electrophysiological research.History and philosophy of the life sciences · 2025Article
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- Quantophrenia and the Promises of Genetics: Do Research Practices (Dis)advantage the «Conservation» of Species?Evolutionary applications · 2025Article
- Tentative renderings: Describing local data infrastructures that support the implementation and evaluation of national evaluation Initiatives.Journal of biomedical informatics · 2025Article
- Domesticating data: Traveling and value-making in the data economy.Social studies of science · 2024Article
- Stakeholder engagement does not guarantee impact: A co-productionist perspective on model-based drought research.Social studies of science · 2024Article
- From 'making lists' to conducting 'well-rounded' studies: Epistemic re-orientations in soil microbial ecology.Social studies of science · 2024Article
- Academic data science: Transdisciplinary and extradisciplinary visions.Social studies of science · 2024Article
- The Intersections between Sociology and STS: A Big Data Approach.Sociological perspectives : SP : official publication of the Pacific Sociological Association · 2023Article
- The practical ethics of repurposing health data: how to acknowledge invisible data work and the need for prioritization.Medicine, health care, and philosophy · 2023Article
- Data sharing and privacy issues in neuroimaging research: Opportunities, obstacles, challenges, and monsters under the bed.Human brain mapping · 2022Review
- The potential of a data centred approach & knowledge graph data representation in chemical safety and drug design.Computational and structural biotechnology journal · 2022Review
- The Conceptualization of Quantitative Reasoning among Introductory Biology Faculty.Journal of microbiology & biology education · 2021Article
- Addressing the global snakebite crisis with geo-spatial analyses - Recent advances and future direction.Toxicon: X · 2021Article
- How can big data shape the field of non-religion studies? And why does it matter?Patterns (New York, N.Y.) · 2021Review
- Data management challenges for artificial intelligence in plant and agricultural research.F1000Research · 2021Article
- Where health and environment meet: the use of invariant parameters in big data analysis.Synthese · 2021Article
Corrections and comments
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Authors and funding
1 author.
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Abstract
Is big data science a whole new way of doing research? And what difference does data quantity make to knowledge production strategies and their outputs? I argue that the novelty of big data science does not lie in the sheer quantity of data involved, but rather in (1) the prominence and status acquired by data as commodity and recognised output, both within and outside of the scientific community; and (2) the methods, infrastructures, technologies, skills and knowledge developed to handle data. These developments generate the impression that data-intensive research is a new mode of doing science, with its own epistemology and norms. To assess this claim, one needs to consider the ways in which data are actually disseminated and used to generate knowledge. Accordingly, this paper reviews the development of sophisticated ways to disseminate, integrate and re-use data acquired on model organisms over the last three decades of work in experimental biology. I focus on online databases as prominent infrastructures set up to organise and interpret such data; and examine the wealth and diversity of expertise, resources and conceptual scaffolding that such databases draw upon. This illuminates some of the conditions under which big data need to be curated to support processes of discovery across biological subfields, which in turn highlights the difficulties caused by the lack of adequate curation for the vast majority of data in the life sciences. In closing, I reflect on the difference that data quantity is making to contemporary biology, the methodological and epistemic challenges of identifying and analyzing data given these developments, and the opportunities and worries associated to big data discourse and methods.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.