The DOI matches its Crossref record; 113 of the 113 references it cites were found in a registry or an index.
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
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“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]
In the citing paper
“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]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Emergence of Scaling in Random Networks
Barabási, A.-L., & Albert, R. (1999) · Science · doi:10.1126/science.286.5439.509 · Cited by 36,135 · cited 1×
“Power-law tails can be generated through a cumulative advantage process (74), known as preferential attachment in network science (75), suggesting that the probability of citing a paper grows with the number of citations that it has already collected.”
An index to quantify an individual’s scientific research output
Hirsch, J. E. (2005) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0507655102 · Cited by 11,669
The file drawer problem and tolerance for null results.
Rosenthal, R. (1979) · Psychological Bulletin · doi:10.1037/0033-2909.86.3.638 · Cited by 9,068
The Matthew Effect in Science: The reward and communication systems of science are considered.
Merton, R. K. (1968) · Science · doi:10.1126/science.159.3810.56 · Cited by 6,720 · cited 2×
“Together, these datadriven efforts complement contributions from related research domains such as the economics (30) and sociology of science (60, 86).”
(1968) · PubMed · Cited by 4,803
The structure of scientific collaboration networks
Newman, M. E. J. (2001) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.98.2.404 · Cited by 4,550 · cited 1×
“A life-cycle analysis of eight fields (10) shows that successful fields undergo a process of knowledge and social unification that leads to a giant connected component in the collaboration network (104), corresponding to a sizeable group of regular coauthors.”
Price, D. J. D. S. (1963) · Columbia University Press eBooks · doi:10.7312/pric91844 · Cited by 4,400
The Essential Tension: Selected Studies in Scientific Tradition and Change
Kuhn, T. S. (1977) · doi:10.7208/chicago/9780226217239.001.0001 · Cited by 3,861
The Increasing Dominance of Teams in Production of Knowledge
Wuchty, S., Jones, B. F., & Uzzi, B. (2007) · Science · doi:10.1126/science.1136099 · Cited by 3,615 · cited 3×
“1 million patents reveals a nearly universal shift toward teams in all branches of science (50) (Fig.”
Self-citationPrice, D. J. D. S. (1965) · Science · doi:10.1126/science.149.3683.510 · Cited by 3,205
Flagged first, then by citations
Research works printed but not linked: 3
- N. J. Cooke M. L. Hilton Eds. Enhancing the Effectiveness of Team Science (National Academies Press 2015).
- H. F. Moed Citation Analysis in Research Evaluation (Springer 2010).
- J. R. Cole H. Zuckerman in The Idea of Social Structure: Papers in Honor of Robert K. Merton L. A. Coser Ed. (Harcourt Brace Jovanovich 1975) pp. 139–174.
Cited by
Sorted by citations
Items 801–900 of 1,002
What drives citations in tourism research?
Huang, S. S., Shao, Y., & Zhang, D. (2024) · Annals of Tourism Research · doi:10.1016/j.annals.2024.103870 · Cited by 2
Using TECHnology to predict the future of biomedical education
Harris, R. A., & Kazdağlı, H. (2024) · FEBS Open Bio · doi:10.1002/2211-5463.13953 · Cited by 2
“The increasing complexity of scientific challenges, particularly in areas such as systems biology, precision medicine, and climate science, requires not just collaboration but deeper integration across disciplines [3,4].”
Fusing structural and temporal information in citation networks for identifying milestone works
Zhou, Y., Gong, F., Wang, Y., Wang, R., & Zeng, A. (2025) · Chaos Solitons & Fractals · doi:10.1016/j.chaos.2025.116004 · Cited by 2
Miranda, J. C. D. C., Souza, R. F., Justi, A. C. A., & Zondervan, E. (2025) · International Journal of Hydrogen Energy · doi:10.1016/j.ijhydene.2025.01.089 · Cited by 2
Xu, H., Ma, M., Chen, L., Wang, C., & Asir, L. M. M. (2025) · Journal of Information Science · doi:10.1177/01655515251330617 · Cited by 2
Knowledge Dynamics in Scientific and Technological Collaboration: The Case of Greece and China
Karampekios, N., & Sioumalas-Christodoulou, K. (2025) · Journal of the Knowledge Economy · doi:10.1007/s13132-025-02803-9 · Cited by 2
“…queries on all the activities associated with a specific international scientific endeavor, which can then be combined with other similar metrics produced by/for other similar endeavors—not excluding national ones—has contributed to what Fortunato et al. (2018) have called the science of science.”
