Dataset for Disentangled Generative AI-Guided Closed-Loop Optimization of Deposition Morphology for 3D Bioprinting Applications

The dataset supports the findings presented in the paper "Generative AI-guided in silico closed-loop optimization of deposition morphology in 3D bioprinting." Keywords: 3D bioprinting; deposition morphology; machine learning; generative artificial intelligence; variational autoencoder File description "ALG-Ph_HA-Ph_rheology_data.zip" contains shear-rate-dependent viscosity, storage modulus, and loss modulus for alginate-phenol (ALG-Ph) and hyaluronic acid-phenol (HA-Ph) conjugated inks. "image_index.csv" contains tabular information on image ID, date, ink type with concentration, shape, irradiation intensity, and whether sodium persulfate (SPS) is present. Note: "completeness_sam" entry determines whether the construct is considered complete or incomplete; however, this entry contains missing data and is not used in this study. "images.zip" contains image files (resized to 800×800 pixels) for training β-CVAE models. The file names are formatted as "resized_IMG_{image ID}.png." The {image ID} can be located in "image_index.csv." The image files include four types of shapes: grid, ichou, star, and nose. Code availability statementThe scripts used for data analysis and machine learning models in this study are available on GitHub at: https://github.com/KORINZ/generative-ai-bioprinting-framework.