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Papers and patents are becoming less disruptive over time

Nature volume 613, pages 138–144 (2023)Cite this article

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Abstract

Theories of scientific and technological change view discovery and invention as endogenous processes1,2, wherein previous accumulated knowledge enables future progress by allowing researchers to, in Newton’s words, ‘stand on the shoulders of giants’3,4,5,6,7. Recent decades have witnessed exponential growth in the volume of new scientific and technological knowledge, thereby creating conditions that should be ripe for major advances8,9. Yet contrary to this view, studies suggest that progress is slowing in several major fields10,11. Here, we analyse these claims at scale across six decades, using data on 45 million papers and 3.9 million patents from six large-scale datasets, together with a new quantitative metric—the CD index12—that characterizes how papers and patents change networks of citations in science and technology. We find that papers and patents are increasingly less likely to break with the past in ways that push science and technology in new directions. This pattern holds universally across fields and is robust across multiple different citation- and text-based metrics1,13,14,15,16,17. Subsequently, we link this decline in disruptiveness to a narrowing in the use of previous knowledge, allowing us to reconcile the patterns we observe with the ‘shoulders of giants’ view. We find that the observed declines are unlikely to be driven by changes in the quality of published science, citation practices or field-specific factors. Overall, our results suggest that slowing rates of disruption may reflect a fundamental shift in the nature of science and technology.

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Fig. 1: Overview of the measurement approach.
Fig. 2: Decline of disruptive science and technology.
Fig. 3: Decline of disruptive science and technology is visible in the changing language of papers and patents.
Fig. 4: Conservation of highly disruptive work.
Fig. 5: CD index of high-quality science over time.
Fig. 6: Papers and patents are using narrower portions of existing knowledge.

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Data availability

Data associated with this study are freely available in a public repository at https://doi.org/10.5281/zenodo.7258379. Our study draws on data from six sources: the American Physical Society, JSTOR, Microsoft Academic Graph, Patents View, PubMed and WoS. Data from Microsoft Academic Graph, Patents View and PubMed are publicly available, and our repository includes complete data for analyses from these sources. Data from the American Physical Society, JSTOR and WoS are not publicly available, and were used under licence from their respective publishers. To facilitate replication, our repository includes limited versions of the data from these sources, which will enable calculation of basic descriptive statistics. The authors will make full versions of these data available upon request and with permission from their respective publishers. Source data are provided with this paper.

Code availability

Open-source code related to this study is available at https://doi.org/10.5281/zenodo.7258379 and http://www.cdindex.info. We used Python v.3.10.6 (pandas v.1.4.3, numpy v.1.23.1, matplotlib v.3.5.2, seaborn v.0.11.2, spacy v.2.2, jupyterlab v.3.4.4) to wrangle, analyse and visualize data and to conduct statistical analyses. We used MariaDB v.10.6.4 to wrangle data. We used R v.4.2.1 (ggplot2 v.3.36, ggrepel v.0.9.0) to visualize data. We used StataMP v.17.0 (reghdfe v.5.7.3) to conduct statistical analyses.

References

  1. Fleming, L. Recombinant uncertainty in technological search. Manage. Sci. 47, 117–132 (2001).

    Article  Google Scholar 

  2. Schumpeter, J. Capitalism, Socialism and Democracy (Perennial, 1942).

  3. Koyré, A. An unpublished letter of Robert Hooke to Isaac Newton. ISIS 43, 312–337 (1952).

    Article  MathSciNet  MATH  Google Scholar 

  4. Popper, K. Conjectures and Refutations: The Growth of Scientific Knowledge (Routledge, 2014).

  5. Fleck, L. Genesis and Development of a Scientific Fact (Univ. Chicago Press, 2012).

  6. Acemoglu, D., Akcigit, U. & Kerr, W. R. Innovation network. Proc. Natl Acad. Sci. USA 113, 11483–11488 (2016).

    Article  ADS  CAS  Google Scholar 

  7. Weitzman, M. L. Recombinant growth. Q. J. Econ. 113, 331–360 (1998).

    Article  MathSciNet  MATH  Google Scholar 

  8. Tria, F., Loreto, V., Servedio, V. D. P. & Strogatz, S. H. The dynamics of correlated novelties. Sci. Rep. 4, 1–8 (2014).

    Article  Google Scholar 

  9. Fink, T. M. A., Reeves, M., Palma, R. & Farr, R. S. Serendipity and strategy in rapid innovation. Nat. Commun. 8, 1–9 (2017).

