2-D slices and Metadata from X-ray μCT Images

This dataset contains derived two‑dimensional slices, segmentation masks, and associated metadata generated from three‑dimensional X‑ray micro‑computed tomography (μCT) volumes of hydrating cement paste. The original volumetric datasets were published by M. Hlobil and I. Kumpová in Data Brief (Vol. 46, p. 108903, 2023, doi: 10.1016/j.dib.2023.108903). The present dataset was prepared to support temporal deep‑learning research on hydration progression. Each 3‑D region of interest (ROI) was converted into a series of 2‑D cross‑sectional slices representing different hydration ages, cement fineness levels, and water‑to‑cement ratios. Corresponding binary masks were generated through semantic segmentation to delineate microstructural features relevant to hydration analysis. Metadata files include ROI identifiers, imaging parameters, and temporal pairing information used for Siamese network training. Contents: 2‑D μCT image slices (PNG format) Segmentation masks (PNG format) Metadata files (CSV format) describing ROI, hydration age, and pairing indices Usage Notes: The dataset is intended for research on temporal representation learning, microstructural evolution, and AI‑based characterization of cement hydration. Users should cite both this dataset and the original publication by Hlobil and Kumpová when using or adapting the data. Citation: Hlobil, M., & Kumpová, I. (2023). A collection of three‑dimensional datasets of hydrating cement paste. Data Brief, 46, 108903. https://doi.org/10.1016/j.dib.2023.108903 (doi.org in Bing)