These files contain data supporting all results reported in Ming-Chiang Chang et. al. Autonomous Materials Exploration by Integrating Automated Phase Identification and AI-Assisted Human Reasoning. In Ming-Chiang Chang et. al. we found by incorporating human input into an expanded SARA-H (SARA with human-in-the-loop) framework, we enhance the efficiency of the underlying reasoning process. Using synthetic benchmarks, we demonstrate the efficiency of our AI implementation and show that the human input can contribute to significant improvement in sampling efficiency. We conduct experimental active learning campaigns using robotic processing of thin-film samples of several oxide material systems, including Bi2O3, SnOx, and Bi–Ti–O, using lateral-gradient laser spike annealing to synthesize and kinetically trap metastable phases. We showcase the utility of human-in-the-loop autonomous experimentation for the Bi–Ti–O system, where we identify extensive processing domains that stabilize δ-Bi2O3 and Bi2Ti2O7, explore dwell-dependent ternary oxide phase behavior, and provide evidence confirming predictions that cationic substitutional doping of TiO2 with Bi inhibits the unfavorable transformation of the metastable anatase to the ground-state rutile phase. The autonomous methods we have developed enable the discovery of new materials and new understanding of materials synthesis and properties.