Turning points: The nature of creativity

Turning Points: The Nature of Creativity

Volumn 9783642191602, Issue , 2012, Pages 1-268

  Chen, Chaomei ^a  

^a Drexel University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CRITICAL THINKING; EXPERIMENTAL SYSTEM; SCIENTIFIC CONTRIBUTIONS; SCIENTIFIC DISCOVERY; TURNING POINTS;

EID: 84949177672     PISSN: None     EISSN: None     Source Type: Book    
DOI: 10.1007/978-3-642-19160-2     Document Type: Book

References (312)

1. Augustine, N.R. (2007). Is America Falling Off the Flat Earth?: National Academies Press.
2. Benderly, B.L. (2010). Does the U.S. produce too many scientists? Scientific American, http://www.scientificamerican.com/article.cfm?id=does-the-us-producetoo-m&offset=6.
3. Collins, R. (1998). The Sociology of Philosophies: A Global Theory of Intellectual Change. Cambridge, MA: Harvard University Press.
4. COSEPUP. (1993). Science, technology, and the federal government: National goals for a new era. Washington, DC: National Academy Press.
5. Eysenck, H.J. (1993). Creativity and personality: Suggestions for a theory. psychological inquiry, 4(3), 147-178.
6. Lowell, B.L., & Salzman, H. (2007). Into the eye of the storm: Assessing the evidence on science and engineering education, quality, and workforce demand: The Urban Institute.
7. National Academy of Sciences, National Academy of Engineering, & Institute of Medicine of the National Academies. (2007). Rising above the gathering storm: Energizing and employing America for a brighter economic future. National Academies Press.
8. NSF. (2007, September 25). Important notice No. 130: Transformative Research. http://www.nsf.gov/pubs/2007/in130/in130.txt. Accessed 14 Aug 2010.
9. Pellegrino, D.A., & Chen, C. (2011). Data repository mapping for influenza protein sequence analysis. Proceedings of 2011 Visualization and Data Analysis (VDA). SPIE.
10. Schubert, A. (1987). Quantitative studies of science a current bibliography. Scientometrics, 12(5-6), 395-412.
11. Schuster, J.A. (2010). Internalist/Externalist Historiography, Encyclopedia of the Scientific Revolution from Copernicus to Newton.
12. Shapin, S. (1992). Discipline and bounding: the history and sociology of science as seen through the externalism-internalism debate. History of Science, 30, 333-369.
13. Yuasa, M. (1962). Center of scientific activity: its shift from the 16th to the 20th century. Japanese Studies in the History of Science, 1, 57-75.
14. Zhao, H., & Jiang, G. (1985). Shifting of world's scientific center and scientists' social ages. Scientometrics, 8(1-2), 59-80.
15. Altshuller, G. (1999). Innovation algorithm: TRIZ, systematic innovation and technical creativity (1st ed.). Worcester, MA: Technical Innovation Center, Inc.
16. Benedek, M., Fink, A., & Neubauer, A.C. (2006). Enhancement of ideational fluency by means of computer-based training. Creativity Research Journal, 18, 317-328.
17. Burton, R.E., & Kebler, R.W. (1960). The 'half-life' of some scientific and technical literatures. American Documentation, 11, 18-22.
18. Campbell, D.T. (1960). Blind variation and selective retentions in creative thought as in other knowledge processes. Psychological Review, 67(6), 380-400.
19. Chen, C., Chen, Y., Horowitz, M., Hou, H., Liu, Z., & Pellegrino, D. (2009). Towards an explanatory and computational theory of scientific discovery. Journal of Informetrics, 3(3), 191-209.
20. Cziko, G.A. (1998). From blind to creative: In defense of Donald Campbell's selectionist theory of human creativity. Journal of Creative Behavior, 32(3), 192-209.
21. Davis, M.S. (1971). That's Interesting! Towards a Phenomenology of Sociology and a Sociology of Phenomenology. Philosophy of the Social Sciences, 1(2), 309-344
22. Friedrich, T.L., & Mumford, M.D. (2009). The Effects of Conflicting Information on Creative Thought: A Source of Performance Improvements or Decrements? Creativity Research Journal, 21(2-3), 265-281.
23. Gruber, H.E. (1992). The evolving systems approach to creative work. In D.B. Wallace & H.E. Gruber (Eds.), Creative People at Work: Twelve Cognitive Case Studies (pp. 3-24). Oxford, England: Oxford University Press.
24. Guilford, J.P. (1967). The Nature of Human Intelligence. New York: McGraw-Hill.
25. Hennessey, B.A., & Amabile, T.M. (2010). Creativity. Annual Review of Psychology, 61, 569-598.
26. Hill, W.E. (1915). My Wife and My Mother-in-Law. Puck, 16, 11.
27. Hudson, L. (1966). Contrary imaginations: A psychological study of the English schoolboy. New York: Schocken.
28. Hunter, S.T., Bedell-Avers, K.E., Hunsicker, C.M., Mumford, M.D., & Ligon, G.S. (2008). Applying multiple knowledge structures in creative thought: Effects on idea generation and problem-solving. Creativity Research Journal, 20(2), 137-154.
29. Kuhn, T.S. (1977). The Essential Tension: Selected Studies in Scientific Tradition and change. Chicago and London: University of Chicago Press.
30. Lauronen, E., Veijola, J., Isohanni, I., Jones, P.B., Nieminen, P., & Isohanni, M. (2004). Links between creativity and mental disorder. Psychiatry, 67(1), 81-98.
31. Lavery, D. (1993). Creative Work: On the Method of Howard Gruber. Journal of Humanistic Psychology, 33(2), 101-121.
32. Maddux, W.W., Adam, H., & Galinsky, A.D. (2010). When in Rome . Learn why the Romans do what they do: how multicultural learning experiences facilitate creativity. Pers Soc Psychol Bull, 36(6), 731-741.
33. Newell, A., Shaw, J.C., & Simon, H.A. (1958). ELEMENTS OF A THEORY OF HUMAN PROBLEM-SOLVING. Psychological Review, 65(3), 151-166.
34. Price, D.D. (1965). Networks of scientific papers. Science, 149, 510-515.
35. Rothenberg, A. (1987). Einstein, Bohr, and creative-thinking in science. History of Science, 25(68), 147-166.
36. Rothenberg, A. (1996). The Janusian process in scientific creativity. Creativity Research Journal, 9(2-3), 207-231.
37. Scott, G.M., Leritz, L.E., & Mumford, M.D. (2004). The effectiveness of creative training: a quantitative review. Creativity Research Journal, 16, 361-388.
