Skip to main content Accessibility help

We use cookies to distinguish you from other users and to provide you with a better experience on our websites. Close this message to accept cookies or find out how to manage your cookie settings.

Close cookie message
×
Home
Hostname: page-component-55597f9d44-xbgml Total loading time: 1.398 Render date: 2022-08-18T11:55:15.070Z Has data issue: true Feature Flags: { "shouldUseShareProductTool": true, "shouldUseHypothesis": true, "isUnsiloEnabled": true, "useRatesEcommerce": false, "useNewApi": true } hasContentIssue true
The Science of Science The Science of Science
Buy print or eBook[Opens in a new window]

Book contents

References

Published online by Cambridge University Press:  07 February 2021

Dashun Wang  and
Albert-László Barabási
Dashun Wang
Affiliation:
Northwestern University, Illinois
Albert-László Barabási
Affiliation:
Northeastern University, Boston
Get access

Summary

A summary is not available for this content so a preview has been provided. Please use the Get access link above for information on how to access this content.
Image of the first page of this content. For PDF version, please use the ‘Save PDF’ preceeding this image.'
Type
Chapter
Information
The Science of Science , pp. 270 - 295
Publisher: Cambridge University Press
Print publication year: 2021

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Purchase

Buy Book

Buy print or eBook[Opens in a new window]

References

Dennis, W., Bibliographies of eminent scientists. Scientific Monthly, 79(3), (1954), 180–183.Google Scholar
Simonton, D. K., Creative productivity: A predictive and explanatory model of career trajectories and landmarks. Psychological Review, 104(1), (1997), 66.CrossRefGoogle Scholar
Brodetsky, S., Newton: Scientist and man. Nature, 150, (1942), 698–699.CrossRefGoogle Scholar
Dong, Y., Ma, H., Shen, Z., et al., A century of science: Globalization of scientific collaborations, citations, and innovations, in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (New York: ACM, 2017), pp. 1437–1446.CrossRefGoogle Scholar
Sinatra, R., Deville, P., Szell, M., et al., A century of physics. Nature Physics, 11(10), (2015), 791–796.CrossRefGoogle Scholar
Goldberger, M. L., Maher, B. A., and Flattau, P. E. E., Doctorate Programs in the United States: Continuity and Change (Washington, DC: The National Academies Press, 1995).Google Scholar
Baird, L., Departmental publication productivity and reputational quality: Disciplinary differences. Tertiary Education and Management, 15(4), 2009), 355–369.CrossRefGoogle Scholar
Ioannidis, J. P., Why most published research findings are false. PLoS Medicine, 2(8), (2005), e124.CrossRefGoogle ScholarPubMed
Shockley, W., On the statistics of individual variations of productivity in research laboratories. Proceedings of the IRE, 45(3), (1957), 279–290.CrossRefGoogle Scholar
Fronczak, P., Fronczak, A., and Hołyst, J. A., Analysis of scientific productivity using maximum entropy principle and fluctuation-dissipation theorem. Physical Review E, 75(2), (2007), 026103.CrossRefGoogle ScholarPubMed
Lotka, A. J., The frequency distribution of scientific productivity. Journal of Washington Academy Sciences, 16(12), (1926), 317–324.Google Scholar
de Solla Price, D., Little Science, Big Science and Beyond (New York: Columbia University Press, 1986).Google Scholar
Lehman, H. C., Men’s creative production rate at different ages and in different countries. The Scientific Monthly, 78, (1954), 321–326.Google Scholar
Allison, P. D. and Stewart, J. A., Productivity differences among scientists: Evidence for accumulative advantage. American Sociological Review, 39(4), (1974), 596–606.Google Scholar
Radicchi, F. and Castellano, C., Analysis of bibliometric indicators for individual scholars in a large data set. Scientometrics, 97(3), (2013), 627–637.Google Scholar
Barabási, A.-L., The Formula: The Universal Laws of Success (London: Hachette, 2018).Google Scholar
Bertsimas, D., Brynjolfsson, E., Reichman, S., et al., OR forum–tenure analytics: Models for predicting research impact. Operations Research, 63(6), (2015), 1246–1261.CrossRefGoogle Scholar
Stephan, P. E., How Economics Shapes Science vol. 1 (Cambridge, MA: Harvard University Press, 2012).CrossRefGoogle Scholar
Clauset, A., Arbesman, S., and Larremore, D. B., Systematic inequality and hierarchy in faculty hiring networks. Science Advances, 1(1), (2015), e1400005.CrossRefGoogle ScholarPubMed
Broad, W. J., The publishing game: Getting more for less. Science, 211(4487), (1981), 1137–1139.CrossRefGoogle Scholar
Smalheiser, N. R. and Torvik, V. I., Author name disambiguation. Annual review of information science and technology, 43(1), (2009), 1–43.CrossRefGoogle Scholar
Ferreira, A. A., Gonçalves, M. A., and Laender, A. H., A brief survey of automatic methods for author name disambiguation. ACM SIGMOD Record, 41(2), (2012), 15–26.CrossRefGoogle Scholar
Torvik, V. I., Weeber, M., Swanson, D. R., et al., A probabilistic similarity metric for Medline records: A model for author name disambiguation. Journal of the American Society for Information Science and Technology, 56(2), (2005), 140–158.CrossRefGoogle Scholar
Hey, A. J. and Walters, P., Einstein’s Mirror (Cambridge, UK: Cambridge University Press, 1997).CrossRefGoogle Scholar
Mermin, D. N., My life with Landau, in Gotsman, E. A., Ne’eman, Y., and Voronel, A., eds., Frontiers of Physics, Proceedings of the Landau Memorial Conference (Oxford: Pergamon Press, 1990), p. 43.Google Scholar
Hirsch, J. E., An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences, 102(46), (2005), 16569–16572.CrossRefGoogle ScholarPubMed
Van Noorden, R., Metrics: A profusion of measures. Nature, 465(7300), (2010), 864–866.CrossRefGoogle ScholarPubMed
Van Raan, A. F., Comparison of the Hirsch-index with standard bibliometric indicators and with peer judgment for 147 chemistry research groups. Scientometrics, 67(3), (2006), 491–502.CrossRefGoogle Scholar
Zhivotovsky, L. and Krutovsky, K., Self-citation can inflate h-index. Scientometrics, 77(2), (2008), 373–375.CrossRefGoogle Scholar
Purvis, A., The h index: playing the numbers game. Trends in Ecology and Evolution, 21(8), (2006), 422.CrossRefGoogle Scholar
Hirsch, J. E., Does the h index have predictive power? Proceedings of the National Academy of Sciences, 104(49), (2007), 19193–19198.CrossRefGoogle Scholar
Cattell, J. M., American Men Of Science: A Biographical Directory (New York: The Science Press, 1910).Google Scholar
Lane, J., Let’s make science metrics more scientific. Nature, 464(7288), (2010), 488–489.CrossRefGoogle ScholarPubMed
Alonso, S., Cabrerizo, F. J., Herrera-Viedma, E., et al., hg-index: A new index to characterize the scientific output of researchers based on the h-and g-indices. Scientometrics, 82(2), (2009), 391–400.CrossRefGoogle Scholar
Alonso, S., Cabrerizo, F. J., Herrera-Viedma, E., et al., h-Index: A review focused in its variants, computation and standardization for different scientific fields. Journal of Informetrics, 3(4), (2009), 273–289.CrossRefGoogle Scholar
Burrell, Q. L., On the h-index, the size of the Hirsch core and Jin’s A-index. Journal of Informetrics, 1(2), (2007), 170–177.CrossRefGoogle Scholar
Cabrerizo, F. J., Alonso, S., Herrera-Viedma, E., et al., q2-Index: Quantitative and qualitative evaluation based on the number and impact of papers in the Hirsch core. Journal of Informetrics, 4(1), (2010), 23–28.CrossRefGoogle Scholar
Jin, B., Liang, L., Rousseau, R., et al., The R-and AR-indices: Complementing the h-index. Chinese science bulletin, 52(6), (2007), 855–863.CrossRefGoogle Scholar
Kosmulski, M., A new Hirsch-type index saves time and works equally well as the original h-index. ISSI Newsletter, 2(3), (2006), 4–6.Google Scholar
Egghe, L., An improvement of the h-index: The g-index. ISSI newsletter, 2(1), (2006), 8–9.Google Scholar
Egghe, L., Theory and practise of the g-index. Scientometrics, 69(1), (2006), 131–152.Google Scholar
Dorogovtsev, S. N. and Mendes, J. F. F., Ranking scientists. Nature Physics, 11(11), (2015), 882–883.Google Scholar
Radicchi, F., Fortunato, S., and Castellano, C., Universality of citation distributions: Toward an objective measure of scientific impact. Proceedings of the National Academy of Sciences, 105(45), (2008), 17268–17272.CrossRefGoogle ScholarPubMed
Kaur, J., Radicchi, F., and Menczer, F., Universality of scholarly impact metrics. Journal of Informetrics, 7(4), (2013), 924–932.CrossRefGoogle Scholar
Sidiropoulos, A., Katsaros, D., and Manolopoulos, Y., Generalized Hirsch h-index for disclosing latent facts in citation networks. Scientometrics, 72(2), (2007), 253–280.CrossRefGoogle Scholar
Hirsch, J., An index to quantify an individual’s scientific research output that takes into account the effect of multiple coauthorship. Scientometrics, 85(3), (2010), 741–754.CrossRefGoogle Scholar
Hirsch, J. E., h α: An index to quantify an individual’s scientific leadership. Scientometrics, 118(2), (2019), 673–686.CrossRefGoogle Scholar
Schreiber, M., A modification of the h-index: The h m-index accounts for multi-authored manuscripts. Journal of Informetrics, 2(3), (2008), 211–216.CrossRefGoogle Scholar
