Delayed recognition, exemplified by the phenomenon of sleeping beauties, presents a compelling narrative within the dynamics of scientific impact and innovation. Our investigation delves into the nuanced facets of delayed acknowledgement, uncovering its profound implications and innovation pathways. Through the analysis of extensive datasets and advanced methodologies, we elucidate the intricate connections between delayed recognition and the realms of scientific and technological influence. Our study not only reveals correlations between atypical combinations of knowledge and the emergence of sleeping beauties but also sheds light on the relationship between delayed recognition and disruptive paradigm shifts in scientific evolution, suggesting their potential role in shaping scientific breakthroughs. Furthermore, our analysis highlights the journey of delayed recognition, often culminating in significant contributions across diverse fields, including notable achievements, such as Nobel-worthy milestones. This article advances our understanding of scientific evolution and the complex landscape of acknowledging pioneering research.
WangDBarabásiA-L. The science of science. Cambridge: Cambridge University Press, 2021.
2.
JusupMHolmePKanazawaK, et al. Social physics. Phys Rep2022; 948: 1–148.
3.
FortunatoSBergstromCTBoernerK, et al. Science of science. Science2018; 359: eaao0185.
4.
WuestmanMHoekmanJFrenkenK. A typology of scientific breakthroughs. Quant Sci Stud2020; 1: 1203–1222.
5.
GarfieldE. Premature discovery or delayed recognition-why. Curr Contents1980; 21: 5–10.
6.
van RaanAFJ. Sleeping beauties in science. Scientometrics2004; 59: 467–472.
7.
BattistonFMusciottoFWangD, et al. Taking census of physics. Nature Rev Phys2019; 1: 89–97.
8.
GlanzelWGarfieldE. The myth of delayed recognition: citation analysis demonstrates that premature discovery, while rare, does occur: nearly all significant research is normally cited soon after publication. Scientist2004; 18: 8–9.
9.
WangRLiSYinQ, et al. Improved PageRank and new indices for academic impact evaluation using AI papers as case studies. J Inf Sci. Epub ahead of print 21 July 2022. DOI: 10.1177/01655515221105038.
10.
MiuraTAsataniKSakataI. Revisiting the uniformity and inconsistency of slow-cited papers in science. J Informet2023; 17: 101378.
11.
CominCHPeronTSilvaFN, et al. Complex systems: features, similarity and connectivity. Phys Rep2020; 861: 1–41.
12.
YeFYBornmannL. ‘Smart girls’ versus ‘sleeping beauties’ in the sciences: the identification of instant and delayed recognition by using the citation angle. J Associa Inform Sci Technol2018; 69: 359–367.
13.
van RaanAFJ. Sleeping beauties cited in patents: is there also a dormitory of inventions?Scientometrics2017; 110: 1123–1156.
14.
van RaanAFJ. Sleeping beauties gain impact in overdrive mode. Scientometrics2021; 126: 4311–4332.
15.
LiJYeFY. Distinguishing sleeping beauties in science. Scientometrics2016; 108: 821–828.
16.
ChuJSGEvansJA. Slowed canonical progress in large fields of science. Proc Nat Acad Sci U S A2021; 118: e2021636118.
17.
LiJYinYFortunatoS, et al. Scientific elite revisited: patterns of productivity, collaboration, authorship and impact. J Roy Soc Interface2020; 17: 20200135.
18.
ParkMLeaheyEFunkRJ. Papers and patents are becoming less disruptive over time. Nature2023; 613: 138–144.
19.
WuLFWangDSEvansJA. Large teams develop and small teams disrupt science and technology. Nature2019; 566: 378.
20.
XuFWuLEvansJ. Flat teams drive scientific innovation. Proc Nat Acad Sci U S A2022; 119: e2200927119.
21.
AcemogluDAkcigitUKerrWR. Innovation network. Proc Nat Acad Sci U S A2016; 113: 11483–11488.
22.
KuhnTS. Historical structure of scientific discovery. Science1962; 136: 760–764.
23.
TrapidoD. How novelty in knowledge earns recognition: the role of consistent identities. Res Policy2015; 44: 1488–1500.
24.
WinninkJJTijssenRJWvan RaanAFJ. Searching for new breakthroughs in science: how effective are computerised detection algorithms?Technol Forecast Social Change2019; 146: 673–686.
25.
ShengLLyuDRuanX, et al. The association between prior knowledge and the disruption of an article. Scientometrics2023; 128: 4731–4751.
26.
AzoulayPGraff-ZivinJUzziB, et al. Toward a more scientific science. Science2018; 361: 1194–1197.
27.
LiuLJonesBFUzziB, et al. Data, measurement and empirical methods in the science of science. Nat Hum Behav2023; 7: 1046–1058.
28.
AhmadpoorMJonesBF. The dual frontier: patented inventions and prior scientific advance. Science2017; 357: 583–587.
29.
DuWMLiZMXieZ. A modified LSTM network to predict the citation counts of papers. J Inf Sci. Epub ahead of print 8 August 2022. DOI: 10.1177/01655515221111000.
30.
UzziBMukherjeeSStringerM, et al. Atypical combinations and scientific impact. Science2013; 342: 468–472.
31.
FunkRJOwen-SmithJ. A dynamic network measure of technological change. Manag Sci2017; 63: 791–817.