Ottino, J. M. (2025) · PNAS Nexus · doi:10.1093/pnasnexus/pgaf174 · Cited by 3
“The “manifesto” of the many authors involved in Fortunato et al. (63), announcing “Science of Science” as a new discipline — the area that focuses on the use of data analysis, complexity, and AI to understand the patterns and mechanisms of scientific breakthroughs and innovative developments — has…”
Measuring how computer science research translates into innovation and development
Cinus, F., Septiandri, A. A., Constantinides, M., & Quercia, D. (2025) · EPJ Data Science · doi:10.1140/epjds/s13688-025-00568-4 · Cited by 3
“The field of scientometrics, leveraging tools from network science, provides a quantitative approach to understanding the dynamics and impact of scientific research [4].”
Attuning Signals in Scientific Crowdfunding: What Works and What Doesn’t?
Hopp, C., Ooms, W., Kaminski, J., & López, M. P. (2025) · IEEE Transactions on Engineering Management · doi:10.1109/tem.2025.3589722 · Cited by 2
“It may even affect individual science metrics, such as one’s number of citations or one’s career perspectives [41], [42].”
Huang, Y., Luo, J., Shetty, V., & Jeon, M. (2025) · Journal of Clinical and Translational Science · doi:10.1017/cts.2025.10124 · Cited by 2
“Integrating concepts, theories, and methods from various disciplines has been shown to foster more innovative and impactful research [8,9].”
Wu, R., Chen, R.-Y., An, L., & Chen, C. (2025) · Journal of Informetrics · doi:10.1016/j.joi.2025.101712 · Cited by 2
A call for early-career teams in interdisciplinary microbiology research
Krinos, A. I., Muratore, D., Bittner, M., Akenga, P. C., Auerbach, L., Dills, M., Faure, E., Flores, E., Levine, N. M., Monteiro, T., et al. (2025) · Nature Microbiology · doi:10.1038/s41564-025-02097-2 · Cited by 2
The innovator's balance between academic social capital and disruptive innovation
Liu, X., Zhou, J., Zeng, A., & Cui, X. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104382 · Cited by 3
The increasing dominance of repeated citations from collaborative research groups in science
Gu, X., & Zeng, A. (2025) · Journal of Informetrics · doi:10.1016/j.joi.2025.101723 · Cited by 3
Huang, Y., Guo, J., Tian, C., Xiang, S., He, Y., & Ma, Y. (2025) · Journal of Data and Information Science · doi:10.2478/jdis-2025-0048 · Cited by 2
Yang, A. J., Guo, J., Shi, Y., Deng, S., & Zhao, S. X. (2025) · Humanities and Social Sciences Communications · doi:10.1057/s41599-025-05887-5 · Cited by 7
“Scienti fi c innovation, encompassing originality, transformative potential, and intellectual breadth, extends beyond mere productivity (Fortunato et al., 2018; Uzzi et al., 2013; Wang et al., 2013).”
Wu, J., Marsh, H. W., Guo, J., Reeve, J., Pekrun, R. H., Dicke, T., Jang, H.-R., & Basarkod, G. (2025) · Learning and Individual Differences · doi:10.1016/j.lindif.2025.102816 · Cited by 2
Toward a roadmap for addressing today's health dilemma–The 101-statement consensus report
Wirnitzer, K., Motevalli, M. S., Tanous, D., Drenowatz, C. J., Moser, M., Cramer, H., Rosemann, T. J., Wagner, K., Michalsen, A., Knechtle, B., et al. (2025) · Frontiers in Nutrition · doi:10.3389/fnut.2025.1676080 · Cited by 3
Citation importance-aware document representation learning for large-scale science mapping
Liang, Z., van Eck, N. J., Wu, X., Mao, J., & Li, G. (2025) · Information Processing & Management · doi:10.1016/j.ipm.2025.104557 · Cited by 3
“These visualizations, often referred to as “science maps”, provide insights into the organization of scientific knowledge, research trends, and collaborative patterns (Fortunato et al., 2018; Murray et al., 2023; Xie & Waltman, 2025).”
