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 makes the knowledge ecosystem vulnerable to perpetuating errors.
“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]
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'.
“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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Self-citationStochastic Dynamical Model of a Growing Citation Network Based on a Self-Exciting Point Process
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An efficient system to fund science: from proposal review to peer-to-peer distributions
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Self-citationDetecting Communities through Network Data
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Self-citationBias against Novelty in Science: A Cautionary Tale for Users of Bibliometric Indicators
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Runaway events dominate the heavy tail of citation distributions
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Characterizing scientific production and consumption in Physics
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Self-citationTurn the scientific method on ourselves
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Characterizing scientific production and consumption in PhysicsPreprint
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Self-citationGetting credit for peer review
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Self-citationOettl, A. (2012) · Nature · doi:10.1038/489496a · Cited by 17 · see citing works
Large Teams Have Developed Science and Technology; Small Teams Have Disrupted ItPreprint
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Self-citation
Flagged first, then by citations
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Cited by
Sorted by citations
The Diversity–Innovation Paradox in Science
Hofstra, B., Kulkarni, V., Galvez, S. M.-N., He, B. D., Jurafsky, D., & McFarland, D. A. (2020) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1915378117 · Cited by 1,000 · see citing works
Historical comparison of gender inequality in scientific careers across countries and disciplines
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Self-citationEmerging Trends in the Social and Behavioral Sciences
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Unequal effects of the COVID-19 pandemic on scientists
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“networks, patent networks, traffic networks, citation networks, and communication networks [1]–[3].”
BackgroundThe preeminence of ethnic diversity in scientific collaboration
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On scientific understanding with artificial intelligence
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“[73] Santo Fortunato, Carl T Bergstrom, Katy Börner,”
BackgroundGender-diverse teams produce more novel and higher-impact scientific ideas
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Self-citationOver-optimization of academic publishing metrics: observing Goodhart's Law in action
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“This research is a large-scale scientometrics study (also referred to as the “science of science” [30]).”
Data-driven materials research enabled by natural language processing and information extraction
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“24 The significant quantity of existing and new published literature.”
BackgroundData-driven modeling and learning in science and engineering
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Research Weaving: Visualizing the Future of Research Synthesis
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The Open Innovation in Science research field: a collaborative conceptualisation approach
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“Adding to the science of science literature (Brown, Deletic, and Wong 2015; Dasgupta and David 1994; Fortunato et al. 2018; Jones, Wuchty, and Uzzi 2008; Van Noorden 2015; Wuchty, Jones, and Uzzi 2007), we argue that a more complete understanding of the available knowledge is needed to manage…”
BackgroundEarly coauthorship with top scientists predicts success in academic careers
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pybliometrics: Scriptable bibliometrics using a Python interface to Scopus
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Anthropogenic biases in chemical reaction data hinder exploratory inorganic synthesis
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How to conduct a meta-analysis in eight steps: A practical guide
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Academic social networks: Modeling, analysis, mining and applications
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“Science of Science (SciSci) characterizes science as a complex, self-organizing and evolving network of academic information (Fortunato et al., 2018).”
The h-index is no longer an effective correlate of scientific reputation
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Hot streaks in artistic, cultural, and scientific careers
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Self-citationMapping migration studies: An empirical analysis of the coming of age of a research field
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Self-citationQuantifying NFT-driven networks in crypto art
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Self-citationIncreasing trend of scientists to switch between topics
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The universal decay of collective memory and attention
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Self-citationTracing the evolution of AI in the past decade and forecasting the emerging trends
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Exploring Gender Bias in Six Key Domains of Academic Science: An Adversarial Collaboration
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“Studies fall into three categories: (a) surveys based on selfreported publications; (b) administrative records from individual universities, granting agencies, or journals; and (c) bibliometric analyses of the published corpus of research over some specified time period (e.g., 2 years, 5 years, 10 years, lifetime), th…”
BackgroundInfluentialTen tips for developing interdisciplinary socio-ecological researchers
Kelly, R., Mackay, M., Nash, K. L., Cvitanovic, C., Allison, E. H., Armitage, D. R., Bonn, A., Cooke, S. J., Frusher, S. D., Fulton, E. A., et al. (2019) · Socio-Ecological Practice Research · doi:10.1007/s42532-019-00018-2 · Cited by 139 · see citing works
Discovering causal relations and equations from data
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Intellectual synthesis in mentorship determines success in academic careers
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Early-career setback and future career impact
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Self-citationSciSciNet: A large-scale open data lake for the science of science research
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Networks behind the morphology and structural design of living systems
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The Elephant in the Room: Race and STEM Diversity
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Scientific elite revisited: patterns of productivity, collaboration, authorship and impact
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Self-citationPublic use and public funding of science
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Self-citationData, measurement and empirical methods in the science of science
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“The science of science closely relates to several strands and communities of research, including metascience, scientometrics, the economics of science, research on research, science and technology studies, the sociology of science, metaknowledge and quantitative science studies 5 .”