    Article  CAS  Google Scholar 

  10. Pammolli, F., Magazzini, L. & Riccaboni, M. The productivity crisis in pharmaceutical R&D. Nat. Rev. Drug Discov. 10, 428–438 (2011).

    Article  CAS  Google Scholar 

  11. Bloom, N., Jones, C. I., Van Reenen, J. & Webb, M. Are ideas getting harder to find? Am. Econ. Rev. 110, 1104–1144 (2020).

    Article  Google Scholar 

  12. Funk, R. J. & Owen-Smith, J. A dynamic network measure of technological change. Manage. Sci. 63, 791–817 (2017).

    Article  Google Scholar 

  13. Bornmann, L., Devarakonda, S., Tekles, A. & Chacko, G. Are disruption index indicators convergently valid? The comparison of several indicator variants with assessments by peers. Quant. Sci. Stud. 1, 1242–1259 (2020).

    Article  Google Scholar 

  14. Uzzi, B., Mukherjee, S., Stringer, M. & Jones, B. Atypical combinations and scientific impact. Science 342, 468–472 (2013).

    Article  ADS  CAS  Google Scholar 

  15. Leydesdorff, L., Tekles, A. & Bornmann, L. A proposal to revise the disruption index. Prof. Inf. 30, e300121 (2021).

  16. Lu, C. et al. Analyzing linguistic complexity and scientific impact. J. Informetr. 13, 817–829 (2019).

    Article  Google Scholar 

  17. Hofstra, B. et al. The diversity–innovation paradox in science. Proc. Natl Acad. Sci. USA 117, 9284–9291 (2020).

    Article  ADS  CAS  Google Scholar 

  18. Jones, B. F. The burden of knowledge and the ‘death of the renaissance man’: is innovation getting harder? Rev. Econ. Stud. 76, 283–317 (2009).

    Article  MATH  Google Scholar 

  19. Gordon, R. J. The Rise and Fall of American Growth (Princeton Univ. Press, 2016).

  20. Chu, J. S. G. & Evans, J. A. Slowed canonical progress in large fields of science. Proc. Natl Acad. Sci. USA 118, e2021636118 (2021).

    Article  CAS  Google Scholar 

  21. Packalen, M. & Bhattacharya, J. NIH funding and the pursuit of edge science. Proc. Natl Acad. Sci. USA 117, 12011–12016 (2020).

    Article  ADS  CAS  Google Scholar 

  22. Jaffe, A. B. & Lerner, J. Innovation and its Discontents: How Our Broken Patent System Is Endangering Innovation and Progress, and What To Do About It (Princeton Univ. Press, 2011).

  23. Horgan, J. The End of Science: Facing the Limits of Knowledge in the Twilight of the Scientific Age (Basic Books, 2015).

  24. Collison, P. & Nielsen, M. Science Is Getting Less Bang for its Buck (Atlantic, 2018).

  25. Nolan, A. Artificial intelligence and the future of science. oecd.ai, https://oecd.ai/en/wonk/ai-future-of-science (25 October 2021).

  26. Effective Policies to Foster High-risk/High-reward Research. OECD Science, Technology, and Industry Policy Papers (OECD, 2021).

  27. Cowen, T. The Great Stagnation: How America Ate All the Low-Hanging Fruit of Modern History, Got Sick, and Will (Eventually) Feel Better (Penguin, 2011).

  28. Einstein, A. The World As I See It (Citadel Press, 1949).

  29. Arthur, W. B. The structure of invention. Res. Policy 36, 274–287 (2007).

    Article  Google Scholar 

  30. Tushman, M. L. & Anderson, P. Technological discontinuities and organizational environments. Adm. Sci. Q. 31, 439–465 (1986).

    Article  Google Scholar 

  31. Kohn, W. & Sham, L. J. Self-consistent equations including exchange and correlation effects. Phys. Rev. 140, A1133 (1965).

    Article  ADS  MathSciNet  Google Scholar 

  32. Watson, J. D. & Crick, F. H. C. Molecular structure of nucleic acids: a structure for deoxyribose nucleic acid. Nature 171, 737–738 (1953).

    Article  ADS  CAS  Google Scholar 

  33. Bornmann, L. & Tekles, A. Disruption index depends on length of citation window. Prof. Inf. 28, e280207 (2019).

  34. Wu, L., Wang, D. & Evans, J. A. Large teams develop and small teams disrupt science and technology. Nature 566, 378–382 (2019).

