LabArchives does more than just store files — it helps you track your code, connect to your data, and document your research process in organized, searchable notebooks. Whether you're working in Python, R, MATLAB, or Jupyter, LabArchives makes your work easier to understand, reproduce, and share.
Store Code, Outputs, and Link to External Data
You can upload code and output files directly into LabArchives, including .py, .R, .csv, .txt, and Jupyter Notebook (.ipynb) files. You can also paste code into Rich Text or Plain Text entries and include notes, setup instructions, or README files.
LabArchives supports Jupyter integration, so you can upload and view notebooks directly in your entries. This makes it easy to combine code, results, and explanations in one place.
If your data is stored elsewhere—like REDCap, other EDC systems, or a network drive—you don’t need to move it. Instead, simply link to those sources from your LabArchives entry.
LabArchives also offers REDCap integration, letting you connect directly to surveys, records, or exports. For other EDC platforms, you can link files or summaries to keep your analysis connected to the source.
To save time, use Folder Monitor to automatically upload updated files from your computer to LabArchives.
Record Your Coding Environment
Use a Rich Text entry in LabArchives to describe the environment your code runs in. This helps others reproduce your work and troubleshoot if needed.
Be sure to include:
- Programming language and version (e.g., Python 3.11)
- Operating system (e.g., Windows 11, macOS 14)
- Environment (e.g., Conda, venv)
- Key libraries or packages (e.g., pandas, NumPy, matplotlib)
A short, clearly formatted entry keeps things easy to find and understand—no extra files needed.
Keep a Data Dictionary
A data dictionary explains the variables and terms used in your code or dataset. It’s essential for making your work understandable to others and for future reuse.
Each entry should include:
- Variable name – used in your code or dataset (e.g., age, bp_systolic)
- Description – what the variable represents
- Data type – number, text, date, etc.
- Units – if applicable (e.g., mmHg, kg)
- Allowed values – for coded data (e.g., 0 = No, 1 = Yes)
- Notes – any relevant context (e.g., how the variable was derived, collected, or coded during the study)
You can create this as a Rich Text entry, Plain Text entry, or upload a spreadsheet. Be sure to keep it updated throughout the project.
Explain Your Decisions
Use Rich Text entries to document important decisions in your coding and analysis process. These notes give context that helps others—and your future self—understand your choices.
Examples include:
- Why you removed or recoded a variable
- Why you chose one method or model over another
- How you fixed a bug or handled unexpected results
Even brief explanations can make your work more transparent and easier to follow.
Using Templates to Stay Consistent
Templates help you stay organized and make your documentation easier to follow. You can create a simple structure that includes:
- Code summary
- Inputs and outputs
- Environment details
- Notes or observations
Using a template ensures that each entry follows the same format, which is especially helpful when working on long-term or team-based projects. It also makes it easier for others to review or continue your work.
To learn more, visit the Can I create Templates in LabArchives? article