BackgroundSelf-citationAutomated discovery of fundamental variables hidden in experimental data
Chen, B., Huang, K., Raghupathi, S., Chandratreya, I. P., Du, Q., & Lipson, H. (2022) · Nature Computational Science · doi:10.1038/s43588-022-00281-6 · Cited by 128 · see citing works
The NIH Open Citation Collection: A public access, broad coverage resource
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“Recently, an interdisciplinary field named “science of science” (SciSci) [558, 559] emerges, aiming to quantify, understand and predict scientific discoveries and the resulting outcomes of individuals and groups.”
BackgroundNew and atypical combinations: An assessment of novelty and interdisciplinarity
Fontana, M., Iori, M., Montobbio, F., & Sinatra, R. (2020) · Research Policy · doi:10.1016/j.respol.2020.104063 · Cited by 115 · see citing works
Self-citationImproving data access democratizes and diversifies science
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Fresh teams are associated with original and multidisciplinary research
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Self-citationArtificial intelligence tools expand scientists' impact but contract science's focus
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“Within this high-dimensional embedding space, we measure knowledge extent as the ‘diameter’ of vector space covered by a sampled batch of papers, which allows us to compare the coverage of topical ground between AI and non-AI papers in each given domain 43,44 (Fig.”
Self-citationScientific prize network predicts who pushes the boundaries of science
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Self-citationEstimating the deep replicability of scientific findings using human and artificial intelligence
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Self-citationNature’s reach: narrow work has broad impact
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Self-citationMethods to account for citation inflation in research evaluation
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Predicting research trends with semantic and neural networks with an application in quantum physics
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“…a broad collection of quantitative methods, from descriptive statistics and data visualization to advanced econometric methods, network science approaches, machine-learning algorithms, mathematical analysis, and computer simulation, including agent-based modeling ’ (Fortunato et al., 2018, p. 1).”
BackgroundMethodInfluentialKrenn, M., Buffoni, L., Coutinho, B. C., Eppel, S., Foster, J. G., Gritsevskiy, A., Lee, H., Lu, Y., Moutinho, J. P., Sanjabi, N., et al. (2023) · Nature Machine Intelligence · doi:10.1038/s42256-023-00735-0 · Cited by 96 · see citing works
“In fact, while scientists tend to study topics in which they are already directly involved [2, 3], often higher scientific impact results from the unexpected combination of more distant domains [10], which foster the search for those surprising and impactful associations.”
BackgroundQi, S., Hua, F., Xu, S., Zhou, Z., & Liu, F. (2021) · PLoS ONE · doi:10.1371/journal.pone.0254988 · Cited by 94 · see citing works
Quantum indistinguishability by path identity and with undetected photons
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Citation recommendation: approaches and datasets
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Multiscale impact of researcher mobility
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Self-citationDeep aspirations: towards a sustainable offshore Blue Economy
Novaglio, C., Bax, N., Boschetti, F., Emad, G. R., Frusher, S. D., Fullbrook, L., Hemer, M., Jennings, S., van Putten, I. E., Robinson, L. M., et al. (2021) · Reviews in Fish Biology and Fisheries · doi:10.1007/s11160-020-09628-6 · Cited by 86 · see citing works
“The current tendency to bias natural science should be avoided and true integration of social sciences should be the ultimate aim (Alexander et al. 2019, Fortunato et al. 2018).”
BackgroundResearch community dynamics behind popular AI benchmarks
Martínez-Plumed, F., Barredo, P., hÉigeartaigh, S. Ó., & Hernández-Orallo, J. (2021) · Nature Machine Intelligence · doi:10.1038/s42256-021-00339-6 · Cited by 86 · see citing works
Scientific laws of research funding to support citations and diffusion of knowledge in life science
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Keyword-citation-keyword network: a new perspective of discipline knowledge structure analysis
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Scientific Developments and New Technological Trajectories in Sensor Research
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The memory of science: Inflation, myopia, and the knowledge network
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Self-citationQuantifying the dynamics of failure across science, startups and security
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Self-citationCitation recommendation: approaches and datasets
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Quantifying the use and potential benefits of artificial intelligence in scientific research
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Self-citationA dataset of publication records for Nobel laureates
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Self-citationMetascience as a Scientific Social Movement
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The memory of science: Inflation, myopia, and the knowledge network
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Untangling the network effects of productivity and prominence among scientists
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New trends in bibliometric APIs: A comparative analysis
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A Scalable Hybrid Research Paper Recommender System for Microsoft Academic
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“nd hours of wasted e‡ort. „e current manual approaches are not scalable, with tens of thousands of papers being published everyday and the number of papers published globally increasing exponentially [11, 12]. To help alleviate this problem, a number of academic search engines have started adding recommender systems i…”
Background10 Guiding principles for learning in the laboratory
Seery, M. K., Agustian, H. Y., Christiansen, F. V., Gammelgaard, B., & Malm, R. H. (2023) · Chemistry Education Research and Practice · doi:10.1039/d3rp00245d · Cited by 72 · see citing works
“…further the development of understanding scientific processes and practices described in previous guiding principles, it is valuable to raise awareness about the extent to which scientific advancements are grounded in work done by large and often multi-disciplinary teams (Fortunato et al. , 2018).”