38. Simonton, D.K. (1999). Origins of Genius: Darwinian Perspectives on Creativity. New York: Oxford University Press.
39. The Delphian Society. (1913). The world's progress, Part III. Hammond: W. B. Conkey Company.
40. Waddell, C. (1998). Creativity and mental illness: is there a link? Can J Psychiatry. 43(2):166-172.
41. Zhao, H., & Jiang, G. (1985). Shifting of world's scientific center and scientists' social ages. Scientometrics, 8(1-2), 59-80.
42. Beadle, G.W. (1974). Recollections. Annual Review of Genetics, 43, 1-13.
43. Ben-Zvi, A. (1976). Hindsight and foresight: A conceptual framework for the analysis of surprise attacks. World Politics, 28(3), 381-395.
44. Betts, R.K. (2007). Two faces of intelligence failure: September 11 and Iraq's missing WMD. Political Science Quarterly, 122(4), 585-606.
45. Bowker, G.C., & Star, S.L. (2000). Sorting things out-Classification and its consequences, Cambridge, MA.: MIT Press.
46. Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377.
47. Drews, J. (2000). Drug discovery: A historical perspective. Science, 287(5460), 1960-1964.
48. French, B.M., & Koeberl, C. (2010). The convincing identification of terrestrial meteorite impact structures: What works, what doesn't, and why. Earth-Science Reviews, 98, 123-170.
49. Greenberg, J.M. (2004). Creating the "Pillars": Multiple meanings of a Hubble image. Public Understanding of Science, 13, 83-95.
50. Heuer, R.J. (1999). Psychology of intelligence analysis. Central Intelligence Agency.
51. Kneller, R. (2010). The importance of new companies for drug discovery: origins of a decade of new drugs. Nature Reviews Drug Discovery, 9, 867-882.
52. Lamb, W.E., Schleich, W.P., Scully, M.O., & Townes, C.H. (1999). Laser physics: Quantum controversy in action. Reviews of Modern Physics, 71, S263-S273.
53. Lipinski, C., & Hopkins, A. (2004). Navigating chemical space for biology and medicine. Nature, 432(7019), 855-861.
54. Perrow, C. (1984). Normal accidents: living with high-risk technologies, Princeton University Press.
55. Scheffer, M., Bascompte, J., Brock, W.A., Brovkin, V., Carpenter, S.R., Dakos, V., et al. (2009). Early-warning signals for critical transitions. Nature, 461(7260), 53-59.
56. Star, S.L. (1989). The structure of Ill-structured solutions: Boundary objects and heterogeneous distributed problem solving. In M. Huhs & L. Gasser (Eds.), Readings in Distributed Artificial Intelligence 3 (pp. 37-54). Menlo Park, CA: Kaufmann.
57. Sternitzke, C. (2010). Knowledge sources, patent protection, and commercialization of pharmaceutical innovations. Research Policy, 39(6), 810-821.
58. Ware, C. (2008). Visual thinking for design. Morgan Kaufmann.
59. Wenger, E. (1998). Communities of practice-learning, meaning, and identity. Cambridge, UK: Cambridge University Press.
60. Wohlstetter, R. (1962). Pearl Harbor: Warning and decisions:. Stanford University Press.
61. Agrawal, A., Cockburn, I., & McHale, J. (2003). Gone but not forgotten: Labor flows, knowledge spillovers, and enduring social capital. NBER Working Paper No. 9950.
62. Anderson, J. (1997). Technology foresight for competitive advantage. Long Range Planning, 30(5), 665-677.
63. Brandes, F. (2009). The UK technology foresight programme: An assessment of expert estimates. Technological Forecasting and Social Change, 76(7), 869-879.
64. Braun, T., Schubert, A., & Zsindely, S. (1997). Nanoscience and nanotechnology on the balance. Scientometrics, 38, 321-325.
65. Chen, C., & Hicks, D. (2004). Tracking knowledge diffusion. Scientometrics, 59(2), 199-211.
66. Chubin, D.E., & Hackett, E.J. (1990). Paperless science: Peer review and U.S. science policy.
67. Comroe, J.H., & Dripps, R.D. (2002). Scientific basis for the support of biomedical science. In R.E. Bulger, E. Heitman & S.J. Reiser (Eds.), The ethical dimensions of the biological and health sciences (2nd ed., pp. 327-340). Cambridge, UK: Cambridge University Press.
68. Cuhls, K. (1998). Technikvorausschau in Japan. Heidelberg: Physica-Springer.
69. Dalkey, N.C. (1969). The Delphi method: An experimental study of group opinion. Santa Monica, CA: The Rand Corporation.
70. Editorial. (2010). Assessing assessment. Nature, 465, 845.
71. Geels, F.W., & Smit, W.A. (2000). Failed technology futures: Pitfalls and lessons from a historical survey. Futures, 32(9-10), 867-885.
72. Goodwin, P., &Wright, G. (2010). The limits of forecasting methods in anticipating rare events. Technological Forecasting and Social Change, 77(3), 355-368.
73. Grupp, H., & Linstone, H.A. (1999). National technology foresight activities around the globe-Resurrection and new paradigms. Technological Forecasting and Social Change, 60(1), 85-94.
74. Hsieh, C. (2010). Explicitly searching for useful inventions: Dynamic relaatedness and the costs of connecting versus synthesizning. Scientometrics.
75. Illinois Institute of Technology. 1969 Technology in retrospect and critical events in science Chicago: The Illinois Institute of Technology Research Institute
76. Jaffe, A., & Trajtenberg, M. (2002). Patents, citations & innovations. The MIT Press.
77. Kaplan, A., Skogstad, A.L., & Girshick, M.A. (1950). The prediction of social and technological events. Public Opinion Quarterly XIV, 93-110.
78. Laudel, G. (2006). The art of getting funded: How scientists adapt to their funding conditions. Science and Public Policy, 33(7), 489-504.
79. MacLean, M., Anderson, J., & Martin, B.R. (1998). Identifying research priorities in public sector funding agencies: Mapping science outputs on to user needs. Technology Analysis & Strategic Management, 10(2), 139-155.
