When some months ago somebody from the University College Dublin invited me to give a 40 minutes seminar on Open Science, I enthusiastically accepted without realizing how difficult it has become, in 2020, with all the amazing things going on around Open Science, to give a proper introduction to Open Research practices in less than an hour.
A couple of weeks after the event, I decided to write a post to highlight what I believe are the absolute key points that have to be part of any broad introduction to Open Science, so here we come.
First and foremost, I always find it useful to tell the audience what we talk about, when we talk about Open Science: the majority of people will definitely know what is the subject at hand, but I believe it's essential to highlight the multiple and somehow diverse facets of it. For quite some time, now, I use the definition created by the Accelerate Open Science project, because I believe it successfully captures these different components.
|Definition of Open Science from the Accelerate Open Science project.|
After this introduction, I like to stress the fact that we do not invest time, efforts, resources, to talk about Open Science for the sake of it, but because it is the only type of Science we should all be doing if we want to have a chance at a Sustainable Future. On top of this, open research practices can definitely help with some of the issues that science has faced in the last decade and still is facing: p-hacking, lack of replication studies, publication bias, lack of data, low statistical power, etc.
Then, I start an introduction to the core of it all: Open Research Practices; here, I could spend hours talking about all sorts of things, of course, but when I don't have enough time available (as for the seminar in Dublin), I necessarily need to pick some topics and leave something else out.
What I have seen to work the best so far is to start with an overview of the current scholarly publishing system: this will resonate with almost everybody in the audience (we all have had to start writing and publishing papers, at one point or another), and will serve as an eye-opening moment for the very early researchers (first or second year PhD candidates) who have not gone through the process yet. This sets the tone for the Open Access part of the talk, which naturally leads into discussing about preprints and licenses.
|A schematic on the current (mostly) paywalled system vs an open access alternative|
When it comes to preprints, I always like to highlight how easy it is these days to submit a preprint (thanks to the many many servers and infrastructures available), and how preprints can help establishing priority of discovery and obtaining wider and more efficient community feedback.
From publishing I then move to the core of a research workflow: data. Here, again, I need to pick key points: I always like to start with stating (hopefully once and for all) that research data are (or at least should be) first-class records of science, which means that we should all care about our data the same way we care about our papers (perhaps more?!), but also that researchers should of course be evaluated for the data they produce (and not only for the amount of papers they publish!).
When it comes to data, I never leave out the FAIR principles (because they are beautiful, because they are super important, and because they help to think about FAIRness for much more than just data). If I have enough time, I talk about my personal story around FAIR and open data and standards for cell migration research (see preprint).
At the end of this (I admit, rather broad, but hopefully useful) introduction to open research practices, I hope to have convinced my audience that
Open Science is Just Science Done RightMoving towards the last part of my talk, I touch upon 'change': how do we make sure this all happens and does not remain utopia? This is perhaps the most complicated message to bring across. Almost a year ago, luckily, I have had the pleasure to listen to Brian Nosek talk about Open Science and in particular about how we can catalyze the culture change that we all need to see for Open Science to become a reality. I learned that the secret seems to be starting from the very bottom of the pyramid (see schema below): we first make it possible, then we make it easy, then we work on communities, setting the right examples, we then make sure that doing open science becomes (also) rewarding, and lastly we design policies to drag along the last portion of people that 'still need to be convinced'.
|A culture change to make Open Science a reality. Diffusion of innovations, E. Rogers, 1962; Image by Jurgen Appelo, Flickr|
The three tips I like to share at this stage with the audience are:
- Embrace incrementalism: change can happen by degrees and every little step counts
- Focus on good science practices, not on social identity
- You are not alone: reach out to the community!
And to close it all: the icing on the cake! Since September 2019, when I gave a keynote talk at the Open Science FAIR in Porto (this here is me), I like to finish my Open Science talks with a message of inclusiveness. I strongly believe that there is to Open Science way more than meets the eye, and that open, collaborative, transparent and inclusive research practices are the way to go if we want to change the currently rather toxic academic culture. And because they say that a picture is worth a thousand words, here you have it:
|Open Science is the way to go to reform the current academic culture and shift the conversation back to things that really matter.|
The slide deck I have prepared for the seminar in Dublin is deposited in Zenodo; the slides, as well as all the images on this blog post are available under a CC-BY-SA license.