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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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“…evaluation of scientific projects and careers, i.e. for assigning grants, tenures, etc.; and on the recognition of a cumulative knowledge about IDR, for example through the claim of discovering essential laws about science itself and its progressive (self)-regularisation (Fortunato et al., 2018).”
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“The latter is instead quantified by the influence that a mentor’s status may have on their protégés’ academic success [12–15].”
BackgroundDebunking revolutionary paradigm shifts: evidence of cumulative scientific progress across science
Krauß, A. (2024) · Proceedings of the Royal Society A Mathematical Physical and Engineering Sciences · doi:10.1098/rspa.2024.0141 · Cited by 20
“These studies adopting a big data approach examine publications and their citations and provide important insights into the dynamics of science [3,5,28].”
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Katchanov, Y. L., Markova, Y. V., & Anatolyevna, S. N. (2019) · Heliyon · doi:10.1016/j.heliyon.2019.e02089 · Cited by 12
“Bibliometrics is a conceptual framework for understanding the statistical properties of scholarly communication [62, 63].”
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Recency predicts bursts in the evolution of author citations
Silva, F. N., Tandon, A., Amancio, D. R., Flammini, A., Menczer, F., Milojević, S., & Fortunato, S. (2020) · Quantitative Science Studies · doi:10.1162/qss_a_00070 · Cited by 11
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“The increasing availability of digital scholarly data offers unprecedented opportunities to explore the structure and evolution of science [19], [20], [21], [22].”
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“In recent years the type and scope of questions addressed with data gathered from these databases has expanded tremendously (Fortunato et al., 2018).”
BackgroundA Heterogeneous Dynamical Graph Neural Networks Approach to Quantify Scientific ImpactPreprint
Zhou, F., Xu, X., Li, C., Trajcevski, G., Zhong, T., & Zhang, K. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2003.12042 · Cited by 19
“F a ( ( n )) S S cited papers/authors) while most of them only have a few neighbors, greatly following the heavy-tailed distribution of citations [Fortunato et al. , 2018].”
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“From a science of science perspective [7], scientists and fields can sometimes get trapped in “fads” controlled by a few prominent and influential authors [22].”
BackgroundMethodA Hypergraph Approach for Estimating Growth Mechanisms of Complex Networks
Inoue, M., Pham, T., & Shimodaira, H. (2022) · IEEE Access · doi:10.1109/access.2022.3143612 · Cited by 11
“From the beginning of the 20th century to present, the average number of co-authors per paper has increased in almost all disciplines [25].”
BackgroundMeta theories of technological innovation based on the analysis of classic texts
Wei, X., Liu, R., & Chen, W. (2023) · Heliyon · doi:10.1016/j.heliyon.2023.e16779 · Cited by 12
“Building upon prior literature [23,24], this study regards classic texts within a given field as the foundation for meta-theoretical classification.”
MethodSelective and deceptive citation in the construction of dueling consensuses
Beers, A. L., Nguyễn, S., Starbird, K., West, J. D., & Spiro, E. S. (2023) · Science Advances · doi:10.1126/sciadv.adh1933 · Cited by 11
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“SciSci, on the other hand, is a set of theoretical and quantitative techniques to unveil the determinants of scientific discovery based on the structure composed of researchers, institutions, and ideas (Bernal, 1939; Fortunato, Bergstrom et al., 2018; Price, 1961, 1963).”
BackgroundHuegel, M. (2024) · Technovation · doi:10.1016/j.technovation.2024.103065 · Cited by 12
Yang, A. J., & Deng, S. (2024) · Quantitative Science Studies · doi:10.1162/qss_a_00323 · Cited by 12
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“In a context where, as noted by Fortunato et al., 43 public policymakers seek to understand the mechanisms that govern the evolution of science, this analysis of collaborative dynamics and thematic orientations becomes essential.”
