Abstract
Research Data Practices (RDP) refer to research activities conducted across the lifespan of data. Characterizing RDP in disciplinary contexts is beneficial for providing data stakeholders with practical understanding of RDP necessary to design data curation services which are tailored to researchers’ need. In this paper, we focus on the five most common types of RDP – collecting data, processing data, analyzing data, representing data, and publishing or citing data. First, we compared the distributions of the five types of RDP across disciplines and observed noticeable differences between disciplines. In addition, we examined the characteristics of each type of RDP under different disciplinary contexts, by developing discipline-specific RDP vocabulary employing the tf-idf approach. Based on the common terms as well as the discipline-specific ones, we found that the five types of RDP can be distinctly conceptualized, while each type of RDP varies by disciplines in terms of their action, object, and instrument.
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References
Chao, T.C., Cragin, M.H., Palmer, C.L.: Data practices and curation vocabulary (DPCVocab): an empirically derived framework of scientific data practices and curatorial processes. J. Am. Soc. Inf. Sci. 66, 616–633 (2015)
Gray, J., Liu, D.T., Nieto-Santisteban, M., Szalay, A., DeWitt, D.J., Heber, G.: Scientific data management in the coming decade. SIGMOD Rec. 34, 34–41 (2005)
Schroeder, R.: Big data: towards a more scientific social science and humanities?. In: Society and the Internet. Oxford University Press, Oxford (2014)
Palmer, C.L., Teffeau, L.C., Pirmann, C.M.: Scholarly information practices in the online environment: themes from the literature and implications for library service development. OCLC Research (2009)
Hey, T., Trefethen, A.: The data deluge: an e-science perspective. In: Grid Computing: Making the Global Infrastructure a Reality, pp. 809–824. Wiley Online Library (2003)
Weller, T., Monroe-Gulick, A.: Understanding methodological and disciplinary differences in the data practices of academic researchers. Libr. Hi Tech. 32, 467–482 (2014)
Thoegersen, J.L.: “Yeah, I Guess That’s Data”: data practices and conceptions among humanities faculty. Portal-Libr. Acad. 18, 491–504 (2018)
Borgman, C., Wallis, J.C., Enyedy, N.: Building digital libraries for scientific data: an exploratory study of data practices in habitat ecology. In: Gonzalo, J., Thanos, C., Verdejo, M.F., Carrasco, R.C. (eds.) ECDL 2006. LNCS, vol. 4172, pp. 170–183. Springer, Heidelberg (2006). https://doi.org/10.1007/11863878_15
Ma, R., Xiao, F.: Data practices in digital history. Int. J. Digit. Curation 15, 21 (2020)
Rolland, B., Lee, C.P.: Beyond trust and reliability: reusing data in collaborative cancer epidemiology research. In: Proceedings of the 2013 Conference on Computer Supported Cooperative Work - CSCW ’13, pp. 435. ACM Press, San Antonio, Texas, USA (2013)
Yoon, A.: “Making a square fit into a circle”: researchers’ experiences reusing qualitative data: “Making a Square Fit into a Circle”: researchers’ experiences reusing qualitative data. Proc. Am. Soc. Info. Sci. Tech. 51, 1–4 (2014)
Yoon, A., Kim, Y.: Social scientists’ data reuse behaviors: exploring the roles of attitudinal beliefs, attitudes, norms, and data repositories. Libr. Inf. Sci. Res. 39, 224–233 (2017)
Borgman, C.L.: Big Data, Little Data, No Data: Scholarship in the Networked World. MIT Press (2015)
Walton, D., Zhang, N.: The epistemology of scientific evidence. Artif. Intell. Law. 21, 173–219 (2013). https://doi.org/10.1007/s10506-012-9132-9
Wang, X., Song, N., Zhou, H., Cheng, H.: The representation of argumentation in scientific papers: a comparative analysis of two research areas. J. Assoc. Inf. Sci. Technol. 73(6), 863–878 (2021). https://doi.org/10.1002/asi.24590
Blake, C.: Beyond genes, proteins, and abstracts: Identifying scientific claims from full-text biomedical articles. J. Biomed. Inform. 43, 173–189 (2010). https://doi.org/10.1016/j.jbi.2009.11.001
Liakata, M., Saha, S., Dobnik, S., Batchelor, C., Rebholz-Schuhmann, D.: Automatic recognition of conceptualization zones in scientific articles and two life science applications. Bioinformatics 28, 991–1000 (2012). https://doi.org/10.1093/bioinformatics/bts071
Chao, T.C., Cragin, M.H., Palmer, C.L.: Data practices and curation vocabulary (DPCVocab). http://hdl.handle.net/2142/44032. Last Accessed 12 Nov 2021
Manning, C.D., Raghavan, P., Schutze, H.S.: Term weighting, and the vector space model. Int. Inf. Retr. 109–133 (2008)
This work is supported by the National Natural Science Foundation of China (Grant No. 72174014 and Grant No. 72010107003).
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Lee, S., Li, W., Zhang, P., Wang, J. (2023). Characterizing Data Practices in Research Papers Across Four Disciplines. In: Sserwanga, I., et al. Information for a Better World: Normality, Virtuality, Physicality, Inclusivity. iConference 2023. Lecture Notes in Computer Science, vol 13971. Springer, Cham. https://doi.org/10.1007/978-3-031-28035-1_26
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