DOI: 10.5061/dryad.7wm37pvp7
title: Data from: Designing data science workshops for data-intensive environmental science research

description: Over the last 20 years, statistics preparation has become vital for a
 broad range of scientific fields, and statistics coursework has been
 readily incorporated into undergraduate and graduate programs.
 However, a gap remains between the computational skills taught in
 statistics service courses and those required for the use of statistics in
 scientific research. Ten years after the publication of
 "Computing in the Statistics Curriculum,'' the nature
 of statistics continues to change, and computing skills are more
 necessary than ever for modern scientific researchers. In this
 paper, we describe research on the design and implementation of a
 suite of data science workshops for environmental science
 graduate students, providing students with the skills necessary
 to retrieve, view, wrangle, visualize, and analyze their data
 using reproducible tools. These workshops help to bridge the gap
 between the computing skills necessary for scientific research and the
 computing skills with which students leave their statistics service
 courses. Moreover, though targeted to environmental science graduate
 students, these workshops are open to the larger academic community. As
 such, they promote the continued learning of the computational
 tools necessary for working with data, and provide resources for
 incorporating data science into the classroom.
description: Surveys from Carpentries style workshops the results of which are
 presented in the accompanying manuscript.  Pre- and
 post-workshop surveys for each workshop (<em>Introduction to
 R</em>, <em>Intermediate R</em>, <em>Data
 Wrangling in R</em>, <em>Data Visualization in
 R</em>) were collected via Google Form.  
 The surveys administered for the fall 2018, spring 2019 academic
 year are included
 as <em>pre_workshop_survey </em>and <em>post_workshop_assessment</em> PDF files.  The raw versions of these data are included in the Excel files ending in <em>survey_raw </em>or <em>assessment_raw</em>. The data files whose name includes <em>survey </em>contain raw data from pre-workshop surveys and the data files whose name includes <em>assessment</em> contain raw data from the post-workshop assessment survey. The annotated RMarkdown files used to clean the pre-workshop surveys and post-workshop assessments are included as <em>workshop_survey_cleaning </em>and <em>workshop_assessment_cleaning</em>, respectively.  The cleaned pre- and post-workshop survey data are included in the Excel files ending in <em>clean</em>.  The summaries and visualizations presented in the manuscript are included in the <em>analysis </em>annotated<em> </em>RMarkdown file. 
description: To clean the data: Open the
 <em>workshop_survey_cleaning</em> and
 <em>workshop_assessment_cleaning</em> annotated RMarkdown
 files.  Load in the data for each respective
 RMarkdown file.  Execute the data cleaning R code
 implemented in each RMarkdown file.  The resulting
 cleaned datasets are the data included whose suffix is
 <em>clean</em>.   To recreate
 the visualizations and summaries included in the manuscript:
 Open the <em>analysis</em> annotated
 RMarkdown file. Read in the cleaned pre- and
 post-workshop surveys -- merged to one Excel file each (survey &
 assessment).  Execute the R code implemented in the
 RMarkdown file, where each section corresponds to a summary or
 visualization presented in the manuscript.