BackgroundA framework for promoting disciplinary diversity and inclusion through epistemic justice
Nyssa, Z., Winkler‐Schor, S., Lôbo, D., Eyster, H. N., & Wright, A. J. (2024) · Conservation Biology · doi:10.1111/cobi.14409 · Cited by 11
“Multiple forms of expertise offer different approaches and theoretical insights that increase innovation and improve problem solving (Fortunato et al., 2018; Hall et al., 2018), and transdisciplinary academic teams outperform specialized teams on measures such as competitive funding bids (Sun et…”
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Bi, W., Chan, H. F., & Torgler, B. (2020) · Humanities and Social Sciences Communications · doi:10.1057/s41599-020-00608-6 · Cited by 11
BIOMEDICAL CONVERGENCE FACILITATED BY THE EMERGENCE OF TECHNOLOGICAL AND INFORMATIC CAPABILITIES
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“significance for knowledge production and scientific development [2].”
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A missing link? Network analysis as an empirical approach for critical physical geography
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Vasan, K., Gysi, D. M., & Barabási, A.-Ĺ. (2023) · iScience · doi:10.1016/j.isci.2023.108361 · Cited by 12
“36,37 Similarly, a pharmaceutical company’s choice of a target for a new drug is influenced by its potential market value and the likelihood that the drug suc-ceeds in clinical trials.”
BackgroundSelf-citationExponential authorship inflation in neuroscience and psychology from the 1950s to the 2020s.
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Empowering open science with reflexive and spatialised indicators
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“The emergence of a new highly interdisciplinary field is coined as a ‘science of science’ by Fortunato et al. (2018).”
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Patterns and evolution of publication and co-authorship in Social Sciences in Mexico
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Higher-order rich-club phenomenon in collaborative research grant networks
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Higher Education and the Exponential Rise of Science: Competition and Collaboration
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Ageing Knowledge Structure in Global River Basins
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Patterns of interest change in stack overflow
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Bratt, S., Langalia, M., & Nanoti, A. (2023) · Frontiers in Big Data · doi:10.3389/fdata.2023.1054655 · Cited by 10
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Idea engines: Unifying innovation & obsolescence from markets & genetic evolution to science
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Is higher team gender diversity correlated with better scientific impact?
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“Team science, a subfield within the broader Science of Science (Fortunato et al., 2018), is dedicated to examining and improving the prerequisites, collaborative dynamics, and resultant outcomes of team-based scientific programs (Stokols et al., 2008).”
InfluentialQuantifying the impact of biobanks and cohort studies
Dorantes-Gilardi, R., Ivey, K. L., Costa, L., Matty, R., Cho, K., Gaziano, J. M., & Barabási, A.-Ĺ. (2025) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2427157122 · Cited by 9
“Here, we fill this gap by relying on big data and the tools of Science of Science [21–24] to identify, catalog, and analyze the characteristics of 2,663 biobanks, mapping out all the publications, grants, patents, clinical trials, and public policy documents where these resources are textually…”
Self-citationA powerful lens for temporal network analysis: temporal motifs
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“Collaborations among researchers have long been studied to understand the innovation dynamics in science and technology [80].”
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Omer, A. A. A., & Dong, Y. (2025) · Publications · doi:10.3390/publications13030040 · Cited by 10
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BackgroundSinghania, M., Gupta, S., Chadha, G. Κ., Braune, É., Dana, L., & Idowu, S. Ο. (2023) · Environmental Science and Pollution Research · doi:10.1007/s11356-023-27828-y · Cited by 10
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Park, H. W., & Yoon, H. Y. (2023) · Journal of Medical Internet Research · doi:10.2196/46328 · Cited by 10
“Analyzing policy document publication citation practices is a new research field, which is currently being explored [27].”
