Designing Data Science Workshops for Data-Intensive Environmental Science Research

Figures & data

Fig. 1 Data analysis cycle (Wickham and Grolemund 2017).

Table 1 Number of faculty members requested for participation and interviewed, by department.

Department	Faculty invited	Faculty
		interviewed
Animal & Range Sciences	7	2
Ecology	15	8
Land Resources and Environmental Sciences	24	8
Plant Sciences & Plant Pathology	15	5

Fig. 2 RStudio Cloud workspace environment for Introduction to R workshop. Every workshop works in an RStudio project, containing a master R Markdown file, a data folder containing the data used in the workshop, and the handout produced for attendees.

Fig. 3 Number of workshop attendees during the 2018–2019 academic year, by department and current occupation, selected from an itemized list of campus departments and occupations in the pre-workshop survey.

Table 2 Workshop attendees’ responses (n = 121) to the pre-workshop survey question “What are your previous statistical experience(s)? List course names,” thematically organized based on content of the course.

Stat courses	Participants
Introductory Statistics	46	(38%)
Applied Statistics	42	(35%)
None	24	(20%)
Discipline Specific Introductory Statistics	20	(17%)
Intermediate Statistics	10	(8%)
Experimental Design	8	(7%)
Probability Theory	6	(5%)
Statistical Computing	3	(3%)
Sampling	3	(3%)
Biostatistics	2	(2%)
Spatial Analysis	2	(2%)
Econometrics	1	(<1%)
Time Series Analysis	1	(<1%)