One thing to think about, which I also struggle with when it comes to large and complicated datasets, is the UI. Even being in the search industry for a long time, it's difficult for me to concretely see how I would use this.

I'd suggest taking a small sample of the dataset that might be reflective of how people would use it, then make that segment public and immediately searchable without registering. eg: One year of articles related to the Olympics.

What I've found is that it's hard for a lot of people to imagine how they would use something without actually using it. So giving people the actual experience of searching the archive and interacting with the results would go a long way.

Again, congrats on the work. This is really impressive work.

https://snewpapers.com/components/b2d40c08-db63-40e8-890f-09...

https://snewpapers.com/components/0fabc8e4-a60b-4f31-9ad1-b0...

https://snewpapers.com/components/cdde790f-4e97-4f2d-a2c2-95...

I've also worked with this data, but only for research purposes:

https://www.finhist.com/bank-runs/episodes/13895.html https://www.finhist.com/bank-runs/index.html

Surprisingly, I found out that layout was the trickiest thing, as newspaper articles often had multiple layers of headers, spanned multiple columns, etc.

Do you have a preferred solution on that?

Thank you everyone for taking the time to look at snewpapers today, enjoy!

- Each month's / year's top news headline

- Left / Right swings of publishers