Using bibliometrics to support the facilitation of cross-disciplinary communication

Journal of the American Society for Information Science and Technology

Volumn 64, Issue 9, 2013, Pages 1768-1779

  Williams, Christopher J ^a   O'Rourke, Michael ^b   Eigenbrode, Sanford D ^a   O'Loughlin, Ian ^c   Crowley, Stephen J ^d  

^a UNIVERSITY OF IDAHO   (United States)

^b MICHIGAN STATE UNIVERSITY   (United States)

^c UNIVERSITY OF IOWA   (United States)

^d BOISE STATE UNIVERSITY   (United States)

Author keywords

collaboration; communication skills; multivariate analysis

Indexed keywords

BIBLIOMETRIC TECHNIQUES; COLLABORATION; COMMUNICATION SKILLS; CROSS-DISCIPLINARY RESEARCH; FUTURE APPLICATIONS; MULTI VARIATE ANALYSIS; MULTIVARIATE STATISTICAL ANALYSIS; STATISTICAL TECHNIQUES;

COMMUNICATION; MULTIVARIANT ANALYSIS; PHILOSOPHICAL ASPECTS;

RESEARCH;

EID: 84882448948     PISSN: 15322882     EISSN: 15322890     Source Type: Journal    
DOI: 10.1002/asi.22874     Document Type: Article

References (61)

