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Document Associé À Des Manifestations Scientifiques Année : 2023

10 years of reproducibility in biomedical research: how can we achieve generalizability and fairness?

Résumé

10 years ago, a series of publications pointed to the difficulty of reproducing scientific findings. This reproducibility crisis was a wake-up call for scientific communities to rethink how we practice and communicate research, and an important driver towards greater transparency and robust results. Ever since, biomedical imaging undertook various efforts to overcome reproducibility issues: From increasing sample sizes for higher statistical power, to data sharing and increased collaborations to acquire such samples, and promoting detailed reporting practices and code sharing to ease computational reproducibility. But where are we standing with respect to reproducible biomedical imaging now? We discuss recent advances and open questions, and focus on how the conversation has moved beyond efforts to reduce false positive findings to broader questions of generalizability and fairness. How does a finding observed in a given group apply to the population at large? How does a finding obtained with one analysis vary when computed using another tool? How does a finding observed in a given group apply to subgroups of that population, in particular to less represented subgroups? How can open science help with the complex questions of building fair algorithms and fairness in who participates in the process of science?

Domaines

Neurosciences
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Dates et versions

hal-04017085 , version 1 (09-03-2023)

Identifiants

  • HAL Id : hal-04017085 , version 1

Citer

Adina Svenja Wagner, Camille Maumet, Melanie Ganz, Cassandra Gould van Praag. 10 years of reproducibility in biomedical research: how can we achieve generalizability and fairness?. ISBI 2023 - 20th IEEE International Symposium on Biomedical Imaging, Apr 2023, Cartagena de Indias, Colombia. , pp.3. ⟨hal-04017085⟩
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