Dataset for Machine Learning-Based Prediction and Optimization of As-Extruded Viability in Extrusion-Based 3D Bioprinting

The dataset supports the findings presented in the paper "Machine learning-based prediction and optimization framework for as-extruded cell viability in extrusion-based 3D bioprinting." Sodium alginate viscosity data: "alg_i1g_viscosity_data.zip" Cross Power Law parameter fitting results: "alg_i1g_viscosity_fittings.zip" Rheological stability measurement: "alg_i1g_contact_angle_data.zip" OpenFOAM simulation results: "alg_i1g_simulation_data.zip" Post-extrusion cell viability results: "cell_viability_data.zip" Keywords: 3D bioprinting; cell viability; shear stress; numerical analysis; machine learning; alginate Code availability statementThe scripts used for data analysis, machine learning models, and numerical simulations in this study are available on GitHub at: https://github.com/KORINZ/in-silico-bioink-viability-prediction