This section aims at sharing various ressources, possibly useful for researchers or students. Some are in English, some are in French. Feel free to use and share them under the following creative commons licence (you can copy, redistribute, and transform the materials, but you must provide appropriate credit, and you cannot use the materials for commercial purposes).
1 | Manuals
This page includes links to manuals (in French) I wrote for colleagues, students, or myself. Any mistakes or typos are my own!
- Personal manual for the Eyelink1000 system (eye tracker used at the CRCN): Click here to download
- Access to eBooks for experiment programming with PsychoPy/Python (written by F. Chetail et al.) : Click here
- Access to eBooks for data analyses with R/RStudio/JASP (written by A. Content et al.) : Click here
- Personal manual for EEG-SSVEPs analyses : click here.
2 | Guidelines and others
Here are several guidelines, some being specifically written for students, other being more general.
Guidelines for students (in French)
- Choisir son thème de mémoire.
- Réaliser son travail personnel de recherche.
- Réaliser son mémoire.
- Je vais imprimer mon travail personnel de recherche / mémoire : CHECK LIST !
- Je prépare mes slides pour la défense : CHECK LIST !
- Stage de recherche: les objectifs
Subscribing mailing lists
If you plan to do research after your master, I advice you to subscribe to scientific lists (which give among other things job offers). Here are three mailing lists in the field of cognitive sciences and psychology:
- BAPS (Belgian Association for Psychological Science): See section ‘Mailing list’
- psy-16 (French mailing list): Send an email to kamel.gana [at] u-bordeaux.fr with your email made of your first and last name
- RISC (Relai d’informations pour les sciences cognitives): See here.
- Comment bien écrire un article scientifique ? [here] (in French, from the course PSYC-E459)
- Comment bien communiquer dans une conférence scientifique ? [here] (in French, from the course PSYC-E459)
- CRCN booklet
3 | Scripts
Here are a few scripts written in R, Python or Matlab for analyses or experimental stimulation. Sharing scripts is always a bit risky, because one may notice some oddities in the way scripting was done (actually, I do find that some command lines are odd in my former scripts), and that’s probably the beauty of programming; the more you practice, the better scripts are. So, given that I’ve learnt so much in other scripts found on the internet, I decided to share my scripts (those sharable!). You just need to know that I’ve learned programming on my own, so scripting is far from being perfect. Feel free to improve it!
- Python script for a lexical decision task (to be run with the Coder of PsychoPy) [here]
- Python script for a two alternative forced choice task (2AFC, to be run with the Coder of PsychoPy). Two items are simultaneously presented and the task is to decide which one is the most wordlike, one being considered so by the experimenter [here]
- Python script for a word recognition task (to be run with the Coder of PsychoPy). An item is very briefly presented and masked. Two alternatives are then presented (two orthographic neighbors) and participants have to select the item they recognize (see Adelman et al., 2010) [here]
- Matlab scripts for EEG stimulation corresponding to an SSVEP oddball experiment [here]
- R script for pre-processing and analyzing data in the lexical decision task [here]
- R script for pre-processing and analyzing data in the syllable counting task [here]
- R script for pre-processing and analyzing data in the length estimation task [here]
- R scripts and manual for pre-processing and analyses of EEG – SSVEPs experiments [here]
4 | Open Science initiatives
From 2016, I started to systematically publish all the raw data of my accepted articles on Open Science Framework (OSF). I also tried to make accessible the data of my previous studies (if you want some that are not available, please ask me). Click here for my public profile on OSF.
I’ve always tried to provide the materials used in my studies in appendices of the papers (usually lists of words or pseudowords), and this is something that I systematically do now. Regarding scripts, from 2016 I’ve started to provide the R scripts used to analyse the raw data of each published study (on OSF). For stimulation, I use PsychoPy or matlab scripts, but I’ve not been able to provide them systematically (especially because most of the stimulation scripts were written before I started to share data, so it would take me a lot of work to make them sharable). However, I try to make them more and more available, so you should find some of them.
So far, I’ve preregistered several experiments (see here for a preregistration in Cortex, and here and here for a pre-registration with OSF) but I don’t do it systematically, because I consider it is not worth doing for all the experiments I conduct (when I do it, it’s usually in the frame of a replication or because I have strong hypotheses opposite to the dominant view). However, now I systematically define a priori the statistical tests and parameters (e.g., for outlier rejection) I’m going to use in case of confirmatory study, and I more and more ask researchers working with me to do so (using for example registration templates available on OSF).
In January 2017, I joined the Peer Reviewers’ Openness (PRO) initiative. More details are offered here, but the basic idea is that no comprehensive review is offered, nor recommendation for publication of any manuscript that does not meet minimal requirements for open science. This includes making available raw data, stimuli, and materials during the reviewing process (for the reviewers) and once the paper is accepted (for the whole community). In case of impossibility, clear reasons should be provided during the reviewing process. The location of these files should be advertised in the manuscript, and all files should be hosted by a reliable third party. The reasons why I joined this initiative are that 1) I find that doing science is easier and faster when data and materials are in open access, 2) despite many researchers agreeing that open science is important, they don’t share data and materials once papers are accepted (indeed, this is time consuming, and one doesn’t want to bother with that once a given paper is accepted), and 3) if we agree that raw data and materials are parts of the publication of a given study, then they should be submitted to the review process as the manuscript.