Understanding Research Data Practices of Civil and Environmental Engineering Graduate Students

Authors

  • Xiaoju Chen Carnegie Mellon University
  • Emily Dommermuth University of Colorado Boulder
  • Jessica G. Benner Carnegie Mellon University
  • Rebecca Kuglitsch University of Colorado Boulder
  • Abbey B. Lewis University of Colorado Boulder
  • Matthew R. Marsteller Carnegie Mellon University
  • Katherine Mika Harvard University
  • Sarah Young Carnegie Mellon University

DOI:

https://doi.org/10.29173/istl2678

Abstract

Research data management is essential for high-quality reproducible research, yet relatively little is known about how research data management is practiced by graduate students in Civil and Environmental Engineering (CEE). Prior research suggests that faculty in CEE delegate research data management to graduate students, prompting this investigation into how graduate students practice data management. This study uses semi-structured interviews and qualitative content analysis to explore how CEE graduate students work with data and practice data management in their research, as well as what resources and support would meet their needs. Many respondents touched on data collection, data management, disseminating research outputs, and collaboration and learning in their interviews. Several themes emerged from the interviews: data quality as a concern, as many CEE graduate students rely on secondary data for research; a gap between values and enacted practices; a connection between disseminating data and reproducibility; and a reliance on peer and self-directed learning for data management education. Based on these themes, the study recommends strategies for librarians and others on campus to better support CEE graduate student research data practices.

Downloads

Download data is not yet available.

References

Antell, K., Foote, J. B., Turner, J., & Shults, B. (2014). Dealing with data: Science librarians’ participation in data management at Association of Research Libraries institutions. College & Research Libraries, 75(4), 557–574. https://doi.org/10.5860/crl.75.4.557

Beaver, D. Deb. (2001). Reflections on scientific collaboration (and its study): Past, present, and future. Scientometrics, 52(3), 365–377. https://doi.org/10.1023/A:1014254214337

Cai, L., & Zhu, Y. (2015). The challenges of data quality and data quality assessment in the big data era. Data Science Journal, 14, 1–10. https://doi.org/10.5334/dsj-2015-002

Carlson, J., Fosmire, M., Miller, C. C., & Nelson, M. S. (2011). Determining data information literacy needs: A study of students and research faculty. portal: Libraries and the Academy, 11(2), 629–657. https://doi.org/10.1353/pla.2011.0022

Carlson, J., & Stowell-Bracke, M. (2013). Data management and sharing from the perspective of graduate students: An examination of the culture and practice at the water quality field station. portal: Libraries and the Academy, 13(4), 343–361. https://doi.org/10.1353/pla.2013.0034

Carnegie Mellon University. (2020). Enrollment Fall 2021. Institutional Research and Analysis. http://www.cmu.edu/ira/Enrollment/index.html

Chen, X., Benner, J., Young, S., & Marsteller, M. R. (2019, June 15-19). Understanding the research practices and service needs of civil and environmental engineering researchers – A grounded theory approach [Paper presentation]. 2019 ASEE Annual Conference & Exposition, Tampa, FL, United States. https://doi.org/10.18260/1-2--33483

Cho, J. Y., & Lee, E.-H. (2014). Reducing confusion about grounded theory and qualitative content analysis: Similarities and differences. The Qualitative Report, 19(32), 1–20. https://doi.org/10.46743/2160-3715/2014.1028

Choudhury, S. (2008). Case study in data curation at Johns Hopkins University. Library Trends, 57(2), 211–220. https://doi.org/10.1353/lib.0.0028

Cooper, D., Springer, R., Benner, J., Bloom, D., Carrillo, E., Carroll, A., Chang, B., Chen, X., Daix, E., Dommermuth, E., Figueriredo, R., Haas, J., Hafner, C., Henshilwood, A., Krogman, A., Kuglitsch, R., Lanteri, S., Lewis, A., Li, L., ... Yu, S. H. (2019). Supporting the changing research practices of civil and environmental engineering scholars. Ithaka S+R. https://doi.org/10.18665/sr.310885

