### Quality control **Questions:** - How can we carry out basic quality control and quality assurance in spreadsheets? **Objectives:** - Apply quality control techniques to identify errors in spreadsheets and limit incorrect data entry. --- ### Quality Assurance  --- ### Quality Control - Don't forget to keep the original data pristine and create a `readme.txt` file - Helpful resource: Cornell University's [guidance](https://data.research.cornell.edu/content/readme) on creating `readme.txt` files --- ### Sorting - Invalid values often sort to the bottom or top of a column - Sort the data on each column one at a time to root out poor data --- ### Conditional Formatting - Use with caution - Can be a good strategy for flagging outliers --- ### Exporting data **Questions:** - How can we export data from spreadsheets in a way that is useful for downstream applications? **Objectives:** - Store spreadsheet data in universal file formats. - Export data from a spreadsheet to a `.csv` file. --- ### Storing data using native formats is problematic - We **do not** recommend using `.xls`, `.xlsx` (Excel), or `.ods` (LibreOffice) file formats - More and more journals and grant agencies are requiring researchers to deposit data in a data repository (most of them do not accept Excel formats) - Storing data in universal, open, and static formats keeps data consistent and reduces the possibility of error --- ### Suggested formats - Tab-delimited (`.tsv`) files - Comma-delimited (`.csv`) files --- ### Advantages of open formats - Most software recognizes these formats - The encoding is simple, so they will continue to be interpretable into the future - These formats work well with version-control software like [Git](http://swcarpentry.github.io/git-novice)