1. Aitchison, J., (1986). The Statistical Analysis of Compositional Data. London: Chapman and Hall.
2. Beers, P.J., &, Bots, P.W.G., (2009). Eliciting conceptual models to support interdisciplinary research. Journal of Information Science, 35, 259-278.
3. Bennett, L.M., Gadlin, H., Levine-Finley, S., (2010). Collaboration and team science: A field guide. Washington, D.C.: National Institutes for Health.
4. Boyack, K.W., Klavans, R., &, Börner, K., (2005). Mapping the backbone of science. Scientometrics, 64 (3), 351-374.
5. Casey-Campbell, M., &, Martens, M.L., (2009). Sticking it all together: A critical assessment of the group cohesion-performance literature. International Journal of Management Reviews, 11 (2), 223-246.
6. Chambers, J., Cleveland, W., Kleiner, B., &, Tukey, P., (1983). Graphical methods for data analysis. Belmont, Calif.: Wadsworth.
7. Chan, E.H.W., Chan, M.W., Scott, D., &, Chan, A.T.S., (2002). Educating the 21st century construction professionals. Journal of Professional Issues in Engineering Education and Practice, 128, 44-51.
8. Cronin, B., (2001). Bibliometrics and beyond: Some thoughts on web-based citation analysis. Journal of Information Science, 27 (1), 1-7.
9. Crowley, S., Eigenbrode, S.D., O'Rourke, M., &, Wulfhorst, J.D., (2010). Localization in cross-disciplinary research: A philosophical approach. Multilingual, 114. Retrieved June 27, 2012, from http://www.multilingual.com/ downloads/114LCDR.pdf
10. Duda, R.O., &, Hart, P.E., (1973). Pattern classification and scene analysis. New York: John Wiley & Sons.
11. Eigenbrode, S.D., O'Rourke, M., Althoff, D., Goldberg, C., Merrill, K., Morse, W., Nielsen-Pincus, M., Stephens, J., Winowiecki, L., Wulfhorst, J.D., &, Bosque-Pérez, N., (2007). Employing philosophical dialogue in collaborative science. Bioscience, 57, 55-64.
12. Frank, L.K., (1961). Interprofessional communication. American Journal of Public Health, 51, 1798-1804.
13. Fryer, D., (2004). Federated search engines. Online, 28 (2), 17-19.
14. Giri, A.K., (2002). The calling of a creative transdisciplinarity. Futures, 34, 103-115.
15. Heemskerk, M., Wilson, K., &, Pavao-Zuckerman, M., (2003). Conceptual models as tools for communication across disciplines. Conservation Ecology, 7, 8. Retrieved June 27, 2012, from http://www.consecol.org/vol7/iss3/art8/.
16. Hijazi, R., (2011). An EM-algorithm based method to deal with rounded zeros in compositional data under dirichlet models. In, J.J. Egozcue, R. Tolosana-Delgado, &, M.I. Ortego, (Eds.), Proceedings of the 4th International Workshop on Compositional Data Analysis (pp. 1-5). Girona, Spain: University of Girona.
17. Hron, K., Templ, M., &, Filzmoser, P., (2010). Imputation of missing values for compositional data using classical and robust methods. Computational Statistics and Data Analysis, 12, 3095-3107.
18. Jantsch, E., (1972). Towards interdisciplinarity and transdisciplinarity in education and innovation. In, L. Apostel, G. Berger, A. Briggs, &, G. Michaud, (Eds.), Interdisciplinarity: Problems of Teaching and Research in Universities (pp. 97-121). Paris: Organization for Economic Cooperation and Development.
19. Johnson, D.E., (1998). Applied multivariate methods for data analysis, Pacific Grove, Calif.: Duxbury Press.
20. Klavans, R., &, Boyack, K.W., (2007). Is there a convergent structure of science? A comparison of maps using the ISI and Scopus databases. In, D. Torres-Salinas, &, H. Moed, (Eds.), Proceedings of the 11th International Conference of the International Society for Scientometrics and Informetrics (pp. 437-448). Madrid, Spain: ISSI.
21. Klavans, R., &, Boyack, K.W., (2009). Toward a consensus map of science. Journal of the American Society for Information Science and Technology, 60 (3), 455-476.
22. Klein, J.T., (1996). Crossing Boundaries: Knowledge, Disciplinarities, and Interdisciplinarities. Charlottesville, VA: University Press of Virginia.
23. Klein, J.T., (2008). Evaluation of interdisciplinary and transdisciplinary research: A literature review. American Journal of Preventive Medicine, 35 (2S), S116-S123.
24. Klein, J.T., (2010). Creating Interdisciplinary Campus Cultures: A Model for Strength and Sustainability. San Francisco: Jossey-Bass.
25. Krzanowski, W.J., (2000). Principles of Multivariate Analysis: A User's Perspective. Oxford: Oxford University Press.
26. Lélé, S., &, Norgaard, R.B., (2005). Practicing interdisciplinarity. BioScience, 55, 967-975.
27. Leydesdorff, L., &, Rafols, I., (2009). A global map of science based on the ISI subject categories. Journal of the American Society for Information Science and Technology, 60 (2), 348-362.
28. Likert, R., (1932). A technique for the measurement of attitudes. Archives of Psychology, 140, 1-55.
29. MacRoberts, M.H., &, MacRoberts, B.R., (1989). Problems of citation analysis: A critical review. Journal of the American Society for Information Science, 40 (5), 342-349.
30. Mardia, K.V., Kent, J.T., &, Bibby, J.M., (1979). Multivariate analysis. London: Academic Press.
31. Martín-Fernández, J., Barceló-Vidal, C., &, Pawlowsky-Glahn, V., (2003). Dealing with zeros and missing values in compositional data sets using nonparametric imputation. Mathematical Geology, 35, 253-278.
32. McDonald, D., Bammer, G., &, Deane, P., (2009). Research Integration Using Dialogue Methods. Canberra, Australia: ANU E Press.