Cheng, Z., Liu, X., Zou, Y., Zhang, W., & Zhou, J. (2026) · European Journal of Education · doi:10.1111/ejed.70455 · Cited by 2
Wilmore, J. T., Forsyth, E., Campbell, C. D., Rowlands, L. J., Hancox, C. C., & Stewart, M. I. (2026) · Journal of Chemical Education · doi:10.1021/acs.jchemed.5c01135 · Cited by 2
Smart and International Campuses in Research Collaborations
Mukminin, A., Marzulina, L., Abdrazakova, A., Harto, K., Fridiyanto, F., Erlina, D., & Amir, R. Z. (2026) · IGI Global eBooks · doi:10.4018/979-8-3373-6831-3.ch012 · Cited by 2
Predictable serendipity: how new tools turn serendipity into systematic breakthroughs
Krauß, A. (2026) · Scientometrics · doi:10.1007/s11192-025-05503-y · Cited by 4
“…and profitable technologies and products they can invest in. Major scientific breakthroughs attract attention more than any other aspect of science and there is tremendous demand for making discoveries more predictable (Clauset et al., 2017; Fortunato et al., 2018; Wang & Barabási, 2021).”
Scholarly discourse of systems thinking within and beyond STEM education
Trujillo, C., Momsen, J. L., Wyse, S. A., Speth, E. B., & Long, T. M. (2026) · International Journal of STEM Education · doi:10.1186/s40594-026-00603-4 · Cited by 2
Váscones-Román, F. F., Váscones-Román, J., & Fuentes-Garcia, S. J. (2026) · Neurosurgical Review · doi:10.1007/s10143-026-04290-1 · Cited by 2
Tsiong, S. (., Feng, J., & Zhang, M. (2026) · Managerial and Decision Economics · doi:10.1002/mde.70115 · Cited by 2
“By introducing new ideas and reshaping the knowledge landscape, technological breakthroughs and disruptive innovations are widely regarded as key drivers of economic growth and industrial transformation (Fortunato et al. 2018; Yang 2025).”
Guidetti, P., Bianchi, C. N., John, B., Pelke, N., & Boudouresque, C. (2026) · Research Integrity and Peer Review · doi:10.1186/s41073-026-00206-1 · Cited by 2
“Also, the volume of scientific literature has shown exponential growth, a sort of deluge of data and information, which does not correspond to more or better knowledge and even less to innovation [42].”
From Profiles to Promising Paths: A Semantic Group Recommender for Novel Academic Topic Discovery
Ayala-Tipan, C., Recalde, L., & Loza-Aguirre, E. (2026) · Information · doi:10.3390/info17070715 · Cited by 2
Evidence-Based Systematic Literature Reviews in the Cloud
Ruiz‐Rube, I., Person, T., Mota, J. M., Dodero, J. M., & González-Toro, Á. R. (2018) · Lecture notes in computer science · doi:10.1007/978-3-030-03496-2_12 · Cited by 2
Minondo, A. (2022) · Scientometrics · doi:10.1007/s11192-022-04267-z · Cited by 2
“Gorodnichenko et al. (2019) found that presenting a paper at major conferences in economics increases the probability of publishing it in a high-quality journal and enhances its visibility.”
BackgroundКолективне авторство в українській науці: маргінальний ефект чи нове явище?
Mryglod, O., & Mryglod, I. M. (2020) · Visnik Nacional noi academii nauk Ukrai ni · doi:10.15407/visn2020.07.034 · Cited by 2
Maximizing Benefits From Punctual Ocean Infrastructure: An Ethical Perspective
Whoriskey, F. G., Barbier, M., Mazur, M., Hahn, T., Kritzer, J. P., & Vallee, R. (2021) · Frontiers in Marine Science · doi:10.3389/fmars.2021.733822 · Cited by 2
Joint Detection of Community and Structural Hole Spanner of Networks in Hyperbolic Space
Nie, Q., Jiang, H., Zhong, S.-D., Wang, Q., Wang, J.-J., Wang, H., & Wu, L.-H. (2022) · Entropy · doi:10.3390/e24070894 · Cited by 3
Mingueza, F. B., Ruiz-Manzanares, G., Gómez‐Gardeñes, J., Tarancón, A., & Íñiguez, D. (2023) · Journal of Complex Networks · doi:10.1093/comnet/cnad012 · Cited by 2
Manfren, M. (2023) · Rivista Tema · doi:10.30682/tema0901c · Cited by 2
Hey, T. (2023) · OECD eBooks · doi:10.1787/7b7b1bce-en · Cited by 4
Schlembach, M. C., & Mischo, W. H. (2023) · Science & Technology Libraries · doi:10.1080/0194262x.2023.2238013 · Cited by 2
“The online availability of detailed bibliographic data covering extended runs of journal titles, particularly through the Web of Science and Scopus , has provided a major benefit to bibliometric analysis and study (Fortunato et al. 2018; Garfield 1955; Visser, van Eck, and Waltman 2021).”
