Designing an Interface for Sharing Quantitative Ethnographic Research Data

  • Zachari SwieckiEmail author
  • Cody Marquart
  • Arjun Sachar
  • Cesar Hinojosa
  • Andrew R. Ruis
  • David Williamson Shaffer
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 1112)


Recently, there have been growing calls to make research data more widely available. While the potential benefits of sharing research data are many, there are also many challenges, including the interpretability, attendability, and complexity of the data. These challenges are particularly salient for research data associated with quantitative ethnographic analyses, which often use relatively novel and sophisticated techniques. In this paper, we explore design considerations for an interface for sharing research data that attempts to address these challenges for quantitative ethnographic analyses. These considerations include: (a) maintaining the consistency of the interpretive space, (b) simplifying model details, (c) including example results and interpretations, and (d) highlighting key affordances in the user interface. To explore these considerations, we describe the design of an interactive visualization of the thematic networks present in the HBO television series, Game of Thrones.


Sharing research data Interface design Epistemic network analysis Quantitative ethnography 



This work was funded in part by the National Science Foundation (DRL-1661036, DRL-1713110), the Wisconsin Alumni Research Foundation, and the Office of the Vice Chancellor for Research and Graduate Education at the University of Wisconsin-Madison. The opinions, findings, and conclusions do not reflect the views of the funding agencies, cooperating institutions, or other individuals.


  1. Beveridge, A., Shan, J.: Network of thrones. Math. Horiz. 23(4), 18–22 (2016)MathSciNetCrossRefGoogle Scholar
  2. Borgman, C.L.: The conundrum of sharing research data. J. Am. Soc. Inform. Sci. Technol. 63(6), 1059–1078 (2012). Scholar
  3. Chang, W., Cheng, J., Allaire, J., Xie, Y., McPherson, J.: shiny: Web Application Framework for R. (Version 1.3.2) (2019).
  4. Feldman, S., Shaw, L.: The epistemological and ethical challenges of archiving and sharing qualitative data. Am. Behav. Sci. 63(6), 699–721 (2019). Scholar
  5. Herder, T., et al.: Supporting teacher’s intervention in student’s virtual collaboration using a network based model. In: Proceedings of the International Conference on Learning Analytics, Sydney, Australia, pp. 21–25 (2018)Google Scholar
  6. Marquart, C.L., Swiecki, Z., Collier, W., Eagan, B., Woodward, R., Shaffer, D.W.: rENA: Epistemic Network Analysis (Version 0.1.3) (2018).
  7. Marquart, C.L., Swiecki, Z., Eagan, B., Shaffer, D.W.: ncodeR (Version 0.1.2) (2018).
  8. Miller, J.W.: Scraping song lyrics from (2017).
  9. Shaffer, D.W.: Quantitative Ethnography. Cathcart Press, Madison (2017)Google Scholar
  10. Shen, H., Huang, J.Z.: Sparse principal component analysis via regularized low rank matrix approximation. J. Multivar. Anal. 99(6), 1015–1034 (2008)MathSciNetCrossRefGoogle Scholar
  11. Tsai, A.C., et al.: Promises and pitfalls of data sharing in qualitative research. Soc. Sci. Med. 169, 191–198 (2016). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.University of WisconsinMadisonUSA
  2. 2.Aalborg University CopenhagenCopenhagenDenmark

Personalised recommendations