Visualizing a field of research: A methodology of systematic scientometric reviews
Autoři:
Chaomei Chen aff001; Min Song aff002
Působiště autorů:
Department of Information Science, College of Computing and Informatics, Drexel University, Philadelphia, Pennsylvania, United States of America
aff001; Department of Information Science, Yonsei University, Seoul, Republic of Korea
aff002
Vyšlo v časopise:
PLoS ONE 14(10)
Kategorie:
Research Article
prolekare.web.journal.doi_sk:
https://doi.org/10.1371/journal.pone.0223994
Souhrn
Systematic scientometric reviews, empowered by computational and visual analytic approaches, offer opportunities to improve the timeliness, accessibility, and reproducibility of studies of the literature of a field of research. On the other hand, effectively and adequately identifying the most representative body of scholarly publications as the basis of subsequent analyses remains a common bottleneck in the current practice. What can we do to reduce the risk of missing something potentially significant? How can we compare different search strategies in terms of the relevance and specificity of topical areas covered? In this study, we introduce a flexible and generic methodology based on a significant extension of the general conceptual framework of citation indexing for delineating the literature of a research field. The method, through cascading citation expansion, provides a practical connection between studies of science from local and global perspectives. We demonstrate an application of the methodology to the research of literature-based discovery (LBD) and compare five datasets constructed based on three use scenarios and corresponding retrieval strategies, namely a query-based lexical search (one dataset), forward expansions starting from a groundbreaking article of LBD (two datasets), and backward expansions starting from a recently published review article by a prominent expert in LBD (two datasets). We particularly discuss the relevance of areas captured by expansion processes with reference to the query-based scientometric visualization. The method used in this study for comparing bibliometric datasets is applicable to comparative studies of search strategies.
Klíčová slova:
Network analysis – Systematic reviews – Citation analysis – Data visualization – Deep learning – Oils – Scientometrics – Information retrieval
Zdroje
1. Price DD. Networks of scientific papers. Science. 1965;149:510–5. doi: 10.1126/science.149.3683.510 14325149
2. Yang H-T, Ju J-H, Wong Y-T, Shmulevich I, Chiang J-H. Literature-based discovery of new candidates for drug repurposing. Briefings in Bioinformatics. 2017;18(3):488–97. doi: 10.1093/bib/bbw030 27113728
3. Bruza P, Weeber M. Literature-Based Discovery: Springer; 2008.
4. Choi B-K, Dayaram T, Parikh N, Wilkins AD, Nagarajan M, Novikov IB, et al. Literature-based automated discovery of tumor suppressor p53 phosphorylation and inhibition by NEK2. PNAS. 2018;115(42):10666–71. doi: 10.1073/pnas.1806643115 30266789
5. Sebastian Y, Siew E, Orimaye SO. Emerging approaches in literature-based discovery: techniques and performance review. The Knowledge Engineering Review. 2017;32(e12):1–35.
6. Swanson DR. Fish oil, Raynaud’s syndrome, and undiscovered public knowledge. Perspectives in Biology and Medicine. 1986;30(1):7–18. doi: 10.1353/pbm.1986.0087 3797213
7. Swanson DR. Undiscovered public knowledge. Library Quarterly. 1986;56(2):103–18.
8. Cobo MJ, López-Herrera AG, Herrera-Viedma E, Herrera F. Science mapping software tools: Review, analysis, and cooperative study among tools. J Am Soc Inf Sci Technol. 2011;62(7):1382–402.
9. Chen C. CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Technol. 2006;57(3):359–77.
10. Chen C. Science Mapping: A Systematic Review of the Literature. Journal of Data and Information Science. 2017;2(2):1–40.
11. van Eck NJ, Waltman L. Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics. 2010;84(2):523–38. doi: 10.1007/s11192-009-0146-3 20585380
12. Chen C, Hu Z, Liu S, Tseng H. Emerging trends in regenerative medicine: A scientometric analysis in CiteSpace. Expert Opinions on Biological Therapy. 2012;12(5):593–608.
