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本数据集用于支撑发表于论文《基于机器学习的挤出式3D生物打印中挤出后细胞活性预测与优化框架》的研究结论。 海藻酸钠粘度数据："alg_i1g_viscosity_data.zip" 交叉幂律（Cross Power Law）参数拟合结果："alg_i1g_viscosity_fittings.zip" 流变稳定性测量数据集："alg_i1g_contact_angle_data.zip" OpenFOAM仿真结果："alg_i1g_simulation_data.zip" 挤出后细胞活性结果："cell_viability_data.zip" 关键词：3D生物打印；细胞活性；剪切应力；数值分析；机器学习；海藻酸钠（alginate） 代码可用性声明：本研究中用于数据分析、机器学习模型构建与数值仿真的脚本已开源至GitHub，地址为：https://github.com/KORINZ/in-silico-bioink-viability-prediction