Egghe, L., Mathematical theory of the h‐and g‐index in case of fractional counting of authorship. Journal of the American Society for Information Science and Technology, 59(10), (2008), 1608–1616.CrossRefGoogle Scholar
Galam, S., Tailor based allocations for multiple authorship: A fractional gh-index. Scientometrics, 89(1), (2011), 365.CrossRefGoogle Scholar
Tscharntke, T., Hochberg, M. E., Rand, T. A. et al., Author sequence and credit for contributions in multiauthored publications. PLoS Biology, 5(1), (2007), e18.CrossRefGoogle ScholarPubMed
Ausloos, M., Assessing the true role of coauthors in the h-index measure of an author scientific impact. Physica A: Statistical Mechanics and its Applications, 422, (2015), 136–142.Google Scholar
Liu, X. Z. and Fang, H., Modifying h-index by allocating credit of multi-authored papers whose author names rank based on contribution. Journal of Informetrics, 6(4), (2012), 557–565.CrossRefGoogle Scholar
Hu, X., Rousseau, R., and Chen, J., In those fields where multiple authorship is the rule, the h-index should be supplemented by role-based h-indices. Journal of Information Science, 36(1), (2010), 73–85.CrossRefGoogle Scholar
Google Scholar. Available online at https://scholar.google.com.Google Scholar
Radicchi, F., Fortunato, S., Markines, B., et al., Diffusion of scientific credits and the ranking of scientists. Physical Review E, 80(5), (2009), 056103.CrossRefGoogle ScholarPubMed
Abbott, A., Cyranoski, D., Jones, N., et al., Metrics: Do metrics matter? Nature News, 465(7300), (2010), 860–862.CrossRefGoogle ScholarPubMed
Pavlou, M. and Diamandis, E. P., The athletes of science. Nature, 478(7369), (2011), 419–419.CrossRefGoogle Scholar
Kuhn, T. S., The Structure of Scientific Revolutions (Chicago: University of Chicago Press, 1962).Google Scholar
Merton, R. K., The Matthew effect in science. Science, 159(3810), (1968), 56–63.CrossRefGoogle Scholar
Simcoe, T. S. and Waguespack, D. M., Status, quality, and attention: What’s in a (missing) name? Management Science, 57(2), (2011), 274–290.CrossRefGoogle Scholar
Tomkins, A., Zhang, M., and Heavlin, W. D., Reviewer bias in single-versus double-blind peer review. Proceedings of the National Academy of Sciences, 114(48), (2017), 12708–12713.CrossRefGoogle ScholarPubMed
McGillivray, B. and De Ranieri, E., Uptake and outcome of manuscripts in Nature journals by review model and author characteristics. Research Integrity and Peer Review 3, (2018), 5, DOI: https://doi.org/10.1186/s41073-018-0049-zGoogle ScholarPubMed
Blank, R. M., The effects of double-blind versus single-blind reviewing: Experimental evidence from the American Economic Review. The American Economic Review, 81(5), (1991), 1041–1067.Google Scholar
Petersen, A. M., Fortunato, S., Pan, R. K., et al., Reputation and impact in academic careers. Proceedings of the National Academy of Sciences, 111 (2014), 15316–15321.CrossRefGoogle ScholarPubMed
Cole, S., Age and scientific performance. American Journal of Sociology, (1979), 958–977.CrossRefGoogle Scholar
Newman, M., Networks: An Introduction (Oxford: Oxford University Press, 2010).CrossRefGoogle Scholar
Barabási, A.-L., Network Science (Cambridge: Cambridge University, 2015).Google Scholar
Fenn, J. B., Mann, M., Meng, C. K., et al., Electrospray ionization for mass spectrometry of large biomolecules. Science, 246(4926), (1989), 64–71.CrossRefGoogle ScholarPubMed
Mazloumian, A., Eom, Y.-H., Helbing, D., et al., How citation boosts promote scientific paradigm shifts and Nobel Prizes. PloS one, 6(5), (2011), e18975.CrossRefGoogle ScholarPubMed
Fang, F. C., Steen, R. G., and Casadevall, A., Misconduct accounts for the majority of retracted scientific publications. Proceedings of the National Academy of Sciences, 109(42), (2012), 17028–17033.CrossRefGoogle ScholarPubMed
Lu, S. F., Jin, G., Uzzi, B., et al., The retraction penalty: Evidence from the Web of Science. Scientific Reports, 3(3146), (2013).Google Scholar
Azoulay, P., Furman, J. L., Krieger, J. L., et al., Retractions. Review of Economics and Statistics, 97(5), (2015), 1118–1136.CrossRefGoogle Scholar
Azoulay, P., Bonatti, A., and Krieger, J. L., The career effects of scandal: Evidence from scientific retractions. Research Policy, 46(9), (2017), 1552–1569.Google Scholar
Jin, G. Z., Jones, B., Feng Lu, S., et al., The Reverse Matthew Effect: Catastrophe and Consequence in Scientific Teams, working paper 19489 (Cambridge, MA: National Bureau of Economic Research, 2013).CrossRefGoogle Scholar
Merton, R. K., Singletons and multiples in scientific discovery: A chapter in the sociology of science. Proceedings of the American Philosophical Society, 105(5), (1961), 470–486.Google Scholar
Azoulay, P., Stuart, T., and Wang, Y., Matthew: Effect or fable? Management Science, 60(1), (2013), 92–109.CrossRefGoogle Scholar
Garfield, E., and Welljams-Dorof, A., Of Nobel class: A citation perspective on high impact research authors. Theoretical Medicine, 13(2), (1992), 117–135.CrossRefGoogle ScholarPubMed
Azoulay, P., Research efficiency: Turn the scientific method on ourselves. Nature, 484(7392), (2012), 31–32.Google ScholarPubMed
Restivo, M., and Van De Rijt, A., Experimental study of informal rewards in peer production. PloS One, 7(3), (2012), e34358.CrossRefGoogle ScholarPubMed
van de Rijt, A., Kang, S. M., Restivo, M., et al., Field experiments of success-breeds-success dynamics. Proceedings of the National Academy of Sciences, 111(19), (2014), 6934–6939.Google ScholarPubMed
Alberts, B., Kirschner, M. W., Tilghman, S., et al., Opinion: Addressing systemic problems in the biomedical research enterprise. Proceedings of the National Academy of Sciences, 112(7), (2015), 1912–1913.CrossRefGoogle ScholarPubMed
Kaiser, J., Biomedical research. The graying of NIH research. Science, 322(5903), (2008), 848–849.CrossRefGoogle ScholarPubMed
Beard, G. M., Legal Responsibility in Old Age (New York: Russells’ American Steam Printing House, 1874) pp. 5–42.Google Scholar
Lehman, H. C., Age and Achievement (Princeton, NJ: Princeton University Press, 1953).CrossRefGoogle Scholar
Dennis, W., Age and productivity among scientists. Science, 123, (1956), 724–725.CrossRefGoogle ScholarPubMed
Dennis, W., Creative productivity between the ages of 20 and 80 years. Journal of Gerontology, 21(1), (1966), 1–8.CrossRefGoogle ScholarPubMed
Jones, B. F., Age and great invention. The Review of Economics and Statistics, 92(1), (2010), 1–14.CrossRefGoogle Scholar
Jones, B., Reedy, E. J., and Weinberg, B. A., Age and Scientific Genius, working paper 19866 (Cambridge, MA: National Bureau of Economic Research, 2014).CrossRefGoogle Scholar
Usher, A. P., A History of Mechanical Inventions, revised edition (North Chelmsford, MA: Courier Corporation, 1954).Google Scholar
Weitzman, M. L., Recombinant growth. Quarterly Journal of Economics, 113(2), (1998), 331–360.CrossRefGoogle Scholar
Uzzi, B., Mukherjee, S., Stringer, M., et al., Atypical combinations and scientific impact. Science, 342(6157), (2013), 468–472.CrossRefGoogle ScholarPubMed
Ericsson, K. A., Krampe, R. T., and Tesch-Römer, C., The role of deliberate practice in the acquisition of expert performance. Psychological Review, 100(3), (1993), 363–406.CrossRefGoogle Scholar
Ericsson, K. A., and Lehmann, A. C., Expert and exceptional performance: Evidence of maximal adaptation to task constraints. Annual Review of Psychology, 47(1), (1996), 273–305.CrossRefGoogle ScholarPubMed
Ericsson, K. A., Hoffman, R. R., Kozbelt, A., et al., The Cambridge Handbook of Expertise and Expert Performance (Cambridge, UK: Cambridge University Press, 2006).Google Scholar
Pelz, D. C., and Andrews, F. M., Scientists in Organizations: Productive Climates for Research and Development (New York: Wiley, 1966).Google Scholar
Bayer, A. E., and Dutton, J. E., Career age and research-professional activities of academic scientists: Tests of alternative nonlinear models and some implications for higher education faculty policies. The Journal of Higher Education, 48(3), (1977), 259–282.Google Scholar
Blackburn, R. T., Behymer, C. E., and Hall, D. E., Research note: Correlates of faculty publications. Sociology of Education, 51(2) (1978), 132–141.CrossRefGoogle Scholar
Matthews, K. R., Calhoun, K. M., Lo, N., et al., The aging of biomedical research in the United States. PLoS ONE, 6(12), (2011), e29738.CrossRefGoogle ScholarPubMed
Adams, C. W., The age at which scientists do their best work. Isis, 36(3/4) (1946), 166–169.Google ScholarPubMed
Zuckerman, H., Scientific Elite: Nobel Laureates in the United States (Piscataway, NJ: Transaction Publishers, 1977).Google Scholar
Simonton, D. K., Career landmarks in science: Individual differences and interdisciplinary contrasts. Developmental Psychology, 27(1), (1991), 119–130.CrossRefGoogle Scholar
Jones, B. F., and Weinberg, B. A., Age dynamics in scientific creativity. Proceedings of the National Academy of Sciences, 108(47), (2011), 18910–18914.CrossRefGoogle ScholarPubMed
Jones, B. F., The burden of knowledge and the “death of the renaissance man”: Is innovation getting harder? The Review of Economic Studies, 76(1), (2009), 283–317.CrossRefGoogle Scholar
