Deep-Learning derived pore pattern data for Globorotaloides hexagonus from Marine Isotope Stages 96-100, ODP Site 202-1241

This dataset contains processed pore-pattern data derived from SEM images of the planktic foraminifer Globorotaloides hexagonus. The measurements were obtained using automated image analysis in Amira 3D Pro (Amira) with a G. hexagonus-based trained deep-learning algorithm, in collaboration with the Micropaleontology Group at the Institute of Geology, University of Hamburg, Germany. The G. hexagonus samples were selected from the Marine Isotope Stages 96–100 interval (~2.5 Ma) at Ocean Drilling Program (ODP) Site 202-1241 and were grouped into high-abundance and low-abundance categories. The pore-pattern parameters include porosity, pore density, and average pore size. For both abundance groups, data were collected from chambers F0 (ultimate/final-formed chamber), F1 (penultimate chamber), F2, and F3.