ArticlePLoS computational biology2019
Perspective: Dimensions of the scientific method.
Article in PLoS computational biology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 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
19 citing papers in PubMed.
- Self-compassion and its association with occupational burnout among young and middle-aged nurses: a cross-sectional study in China.Frontiers in psychology · 2026Article
- Research Hypothesis: A Brief History, Central Role in Scientific Inquiry, and Characteristics.Addiction & health · 2025Article
- An open-source software for building and simulating ordinary differential equation models in biology.PloS one · 2025Article
- Beyond genes-for-behaviour: The potential for genomics to resolve long-standing questions in avian brood parasitism.Ecology and evolution · 2024Review
- Unveiling the Dynamics behind Glioblastoma Multiforme Single-Cell Data Heterogeneity.International journal of molecular sciences · 2024Article
- Integrity at stake: confronting "publish or perish" in the developing world and emerging economies.Frontiers in medicine · 2024Article
- Machine-guided discovery of a real-world rogue wave model.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Perspectives of (/memorandum for) systems thinking on COVID-19 pandemic and pathology.Journal of evaluation in clinical practice · 2023Article
- What's next for computational systems biology?Frontiers in systems biology · 2023Review
- Severe testing with high-dimensional omics data for enhancing biomedical scientific discovery.NPJ systems biology and applications · 2022Article
- Article
- Establishing an environment in which rigorous scientific inquiry is practiced: a personal journey.Nucleic acids research · 2022Article
- Finding your scientific story by writing backwards.Marine life science & technology · 2022Article
- Article
- Biologically grounded scientific methods: The challenges ahead for combating epidemics.Methods (San Diego, Calif.) · 2021Review
- A proof of concept study for machine learning application to stenosis detection.Medical & biological engineering & computing · 2021Article
- Mathematical Modelling in Biomedicine: A Primer for the Curious and the Skeptic.International journal of molecular sciences · 2021Review
- Science serving justice: opportunities for enhancing integrity in forensic science in Africa.Forensic sciences research · 2021Article
- Agent-Based Modeling and Simulation in Mathematics and Biology Education.Bulletin of mathematical biology · 2020Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
The scientific method has been guiding biological research for a long time. It not only prescribes the order and types of activities that give a scientific study validity and a stamp of approval but also has substantially shaped how we collectively think about the endeavor of investigating nature. The advent of high-throughput data generation, data mining, and advanced computational modeling has thrown the formerly undisputed, monolithic status of the scientific method into turmoil. On the one hand, the new approaches are clearly successful and expect the same acceptance as the traditional methods, but on the other hand, they replace much of the hypothesis-driven reasoning with inductive argumentation, which philosophers of science consider problematic. Intrigued by the enormous wealth of data and the power of machine learning, some scientists have even argued that significant correlations within datasets could make the entire quest for causation obsolete. Many of these issues have been passionately debated during the past two decades, often with scant agreement. It is proffered here that hypothesis-driven, data-mining-inspired, and "allochthonous" knowledge acquisition, based on mathematical and computational models, are vectors spanning a 3D space of an expanded scientific method. The combination of methods within this space will most certainly shape our thinking about nature, with implications for experimental design, peer review and funding, sharing of result, education, medical diagnostics, and even questions of litigation.
Indexed as
Identifiers
What Socratic 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 Socratic graph.