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Science of science
Open accessAbstract
The whys and wherefores of SciSci: The science of science (SciSci) is based on a transdisciplinary approach that uses large data sets to study the mechanisms underlying the doing of science—from the choice of a research problem to career trajectories and progress within a field. In a Review, Fortunato et al. explain that the underlying rationale is that with a deeper understanding of the precursors of impactful science, it will be possible to develop systems and policies that improve each scientist's ability to succeed and enhance the prospects of science as a whole. Science , this issue p. eaao0185
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“the cumulative nature of scientific research [12] makes the knowledge ecosystem vulnerable to perpetuating errors [13], [14]”
From LLM hallucinations in the wild: Large-scale evidence from non-existent citations (2026) · cites this paper as [12]
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“Specifically, we believe that practices fundamental to the business models of paper mills leave a collaboration fingerprint that can be identified using techniques familiar to the ‘Science of Science’12.”
From Identifying fabricated networks within authorship-for-sale enterprises (2024) · cites this paper as [12]
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“Our findings support what Fortunato et al. (2018) asserted: “Science often behaves like an economic system with a one-dimensional ‘currency’ of citation counts.”
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“…issues[42] In such a situation, the investigation of scholarly articles with innovative and useful ideas may lead to a better understanding of the underlying internal (e.g., structural and contextual) and external (e.g., citations and social) factors leading to competent and useful research [25].”
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“…analysis, similar to other related studies (Babbar et al., 2020; Card and DellaVigna, 2013; Kumar and Singhal, 2024; Mithas and Silveira, 2024; Swan-son, 2004), to provide a foundation for the science of science as it applies to research related to business and economics (Fortunato et al., 2018).”
Interfaces for Science: Conceptualizing an Interactive Graphical Interface
Azevedo, B., Baptista, A. A., Sá, J. O. E., Branco, P., & Cuesta, R. T. (2019) · Lecture notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering · doi:10.1007/978-3-030-06134-0_3 · Cited by 3
Text Mining Gene Selection to Understand Pathological Phenotype Using Biological Big Data
Desterke, C., Lorenzo, H. K., & Candelier, J. (2021) · Exon Publications eBooks · doi:10.36255/exonpublications.bioinformatics.2021.ch1 · Cited by 4
The U.S. Academic Fisheries Co-Authorship Network Under the Lens of Diversity and Inclusion
Arismendi, I., McLaughlin, K. R., & Penaluna, B. E. (2021) · Fisheries · doi:10.1002/fsh.10618 · Cited by 3
Chen, W., Wang, X., Sun, Y., Hu, Q., Huang, L., Jiang, Y., Singh, A. K., Mak, T., & Yang, M. (2021) · IEEE Access · doi:10.1109/access.2021.3123106 · Cited by 3
“Specifically, it identifies the features or dynamics of a specific discipline of science or the whole science, focusing on how knowledge, publications, and scientist behaviors would evolve [15].”
BackgroundMethodReinmann, G., & Rhein, R. (2023) · transcript Verlag eBooks · doi:10.14361/9783839462959 · Cited by 7
Krauß, A. (2024) · Oxford University Press eBooks · doi:10.1093/9780198937401.003.0013 · Cited by 3
Evaluation of Session-Suitability of Papers in Conference Programs
Gündoğan, E., & Kaya, M. (2019) · International Artificial Intelligence and Data Processing Symposium (IDAP) · doi:10.1109/idap.2019.8875905 · Cited by 3
Viral Science: Masks, Speed Bumps, and Guard Rails
Mani, G., & Hope, T. (2020) · Patterns · doi:10.1016/j.patter.2020.100101 · Cited by 3
Yan, X., Ruan, G., Nikolov, D., Hutchinson, M., Kankanamalage, C. P., Serrette, B., McCombs, J. R., Walsh, A., Tuna, E., & Pentchev, V. (2021) · ACM International Conference on Information and Knowledge Management (CIKM) · doi:10.1145/3459637.3481898 · Cited by 3
“Large bibliographic data sets such as Web of Science (WoS [2]), and Microsoft Academic Graph (MAG [26]) hold the promise of revo-lutionizing the scientific enterprise when combined with state-of-the-science computational capabilities [6].”
BackgroundTran, N. M., & Xie, Y. (2021) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata52589.2021.9671785 · Cited by 3
“Predicting future trends in science by completing semantic networks using machine learning approaches has drawn a lot of attention in recent years [5], [7], [14], [17], [20].”
BackgroundCioffi, A., & Peroni, S. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-16802-4_42 · Cited by 3
“At the same time, the structured format allowed us to valorise the citation graph connecting the scientific literature [7].”