Cox, A. M., Kennan, M. A., Lyon, L., & Pinfield, S. (2017). Developments in research data management in academic libraries: Towards an understanding of research data service maturity. Journal of the Association for Information Science and Technology, 68(9), 2182–2200. https://doi.org/10.1002/asi.23781

Cox, A. M., & Pinfield, S. (2014). Research data management and libraries: Current activities and future priorities. Journal of Librarianship and Information Science, 46(4), 299–316. https://doi.org/10.1177/0961000613492542

FORCE11. (2014). The FAIR data principles. FORCE11: The Future of Research Communications and e-Scholarship. https://force11.org/info/the-fair-data-principles/

Gardner, S. K. (2007). “I heard it through the grapevine”: Doctoral student socialization in chemistry and history. Higher Education, 54(5), 723–740. https://doi.org/10.1007/s10734-006-9020-x

Ivey, S. S., Best, R. M., Camp, C. V., & Palazolo, P. J. (2012, June 10-13). Transforming a civil engineering curriculum through GIS integration [Paper presentation]. 2012 ASEE Annual Conference & Exposition, San Antonio, TX. United States. https://doi.org/10.18260/1-2--22130

Jahnke, L., Asher, A. D., Keralis, S. D. C., & Henry, C. (2012). The problem of data. Council on Library and Information Resources and Digital Library Federation. http://www.clir.org/pubs/reports/pub154

Johnston, L., & Jeffryes, J. (2014). Data management skills needed by structural engineering students: Case study at the University of Minnesota. Journal of Professional Issues in Engineering Education and Practice, 140(2), 1–8. https://doi.org/10.1061/(ASCE)EI.1943-5541.0000154

Kim, J. (2013). Data sharing and its implications for academic libraries. New Library World, 114(11/12), 494–506. https://doi.org/10.1108/NLW-06-2013-0051

Kuglitsch, R., Dommermuth, E., & Lewis, A. (2018). Research practices of civil and environmental engineering scholars. University Libraries University of Colorado Boulder. https://scholar.colorado.edu/libr_facpapers/132

Lage, K., Losoff, B., & Maness, J. (2011). Receptivity to library involvement in scientific data curation: A case study at the University of Colorado Boulder. portal: Libraries and the Academy 11(4), 915-937. https://doi.org/10.1353/pla.2011.0049

Latham, B. (2017). Research data management: Defining roles, prioritizing services, and enumerating challenges. The Journal of Academic Librarianship, 43(3), 263–265. https://doi.org/10.1016/j.acalib.2017.04.004

Montgomery, J. L., Harmon, T., Kaiser, W., Sanderson, A., Hass, C. N., Hooper, R., Minsker, B., Schnoor, J., Clesceri, N., Graham, W., & Brezonik, P. (2007). The WATERS Network: An integrated environmental observatory network for water research. Environmental Science & Technology, 41(19), 6642–6647. https://doi.org/10.1021/es072618f

Newton, M. P., Miller, C. C., & Bracke, M. S. (2010). Librarian roles in institutional repository data set collecting: Outcomes of a research library task force. Collection Management, 36(1), 53–67. https://doi.org/10/d65c3b

Pasek, J. E., & Mayer, J. (2019). Education needs in research data management for science-based disciplines: Self-assessment surveys of graduate students and faculty at two public universities. Issues in Science and Technology Librarianship, 92. https://doi.org/10.29173/istl12

Pejşa, S., & Song, C. (2013, July 22-26). Publishing earthquake engineering research data [Paper presentation]. Proceedings of the 13th ACM/IEEE-CS Joint Conference on Digital Libraries, Indianapolis, IN, United States. https://doi.org/10.1145/2467696.2467758

Petters, J. L., Brooks, G. C., Smith, J. A., & Haas, C. A. (2019). The Impact of targeted data management training for field research projects – A case study. Data Science Journal, 18(43), 1–7. https://doi.org/10.5334/dsj-2019-043

Pinfield, S., Cox, A. M., & Smith, J. (2014). Research data management and libraries: Relationships, activities, drivers and influences. PLoS One, 9(12), 1–28. https://doi.org/10.1371/journal.pone.0114734