Smith, T. B., Mao, R., Korotchenko, S., & Krohn, M. D. (2023) · Journal of Quantitative Criminology · doi:10.1007/s10940-023-09574-z · Cited by 9
“Scholars in the science of science (SciSci) (Fortunato et al. 2018) refer to fields like criminology as specialty areas —a complex and specialized area of research that borrows from a wide range of disciplines to focus on a narrow range of issues (i.e. law and criminal justice) that are difficult…”
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“Motivated by the above findings, in this paper we examine the temporal evolution of academic impact rankings, that is the ranking of authors in a given discipline based on the citations they accrue over a period of time – we are fully aware that citations are not a comprehensive measure of academic impact, yet citatio…”
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Effective Altruism as an Ethical Lens on Research Priorities
Garrett, K. A., Alcalá-Briseño, R. I., Andersen, K. F., Brawner, J. T., Choudhury, R. A., Delaquis, E., Fayette, J., Poudel, R., Purves, D., Rothschild, J., et al. (2019) · Phytopathology · doi:10.1094/phyto-05-19-0168-rvw · Cited by 10
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“Efforts often given for granted by major players in scholarly knowledge analytics and dismissed in one sentence where a list of data sources, often paywalled and thus not available to the general public, is provided [5].”
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Li, H., Wu, M., Wang, Y., & Zeng, A. (2022) · Journal of Informetrics · doi:10.1016/j.joi.2022.101321 · Cited by 11
Hou, T., Li, J., & Lin, J. (2022) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2022.3212793 · Cited by 10
“However, due to the exponential increase in knowledge [4], [7], searching for and recombining useful knowledge pieces are becoming increasingly difficult [25].”
BackgroundFinding Temporal Trends of Scientific Concepts.
Färber, M., & Jatowt, A. (2019) · Cited by 10
Lamirel, J.-C., Chen, Y., Cuxac, P., Shehabi, S. A., Dugué, N., & Liu, Z. (2020) · Scientometrics · doi:10.1007/s11192-020-03503-8 · Cited by 10
“Two major blocks of knowledge, corresponding to 2 complementary paths of development of Science of Science in China, were thus isolated—scientometrics, focusing on quantitative analysis, and scientific studies, focusing on philosophical analysis.”
ResultInfluentialComplex networks theory and precursors of financial crashes
Soloviev, V., Solovieva, V., Tuliakova, A., Hostryk, A., & Pichl, L. (2020) · doi:10.31812/123456789/4119 · Cited by 10
“Complex networks include electrical, transport, information, social, economic, biological, neural and other networks [1; 6; 10; 20; 18; 22; 24; 31].”
BackgroundDu, A., Head, M., & Brede, M. (2024) · Journal of Informetrics · doi:10.1016/j.joi.2024.101634 · Cited by 9
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Light, R., Gullickson, A., & Harrison, J. A. (2025) · PLoS ONE · doi:10.1371/journal.pone.0316913 · Cited by 9
Unveiling citation peaks: How innovation faces delayed recognition in science
Wu, R., & Shi, W. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104100 · Cited by 10
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Meta-HCI: First Workshop on Meta-Research in HCI
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“Self-reflective [85] and meta-scientific contributions [30, 47] are critically important to advance the HCI field as a whole.”
Comparative analysis of smart city scientific research trends in the USA and China
Lai, Y., & Zhao, H. (2025) · Nature Cities · doi:10.1038/s44284-025-00305-y · Cited by 9
“Scientific Scientific knowledge, as manifested in research papers, books, patents and other scholarly outputs, involves the systematic organization of concepts and relationships within scientific disciplines and broader fields 7 .”
Moreira‐Filho, J. T., Ranganath, D., Tieghi, R. S., Patton, R. M., Sutherland, V. L., Schmitt, C., Rooney, A. A., Fostel, J. M., Walker, V. R., Saddler, T. O., et al. (2025) · Wiley Interdisciplinary Reviews Computational Molecular Science · doi:10.1002/wcms.70047 · Cited by 12
Ramirez, A. M., & Petersen, A. M. (2025) · Research Policy · doi:10.1016/j.respol.2025.105370 · Cited by 17
Self-citationTracing Long-Term Outcomes of Basic Research Using Citation Networks
Onken, J., Miklos, A. C., & Aragon, R. (2020) · Frontiers in Research Metrics and Analytics · doi:10.3389/frma.2020.00005 · Cited by 9
“The increase in SoS studies has been fueled, in part, by greater access to digital data on the science, technology, and innovation enterprise (National Research Council, 2014b; Fortunato et al., 2018; Waldman and Lariviere, 2020).”
