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## Data | |
Keep your data (e.g. from evaluations here) | |
* If you involved human subjects in any form, you will require ethical permission. | |
* Keep records of all items related to ethics in `data/ethics`. There are templates for scripts, guidance provided. | |
* **You must have scanned PDFs of signed checklists in this folder**, or PDFs of ethics confirmations from other sources | |
* Ensure you remain GDPR compliant. In general: | |
* Never collect personally identifiable information if at all possible. | |
* Pseudonymise identifiers for subjects. | |
* Use coarse demographic values unless you need specific information (for example, if you need age ranges, collect ranges, not specific ages) | |
* Ensure you have explicit consent for the storage and use of data from human subjects | |
* DO NOT STORE PERSONALLY IDENTIFIABLE INFORMATION ON REMOTE SERVERS (no Dropbox, Github, etc.) | |
* Keep a written description of the data, what is contained, and how it was captured in `data/readme.md` | |
* Record all raw data as an immutable store. **Never modify captured data.** | |
* Keep this under `data/raw` | |
* This could be logs, questionnaire responses, computation results | |
* Write scripts to produced processed data from these (e.g. tidy dataframes, excel sheets, csv files, HDF5 files, sqlite databases) | |
* Write scripts that process these into results, visualisations, tables that you include in your project. | |
* If you use Jupyter/RStudio notebooks, place these in `data/notebooks` and name them carefully (not "Untitled1", "Untitled2"). | |
* You may need to remove the `data/` folder from version control if the data size is too large or you are bound by confidentiality. | |
* If you do so **make sure you have good backups** | |