BackgroundXu, X., Zeng, Z., & Chang, Y. (2023) · Learned Publishing · doi:10.1002/leap.1571 · Cited by 2
“…has limitations due to issues such as highly skewed citation distributions (Kiesslich et al., 2021), heterogeneity of citation densities (Kim & Chung, 2018), and citation variation among academic disciplines (Fortunato et al., 2018), which have been widely recognized and partially addressed.”
BackgroundRanking academic institutions by means of institution–publication networks
Cao, H., Gao, C., & Wang, Z. (2023) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2023.129075 · Cited by 2
Citation Impact of Institutional Repositories in Selected Higher Learning Institutions in Tanzania
Msoffe, G., & Buhomoli, O. S. (2023) · East African Journal of Science Technology and Innovation · doi:10.37425/eajsti.v4i3.717 · Cited by 2
A new evolutional model for institutional field knowledge flow network
Guo, J., Wang, K., Liao, X., & Liu, X. (2024) · Journal of Data and Information Science · doi:10.2478/jdis-2024-0009 · Cited by 2
Progress in the valorization of biomass: a statistical perspective
Barabás, B., Fülöp, O., Nagy, M., & Pályi, G. (2024) · Journal of Mathematical Chemistry · doi:10.1007/s10910-024-01575-y · Cited by 2
A complement to the novel disruption indicator based on knowledge entities
Tong, T., Wang, W., & Ye, F. Y. (2024) · Journal of Informetrics · doi:10.1016/j.joi.2024.101524 · Cited by 2
Evaluation of Brazilian women’s participation in the CNPQ in the field of medical research
Keffer, G. M., de Sousa, Á. A. D., de Oliveira, F. E. S., de Magalhães, M. J. D. S., OLIVEIRA, E. A., & Júnior, H. M. (2024) · Revista Brasileira de Educação Médica · doi:10.1590/1981-5271v48.2-2023-0271.ing · Cited by 2
Malaterre, C., & Lareau, F. (2024) · Scientometrics · doi:10.1007/s11192-024-05047-7 · Cited by 2
“…for instance to uncover latent social structures or “hidden colleges” between interacting scientists (Crane, 1969), or to examine the dynamics of science, including the role of networks and institutions over such issues as problem selection, discovery, or collaboration (Fortunato et al., 2018).”
SSRES: A Student Academic Paper Social Recommendation Model Based on a Heterogeneous Graph Approach
Guo, Y., & Zhou, Z. (2024) · Mathematics · doi:10.3390/math12111667 · Cited by 4
Application of Neuroscience Methods in HRDM for Brain-Based Human Capital Optimization
Patangia, B., Rithani, J. M., & Alfonso, T. (2024) · Advances in human resources management and organizational development book series · doi:10.4018/979-8-3693-1858-4.ch013 · Cited by 4
Wang, J., & Mao, Z. (2024) · International Journal of Sport and Exercise Psychology · doi:10.1080/1612197x.2024.2406533 · Cited by 2
“On the other side of the coin, scientometric reviews can give information about the current developments, hotspots and frontiers in a research field (Chen & Song, 2019; Fortunato et al., 2018).”
Niu, L., Si, X., & Ma, F. (2024) · The Electronic Library · doi:10.1108/el-06-2023-0145 · Cited by 2
Large language models recover scientific collaboration networks from text
Jeyaram, R., Ward, R., & Santolini, M. (2024) · Applied Network Science · doi:10.1007/s41109-024-00658-8 · Cited by 4
Researching Organic Solar Cells from the Perspective of Literature Big Data Analysis
Wang, Q., Liu, Z., Zhao, S., Yan, Y., Li, X., Zhang, Y., & Zhang, X. (2024) · Advanced Intelligent Systems · doi:10.1002/aisy.202400306 · Cited by 2
A Decade of Vertebrate Palaeontology Research in the UK: Bibliometric and Topic Modelling Analysis
Wang, H., Han, D., & Pan, Z. (2024) · Geosciences · doi:10.3390/geosciences14110314 · Cited by 2
López, E. D., Thielicke, V., & Arya, N. (2024) · Societies · doi:10.3390/soc14120245 · Cited by 2
“Almost every society asks their students and scholars at some point to produce more or less tangible results [73] (pp. 347–349), [74] (p. 3).”
