Objectifs
- This meeting is not a lecture course, but an exchange session
dedicated to unorthodox practices in the field of research and more
particularly, in the field of data science, but not only.
- If there is a (very) dark side of research practices, there are
also subtle margins within it could be helpful to move forward. And this
time, it’s not about tricking. It’s about being more understandable.
When cheating is not cheating.
Talks are delivered in English.
Vidéos des interventions
Programme
The event is organized in partnership with the doctoral school “Mathematics and Computing”.
Talks are delivered in English. Contact : Sabrina Granger
This meeting is not a lecture course, but an exchange session
dedicated to unorthodox practices in the field of research and more
particularly, in the field of data science, but not only.
If there is a (very) dark side of research practices, there are also
subtle margins within it could be helpful to move forward. And this
time, it’s not about tricking. It’s about being more understandable.
When cheating is not cheating.
The point of this workshop is to help you to know where you stand, to give you a compass in a tortured landscape.
Will you meet the challenge of orthodoxe research practices?
To start the fight, first, you should know your enemy : p-hacking, low statistical power, failure to control bias, poor quality control, falsified data, writing your own peer-review, but also working with predatory publishers, keeping your code veiled…
Do you see patterns in random data (aka apophenia ; Munafò et al., 2017)? Do you have « the tendency to focus on evidence that is in line with our expectations or favoured explanation » (aka confirmation bias ; Munafò et al., 2017)? Or maybe have you heard of « the tendency to see an event as having been predictable only after it has occurred » (aka hindsight bias ; Munafò et al., 2017)?
But you also should be aware of the wide and sometimes surprising
range of possibilities to produce more understandable results and thus, a
better research. Get the grip, it’s up to you!
Nicolas Rougier : “Ten Simple Rules for Scientific Fraud & Misconduct”
Abstract : « We obviously do not encourage scientific fraud nor
misconduct. The goal of this talk is to alert the audience to problems
that have arisen in part due to the Publish or Perish imperative, which
has driven a number of researchers to cross the Rubicon without the full
appreciation of the consequences. Choosing fraud will hurt science, end
careers, and could have impacts on life outside of the lab. If you’re
tempted (even slightly) to beautify your results, keep in mind that the
benefits are probably not worth the risks », (N. Rougier, J. Timmer 2017)
Nicolas Rougier est chercheur Inria en neurosciences
computationelles travaillant à l’institut des maladies
neurodégénératives à Bordeaux. Il a co-fondé le journal ReScience qui est spécialisé dans la publication de réplication en sciences computationelles.
Christophe Bontemps : « How To Lie With Graphics? »
Abstract : « According to Mark Twain « There are three kinds of lies:
lies, damned lies, and statistics’’. Today, with the emergence of
so-called Data Science and self-proclaimed data scientists, we observe
that graphical lies are everywhere. They are even more powerful than
spurious statistics. Many graphics in blogs, newspapers, and TV convey
information that is misleading, by mistake or on purpose. I propose a
short tutorial to visual fallacies and lies. My goal here is not to
encourage cheating and lying, but on the contrary to highlight the
techniques used to elaborate misleading data visualizations. This
introduction should help researchers, citizens, (data) journalists and
decision makers to distinguish visual lies from consistent graphics. »
Chr. Bontemps
Christophe Bontemps est ingénieur de recherche à l’INRA au sein
de la Toulouse School of Economics. Economètre-Statisticien de
formation, il enseigne la visualisation des données depuis plusieurs
années et est co-organisateur du Meetup Toulouse Dataviz.
Sources
Munafò, Marcus R., Brian A. Nosek, Dorothy V. M. Bishop, Katherine S.
Button, Christopher D. Chambers, Nathalie Percie du Sert, Uri
Simonsohn, Eric-Jan Wagenmakers, Jennifer J. Ware, and John P. A.
Ioannidis. 2017. ‘A Manifesto for Reproducible Science’. Nature Human Behaviour 1 (1): 0021. https://doi.org/10.1038/s41562-016-0021.
Rougier, Nicolas P., and John Timmer. 2017. “Ten Simple Rules for Scientific Fraud & Misconduct”. https://hal.inria.fr/hal-01562601.