80. Martin, B.R. (1995). Foresight in science and technology. Technology Analysis & Strategic Management, 7(2), 139-168.
81. Martin, B.R. (2010). The origins of the concept of 'foresight' in science and technology: An insider's perspective. Technological Forecasting and Social Change, 77(9), 1438-1447.
82. Meyer, M. (2000). What is special about patent citations? Differences between scientific and patent citations. Scientometrics, 49(1), 93-123.
83. Miles, I. (2010). The development of technology foresight: A review. Technological Forecasting and Social Change, 77(9), 1448-1456.
84. Narin, F., & Olivastro, D. (1992). Linkage between technology and science. Research Policy, 21, 237-249.
85. Pimentel, G.C. (1985). Opportunities in Chemistry. Washington, DC: National Academy Press.
86. Rowe, G., Wright, G., & McColl, A. (2005). Judgment change during Delphi-like procedures: The role of majority influence, expertise, and confidence. Technological Forecasting and Social Change, 72(4), 377-399.
87. Saka, A., Igami, M., & Kuwahara, T. (2008). Science map 2006: study on hot research areas (2001-2006 by Bibliometric method). National Institute of Science and Technology Policy (NISTEP).
88. Singh, J. (2004, January 9). Social networks as determinants of knowledge diffusion patterns. Retrieved March 24, 2004, from http://www.people.hbs.edu/jsingh/academic/jasjit singhnetworks.pdf
89. Sorenson, O., & Fleming, L. (2004). Science and the diffusion of knowledge. Research Policy, 33(10), 1615-1634.
90. The Science Coalition. (2010). Sparking economic growth: How federally funded university research creates innovation, new companies, and jobs. The Science Coalition.
91. Tichy, G. (2004). The over-optimism among experts in assessment and foresight. Technological Forecasting and Social Change, 71(4), 341-363.
92. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge University Press.
93. Westheimer, F.H. (1965). Chemistry: Opportunities and needs. Washington, DC: National Academy of Sciences.
94. Adar, E., Zhang, L., Adamic, L.A., & Lukose, R.M. (2004). Implicit structure and the dynamics of blogspace. In Proceedings of the workshop on the weblogging ecosystem at 13th international world wide web conference.
95. Albert, R., & Barabasi, A. (2002). Statistical mechanics of complex networks. Reviews of Modern Physics, 74(1), 47-97.
96. Anderson, T., Schum, D., & Twining, W. (2005). Analysis of evidence. (2nd ed.). Cambridge, England: Cambridge University Press.
97. Antonakis, J., & Lalive, R. (2008). Quantifying scholarly impact: IQp versus the Hirsch h Journal of the American Society for Information Science and Technology, 59(6), 956-969.
98. Barabási, A.L., Jeong, H., Néda, Z., Ravasz, E., Schubert, A., & Vicsek, T. (2002). Evolution of the social network of scientific collaborations. Physica A, 311, 590-614.
99. Bartels, L. (1988). Issue voting under uncertainty: An empirical test. American Journal of Political Science, 30, 709-728.
100. Bederson, B.B., & Shneiderman, B. (2003). Theories for understanding information visualization. In the craft of information visualization: Readings and reflections (pp. 349-351): Morgan Kaufmann.
101. Bettencourt, L.M.A., Castillo-Chavez, C., Kaiser, D., & Wojick, D.E. (2006). Report for the office of scientific and technical information: Population modeling of the emergence and development of scientific fields.
102. Bettencourt, L.M.A., Kaiser, D.I., Kaur, J., Castillo-Chavez, C., & Wojick, D.E. (2008). Population modeling of the emergence and development of scientific fields. Scientometrics, 75(3), 495-518.
103. Bradshaw, G.F., Langley, P.W., & Simon, H.A. (1983). Studying scientific discovery by computer simulation. Science, 222(4627), 971-975.
104. Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25(2), 163-177.
105. Brannigan, A., & Wanner, R.A. (1983). Historical distributions of multiple discoveries and theories of scientific change. Social Studies of Science, 13, 417-435.
106. Brody, T., Harnad, S., & Carr, L. (2006). Earlier web usage statistics as predictors of later citation impact. Journal of the American Association for Information Science and Technology, 57(8), 1060-1072.
107. Brush, S.G. (1994). Dynamics of theory change: The role of predictions. In Proceedings of the 1994 Biennial Meeting of the Philosophy of Science Association(pp. 133-145). East Lansing, MI.
108. Brush, S.G. (1995). Prediction and theory evaluation in physics and astronomy. In A.J. Kox & D.M. Siegel (Eds.), No Truth Except in the Details (pp. 299-318). Dordrecht: Kluwer Academic Publishers.
109. Burt, R.S. (1992). Structural holes: The social structure of competition. Cambridge, Massachusetts: Harvard University Press.
110. Burt, R.S. (2001). The social capital of structural holes. In N.F. Guillen, R. Collins, P. England & M. Meyer (Eds.), New directions in economic sociology. New York: Russell Sage Foundation.
111. Burt, R.S. (2004). Structural holes and good ideas. American Journal of Sociology, 110(2), 349-399.
112. Burt, R.S. (2005). Brokerage and closure: An introduction to social capital. Oxford, UK: Oxford University Press.
113. Cahn, R.W. (1970). Case histories of innovations. Nature, 225, 693-695.
114. Chen, C. (2003). Mapping scientific frontiers: The quest for knowledge visualization. London: Springer.
115. Chen, C. (2004). Searching for intellectual turning points: Progressive knowledge domain visualization. Proc. Natl. Acad. Sci. USA, 101(suppl), 5303-5310.
116. Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377.
117. Chen, C. (2008). An information-theoretic view of visual analytics. IEEE Computer Graphics & Applications, 28(1), 18-23.
118. Chen, C., Chen, Y., Horowitz, M., Hou, H., Liu, Z., & Pellegrino, D. (2009). Towards an explanatory and computational theory of scientific discovery. Journal of Informetrics, 3(3), 191-209.
119. Chen, C., & Kuljis, J. (2003). The rising landscape: A visual exploration of superstring revolutions in physics. Journal of the American Society for Information Science and Technology, 54(5), 435-446.
120. Chubin, D.E. (1976). The conceptualization of scientific specialties. The Sociological Quarterly, 17(4), 448-476.
121. Collins, R. (1998). The sociology of philosophies: A global theory of intellectual change. Cambridge, MA: Harvard University Press.
