This article uses the characteristics of citation curves in emerging research topics (ERTs) and combines them with the ERTs’ knowledge bases to draw conclusions by comparing their development patterns. The goal of this study is to enrich the toolset for predicting breakthroughs in scientific research. A set of multidimensional and practical bibliometric indicators is used to identify ERTs, to further identify the knowledge bases of ERTs and construct citation curves for both ERTs and their knowledge bases. The development trends of the citation curves of ERTs and their knowledge bases in different time periods are compared and analysed from two dimensions: knowledge transition and continuous growth. We use the field of stem cell research to test our method. Based on the outcome of the analysis, we can assess the breakthrough potential of ERTs. The stratification, transition and recent changes of the citation curve can be used as a basis for analysing and assessing the ERTs’ breakthrough potential. The combination of different citation diffusion patterns of ERTs and their knowledge bases can improve the effectiveness of identifying ERTs that can become breakthrough innovations.
ZhangJZhangX.Overview of radical innovation identification based on scientific references in patents. J China Soc Sci Tech Inform2016; 35: 955–962.
2.
KuhnT.The structure of scientific revolutions. Chicago, IL: The University of Chicago Press, 1962.
3.
FortunatoSBergstromCTBörnerK, et al. Science of science. Science2018; 359: eaao0185.
4.
MinCBuYSunJ.Predicting scientific breakthroughs based on knowledge structure variations. Technol Forecast Soc2021; 164: 120502.
5.
MertonRK.The sociology of science: theoretical and empirical investigations. Chicago, IL: The University of Chicago Press, 1973.
6.
Brad WrayK. Kuhn and the discovery of paradigms. Philos Soc Sci2011; 41: 380–397.
7.
AndersenHBarkerPChenX.The cognitive structure of scientific revolutions. Cambridge: Cambridge University Press, 2006.
8.
DuJSunYZhangY, et al. Characterizing and detecting the early scientometric signs of the potential transformative research. Bullet Natl Nat Sci Found China2019; 33: 88–98.
9.
LuoRXuHLiuY.Identifying scientific breakthrough topics using structure entropy: taking the study field of genetic engineering vaccines as an experimentation. Inform Stud: Theor Appl2021; 44: 106–114.
10.
KoshlandDE.The cha-cha-cha theory of scientific discovery. Science2007; 317: 761–762.
11.
JiangJLiXYinT, et al. Formation of breakthrough technology innovation: review and outlook. R D Manag2017; 29: 109–120.
12.
LiZLiuCLuoH.Analysis of essence and nurturing of disruptive technology: highlighting basic science researches behind disruptive technologies. Glob Sci Technol Econ Outlook2016; 31: 53–61.
13.
LiangZDengYHongY.From transformative research to transformative innovation: evolution of concept and implication for policy. Sci Soc2017; 7: 94–106.
14.
WinninkJ.Early-stage detection of breakthrough-class scientific research: using micro-level citation dynamics. PhD Thesis, Leiden University, Leiden, 2017.
15.
WinninkJTijssenRJVan RaanA.Searching for new breakthroughs in science: how effective are computerised detection algorithms?Technol Forecast Soc2019; 146: 673–686.
16.
WangXLiuZBaiR, et al. The method of research front topic detection based on the fund project data. Libr Inform Serv2017; 61: 87–98.
17.
MatsumuraNMatsuoYOhsawaY, et al. Discovering emerging topics from WWW. J Conting Crisis Man2002; 10: 73–81.
18.
XuHYueZLuoR, et al. A study on the multidimensional scientometric indicators to detect the emerging topics. In: Proceedings of the 17th international conference of the international society for scientometrics and informetrics (ISSI), Rome, 2–5 September 2019. Leuven: ISSI Society.
19.
MinCBuYWuD, et al. Identifying citation patterns of scientific breakthroughs: a perspective of dynamic citation process. Inform Process Manag2021; 58: 102428.
20.
WinninkJTijssenRJ.R&D dynamics and scientific breakthroughs in HIV/AIDS drugs development: the case of integrase inhibitors. Scientometrics2014; 101: 1–16.
21.
WinninkJTijssenRJVan RaanAF.Theory-changing breakthroughs in science: the impact of research teamwork on scientific discoveries. J Assoc Inf Sci Tech2016; 67: 1210–1223.
22.