Jones, B. F., As science evolves, how can science policy? Innovation Policy and the Economy, 11 (2011), 103–131.Google Scholar
Machlup, F., The Production and Distribution of Knowledge in the United States (Princeton, NJ: Princeton University Press, 1962).Google Scholar
Fortunato, S., Growing time lag threatens Nobels. Nature, 508(7495), (2014), 186–186.CrossRefGoogle ScholarPubMed
Cassidy, D. C., Uncertainty: The Life and Science of Werner Heisenberg (New York: Freeman, 1992), p. 1.Google Scholar
Weinberg, B. A., and Galenson, D. W., Creative Careers: The Life Cycles of Nobel Laureates in Economics, working paper 11799 (Cambridge, MA: National Bureau of Economic Research, 2005).CrossRefGoogle Scholar
Rappa, M. and Debackere, K., Youth and scientific innovation: The role of young scientists in the development of a new field. Minerva, 31(1), (1993), 1–20.CrossRefGoogle Scholar
Packalen, M. and Bhattacharya, J., Age and the Trying Out of New Ideas. working paper 20920 (Cambridge, MA: National Bureau of Economic Research, 2015).CrossRefGoogle Scholar
Galenson, D. W., Painting Outside the Lines: Patterns of Creativity in Modern Art (Cambridge, MA: Harvard University Press, 2009).Google Scholar
Galenson, D. W., Old Masters and Young Geniuses: The Two Life Cycles of Artistic Creativity (Princeton, NJ: Princeton University Press, 2011).CrossRefGoogle Scholar
Hull, D. L., Tessner, P. D., and Diamond, A. M., Planck’s principle. Science, 202(4369), (1978), 717–723.CrossRefGoogle ScholarPubMed
Azoulay, P., Zivin, J. S., and Wang, J., Superstar extinction. Quarterly Journal of Economics, 125(2), (2010), 549–589.CrossRefGoogle Scholar
Sinatra, R., Wang, D., Deville, P., et al., Quantifying the evolution of individual scientific impact. Science, 354(6312), (2016), aaf5239.CrossRefGoogle ScholarPubMed
Liu, L., Wang, Y., Sinatra, R., et al., Hot streaks in artistic, cultural, and scientific careers. Nature, 559, (2018), 396–399.CrossRefGoogle ScholarPubMed
Simonton, D. K., Creative productivity, age, and stress: A biographical time-series analysis of 10 classical composers. Journal of Personality and Social Psychology, 35(11), (1977), 791–804.CrossRefGoogle ScholarPubMed
Simonton, D. K., Quality, quantity, and age: The careers of ten distinguished psychologists. International Journal of Aging & Human Development, 21(4), (1985), 241–254.CrossRefGoogle ScholarPubMed
Simonton, D. K., Genius, Creativity, and Leadership: Historiometric Inquiries (Cambridge, MA; Harvard University Press, 1984).CrossRefGoogle Scholar
Simonton, D. K., Scientific Genius: A Psychology of Science (Cambridge, UK: Cambridge University Press, 1988).Google Scholar
Li, J., Yin, Y., Fortunato, S., et al., Nobel laureates are almost the same as us. Nature Reviews Physics, 1(5), (2019), 301–303.CrossRefGoogle Scholar
Azoulay, P., Jones, B. F., Kim, N. J. D., et al., Age and High-Growth Entrepreneurship working paper 24489 (Cambridge, MA: National Bureau of Economic Research, 2018).CrossRefGoogle Scholar
Azoulay, P., Jones, B., King, J. D., et al., Research: The average age of a successful startup founder is 45. Harvard Business Review, (2018), July 11.Google Scholar
Powdthavee, N., Riyanto, Y. E., and Knetsch, J. L., Lower-rated publications do lower academics’ judgments of publication lists: Evidence from a survey experiment of economists. Journal of Economic Psychology, 66, (2018), 33–44.CrossRefGoogle Scholar
Gilovich, T., Vallone, R., and Tversky, A., The hot hand in basketball: On the misperception of random sequences. Cognitive Psychology, 17(3), (1985), 295–314.CrossRefGoogle Scholar
Miller, J. and Sanjurjo, A., Surprised by the hot hand fallacy? A truth in the law of small numbers. Econometrica, 86(6), (2018), 2019–2047, DOI: https://doi.org/10.3982/ECTA14943.CrossRefGoogle Scholar
Ayton, P., and Fischer, I., The hot hand fallacy and the gambler’s fallacy: Two faces of subjective randomness? Memory & Cognition, 32(8), (2004), 1369–1378.CrossRefGoogle ScholarPubMed
Rabin, M., and Vayanos, D., The gambler’s and hot-hand fallacies: Theory and applications. Review of Economic Studies, 77(2), (2010), 730–778.CrossRefGoogle Scholar
Xu, J. M., and Harvey, N., Carry on winning: The gamblers’ fallacy creates hot hand effects in online gambling. Cognition, 131(2), (2014), 173–180.CrossRefGoogle ScholarPubMed
Csapo, P. and Raab, M., Correction “Hand down, Man down.” Analysis of defensive adjustments in response to the hot hand in basketball using novel defense metrics (vol. 9, e114184, 2014). PLoS One, 10(4), (2015), e0124982.CrossRefGoogle ScholarPubMed
Barabási, A.-L., The origin of bursts and heavy tails in human dynamics. Nature, 435(7039), (2005), 207–211.CrossRefGoogle ScholarPubMed
Vázquez, A., Oliveira, J. G., Dezsö, Z., et al., Modeling bursts and heavy tails in human dynamics. Physical Review E, 73(3), (2006), 036127.CrossRefGoogle ScholarPubMed
Barabási, A.-L., Bursts: The Hidden Patterns Behind Everything We Do, From Your E-mail to Bloody Crusades (New York: Penguin, 2010).Google Scholar
Abbott, B. P, Abbott, R., Abbott, T. D., et al., Observation of gravitational waves from a binary black hole merger. Physical Review Letters, 116(6), (2016), 061102.CrossRefGoogle ScholarPubMed
Wuchty, S., Jones, B.F., and Uzzi, B., The increasing dominance of teams in production of knowledge. Science, 316(5827), (2007), 1036–1039.CrossRefGoogle ScholarPubMed
Cooke, N. J. and Hilton, M. L. (eds.), Enhancing the Effectiveness of Team Science (Washington, DC: National Academies Press, 2015).Google Scholar
Drake, N., What is the human genome worth? Nature News, (2011), DOI: https://doi.org/10.1038/news.2011.281.CrossRefGoogle Scholar
Whitfield, J., Group theory. Nature, 455(7214), (2008), 720–723.CrossRefGoogle ScholarPubMed
Valderas, J. M., Why do team-authored papers get cited more? Science, 317(5844), (2007), 1496–1498.CrossRefGoogle ScholarPubMed
Leahey, E., From solo investigator to team scientist: Trends in the practice and study of research collaboration. Annual Review of Sociology, 42, (2016), 81–100.CrossRefGoogle Scholar
Rawlings, C. M. and McFarland, D. A., Influence flows in the academy: Using affiliation networks to assess peer effects among researchers. Social Science Research, 40(3), (2011), 1001–1017.CrossRefGoogle Scholar
Jones, B. F., Wuchty, S., and Uzzi, B., Multi-university research teams: shifting impact, geography, and stratification in science. Science, 322(5905), (2008), 1259–1262.CrossRefGoogle Scholar
Xie, Y. and Killewald, A. A., Is American Science in Decline? (Cambridge, MA: Harvard University Press, 2012).CrossRefGoogle Scholar
Adams, J., Collaborations: The fourth age of research. Nature, 497(7451), (2013), 557–560.CrossRefGoogle ScholarPubMed
Xie, Y., “Undemocracy”: Inequalities in science. Science, 344(6186), (2014), 809–810.CrossRefGoogle Scholar
Bikard, M., Murray, F., and Gans, J. S., Exploring trade-offs in the organization of scientific work: Collaboration and scientific reward. Management Science, 61(7), (2015), 1473–1495.CrossRefGoogle Scholar
Manski, C. F., Identification of endogenous social effects: The reflection problem. The Review of Economic Studies, 60(3), (1993), 531–542.CrossRefGoogle Scholar
Sacerdote, B., Peer effects with random assignment: Results for Dartmouth roommates. The Quarterly Journal of Economics, 116(2), (2001), 681–704.CrossRefGoogle Scholar
Mas, A. and Moretti, E., Peers at work. The American Economic Review, 99(1), (2009), 112–145.CrossRefGoogle Scholar
Herbst, D. and Mas, A., Peer effects on worker output in the laboratory generalize to the field. Science, 350(6260), (2015), 545–549.CrossRefGoogle ScholarPubMed
Agrawal, A. K., McHale, J., and Oettl, A., Why Stars Matter working paper 20012 (Cambrdige, MA: National Bureau of Economic Research, 2014).CrossRefGoogle Scholar
Angrist, J. D. and Pischke, J. -S., Mostly Harmless Econometrics: An Empiricist’s Companion (Princeton, NJ: Princeton University Press, 2008).CrossRefGoogle Scholar
Borjas, G. J. and Doran, K. B., Which peers matter? The relative impacts of collaborators, colleagues, and competitors. Review of Economics and Statistics, 97(5), (2015), 1104–1117.CrossRefGoogle Scholar
Waldinger, F., Peer effects in science: Evidence from the dismissal of scientists in Nazi Germany. The Review of Economic Studies, 79(2), (2011), 838–861.CrossRefGoogle Scholar
Crane, D., Invisible Colleges: Diffusion of Knowledge in Scientific Communities (Chicago: University of Chicago Press, 1972).Google Scholar
Oettl, A., Sociology: Honour the helpful. Nature, 489(7417), (2012), 496–497.CrossRefGoogle ScholarPubMed
Oettl, A., Reconceptualizing stars: Scientist helpfulness and peer performance. Management Science, 58(6), (2012), 1122–1140.CrossRefGoogle Scholar
Grossman, J. W., Patterns of research in mathematics. Notices of the AMS, 52(1), (2005), 35–41.Google Scholar
Palla, G., Barabási, A.-L., and Vicsek, T., Quantifying social group evolution. Nature, 446(7136), (2007), 664–667.CrossRefGoogle ScholarPubMed
Grossman, J. W. and Ion, P. D., On a portion of the well-known collaboration graph. Congressus Numerantium, 108, (1995), 129–132.Google Scholar