Designing a Dynamic Network of Knowledge of Micro-learning Content
Singh, P. J., & Gera, R. (2024) · World Conference on Complex Systems · doi:10.1109/wccs62745.2024.10765575 · Cited by 2
“By examining where instructors focus their material, and comparing to current SciSci studies, we can identify gaps where the next generation of students and potential researchers may need more learning content, and where we may shift resources away from heavily populated study areas [2].”
Иванов, Д. В., & Deviatko, I. F. (2024) · Monitoring obŝestvennogo mneniâ: èkonomičeskie i socialʹnye peremeny · doi:10.14515/monitoring.2024.6.2627 · Cited by 1
Taşkın, Z. (2025) · Journal of the Association for Information Science and Technology · doi:10.1002/asi.24982 · Cited by 6
“” (Chudlarsky & Dvorak, 2019) compares the coverage and feasibility of using Crossref's Open Citation Index (COCI) against Web of Science for research evaluation purposes.”
InfluentialDiscovering emergent connections in quantum physics research via dynamic word embeddings
Frohnert, F., Gu, X., Krenn, M., & van Nieuwenburg, E. (2025) · Machine Learning Science and Technology · doi:10.1088/2632-2153/adb00a · Cited by 5
“Overcoming this isolation can help researchers from leveraging advancements in adjacent fields that could accelerate progress in their own field [2–5].”
Деспотули, А. Л., Kazmiruk, V. V., Despotuli, A., & Andreeva, A. V. (2025) · Energies · doi:10.3390/en18051084 · Cited by 2
LLMs4Synthesis: Leveraging Large Language Models for Scientific Synthesis
Giglou, H. B., D’Souza, J., & Auer, S. (2024) · ACM/IEEE Joint Conference on Digital Libraries · doi:10.1145/3677389.3702565 · Cited by 14
“The motivation for this endeavor stems from the growing complexity and volume of scientific literature [7, 16, 41], which challenges researchers in achieving timely, comprehensive knowledge syntheses.”
Molecular biology and design research
Molins-Pitarch, C., Krebs, J., & Brena, D. (2025) · EMBO Reports · doi:10.1038/s44319-025-00434-4 · Cited by 2
“Yet, as scienti fi c research has grown increasingly specialized (Fortunato et al, 2018), communicating complex concepts to others, even within the scienti fi c community, has become a signi fi cant challenge, and molecular biology is no exception.”
Understanding knowledge growth in scientific collaboration process: Evidence from NSFC projects
Yao, Z., Huang, X., Song, H., Rong, G., & Ma, F. (2025) · Journal of Informetrics · doi:10.1016/j.joi.2025.101664 · Cited by 2
CTEA: Camouflaged topological element attack via causal influence discovery
Jia, J., Gao, P., Luo, M., Wu, C., & Guo, J. (2025) · Expert Systems with Applications · doi:10.1016/j.eswa.2025.129160 · Cited by 2
The scientometric approach to Code Biology: What the title tells about the field
Paredes, O., & Prinz, R. (2025) · Biosystems · doi:10.1016/j.biosystems.2025.105552 · Cited by 2
Team careers in science: formation, composition and success of persistent collaborations
Chowdhary, S., Musciotto, F., Gallo, L., & Battiston, F. (2026) · npj Complexity · doi:10.1038/s44260-026-00081-3 · Cited by 8
Crossing borders in the 19th century and now — two examples of weaving a scientific network
Folk, & Holovatch, Y. V. (2020) · Condensed Matter Physics · doi:10.5488/cmp.23.23001 · Cited by 2
Influence in economics and aging
Jelnov, P., & Weiss, Y. (2021) · Labour Economics · doi:10.1016/j.labeco.2021.101992 · Cited by 2
Yair, G. (2021) · International series in management science/operations research/International series in operations research & management science · doi:10.1007/978-3-030-74051-1_4 · Cited by 2
Re‐imagining Priorities for Chemistry: A Central Science for “Freedom from Fear and Want”
Matlin, S. A., Krief, A., Hopf, H., & Mehta, G. (2021) · Angewandte Chemie · doi:10.1002/ange.202108067 · Cited by 2
Timchenko, M. V., & Kishko, O. N. (2023) · Perspectives of science and education · doi:10.32744/pse.2023.1.12 · Cited by 2
Djidjian, R., & Hovhannisyan, H. (2023) · wisdom · doi:10.24234/wisdom.v25i1.979 · Cited by 2
The scientific applications of big data in science of science
(2022) · Data Science and Informetrics · doi:10.59494/dsi.2022.3.3 · Cited by 2
“…scientific research activities, scientific research behavior and scientific research achievements is constantly improving, and the accessibility of structured digital information with in-depth index is increasingly improved, so more and more data analysis tools can be used (Fortunato et al., 2018).”