CaoYXuHWuH, et al. Study on radical innovation prediction to emerging technology topics based on citation curve fitting – taking the field of stem cells as an example. Libr Inform Serv2020; 64: 100–113.
23.
ManyikaJChuiMBughinJ, et al. Disruptive technologies: advances that will transform life, business, and the global economy. San Francisco, CA: McKinsey Global Institute, 2013.
24.
Thomson Reuters. The future is open, 2015 state of innovation. Estados Unidos: Thomson Reuters, 2015.
Chinese Academy of Sciences. 2019 scientific development report. Beijing, China: Science Press, 2020.
27.
SmallH.A co-citation model of a scientific specialty: a longitudinal study of collagen research. Soc Stud Sci1977; 7: 139–166.
28.
DahlinKBBehrensDM.When is an invention really radical?: Defining and measuring technological radicalness. Res Policy2005; 34: 717–737.
29.
SchoenmakersWDuystersG.The technological origins of radical inventions. Res Policy2010; 39: 1051–1059.
30.
SmallHBoyackKWKlavansR.Identifying emerging topics in science and technology. Res Policy2014; 43: 1450–1467.
31.
SchneiderJWCostasR.Identifying potential “breakthrough” publications using refined citation analyses: three related explorative approaches. J Assoc Inf Sci Tech2017; 68: 709–723.
32.
SmallH.Referencing as cooperation or competition. In: SugimotoCR (ed.) Theories of informetrics and scholarly communication: a Festschrift in honor of Blaise Cronin. Berlin: Walter de Gruyter GmbH, 2016, pp. 49–71.
33.
StaudtJYuHLightRP, et al. High-impact and transformative science (HITS) metrics: definition, exemplification, and comparison. PLoS One2018; 13: e0200597.
34.
MinCBuYSunJ.Comparing breakthrough and non-breakthrough papers from early citing structures. In: Proceedings of the 17th international conference of the international society for scientometrics and informetrics (ISSI), Rome, 2–5 September 2019, pp. 1110–1115. Leuven: ISSI Society.
35.
KleinbergJ.Bursty and hierarchical structure in streams. Data Min Knowl Disc2003; 7: 373–397.
36.
SoodATellisGJ.Technological evolution and radical innovation. J Marketing2005; 69: 152–168.
37.
TellisGJ.Disruptive technology or visionary leadership?J Prod Innovat Manag2006; 23: 34–38.
38.
YoonJKimK.Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks. Scientometrics2011; 88: 213–228.
39.
ZhangJZhangX.Identification of radical innovation based on mutation of cited scientific knowledge. J China Soc Sci Tech Inform2014; 33: 259–266.
40.
SunXDingK.Identifying and tracking scientific and technological knowledge memes from citation networks of publications and patents. Scientometrics2018; 116: 1735–1748.
41.
LuoSKouCJinJ, et al. Disruptive technology prediction based on outlier patents: traditional Chinese medicine patents as an example. Inform Stud: Theor Appl2019; 42: 165–170.
42.
JiaWXieYZhaoY, et al. Research on disruptive technology recognition of China’s electronic information and communication industry based on patent influence. J Global Inform Manag2021; 29: 148–165.
GarfieldE.Delayed recognition in scientific discovery-citation frequency-analysis aids the search for case-histories. Curr Contents1989; 23: 3–9.
46.
Garfield E. More delayed recognition. 1. Examples from the genetics of color-blindness, the entropy of short-term-memory, phosphoinositides, and polymer rheology. Curr Contents1989; 38: 3–8.
47.
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.
48.
Van RaanAF. Sleeping beauties in science. Scientometrics2004; 59: 467–472.
49.
CampanarioJM.Rejecting and resisting Nobel class discoveries: accounts by Nobel laureates. Scientometrics2009; 81: 549–565.
50.
Van RaanAF. Dormitory of physical and engineering sciences: sleeping beauties may be sleeping innovations. PLoS One2015; 10: e0139786.
51.
FangH.An explanation of resisted discoveries based on construal-level theory. Sci Eng Ethics2015; 21: 41–50.
52.
Van RaanAF. Sleeping beauties cited in patents: is there also a dormitory of inventions?Scientometrics2017; 110: 1123–1156.
53.
DuJ.A study on systematic identification of “Sleeping Beauty” publications and on their awaking mechanism. PhD Thesis, Nanjing University, Nanjing, China, 2017.
54.
Van RaanAFWinninkJJ. Do younger Sleeping Beauties prefer a technological prince?Scientometrics2018; 114: 701–717.