    Article  ADS  CAS  Google Scholar 

  35. Kuhn, T. S. The Structure of Scientific Revolutions (Univ. Chicago Press, 1962).

  36. Brad Wray, K. Kuhn and the discovery of paradigms. Philos. Soc. Sci. 41, 380–397 (2011).

    Article  Google Scholar 

  37. Ioannidis, J. P. A. Why most published research findings are false. PLoS Med. 2, e124 (2005).

    Article  Google Scholar 

  38. Li, J., Yin, Y., Fortunato, S. & Wang, D. A dataset of publication records for Nobel laureates. Sci. Data 6, 1–10 (2019).

    Article  Google Scholar 

  39. Bornmann, L. & Marx, W. Methods for the generation of normalized citation impact scores in bibliometrics: which method best reflects the judgements of experts? J. Informetr. 9, 408–418 (2015).

    Article  Google Scholar 

  40. Waltman, L. A review of the literature on citation impact indicators. J. Informetr. 10, 365–391 (2016).

    Article  Google Scholar 

  41. Waltman, L. & van Eck, N. J. in Springer Handbook of Science and Technology Indicators (eds. Glänzel, W. et al.) 281–300 (Springer, 2019).

  42. Bornmann, L. How can citation impact in bibliometrics be normalized? A new approach combining citing-side normalization and citation percentiles. Quant. Sci. Stud. 1, 1553–1569 (2020).

    Article  Google Scholar 

  43. Petersen, A. M., Pan, R. K., Pammolli, F. & Fortunato, S. Methods to account for citation inflation in research evaluation. Res. Policy 48, 1855–1865 (2019).

    Article  Google Scholar 

  44. Bornmann, L. & Mutz, R. Growth rates of modern science: a bibliometric analysis based on the number of publications and cited references. J. Assoc. Inf. Sci. Technol. 66, 2215–2222 (2015).

    Article  CAS  Google Scholar 

  45. Bornmann, L., Haunschild, R. & Mutz, R. Growth rates of modern science: a latent piecewise growth curve approach to model publication numbers from established and new literature databases. Humanit. Soc. Sci. Commun. 8, 1–15 (2021).

    Article  Google Scholar 

  46. Jones, B. F. & Weinberg, B. A. Age dynamics in scientific creativity. Proc. Natl Acad. Sci. USA 108, 18910–18914 (2011).

    Article  ADS  CAS  Google Scholar 

  47. Bonzi, S. & Snyder, H. Motivations for citation: a comparison of self citation and citation to others. Scientometrics 21, 245–254 (1991).

    Article  Google Scholar 

  48. Fowler, J. & Aksnes, D. Does self-citation pay? Scientometrics 72, 427–437 (2007).

    Article  Google Scholar 

  49. King, M. M., Bergstrom, C. T., Correll, S. J., Jacquet, J. & West, J. D. Men set their own cites high: gender and self-citation across fields and over time. Socius 3, 2378023117738903 (2017).

    Article  Google Scholar 

  50. Mukherjee, S., Romero, D. M., Jones, B. & Uzzi, B. The nearly universal link between the age of past knowledge and tomorrow’s breakthroughs in science and technology: the hotspot. Sci. Adv. 3, e1601315 (2017).

    Article  ADS  Google Scholar 

  51. Merton, R. K. Singletons and multiples in scientific discovery: a chapter in the sociology of science. Proc. Am. Philos. Soc. 105, 470–486 (1961).

    Google Scholar 

  52. Wang, D., Song, C. & Barabási, A.-L. Quantifying long-term scientific impact. Science 342, 127–132 (2013).

    Article  ADS  CAS  Google Scholar 

  53. Leahey, E. Not by productivity alone: how visibility and specialization contribute to academic earnings. Am. Sociol. Rev. 72, 533–561 (2007).

    Article  Google Scholar 

  54. Tahamtan, I. & Bornmann, L. Core elements in the process of citing publications: conceptual overview of the literature. J. Informetr. 12, 203–216 (2018).

    Article  Google Scholar 

  55. Tahamtan, I. & Bornmann, L. What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018. Scientometrics 121, 1635–1684 (2019).

    Article  Google Scholar 

  56. Bhattacharya, J. & Packalen, M. Stagnation and Scientific Incentives (Working Paper 26752), https://www.nber.org/papers/w26752 (2020).

