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Articles

Designing Data Science Workshops for Data-Intensive Environmental Science Research

ORCID Icon, &
Pages S83-S94 | Published online: 22 Mar 2021

References

  • American Statistical Association Undergraduate Guidelines Workgroup (2014), 2014 Curriculum Guidelines for Undergraduate Programs in Statistical Science, Alexandria, VA: American Statistical Association.
  • Andelman, S. J., Bowles, C. M., Willig, M. R., and Waide, R. B. (2004), “Understanding Environmental Complexity Through a Distributed Knowledge Network,” BioScience, 54, 240–246. DOI: 10.1641/0006-3568(2004)054[0240:UECTAD.2.0.CO;2]
  • ASA GAISE College Group (2016), Guidelines for Assessment and Instruction in Statistics Education College Report 2016, Alexandria, VA: American Statistical Association.
  • Baumer, B. (2015), “A Data Science Course for Undergraduates: Thinking With Data,” The American Statistician, 69, 334–342. DOI: 10.1080/00031305.2015.1081105.
  • Baumer, B., Cetinkaya-Rundel, M., Bray, A., Loi, L., and Horton, N. J. (2014), “R Markdown: Integrating a Reproducible Analysis Tool Into Introductory Statistics,” Technology Innovations in Statistics Education, 8, 1–22.
  • Baumer, B. S., Horton, N. J., and Wickham, H. (2015), “Setting the Stage for Data Science: Integration of Data Management Skills in Introductory and Second Courses in Statistics,” CHANCE, 28, 40–50. DOI: 10.1080/09332480.2015.1042739.
  • Cassey, P., and Blackburn, T. M. (2006), “Reproducibility and Repeatability in Ecology,” BioScience, 56, 98. DOI: 10.1641/0006-3568(2006)56[958:RARIE.2.0.CO;2]
  • Çetinkaya-Rundel, M. (2018), “Intro Stats, Intro Data Science: Do We Need Both?,” Presented at the 2018 Joint Statistical Meetings.
  • Çetinkaya-Rundel, M., and Rundel, C. (2018), “Infrastructure and Tools for Teaching Computing Throughout the Statistical Curriculum,” The American Statistician, 72, 58–65. DOI: 10.1080/00031305.2017.1397549.
  • Cobb, G. (2015), “Mere Renovation Is Too Little Too Late: We Need to Rethink Our Undergraduate Curriculum From the Ground Up,” The American Statistician, 69, 266–282. DOI: 10.1080/00031305.2015.1093029.
  • Cobb, P. A., Confrey, J., diSessa, A. A., Lehrer, R., and Schauble, L. (2003), “Design Experiments in Educational Research,” Educational Researcher, 32, 9–13. DOI: 10.3102/0013189X032001009.
  • Dodds, Z., Alvarado, C., Kuenning, G., and Libeskind-Hadas, R. (2007), “Breadth-first CS-1 for Scientists,” in Proceedings of the 2007 Innovation and Technology in Computer Science Education (ITiCSE), ACM.
  • Dodds, Z., Libeskind-Hadas, R., Alvarado, C., and Kuenning, G. (2008), “Evaluating a Breadth-First CS-1 for Scientists,” in Proceedings of the 2008 Special Interest Group on Computer Science Education (SIGCSE), ACM.
  • Eglen, S. J. (2009), “A Quick Guide to Teaching R Programming to Computational Biology Students,” PLOS Computational Biology, 5, 1–4. DOI: 10.1371/journal.pcbi.1000482.
  • Ellison, A. M. (2010), “Repeatability and Transparency in Ecological Research,” Ecology, 91, 2536–2539. DOI: 10.1890/09-0032.1.
  • Ernest, M., Brown, J., Valone, T., and White, E. P. (2018), “Portal Project Teaching Database, available at DOI: 10.6084/m9.figshare.1314459..
  • Fishman, B. J., Penuel, W. R., Allen, A.-R., Cheng, B. H., and Sabelli, N. (2013), “Design-Based Implementation Research: An Emerging Model for Transforming the Relationship of Research and Practice,” Yearbook of the National Society for the Study of Education, 112(2), 136–156.
  • Gould, R. (2010), “Statistics and the Modern Student,” International Statistics Reveiw, 78, 297–315. DOI: 10.1111/j.1751-5823.2010.00117.x.
