Published September 9, 2025 | Version v3
Software documentation Open

An Open-Source Framework for Advanced Correlation Analysis: The KARL Lab Correlation Tool (Pro Edition)

Authors/Creators

  • 1. ROR icon University of Barisal
  • 2. KARL
  • 3. ROR icon Bangladesh Council of Scientific and Industrial Research
  • 4. Kogakuin Daigaku - Hachioji Campus

Contributors

Project leader:

  • 1. ROR icon University of Barisal

Description

The KARL Lab Correlation Tool (Pro Edition) is an open-source, web-based framework designed to streamline advanced correlation analysis for researchers, educators, and analysts. Built with Python and Streamlit, it integrates data ingestion, statistical computation, interactive visualization, and publication-ready export into a single platform. The tool supports multiple correlation methods (Pearson, Spearman, Kendall), offers heatmaps, scatter plots, pair plots, and a unique Smart Insights feature that highlights the strongest correlations automatically.

To ensure usability across disciplines, it provides a no-code interface, compatibility with common data formats (CSV, Excel), and customizable outputs in high-resolution (PNG, JPG, TIFF) tailored for journal submission standards. Unlike fragmented workflows that rely on separate statistical software and visualization packages, the KARL Lab Correlation Tool unifies the process—accelerating discovery, enhancing reproducibility, and lowering barriers for users without programming expertise.

With its focus on accessibility, reproducibility, and professional-quality outputs, this framework serves as a valuable resource for scientific research, data science, business analytics, and education

Files

An Open-Source Framework for Advanced Correlation Analysis The KARL Lab Correlation Tool.pdf

Files (1.0 MB)

Additional details

Dates

Other
2025-09-03

Software

Repository URL
https://github.com/dev-rayhan-byte/Correlations-APPbyKARL/tree/v2.3.4
Programming language
Python
Development Status
Active

References

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  • Rayhan Miah, & Md Nurnabe Sagor. (2025). A High-Fidelity XGBoost Framework for Accurate Efficiency Prediction and Parameter Analysis in Perovskite Solar Cells Optimization. Zenodo. https://doi.org/10.5281/zenodo.15620092
  • Moreland, Kenneth. 2009. "Diverging Color Maps for Scientific Visualization." Pp. 92–103 in Advances in Visual Computing, edited by G. Bebis, R. Boyle, B. Parvin, D. Koracin, Y. Kuno, J. Wang, R. Pajarola, P. Lindstrom, A. Hinkenjann, M. L. Encarnação, C. T. Silva, and D. Coming. Berlin, Heidelberg: Springer.
  • Rayhan Miah. (2025). dev-rayhan-byte/Correlations-APPbyKARL: Release v2.3.4 — KARL Lab Correlation Tool (Pro Edition) (v2.3.4). Zenodo. https://doi.org/10.5281/zenodo.17046776
  • McKinney, Wes. 2010. "Data Structures for Statistical Computing in Python." Scipy. doi:10.25080/Majora-92bf1922-00a.
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  • Cadeddu, A., Canepa, L., Capiandaca, F., & Mossad, A. (2020). Streamlit: An open-source app framework for Machine Learning and Data Science. Streamlit Inc. n.d.