122. Crane, D. (1972). Invisible colleges: diffusion of knowledge in scientific communities. Chicago, Illinois: University of Chicago Press.
123. Dalen, H.P.v., & Henkens, K. (2005). Signals in science-on the importance of signaling in gaining attention in science. Scientometrics, 64(2), 209-233.
124. Davis, M.S. (1971a). That's interesting! Towards a phenomenology of sociology and a sociology of phenomenology. Philosophy of the Social Sciences, 1(2), 309-344
125. Davis, M.S. (1971b). That's interesting! Towards a phenomenology of sociology and a sociology of phenomenology. Phil. Soc. Sci., 1, 309-344.
126. Dorigo, M., & Gambardella, L.M. (1997). Ant colony system: A cooperative learning approach to the traveling salesman problem. IEEE Transactions on Evolutionary Computation, 1(1), 53-66.
127. Dunbar, K. (1993). Concept discovery in a scientific domain. Cognitive Science, 17, 397-434.
128. Evans, M., & Kaufman, M. (1981). Establishment in culture of pluripotential cells from mouse embryos. Nature, 292(5819), 154-156.
129. Fleming, L., Mingo, S., & Chen, D. (2007). Collaborative brokerage, generative creativity, and creative success. Administrative Science Quarterly, 52, 443-475.
130. Fortunato, S., Flammini, A., & Menczer, F. (2006). Scale-free network growth by ranking. Phys. Rev. Lett., 96, 218701.
131. Freeman, L.C. (1977). A set of measuring centrality based on betweenness. Sociometry, 40, 35-41.
132. Fuchs, S. (1993). A sociological theory of scientific change. Social Forces, 71(4), 933-953.
133. Garfield, E. (1992). Of Nobel class: Part 2. Forecasting Nobel Prizes using citation data and the odds against it. Current Contents, 35, 3-12.
134. Garfield, E., & Welljamsdorof, A. (1992). Of Nobel class-a citation perspective on high-impact research authors. Theoretical Medicine, 13(2), 117-135.
135. Gill, J. (2005). An entropy measure of uncertainty in vote choice. Electorial Studies, 24, 371-392.
136. Girvan, M., & Newman, M.E.J. (2002). Community structure in social and biolog ical networks. Proc. Natl. Acad. Sci. USA, 99, 7821-7826.
137. Gladwell, M. (2007, 01/08/2007). Open secrets: Enron, intelligence, and the perils of too much information. The New Yorker.
138. Glymour, C. (2004). The automation of discovery. Daedelus, Winter, 69-77.
139. Goffman, W., & Harmon, G. (1971). Mathematical approach to the prediction of scientific discovery. Nature, 229, 103-104.
140. Goffman, W., & Newill, V.A. (1964). Generalisation of epidemic theory: An application to the transmission of ideas. Nature, 204, 225-228.
141. Gordon, M.D., & Lindsay, R.K. (1996). Toward discovery support systems: A replication, re-examination, and extension of Swanson's work on literaturebased discovery of a connection between Raynaud's and fish oil. Journal of the American Society for Information Science, 47(2), 116-128.
142. Griffith, B.C., & Mullins, N.C. (1977). Coherent social groups in scientific change. Science, 177(4053), 959-964.
143. Gruhl, D., Guha, R., Liben-Nowell, D., & Tomkins, A. (2004). Information diffusion through blogspace. New York, NY.
144. Guiffre, K. (1999). Sandpiles of opportunity: Success in the art world. Social Forces, 77(3), 815-832.
145. Hansen, M.T. (1999). The search-transfer problem: The role of weak ties in sharing knowledge across organization subunits. Administrative Science Quarterly, 44(1), 82-111.
146. Heinze, T., & Bauer, G. (2007). Characterizing creative scientists in nano-S&T: Productivity, multidisciplinarity, and network brokerage in a longitudinal perspective Scientometrics, 70(3), 811-830.
147. Heinze, T., Shapira, P., Senker, J., & Kuhlmann, S. (2007). Identifying creative research accomplishments: Methodology and results for nanotechnology and human genetics Scientometrics, 70(1), 125-152.
148. Hilderman, R.J., & Hamilton, H.J. (2001). Knowledge discovery and measures of interest. Norwell, MA: Kluwer Academic Publishers.
149. Hirsch, J.E. (2005a). An index to quantify an individual's scientific output. PNAS, 102, 16569.
150. Hirsch, J.E. (2005b). An index to quantify an individual's scientific research output. Proceedings of the national academy of sciences of the United States of America, 102, 16569.
151. Holton, G., Chang, H., & Jurkowitz, E. (1996). How a scientific discovery is made: A case history. American Scientist, 84(4), 364-375.
152. Hummon, N.P., & Doreian, P. (1989). Connectivity in a citation network-the development of DNA theory. Social Networks, 11(1), 39-63.
153. Ioannidis, J.P., & Trikalinos, T.A. (2005). Early extreme contradictory estimates may appear in published research: The Proteus phenomenon in molecular genetics research and randomized trials. J Clin Epidemiol, 58, 543-549.
154. Itti, L., & Baldi, P. (2005). A principled approach to detecting surprising events in video. In proceedings of the IEEE conference on computer vision and pattern recognition (CVPR)(pp. 631-637.
155. Katz, E., & Lazarsfeld, P. (1955). Personal Influence. New York: The Free Press.
156. Klahr, D., & Simon, H.A. (1999). Studies of scientific discovery: Complementary approaches and convergent findings. Psychological Bulletin, 125(5), 524-543.
157. Kleinberg, J. (2002). Bursty and hierarchical structure in streams. Proceedings of the 8th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 91-101). ACM Press.
158. Kuhn, T.S. (1962). The structure of scientific revolutions. Chicago: University of Chicago Press.
159. Kuhn, T.S. (1970). The structure of scientific revolutions. (2nd ed.): University of Chicago Press.
160. Kullback, S., & Leibler, R.A. (1951). On information and suffciency. Annals of Mathematical Statistics, 22, 79-86.
161. Kumar, R., Novak, J., Raghavan, P., & Tomkins, A. (2003). On the bursty evolution of blogspace. In proceedings of the WWW2003(pp. 477). Budapest, Hungary.