13. Shen S, Cheng C, Yang J, Yang S. Visualized analysis of developing trends and hot topics in natural disaster research. PLoS One. 2018;13(1):e0191250. doi: 10.1371/journal.pone.0191250 29351350
14. Zhang C, Xu T, Feng H, Chen S. Greenhouse Gas Emissions from Landfills: A Review and Bibliometric Analysis. Sustainability. 2019;11(8):2282.
15. Li M, Porter AL, Suominen A. Insights into relationships between disruptive technology/innovation and emerging technology: A bibliometric perspective. Technological Forecasting and Social Change. 2018;129:285–96.
16. Kullenberg C, Kasperowski D. What Is Citizen Science?–A Scientometric Meta-Analysis. PLoS One. 2016;11(1):e0147152. doi: 10.1371/journal.pone.0147152 26766577
17. Haunschild R, Bornmann L, Marx W. Climate Change Research in View of Bibliometrics. PLoS One. 2016;11(7):e0160393. doi: 10.1371/journal.pone.0160393 27472663
18. Klavans R, Boyack KW. Using Global Mapping to Create More Accurate Document-Level Maps of Research Fields. J Am Soc Inf Sci Technol. 2011;62(1):1–18.
19. Leydesdorff L, Rafols I. A Global Map of Science Based on the ISI Subject Categories. J AM SOC INF SCI TEC. 2009;60(2):348–62.
20. Borner K, Klavans R, Patek M, Zoss AM, Biberstine JR, Light RP, et al. Design and Update of a Classification System: The UCSD Map of Science. PLoS One. 2012;7(7):10.
21. Chen C. Predictive effects of structural variation on citation counts. J Am Soc Inf Sci Technol. 2012;63(3):431–49.
22. Chen C, Song M. Representing Scientific Knowledge: The Role of Uncertainty: Springer; 2017.
23. Garfield E. From the science of science to Scientometrics visualizing the history of science with HistCite software. Journal of Informetrics. 2009;3(3):173–9.
24. Zitt M, Bassecoulard E. Dlineating complex scientific fields by an hybrid lexical-citation method: An application to nanosciences. Information Processing & Management. 2006;42(6):1513–31.
25. Porter AL, Youtie Y, Shapira P, Schoeneck DJ. Refining search terms for nanotechnology. Journal of Nanopartical Research. 2008;10(5):715–28.
26. Kostoff RN, Koytcheff RG, Lau CGY. Technical structure of the global nanoscience and nanotechnology literature. Journal of Nanopartical Research. 2007;9(5):701–24.
27. Huang Y, Schuehle J, Porter AL, Youtie J. A systematic method to create search strategies for emerging technologies based on the Web of Science: illustrated for ‘Big Data’. Scientometrics. 2015;105(3):2005–22.
28. Smalheiser N. Rediscovering Don Swanson: the past, present and future of literature-based discovery. Journal of Data and Information Science. 2017;2(4):43–64. doi: 10.1515/jdis-2017-0019 29355246
29. Swanson DR. 2 MEDICAL LITERATURES THAT ARE LOGICALLY BUT NOT BIBLIOGRAPHICALLY CONNECTED. J Am Soc Inf Sci. 1987;38(4):228–33.
30. Swanson DR. MIGRAINE AND MAGNESIUM—11 NEGLECTED CONNECTIONS. Perspectives in Biology and Medicine. 1988;31(4):526–57.
31. Smalheiser NR, Swanson DR. Using ARROWSMITH: a computer-assisted approach to formulating and assessing scientific hypotheses. Comput Meth Programs Biomed. 1998;57(3):149–53.
32. Swanson DR, Smalheiser NR. An interactive system for finding complementary literatures: A stimulus to scientific discovery. Artificial Intelligence. 1997;91(2):183–203.