Success and luck in creative careers
Janosov, M., Battiston, F., & Sinatra, R. (2020) · EPJ Data Science · doi:10.1140/epjds/s13688-020-00227-w · Cited by 67 · see citing works
Self-citationDeep Graph Learning for Anomalous Citation Detection
Liu, J., Xia, F., Xu, F., Ren, J. F., & Liu, H. (2022) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3145092 · Cited by 71 · see citing works
The evolution of IJHCS and CHI: A quantitative analysis
Mannocci, A., Osborne, F., & Motta, E. (2019) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2019.05.009 · Cited by 69 · see citing works
Kiran, M., Xie, Y., Anjum, N., Ball, G., Pierscionek, B. K., & Russell, D. (2025) · Frontiers in Digital Health · doi:10.3389/fdgth.2025.1557467 · Cited by 81 · see citing works
S&T indicators in the wild: Contextualization and participation for responsible metrics
Ràfols, I. (2018) · Research Evaluation · doi:10.1093/reseval/rvy030 · Cited by 65 · see citing works
“Interactive methods allow the users to change classifications or analytical perspectives, and to obtain new analyses dynamically (on-the-fly) (Eck and Waltman, 2017; Fortunato et al., 2018).”
Decoding team and individual impact in science and invention
Ahmadpoor, M., & Jones, B. F. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1812341116 · Cited by 60 · see citing works
Shifu2: A Network Representation Learning Based Model for Advisor-advisee Relationship Mining
Liu, J., Xia, F., Wang, L., Xu, B., Kong, X., Tong, H., & King, I. (2019) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2019.2946825 · Cited by 64 · see citing works
“Obviously, the data can form various types of networks within the science of science [3].”
BackgroundWróbel, K., Gil, M., & Montewka, J. (2020) · Safety Science · doi:10.1016/j.ssci.2020.104797 · Cited by 63 · see citing works
Motif discovery in networks: A survey
Yu, S., Feng, Y., Zhang, D., Bedru, H. D., Xu, B., & Xia, F. (2020) · Computer Science Review · doi:10.1016/j.cosrev.2020.100267 · Cited by 63 · see citing works
Breaking Out of the Ivory Tower: A Large-scale Analysis of Patent Citations to HCI Research
Cao, H., Lu, Y., Deng, Y., McFarland, D. A., & Bernstein, M. S. (2023) · CHI Conference on Human Factors in Computing Systems · doi:10.1145/3544548.3581108 · Cited by 63 · see citing works
Understanding the onset of hot streaks across artistic, cultural, and scientific careers
Liu, L., Dehmamy, N., Chown, J., Giles, C. L., & Wang, D. (2021) · Nature Communications · doi:10.1038/s41467-021-25477-8 · Cited by 81 · see citing works
Self-citationSmith, T. B., Vacca, R., Mantegazza, L., & Capua, I. (2021) · Scientific Reports · doi:10.1038/s41598-021-01801-6 · Cited by 60 · see citing works
Hu, K., Liu, J., Li, B., Liu, L., Gharibzahedi, S. M. T., Su, Y., Jiang, Y., Tan, J., Yingkuan, W., & Guo, Y. (2019) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2019.01.011 · Cited by 61 · see citing works
3D Point Cloud for Cultural Heritage: A Scientometric Survey
Yang, S., Xu, S., & Huang, W. (2022) · Remote Sensing · doi:10.3390/rs14215542 · Cited by 65 · see citing works
Toward a cohesive understanding of ecological complexity
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“We refer particularly to ( 39 , 65 , 75 , 84 , 85 , 128 ) for comprehensive reviews of metrics designed to measure complexity.”
InfluentialQuantitative research assessment: using metrics against gamed metrics
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“Concurrently, there are several efforts to try to improve metrics and make them available for all scientists, authors, and papers in ways that allow for proper standardization and more legitimate use [7–9].”
- 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 · 2017: 3 · 2015: 1
- 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 › Decision Sciences › Statistics, Probability and Uncertainty › scientometrics and bibliometrics research
- Social Sciences › Decision Sciences › Information Systems and Management › 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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