Radecki, J., & Springer, R. (2020). Research data services in US higher education: A web-based inventory. Ithaka S+R. https://doi.org/10.18665/sr.314397

Rolando, L., Carlson, J., Hswe, P., Parham, S. W., Westra, B., & Whitmire, A. L. (2015). Data management plans as a research tool. Bulletin of the Association for Information Science and Technology, 41(5), 43–45. https://doi.org/10.1002/bult.2015.1720410510

Sadiq, S., & Indulska, M. (2017). Open data: Quality over quantity. International Journal of Information Management, 37(3), 150–154. https://doi.org/10.1016/j.ijinfomgt.2017.01.003

Sapp Nelson, M. (2015). Data strategies: The research says. Purdue University. https://10.5703/1288284315525

Satheesan, S. P., Alameda, J., Bradley, S., Dietze, M., Galewsky, B., Jansen, G., Kooper, R., Kumar, P., Lee, J., Marciano, R., Marini, L., Minsker, B. S., Navarro, C. M., Schmidt, A., Slavenas, M., Sullivan, W. C., Zhang, B., Zhao, Y., Zharnitsky, I., & McHenry, K. (2018, July 22-26). Brown Dog: Making the digital world a better place, a few files at a time [Paper presentation]. Proceedings of the Practice and Experience on Advanced Research Computing, Pittsburgh, PA, United States. https://doi.org/10.1145/3219104.3219132

Schröder, W., & Nickel, S. (2020). Research data management as an integral part of the research process of empirical disciplines using landscape ecology as an example. Data Science Journal, 19(26), 1–14. https://doi.org/10.5334/dsj-2020-026

Shahi, A., Haas, C. T., West, J. S., & Akinci, B. (2014). Workflow-based construction research data management and dissemination. Journal of Computing in Civil Engineering, 28(2), 244–252. https://doi.org/10.1061/(ASCE)CP.1943-5487.0000251

Sharma, S., & Qin, J. (2014). Data management: Graduate student’s awareness of practices and policies. Proceedings of the American Society for Information Science and Technology, 51(1), 1–3. https://doi.org/10.1002/meet.2014.14505101130

Tang, R., & Hu, Z. (2019). Providing research data management (RDM) services in libraries: Preparedness, roles, challenges, and training for RDM practice. Data and Information Management, 3(2), 84–101. https://doi.org/10.2478/dim-2019-0009

Trisovic, A., Mika, K., Boyd, C., Feger, S., & Crosas, M. (2021). Repository approaches to improving the quality of shared data and code. Data, 6(2), 15. https://doi.org/10.3390/data6020015

UNESCO. (2020). Open science: Making science more accessible, inclusive and equitable for the benefit of all. https://en.unesco.org/science-sustainable-future/open-science

University of Colorado Boulder. (2020). University of Colorado Boulder IR. Tableau Public. https://public.tableau.com/app/profile/university.of.colorado.boulder.ir

U.S. News & World Report. (2021). Best engineering schools ranked in 2021. https://www.usnews.com/best-graduate-schools/top-engineering-schools/eng-rankings

Valentino, M., & Boock, M. (2015). Data management for graduate students: A case study at Oregon State University. Practical Academic Librarianship, 5(2), 77–91.

Wiley, C. A., & Kerby, E. E. (2018). Managing research data: Graduate student and postdoctoral researcher perspectives. Issues in Science and Technology Librarianship, 89. https://doi.org/10.5062/F4FN14FJ

Witt, M. (2012). Co-designing, co-developing, and co-implementing an institutional data repository service. Journal of Library Administration, 52(2), 172–188. https://doi.org/10.1080/01930826.2012.655607

Downloads

Published

2022-08-16

How to Cite

Chen, X., Dommermuth, E., Benner, J. G. ., Kuglitsch, R., Lewis, A. B. ., Marsteller, M. R. ., … Young, S. . (2022). Understanding Research Data Practices of Civil and Environmental Engineering Graduate Students. Issues in Science and Technology Librarianship, (100). https://doi.org/10.29173/istl2678

Issue

Section

Refereed Articles
Share |