  57. Azoulay, P., Graff Zivin, J. S. & Manso, G. Incentives and creativity: evidence from the academic life sciences. RAND J. Econ. 42, 527–554 (2011).

    Article  Google Scholar 

  58. Baltimore, D. Viral RNA-dependent DNA polymerase: RNA-dependent DNA polymerase in virions of RNA tumour viruses. Nature 226, 1209–1211 (1970).

    Article  ADS  CAS  Google Scholar 

  59. Page, L. Method for node ranking in a linked database. US patent 6,285,999 (2001).

  60. Axel, R., Wigler, M. H. & Silverstein, S. J. Processes for inserting DNA into eucaryotic cells and for producing proteinaceous materials. US patent 4,634,665 (1983).

  61. Hawbaker, M. S. Soybean variety SE90346. US patent 6,958,436 (2005).

  62. Katsuki, T. & Sharpless, K. B. The first practical method for asymmetric epoxidation. J. Am. Chem. Soc. 102, 5974–5976 (1980).

  63. Riess, A. G., et al. Observational evidence from supernovae for an accelerating universe and a cosmological constant. Astron. J. 116, 1009 (1998).

  64. Dirac, P. A. M. The quantum theory of the electron. Proc. R. Soc. Lond. A Math. Phys. Sci. 117, 610–624 (1928).

    ADS  MATH  Google Scholar 

  65. Sanger, F., Nicklen, S. & Coulson, A. R. DNA sequencing with chain-terminating inhibitors. Proc. Natl Acad. Sci. USA 74, 5463–5467 (1977).

    Article  ADS  CAS  Google Scholar 

  66. Bednorz, J. G. & Müller, K. A. Possible high Tc superconductivity in the Ba-La-Cu-O system. Z. Phys. B Condens. Matter 64, 189–193 (1986).

  67. Wuchty, S., Jones, B. F. & Uzzi, B. The increasing dominance of teams in production of knowledge. Science 316, 1036–1039 (2007).

    Article  ADS  CAS  Google Scholar 

  68. Guimera, R., Uzzi, B., Spiro, J. & Amaral, L. A. N. Team assembly mechanisms determine collaboration network structure and team performance. Science 308, 697–702 (2005).

    Article  ADS  CAS  Google Scholar 

  69. Jones, B. F., Wuchty, S. & Uzzi, B. Multi-university research teams: shifting impact, geography, and stratification in science. Science 322, 1259–1262 (2008).

    Article  ADS  CAS  Google Scholar 

  70. Grömping, U. Estimators of relative importance in linear regression based on variance decomposition. Am. Stat. 61, 139–147 (2007).

    Article  MathSciNet  Google Scholar 

  71. Mukherjee, S., Uzzi, B., Jones, B. & Stringer, M. A new method for identifying recombinations of existing knowledge associated with high-impact innovation. J. Prod. Innov. Manage. 33, 224–236 (2016).

    Article  Google Scholar 

  72. Christianson, N. H., Sizemore Blevins, A. & Bassett, D. S. Architecture and evolution of semantic networks in mathematics texts. Proc. R. Soc. A 476, 20190741 (2020).

    Article  ADS  MathSciNet  MATH  Google Scholar 

  73. Newman, M. E. J. The structure of scientific collaboration networks. Proc. Natl Acad. Sci. USA 98, 404–409 (2001).

    Article  ADS  MathSciNet  CAS  MATH  Google Scholar 

  74. Newman, M. E. J. Scientific collaboration networks. I. Network construction and fundamental results. Phys. Rev. E 64, 016131 (2001).

    Article  ADS  CAS  Google Scholar 

  75. Uzzi, B. & Spiro, J. Collaboration and creativity: the small world problem. Am. J. Sociol. 111, 447–504 (2005).

    Article  Google Scholar 

  76. Funk, R. J. Making the most of where you are: geography, networks, and innovation in organizations. Acad. Manage. J. 57, 193–222 (2014).

    Article  Google Scholar 

  77. Barabási, A.-L. Network Science (Cambridge Univ. Press, 2016).

  78. Blau, D. M. & Weinberg, B. A. Why the US science and engineering workforce is aging rapidly. Proc. Natl Acad. Sci. USA 114, 3879–3884 (2017).

    Article  ADS  CAS  Google Scholar 

  79. Cui, H., Wu, L. & Evans, J. A. Aging scientists and slowed advance. Preprint at https://doi.org/10.48550/arXiv.2202.04044 (2022).