  • Green, J. L., Hastings, A., Arzberger, P., Ayala, F. J., Cottingham, K. L., Cuddington, K., Davis, F., Dunne, J. A., Fortin, M.-J., Gerber, L., and Neubert, M. (2005), “Complexity in Ecology and Conservation: Mathematical, Statistical, and Computational Challenges,” BioScience, 55, 501–510. DOI: 10.1641/0006-3568(2005)055[0501:CIEACM.2.0.CO;2]
  • Hampton, S. E., Jones, M. B., Wasser, L. A., Schildhauer, M. P., Supp, S. R., Brun, J., Hernandez, R. R., Boettiger, C., Collins, S. L., Gross, L. J., Fernandez, D. S., Budden, A., White, E. P., Teal, T. K., Labou, S. G., and Aukema, J. E. (2017), “Skills and Knowledge for Data-Intensive Environmental Research,” BioScience, 67, 546–557. DOI: 10.1093/biosci/bix025.
  • Hardin, J., Hoerl, R., Horton, N. J., Nolan, D., Baumer, B., Hall-Holt, O., Murrell, P., Peng, R., Roback, P., Lang, D. T., and Ward, M. D. (2015), “Data Science in Statistics Curricula: Preparing Students to ‘Think With Data’,” The American Statistician, 69, 343–353. DOI: 10.1080/00031305.2015.1077729.
  • Hastings, A., Arzberger, P., Bolker, B., Collins, S., Ives, A. R., Johnson, N. A., and Palmer, M. A. (2005), “Quantitative Bioscience for the 21st Century,” BioScience, 55, 511–517. DOI: 10.1641/0006-3568(2005)055[0511:QBFTSC.2.0.CO;2]
  • He, X., Madigan, D., Yu, B., and Wellner, J. (2019), “Statistics at a Crossroads: Who Is for the Challenge,” Technical Report, The National Science Foundation.
  • Henry, L., and Wickham, H. (2020), “purrr: Functional Programming Tools,” R package version 0.3.4.
  • Hernandez, R. R., Mayernik, M. S., Murphy-Mariscal, M. L., and Allen, M. F. (2012), “Advanced Technologies and Data Management Practices in Environmental Science: Lessons From Academia,” BioScience, 62, 1067–1076. DOI: 10.1525/bio.2012.62.12.8.
  • Horton, N. J., and Hardin, J. S. (2015), “Teaching the Next Generation of Statistics Students to ‘Think With Data’: Special Issue on Statistics and the Undergraduate Curriculum,” The American Statistician, 69, 259–265. DOI: 10.1080/00031305.2015.1094283.
  • Kaplan, D. (2018), “Teaching Stats for Data Science,” The American Statistician, 72, 89–96. DOI: 10.1080/00031305.2017.1398107.
  • Kelling, S., Hochachka, W. M., Fink, D., Riedewald, M., Caruana, R., Ballard, G., and Hooker, G. (2009), “Data-Intensive Science: A New Paradigm for Biodiversity Studies,” BioScience, 59, 613–620. DOI: 10.1525/bio.2009.59.7.12.
  • Kitzes, J., Turek, D., and Deniz, F. (2018), The Practice of Reproducible Research: Case Studies and Lessons From the Data-Intensive Sciences, Okland, CA: University of California Press.
  • Lai, J., Lortie, C. J., Muenchen, R. A., Yang, J., and Ma, K. (2019), “Evaluating the Popularity of R in Ecology,” Ecosphere, 10, e02567. DOI: 10.1002/ecs2.2567.
  • Manyika, J., Chui, M., Brown, B., Bughin, J., Dobbs, R., Roxburgh, C., and Byers, A. H. (2011), “Big Data: The Next Frontier for Innovation, Competition, and Productivity,” Technical Report, McKinsey Global Institute.
  • McNamara, A. (2018), “Imagining the Future of Statistical Education Software,” in Proceedings of the 10th International Conference on Teaching Statistics (ICOTS).
  • Miles, M. B., Huberman, A. M., and Saldana, J. (2014), Qualitative Data Analysis: A Methods Sourcebook (3rd ed.), Thousand Oaks, CA: SAGE.
  • Mislan, K., Heer, J. M., and White, E. P. (2016), “Elevating the Status of Code in Ecology,” Trends in Ecology & Evolution, 31, 4–7.
  • Morrison, C., Wardle, C., and Castley, J. (2016), “Repeatability and Reproducibility of Population Viability Analysis (PVA) and the Implications for Threatened Species Management,” Frontiers in Ecology and Evolution, 4, 98. DOI: 10.3389/fevo.2016.00098.
  • National Academies of Sciences, Engineering, and Medicine (2018), Data Science for Undergraduates: Opportunities and Options, Washington, DC: The National Academies Press.
