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

<p>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."</p> <ul> <li>Sodium alginate viscosity data: "alg_i1g_viscosity_data.zip"</li> <li>Cross Power Law parameter fitting results: "alg_i1g_viscosity_fittings.zip"</li> <li>Rheological stability measurement: "alg_i1g_contact_angle_data.zip"</li> <li>OpenFOAM simulation results: "alg_i1g_simulation_data.zip"</li> <li>Post-extrusion cell viability results: "cell_viability_data.zip"</li> </ul> <p><strong>Keywords:</strong> 3D bioprinting; cell viability; shear stress; numerical analysis; machine learning; alginate<br><br></p> <p><strong>Code availability statement</strong><br>The scripts used for data analysis, machine learning models, and numerical simulations in this study are available on GitHub at:&nbsp;<a href="https://github.com/KORINZ/in-silico-bioink-viability-prediction">https://github.com/KORINZ/in-silico-bioink-viability-prediction</a></p>