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001019167 0247_ $$2datacite_doi$$a10.34734/FZJ-2023-05213
001019167 037__ $$aFZJ-2023-05213
001019167 1001_ $$0P:(DE-Juel1)178612$$aWagner, Adina Svenja$$b0$$ufzj
001019167 1112_ $$aISBI 2023$$cCartagena de Indias$$d2023-04-18 - 2023-04-21$$wColombia
001019167 245__ $$a10 years of reproducibility in biomedical research: howcan we achieve generalizability and fairness?
001019167 260__ $$c2023
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001019167 520__ $$a10 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?
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001019167 7001_ $$0P:(DE-HGF)0$$aMaumet, Camille$$b1$$eCorresponding author
001019167 7001_ $$0P:(DE-HGF)0$$aGanz, Melanie$$b2
001019167 7001_ $$0P:(DE-HGF)0$$aPraag, Cassandra Could van$$b3
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