162. Kumar, R., Novak, J., Raghavan, P., & Tomkins, A. (2004). Structure and evolution of blogspace. Communications of the ACM, 47(12), 35-39.
163. Laudan, L., Donovan, A., Laudan, R., Barker, P., Brown, H., Leplin, J., et al. (1986). Scientific change-philosophical models and historical research. Synthese, 69(2), 141-223.
164. Lazarsfeld, P.F., Berelson, B., & Gaudet, H. (1944). The people's choice: How the voter makes up his mind in a presidential campaign. New York: Columbia University Press.
165. Leydesdorff, L. (2007). Betweenness centrality as an indicator of the interdisciplinarity of scientific journals. Journal of the American Society for Information Science and Technology, 58(9), 1303-1319.
166. Liben-Nowell, D., & Kleinberg, J. (2008). Tracing information flow on a global scale using Internet chain-letter data. PNAS, 105(12), 4633-4638.
167. Lindahal, B.I.B. (1992). Discovery, theory change, and the Nobel Prize: On the mechanism of scientific evolution. Theoretical Medicine, 13(2), 97-231.
168. Lindsay, R.K., & Gordon, M.D. (1999). Literature-based discovery by lexical statistics. Journal of the American Society for Information Science, 50(7), 574-587.
169. Liqiang, G., & Howard, J.H. (2006). Interestingness measures for data mining: A survey. ACM Computing Surveys, 38(3), 9.
170. Lokker, C., McKibbon, K.A., McKinlay, R.J., Wilczynski, N.L., & Haynes, R.B. (2008). Prediction of citation counts for clinical articles at two years using data available within three weeks of publication: retrospective cohort study. BMJ, 336(7645), 655-657.
171. Marshall, B.J. (2005). Helicobacter connections. Nobel Lecture.
172. Marshall, B.J., & Warren, J.R. (1984). Unidentified curved bacilli in the stomach of patients with gastritis and peptic ulceration. Lancet, 16(1), 1311-1315.
173. Mayer, R.E. (1995). The search for insight: Grappling with Gestalt Psychology's unanswered questions. In R.J. Sternberg & J.E. Davidson (Eds.), The Nature of Insight (pp. 3-32). Cambridge, MA: The MIT Press.
174. Morris, S.A., & Van der Veer Martens, B. (2008). Mapping research specialties. Annual Review of Information Science and Technology, 42, 213-295.
175. Mullins, N.C., Hargens, L.L., Hecht, P.K., & Kick, E.L. (1977). The group structure of cocitation clusters: A comparative study. American Sociological Review, 42(4), 552-562.
176. Newman, M.E.J. (2001). The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. USA, 98, 404-409.
177. Nowakowska, M. (1973). An epidemical spread of scientific objects: an attempt of empirical approach to some problems of meta-science. Theory and Decision, 3, 262-297.
178. NSF. (2007). Important Notice No. 130: Transformative research. Retrieved Nov 19, 2008, 2008, from http://www.nsf.gov/pubs/2007/in130/in130.jsp
179. Perkins, D.N. (1995). Insight in minds and genes. In R.J. Sternberg & J.E. Davidson (Eds.), The nature of insight (pp. 495-534). Cambridge, MA: MIT Press.
180. Perneger, T.V. (2004). Relation between online "hit counts" and subsequent citations: prospective study of research papers in the BMJ. BMJ, 329, 546-547.
181. Pincock, S. (2005). Nobel Prize winners Robin Warren and Barry Marshall. Lancet, 366(9495), 1429.
182. Pirolli, P. (2007). Information foraging theory: Adaptive interaction with information. Oxford, England: Oxford University Press.
183. Radder, H. (1997). Philosophy and history of science: Beyond the Kuhnian paradigm. Studies in History and Philosophy of Science, 28(4), 633-655.
184. Redner, S. (2004). Citation statistics from more than a century of Physical Review. Phys. Rev. E (Submitted for Publication).
185. Sandstrom, P.E. (1999). Scholars as subsistence foragers. Bulletin of the American Society for Information Science, 25(3), 17-20.
186. Schaffner, K.F. (1992). Theory change in immunology part I: extended theories and scientific progress. Theoretical Medicine, 13(2), 175-189.
187. Schwarz, J.H. (1982). Superstring theory. Physics Reports-Review Section of Physics Letters, 89(3), 224-322.
188. Shibata, N., Kajikawa, Y., & Matsushima, K. (2007). Topological analysis of citation networks to discover the future core articles Journal of the American Society for Information Science and Technology, 58(6), 872-882.
189. Shneiderman, B. (2002). Leonardo's laptop: Human needs and the new computing technologies. Cambridge, MA: MIT Press.
190. Shneiderman, B. (2007). Creativity support tools: accelerating discovery and innovation. Communications of the ACM, 50(12), 20-32.
191. Simon, H.A. (1981). The sciences of the artificial. Cambridge, MA: MIT Press.
192. Simon, H.A., Langley, P.W., & Bradshaw, G.L. (1981a). Scientific discovery as problem-solving. Synthese, 47, 1-27.
193. Smalheiser, N.R., & Swanson, D.R. (1996). Indomethacin and Alzheimer's disease. Neurology, 46, 583.
194. Small, H. (1999). A passage through science: Crossing disciplinary boundaries. Library Trends, 48(1), 72-108.
195. Small, H. (2000). Charting pathways through science: Exploring Garfield's vision of a unified index to science. In B. Cronin & H.B. Atkins (Eds.), The web of knowledge: A festschrift in honor of eugene garfield (pp. 449-473). Medford, NJ: Information Today, Inc.
196. Small, H., & Crane, D. (1979). Specialties and disciplines in science and social science. Scientometrics, 1, 445-461.
197. Snijders, T.A.B. (2001). The statistical evaluation of social network dynamics. In M.E. Sobel & M.P. Becker (Eds.), Sociological methodology (pp. 361-395). Boston and London: Basil Blackwell.
198. Soofi, E.S., & Retzer, J.J. (2002). Information indices: unification and applications. Journal of Econometrics, 107, 17-40.
199. Stewart, J.A. (1990). Drifting continents and colliding paradigms: Perspectives on the geoscience revolution. Bloomington, IN: Indiana University Press.
200. Sullivan, D., Koester, D., White, D.H., & Kern, R. (1980). Understanding rapid theoretical change in particle physics: A month-by-month co-citation analysis. Scientometrics, 2(4), 309-319.
