Journal of Intelligent and Multidisciplinary Computing

Meta-Science and Knowledge Discovery aim to understand, improve, and accelerate the scientific enterprise itself using intelligent computation. This field analyzes how science progresses—how discoveries emerge, how collaborations form, and how ideas evolve across disciplines. AI-driven literature mining extracts concepts, hypotheses, and methodologies from millions of scientific publications. Knowledge graphs connect theories, results, datasets, and researchers to map the structure of global scientific knowledge. Machine learning identifies research gaps, predicts emerging fields, and recommends future directions for innovation. Meta-science evaluates reproducibility, experimental design quality, and methodological rigor. Interdisciplinary teams spanning philosophy of science, informatics, bibliometrics, and machine learning collaborate to create more transparent, efficient scientific ecosystems. Automated hypothesis generation and intelligent experiment design accelerate discovery cycles. Ethical and equitable science policy is supported by data-driven insights. Ultimately, this field enhances how humanity produces, validates, and disseminates knowledge.