ArticleeLife2019
The challenges of big data biology.
Article in eLife, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 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
25 citing papers in PubMed.
- Progress and ongoing conceptual challenges "on the way to integrative human neuroscience"-ten years after.Frontiers in integrative neuroscience · 2026Article
- Bottlenecks in advancing and applying multiomic data integration-common data resources as rate-limiting drivers-the high-impact use case of atherosclerotic cardiovascular disease.Briefings in bioinformatics · 2025Review
- From biological data to oscillator models using SINDy.iScience · 2024Article
- Enhancing Transparency in Defining Studied Drugs: The Open-Source Living DiAna Dictionary for Standardizing Drug Names in the FAERS.Drug safety · 2024Article
- A Practical Guide to Evaluating and Using Big Data in Digestive Disease Research.Gastroenterology · 2024Article
- Infrastructuring Educational Genomics: Associations, Architectures, and Apparatuses.Postdigital science and education · 2024Article
- "Molecular Biology"-Pleonasm or Denotation for a Discipline of Its Own? Reflections on the Origins of Molecular Biology and Its Situation Today.Biomolecules · 2023Review
- Challenges and Opportunities in Accessing and Analysing FAERS Data: A Call Towards a Collaborative Approach.Drug safety · 2023Article
- Unified and pluralistic ideals for data sharing and reuse in biodiversity.Database : the journal of biological databases and curation · 2023Article
- Artificial intelligence and machine-learning approaches in structure and ligand-based discovery of drugs affecting central nervous system.Molecular diversity · 2023Review
- Perspectives of (/memorandum for) systems thinking on COVID-19 pandemic and pathology.Journal of evaluation in clinical practice · 2023Article
- Leveraging big data to uncover the eco-evolutionary factors shaping behavioural development.Proceedings. Biological sciences · 2023Review
- PhyloMatcher: a tool for resolving conflicts in taxonomic nomenclature.Bioinformatics advances · 2023Article
- iProX in 2021: connecting proteomics data sharing with big data.Nucleic acids research · 2022Article
- Post-translational Modifications in Brain Diseases: A Future for Biomarkers.Advances in experimental medicine and biology · 2022Article
- Artifact3-D: New software for accurate, objective and efficient 3D analysis and documentation of archaeological artifacts.PloS one · 2022Article
- Variability in Retinal Neuron Populations and Associated Variations in Mass Transport Systems of the Retina in Health and Aging.Frontiers in aging neuroscience · 2022Review
- From Reductionism to Reintegration: Solving society's most pressing problems requires building bridges between data types across the life sciences.PLoS biology · 2021Article
- Data Management and Modeling in Plant Biology.Frontiers in plant science · 2021Review
- Data management challenges for artificial intelligence in plant and agricultural research.F1000Research · 2021Article
Corrections and comments
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Authors and funding
1 author.
Funding
Abstract
The availability of big data has the potential to transform many areas of the life sciences and usher in new ways of doing research. Here, I argue that big data biology also raises fundamental questions in the philosophy of science: for example, what is a good dataset, and how can reliable knowledge be extracted from big data? Collaborations between biologists, data scientists and philosophers of science will help us to answer these and other questions.
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What OpenQuestion holds
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.