Perversely, this is almost the exact opposite of how to solve the problem.

Speaking from experience, the real problem with scientific data (genomics, in particular) is when it is exchanged in plain-text format and then loaded IN to Excel. Automatic type inference is what fails.

Data already in Excel format can be created/modified/exchanged without unintentional conversion if the column type is correctly specified as "TEXT".

True, but how do you trust something that started as a CSV and was digested into Excel?

Unless you've done it yourself carefully, you can't

Speaking from experience, that would be most people in life sciences laboratories.

Look, I get the impulse, and might have agreed with you 10 years ago. At the end of the day, though, we have to work with non-computationally savvy people who (reasonably) want to look at their data sometimes. Not every lab tech or PI can or should learn Python/R/your favorite scripting language, and frameworks like Galaxy take time to set up and maintain, etc etc. Our job is to meet the users where they are and push the biology forward. Any time that I can push them onto a better path, I'll do that, but sometimes the right move is to tell them: "Yeah, go ahead and play with the data in excel, then tell me what you find", and I'll code something proper up afterwards to verify it, get solid stats and a make a pretty visualization.

> Not every lab tech or PI can or should learn Python/R/your favorite scripting language

The amount of learning required is minimal, even more when compared to the contortionist required to process data in Excel.

Working in the enterprise tech world, I'd go completely broke.

You arent in management, nor a very flexible person?

Businesses use Excel for a reason. Nice that MS finally gave that setting, but renaming genes to be able to use a popular tool also works.