Data Analysis Methods Encyclopedia is an open-source project for building a structured encyclopedia of data analysis methods.
The goal of the project is not only to describe well-known analytical methods, but also to organize rare, emerging, frontier, and hybrid methods across data science, artificial intelligence, bioinformatics, longevity research, and quantum-inspired computing.
This encyclopedia is designed as a living research atlas of data analysis methods.
It will include:
- classical data analysis methods
- machine learning and advanced AI methods
- quantum and quantum-inspired methods
- bioinformatics, longevity, and systems biology methods
- rare and emerging methods
- frontier methods under observation
- method lineage and comparison structures
- a master index for navigation and research use
The project is intended to help researchers, students, developers, and analysts study existing methods, compare them, discover less-known approaches, and explore possible combinations for new hybrid methods.
The project is currently in the preparation stage.
The first public version of the encyclopedia will be uploaded to GitHub later. After the first version is available, I plan to develop a website for easier navigation and public access.
The encyclopedia will be organized into several main volumes:
- General Data Analysis Methods
- AI, Machine Learning, and Advanced AI Methods
- Quantum Computing and Quantum-Inspired Methods
- Bioinformatics, Longevity, and Systems Biology Methods
- Rare, Emerging, and Frontier Methods Observatory
- Hybrid Method Invention Lab
- Master Method Index
Each method will be described using a structured method passport, including its task, assumptions, input and output, mathematical idea, suitable data types, Python libraries, evaluation metrics, limitations, interpretability, data leakage risks, ethical risks, primary sources, documentation, and related methods. Each method will have a functioning real code example
The long-term goal is to create a public, research-oriented knowledge base that can support:
- learning data analysis methods
- finding appropriate methods for specific tasks
- comparing method families
- tracking new and emerging methods
- identifying rare methods
- analyzing method genealogy
- designing hybrid analytical approaches
- developing new method candidates
I would be happy to receive collaboration, suggestions, corrections, and methodological help for this open-source project.
Possible contributions may include:
- suggesting methods to include
- helping verify method descriptions
- adding references and documentation links
- improving taxonomy and classification
- contributing Python examples
- reviewing method passports
- helping with the future website
- proposing rare, emerging, or hybrid methods
Eva Ticina Comenius University in Bratislava
ORCID: https://orcid.org/0009-0004-2478-4054 LinkedIn: https://sk.linkedin.com/in/eva-ticina-088a04206