IC4SD-Wood-Eucalyptus: A macroscopic transverse-section image dataset with metadata, split manifests, and...

IC4SD-Wood-Eucalyptus is a macroscopic transverse-section image dataset for fine-grained Eucalyptus wood identification. The dataset contains 2910 images from 86 physical wood specimens collected across two acquisition periods (2023-2024), acquired at 50 $\times$ magnification from eight classes: seven Eucalyptus species (E. camaldulensis, E. cladocalyx, E. deglupta, E. diversicolor, E. grandis, E. microcorys, and E. saligna) and one Myrtaceae outgroup, Syzygium hemisphericum. The release includes raw images, class labels, original image dimensions, SHA-256 file hashes, physical-specimen group identifiers, perceptual-hash component identifiers, two split manifests, and leakage-audit outputs. The two distributed split manifests comprise a specimen-stratified reference split and a strict pHash-clean group-disjoint split; both are constructed from group identifiers anchored to physical-specimen subfolders and are audited for perceptual near-duplicate leakage. The strict split contains 2025 training images, 437 validation images, and 448 test images, distributed across 56, 15, and 15 physical-specimen groups respectively, with all eight classes represented in every partition. The accompanying leakage-audit files document group overlap, exact file-hash overlap, filename overlap, and perceptual near-duplicate checks at two Hamming-distance thresholds. The dataset is intended to support reproducible benchmarking of macroscopic wood-image classifiers and to provide an example of leakage-aware data organization for biological image classification. The dataset is available at https://doi.org/10.5281/zenodo.21216065.