  80. Azoulay, P., Fons-Rosen, C. & Graff Zivin, J. S. Does science advance one funeral at a time? Am. Econ. Rev. 109, 2889–2920 (2019).

    Article  Google Scholar 

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Acknowledgements

This study was supported by the National Science Foundation (grant Nos. 1829168, 1932596 and 1829302).

Author information

Authors and Affiliations

  1. Carlson School of Management, University of Minnesota, Minneapolis, MN, USA

    Michael Park & Russell J. Funk

  2. School of Sociology, University of Arizona, Tucson, AZ, USA

    Erin Leahey

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  1. Michael Park
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  2. Erin Leahey
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  3. Russell J. Funk
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Contributions

R.J.F. and E.L. collaboratively contributed to the conception and design of the study. R.J.F. and M.P. collaboratively contributed to the acquisition, analysis and interpretation of the data. R.J.F. created software used in the study. R.J.F., E.L. and M.P. collaboratively drafted and revised the manuscript.

Corresponding author

Correspondence to Russell J. Funk.

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The authors declare no competing interests.

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Nature thanks Diana Hicks and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data figures and tables

Extended Data Fig. 1 Distribution of CD5.

This figure gives an overview of the distribution of CD5 for papers (n = 24,659,076) and patents (n = 3,912,353). Panels a and c show counts of papers and patents over discrete intervals of CD5. Panels b and d show the distribution of CD5 over time, within 10 (papers) and 5 (patents) year intervals, using letter-value plots. These plots are similar to boxplots, but generally provide more reliable summaries for large datasets. They are drawn by identifying the median of the underlying distribution and then recursively drawing boxes outward from there in either direction that encompass half of the remaining data.

Source data

Extended Data Fig. 2 CD index measured using alternative forward citation windows.

This figure evaluates the sensitivity of our results to the use of different forward citation windows when computing the CD index for papers (n = 24,659,076) and patents (n = 3,912,353). In the main text, the index is computed based on citations made to papers and patents and their backward references as of 5 years after the year of publication. a and c plot the CD index using a longer, 10 year forward window, for papers and patents, respectively. b and d plot the CD index using all forward citations made to sample papers and patents as of the year 2017. Shaded bands correspond to 95% confidence intervals. Overall, the results mirror those reported in the main text, although the decline is somewhat steeper using longer forward citation windows, suggesting our primary results may represent a more conservative estimate.

Source data

Extended Data Fig. 3 Diversity of language use in science and technology over time.

This figure shows changes in the ratio of unique to total words (also known as the type-token ratio) over time based on data from the abstracts of papers (a, n = 76 WoS research area × year observations) and patents (b, n = 229 NBER technology category × year observations). For papers, lines correspond to WoS research areas; for patents, lines correspond to NBER technology categories. For paper abstracts, lines begin in 1992 because WoS does not reliably record abstracts for papers published prior to the early 1990s. The ratio of unique to total words is computed separately by field (i.e., the uniqueness of words and total word counts are determined within WoS research areas and NBER technology categories). If disruption is decreasing, we may plausibly expect to see a decrease in the diversity of words used by scientists and inventors, as discoveries and inventions will be less likely to create departures from the status quo, and will therefore be less likely to need to introduce new terminology. For both papers and patents, we observe declining diversity in word use over time, which is consistent with this expectation and corroborates our findings using the CD index.

Source data

Extended Data Fig. 4 Declining combinatorial novelty.

This figure shows changing patterns in the combinatorial novelty/conventionality of papers (a, n = 24,659,076) and patents (b, n = 3,912,353), using a previously proposed measure of “atypical combinations”14. The measure quantifies the degree to which the prior work cited by a paper or patent would be expected by chance. For papers, we follow prior work14 and consider combinations of cited journals. If a paper made three citations to prior work, and that work was published in three different journals—Nature, Cell, and Science—then there are three combinations—Nature × Cell, Nature × Science, and Science × Cell. To determine the degree to which each combination would be expected by chance, the frequency of observed pairings is compared to those in 10 “rewired” copies of the overall citation network, using a z-score. For patents, there is no natural analogue to journals, and therefore we consider pairings of primary United States Patent Classification (USPC) system codes. We present the results of this analysis following the approach of prior work14, which plots the cumulative distribution function of the measure. In general, there is a rightward shift in the cumulative distributions over time, suggesting that for both papers and patents, combinations are more conventional than would be expected by chance, consistent with what we would anticipate based on our results using the CD index. For patents, there is also a smaller shift in the opposite direction on the left side of the distribution, suggesting that novel patents in recent decades are somewhat more novel than novel patents in earlier decades. Overall, however, the bulk of the distribution is moving rightward, indicating greater conventionality.