Grossman, J. W., The evolution of the mathematical research collaboration graph. Congressus Numerantium, 158, (2002), 201–212.Google Scholar
Barabási, A. -L, Jeong, H., Neda, Z., et al., Evolution of the social network of scientific collaborations. Physica A: Statistical Mechanics and its Applications, 311(3), (2002), 590–614.CrossRefGoogle Scholar
Newman, M. E., Coauthorship networks and patterns of scientific collaboration. Proceedings of the National Academy of Sciences, 101(suppl 1), (2004), 5200–5205.CrossRefGoogle ScholarPubMed
Newman, M. E., The structure of scientific collaboration networks. Proceedings of the National Academy of Sciences, 98(2), (2001), 404–409.CrossRefGoogle ScholarPubMed
Grossman, J., The Erdős Number Project at Oakland University (2018), available online at https://oakland.edu/enp/.Google Scholar
Watts, D. J. and Strogatz, S. H., Collective dynamics of “small-world” networks. Nature, 393(6684), (1998), 440–442.CrossRefGoogle ScholarPubMed
Uzzi, B. and Spiro, J., Collaboration and creativity: The small world Problem1. American Journal of Sociology, 111(2), (2005), 447–504.CrossRefGoogle Scholar
Muir, W. M., Group selection for adaptation to multiple-hen cages: Selection program and direct responses. Poultry Science, 75(4), (1996), 447–458.CrossRefGoogle ScholarPubMed
Wilson, D. S., Evolution for Everyone: How Darwin’s Theory Can Change the Way We Think About Our Lives (McHenry, IL: Delta, 2007).Google Scholar
Marks, M. A., Mathieu, J. E., and Zaccaro, S. J., A temporally based framework and taxonomy of team processes. Academy of Management Review, 26(3), (2001), 356–376.CrossRefGoogle Scholar
Scott, J., Discord turns academe’s hot team cold: The self-destruction of the English department at Duke. The New York Times, (November 21, 1998).Google Scholar
Yaffe, D., The department that fell to Earth: The deflation of Duke English. Lingua Franca: The Review of Academic Life, 9(1), (1999), 24–31.Google Scholar
Swaab, R. I., Schaerer, M., Anicich, E. M., et al., The too-much-talent effect team interdependence determines when more talent is too much or not enough. Psychological Science, 25(8), (2014), 1581–1591.CrossRefGoogle ScholarPubMed
Ronay, R., Greenaway, K., Anicich, E. M., et al., The path to glory is paved with hierarchy when hierarchical differentiation increases group effectiveness. Psychological Science, 23(6), (2012), 669–677.CrossRefGoogle ScholarPubMed
Groysberg, B., Polzer, J. T., and Elfenbein, H. A., Too many cooks spoil the broth: How high-status individuals decrease group effectiveness. Organization Science, 22(3), (2011), 722–737.CrossRefGoogle Scholar
Uzzi, B., Wuchty, S., Spiro, J., et al., Scientific teams and networks change the face of knowledge creation, in Vedres, B. and Scotti, M. (eds.), Networks in Social Policy Problems (Cambridge: Cambridge University Press, 2012), pp. 47–59.Google Scholar
Freeman, R. B. and Huang, W., Collaboration: Strength in diversity. Nature, 513(7518), (2014), 305–305.CrossRefGoogle ScholarPubMed
Freeman, R. B. and Huang, W., Collaborating With People Like Me: Ethnic Coauthorship Within the US, working paper 19905, (Cambridge, MA: National Bureau of Economic Research, 2014).CrossRefGoogle Scholar
Smith, M. J., Weinberger, C., Bruna, E. M., et al., The scientific impact of nations: Journal placement and citation performance. PloS One, 9(10), (2014), e109195.CrossRefGoogle ScholarPubMed
AlShebli, B. K., Rahwan, T., and Woon, W. L., The preeminence of ethnic diversity in scientific collaboration. Nature Communications, 9(1), (2018), 5163.CrossRefGoogle ScholarPubMed
Powell, K., These labs are remarkably diverse: Here’s why they’re winning at science. Nature, 558(7708), (2018), 19–22.CrossRefGoogle ScholarPubMed
Cummings, J. N., Kiesler, S., Bosagh Zadeh, R., et al., Group heterogeneity increases the risks of large group size a longitudinal study of productivity in research groups. Psychological Science, 24(6), (2013), 880–890.CrossRefGoogle Scholar
Deary, I. J., Looking Down on Human Intelligence: From Psychometrics to the Brain (Oxford: Oxford University Press, 2000).CrossRefGoogle Scholar
Spearman, C., “General Intelligence,” objectively determined and measured. The American Journal of Psychology, 15(2), (1904), 201–292.CrossRefGoogle Scholar
Woolley, A.W., Chabris, C. F., Pentland, A., et al., Evidence for a collective intelligence factor in the performance of human groups. Science, 330(6004), (2010), 686–688.CrossRefGoogle ScholarPubMed
Guimera, R., Uzzi, B., Spiro, J., et al., Team assembly mechanisms determine collaboration network structure and team performance. Science, 308(5722), (2005), 697–702.CrossRefGoogle ScholarPubMed
De Vaan, M., Stark, D., and Vedres, B., Game changer: The topology of creativity. American Journal of Sociology, 120(4), (2015), 1144–1194.CrossRefGoogle ScholarPubMed
Vedres, B., Forbidden triads and creative success in jazz: The Miles Davis factor. Applied Network Science, 2(1), (2017), 31.CrossRefGoogle Scholar
Petersen, A. M., Quantifying the impact of weak, strong, and super ties in scientific careers. Proceedings of the National Academy of Sciences, 112(34), (2015), E4671–E4680.CrossRefGoogle ScholarPubMed
Dahlander, L. and McFarland, D. A., Ties that last tie formation and persistence in research collaborations over time. Administrative Science Quarterly, 58(1), (2013), 69–110.CrossRefGoogle Scholar
Brown, M. S. and Goldstein, J. L., A receptor-mediated pathway for cholesterol homeostasis. Science, 232(4746), (1986), 34–47.CrossRefGoogle ScholarPubMed
Heron, M., Deaths: Leading causes for 2012. National Vital Statistics Reports, 64(10), (2015).Google ScholarPubMed
Aad, G., Abbott, B., Abdallah, J., et al., Combined measurement of the Higgs boson mass in pp collisions at √s= 7 and 8 TeV with the ATLAS and CMS experiments. Physical Review Letters, 114(19), (2015), 191803.CrossRefGoogle Scholar
Castelvecchi, D., Physics paper sets record with more than 5,000 authors. Nature News, May 15, 2015.CrossRefGoogle Scholar
Milojevic, S., Principles of scientific research team formation and evolution. Proceedings of the National Academy of Sciences, 111(11), (2014), 3984–3989.CrossRefGoogle Scholar
Klug, M. and Bagrow, J. P., Understanding the group dynamics and success of teams. Royal Society Open Science, 3(4), (2016), 160007.CrossRefGoogle Scholar
Paulus, P. B., Kohn, N. W., Arditti, L. E., et al., Understanding the group size effect in electronic brainstorming. Small Group Research, 44(3), (2013), 332–352.CrossRefGoogle Scholar
Lakhani, K. R., Boudreau, K. J., Loh, P.-R., et al., Prize-based contests can provide solutions to computational biology problems. Nature Biotechnology, 31(2), (2013), 108–111.CrossRefGoogle ScholarPubMed
Barber, S. J., Harris, C. B., and Rajaram, S., Why two heads apart are better than two heads together: Multiple mechanisms underlie the collaborative inhibition effect in memory. Journal of Experimental Psychology: Learning Memory and Cognition, 41(2), (2015), 559–566.Google ScholarPubMed
Minson, J. A. and Mueller, J. S., The cost of collaboration: Why joint decision-making exacerbates rejection of outside information. Psychological Science, 23(3), (2012), 219–224.CrossRefGoogle ScholarPubMed
Greenstein, S. and Zhu, F., Open content, Linus’ law, and neutral point of view. Information Systems Research, 27(3), (2016), 618–635.CrossRefGoogle Scholar
Christensen, C. M., and Christensen, C. M., The Innovator’s Dilemma: The Revolutionary Book That Will Change the Way You do Business (New York: Harper Business Essentials, 2003).Google Scholar
Bak, P., Tang, C., and Wiesenfeld, K., Self-organized criticality: An explanation of the 1/f noise. Physical Review Letters, 59(4), (1987), 381–384.CrossRefGoogle ScholarPubMed
Davis, K.B., Mewes, M. -O., Andrews, M. R., et al., Bose–Einstein condensation in a gas of sodium atoms. Physical Review Letters, 75(22), (1995), 3969–3973.CrossRefGoogle Scholar
Wu, L., Wang, D., and Evans, J. A., Large teams develop and small teams disrupt science and technology. Nature, 566(7744), (2019), 378–382.CrossRefGoogle ScholarPubMed
Funk, R. J., and Owen-Smith, J., A dynamic network measure of technological change. Management Science, 63(3), (2017), 791-817.CrossRefGoogle Scholar
Einstein, A., Die feldgleichungen der gravitation. Sitzung der physikalische-mathematischen Klasse, 25, (1915), 844–847.Google Scholar
Cummings, J. N. and Kiesler, S., Coordination costs and project outcomes in multi-university collaborations. Research Policy, 36(10), (2007), 1620–1634.CrossRefGoogle Scholar
Biagioli, M. and Galison, P., Scientific Authorship: Credit and Intellectual Property in Science (Abingdon, UK: Routledge, 2014).CrossRefGoogle Scholar
Corrêa Jr, E. A., Silva, F. N., da F. Costa, L., et al., Patterns of authors contribution in scientific manuscripts. Journal of Informetrics, 11(22), (2016), 498–510.CrossRefGoogle Scholar
Larivière, V., Desrochers, N., Macaluso, B., et al., Contributorship and division of labor in knowledge production. Social Studies of Science, 46(3), (2016), 417–435.CrossRefGoogle ScholarPubMed
