The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
Data-driven predictions in the science of science
Open accessAbstract
The desire to predict discoveries—to have some idea, in advance, of what will be discovered, by whom, when, and where—pervades nearly all aspects of modern science, from individual scientists to publishers, from funding agencies to hiring committees. In this Essay, we survey the emerging and interdisciplinary field of the “science of science” and what it teaches us about the predictability of scientific discovery. We then discuss future opportunities for improving predictions derived from the science of science and its potential impact, positive and negative, on the scientific community.
Overview
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Citations
Cites
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Self-citationQuantifying the evolution of individual scientific impact
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Self-citation
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Cited by
Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojević, S., Petersen, A. M., Radicchi, F., Sinatra, R., Uzzi, B., et al. (2018) · Science · doi:10.1126/science.aao0185 · Cited by 1,497
Self-citationData-driven modeling and learning in science and engineering
Montáns, F. J., Chinesta, F., Gómez-Bombarelli, R., & Kutz, J. N. (2019) · Comptes Rendus. Mécanique · doi:10.1016/j.crme.2019.11.009 · Cited by 299
Dynamics of cross-platform attention to retracted papers
Peng, H., Romero, D. M., & Horvát, E.-Á. (2022) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2119086119 · Cited by 57
Metadata
- Authors
- Clauset, A., Larremore, D. B., & Sinatra, R.
- Venue
- Science
- Published
- 2017-02-03
- Volume
- 355
- Issue
- 6324
- Pages
- 477-480
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- OpenAlex
- W2585057539
- Cited by
- 265
- References
- 25
- FWCI
- 21.91
- Type
- journal article
- Language
- en
- Open access
- green
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Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