Extended Data Fig. 5 Contribution of field, year, and author effects.

This figure shows the relative contribution of field, year, and author fixed effects to the adjusted R2 in regression models predicting CD5. The top bar shows the results for papers (n = 80,607,091 paper × author observations); the bottom bar shows the results for patents (n = 8,319,826 patent × inventor observations). The results suggest that for both papers and patents, stable characteristics of authors contribute significantly to patterns of disruptiveness. Moreover, relatively little of the variation is accounted for by field-specific factors.

Source data

Extended Data Fig. 6 CD index over time across data sources.

This figure shows changes in CD5 over time across four additional data sources (the WoS [n = 24,659,076] and Patents View [n = 3,912,353] lines are included for reference): JSTOR (n = 1,703,353), the American Physical Society corpus (n = 478,373), Microsoft Academic Graph (n = 1,000,000), and PubMed (n = 16,774,282). Colours indicate the six different data sources. Shaded bands correspond to 95% confidence intervals. The figure indicates that the decline in disruption is unlikely to be driven by our sample choice of WoS papers and Patents View patents.

Source data

Extended Data Fig. 7 Alternative measures of disruption.

This figure shows the decline in the disruption of papers (a, n = 100,000) and patents (b, n = 100,000) based on two alternative measures of disruption. The blue lines calculate disruption using a measure proposed in Bornmann et al.13, \({{DI}}_{l}^{{nok}}\) where l = 5, which makes the measure more resilient to marginal changes in the number of papers or patents that only cite the focal work’s references. The orange lines calculate disruption using a measure proposed in Leydesdorff et al.15, DI*, which makes the measure less sensitive to small changes in the forward citation patterns of papers or patents that make no backward citations. Shaded bands correspond to 95% confidence intervals. With both alternative measures, we observe decreases in disruption for papers and patents, suggesting that the decline is not an artefact of our operationalization of disruption.

Source data

Extended Data Fig. 8 Robustness to changes in publication, citation, and authorship practices.

This figure evaluates whether declines in disruptiveness may be attributable to changes in publication, citation, and authorship practices for papers (n = 24,659,076) and patents (n = 3,912,353). Panels a and d adjust for these changes using a normalization approach. We present two alternative versions of the CD index, both of which account for the tendency for papers and patents to cite more prior work over time. Blue lines indicate normalization at the paper level (accounting for the number of citations made by the focal paper/patent). Orange lines indicate normalization at the field and year level (accounting for the mean number of citations made by papers/patents in the focal field and year). Panels b (papers) and e (patents) adjust for changes in publication, citation, and authorship practices using a regression approach. The panels show predicted values of CD5 based on regressions reported in Models 4 (papers) and 8 (patents) of Supplementary Table 1, which adjust for field × year—Number of new papers/patents, Mean number of papers/patents cited, Mean number of authors/inventors per paper/patent—and paper/patent-level—Number of papers/patents cited, Number of unlinked references—characteristics. Predictions are made separately for each year indicator included in the models; we then connect these separate predictions with lines to aid interpretation. Finally, Panels c (papers) and f (patents) adjust for changes in publication, citation, and authorship practices using a simulation approach. The panels plot z-scores that compare values of CD5 obtained from the observed citation networks to those obtained from randomly rewired copies of the observed networks. Across all six panels, shaded bands correspond to 95% confidence intervals.

Source data

Extended Data Fig. 9 Growth of scientific and technological knowledge.

This figure shows the number of papers (n = 24,659,076) published (a) and patents (n = 3,912,353) granted (b) over time. For papers, lines correspond to WoS research areas; for patents, lines correspond to NBER technology categories.

Source data

Extended Data Table 1 Regression models of disruptiveness and the use of prior knowledge
Full size table

Supplementary information

Supplementary Information

Supplementary Sections 1–3, Tables 1–3 and References.

Reporting summary

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Park, M., Leahey, E. & Funk, R.J. Papers and patents are becoming less disruptive over time. Nature 613, 138–144 (2023). https://doi.org/10.1038/s41586-022-05543-x

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