HBP survey plots

This repository hosts the survey plots of the paper Interdisciplinary and collaborative training in neuroscience: Insights from the Human Brain Project Education Programme. The figures 8-11 in the paper are created by the Python file plot_survey_results.py. The plots are controlled via arguments which can be specified via the terminal (for details see Usage).

Figure 3 is created by the script pi_survey_plot.py and visualizes the survey results conducted among speakers and tutors at HBP SGA3 education events.

The script deviation_plots.py visualizes the figure 1 in the annex.These are the YRE survey votes deviations depicted as box plots.

Another file is plot_teaslides_survey.py, which was used in an early version of the paper, and is uploaded here for completeness. It is a single figure and only shows the participants' (average) Overall Satisfaction pooled from all Tea and Slides online events.

Description

The script plot_survey_results.py plots a figure depending on the input:

• If the input is SGA1-2_summary2.xlsx, the figure depicts the pooled average results from participants across several HBP conferences conducted in SGA1 and SGA2. It has three subplots (Benefit for participant, Quality of Lectures and Overall Satisfaction) and visualizes the survey outcome of several questions as horizontal stacked bars.
• If the input is SGA3_summary.xlsx the plots shows the outcomes for the SGA3 phase. This plot has four subfigures (Benefit for participant, Quality, Virtual format and Overall Satisfaction).
• The plot with the input YRE Surveys_SGA1-2_summary.xlsx displays the average survey results of the Young Researcher Events held in SGA1 and SGA2. The structure follows the plot as for the HBP conferences in SGA1 and SGA2.
• Similarly, the plot with the input YRE Surveys_SGA3_summary.xlsx shows the results of the Young Researcher Events in SGA3.

Requirements

There are not many requirements to run the code, the additional, three Python packages are:

• matplotlib
• pandas
• seaborn

Optionally to create a Likert graph (can be installed via pip install plot-likert):

• plot_likert

Usage

HBP Student Conference survey

The file plot_survey_results.py requires the input data SGA1-2_summary2.xlsx, SGA3_summary.xlsx YRE Surveys_SGA1-2_summary.xlsx or YRE Surveys_SGA3_summary.xlsx. These files are located in the main repository. To load one of the files and plot a figure use the argument --filename, e.g. python plot_survey_results.py --filename SGA1-2_summary2.xlsx.

• --filename, requires the name of the file as a string. A path can also be supplied. If no filename is given then the script assumes the file SGA1-2_summary2.xlsx.
• --total_mean, if specified it prints out the average response rate of the participants.
• --total_std, if specified it prints out the standard deviation of the participants' response rate.
• --plot_type, defines the type of the plot. Available are stacked plotting horizontal stacked histograms or likert for Likert graphs. If not specified the default is stacked.
• --percentage, defines if percentages or absolute values will be printed on the bars.
• Thus, if no arguments are given, i.e. running only python plot_survey_results.py, the script assumes that python plot_survey_results.py --filename SGA1-2_summary2.xlsx is called.

PI survey

The script pi_survey_plot.py requires the input data PI Survey_Data.xlsx (hard-coded) and has only one argument:

• --percentage: Defines whether percentage or absolute value will be visualized on the bars.

Deviation plots

The script deviation_plots.py requires YRE Surveys_SGA1-2_summary.xlsx or YRE Surveys_SGA3_summary.xlsx as input. This script has an additional argument success_rate, which plots all votes as box plots. However, the resulting plot is not added to the paper.

• --filename, requires the name of the file as a string. A path can also be supplied. If no filename is given then the script assumes the file SGA1-2_summary2.xlsx.
• --success_rate, a boxplot illustrating the total success rate. This argument is optional.
• --save, indicates whether the plots should be saved. Default is False.

For the final version of the manuscript we decided not use the plot for the tea and slides survey. The script plot_teaslides_survey.py follows the same command scheme as above. The file requires the input HBPTeaandSlides_overall_satisfaction.xlsx.

Figure specific commands

Below are the arguments to create the figures from the manuscript:

• Fig.3: pi_survey_plot.py --percentage
• Fig.8: python plot_survey_results.py --filename SGA1-2_summary2.xlsx --plot_type stacked --percentage
• Fig.9: python plot_survey_results.py --filename SGA3_summary.xlsx --percentage
• Fig.10: python plot_survey_results.py --filename YRE\ Surveys_SGA1-2_summary.xlsx --percentage
• Fig.11: python plot_survey_results.py --filename YRE\ Surveys_SGA3_summary.xlsx --percentage
• Annex Fig.1 (top): python deviation_plots.py --filename YRE\ Surveys_SGA1-2_summary.xlsx
• Annex Fig.1 (bottom): python deviation_plots.py --filename YRE\ Surveys_SGA3_summary.xlsx