RBiblioSynth : Bibliometric Analysis and Report Automation

Overview

RBiblioSynth is a modular framework for automating bibliometric analysis and systematic review reporting.
It combines Bibliometrix (R), custom R6 classes, and LLM-powered PDF/text analysis to streamline the literature review process.
The main focus is power systems frequency estimation as an example dataset, but the framework is designed to be domain-agnostic.

The goal is to analyze, detect, and report trends, anomalies, and patterns in bibliometric data and integrate them with enriched insights from academic papers, producing IEEE-style outputs (plots, indices, LaTeX reports).

Key Features

• Modular R6 Architecture: Independent modules for ingestion, cleaning, analysis, and visualization.
• Regression Modeling: Logistic, Gompertz, power law, and linear models for annual production and other indicators.
• Exploratory Data Analysis (EDA): Automated detection of anomalies, outliers, and change points.
• Country/Institution Analysis: Global, regional, and institutional publication/collaboration indicators with Gini and Lorenz analysis.
• Clustering & Thematic Analysis: Emerging themes, co-word analysis, and topic evolution.
• Automated Plots: IEEE-ready visualizations (quadrants, growth curves, Lorenz, networks).
• LLM-Powered PDF Analysis: Combine bibliometric insights with textual analysis of PDFs for qualitative review.
• Integrated Literature Review Report: Generate structured LaTeX/Markdown/PDF reports combining quantitative + qualitative results.
• Zotero & PDF Pipelines: Enriched workflows for integrating user libraries and annotated PDFs.

Roadmap (Modules)

Bibliometric (R6 in R)

• M0: Setup & Config (shared utils, IO, plotting, IEEE themes)
• M1: Data Ingestion (Scopus/WoS/IEEE .bib + PRISMA deduplication)
• M2: Annual Production (growth, models, regression, trends, anomalies)
• M3: Countries (TP, TC, SCP, MCP, Gini, fits, top/bottom analysis)
• [?] M4: Institutions (collaboration networks, quadrants, indicators)
• M5: Authors & Documents (h-index, g-index, most cited, author growth)
• M6: Clustering & Themes (co-word, Louvain, topic evolution)
• M7: Conceptual & Social Structure (collaboration networks, betweenness)
• M8: Automated Bibliometric Report (JSON + CSV + plots)

LLM / AI Integration

• M9: LLM Report Generator (combine bibliometric insights with text analysis)
• M10: Zotero Integration (tagging, notes, thematic structuring)
• M11: Multi-PDF Analysis (summaries, gaps, synthesis from full texts)
• M12: Auto LaTeX Paper Report (IEEE-ready, modular sections)

Current Focus

• Finalizing M3 (Countries) with inequality metrics (Gini, Lorenz) and normality tests.
• Structuring M4 (Institutions) using robust affiliation parsing and meta cleaning.
• Preparing baseline IEEE-style paper combining quantitative bibliometric results with qualitative review.

Project Goals

RBiblioSynth aims to:

1. Automate systematic literature reviews through a hybrid bibliometric + qualitative approach.
2. Provide reproducible, IEEE-ready outputs (plots, tables, LaTeX reports).
3. Accelerate research by identifying trends, emerging themes, gaps, and potential benchmarks.
4. Enable scalability to other domains beyond power systems frequency estimation.

Next Steps

• Complete M3 (Countries) analysis module.
• Implement M4 (Institutions) collaboration quadrants.
• Integrate qualitative LLM review pipeline for enriched insights.
• Draft conference paper version (6 pages, IEEE template).