📄 00_README.md
📁 01_Data ▶️
📁 02_Analysis ▶️
📁 03_Reports ▶️
📁 99_Archive Older versions and obsolete files
Mr. Isaac Tai
Assistant Librarian (User Services)
Email: cytai@eduhk.hk
Tel: (852) 2948-6681
We warmly welcome faculty members, researchers, and academic departments to partner with us in organizing tailored workshops that support students' research skills development and academic success.
Keeping your files well-organized isn't just 'nice to have'--it's the backbone of reproducible, collaborative, and long-lasting research. Here are some benefits of why data organization is important to your research.
Folder structure design in research data management refers to the organized arrangement of digital folders and files to systematically store, access, and manage research data. A well-designed folder structure ensures consistency, makes data easy to locate, and supports collaboration across research teams.
Click the folders below to expand and explore the recommended structure:
File naming in research data management involves creating clear, consistent, and descriptive names for files to make them easy to identify, organize, and retrieve. Good file naming practices help avoid confusion, support collaboration, and ensure long-term usability of research data.
SpineReview_TK_20250720_SurveyData_v01.csvManuscript_CYT_20250725_Draft_v1.0.docxAnalysis_Script_JD_20250722_Clustering_v02.pydata final FINAL.xlsx
survey@results#2.csv
stuff.docx
Version control in research data management is the practice of tracking and managing changes to data files, code, and documents over time. It allows researchers to record revisions, revert to earlier versions, and collaborate without overwriting each other's work.
Click on version points to see the evolution of a file:
First complete analysis with basic visualizations
Fixed data cleaning issues and updated charts
Added correlation analysis and statistical tests
Complete restructure with new methodology
Distributed version control for code and small files
✅ Best for: Code, scripts, documentation
❌ Avoid for: Large datasets
Data Version Control for large datasets
✅ Best for: Large datasets, ML models
❌ Learning curve required
Simple manual versioning with timestamps
✅ Best for: Simple projects, beginners
❌ Manual process, limited features
| Category | Key Practices | Example Tools |
|---|---|---|
|
🗂️ Folder Structure
|
|
File Explorer, Finder |
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🏷️ File Naming
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Bulk Rename Utility |
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🌿 Versioning
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Git, DVC, Git LFS |
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☁️ Backup & Sync
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OneDrive, rsync, cron |