Slone, R. M., Coauthors’ contributions to major papers published in the AJR: frequency of undeserved coauthorship. American Journal of Roentgenology, 167(3), (1996), 571–579.CrossRefGoogle ScholarPubMed
Campbell, P., Policy on papers’ contributors. Nature, 399(6735), (1999), 393.Google Scholar
Ilakovac, V., Fister, K., Marusic, M., et al., Reliability of disclosure forms of authors’ contributions. Canadian Medical Association Journal, 176(1), (2007), 41–46.CrossRefGoogle ScholarPubMed
Deacon, R., Hurley, M. J., Rebolledo, C. M., et al., Nrf2: a novel therapeutic target in fragile X syndrome is modulated by NNZ2566. Genes, Brain, and Behavior, 16(7), (2017), 1–10.CrossRefGoogle ScholarPubMed
Conte, M. L., Maat, S. L., and Omary, M. B., Increased co-first authorships in biomedical and clinical publications: a call for recognition. The FASEB Journal, 27(10), (2013), 3902–3904.CrossRefGoogle Scholar
Dubnansky, E. and Omary, M. B., Acknowledging joint first authors of published work: the time has come. Gastroenterology, 143(4), (2012), 879–880.CrossRefGoogle ScholarPubMed
Omary, M. B., Wallace, M. B., El-Omar, E. M., et al., A multi-journal partnership to highlight joint first-authors of manuscripts. Gut, 64(2), (2015), 189.CrossRefGoogle ScholarPubMed
Drubin, D. G., MBoC improves recognition of co-first authors. Molecular Biology of the Cell, 25(13), (2014), 1937.CrossRefGoogle ScholarPubMed
Waltman, L., An empirical analysis of the use of alphabetical authorship in scientific publishing. Journal of Informetrics, 6(4), (2012), 700–711.CrossRefGoogle Scholar
Jabbehdari, S. and Walsh, J. P., Authorship norms and project structures in science. Science, Technology, and Human Values, 42(5), (2017), 872–900.CrossRefGoogle Scholar
Heffner, A. G., Authorship recognition of subordinates in collaborative research. Social Studies of Science, 9(3), (1979), 377–384.CrossRefGoogle Scholar
Shapin, S., The invisible technician. American Scientist, 77(6), (1989), 554–563.Google Scholar
Schott, G., Mechanica hydraulico-pneumatica (Wurzburg, 1657).Google Scholar
Rossi, A. S., Women in science: Why so few? Science, 148(3674), (1965), 1196–1202.CrossRefGoogle ScholarPubMed
Xie, Y. and Shauman, K. A., Women in Science: Career Processes and Outcomes (Cambridge, MA: Harvard University Press, 2003).Google Scholar
Ceci, S. J., Ginther, D. K., Kahn, S., et al., Women in academic science: A changing landscape. Psychological Science in the Public Interest, 15(3), (2014), 75–141.CrossRefGoogle ScholarPubMed
Ginther, D. K. and Kahn, S., Women in economics: moving up or falling off the academic career ladder? The Journal of Economic Perspectives, 18(3), (2004), 193–214.CrossRefGoogle Scholar
Sarsons, H., Gender differences in recognition for group work. Working paper (2017), available online at https://scholar.harvard.edu/files/sarsons/files/full_v6.pdf.Google Scholar
Niederle, M. and Vesterlund, L., Do women shy away from competition? Do men compete too much? The Quarterly Journal of Economics, 122(3), (2007), 1067–1101.CrossRefGoogle Scholar
Thomas, W. I. and Thomas, D. S., The Child in America: Behavior Problems and Programs. (New York: A. A. Knopf. 1928).Google Scholar
Merton, R. K., The Thomas theorem and the Matthew effect. Social Forces, 74(2), (1995), 379–422.CrossRefGoogle Scholar
Merton, R. K., The Sociology of Science: Theoretical and Empirical Investigations (Chicago: University of Chicago Press, 1973).Google Scholar
Arnison, G., Astbury, A., Aubert, B., et al., Experimental observation of isolated large transverse energy electrons with associated missing energy at sqrt (s)= 540 GeV. Physics Letters B, 122 (1983), 103–116.CrossRefGoogle Scholar
Shen, H.-W. and Barabási, A.-L., Collective credit allocation in science. Proceedings of the National Academy of Sciences, 111(34), (2014), 12325–12330.CrossRefGoogle Scholar
Englert, F. and Brout, R., Broken symmetry and the mass of gauge vector mesons. Physical Review Letters, 13(9), (1964), 321–323.CrossRefGoogle Scholar
Higgs, P. W., Broken symmetries and the masses of gauge bosons. Physical Review Letters, 13(16), (1964), 508–509.CrossRefGoogle Scholar
Guralnik, G. S., Hagen, C. R., and Kibble, T. W., Global conservation laws and massless particles. Physical Review Letters, 13(20), (1964), 585–587.CrossRefGoogle Scholar
Maury, J. -P., Newton: Understanding the Cosmos (London: Thames & Hudson, 1992).Google Scholar
de Solla Price, D., Science Since Babylon (New Haven, CT: Yale University Press, 1961).Google Scholar
Gilbert, G. N. and Woolgar, S., The quantitative study of science: An examination of the literature. Science Studies, 4(3), (1974), 279–294.CrossRefGoogle Scholar
Khabsa, M. and Giles, C. L., The number of scholarly documents on the public web. PLoS One, 9(5), (2014), e93949.CrossRefGoogle ScholarPubMed
Sinha, A., Shen, Z., Song, Y., et al., An overview of Microsoft Academic Service (MAS) and applications, in WWW ’15 Companion: Proceedings of the 24th International Conference on World Wide Web (New York: ACM, 2015), pp. 243–246.CrossRefGoogle Scholar
The Works of Francis Bacon vol. IV: Translations of the Philosophical Works ed. Spedding, J., Ellis, R. L., Heath, D. D. (London: Longmans & Co., 1875), p. 109.Google Scholar
Baldwin, M., “Keeping in the race”: Physics, publication speed and national publishing strategies in Nature, 1895–1939. The British Journal for the History of Science, 47(2), (2014), 257–279.CrossRefGoogle ScholarPubMed
Editorial, Form follows need. Nature Physics, 12, (2016), 285.Google Scholar
Csiszar, A., The Scientific Journal: Authorship and the Politics of Knowledge in the Nineteenth Century (Chicago: University of Chicago Press, 2018).CrossRefGoogle Scholar
Wendler, C., Bridgeman, B., Cline, F., et al., The Path Forward: The Future of Graduate Education in the United States (Prnceton, NJ: Educational Testing Service, 2010).Google Scholar
Council of Graduate Schools, PhD Completion and Attrition: Policy, Numbers, Leadership, and Next Steps (Washington, DC: Council of Graduate Schools, 2004).Google Scholar
Schillebeeckx, M., Maricque, B., and Lewis, C., The missing piece to changing the university culture. Nature Biotechnology, 31(10), (2013), 938–941.CrossRefGoogle ScholarPubMed
Cyranoski, D., Gilbert, N., Ledford, H., et al., Education: The PhD factory. Nature News, 472(7343), (2011), 276–279.CrossRefGoogle Scholar
Yin, Y. and Wang, D., The time dimension of science: Connecting the past to the future. Journal of Informetrics, 11(2), (2017), 608–621.CrossRefGoogle Scholar
Vale, R. D., Accelerating scientific publication in biology. Proceedings of the National Academy of Sciences, 112(44), (2015), 13439–13446.CrossRefGoogle ScholarPubMed
Woolston, C., Graduate survey: A love–hurt relationship. Nature, 550(7677), (2017), 549–552.CrossRefGoogle Scholar
Powell, K., The future of the postdoc. Nature, 520(7546), (2015), 144.CrossRefGoogle ScholarPubMed
Zolas, N., Goldschlag, N., Jarmin, R., et al., Wrapping it up in a person: Examining employment and earnings outcomes for PhD recipients. Science, 350(6266), (2015), 1367–1371.CrossRefGoogle Scholar
Editorial, Make the most of PhDs. Nature News, 528(7580), (2015), 7.Google Scholar
Bloom, N., Jones, C. I., Van Reenen, J., et al., Are Ideas Getting Harder to Find? working paper 23782 (Cambridge, MA: National Bureau of Economic Research, 2017).CrossRefGoogle Scholar
Milojevic, S., Quantifying the cognitive extent of science. Journal of Informetrics, 9(4), (2015), 962–973.CrossRefGoogle Scholar
Van Noorden, R., Maher, B., and Nuzzo, R., The top 100 papers. Nature, 514(7524), (2014), 550–553.CrossRefGoogle ScholarPubMed
de Solla Price, D. J., Networks of scientific papers. Science, 149(3683), (1965), 510–515.CrossRefGoogle Scholar
Garfield, E. and Sher, I. H., New factors in the evaluation of scientific literature through citation indexing. American Documentation, 14(3), (1963), 195–201.CrossRefGoogle Scholar
Pareto, V., Cours d’économie politique (Geneva: Librairie Droz, 1964).CrossRefGoogle Scholar
Vázquez, A., Statistics of citation networks. arXiv preprint https://arxiv.org/abs/cond-mat/0105031, (2001).Google Scholar
Lehmann, S., Lautrup, B., and Jackson, A. D., Citation networks in high energy physics. Physical Review E, 68(2), (2003) 026113.CrossRefGoogle ScholarPubMed
Seglen, P. O., The skewness of science. Journal of the American Society for Information Science, 43(9), (1992) 628–638.3.0.CO;2-0>CrossRefGoogle Scholar
Bommarito, I. I., Michael, J., and Katz, D. M., Properties of the United States code citation network. arXiv preprint https://arxiv.org/abs/0911.1751, (2009).CrossRefGoogle Scholar
Eom, Y.-H. and Fortunato, S., Characterizing and modeling citation dynamics. PloS One, 6(9), (2011), e24926.CrossRefGoogle ScholarPubMed
Menczer, F., Evolution of document networks. Proceedings of the National Academy of Sciences, 101(suppl 1), (2004), 5261–5265.CrossRefGoogle ScholarPubMed
Radicchi, F. and Castellano, C., Rescaling citations of publications in physics. Physical Review E, 83(4), (2011), 046116.CrossRefGoogle ScholarPubMed
Redner, S., Citation statistics from 110 years of Physical Review. Physics Today, 58 (2005), 49–54.CrossRefGoogle Scholar