201. Swanson, D.R. (1986a). Fish oil, Raynaud's syndrome, and undiscovered public knowledge. Perspectives in Biology and Medicine(30), 7-18.
202. Swanson, D.R. (1986b). Undiscovered public knowledge. Library Quarterly, 56(2), 103-118.
203. Swanson, D.R. (1987). Two medical literatures that are logically but not bibliographically connected. Journal of the American Society for Information Science, 38, 228-233.
204. Swanson, D.R. (1988). Migraine and magnesium: Eleven neglected connections. Perspectives in Biology and Medicine, 31, 526-557.
205. Swanson, D.R., & Smalheiser, N.R. (1999). Implicit text linkages between Medline records: Using Arrowsmith as an aid to scientific discovery. Library Trends, 48, 48-59.
206. Thagard, P. (1992). Conceptual revolutions. Princeton, New Jersey: Princeton University Press.
207. Thomas, J.J., & Cook, K.A. (Eds.). (2005). Illuminating the path: The research and development agenda for visual analytics, Los Alamitos, CA: IEEE Computer Society Press.
208. Valente, T.W. (1996). Social network thresholds in the diffusion of innovations. Social Networks, 18, 69-89.
209. Wagner-Dobler, R. (1999). William Goffman's "Mathematical approach to the prediction of scientific discovery" and its application to logic, revisited. Scientometrics, 46(3), 635-645.
210. Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and applications. Cambridge University Press.
211. Bonacich, P. (1987). Power and centrality: A family of measures. American Journal of Sociology, 92, 1170-1182.
212. Brandes, U. (2001). A faster algorithm for betweenness centrality. Journal of Mathematical Sociology, 25(2), 163-177.
213. Bursill, H. (1859). Hand shadows to be thrown upon the wall. Griffith and Farran.
214. Carmel, D., Roitman, H., & Zwerdling, N. (2009). Enhancing cluster labeling using wikipedia. Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval (pp. 139-146).
215. Chen, C. (2004). Searching for intellectual turning points: Progressive knowledge domain visualization. Proc. Natl. Acad. Sci. USA, 101(suppl), 5303-5310.
216. Chen, C. (2005). The centrality of pivotal points in the evolution of scientific networks.Proceedings of the international conference on intelligent user interfaces (IUI 2005) (pp. 98-105). ACM Press.
217. Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377.
218. Chen, C., Chen, Y., Horowitz, M., Hou, H., Liu, Z., & Pellegrino, D. (2009a). Towards an explanatory and computational theory of scientific discovery. Journal of Informetrics, 3(3), 191-209.
219. Chen, C., Ibekwe-SanJuan, F., & Hou, J. (2010). The structure and dynamics of co-citation clusters: A multiple-perspective co-citation analysis. Journal of the American Society for Information Science and Technology, 61(7), 1386-1409.
220. Chen, C., & Kuljis, J. (2003). The rising landscape: A visual exploration of superstring revolutions in physics. Journal of the American Society for Information Science and Technology, 54(5), 435-446.
221. Chen, C., Zhang, J., & Vogeley, M.S. (2009b). Mapping the global impact of Sloan digital sky survey. IEEE Intelligent Systems, 24(4), 74-77.
222. Cronin, B. (1981). Agreement and divergence on referencing practice. Journal of Information Science, 3(1), 27-33.
223. Deerwester, S., Dumais, S.T., landauer, T.K., Furnas, G.W., & Harshman, R.A. (1990). Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6), 391-407.
224. Dunning, T. (1993). Accurate methods for the statistics of surprise and coincidence. Computational Linguistics, 19(1), 61-74.
225. Fiszman, M., Demner-Fushman, D., Kilicoglu, H., & Rindflesch, T.C. (2009). Automatic summarization of MEDLINE citations for evidence-based medical treatment: A topic-oriented evaluation. Journal of Biomedical Informatics, 42, 801-813.
226. Freeman, L.C. (1977). A set of measuring centrality based on betweenness. Sociometry, 40, 35-41.
227. French, B.M., & Koeberl, C. (2010). The convincing identification of terrestrial meteorite impact structures: What works, what doesn't, and why. Earth-Science Reviews, 98, 123-170.
228. Garfield, E. (1979). Citation indexing: Its theory and applications in science, technology, and humanities. New York: John Wiley.
229. Garfield, E. (2004). Historiographic mapping of knowledge domains literature. Journal of Information Science, 30(2), 119-145.
230. Jaccard, P. (1901). Éude comparative de la distribution florale dans une portion des Alpes et des Jura. Bulletin del la Société Vaudoise des Sciences Naturelles, 37, 547-579.
231. Kamada, T., & Kawai, S. (1989). An algorithm for drawing general undirected graphs. Information Processing Letters, 31(1), 7-15.
232. Kiss, C., & Bichler, M. (2008). Identification of influencers: Measuring influence in customer networks. Decision Support Systems, 46(1), 233-253.
233. Kleinberg, J. (2002). Bursty and hierarchical structure in streams. Proceedings of Proceedings of the 8th ACM SIGKDD international conference on knowledge discovery and data mining (pp. 91-101). ACM Press.
234. Kumar, R., Novak, J., Raghavan, P., & Tomkins, A. (2003). On the bursty evolution of blogspace. Proceedings of WWW2003 (pp. 568-576). ACM Press.
235. Luxburg U.V. 2006 A tutorial on spectral clustering. http://www.kyb.mpg.de/publications/attachments/Luxburg06TR%5B0%5D.pdf Accessed 1 Oct 2002
236. Luxburg, U.v., Bousquet, O., & Belkin, M. (2009). Limits of spectral clustering, from 〈 http://kyb.mpg.de/publications/pdfs/pdf2775.pdf 〉
237. Morris, S.A., & Van der Veer Martens, B. (2008). Mapping research specialties. Annual Review of Information Science and Technology, 42, 213-295.
238. Morris, S.A., Yen, G., Wu, Z., & Asnake, B. (2003). Time line visualization of research fronts. Journal of the American Society for Information Science and Technology, 54(5), 413-422.
239. Newman, M.E.J. (2006). Modularity and community structure in networks. PNAS, 103(23), 8577-8582.
240. Ng, A.Y., Jordan, M.I., & Weiss, Y. (2002). On spectral clustering: Analysis and an algorithm. Advanced in Neural Information Processing Systems, 14(2), 849-856.
