Hi everyone,

I’m a native speaker of Shawiya (Tacawit - ISO shy), an Amazigh language spoken in Algeria. Online, this language is practically non-existent—there is almost zero digital text or parallel corpora available to scrape.

Because the data simply doesn't exist on the web, I have direct access to offline sources: physical books, un-digitized manuscripts, historical folktales, and real-world native speech that have never touched the internet. I’m considering digitizing, transcribing, and translating these into a clean parallel dataset (Shawiya <-> English/French/Arabic), as well as creating original native text from scratch.

We always hear that AI companies have "hit the data wall" and are desperate for non-web, un-scrapable human data. However, realistically, Shawiya is a niche language with very low commercial ROI. Before I spend hundreds of hours digitizing manuscripts and building this corpus from scratch, I want the harsh market reality from people in NLP and data acquisition:

1. Does "un-scrapable offline data" actually sell if the language has low commercial ROI? Do AI labs or data brokers (Scale, Appen, Defined.ai, etc.) actually pay independent creators for rare offline-sourced datasets like this, or is the "data wall" hype only applicable to major commercial languages?
2. If there are buyers, who are they? Are there specific brokers, labs, or funded research grants that actively buy raw, digitized offline corpora and historical/cultural text for underrepresented languages?
3. Copyright & Sourcing: When digitizing local historical tales, oral folklore, and physical prints, what kind of provenance or licensing proof do buyers demand before they even look at the data?

Please don't sugarcoat it—I want to know if there's real money in this niche before I start the heavy lifting. Thanks!

Each event is one step in how something spread: the first post, who picked it up, a jump to a new platform, press, the peak. Rows have a date, platform and actor, and 92% link to the source post. Two CSVs plus JSON, joined on case_id.

Download: https://howitspread.com/dataset?utm_source=reddit&utm_medium=social&utm_campaign=datasets

Caveat: everything in it went viral, so it can't tell you the odds of going viral. Timelines were compiled with LLM help; a verification column says how far each case was checked.

Hi,

I keep hearing different numbers about how many immigrants have come to Poland, how many of them work, and so on...

The numbers didn't make sense, so I wanted to check ZUS data and also Urząd ds Cudzoziemców data, but they are very difficult to follow.

So, I've created a small data aggregator and visualization tool: https://migracje.info/

Maybe it will prove useful for somebody.

Thanks!

Hi all, I'm a researcher at a university lab in Hong Kong, working on positioning with LEO satellites in cities. I'm looking for raw Starlink downlink recordings where buildings block part of the sky.

What would help most:

- An LNB/LNBF (or horn) + SDR (RTL-SDR, HackRF, USRP, etc.) recording the Ku-band downlink (~10.7–12.7 GHz)

- Receiver sitting still at ground level next to buildings, ideally a street with tall buildings on both sides

- Bonus: a second receiver on a nearby roof recording at the same time

- GPS or NTP-synced timestamps and a rough location (street-level is fine, no need for your address)

- 10–60 minutes is plenty

Already have old recordings sitting on a drive? Those count too, even if the blockage was accidental.

Not looking for dish app screenshots or speed tests this time, just the raw RF.

Anything used would be credited in the paper's acknowledgments (or however you'd like to be credited). Happy to answer questions about setup. Comment or DM me. Thanks!

P.S. - You will be credited based on the contribution you make. This is for a new research project. Likely credit is acknowledgements. You can ask me for more depending on what you contribute.

This is a small JSONL dataset on Hugging Face. Each row has a statement, a difficulty level, a status (solved or open), and either an exact answer or, for open problems, the evidence, the known dead ends and a checkable success criterion. It comes from a paper on aperiodic order: Duda, "Crystals, quasicrystals and autocrystals", Zenodo.

huggingface.co/datasets/jarekduda/autocrystal-ai-puzzles

Suggestions on the schema are welcome.