Stringer, M. J., Sales-Pardo, M., and Amaral, L. A. N., Effectiveness of journal ranking schemes as a tool for locating information. PloS One, 3(2), (2008), e1683.CrossRefGoogle ScholarPubMed
Castellano, C. and Radicchi, F., On the fairness of using relative indicators for comparing citation performance in different disciplines. Archivum immunologiae et therapiae experimentalis, 57(2), (2009), 85–90.CrossRefGoogle ScholarPubMed
Stringer, M. J., Sales-Pardo, M., and Amaral, L. A. N., Statistical validation of a global model for the distribution of the ultimate number of citations accrued by papers published in a scientific journal. Journal of the American Society for Information Science and Technology, 61(7), (2010), 1377–1385.CrossRefGoogle Scholar
Wallace, M. L., Larivière, V., and Gingras, Y., Modeling a century of citation distributions. Journal of Informetrics, 3(4), (2009), 296–303.CrossRefGoogle Scholar
Anastasiadis, A. D., de Albuquerque, M. P., de Albuquerque, M. P., et al., Tsallis q-exponential describes the distribution of scientific citations: A new characterization of the impact. Scientometrics, 83(1), (2010), 205–218.CrossRefGoogle Scholar
van Raan, A. F., Two-step competition process leads to quasi power-law income distributions: Application to scientific publication and citation distributions. Physica A: Statistical Mechanics and its Applications, 298(3), (2001), 530–536.CrossRefGoogle Scholar
Van Raan, A. F., Competition amongst scientists for publication status: Toward a model of scientific publication and citation distributions. Scientometrics, 51(1), (2001) 347–357.CrossRefGoogle Scholar
Kryssanov, V. V., Kuleshov, E. L., and Rinaldo, F. J. et al., We cite as we communicate: A communication model for the citation process. arXiv preprint https://arxiv.org/abs/cs/0703115, (2007).Google Scholar
Barabási, A. -L., Song, C., and Wang, D., Publishing: Handful of papers dominates citation. Nature, 491(7422), (2012), 40.CrossRefGoogle ScholarPubMed
Aksnes, D. W., Citation rates and perceptions of scientific contribution. Journal of the American Society for Information Science and Technology, 57(2), (2006), 169–185.CrossRefGoogle Scholar
Radicchi, F., In science “there is no bad publicity”: Papers criticized in comments have high scientific impact. Scientific Reports, 2 (2012), 815.CrossRefGoogle ScholarPubMed
Moravcsik, M. J. and Murugesan, P., Some results on the function and quality of citations. Social Studies of Science, 5(1), (1975), 86–92.CrossRefGoogle Scholar
Cole, J. R. and Cole, S., Social Stratification in Science (Chicago: University of Chicago Press, 1973).Google Scholar
Cronin, B., Research brief rates of return to citation. Journal of Documentation, 52(2), (1996), 188–197.CrossRefGoogle Scholar
Lawani, S. M. and Bayer, A. E., Validity of citation criteria for assessing the influence of scientific publications: New evidence with peer assessment. Journal of the American Society for Information Science, 34(1), (1983), 59–66.CrossRefGoogle Scholar
Luukkonen, T., Citation indicators and peer review: Their time-scales, criteria of evaluation, and biases. Research Evaluation, 1(1), (1991), 21–30.CrossRefGoogle Scholar
Oppenheim, C. and Renn, S. P., Highly cited old papers and the reasons why they continue to be cited. Journal of the American Society for Information Science, 29(5), (1978), 225–231.CrossRefGoogle Scholar
Rinia, E. J., van Leeuwen, T. N., van Vuren, H. G., et al., Comparative analysis of a set of bibliometric indicators and central peer review criteria: Evaluation of condensed matter physics in the Netherlands. Research policy, 27(1), (1998), 95–107.CrossRefGoogle Scholar
Radicchi, F., Weissman, A., and Bollen, J., Quantifying perceived impact of scientific publications. Journal of Informetrics, 11(3), (2017), 704–712.CrossRefGoogle Scholar
Jaffe, A. B., Patents, patent citations, and the dynamics of technological change. NBER Reporter, (1998, summer), 8–11.Google Scholar
Jaffe, A. B., Fogarty, M. S., and Banks, B. A., Evidence from patents and patent citations on the impact of NASA and other federal labs on commercial innovation. The Journal of Industrial Economics, 46(2), (1998), 183–205.CrossRefGoogle Scholar
Trajtenberg, M., A penny for your quotes: patent citations and the value of innovations. The Rand Journal of Economics, 221(1), (1990), 172–187.CrossRefGoogle Scholar
Hall, B. H., Jaffe, A. B., and Trajtenberg, M., Market Value and Patent Citations: A First Look, working paper 7741, (Cambridge, MA: National Bureau of Economic Research, 2000).CrossRefGoogle Scholar
Harhoff, D., Narin, F., Scherer, F. M., et al., Citation frequency and the value of patented inventions. Review of Economics and Statistics, 81(3), (1999), 511–515.CrossRefGoogle Scholar
de Solla Price, D., A general theory of bibliometric and other cumulative advantage processes. Journal of the American Society for Information Science, 27(5), (1976), 292–306.CrossRefGoogle Scholar
Wang, D., Song, C., and Barabási, A. -L., Quantifying long-term scientific impact. Science, 342(6154), (2013), 127–132.CrossRefGoogle ScholarPubMed
Eggenberger, F. and Pólya, G., Über die statistik verketteter vorgänge. ZAMM-Journal of Applied Mathematics and Mechanics/Zeitschrift für Angewandte Mathematik und Mechanik, 3(4), (1923), 279–289.CrossRefGoogle Scholar
Yule, G. U., A mathematical theory of evolution, based on the conclusions of Dr. JC Willis, FRS. Philosophical Transactions of the Royal Society of London. Series B, Containing Papers of a Biological Character, 213 (1925), 21–87.Google Scholar
Gibrat, R., Les inégalités économiques (Paris: Recueil Sirey, 1931).Google Scholar
Zipf, G. K., Human Behavior and the Principle of Least Effort (Boston, MA: Addison-Wesley Press, 1949).Google Scholar
Simon, H. A., On a class of skew distribution functions. Biometrika, 42 (3/4), (1955), 425–440.CrossRefGoogle Scholar
Barabási, A.-L. and Albert, R., Emergence of scaling in random networks. Science, 286(5439), (1999), 509–512.CrossRefGoogle ScholarPubMed
Newman, M. E .J., The first-mover advantage in scientific publication. EPL (Europhysics Letters), 86(6), (2009), 68001.CrossRefGoogle Scholar
Bardeen, J., Cooper, L. N., and Schrieffer, J. R., Theory of superconductivity. Physical Review, 108(5), (1957), 1175–1204.CrossRefGoogle Scholar
Bianconi, G. and Barabási, A. -L., Competition and multiscaling in evolving networks. EPL (Europhysics Letters), 54(4), (2001), 436–442.CrossRefGoogle Scholar
Bianconi, G. and Barabási, A. -L., Bose–Einstein condensation in complex networks. Physical Review Letters,. 86(24), (2001) 5632.CrossRefGoogle ScholarPubMed
Fleming, L., Mingo, S., and Chen, D., Collaborative brokerage, generative creativity, and creative success. Administrative Science Quarterly, 52(3), (2007), 443-475.CrossRefGoogle Scholar
Youn, H., Strumsky, D., Bettencourt, L. M. A., et al., Invention as a combinatorial process: Evidence from US patents. Journal of The Royal Society Interface, 12(106), (2015), 20150272.CrossRefGoogle ScholarPubMed
Wang, J., Veugelers, R., and Stephan, P., Bias against novelty in science: A cautionary tale for users of bibliometric indicators. Research Policy, 46(8), (2017), 1416–1436.CrossRefGoogle Scholar
Lee, Y. -N., Walsh, J. P., and Wang, J., Creativity in scientific teams: Unpacking novelty and impact. Research Policy, 44(3), (2015), 684–697.CrossRefGoogle Scholar
Phiel, C. J., Zhang, F., Huang, E. Y., et al., Histone deacetylase is a direct target of valproic acid, a potent anticonvulsant, mood stabilizer, and teratogen. Journal of Biological Chemistry, 276(39), (2001), 36734–36741.CrossRefGoogle ScholarPubMed
Stephan, P., Veugelers, R., and Wang, J., Reviewers are blinkered by bibliometrics. Nature News,. 544(7651), (2017), 411.CrossRefGoogle ScholarPubMed
Van Noorden, R., Interdisciplinary research by the numbers. Nature, 525(7569), (2015), 306–307.CrossRefGoogle ScholarPubMed
Wagner, C. S., Roessner, J. D., Bobb, K., et al., Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature. Journal of Informetrics, 5(1), (2011), 14–26.CrossRefGoogle Scholar
Larivière, V., Haustein, S., and Börner, K., Long-distance interdisciplinarity leads to higher scientific impact. PLoS ONE, 10(3), (2015), e0122565.CrossRefGoogle ScholarPubMed
Leahey, E., and Moody, J., Sociological innovation through subfield integration. Social Currents, 1(3), (2014), 228-256.CrossRefGoogle Scholar
Foster, J. G., Rzhetsky, A., and Evans, J.A., Tradition and innovation in scientists’ research strategies. American Sociological Review, 80(5), (2015), 875–908.CrossRefGoogle Scholar
Fleming, L., Breakthroughs and the “long tail” of innovation. MIT Sloan Management Review, 49(1), (2007), 69.Google Scholar
Boudreau, K. J., Guinan, E., Lakhani, K. R., et al., Looking across and looking beyond the knowledge frontier: Intellectual distance, novelty, and resource allocation in science. Management Science, 62(10), (2016), 2765–2783.CrossRefGoogle Scholar
Bromham, L., Dinnage, R., and Hua, X., Interdisciplinary research has consistently lower funding success. Nature, 534(7609), (2016), 684–687.CrossRefGoogle ScholarPubMed
