asudhakar11

It should be able to tell you what assumptions it made. “Sales is $X because I assumed ARR and calendar year”. You’re then able to say great that’s what I wanted or “no I want bookings and fiscal year”.

Why weren’t you able to show usable insights from data?

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Possible to have small bad communities no? I'd argue % of bad actors (vs size) is the tipping metric. Also smaller communities like HN probably have a larger % of bad actors (small denominator).

thanks @vikrampatel. Pulling the data from a few places. The concept graphs are w2v and Wikipedia. The funding data is from sources like the NSF (I capture 90% of the $60bn of annual public funded research). Papers are from sources like Elsevier. Patents are from the USPTO. I'd love to pull in the 4th innovation leg, startups, but Angel and Crunchbase are pretty tight with that info.

Thanks @fasicle. That's a great idea and I'll work on it asap. Some limitations will be English speaking countries (my own limitation!) and data availability, but I'll look into it.

Thanks @kyunbit! I'll point you to a cleaned up front end soon :)

Valid question @chinjoshi. Our funding data's pulled from sources like the NSF. These account for 90% of the $60bn of annual research spend in the US. Our patent data is pulled directly from the USPTO. And our papers published data is pulled form technical publishers like Elsevier. What gets missed out is private funding which accounts for <$5bn of the $60bn.

@ericsun, sure, I use a combination of W2v and Wikipedia for the concepts. I then query a repository that I assemble (funding, papers and patent data).

@franklee2, sure. just change the text after the = sign. It needs some work, but you'll get an overall idea of who's doing significant work in the topic! It's a summary of funding, papers and patent data.

Reddit AMA by Columbia CS prof on 3/6 at 10.30a PST on DNA computers. The link shows you who else has done work in the topic (needs better UI!). What do you think?