BackgroundBreakthrough potential of emerging research topics based on citation diffusion features
Xu, H., Winnink, J., Pang, H., Wen, S., & Chen, L. (2022) · Journal of Information Science · doi:10.1177/01655515211061219 · Cited by 9
Research strategies in click chemistry: Measuring its cognitive contents and knowledge flow
Zhu, J., Chen, M., He, L., Li, Q., & Jiang, X. (2022) · Chinese Chemical Letters · doi:10.1016/j.cclet.2022.107936 · Cited by 9
Predicting the Evolution of Scientific Communities by Interpretable Machine Learning Approaches
Tian, Y., Li, G., & Mao, J. (2022) · Journal of Informetrics · doi:10.1016/j.joi.2023.101399 · Cited by 9
Li, J., Wu, W.-H., Li, Z.-Z., Wang, W.-X., & Zhang, B. (2023) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2023.1131769 · Cited by 10
How to detect the sleeping beauty papers and princes in technology considering indirect citations?
Feng, S., Li, H., & Qi, Y. (2023) · Journal of Informetrics · doi:10.1016/j.joi.2023.101431 · Cited by 9
The structure of segregation in co-authorship networks and its impact on scientific production
Jaramillo, A. M., Williams, H. T. P., Perra, N., & de Menezes, R. P. (2023) · EPJ Data Science · doi:10.1140/epjds/s13688-023-00411-8 · Cited by 13
Arroyave, F. J., Jenkins, J. S., Shackelton, S., Jackson, B. K., & Petersen, A. M. (2024) · Journal of Environmental Management · doi:10.1016/j.jenvman.2024.120699 · Cited by 11
Self-citationImpact of Open Access Policy on Brazilian Science and Global Trends
Figueiredo, C. P., Neves, A. A. B., Pimentel, F., PIMENTEL, D., Mota-Araujo, H. P., Bem, A. F. D., Neto, B. A. D., & Mcmanus, C. M. (2024) · Anais da Academia Brasileira de Ciências · doi:10.1590/0001-3765202420231068 · Cited by 9
Towards a More Realistic Citation Model: The Key Role of Research Team Sizes
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“Namely, a larger number of authors per paper means that more individuals will be aware of a paper’s existence, leading to increased capability to ensure higher visibility [30].”
BackgroundSelf-citationGroup Decision-Making Based on Artificial Intelligence: A Bibliometric Analysis
Heradio, R., Fernández-Amorós, D., Cerrada, C., & Cobo, M. J. (2020) · Mathematics · doi:10.3390/math8091566 · Cited by 9
“The science of science provides a collection of techniques to analyze scientific documents to identify patterns and trends [29–32].”
Support for Young Researchers: Foreign Practices and the Possibility of their Application in Russia
Ilina, I. E., Zharova, E., & Koroleva, N. (2020) · Integration of Education · doi:10.15507/1991-9468.100.024.202003.352-376 · Cited by 9
Predicting the Evolution of Physics Research from a Complex Network Perspective
Liu, W., Saganowski, S., Kazienko, P., & Cheong, S. A. (2019) · Entropy · doi:10.3390/e21121152 · Cited by 11
“roups16, in our case groups correspond to TCs. The GEP process consists of six main steps: (1) time window definition, (2) temporal network creation, (3) group detection, (4) group evolution tracking, (5) evolution chain identification and feature calculation, and (6) classification using machine learning techniques. Tha…”
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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