ArticlePhilosophical transactions. Series A, Mathematical, physical, and engineering sciences2019
Big data: the end of the scientific method?
Article in Philosophical transactions. Series A, Mathematical, physical, and engineering sciences, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 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
24 citing papers in PubMed.
- Equilibrium and Nonequilibrium Ensemble Methods for Accurate, Precise and Reproducible Absolute Binding Free Energy Calculations.Journal of chemical theory and computation · 2025Article
- The role of artificial intelligence in pandemic responses: from epidemiological modeling to vaccine development.Molecular biomedicine · 2025Review
- Artificial Intelligence Must Be Made More Scientific.Journal of chemical information and modeling · 2024Article
- When we talk about Big Data, What do we really mean? Toward a more precise definition of Big Data.Frontiers in big data · 2024Review
- How is Big Data reshaping preclinical aging research?Lab animal · 2023Review
- Ensemble-Based Approaches Ensure Reliability and Reproducibility.Journal of chemical information and modeling · 2023Article
- Challenges and best practices for digital unstructured data enrichment in health research: A systematic narrative review.PLOS digital health · 2023Article
- Generative Models as an Emerging Paradigm in the Chemical Sciences.Journal of the American Chemical Society · 2023Review
- Embracing complexity in sepsis.Critical care (London, England) · 2023Review
- Will Big Data and personalized medicine do the gender dimension justice?AI & society · 2023Article
- Large Scale Study of Ligand-Protein Relative Binding Free Energy Calculations: Actionable Predictions from Statistically Robust Protocols.Journal of chemical theory and computation · 2022Article
- Biologically grounded scientific methods: The challenges ahead for combating epidemics.Methods (San Diego, Calif.) · 2021Review
- Great future or greedy venture: Precision medicine needs philosophy.Health science reports · 2021Review
- Ensembles Are Required to Handle Aleatoric and Parametric Uncertainty in Molecular Dynamics Simulation.Journal of chemical theory and computation · 2021Article
- Data Analysis will not Result in Knowledge Production about Sepsis.Indian journal of critical care medicine : peer-reviewed, official publication of Indian Society of Critical Care Medicine · 2021Article
- When we can trust computers (and when we can't).Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
- Uncertainty quantification in classical molecular dynamics.Philosophical transactions. Series A, Mathematical, physical, and engineering sciences · 2021Article
- Data-driven cardiovascular flow modelling: examples and opportunities.Journal of the Royal Society, Interface · 2021Review
- Tracking droplets in soft granular flows with deep learning techniques.European physical journal plus · 2021Article
- Rapid, accurate, precise and reproducible ligand-protein binding free energy prediction.Interface focus · 2020Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
For it is not the abundance of knowledge, but the interior feeling and taste of things, which is accustomed to satisfy the desire of the soul. (Saint Ignatius of Loyola). We argue that the boldest claims of big data (BD) are in need of revision and toning-down, in view of a few basic lessons learned from the science of complex systems. We point out that, once the most extravagant claims of BD are properly discarded, a synergistic merging of BD with big theory offers considerable potential to spawn a new scientific paradigm capable of overcoming some of the major barriers confronted by the modern scientific method originating with Galileo. These obstacles are due to the presence of nonlinearity, non-locality and hyperdimensions which one encounters frequently in multi-scale modelling of complex systems. This article is part of the theme issue 'Multiscale modelling, simulation and computing: from the desktop to the exascale'.
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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.