55.
Van RaanAFWinninkJJ. The occurrence of ‘Sleeping Beauty’ publications in medical research: their scientific impact and technological relevance. PLoS One2019; 14: e0223373.
XuLWangF.Scientific frontier prediction model based on support vector machine and improved particle swarm optimization. Inform Sciences2019; 37: 22–28.
58.
EulaertsOJoannyGGiraldiJ, et al. Weak signals in science and technologies: 2019 report. Luxembourg: Publications Office of the European Union, 2019.
59.
JoannyGPeraniSEulaertsO. Detection of disruptive technologies by automated identification of weak signals in technology development. In: Proceedings of the 17th international conference of the international society for scientometrics and informetrics (ISSI), Rome, 2–5 September 2019, pp. 2644–2645. Leuven: ISSI Society.
60.
LuoR. Identification and prediction of the field’s scientific breakthrough based on entropy theory. Master Thesis, University of Chinese Academy of Sciences, Beijing, China, 2020.
61.
SquicciariniMDernisHCriscuoloC.Measuring patent quality: indicators of technological and economic value. Paris: OECD Publishing, 2013.
RotoloDHicksDMartinBR.What is an emerging technology?Res Policy2015; 44: 1827–1843.
64.
WangQ.A bibliometric model for identifying emerging research topics. J Assoc Inf Sci Tech2018; 69: 290–304.
65.
CozzensSGatchairSKangJ, et al. Emerging technologies: quantitative identification and measurement. Technol Anal Strateg2010; 22: 361–376.
66.
XuHWinninkJYueZ, et al. Multidimensional Scientometric indicators for the detection of emerging research topics. Technol Forecast Soc2021; 163: 120490.
67.
MartinBR.Foresight in science and technology. Technol Anal Strateg1995; 7: 139–168.
68.
DayGSSchoemakerPJ.Peripheral vision: detecting the weak signals that will make or break your company. Boston, MA: Harvard Business Press, 2006.
69.
PorterALRoessnerJDJinX-Y, et al. Measuring national ‘emerging technology’ capabilities. Sci Publ Policy2002; 29: 189–200.
70.
HungS-CChuY-Y.Stimulating new industries from emerging technologies: challenges for the public sector. Technovation2006; 26: 104–110.
71.
BurmaogluSSartenaerOPorterA, et al. Analysing the theoretical roots of technology emergence: an evolutionary perspective. Scientometrics2019; 119: 97–118.
72.
De HaanJ. How emergence arises. Ecol Complex2006; 3: 293–301.
73.
StirlingA.Risk, precaution and science: towards a more constructive policy debate. Talking point on the precautionary principle. EMBO Rep2007; 8: 309–315.
74.
XuSHaoLAnX, et al. Review on emerging research topics with key-route main path analysis. Scientometrics2020; 122: 607–624.
75.
WaltmanLVan EckNJ.A new methodology for constructing a publication-level classification system of science. J Am Soc Inf Sci Tec2012; 63: 2378–2392.
76.
SmallH.Co-citation in the scientific literature: a new measure of the relationship between two documents. J Am Soc Inform Sci1973; 24: 265–269.
77.
SmallHGriffithBC.The structure of scientific literatures I: identifying and graphing specialties. Sci Stud1974; 4: 17–40.
78.
GlänzelWThijsB.Using ‘core documents’ for detecting and labelling new emerging topics. Scientometrics2012; 91: 399–416.
79.
IwamiSMoriJSakataI, et al. Detection method of emerging leading papers using time transition. Scientometrics2014; 101: 1515–1533.
80.
JarićIKnežević-JarićJLenhardtM.Relative age of references as a tool to identify emerging research fields with an application to the field of ecology and environmental sciences. Scientometrics2014; 100: 519–529.
81.
SorianoASÁlvarezCLValdésRMT. Bibliometric analysis to identify an emerging research area: Public Relations Intelligence – a challenge to strengthen technological observatories in the network society. Scientometrics2018; 115: 1591–1614.
LeeWH.How to identify emerging research fields using scientometrics: an example in the field of Information Security. Scientometrics2008; 76: 503–525.
84.
TakedaYKajikawaY.Optics: a bibliometric approach to detect emerging research domains and intellectual bases. Scientometrics2009; 78: 543–558.
85.
