RISP is a Python-based research analytics engine for quantitative evaluation of citation data. It provides a structured environment for computing research indices, generating statistical summaries, and analyzing citation distributions. The system is designed with modularity and extensibility in mind, enabling progressive expansion from basic metric computation toward deeper scientometric analysis. RISP emphasizes reproducible analysis, clear reporting, and architectural evolution over interface design.
Computation of citation-based indices (e.g., h-index, i10-index)
Statistical profiling of research output (mean, median, dispersion, range)
Distribution-aware analysis including outlier detection
Querying and filtering of citation datasets
Structured text-based report generation
Persistent dataset management
Version 1 established foundational index computation and statistical analysis.
Version 2 introduced structured data management, modular utilities, extended statistical reporting, and distribution-level diagnostics.
The platform is evolving toward a broader computational framework for modeling and interpreting research impact. This project is also meant to work as a sandbox.