A python pipeline for interactive visualization of systematic literature review data

Systematic Literature Reviews (SLRs) generate complex, multidimensional datasets. A major bottleneck in bibliometric analysis is the prevalence of multi-value cells, which are instances where a single study utilizes multiple tools, methodologies, or platforms. These are typically stored as semicolon-delimited strings in standard spreadsheets. Generic data visualization software struggles to parse these one-to-many relationships natively, forcing researchers to rely on manual data cleaning and external graphic design tools. To address this, we introduce an open-source Python pipeline and interactive Streamlit web application specifically engineered to automate the Extract, Transform, Load (ETL) process for SLR datasets. The software provides a robust tokenization engine that dynamically splits arrays, applies exclusion logic, and maps hierarchical relationships. By abstracting the complexities of three rendering engines (Matplotlib, Plotly, and Graphviz), the tool enables users to generate publication-ready visualizations. These outputs range from automated PRISMA flow diagrams to relational Sankey diagrams and interactive hierarchical charts, all without requiring programming expertise. This methodology significantly reduces the manual overhead of SLR reporting, enhances analytical reproducibility, and aligns with FAIR data principles.