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@INPROCEEDINGS{Wagner:1019167,
author = {Wagner, Adina Svenja and Maumet, Camille and Ganz, Melanie
and Praag, Cassandra Could van},
title = {10 years of reproducibility in biomedical research: howcan
we achieve generalizability and fairness?},
reportid = {FZJ-2023-05213},
year = {2023},
abstract = {10 years ago, a series of publications pointed to the
difficulty of reproducing scientific findings.
Thisreproducibility crisis was a wake-up call for scientific
communities to rethink how we practice andcommunicate
research, and an important driver towards greater
transparency and robust results. Ever since,biomedical
imaging undertook various efforts to overcome
reproducibility issues: From increasing samplesizes for
higher statistical power, to data sharing and increased
collaborations to acquire such samples, andpromoting
detailed reporting practices and code sharing to ease
computational reproducibility.But where are we standing with
respect to reproducible biomedical imaging now? We discuss
recentadvances and open questions, and focus on how the
conversation has moved beyond efforts to reduce
falsepositive findings to broader questions of
generalizability and fairness. How does a finding observed
in agiven group apply to the population at large? How does a
finding obtained with one analysis vary whencomputed using
another tool? How does a finding observed in a given group
apply to subgroups of thatpopulation, in particular to less
represented subgroups? How can open science help with the
complexquestions of building fair algorithms and fairness in
who participates in the process of science?},
month = {Apr},
date = {2023-04-18},
organization = {ISBI 2023, Cartagena de Indias
(Colombia), 18 Apr 2023 - 21 Apr 2023},
subtyp = {After Call},
cin = {INM-7},
cid = {I:(DE-Juel1)INM-7-20090406},
pnm = {5254 - Neuroscientific Data Analytics and AI (POF4-525)},
pid = {G:(DE-HGF)POF4-5254},
typ = {PUB:(DE-HGF)6},
doi = {10.34734/FZJ-2023-05213},
url = {https://juser.fz-juelich.de/record/1019167},
}