Scientific data practices
Never modify any source files. Do all data cleaning programmatically, and never overwrite or touch original files once theyβre generated. Save any adjustments as separate derived files so the raw data stays untouched.
- Donβt encode information in cell colors in Excel.
- Variable names should be short but descriptive.
- Store output data as
.csvwhen possible. - Read up on tidy data.
- Write a script that outputs every number you report in a paper.
Once your data is clean and backed up, see Data analysis for how we explore, quantify, and report it with uncertainty.
Back up your files
The labβs shared Box folder is the system of record for all data, documents, photos, and videos. UC Davis provides it with unlimited storage, and it keeps a version history of recent changes. Work out of Box so your files are backed up at all times.
Once a month, back up your Box data to an external lab hard drive. Keeping a second copy off the cloud is your responsibility as much as the PIβs.
- Donβt use spaces in file names.
- Donβt load data files directly into Notion; keep data in Box and keep only lab documentation in Notion.
- Use the format
YYYY_MM_DD_LastName_FileDescription.extfor shared files, and consider it for personal files too.
Share and archive on publication
We manage data to be FAIR: findable, accessible, interoperable, and reusable. In practice that means:
- When a paper is published, its data (both raw and derived) are made public with a citable DOI, and the associated code repositories are made public alongside it. Please follow the lab template for setting up your GitHub repository README.
- Data are archived for the long term so the work stays reproducible after the project ends.
Operational details, including retention periods and where physical backups live, are in the member portal.
Version control
All code-based projects must be version-controlled and linked to a remote repository in the UC-BIRD-Lab organization. For Git setup, workflow, and naming conventions, see Code.
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