The application backing use case is best suited for when you have parts of your database that get updated periodically and need human review. So you have a prod

The application backing use case is best suited for when you have parts of your database that get updated periodically and need human review. So you have a production database that you serve to your customers. Then you have a branch / fork of that (dev) that your development team adds batches of products to. Once a week you do a data release: submit a PR from dev -> prod, have somebody review all the new copy, and merge it once you're happy. If there's a big mistake, just back it out again. We have several paying customers building products around this workflow. As for lots of people collaborating on data together, we have started a data bounties program where we pay volunteers to assemble large datasets. Two have completed so far, and a third is in progress. For the first one, we paid $25k to assemble precinct-level voting data for the 2016 and 2020 presidential elections. For the second, we paid $10k to get procedure prices for US hospitals. You can read about them here: https://www.dolthub.com/blog/2021-02-15-election-bounty-review/ https://www.dolthub.com/blog/2021-02-15-election-bounty-revi... https://www.dolthub.com/blog/2021-03-03-hpt-bounty-review/ https://www.dolthub.com/blog/2021-03-03-hpt-bounty-review/ What's cool is that novices can make a really good income from data entry as a side gig, and it's two orders of magnitude cheaper than hiring a firm to build data sets for you. You're right that the site is kind of vague about what dolt is "for." It's a really general, very multi-purpose tool that we think will get used a lot of places. Here's a blog we wrote a while back about some of the use cases we envision. https://www.dolthub.com/blog/2020-03-30-dolt-use-cases/ https://www.dolthub.com/blog/2020-03-30-dolt-use-cases/