BackgroundA study of publication trajectories of the Brazilian Computer Science community
Albertini, M. K., Backes, A. R., & SÁ, A. L. D. (2019) · Anais da Academia Brasileira de Ciências · doi:10.1590/0001-3765201920180559 · Cited by 3
Realpe-Gómez, J. (2018) · arXiv (Cornell University) · doi:10.13140/rg.2.2.21301.32482 · Cited by 3
Contributions towards understanding and building sustainable sciencePreprint
Hartgerink, C. H. J. (2019) · Thesis Commons (OSF Preprints) · doi:10.31237/osf.io/4wtpc · Cited by 3
Reciprocity and Success in Academic CareersPreprint
Li, W., Aste, T., Caccioli, F., & Livan, G. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3229271 · Cited by 3
How to survive in the academic jungle? Protection strategies against predatory journals
Corrêa, C. L. (2022) · Fisioterapia em Movimento · doi:10.1590/fm.2022.35001 · Cited by 3
Lemaitre, J. C., Popelka, D., Ribotta, B., Westlake, H. E., Chakrabarti, S., Xiaoxue, L., Hanson, M. A., Jiang, H., Cara, F. D., Kurant, E., et al. (2026) · eLife · doi:10.7554/elife.108403.1 · Cited by 3
LLM hallucinations in the wild: Large-scale evidence from non-existent citations
Zhao, Z., Wang, Y., Stuart, T., De Vaan, M., Ginsparg, P., & Yin, Y. (2026) · arXiv · doi:10.48550/arxiv.2605.07723 · Cited by 3
“While the widespread use of GenAI in scientific production has already reshaped productivity dynamics and quality signals [11], the cumulative nature of scientific research [12] makes the knowledge ecosystem vulnerable to perpetuating errors [13], [14].”
How coupled are capillary electrophoresis and mass spectrometry?
Ceribeli, C., de Arruda, H. F., & Costa, L. D. F. (2021) · Scientometrics · doi:10.1007/s11192-021-03923-0 · Cited by 7
Consistency pays off in science
Erkol, Ş., Sikdar, S., Radicchi, F., & Fortunato, S. (2023) · Quantitative Science Studies · doi:10.1162/qss_a_00252 · Cited by 2
“The number of scientific papers has been growing exponentially for over a century [1, 2].”
BackgroundSelf-citationDu, C., Yao, K., Zhu, H., Wang, D., Zhuang, F., & Xiong, H. (2024) · Knowledge and Information Systems · doi:10.1007/s10115-023-02005-2 · Cited by 2
“Driven by a vast amount of scientific data, Science of Science is flourishing to identify fundamental drivers of science and develop predictive models to capture its evolution [1].”
Self-citationIndependent Russian medical science: is there any?
Dyachenko, E. L., Агафонов, Ю., Губа, К. С., & Gelvikh, A. (2024) · Scientometrics · doi:10.1007/s11192-024-05135-8 · Cited by 2
“…shown that across various disciplines in both natural and social science s, the prevalence of co-authored papers and the average number of co-authors per paper have been on the rise (Fanelli & Larivière, 2016; Fortunato et al., 2018; Glänzel & Schubert, 2004; Henriksen, 2016; Wuchty et al., 2007).”
Idea to Ink: Lessons From Failure to Publish
Seno‐Alday, S. (2024) · Organizational Behavior Teaching Review · doi:10.1177/10525629241289334 · Cited by 2
“Indeed, studies have shown that the most influential ideas are clearly contextualized in established knowledge (Fortunato et al., 2018).”
Metadata
- Authors
- Fortunato, S., Bergstrom, C. T., Börner, K., Evans, J. A., Helbing, D., Milojević, S., Petersen, A. M., Radicchi, F., Sinatra, R., Uzzi, B., Vespignani, A., Waltman, L., Wang, D., & Barabási, A.-L.
- Affiliations
- Indiana University Bloomington (US)
- University of Washington (US)
- University of Chicago (US)
- ETH Zurich (CH)
- University of California, Merced (US)
- Northeastern University (US)
- Northwestern University (US)
- Leiden University (NL)
- Center for Network Science, Central European University, Budapest 1052, Hungary.
- Corresponding author
- Fortunato, Santo
- ORCID
- Venue
- Science
- Published
- 2018-03-02
- Volume
- 359
- Issue
- 6379
- ISSN
- 0036-8075 (ISSN-L)
- 1095-9203
- PMC
- PMC5949209
- OpenAlex
- W2793071066
- Cited by
- 1,497
- References
- 113
- Citations by year
- 2026: 222 · 2025: 223 · 2024: 223 · 2023: 197 · 2022: 164 · 2021: 180 · 2020: 126 · 2019: 113 · 2018: 43
- FWCI
- 22.72
- Type
- journal article
- Language
- en
- Open access
- Licence
- other-oa
- Locations
- Science (publishedVersion)
- PubMed (publishedVersion)
- Research at the University of Copenhagen (University of Copenhagen) (publishedVersion, other-oa)
- Europe PMC (PubMed Central) (acceptedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- IUScholarWorks Open (Indiana University) (submittedVersion)
- Funders
- NIH Office of the Director (P01 AG039347, U01CA198934, IIS-0910664)
- NSF Office of the Director (NCSE 1538763, EAGER1566393, NCN CP Supplement 1553044)
- AFOSR (FA9550-15-1-0077, FA9550-15-1-0162, FA9550-17-1-0089)
- European Commission (641191)
- Air Force Office of Scientific Research (FA9550-15-1-0364)
- Army Research Office (W911NF-15-1-0577)
- Topics
- Social Sciences › Library and Information Sciences › scientometrics and bibliometrics research
- Social Sciences › Business, Management and Accounting › Management of Technology and Innovation › Research, Science, and Academia
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
- Keywords
- science of science, research output, science policy, scientific careers, research funding, research frontiers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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