ArticleProceedings of the National Academy of Sciences of the United States of America2015
Choosing experiments to accelerate collective discovery.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 57 papers, 1 of them a synthesis that pooled it.
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
57 citing papers in PubMed, 1 synthesis or guideline pooled it.
- NIH funding and the pursuit of edge science.Proceedings of the National Academy of Sciences of the United States of America · 2020Pooled it
- What breaks when federal commitments break: comment on Beccia et al.SSM. Mental health · 2026Article
- Structure of scientific knowledge flows to intergovernmental organizations.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Organizing across disciplines to tackle shared computational challenges.Patterns (New York, N.Y.) · 2026Article
- Efficient structure learning of gene regulatory networks with Bayesian active learning.BMC bioinformatics · 2025Article
- Article
- Quantifying the dynamics of peak disruption in scientific careers.Scientific reports · 2025Article
- Unmeasured human transcription factor ChIP-seq data shape functional genomics and demand strategic prioritization.Briefings in functional genomics · 2025Article
- The dynamics of higher-order novelties.Nature communications · 2025Article
- Article
- Rationalizing risk aversion in science: Why incentives to work hard clash with incentives to take risks.PLoS biology · 2024Article
- The Promises and Possibilities of Artificial Intelligence in the Delivery of Behavior Analytic Services.Behavior analysis in practice · 2024Review
- Idea engines: Unifying innovation & obsolescence from markets & genetic evolution to science.Proceedings of the National Academy of Sciences of the United States of America · 2024Article
- Identifying the perceived local properties of networks reconstructed from biased random walks.PloS one · 2024Article
- Detecting possible pairs of materials for composites using a material word co-occurrence network.PloS one · 2024Article
- Article
- Interacting innovation processes.Scientific reports · 2023Article
- Accelerating science with human-aware artificial intelligence.Nature human behaviour · 2023Article
- Data, measurement and empirical methods in the science of science.Nature human behaviour · 2023Review
- Identifying Trends in the Open-Access Behavior Analytic Literature via Computational Analyses (I): Simple Descriptions of Text.The Analysis of verbal behavior · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
Abstract
A scientist's choice of research problem affects his or her personal career trajectory. Scientists' combined choices affect the direction and efficiency of scientific discovery as a whole. In this paper, we infer preferences that shape problem selection from patterns of published findings and then quantify their efficiency. We represent research problems as links between scientific entities in a knowledge network. We then build a generative model of discovery informed by qualitative research on scientific problem selection. We map salient features from this literature to key network properties: an entity's importance corresponds to its degree centrality, and a problem's difficulty corresponds to the network distance it spans. Drawing on millions of papers and patents published over 30 years, we use this model to infer the typical research strategy used to explore chemical relationships in biomedicine. This strategy generates conservative research choices focused on building up knowledge around important molecules. These choices become more conservative over time. The observed strategy is efficient for initial exploration of the network and supports scientific careers that require steady output, but is inefficient for science as a whole. Through supercomputer experiments on a sample of the network, we study thousands of alternatives and identify strategies much more efficient at exploring mature knowledge networks. We find that increased risk-taking and the publication of experimental failures would substantially improve the speed of discovery. We consider institutional shifts in grant making, evaluation, and publication that would help realize these efficiencies.
Indexed as
Identifiers
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.