33. Milligan, G.W., Soon, S.C., &, Sokol, L.M., (1983). The effect of cluster size, dimensionality, and the number of clusters on recovery of true cluster structure. IEEE Transactions on Pattern Analysis and Machine Intelligence, 5 (1), 40-47.
34. Morillo, F., Bordons, M., &, Gómez, I., (2001). An approach to interdisciplinarity through bibliometric indicators. Scientometrics, 51, 203-222.
35. Morillo, F., Bordons, M., &, Gómez, I., (2003). Interdisciplinarity in science: A tentative typology of disciplines and research areas. Journal of the American Society for Information Science and Technology, 54 (13), 1237-1249.
36. Morse, W.C., Nielsen-Pincus, M., Force, J.E., &, Wulfhorst, J.D., (2007). Bridges and barriers to developing and conducting interdisciplinary graduate-student team research. Ecology and Society, 12, 8. Retrieved June 27, 2012, from http://www.ecologyandsociety.org/vol12/iss2/art8/.
37. National Academy of Sciences (NAS), Committee on Facilitating Interdisciplinary Research and Committee on Science Engineering and Public Policy. (2004). Facilitating Interdisciplinary Research. Washington, D.C.: National Academies Press.
38. National Institutes for Health (NIH). (2011). About the NIH Roadmap. Retrieved June 27, 2012, from http://commonfund.nih.gov/aboutroadmap.aspx.
39. National Science Foundation (NSF). (2012). Introduction to interdisciplinary research. Retrieved June 27, 2012, from http://www.nsf.gov/od/ oia/additional-resources/interdisciplinary-research/.
40. Nikitina, S., (2005). Pathways of interdisciplinary cognition. Cognition and Instruction, 23, 389-425.
41. O'Rourke, M., &, Crowley, S., (2012). Philosophical intervention and cross-disciplinary science: The story of the Toolbox Project. Synthese. doi 10.1007/s11229-012-0175-y.
42. Palmer, C.L., (2010) Information research on interdisciplinarity. In, R. Frodeman, J.T. Klein, &, C. Mitcham, (Eds.), The Oxford Handbook of Interdisciplinarity (pp. 174-188). Oxford: Oxford University Press.
43. Porter, A.L., &, Rafols, I., (2009). Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics, 81, 719-745.
44. Porter, A.L., Roessner, J.D., Cohen, A.S., &, Perreault, M., (2006). Interdisciplinary research: Meaning, metrics, and nurture. Research Evaluation, 15, 187-195.
45. Porter, A.L., Roessner, J.D., &, Heberger, A.E., (2008). How interdisciplinary is a given body of research? Research Evaluation, 17, 273-282.
46. R Development Core Team (2010). R: A Language and Environment for Statistical Computing. Vienna: R Foundation for Statistical Computing. Retrieved June 27, 2012, from http://www.R-project.org.
47. Rafols, I., &, Meyer, M., (2010). Diversity and network coherence as indicators of interdisciplinarity: Case studies in bionanoscience. Scientometrics, 82, 263-287.
48. Rafols, I., Porter, A.L., &, Leydesdorff, L., (2010). Science overlay maps: A new tool for research policy and library management. Journal of the American Society for Information Science and Technology, 61, 1871-1887.
49. Research Evaluation. (2006). Special issue on the assessment of interdisciplinary research. Research Evaluation, 15 (1), 2-80.
50. Rosvall, M., &, Bergstrom, C.T., (2008). Maps of random walks on complex networks reveal community structure. Proceedings of the National Academy of Sciences U S A, 105, 1118-1123.
51. Sarle, W.S., (1983). Cubic Clustering Criterion. SAS Technical Report A-108, Cary, N.C.: SAS Institute Inc.
52. SAS Institute Inc. (2000-2004). SAS 9.2 Help and Documentation, Cary, N.C.: SAS Institute Inc.
53. Shrum, W., Genuth, J., &, Chompalov, I., (2007). Structures of Scientific Collaboration. Cambridge, Mass.: The MIT Press.
54. Small, H., (1999). Visualizing science by citation mapping. Journal of the American Society for Information Science, 50 (9), 799-813.
55. Stokols, D., Hall, K.L., Taylor, B.K., &, Moser, R.P., (2008). The science of team science: Overview of the field and introduction to the supplement. American Journal of Preventive Medicine, 35, S77-S89.
56. Wagner, C.S., Roessner, J.D., Bobb, K., Klein, J.T., Boyack, K.W., Keyton, J., Rafols, I., &, Börner, K., (2011). Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature. Journal of Informetrics, 165, 14-26.
57. Warnick, W., (2010). Federated search as a transformational technology enabling knowledge discovery: The role of WorldWideScience.org. Interlending & Document Supply, 38 (2), 82-92.
58. Webber, S.S., (2002). Leadership and trust facilitating cross-functional team success. Journal of Management Development, 21 (3), 201-214.
59. Weingart, P., (2010) A short history of knowledge formations. In, R. Frodeman, J. T. Klein, and, C. Mitcham, (Eds.), The Oxford Handbook of Interdisciplinarity (pp. 3-14). Oxford: Oxford University Press.
60. Winowiecki, L., Smukler, S., Shirley, K., Remans, R., Peltier, G., Lothes, E., King, E., Comita, L., Baptista, S., &, Alkema, L., (2011). Tools for enhancing interdisciplinary communication. Sustainability: Science, Practice, & Policy, 7, 1. Retrieved June 27, 2012, from http://sspp. proquest.com/archives/vol7iss1/communityessay.winowiecki.html.
61. Yang, C.H., Park, H.W., &, Heo, J., (2010). A network analysis of interdisciplinary research relationships: the Korean government's R&D grant program. Scientometrics, 83, 77-92.