Kim, J., Lee, C. -Y., and Cho, Y., Technological diversification, core-technology competence, and firm growth. Research Policy, 45(1), (2016), 113–124.CrossRefGoogle Scholar
Phillips, D. P., Kanter, E. J., Bednarczyk, B., et al., Importance of the lay press in the transmission of medical knowledge to the scientific community. The New England Journal of Medicine, 325(16), (1991), 1180–1183.CrossRefGoogle ScholarPubMed
Gonon, F., Konsman, J. -P., Cohen, D., et al., Why most biomedical findings echoed by newspapers turn out to be false: The case of attention deficit hyperactivity disorder. PLoS One, 7(9), (2012), e44275.CrossRefGoogle ScholarPubMed
Dumas-Mallet, E., Smith, A., Boraud, T., et al., Poor replication validity of biomedical association studies reported by newspapers. PLoS One, 12(2), (2017), e0172650.CrossRefGoogle ScholarPubMed
Peng, R. D., Reproducible research in computational science. Science, 334(6060), (2011), 1226–1227.CrossRefGoogle ScholarPubMed
Open Science Collaboration, Aarts, A., Anderson, J., et al., Estimating the reproducibility of psychological science. Science, 349(6251), (2015), aac4716.Google Scholar
Wakefield, A. J., Murch, S. H., Anthony, A., et al., RETRACTED: Ileal-lymphoid-nodular hyperplasia, non-specific colitis, and pervasive developmental disorder in children. The Lancet, 351(1998), 637–641.CrossRefGoogle ScholarPubMed
Catalini, C., Lacetera, N., and Oettl, A., The incidence and role of negative citations in science. Proceedings of the National Academy of Sciences, 112(45), (2015), 13823–13826.CrossRefGoogle Scholar
Fang, F. C. and Casadevall, A., Retracted science and the retraction index. Infection and Immunity, 79(10), (2011), 3855–3859.CrossRefGoogle ScholarPubMed
Sandison, A., Densities of use, and absence of obsolescence, in physics journals at MIT. Journal of the American Society for Information Science, 25(3), (1974), 172–182.CrossRefGoogle Scholar
Candia, C., Jara-Figueroa, C., Rodriguez-Sickert, C., et al., The universal decay of collective memory and attention. Nature Human Behaviour, 3(1), (2019), 82–91.CrossRefGoogle ScholarPubMed
Mukherjee, S., Romero, D. M., Jones, B., et al., The age of past knowledge and tomorrow’s scientific and technological breakthroughs. Science Advances, 3( 4), (2017), e1601315.Google Scholar
Odlyzko, A., The rapid evolution of scholarly communication. Learned Publishing, 15(1), (2002), 7–19.CrossRefGoogle Scholar
Larivière, V., Archambault, É., and Gingras, Y., Long-term variations in the aging of scientific literature: From exponential growth to steady-state science (1900–2004). Journal of the American Society for Information Science and Technology, 59(2), (2008), 288–296.CrossRefGoogle Scholar
Verstak, A., Acharya, A. Suzuki, H. et al., On the shoulders of giants: The growing impact of older articles. arXiv preprint https://arxiv.org/abs/1411.0275, (2014).Google Scholar
Evans, J. A., Electronic publication and the narrowing of science and scholarship. Science, 321(5887), (2008), 395–399.CrossRefGoogle ScholarPubMed
Burnham, J. C., The evolution of editorial peer review. Journal of the American Medical Association, 263(10), (1990), 1323–1329.CrossRefGoogle ScholarPubMed
Spier, R., The history of the peer-review process. Trends in Biotechnology, 20(8), (2002), 357–358.CrossRefGoogle ScholarPubMed
Burrell, Q. L., Modelling citation age data: Simple graphical methods from reliability theory. Scientometrics, 55(2), (2002), 273–285.CrossRefGoogle Scholar
Glänzel, W., Towards a model for diachronous and synchronous citation analyses. Scientometrics, 60(3), (2004), 511–522.CrossRefGoogle Scholar
Nakamoto, H., Synchronous and diachronous citation distribution, in Egghe, L. and Rousseau, R. (eds.), Informetrics 87/88: Select Proceedings of the First International Conference on Bibliometrics and Theoretical Aspects of Information Retrieval (Amsterdam: Elsevier Science Publishers, 1988).Google Scholar
Pan, R. K., Petersena, A. M., Pammolli, F., et al., The memory of science: Inflation, myopia, and the knowledge network. Journal of Informetrics, 12, (2016), 656–678.CrossRefGoogle Scholar
Parolo, P. D. B., Pan, R. K., Ghosh, R., et al., Attention decay in science. Journal of Informetrics, 9(4), (2015), 734–745.CrossRefGoogle Scholar
Van Raan, A. F., Sleeping beauties in science. Scientometrics, 59(3), (2004), 467–472.CrossRefGoogle Scholar
Ke, Q., Ferrara, E., Radicchi, F., et al., Defining and identifying Sleeping Beauties in science. Proceedings of the National Academy of Sciences, 112(24), (2015), 7426–7431.CrossRefGoogle Scholar
He, Z., Lei, Z., and Wang, D., Modeling citation dynamics of “atypical” articles. Journal of the Association for Information Science and Technology, 69(9), (2018), 1148–1160.CrossRefGoogle Scholar
Garfield, E., Citation indexes for science. Science, 122(3159), (1955), 108–111.CrossRefGoogle ScholarPubMed
Erdős, P., and Rényi, A., On the evolution of random graphs. Publications of the Mathematical Institute of the Hungarian Academy of Sciences, 5(1), (1960), 17–61.Google Scholar
Evans, J. A., Future science. Science, 342(44), (2013), 44–45.CrossRefGoogle ScholarPubMed
Harari, Y. N., Sapiens: A Brief History of Humankind (London: Random House, 2014).Google Scholar
Evans, J. A., and Foster, J. G., Metaknowledge. Science, 331(6018), (2011), 721–725.CrossRefGoogle ScholarPubMed
King, R. D., Rowland, J., Olive, S. G. et al., The automation of science. Science, 324(5923), (2009), 85–89.CrossRefGoogle ScholarPubMed
Evans, J., and Rzhetsky, A., Machine science. Science, 329(5990), (2010), 399–400.CrossRefGoogle ScholarPubMed
Swanson, D. R., Migraine and magnesium: Eleven neglected connections. Perspectives in Biology and Medicine, 31(4), (1988), 526–557.CrossRefGoogle ScholarPubMed
Rzhetsky, A., Foster, J. G., Foster, I. T., et al., Choosing experiments to accelerate collective discovery. Proceedings of the National Academy of Sciences, 112(47), (2015), 14569–14574.CrossRefGoogle ScholarPubMed
Azoulay, P., Graff-Zivin, J., Uzzi, B., et al., Toward a more scientific science. Science, 361(6408), (2018), 1194–1197.CrossRefGoogle Scholar
Greenberg, S. A., How citation distortions create unfounded authority: analysis of a citation network. The BMJ, 339, (2009), b2680.CrossRefGoogle ScholarPubMed
Gerber, A. S., and Malhotra, N., Publication bias in empirical sociological research: Do arbitrary significance levels distort published results? Sociological Methods and Research, 37(1), (2008), 3–30.CrossRefGoogle Scholar
Benjamin, D. J., Berger, J. O., Johannesson, M., et al., Redefine statistical significance. Nature Human Behaviour, 2(1), (2018), 6–10.CrossRefGoogle ScholarPubMed
Efthimiou, O., and Allison, S. T., Heroism science: Frameworks for an emerging field. Journal of Humanistic Psychology,. 58(5), (2018), 556–570.CrossRefGoogle Scholar
Nosek, B. A., Ebersole, C. R., DeHaven, A. C., et al., The preregistration revolution. Proceedings of the National Academy of Sciences, 115(11), (2018), 2600–2606.CrossRefGoogle ScholarPubMed
Kuhn, T. S., The Essential Tension: Selected Studies in Scientific Tradition and Change (Chicago: University of Chicago Press, 1977).CrossRefGoogle Scholar
Bourdieu, P., The specificity of the scientific field and the social conditions of the progress of reasons. Social Science Information, 14(6), (1975), 19–47.CrossRefGoogle Scholar
Yao, L., Li, Y., Ghosh, S., et al., Health ROI as a measure of misalignment of biomedical needs and resources. Nature Biotechnology, 33(8), (2015), 807–811.CrossRefGoogle ScholarPubMed
Willett, W., Nutritional Epidemiology (New York: Oxford University Press, 2012).CrossRefGoogle Scholar
Spector, J. M., Harrison, R. S., and Fishman, M.C., Fundamental science behind today’s important medicines. Science Translational Medicine, 10(438), (2018), eaaq1787.CrossRefGoogle ScholarPubMed
Senior, A., Jumper, J., and Hassabis, D., AlphaFold: Using AI for scientific discovery. Deepmind article/blog post available online at https://bit.ly/34PXtzA (2020).Google Scholar
Harari, Y. N., Reboot for the AI revolution. Nature News, 550(7676), (2017), 324–327.CrossRefGoogle ScholarPubMed
Krizhevsky, A., Sutskever, I., and Hinton, G. E.. ImageNet classification with deep convolutional neural networks, in Advances in Neural Information Processing Systems 25 (NIPS 2012) (San Diego, CA: NIPS Foundation, 2012).Google Scholar
Farabet, C., Couprie, C., Najman, L., et al., Learning hierarchical features for scene labeling. IEEE Transactions on Pattern Analysis and Machine Intelligence, 35(8), (2012), 1915–1929.CrossRefGoogle Scholar
Tompson, J. J., Jain, A., LeCun, Y., et al., Joint training of a convolutional network and a graphical model for human pose estimation, in Advances in Neural Information Processing Systems 27 (NIPS 2014) (San Diego, CA: NIPS Foundation, 2014).Google Scholar
Szegedy, C., Liu, W., Jia, Y., et al., Going deeper with convolutions, in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (Piscataway, NJ: IEEE, 2015), pp. 1–9.Google Scholar
