<?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>ShapeDomainLab: A Controlled Synthetic Dataset of Simple Geometric Shapes for Studying Domain Robustness of Computer Vision Models</titl><IDNo agency="DOI">doi:10.48788/DVUA/T8V83V</IDNo></titlStmt><distStmt><distrbtr source="archive">DataverseUA</distrbtr><distDate>2026-08-13</distDate></distStmt><verStmt source="archive"><version date="2026-08-13" type="RELEASED">1</version></verStmt><biblCit>Galelyuka, Igor; Romanov, Volodymyr, 2026, "ShapeDomainLab: A Controlled Synthetic Dataset of Simple Geometric Shapes for Studying Domain Robustness of Computer Vision Models", https://doi.org/10.48788/DVUA/T8V83V, DataverseUA, V1, UNF:6:bqdw6+LCffW4aqx10ClhDA== [fileUNF]</biblCit></citation></docDscr><stdyDscr><citation><titlStmt><titl>ShapeDomainLab: A Controlled Synthetic Dataset of Simple Geometric Shapes for Studying Domain Robustness of Computer Vision Models</titl><altTitl>ShapeDomainLab: контрольований синтетичний датасет простих геометричних фігур для дослідження доменної стійкості моделей комп’ютерного зору</altTitl><IDNo agency="DOI">doi:10.48788/DVUA/T8V83V</IDNo></titlStmt><rspStmt><AuthEnty affiliation="V.M. Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine">Galelyuka, Igor</AuthEnty><AuthEnty affiliation="V.M. Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine">Romanov, Volodymyr</AuthEnty></rspStmt><prodStmt><software version="1.0.0">ShapeDomainLab (Shape Domain Laboratory)</software></prodStmt><distStmt><distrbtr source="archive">DataverseUA</distrbtr><contact affiliation="V.M. Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine" email="gib@nas.gov.ua">Galelyuka, Igor</contact><depositr>Galelyuka, Igor</depositr><depDate>2026-08-11</depDate></distStmt><holdings URI="https://doi.org/10.48788/DVUA/T8V83V"/></citation><stdyInfo><subject><keyword xml:lang="en">Computer and Information Science</keyword><keyword>synthetic data</keyword><keyword>geometric shapes</keyword><keyword>computer vision</keyword><keyword>domain shift</keyword><keyword>domain robustness</keyword><keyword>segmentation</keyword><keyword>reproducibility</keyword></subject><abstract date="2026-08-11">&lt;b>## Motivation and scope&lt;/b>&lt;br>
Motivation: controlled study of domain shift, domain robustness, classification and segmentation; this is not a substitute for natural data.&lt;br>&lt;br>

&lt;b>## Composition&lt;/b>&lt;br>
Six domains (clean, rotation, geometry, noise, clipping, composite); five classes (circle, octagon, rectangle, square, triangle); 51,000 64*64 grayscale PNG images and 51,000 binary L-mode PNG masks. Per domain: 5,000 train, 1,000 validation and 2,500 test images.&lt;br>&lt;br>

&lt;b>## Generation and splits&lt;/b>&lt;br>
Images are reproducibly generated controlled 2D shape rasters. Metadata schema version 2.1 records rendering parameters and file linkage. Train/validation/test splits are independent within each domain.&lt;br>&lt;br>

&lt;b>## Intended uses and limitations&lt;/b>&lt;br>
Suitable for controlled classification, segmentation, invariance and educational experiments. It has no natural textures, camera model, complex scenes or external validity for natural imagery. Strong clipping can reduce semantic identifiability.</abstract><sumDscr/><notes>Exact SHA-256 screening found 143 cross-domain pixel-identical groups: 41 expected parameter collisions and 102 expected symmetry cases; no group has different class labels. Fifty groups span train/test across domains. 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