  • Nolan, D., and Perrett, J. (2016), “Teaching and Learning Data Visualization: Ideas and Assignments,” The American Statistician, 70, 260–269. DOI: 10.1080/00031305.2015.1123651.
  • Nolan, D., and Temple Lang, D. (2010), “Computing in the Statistics Curriculum,” The American Statistician, 64, 97–107. DOI: 10.1198/tast.2010.09132.
  • Nolan, D., and Temple Lang, D. (2015), “Explorations in Statistics Research: An Approach to Expose Undergraduates to Authentic Data Analysis,” The American Statistician, 69, 292–299.
  • O’Neill, D. K. (2012), “Designs That Fly: What the History of Aeronautics Tells Us About the Future of Design-Based Research in Education,” International Journal of Research and Method in Education, 35, 119–140.
  • Powers, S. M., and Hampton, S. E. (2019), “Open Science, Reproducibility, and Transparency in Ecology,” Ecological Applications, 29, e01822. DOI: 10.1002/eap.1822.
  • Ross, Z., Wickham, H., and Robinson, D. (2017), “Declutter Your R Workflow With Tidy Tools,” Technical Report, PeerJ Preprints.
  • RStudio Team (2015a), RStudio Cloud, Boston, MA: RStudio, Inc.
  • RStudio Team (2015b), RStudio: Integrated Development Environment for R, Boston, MA: RStudio, Inc.
  • Strasser, C. A., and Hampton, S. E. (2012), “The Fractured Lab Notebook: Undergraduates and Ecological Data Management Training in the United States,” Ecosphere, 3, 1–18. DOI: 10.1890/ES12-00139.1.
  • Teal, T. K., Cranston, K. A., Lapp, H., White, E., Wilson, G., Ram, K., and Pawlik, A. (2015), “Data Carpentry: Workshops to Increase Data Literacy for Researchers,” International Journal of Digital Curation, 10, 343–353. DOI: 10.2218/ijdc.v10i1.351.
  • Theobold, A., and Hancock, S. (2019), “How Environmental Science Graduate Students Acquire Statistical Computing Skills,” Statistics Education Research Journal, 18, 68–85.
  • Theobold, A. S., Hancock, S. A., and Mannheimer, S. (2020), “Data From: Designing Data Science Workshops for Data-Intensive Environmental Science Research,” Dryad, available at DOI: 10.5061/dryad.7wm37pvp7.
  • Thomas, W., and Naupaka, Z. (eds). (2016), “Software Carpentry: R for Reproducible Scientific Analysis.” Version 2016.06, available at https://github.com/swcarpentry/r-novice-gapminder.
  • Wickham, H. (2014), “Tidy Data,” The Journal of Statistical Software, 59, 1–23. DOI: 10.18637/jss.v059.i10.
  • Wickham, H. (2016), ggplot2: Elegant Graphics for Data Analysis, New York: Springer-Verlag.
  • Wickham, H., Averick, M., et al. (2019), “Welcome to the tidyverse,” Journal of Open Source Software, 4, 1686. DOI: 10.21105/joss.01686.
  • Wickham, H., and Grolemund, G. (2017), R for Data Science, Sebastopol, CA: O’Reilly.
  • Wickham, H., and Henry, L. (2018), “tidyr: Tidy Messy Data,” R Package Version 0.8.0.
  • Wickham, H., Romain François, R., Henry, L., and Muller, K. (2018), “dplyr: A Grammar of Data Manipulation,” R Package Version 0.7.5.
  • Wilson, G. (2006), “Software Carpentry: Getting Scientists to Write Better Code by Making Them More Productive,” Computing in Science & Engineering, 8, 66–69.
  • Wilson, G., Alvarado, C., Campbell, J., Landau, R., and Sedgewich, R. (2008), “CS-1 for Scientists,” in Proceedings of the 2008 Special Interest Group on Computer Science Education (SIGCSE), ACM, pp. 36–37.
  • Wing, J. (2006), “Computational Thinking,” Communications of the ACM, 49, 33–35. DOI: 10.1145/1118178.1118215.
  • Word, K. R., Jordan, K., Becker, E., Williams, J., Reynolds, P., Hodge, A., Belkin, M., Marwick, B., and Teal, T. (2017), “When Do Workshops Work? A Response to the ‘Null Effects’ Paper From Feldon et al.,” Technical Report, Software Carpentry.
  • Zhian N. Kamvar. (2018), “datacarpentry/R-ecology-lesson: Data Carpentry: Data Analysis and Visualization in R for Ecologists,” Ana Costa Conrado, M.V. F. Auriel, S. Brian, and M. Francois, eds.