<?xml version='1.0' encoding='UTF-8'?><codeBook xmlns="ddi:codebook:2_5" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance" xsi:schemaLocation="ddi:codebook:2_5 https://ddialliance.org/Specification/DDI-Codebook/2.5/XMLSchema/codebook.xsd" version="2.5"><docDscr><citation><titlStmt><titl>AIMOS - pre-trained models</titl><IDNo agency="DOI">doi:10.7910/DVN/G6VLZN</IDNo></titlStmt><distStmt><distrbtr source="archive">Harvard Dataverse</distrbtr><distDate>2020-04-28</distDate></distStmt><verStmt source="archive"><version date="2020-04-28" type="RELEASED">1</version></verStmt><biblCit>Schoppe, Oliver, 2020, "AIMOS - pre-trained models", https://doi.org/10.7910/DVN/G6VLZN, Harvard Dataverse, V1</biblCit></citation></docDscr><stdyDscr><citation><titlStmt><titl>AIMOS - pre-trained models</titl><IDNo agency="DOI">doi:10.7910/DVN/G6VLZN</IDNo></titlStmt><rspStmt><AuthEnty affiliation="Technische Universität München (TUM)">Schoppe, Oliver</AuthEnty></rspStmt><prodStmt/><distStmt><distrbtr source="archive">Harvard Dataverse</distrbtr><contact affiliation="Technische Universität München (TUM)" email="oli@mytum.de">Schoppe, Oliver</contact><depositr>Schoppe, Oliver</depositr><depDate>2020-04-27</depDate></distStmt><holdings URI="https://doi.org/10.7910/DVN/G6VLZN"/></citation><stdyInfo><subject><keyword xml:lang="en">Computer and Information Science</keyword><keyword xml:lang="en">Medicine, Health and Life Sciences</keyword></subject><abstract>Pre-trained models for AIMOS &#xd;
* Unet768 for native micro-CT&#xd;
* Unet768 for contrast-enhanced micro-CT&#xd;
* Unet768 for light-sheet microscopy</abstract><sumDscr/></stdyInfo><method><dataColl><sources/></dataColl><anlyInfo/></method><dataAccs><setAvail/><useStmt/><notes type="DVN:TOU" level="dv">&lt;a href="http://creativecommons.org/publicdomain/zero/1.0">CC0 1.0&lt;/a></notes></dataAccs><othrStdyMat><relStdy>https://www.nature.com/articles/sdata2018294,&#xd;
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