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Data · dataset · 2026

Systematic Diagnosis of Cross-Domain Performance Degradation in PCB Defect Detection: A PKU-Source Study — Data, Analysis Outputs, and Reproducibility Resources

Listed in Teesside University Research Data Repository

Description

This repository provides the data, analysis outputs, protocols, source data, and reproducibility resources supporting the study “Systematic Diagnosis of Cross-Domain Performance Degradation in PCB Defect Detection: A PKU-Source Study.” The study uses PKU as the sole source domain and MIXED-clean, SELF, and DeepPCB as observational target domains under a common five-class ontology consisting of mouse_bite, open_circuit, short, spur, and spurious_copper.

The repository contains the study-specific dataset manifests and ontology mappings, experimental and evaluation protocols, annotation-audit and adjudication records, full-domain evaluation outputs, positive-control results, controlled-imaging and selected-target analyses, domain-characterization and robustness outputs, statistical results, and source data underlying the supplementary tables. It also provides the custom scripts, notebooks, configurations, environment specifications, and other resources required to trace and reproduce the reported analyses.

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Third-party public PCB datasets are not redistributed unless their original licenses explicitly permit redistribution. Their original sources are identified in the associated manuscript and repository documentation. Historical or superseded experimental branches are excluded from the manuscript-facing evidence package unless explicitly retained and labeled for provenance or audit purposes.

The repository is intended to provide a traceable link from the manuscript claims to the corresponding protocols, code, formal results, supplementary source data, and provenance records.

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Provenance · 1 source records, 12 field assertions
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Teesside University Research Data Repositoryoai:data.mendeley.com/p9ts8kz6gd.114 h agoJSON v1
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concepts[field].anzsrc:group:4602mapping · researchdata tees ac ukvocabulary-mapper@1.0.0keywords['Artificial Intelligence']
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