Mikolov, T., Deoras, A., Povey, D., et al., Strategies for training large scale neural network language models, in 2011 IEEE Workshop on Automatic Speech Recognition & Understanding (Piscataway, NJ: IEEE, 2011), pp. 196–201.CrossRefGoogle Scholar
Hinton, G., Deng, L., Yu, D., et al., Deep neural networks for acoustic modeling in speech recognition. IEEE Signal Processing Magazine, 29(6), (2012), 82–97.CrossRefGoogle Scholar
Sainath, T. N., Mohamed, A., Kingsbury, B., et al. Deep convolutional neural networks for LVCSR, in 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (Piscataway, NJ: IEEE, 2013), pp. 8614–8618.CrossRefGoogle Scholar
Bordes, A., Chopra, S., and Weston, J., Question answering with subgraph embeddings. arXiv preprint https://arxiv.org/pdf/1406.3676.pdf, (2014).CrossRefGoogle Scholar
Jean, S., Cho, K., Memisevic, R., et al., On using very large target vocabulary for neural machine translation. arXiv preprint https://arxiv.org/abs/1412.2007, (2014).Google Scholar
Sutskever, I., Vinyals, O., and Le, Q. V.. Sequence to sequence learning with neural networks, in Advances in Neural Information Processing Systems 27 (NIPS 2014) (San Diego, CA: NIPS Foundation, 2014).Google Scholar
Ma, J., Sheridan, R. P., Liaw, A., et al., Deep neural nets as a method for quantitative structure–activity relationships. Journal of Chemical Information and Modeling,. 55(2), (2015), 263–274.CrossRefGoogle ScholarPubMed
Ciodaro, T., Deva, D., de Seixas, J. M., et al., Online particle detection with neural networks based on topological calorimetry information. Journal of Physics: Conference Series, 368, (2012), 012030.Google Scholar
Kaggle. Higgs boson machine learning challenge. Available online at www.kaggle.com/c/higgs-boson/overview (2014).Google Scholar
Helmstaedter, M., Briggman, K., Turaga, S., et al., Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature, 500(7461), (2013), 168–174.CrossRefGoogle ScholarPubMed
Leung, M. K., Xiong, H. Y., Lee, L., et al., Deep learning of the tissue-regulated splicing code. Bioinformatics, 30(12), (2014), i121–i129.CrossRefGoogle ScholarPubMed
Xiong, H. Y., Alipanahi, B., Lee, L., et al., The human splicing code reveals new insights into the genetic determinants of disease. Science, 347(6218), (2015), 1254806.CrossRefGoogle ScholarPubMed
Silver, D., Hubert, T., Schrittwieser, J., et al., A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419), (2018), 1140–1144.CrossRefGoogle ScholarPubMed
De Fauw, J., Ledsam, J. R., Romera-Parede, B., et al., Clinically applicable deep learning for diagnosis and referral in retinal disease. Nature Medicine, 24(9), (2018), 1342–1350.CrossRefGoogle ScholarPubMed
Esteva, A., Kuprel, B., Novoa, R. A., et al., Dermatologist-level classification of skin cancer with deep neural networks. Nature, 542(7639), (2017), 115–118.CrossRefGoogle ScholarPubMed
Titano, J. J., Badgeley, M., Schefflein, J., et al., Automated deep-neural-network surveillance of cranial images for acute neurologic events. Nature Medicine, 24(9), (2018), 1337–1341.CrossRefGoogle ScholarPubMed
Nosek, B. A. and Errington, T. M., Reproducibility in cancer biology: Making sense of replications. Elife, 6, (2017), e23383.CrossRefGoogle ScholarPubMed
Camerer, C. F., Dreber, A., Forsell, E., et al., Evaluating replicability of laboratory experiments in economics. Science, 351(6280), (2016), 1433–1436.CrossRefGoogle ScholarPubMed
Camerer, C. F., Dreber, A., Holzmeister, F., et al., Evaluating the replicability of social science experiments in Nature and Science between 2010 and 2015. Nature Human Behaviour, 2(9), (2018), 637–644.CrossRefGoogle ScholarPubMed
Chang, A. and Li, P., Is economics research replicable? Sixty published papers from thirteen journals say “usually not.” Finance and Economics Discussion Series 2015-083. Washington, DC: Board of Governors of the Federal Reserve System. Available online at https://bit.ly/34RI3uy, (2015).CrossRefGoogle Scholar
Wu, Y., Yang, Y., and Uzzi, B., An artificial and human intelligence approach to the replication problem in science. [Unpublished data.]Google Scholar
Tegmark, M., Life 3.0: Being Human in the Age of Artificial Intelligence (New York: Alfred A. Knopf, ( 2017).Google Scholar
Dastin, J., Amazon scraps secret AI recruiting tool that showed bias against women. Reuters news article, available online at https://bit.ly/3cChuwe, (October 10, 2018).Google Scholar
Wang, Y., Jones, B. F., and Wang, D., Early-career setback and future career impact. Nature Communications, 10, (2019), 4331.CrossRefGoogle ScholarPubMed
Bol, T., de Vaan, M., and van de Rijt, A., The Matthew effect in science funding. Proceedings of the National Academy of Sciences, 115(19), (2018), 4887–4890.CrossRefGoogle Scholar
Calcagno, V., Demoinet, E., Gollner, K., et al., Flows of research manuscripts among scientific journals reveal hidden submission patterns. Science, 338(6110), (2012), 1065–1069.CrossRefGoogle ScholarPubMed
Azoulay, P., Small-team science is beautiful. Nature, 566(7744), (2019), 330–332.CrossRefGoogle Scholar
Haustein, S., Peters, I., Sugimoto, C. R., et al., Tweeting biomedicine: An analysis of tweets and citations in the biomedical literature. Journal of the Association for Information Science and Technology, 65(4), (2014), 656–669.CrossRefGoogle Scholar
Perneger, T. V., Relation between online “hit counts” and subsequent citations: Prospective study of research papers in The BMJ. The BMJ, 329(7465), (2004), 546–547.CrossRefGoogle ScholarPubMed
Li, D., Azoulay, P. and Sampat, B. N., The applied value of public investments in biomedical research. Science,. 356(6333), (2017), 78–81.CrossRefGoogle ScholarPubMed
Ahmadpoor, M. and Jones, B. F., The dual frontier: Patented inventions and prior scientific advance. Science,. 357(6351), (2017), 583–587.CrossRefGoogle ScholarPubMed
Duckworth, A. and Duckworth, A., Grit: The Power of Passion and Perseverance (New York: Scribner, 2016).Google Scholar
Angrist, J. D. and Pischke, J.-S., The credibility revolution in empirical economics: How better research design is taking the con out of econometrics. Journal of Economic Perspectives, 24(2), (2010), 3–30.CrossRefGoogle Scholar
Boudreau, K. J., Brady, T., Ganguli, I., et al., A field experiment on search costs and the formation of scientific collaborations. Review of Economics and Statistics, 99(4), (2017), 565–576.CrossRefGoogle ScholarPubMed
Kleven, H. J., Language trends in public economics. Slides available online at https://bit.ly/2RSSTuT, (2018).Google Scholar
Ruhm, C. J., Deaths of Despair or Drug Problems?, working paper 24188 (Cambridge, MA: National Bureau of Economic Research, 2018).CrossRefGoogle Scholar
Redner, S., How popular is your paper? An empirical study of the citation distribution. The European Physical Journal B: Condensed Matter and Complex Systems, 4(2), (1998), 131–134.CrossRefGoogle Scholar
Jeong, H., Néda, Z., and Barabási, A.-L., Measuring preferential attachment in evolving networks. Europhysics Letters, 61(4), (2003), 567.CrossRefGoogle Scholar
Newman, M. E., Clustering and preferential attachment in growing networks. Physical Review E, 64(2), (2001), 025102.CrossRefGoogle ScholarPubMed
Krapivsky, P. L. and Redner, S., Organization of growing random networks. Physical Review E, 63(6), (2001), 066123.CrossRefGoogle ScholarPubMed
Peterson, G. J., Pressé, S., and Dill, K.A., Nonuniversal power law scaling in the probability distribution of scientific citations. Proceedings of the National Academy of Sciences, 107(37), (2010), 16023–16027.CrossRefGoogle ScholarPubMed
Simkin, M. V. and Roychowdhury, V. P., Do copied citations create renowned papers? Annals of Improbable Research, 11(1), (2005), 24–27.CrossRefGoogle Scholar
Simkin, M. V. and Roychowdhury, V. P., A mathematical theory of citing. Journal of the American Society for Information Science and Technology, 58(11), (2007), 1661–1673.CrossRefGoogle Scholar
Bentley, R. A., Hahn, M.W., and Shennan, S. J., Random drift and culture change. Proceedings of the Royal Society of London. Series B: Biological Sciences, 271(1547), (2004), 1443–1450.CrossRefGoogle ScholarPubMed
Vazquez, A., Knowing a network by walking on it: Emergence of scaling. arXiv preprint https://arxiv.org/pdf/cond-mat/0006132v1.pdf, (2000).Google Scholar
Kleinberg, J. M., Kumar, R., Raghavan, P., et al., The web as a graph: Measurements, models, and methods, in Lecture Notes in Computer Science, vol. 1627, Computing and Combinatorics, (Berlin: Springer-Verlag, 1999), pp. 1–17.Google Scholar
Perdew, J. P. and Wang, Y., Accurate and simple analytic representation of the electron–gas correlation energy. Physical Review B, 45(23), (1992), 13244.CrossRefGoogle ScholarPubMed
Simkin, M. V. and Roychowdhury, V. P., Read before you cite! arXiv preprint https://arxiv.org/pdf/cond-mat/0212043.pdf, (2002).Google Scholar
Kosterlitz, J. M. and Thouless, D. J., Ordering, metastability and phase transitions in two-dimensional systems. Journal of Physics C: Solid State Physics, 6(7), (1973), 1181–1203.CrossRefGoogle Scholar
Radicchi, F. and Castellano, C., A reverse engineering approach to the suppression of citation biases reveals universal properties of citation distributions. PLoS One, 7(3), (2012), e33833.CrossRefGoogle ScholarPubMed