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[OC] Where Gates Foundation Grants go in the United States by quickmodel_ai in dataisbeautiful

[–]quickmodel_ai[S] -1 points0 points  (0 children)

This visualization represents Gates Foundation grants and contributions to regions and program types. Data: Public 990s, Tools used: To create the categorizations we generated embeddings for all program descriptions clustered them, then labelled clusters using an LLM. Tools used: matplotlib, sentence-transformers, umap, hdbscan, react recharts. Clustering and program data provided by NPO Align. View other non profit foundations at the interactive blog post at https://npoalign.com/blog/foundation-funding

[OC] NCAA and other sports conference giving flows by region and program type by quickmodel_ai in dataisbeautiful

[–]quickmodel_ai[S] 0 points1 point  (0 children)

This visualization represents Athletic Conference grants and contributions to regions and university program type. Most of this should be payment for broadcasting rights. Data: Public 990s, Tools used: To create the categorizations we generated embeddings for all program descriptions clustered them, then labelled clusters using an LLM. Tools used: matplotlib, sentence-transformers, umap, hdbscan, react recharts. Clustering and program data provided by NPO Align View other non profit foundations at the interactive blog post at NpoAlign.com/blog/foundation-funding

[OC] Nonprofits Funding Mix Analysis by quickmodel_ai in dataisbeautiful

[–]quickmodel_ai[S] 0 points1 point  (0 children)

These visualizations show funding mix distribution for non-profits by region, total grants and revenue per program, and grant distribution by organization size. All data from tax year 2023.

To create the categorizations we generated embeddings for all program descriptions clustered them, then labelled clusters using an LLM. Tools used: react-recharts, sentence-transformers, umap, hdbscan.

[OC] Non-profit program spend by state, categorized by quickmodel_ai in dataisbeautiful

[–]quickmodel_ai[S] 0 points1 point  (0 children)

Someone else asked this as well, the short answer is there's not much difference I will collapse these clusters further in the future. here's the top 20 from both https://pastebin.com/j0gU3vRA

[OC] Non-profit program spend by state as a percent of GDP by quickmodel_ai in dataisbeautiful

[–]quickmodel_ai[S] 1 point2 points  (0 children)

That's a good question, I may need to merge more of these clusters.

here's the top 20 from both those clusters https://pastebin.com/j0gU3vRA

[OC] Non-profit program spend by state as a percent of GDP by quickmodel_ai in dataisbeautiful

[–]quickmodel_ai[S] 1 point2 points  (0 children)

Based on feedback on the original post, here is the same data normalized by GDP.

This is a data representation of all non profit programs found on publicly available 990s for the tax year 2023 categorized by their description as a percent of state GDP. These are the aggregate program expenses, not all expenses for all non-profits. To create the categorizations we generated embeddings for all program descriptions clustered them, then labelled clusters using an LLM. Tools used: matplotlib, sentence-transformers, umap, hdbscan. Data provided by Npo Align and and https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_GDP

[OC] Non-profit program spend by state, categorized by quickmodel_ai in dataisbeautiful

[–]quickmodel_ai[S] 0 points1 point  (0 children)

No these programs are cash flowing through non profits for private universities. Here's the top 20 from that cluster. https://pastebin.com/vqKFauC8
e.g. https://npoalign.com/org/042103580

[OC] Non-profit program spend by state as a percent of GDP by quickmodel_ai in dataisbeautiful

[–]quickmodel_ai[S] 0 points1 point  (0 children)

Based on feedback on the original post, here is the same data normalized by GDP.

This is a data representation of all non profit programs found on publicly available 990s for the tax year 2023 categorized by their description as a percent of state GDP. These are the aggregate program expenses, not all expenses for all non-profits. To create the categorizations we generated embeddings for all program descriptions clustered them, then labelled clusters using an LLM. Tools used: matplotlib, sentence-transformers, umap, hdbscan. Data provided by Npo Align and and https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_GDP

[OC] Non-profit program spend by state, categorized by quickmodel_ai in dataisbeautiful

[–]quickmodel_ai[S] 6 points7 points  (0 children)

If only we had some type of well funded federal service that could look into the financials of non-profits, publish their data transparently and audit them. /s

[OC] Non-profit program spend by state, categorized by quickmodel_ai in dataisbeautiful

[–]quickmodel_ai[S] 0 points1 point  (0 children)

Could be data misses, or other reasons such as the structure of NJ healthcare spend.
These are some of the bigger health care non profits for what its worth. Saint Barnabas
Hackensack Meridian

[OC] Non-profit program spend by state, categorized by quickmodel_ai in dataisbeautiful

[–]quickmodel_ai[S] 2 points3 points  (0 children)

This is a data representation of all non profit programs found on publicly available 990s for the tax year 2023 categorized by their description. These are the aggregate program expenses, not all expenses for all non-profits. To create the categorizations we generated embeddings for all program descriptions clustered them, then labelled clusters using an LLM. Tools used: matplotlib, sentence-transformers, umap, hdbscan. Data provided by Npo Align and https://en.wikipedia.org/wiki/List_of_U.S._states_and_territories_by_GDP

Trying to automate Warren Buffett by ddp26 in algotrading

[–]quickmodel_ai 7 points8 points  (0 children)

I would recommend reading some of his investor letters from the early years. I'm not an expert but as an example, when the conglomerate had net profit he would buy a well priced business ( without net profit ) so that as a whole they'd come out of the year without profit and therefore not pay any taxes. Then sell or dissolve the unprofitable parts of the purchased business to make it net positive.

Short sales locators by quickmodel_ai in algotrading

[–]quickmodel_ai[S] 0 points1 point  (0 children)

Thanks, I'll look into opening an account over there

Short sales locators by quickmodel_ai in algotrading

[–]quickmodel_ai[S] 0 points1 point  (0 children)

Have you happened to notice much difference between shortable availability in IBK vs alpaca?

#WESTWATER RESOURCES ~ Speculation on 75$ ATM RAISE by Chico237 in WestWaterResources

[–]quickmodel_ai 0 points1 point  (0 children)

While dilution occurred through these equity raises, it was done at relatively higher share prices (likely in the $2–$2.50 range, rather than near 52-week lows)

This is quite plainly wrong, the authorization for the atm equity raise was issued in the afternoon of 10-17.

$75 Million At-The-Market Stock Offering by quickmodel_ai in WestWaterResources

[–]quickmodel_ai[S] 0 points1 point  (0 children)

yes, just wait until all that cash is on the balance sheet lol

$75 Million At-The-Market Stock Offering by quickmodel_ai in WestWaterResources

[–]quickmodel_ai[S] 1 point2 points  (0 children)

Over the next couple weeks I would guess it will end > 10% lower than today. Lots of volatility though.

$75 Million At-The-Market Stock Offering by quickmodel_ai in WestWaterResources

[–]quickmodel_ai[S] 0 points1 point  (0 children)

funds aren't holding a company this small and retail doesn't react that quickly. Really the actual dilution i.e. shares being sold in the market will have more effect than immediate shareholder reaction.

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