MethodThe evolution of evolutionary linguistics
Brar, P., & Camargo, C. Q. (2023) · Journal of Language Evolution · doi:10.1093/jole/lzae003 · Cited by 2
The Science of Science and a Multilayer Network Approach to Scientists' Ranking
Sideris, G., Katsaros, D., Sidiropoulos, A., & Manolopoulos, Y. (2018) · International Database Engineering and Applications Symposium (IDEAS) · doi:10.1145/3216122.3229862 · Cited by 2
“Its aim is mainly to bring awareness about the tremendous opportunities that open in the Science of Science context rather than become a yet another index.”
BackgroundInfluentialDu, Z., Cai, Q., Ertem, Z., & Bai, Y. (2019) · Physics of Life Reviews · doi:10.1016/j.plrev.2019.01.014 · Cited by 2
“…computations and applications However, a fundamental challenge going forward is accounting for the potential life cycle of this research direction We suggest that further insights can be gained by carrying out studies on science of science [15,16] of Physarum -based modelling and computations.”
BackgroundNagy, G., & Molnár, G. (2018) · Magyar Pedagógia · doi:10.17670/mped.2018.3.203 · Cited by 2
Enriching and analyzing small citation networks: A case study on transistor’s history
Benatti, A., de Arruda, H. F., Silva, F. N., & Costa, L. D. F. (2021) · Physica A Statistical Mechanics and its Applications · doi:10.1016/j.physa.2021.125901 · Cited by 2
MAVAC: Mapping and Visualization of Academic Collaborations with a Focus on Diversity
McNichols, L., Pineda, S., Sauerborn, E., Tat, B., Yoo, K., Lehr, J., Wood, Z. J., & Migler, T. (2021) · Springer proceedings in complexity · doi:10.1007/978-3-030-81854-8_8 · Cited by 2
The Global Amylase Research Trend in Food Science Technology: A Data-Driven Analysis
Ban, X., Guo, Y., Kaustubh, B., Li, C., Gu, Z., Hu, K., & Li, Z. (2021) · Food Reviews International · doi:10.1080/87559129.2021.1961267 · Cited by 2
Associations between author-level metrics in subsequent time periods
Brito, A. C. M., Silva, F. N., & Amancio, D. R. (2021) · RePEc: Research Papers in Economics · Cited by 2
Glenski, M., & Volkova, S. (2021) · doi:10.18653/v1/2021.cinlp-1.7 · Cited by 2
(2019) · Nature Physics · doi:10.1038/s41567-019-0667-5 · Cited by 2
Use of Publication Dynamics to Distinguish Cancer Genes and Bystander Genes
Bányai, L., Trexler, M., & Patthy, L. (2022) · Genes · doi:10.3390/genes13071105 · Cited by 2
Heavy-tailed distribution of the number of papers within scientific journals
Delabays, R., & Tyloo, M. (2022) · Quantitative Science Studies · doi:10.1162/qss_a_00201 · Cited by 3
Koponen, I. T., & Södervik, I. (2022) · Entropy · doi:10.3390/e24081058 · Cited by 2
Fotovoz como metodología innovadora en educación superior
Rodríguez, D. I. M., Rodríguez, C. C., & Sebastián, J. B. (2022) · TECHNO REVIEW International Technology Science and Society Review /Revista Internacional de Tecnología Ciencia y Sociedad · doi:10.37467/revtechno.v11.3795 · Cited by 2
Is Lottery a Better Way of Resource Distribution Than Baseline Funding?