RocheIBesagniDFrançoisC, et al. Identification and characterisation of technological topics in the field of molecular biology. Scientometrics2010; 82: 663–676.
86.
TuY-NSengJ-L.Indices of novelty for emerging topic detection. Inform Process Manag2012; 48: 303–325.
87.
AroraSKYoutieJShapiraP, et al. Entry strategies in an emerging technology: a pilot web-based study of graphene firms. Scientometrics2013; 95: 1189–1207.
88.
HuC-PHuJ-MDengS-L, et al. A co-word analysis of library and information science in China. Scientometrics2013; 97: 369–382.
89.
HansmannTNiemeyerP.Big data–characterizing an emerging research field using topic models. In: Proceedings of the 2014 IEEE/WIC/ACM international joint conferences on web intelligence (WI) and intelligent agent technologies (IAT), Warsaw, 11–14 August 2014, vol. 1, pp. 43–51. Washington, DC: IEEE.
90.
YanE.Research dynamics: measuring the continuity and popularity of research topics. J Informetr2014; 8: 98–110.
91.
LiuZYinYLiuW, et al. Visualizing the intellectual structure and evolution of innovation systems research: a bibliometric analysis. Scientometrics2015; 103: 135–158.
92.
WeismayerCPezenkaI.Identifying emerging research fields: a longitudinal latent semantic keyword analysis. Scientometrics2017; 113: 1757–1785.
93.
JoungJKimK.Monitoring emerging technologies for technology planning using technical keyword based analysis from patent data. Technol Forecast Soc2017; 114: 281–292.
94.
LiM.Visualizing dispersed risk signals for a specific emerging technology: a novel approach of Keywords Aggregation across Topics (KAaT). In: Proceedings of the 8th annual global techmining conference, Leiden, 11 September 2018. Atlanta, GA: Global TechMining (GTM).
95.
CarleySFNewmanNCPorterAL, et al. An indicator of technical emergence. Scientometrics2018; 115: 35–49.
96.
PorterALGarnerJCarleySF, et al. Emergence scoring to identify frontier R&D topics and key players. Technol Forecast Soc2019; 146: 628–643.
97.
ChenC.CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Tec2006; 57: 359–377.
98.
GuoHWeingartSBörnerK.Mixed-indicators model for identifying emerging research areas. Scientometrics2011; 89: 421–435.
99.
ZittMLeluABassecoulardE.Hybrid citation-word representations in science mapping: portolan charts of research fields?J Am Soc Inf Sci Tec2011; 62: 19–39.
100.
HuangYZhuDQianY, et al. A hybrid method to trace technology evolution pathways: a case study of 3D printing. Scientometrics2017; 111: 185–204.
101.
MinCDingYLiJ, et al. Innovation or imitation: the diffusion of citations. J Assoc Inf Sci Tech2018; 69: 1271–1282.
102.
AvramescuA.Actuality and obsolescence of scientific literature. J Am Soc Inform Sci1979; 30: 296–303.
103.
LiJJiangMLiY.Citation curves of Nobel laureates. J Libr Sci China2014; 40: 41–49.
104.
LiLMinCSunJ.On the quantification and distribution of citation peaks. J China Soc Sci Tech Inform2019; 38: 697–708.
105.
EggheLRavichandra RaoIK.Citation age data and the obsolescence function: fits and explanations. Inform Process Manag1992; 28: 201–217.
106.
WinninkJTijssenRJ.Early stage identification of breakthroughs at the interface of science and technology: lessons drawn from a landmark publication. Scientometrics2015; 102: 113–134.
107.
SmallHBoyackKKlavansR.Identifying emerging topics by combining direct citation and co-citation. In: Proceedings of the 14th international conference of the international society for scientometrics and informetrics (ISSI), Vienna, 15–19 July 2013, Leuven: ISSI Society.
EomY-HFortunatoS.Characterizing and modeling citation dynamics. PLoS One2011; 6: e24926.
110.
StentGS.Prematurity and uniqueness in scientific discovery. Sci Am1972; 227: 84–93.
111.
WyattHV.Knowledge and prematurity: the journey from transformation to DNA. Perspect Biol Med1975; 18: 149–156.
112.
ColeS.Professional standing and the reception of scientific discoveries. Am J Sociol1970; 76: 286–306.
113.
MinCSunJPeiL, et al. Measuring delayed recognition for papers: uneven weighted summation and total citations. J Informetr2016; 10: 1153–1165.