241. Rousseeuw, P.J. (1987). Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Computational and Applied Mathematics, 20, 53-65.
242. Salton, G., Yang, C.S., & Wong, A. (1975). A vector space model for information retrieval. Communications of the ACM, 18(11), 613-620.
243. Schneider, J.W. (2009). Mapping of cross-reference activity between journals by use of multidimensional unfolding: Implications for mapping studies. In B. Larsen & J. Leta (Eds.), Proceedings of 12th international conference on scientometrics and informetrics (ISSI 2009) (pp. 443-454). BIREME/PAHO/WHO and Federal University of Rio de Janeiro.
244. Schvaneveldt, R.W. (Ed.). (1990). Pathfinder associative networks: Studies in knowledge organization. Norwood, New Jersey: Ablex Publishing Corporations.
245. Schwarz, J.H. (1982). Superstring theory. Physics Reports-Review Section of Physics Letters, 89(3), 224-322.
246. Schwarz, J.H. (1996). The second superstring revolution. http://arxiv.org/PS cache/hep-th/pdf/9607/9607067.pdf. Accessed 1 Oct 2002.
247. Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(8), 888-905.
248. Shibata, N., Kajikawa, Y., Taked, Y., & Matsushima, K. (2008). Detecting emerging research fronts based on topological measures in citation networks of scien tific publications. Technovation, 28(11), 758-775.
249. Small, H. (1973).Co-citation in the scientific literature:A new measure of the relationship between two documents. Journal of the American Society for Information Science, 24, 265-269.
250. Small, H. (1986). The synthesis of specialty narratives from co-citation clusters. Journal of the American Society for Information Science, 37(3), 97-110.
251. Small, H.G. (1977). A co-citation model of a scientific specialty: A longitudinal study of collagen research. Social Studies of Science, 7, 139-166.
252. Sparck Jones, K. (1999). Automatic summarizing: Factors and directions. In I. Mani & M.T. Maybury (Eds.), Advances in automatic text summarization (pp. 2-12). Cambridge, MA: MIT Press.
253. Teufel, S., & Moens, M. (2002). Summarizing scientific articles: Experiments with relevance and rhetorical status. Computational Linguistics, 28(4), 409-445.
254. White, H.D. (2007a). Combining bibliometrics, information retrieval, and relevance theory, Part 1: First examples of a synthesis. Journal of the American Society for Information Science and Technology, 58(4), 536-559.
255. White, H.D. (2007b). Combining bibliometrics, information retrieval, and relevance theory, Part 2: Some implications for information science. Journal of the American Society for Information Science and Technology, 58(4), 583-605.
256. Witten, I.H., & Frank, E. (1999). Data mining: Practical machine learning tools and techniques with Java implementations. San Francisco, CA: Morgan Kaufmann.
257. Zhao, D.Z., & Strotmann, A. (2008). Evolution of research activities and intellectual influences in information science 1996-2005: Introducing author bibliographiccoupling analysis. Journal of the American Society for Information Science and Technology, 59(13), 2070-2086.
258. Budiu, R., Pirolli, P., & Fleetwood, M. (2006). Navigation in degree of interest trees. http://www2.parc.com/istl/groups/uir/publications/items/UIR-2006-02-Budiu-NavigationinDOITrees.pdf. Accessed June 1, 2010.
259. Burt, R.S. (2004). Structural holes and good ideas. American Journal of Sociology, 110(2), 349-399.
260. Burt, R.S. (2005). Brokerage and closure. New York, NY: Oxford University Press.
261. Callon, M., Courtial, J.P., Turner, W.A., & Bauin, S. (1983). From translations to problematic networks-An introduction to co-word analysis. Social Science Information Sur Les Sciences Sociales, 22(2), 191-235.
262. Card, S., & Nation, D. (2002). Degree-of-interest trees: A component of an attentionreactive user interface. Proceedings of AVI (pp. 231-245).
263. Chalmers, M. (1992). BEAD: Explorations in information visualisation. In proceedings of the SIGIR '92(pp. 330-337). Copenhagen, Denmark. ACM Press.
264. Chang, C.-C., & Lin, C.-J. (2001). LIBSVM: A library for support vector machines. http://www.csie.ntu.edu.tw/~cjlin/libsvm.
265. Chen, C. (2004). Searching for intellectual turning points: Progressive knowledge domain visualization. Proc. Natl. Acad. Sci. USA, 101(suppl), 5303-5310.
266. Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377.
267. Chen, C., Chen, Y., Horowitz, M., Hou, H., Liu, Z., & Pellegrino, D. (2009). Towards an explanatory and computational theory of scientific discovery. Journal of Informetrics, 3(3), 191-209.
268. Chen, C., Ibekwe-SanJuan, F., SanJuan, E., &Weaver, C. (2006). Visual analysis of conflicting opinions. In Proceedings of the IEEE Symposium on Visual Analytics Science and Technology (VAST)(pp. 59-66). Baltimore, MA.
269. Daille, B. (2003). Conceptual structuring through term variations. In proceedings of the Proceedings of the ACL-2003 workshop on multiWord expressions: Analysis, acquisition and treatment(pp. 9-16). Saporro, Japan.
270. Darwin, C. (1872). The origin of species. (6th ed.): Project. Gutenberg.
271. Fiszman, M., Demner-Fushman, D., Kilicoglu, H., & Rindflesch, T.C. (2009). Automatic summarization of MEDLINE citations for evidence-based medical treatment: A topic-oriented evaluation. Journal of Biomedical Informatics, 42, 801-813.
272. Fry, B. (2009). On the origin of species: The preservation of favoured traces. http://benfry.com/traces/.
273. Furnas, G.W. (1986). Generalized fisheye views. In proceedings of the CHI '86(pp. 16-23. ACM Press.
274. Ham, F.v., Wattenberg, M., & Viéas, F.B. (2009). Mapping text with phrase nets. IEEE Transactions on Visualization and Computer Graphics, 15(6), 1169-
275. Havre, S., Hetzler, E., Whitney, P., & Nowell, L. (2002). ThemeRiver: Visualizing thematic changes in large document collections. IEEE Transactions on Visualization and Computer Graphics, 8(1), 9-20.
