Bonjour et meilleurs voeux !! Je nous souhaite des données FAIRisées et des entrepôts de confiance 
Je vous conseille la lecture du billet « On abhorrent science and the weaponization of genomic data » de Sasha Gusev (généticien et bioinformaticien à Harvard Medical School) qui fait un point sur les débats récents concernant l’accès aux données génomiques (données de séquençage) dans un contexte où certains en feraient un usage non-éthique (pour identifier certains marqueurs génétiques de populations sans le consentement de ces populations, pour défendre un point de vue racialiste, etc.).
Voici ses principaux arguments tels qu’il les résume lui-même (les points 1 et 3 portent spécifiquement sur le partage et l’accès aux données) :
- Studies should follow the NIH recommendations and strive for the broadest possible consent for research and data sharing. As suggested by Bird & Carlson, that also means investigators need to do a better job of articulating the potential harms to their participants. It is simply untenable that a person consenting to research that improves human health, typically in a clinical/hospital setting, is fully informed that they have also consented to the study of factors like educational attainment, income inequality, or spousal choice if those concepts can be in some way be connected to “health”.
- Placing post hoc limitations on broad research areas like intelligence or substance use is inappropriate gatekeeping, a violation of the consent decisions made by participants, and a barrier to scientific progress.
- Individuals who leak sensitive data should be categorically banned from accessing other datasets and should be treated by the community the same way as other scientific misconduct or fraud.
- The community should demand a high standard of scientific validity and rigor for research topics that have broad public impact and potential for misinterpretation. This includes not just comparisons across race, but across sex, wealth, geography, etc where confounding is also a major concern and potential misinterpretation is high. Studies should strive to define what they are estimating, on what scale, articulate all sources of confounding, and pre-register their analysis plans.
- A commitment to open data access also requires scientists within the field to be actively critical of work that does not meet those high standards of rigor. This includes writing commentaries and critical reviews but, more importantly, accessible content for the public. The field has long operated under the assumptions that all traits are highly heritable and GWAS estimates are largely un-confounded and causal; assumptions that have proven particularly inaccurate for behavioral phenotypes. Such persistent misinterpretations need to be redressed and we should be vigilant about overstating findings in the future.