Article (Scientific journals)
Gender bias in (neuro)science: facts, consequences and solutions
Schrouff, Jessica; Pischedda; Genon, Sarah et al.
2019In European Journal of Neuroscience, 50 (7), p. 3094-3100
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Abstract :
[en] Women neuroscientists (please note that we refer to all who identify as such) are still underrepresented in various aspects of academic life. The efforts of the community to mitigate this issue are growing but can elicit adverse reactions (Moghaddam & Gur, 2016). In this opinion paper, we discuss the different approaches that have been taken at institutional, organizational and individual levels to counter gender bias and aim at addressing unfavorable comments. We base our reasoning on empirical data and on the feedback received after the release of the Women in Neuroscience Repository (WiNRepo, see Supplementary Table S1.a), an initiative we created to increase the visibility of women in neuroscience. While this feedback originated mainly from oral conversations and was not rigorously quantified, we believe the frequency of the comments justify their discussion, as performed in (Moghaddam & Gur, 2016). The aim of this piece (supported by a list of signatories, see Supplementary Table S2) is therefore to ‘debunk the myths’ related to gender bias and to affirmative actions in academia, as well as to propose concrete measures that can be implemented to counter such bias.
Disciplines :
Social & behavioral sciences, psychology: Multidisciplinary, general & others
Author, co-author :
Schrouff, Jessica 
Pischedda
Genon, Sarah ;  Université de Liège - ULiège > Département des sciences cliniques > Neuroimagerie des troubles de la mémoire et revalid. cogn.
Fryns, Gregory
Pinho, Ana Luísa
Vassena, Eliana
Liuzzi, Antonietta
Ferreira, Fabio
Language :
English
Title :
Gender bias in (neuro)science: facts, consequences and solutions
Publication date :
October 2019
Journal title :
European Journal of Neuroscience
ISSN :
0953-816X
eISSN :
1460-9568
Publisher :
Blackwell, Oxford, United Kingdom
Volume :
50
Issue :
7
Pages :
3094-3100
Peer reviewed :
Peer Reviewed verified by ORBi
European Projects :
H2020 - 654038 - DecoMP_ECoG - Decoding memory processing from experimental and spontaneous human brain activity using intracranial electrophysiological recordings and machine learning based methods.
Funders :
CE - Commission Européenne [BE]
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