276. Heer, J. (2007). The prefuse visualization toolkit. http://prefuse.org/.
277. Heer, J., & Card, S.K. (2004). DOI Trees revisited: Scalable, space-constrained visualization of hierarchical data. Proceedings of AVI (pp. 421-424).
278. Hetzler, B., Whitney, P., Martucci, L., & Thomas, J. (1998). Multi-faceted insight through interoperable visual information analysis paradigms. In Proceedings of the IEEE Information Visualization '98(pp. 137-144). Los Alamitos, CA: IEEE Computer Society Press.
279. Ibekwe-SanJuan, F. (1998). A linguistic and mathematical method for mapping thematic trends from texts. In Proceedings of the 13th European Conference on Artificial Intelligence (ECAI'98). (pp. 170-174). Brighton, UK.
280. Ibekwe-SanJuan, F., & SanJuan, E. (2004). Mining textual data through term variant clustering: The TermWatch system. In Proceedings of the Recherche d'Information assistée par ordinateur(RIAO 2004)(pp. 487-503). University of Avignon, France.
281. Kohonen, T. (1995). Self-organizing maps. Springer.
282. Paley, W.B. (2002). TextArc. http://www.textarc.org/.
283. Pang, B., & Lee, L. (2004). A sentimental education: Sentiment analysis using subjectivity summarization based on minimum cuts. In Proceedings of the ACL.
284. PNNL. IN-SPIRE. http://in-spire.pnl.gov/.
285. Rip, A., & Courtial, J.P. (1984). Co-word maps of biotechnology-An example of cognitive scientometrics. Scientometrics, 6(6), 381-400.
286. Shneiderman, B. (1996). The eyes have it: A task by data type taxonomy for information visualization. In Proceedings of the IEEE Workshop on Visual Language( pp. 336-343). Boulder, CO: IEEE Computer Society Press.
287. Sparck Jones, K. (1999). Automatic summarizing: Factors and directions. In I. Mani & M.T. Maybury (Eds.), Advances in Automatic Text Summarization (pp. 2-12). Cambridge, MA: MIT Press.
288. Swanson, D.R. (1986). Fish oil, Raynaud's syndrome, and undiscovered public knowledge. Perspectives in Biology and Medicine, (30), 7-18.
289. Teufel, S., & Moens, M. (2002). Summarizing scientific articles: Experiments with relevance and rhetorical status. Computational Linguistics, 28(4), 409-445.
290. Thagard, P. (1992). Conceptual revolutions. Princeton, New Jersey: Princeton University Press.
291. Thomas, J.J., & Cook, K.A. (Eds.). (2005). Illuminating the path: The research and development agenda for visual analytics. IEEE Computer Society Press.
292. Tijssen, R.J.W., & Vanraan, A.F.J. (1989). Mapping co-word structures-a comparison of multidimensional-scaling and leximappe. Scientometrics, 15(3-4), 283-295.
293. Toutanova, K., Klein, D., Manning, C., & Singer, Y. (2003). Feature-rich part-ofspeech tagging with a cyclic dependency network. Proceedings of HLT-NAACL 2003 (pp. 252-259).
294. Viégas, F.B., Wattenberg, M., Ham, F.v., Kriss, J., & McKeon, M. (2007). Many eyes: A site for visualization at Internet scale. IEEE Transactions on Visualization and Computer Graphics, 13(6), 1121-1128.
295. Wattenberg, M., & Viégas, F.B. (2008). The word tree: an interactive visual concordance. IEEE Transactions on Visualization and Computer Graphics, 14(6), 1221-1228.
296. Witten, I.H., & Frank, E. (1999). Data mining: Practical machine learning tools and techniques with Java implementations. San Francisco, CA: Morgan Kaufmann.
297. Yang, Y., & Pedersen, J.O. (1997). A comparative study on feature selection in text categorization. In J.D.H. Fisher (Ed.), Proceedings of the The 14th International Conference on Machine Learning (ICML'97)(pp. 412-420). Nashville, US. Morgan Kaufmann Publishers.
298. Chen, C. (2003). Mapping scientific frontiers: The quest for knowledge visualization. London: Springer.
299. Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359-377.
300. Chen, C., Chen, Y., Horowitz, M., Hou, H., Liu, Z., & Pellegrino, D. (2009). Towards an explanatory and computational theory of scientific discovery. Journal of Informetrics, 3(3), 191-209.
301. Chen, C., Ibekwe-SanJuan, F., & Hou, J. (2010). The Structure and dynamics of co-citation clusters: A multiple-perspective Co-Citation Analysis. Journal of the American Society for Information Science and Technology, 61(7), 1386-1409.
302. Gold, T. (1968). Rotating neutron stars as origin of pulsating radio sources. Nature, 218(5143), 731-732.
303. Härynen, M. (2007). Breakthrough research: Funding for high-risk research at the Academy of Finland. Helsinki: The Academy of Finland.
304. Hewish, A., Bell, S.J., Pilkington, J.D.H., Scott, P.F., & Collins, R.A. (1968). Observation of a rapidly pulsating radio source. Nature, 217(5130), 709-713.
305. Lee, Y.G., Lee, J.D., Song, Y.I., & Lee, S.J. (2007). An in-depth empirical analysis of patent citation counts using zero-inflated count data model: The case of KIST. Scientometrics, 70(1), 27-39.
306. Lipinski, C., & Hopkins, A. (2004). Navigating chemical space for biology and medicine. Nature, 432(7019), 855-861.
307. Lokker, C., & Walter, S.D. (2010). Prediction of citation counts: a comparison of results from alternative statistical models. Retrieved Oct 15, 2010, 2010, from http://www.bmj.com/content/336/7645/655/reply.
308. Meadows, A.J., & O'Connor, J.G. (1971). Bibliographical statistics as a guide to growth points in science. Science Studies, 1(1), 95-99.
309. NSF. (2007, September 25). Important notice No. 130: Transformative research. http://www.nsf.gov/pubs/2007/in130/in130.txt. Accessed 14 Aug 2010.
310. Price, D.D. (1965). Networks of scientific papers. Science, 149, 510-515.
311. Swanson, D.R. (1986). Fish oil, Raynaud's syndrome, and undiscovered public knowledge. Perspectives in Biology and Medicine, (30), 7-18.
312. Takeda, Y., & Kajikawa, Y. (2010). Tracking modularity in citation networks. Scientometrics, 83(3), 783-792.