114.
RednerS.Citation characteristics from 110 years ofPhysical Review. Phys Today2005; 58: 49–54.
115.
BaumgartnerSELeydesdorffL.Group-based trajectory modeling (GBTM) of citations in scholarly literature: dynamic qualities of ‘transient’ and ‘sticky knowledge claims’. J Assoc Inf Sci Tech2014; 65: 797–811.
116.
PonomarevIVWilliamsDELawtonBK, et al. Breakthrough paper indicator: early detection and measurement of ground-breaking research. In: Proceedings of the 11th international conference on CRIS, Prague, 6–9 June 2012. Nijmegen: CRIS.
117.
PonomarevIVWilliamsDEHackettCJ, et al. Predicting highly cited papers: a method for early detection of candidate breakthroughs. Technol Forecast Soc2014; 81: 49–55.
118.
WangJVeugelersRStephanP.Bias against novelty in science: a cautionary tale for users of bibliometric indicators. Res Policy2017; 46: 1416–1436.
119.
ZhangLLiuY.Study on the transition of technology trajectory and the evolution of technological innovation. Stud Sci Sci2015; 33: 137–145.
120.
LiuZXuHLuoR, et al. Research on scientific and technological interaction patterns based on topic relevance analysis. J China Soc Sci Tech Inform2019; 38: 997–1011.
121.
HollingsworthJR. Scientific discoveries: an institutionalist and path-dependent perspective. In: HannawayC (ed.) Biomedicine in the twentieth century: practices, policies, and politics. Amsterdam: IOS Press, 2008, pp. 317–353.
122.
NoyonsECVan RaanAF.Monitoring scientific developments from a dynamic perspective: self-organized structuring to map neural network research. J Am Soc Inform Sci1998; 49: 68–81.
123.
PopperK.The logic of scientific discovery. London: Routledge, 2005.
124.
HeimeriksGBoschmaR.The path- and place-dependent nature of scientific knowledge production in biotech 1986–2008. J Econ Geogr2014; 14: 339–364.
HuZ-YZengR-QQinX-C, et al. A method of biomedical knowledge discovery by literature mining based on SPO predications: a case study of induced pluripotent stem cells. In: PernerP (ed.) Machine learning and data mining in pattern recognition. Cham: Springer International Publishing, 2018, pp. 383–393.
127.
BolgerFWrightG.Use of expert knowledge to anticipate the future: issues, analysis and directions. Int J Forecasting2017; 33: 230–243.
128.
AyyazAKumarSSangiorgiB, et al. Single-cell transcriptomes of the regenerating intestine reveal a revival stem cell. Nature2019; 569: 121–125.
129.
ReesWDTandunRYauE, et al. Regenerative intestinal stem cells induced by acute and chronic injury: the saving grace of the epithelium?Front Cell Dev Biol2020; 8: 583919.
130.
MukherjeeSParicioNSokolNS.A stress-responsive miRNA regulates BMP signaling to maintain tissue homeostasis. P Natl Acad Sci USA2021; 118: e2022583118.
131.
KasperDMHintzenJWuY, et al. The N-glycome regulates the endothelial-to-hematopoietic transition. Science2020; 370: 1186–1191.
132.
ArumugamKShinWSchiavoneV, et al. The master regulator protein BAZ2B can reprogram human hematopoietic lineage-committed progenitors into a multipotent state. Cell Rep2020; 33: 108474.
133.
TakayamaNMurisonATakayanagiS, et al. The transition from quiescent to activated states in human hematopoietic stem cells is governed by dynamic 3D genome reorganization. Cell Stem Cell2021; 28: 488.e10–501.e10.
134.
SullivanWJMullenPJSchmidEW, et al. Extracellular matrix remodeling regulates glucose metabolism through TXNIP destabilization. Cell2018; 175: 117.e21–132.e21.
135.
StantonAETongXYangF.Extracellular matrix type modulates mechanotransduction of stem cells. Acta Biomater2019; 96: 310–320.
136.
WangH.Bendamustine, gemcitabine, and vinorelbine before autologous hematopoietic stem cell transplantation for relapsed/refractory Hodgkin’s lymphoma are better salvage chemotherapy regimens, https://www.bjcancer.org/Mobile/Article/Index/4091 (2016, accessed 12 November 2020).
137.
LillquistEGreenS.The discipline dependence of citation statistics. Scientometrics2010; 84: 749–762.