Sakamoto, T. (2023) · Philosophy of Science · doi:10.1017/psa.2023.49 · Cited by 2
Criteria and systems for individual assessment of a scientist: world experience
Balanchuk, I. S., & Mykhalchenkova, O. Y. (2023) · Science Technologies Innovation · doi:10.35668/2520-6524-2023-4-09 · Cited by 2
Malaterre, C., & Lareau, F. (2023) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2023.305 · Cited by 2
“…on latent social structures or “hidden colleges” (Crane, 1969) and to a broader understanding of the dynamics of science, including the role of networks of scientists and institutions over such issues as problem selection, discovery, collaboration or even career dynamics (Fortunato et al., 2018).”
BackgroundHale, J. F., Thorndyke, L. E., Milner, R. J., & Vitello-Cicciu, J. M. (2024) · Journal of Professional Nursing · doi:10.1016/j.profnurs.2024.02.003 · Cited by 2
The Effect of the COVID Pandemic on Clinical Psychology Research: A Bibliometric Analysis
Anders, J., & Vitevitch, M. S. (2024) · Behavioral Sciences · doi:10.3390/bs14060463 · Cited by 3
“The science of science uses various analysis techniques and measures to examine how science itself works [17], with the goal of discovering general properties, trends, or characteristics that might help science administrators and policy makers, scientists in an entire discipline, or individual…”
BackgroundMethodInfluentialEl rol de las redes nacionales de investigación y educación en las Ciencias Sociales
Cadenas, L. E. (2020) · Anuario electrónico de estudios en Comunicación Social Disertaciones · doi:10.12804/revistas.urosario.edu.co/disertaciones/a.7608 · Cited by 2
Rough Sets: A Bibliometric Analysis from 2014 to 2018
Heradio, R., Fernández-Amorós, D., Moral-Muñoz, J. A., & Cobo, M. J. (2020) · Hawaii International Conference on System Sciences (HICSS) · doi:10.24251/hicss.2020.215 · Cited by 2
“Particularly, the science of science [8] is delved to analyze scientific documents to detect trends and patterns, employing bibliometric and science mapping analysis [9, 10, 8].”
BackgroundDynamics of Scientific Collaboration Networks Due to Academic Migrations
Paraskevopoulos, P., Boldrini, C., Passarella, A., & Conti, M. (2020) · Lecture notes in computer science · doi:10.1007/978-3-030-60975-7_21 · Cited by 2
“Mobility is an important aspect of a researcher’s life, affecting the career of the scientist in many ways [3,4,7,9,12,13,8].”
Learning Global Proliferation Expertise Evolution Using AI-Driven Analytics and Public Information
Glenski, M., Ayton, E., Soni, S. A., Saldanha, E., Arendt, D., Quiter, B. J., Cooper, R., & Volkova, S. (2022) · IEEE Transactions on Nuclear Science · doi:10.1109/tns.2022.3162216 · Cited by 2
Optimization in AEC-AI 4.0 Industry Multi-level Adaptive Methodology for Knowledge Mapping
Maureira, C., Allende‐Cid, H., & García, J. (2022) · Lecture notes in networks and systems · doi:10.1007/978-981-19-2397-5_65 · Cited by 2
A Simple, interpretable method to identify surprising topic shifts in scientific fields
Cheng, L., Foster, J. G., & Lee, H. (2022) · Frontiers in Research Metrics and Analytics · doi:10.3389/frma.2022.1001754 · Cited by 2
Many-Citednesss: Citations Measure More than Just Scientific ImpactPreprint
D’Ippoliti, C. (2017) · SSRN Electronic Journal · doi:10.2139/ssrn.2993971 · Cited by 2
International differences in basic research grant funding - a sytematic comparison.
WIFO, Janger, J., Schmidt, N. C., & Strauss, A. W. (2019) · doi:10.22163/fteval.2019.345 · Cited by 2
Analysis of author gender in TPB, 1991–2018
Severson, A. L., Uricchio, L. H., Arbisser, I. M., Glassberg, E. C., & Rosenberg, N. A. (2019) · Theoretical Population Biology · doi:10.1016/j.tpb.2019.04.001 · Cited by 2
The wisdom of the few: Predicting collective success from individual behaviorPreprint
Mariani, M. S., Gimenez, Y., Brea, J., Minnoni, M., Algesheimer, R., & Tessone, C. J. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2001.04777 · Cited by 2
“Data-driven success predictions have recently gained traction in various scientific disciplines beyond management science, including the science of science (Fortunato et al. 2018), social sciences (Hofman et al. 2017), and macroeconomics (Tacchella et al. 2018), among others.”
Background
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 ›
Are you an author, or do you see a problem with this page? Request a review
