Libraries at University of Nebraska-Lincoln

 

Date of this Version

Summer 6-15-2020

Document Type

Article

Citation

  1. Data science. Wikipedia. https://en.wikipedia.org/wiki/Data_science (accessed on 12 April 2019).
  1. Pillai, K. G. S. Authorship patterns in physics literature: An informetric study on citations in doctoral theses of the Indian Institute of Science. Ann. of Lib. & Inf. Stu., 2007, 54 (2), 90-94. http://nopr.niscair.res.in/bitstream/123456789/3248/4/ALIS%2054%282%29%2090-94.pdf (accessed on 15 April 2019).
  1. Nalimov, V.V. & Mulchenko, Z.M. Study of science development as an information process. Scientometrics, 1989, 15, 33-43.
  1. Khiste, G. P.; Maske, D.B. & Deshmukh, R. K. Big data output in Jgate during 2013 to 2017: A bibliometrics analysis. Int. J. of Scientific Research in Com. Sci., Eng. and Inf. Tech, 2018 3(1), 1252-1257.
  1. Liao, H.; Tang, M.; Luo, L.; Li, C.; Chiclana, F. & Zeng, X. J. A bibliometric analysis and visualization of medical big data research. Sustainability, 2018, 10(1), 166.
  1. Sarkar, Arindam & Pal, Ashok. Where does data science research stand in the 21st century: Observation from the standpoint of a scientometric analysis. Lib. Phi. and Pra. (e-journal), 2019, 2561, 1-9. https://digitalcommons.unl.edu/libphilprac/2561/ (accessed on 22 April 2019).
  1. Noruzi, Alireza. YouTube in scientific research: A bibliometric analysis. Webology, 2017, 14(1), http://www.webology.org/2017/v14n1/editorial23.pdf
  1. Annual growth rate. Wikipedia. https://en.wikipedia.org/wiki/Annual_growth_rate (accessed on 12 April 2019).

  1. Velmurugan, C. & Radhakrishnan, N. Malaysian Journal of Library and Information Science: A scientometric profile. J. Scientometric Res., 2016, 5(1), 62-70. doi: 10.5530/jscires.5.1.9
  1. Subramanyam, K. Bibliometric studies of research in collaboration: A review. J. of Inf. Sc., 1983, 6 (1), 33-38.
  1. Elango, B. & Rajendran, P. Authorship trends and collaboration pattern in the marine sciences literature: A scientometric study. Int. J. Inf. Dissemination Technol., 2012, 2(3), 166- 169. http://www.ijidt.com/ index.php/ijidt/article/viewFile/91/91 (accessed on 24 April 2019).
  1. Van Eck, N.J. & Waltman, L. Visualizing bibliometric networks. In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring scholarly impact: Methods and practice, 2014, 285–320.
  1. Sen, B.K. Lotka’s law: A view point. Ann. Lib. & Inf. Stu., 2010, 57(2), 166-67.

Abstract

The present study tries to focus on the various facets of authorship pattern in data science during 2001-2018. Annual growth rate of articles, authorship pattern, author productivity rate, degree of collaboration, author collaboration network visualization and finally the application of Lotka’s law are the major thrust of this research. The highest AGR 46.43% was noticed in the year 2016 followed by 39.53% in 2014 and 37.67% in 2015. The lowest AGR -20.75% was noticed in the year 2002. Only 21.83% articles were published by single author whereas 78.17% articles contributed by two or more than two authors. The lowest AAPP was 2.25 with highest PPA was 0.44 observed in the year 2015. On the other side, highest AAPP at 3.88 with lowest PPA at 0.25 is seen in the year 2002. The study reflects that overall Degree of Collaboration is 0.78 that indicates large number of collaboration among the authors. The highest Collaborative Index 5.06 is seen in the year 2001 and minimum Collaborative Index 2.63 is in the year 2015. Seventy authors with greatest total link strength have been represented through VOSviewer’s author collaboration network. Finally it can be mentioned that the data set derived from this research largely follows Lotaka’s law of author productivity.

Share

COinS