Feedback on the first “Fair in biological practices” course, run 19th-22th October 2021

Public page for workshop is here

Summary

This course covering FAIR and Open Research practices for biological sciences stretched across four afternoons (4 - 4 1/2h each afternoon) and was received very well among the audience. The fantastic feedback received, summarised below, reflects the efforts of the whole team involved in putting together and hosting the first run of the course.

Based on suggestions from users and discussions with the tutors and helpers, here is our general feedback, and some personal suggestions on what should be changed in the future.

• the exercises worked well and students were generally engaged
• however, some students likely prefer to type their opinions and thoughts as oppose to speak up
• the speed was unanimously good
• the selection of topics and discussed resources proved to be broad and extensive
• 10min breaks after each episode to allow for enough time to get away from the screen
• the introduction to the course and online delivery was slow (20min) and should be streamlined next time to keep people engaged
• currently the workshop containes an episode targeting UoE studes and focused on avaialble resources at the UoE. Half an hour parallel sessions on topics like collaboration or anonymisation are considered once this course go beyond UoE attendees

Teaching outcome

Below there are students statements what they are planning to do in their future journey to be FAIRproductive

• For each paper I publish I will make a Zenodo deposit with all the data files
• I’m going to take a very deep breath and make a lot of changes starting with using GitHub for all code instead of using folders
• Proactively think about how reusable and sharable my code is. Don’t just think about myself.
• Start using ELN
• Write data management plan for my PhD
• Reorganise my folder structure
• Finally make a personalised commit message template
• Attempt to start using an ELN. Review and restructure current data storage and add read me files.
• Write protocols on protocols.io
• Back up my data at three different locations
• will practice to make a data management plan
• Proactively think about data storage and sharing as I’m generating data
• Think about how the structure of data management in general.
• Stop putting the date at the beginning of all my filenames
• I will never put the date at the beginning of any folders
• I’d like to use Jupyter notebooks for R code because it makes it very easy to describe my work using RMarkdown. But in the meantime I’m going to start using RMarkdown using RStudio instead of just writing R code and descriptions in separate folders
• Write more metadata for others to understand my work
• Think as a user of my data instead of just a creator
• Create templates for code I have written over the last year

Detailed comments

What did you like:

• Well rounded consideration of open science
• Very clear slides
• Good links to available resources
• Fair and balanced, areas which were personal opinion were made clear
• The pad was great - motivated participants to answer and get involved
• engaging delivery
• great examples (conversion from PDF to excel was a great exemplar of where things go wrong!)
• Mix of lexture context and exercises was really balanced
• Explanation of FAIR
• The exercises in groups
• Very interactive
• Keep you busy and not boring
• The collaborative pad is amazing!
• Did not expect to earn anything new regarding bash. I was plesantly suprised.
• Information about metadata
• The tempo helps keep me focused on the topics
• I do use protocols io and benchling, but this lesson really encouraged me to use it more
• It was great that lots of resources and softwares were mentioned
• gave many examples
• good examples and logic for file naming…something I have struggled with in the past
• Learned about new resources I wasn’t aware of
• Learned ways to improve my current system
• I know about the digital laboratory notebook for the first time and considered introducing it to my laboratory
• Explanation of the jupyter notebooks was very helpful. It’s something I would like to start using for all my analyses to help my PI and anyone else understand what I’m doing. I had read about them but thought they were complicated…this helped illustrate how easy and powerful they are to use
• Seeing how people who work in different disciplines work

What can we improve:

• The overall course structure was really clear (the theme for each day) but I couldn’t see a clear structure within each day?
• It wasn’t so clear when to expect breaks (and 9 minutes with an exercise to do isn’t really long enough to get away from the screen)
• Participation in my break out group was very poor. Only two people really participating
• A couple of times some of the most contentious issues (like whether data sharing can lead to scooping) were navigated without discussing the pros and cons.
• Allow time for breakout groups to introduce each other to establish trust, context etc
• Introducing the participants (we tried that it takes too long)
• An extra break
• Four hours looking at a screen feels longer than I thought
• a list of the resources mentioned (OpenRefine, Antname?, etc) (those are actually available in students notebooks and the course materials)
• Data transfer (which is part of data management)? secure sharing of data?

If the lessons could be 5 minutes longer, what would you add or spend more time on:

• The good, The bad and the ugly of coding practices
• Ontologies
• Re-using other people’s code

** What topics can we shorten:** * excel * jupyter notebooks (that has been vetoed by others)