33. Smalheiser NR, Swanson DR. Linking estrogen to Alzheimer’s disease: An informatics approach. Neurology. 1996;47(3):809–10. doi: 10.1212/wnl.47.3.809 8797484
34. Weeber M, Klein H, de Jong-van den Berg LTW, Vos R. Using concepts in literature-based discovery: Simulating Swanson’s Raynaud-fish oil and migraine-magnesium discoveries. J Am Soc Inf Sci Technol. 2001;52(7):548–57.
35. Gordon MD, Lindsay RK. Toward discovery support systems: A replication, re-examination, and extension of Swanson’s work on literature-based discovery of a connection between Raynaud’s and fish oil. J Am Soc Inf Sci. 1996;47(2):116–28.
36. Kim YH, Song M. A context-based ABC model for literature-based discovery. PLoS One. 2019;14(4).
37. Shneider AM. Four stages of a scientific discipline: four types of scientists. Trends in biochemical sciences. 2009;34(5):217–23. doi: 10.1016/j.tibs.2009.02.002 19362484
38. Klavans R, Boyack KW. Toward a Consensus Map of Science. J Am Soc Inf Sci Technol. 2009;60(3):455–76.
39. Chen C, Leydesdorff L. Patterns of connections and movements in dual-map overlays: A new method of publication portfolio analysis. J Am Soc Inf Sci Technol. 2014;62(2):334–51.
40. Chen C. Searching for intellectual turning points: Progressive knowledge domain visualization. Proc Natl Acad Sci U S A. 2004;101:5303–10. doi: 10.1073/pnas.0307513100 14724295
41. Börner K, Chen C, Boyack KW. Visualizing knowledge domains. Annual Review of Information Science and Technology. 2003;37(1):179–255.
42. Pirolli P, Schank P, Hearst M, Diehl C, editors. Scatter/Gather browsing communicates the topic structure of a very large text collection. the Conference on Human Factors in Computing Systems (CHI ‘96); 1996 April 1996; Vancouver, BC: ACM Press.
43. Miller GA. WORDNET—A LEXICAL DATABASE FOR ENGLISH. Communications of the Acm. 1995;38(11):39–41.
44. Aronson AR, Lang FM. An overview of MetaMap: historical perspective and recent advances. Journal of the American Medical Informatics Association. 2010;17(3):229–36. doi: 10.1136/jamia.2009.002733 20442139
45. Bodenreider O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Research. 2004;32:D267–D70. doi: 10.1093/nar/gkh061 14681409
46. Deerwester S, Dumais ST, Landauer TK, Furnas GW, Harshman RA. Indexing by Latent Semantic Analysis. J Am Soc Inf Sci. 1990;41(6):391–407.
47. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J. Distributed representations of words and phrases and their compositionality. Proceedings of the 26th International Conference on Neural Information Processing Systems (NIPS’13); December 05–10, 2013; Lake Tahoe, Nevada2013. p. 3111–9.
48. Garfield E. Citation indexing for studying science. Nature. 1970;227:669–71. doi: 10.1038/227669a0 4914589
49. Merton RK. Priorities in scientific discoveries. American Sociological Review. 1957;22:635–59.
50. Chen C. Cascading citation expansion. Journal of Information Science Theory and Practice. 2018;6(2):6–23.
51. Liu JS, Chen H-H, Ho MH-C, Li Y-C. Citations with different levels of relevancy: Tracing the main paths of legal opinions. Journal of the Association for Information Science and Technology. 2014;65 (12):2479–88.
52. Hummon NP, Doreian P. Connectivity in a citation network: The development of DNA theory. Social Networks. 1989;11(1):39–63.
53. Chen C, Ibekwe-SanJuan F, Hou JH. The Structure and Dynamics of Cocitation Clusters: A Multiple-Perspective Cocitation Analysis. J Am Soc Inf Sci Technol. 2010;61(7):1386–409.
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