32.
LiJYinYFortunatoS, et al. A dataset of publication records for Nobel laureates. Sci Data2019; 6: 33.
33.
GlänzelWSchlemmerBThijsB. Better late than never? On the chance to become highly cited only beyond the standard bibliometric time horizon. Scientometrics2003; 58: 571–586.
34.
BarberB. Resistance by scientists to scientific discovery. Science1961; 134: 596–602.
O’ReillyMSHolmgrenLShingY, et al. Angiostatin: a novel angiogenesis inhibitor that mediates the suppression of metastases by a Lewis lung carcinoma. Cell1994; 79: 315–328.
37.
DuJWuY. A parameter-free index for identifying under-cited sleeping beauties in science. Scientometrics2018; 116: 959–971.
38.
KeQFerraraERadicchiF, et al. Defining and identifying Sleeping Beauties in science. Proc Nat Acad Sci U S A2015; 112: 7426–7431.
39.
CostasRvan LeeuwenTNvan RaanAFJ. Is scientific literature subject to a ‘Sell-By-Date’? A general methodology to analyze the ‘durability’ of scientific documents. J Am Soc Inform Sci Technol2010; 61: 329–339.
40.
DongXLinKGaoY, et al. Nobel citation effects on scientific publications: a case study in physics. Inform Process Manag2023; 60: 103410.
41.
FengSLiHQiY. How to detect the sleeping beauty papers and princes in technology considering indirect citations?J Informet2023; 17: 101431.
42.
WangJVeugelersRStephanP. Bias against novelty in science: a cautionary tale for users of bibliometric indicators. Res Policy2017; 46: 1416–1436.
43.
YangAJHuHZhaoY, et al. From consolidation to disruption: a novel way to measure the impact of scientists and identify laureates. Infor Process Manag2023; 60: 103420.
44.
YangAJDengSWangH, et al. Disruptive coefficient and 2-step disruptive coefficient: novel measures for identifying vital nodes in complex networks. J Informet2023; 17: 101411.
45.
KokolPBlažun VošnerHZavršnikJ, et al. Sleeping beauties in health informatics research. Scientometrics2022; 127: 5073–5081.
46.
RuanXAoWLyuD, et al. Effect of the topic-combination novelty on the disruption and impact of scientific articles: evidence from PubMed. J Inf Sci. Epub ahead of print 15 March 2023. DOI: 10.1177/01655515231161133.
47.
LinZYinYLiuL, et al. SciSciNet: a large-scale open data lake for the science of science research. Sci Data2023; 10: 315.
48.
HuangC-KNeylonCBrookes-KenworthyC, et al. Comparison of bibliographic data sources: implications for the robustness of university rankings. Quant Sci Stud2020; 1: 445–478.
49.
WangJ. Citation time window choice for research impact evaluation. Scientometrics2013; 94: 851–872.
50.
MukherjeeSRomeroDMJonesB, et al. The nearly universal link between the age of past knowledge and tomorrow’s breakthroughs in science and technology: the hotspot. Sci Adv2017; 3: e1601315.
51.
HoY-SHartleyJ. Sleeping beauties in psychology. Scientometrics2017; 110: 301–305.
YoudenWJ. Index for rating diagnostic tests. Cancer1950; 3: 32–35.
54.
ZengAShenZSZhouJL, et al. The science of science: from the perspective of complex systems. Phys Rep2017; 714: 1–73.
55.
MarxMFuegiA. Reliance on science by inventors: hybrid extraction of in-text patent-to-article citations. J Econom Manag Strat2022; 31: 369–392.
56.
YangAJGongHWangY, et al. Rescaling the disruption index reveals the universality of disruption distributions in science. Scientometrics2024; 129: 561–580.
57.
LeeY-NWalshJPWangJ. Creativity in scientific teams: unpacking novelty and impact. Res Policy2015; 44: 684–697.
58.
GatesAJBarabásiA-L. Reproducible science of science at scale: pySciSci. Quan Sci Stud2023; 4: 700–710.
59.
ShorBBafumiJKeeleL, et al. A Bayesian multilevel modeling approach to time-series cross-sectional data. Politi Analy2007; 15: 165–181.
60.
WagnerCSWhetsellTAMukherjeeS. International research collaboration: novelty, conventionality, and atypicality in knowledge recombination. Res Policy2019; 48: 1260–1270.
61.
YangAJWuLZhangQ, et al. The k-step h-index in citation networks at the paper, author, and institution levels. J Informet2023; 17: 101456.
62.
YangAJ. Unveiling the impact and dual innovation of funded research. J Informet2024; 18: 101480.
63.
SinatraRWangDDevilleP, et al. Quantifying the evolution of individual scientific impact. Science2016; 354: aaf5239.
HofstraBKulkarniVVMunoz-Najar GalvezS, et al. The Diversity-Innovation Paradox in Science. Proc Nat Acad Sci U S A2020; 117: 9284–9291.
66.
TeplitskiyMPengHBlascoA, et al. Is novel research worth doing? Evidence from peer review at 49 journals. Proc Nat Acad Sci U S A2022; 119: e2118046119.
67.
MarxMFuegiA. Reliance on science: worldwide front-page patent citations to scientific articles. Strat Manag J2020; 41: 1572–1594.
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