(This article was first published on R – christopher lortie, and kindly contributed to R-bloggers)
What makes an open science workshop effective or successful*?
Over the last 15 years, I have had the good fortune to participate in workshops as a student and sometimes as an instructor. Consistently, there were beneficial discovery experiences, and at times, some of the processes highlighted have been transformative. Last year, I had the good fortune to participate in Software Carpentry at UCSB and Software Carpentry at YorkU, and in the past, attend (in part) workshops such as Open Science for Synthesis. Several of us are now deciding what to attend as students in 2017. I have been wondering about the potential efficacy of the workshop model and why it seems that they are so relatively effective. I propose that the answer is expectations. Here is a set of brief lists of observations from workshops that lead me to this conclusion.
*Note: I define a workshop as effective or successful when it provides me with something practical that I did not have before the workshop. Practical outcomes can include tools, ideas, workflows, insights, or novel viewpoints from discussion. Anything that helps me do better open science. Efficacy for me is relative to learning by myself (i.e. through reading, watching webinars, or stuggling with code or data), asking for help from others, taking an online course (that I always give up on), or attending a scientific conference.
Delivery elements of an open science training workshop
Lectures
Tutorials
Demonstrations
Q & A sessions
Hands-on exercises
Webinars or group-viewing recorded vignettes.
Summary expectations from this list: a workshop will offer me content in more than one way unlike a more traditional course offering. I can ask questions right there on the spot about content and get an answer.
Content elements of an open science training workshop
Data and code
Slide decks
Advanced discussion
Experts that can address basic and advanced queries
A curated list of additional resources
Opinions from the experts on the ‘best’ way to do something
A list of problems or questions that need to addressed or solved both routinely and in specific contexts when doing science
A toolkit in some form associated with the specific focus of the workshop.
Summary of expectations from this list: the best, most useful content is curated. It is contemporary, and it would be a challenge for me to find out this on my own.
Pedagogical elements of an open science training workshop
Organized to reflect authentic challenges
Uses problem-based learning
Content is very contemporary
Very light on lecture and heavy on practical application
Reasonably small groups
Will include team science and networks to learn and solve problems
Short duration, high intensity
Will use an open science tool for discussion and collective note taking
Will be organized by major concepts such as data & meta-data, workflows, code, data repositories OR will be organized around a central problem or theme, and we will work together through the steps to solve a problem
There will be a specific, quantifiable outcome for the participants (i.e. we will learn how to do or use a specific set of tools for future work).
Summary of expectations from this list: the training and learning experience will emulate a scientific working group that has convened to solve a problem. In this case, how can we all get better at doing a certain set of scientific activities versus can a group aggregate and summarize a global alpine dataset for instance. These collaborative solving-models need not be exclusive.
Higher-order expectations that summarize all these open science workshop elements
Experts, curated content, and contemporary tools.
Everyone is focussed exclusively on the workshop, i.e. we all try to put our lives on hold to teach and learn together rapidly for a short time.
Experiences are authentic and focus on problem solving.
I will have to work trying things, but the slope of the learning curve/climb will be mediated by the workshop process.
There will be some, but not too much, lecturing to give me the big picture highlights of why I need to know/use a specific concept or tool.
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