r/datasets

Disclosure up front: I built the project this dataset backs, and I am sharing the raw data here because it is genuinely useful for anyone mining founder or engineering signals.

This is Q3 2026 engineering momentum across 350+ startup GitHub organizations in 15 sectors (web3, data infrastructure, enterprise SaaS, robotics, healthcare, legal tech, space tech, and more). Updated weekly.

What is inside, per org:

• 14-day commit velocity and velocity change %
• contributor count and growth
• new-repo creation
• a signal label (engineering hiring burst / deploy frequency spike / infrastructure buildout / framework migration)
• funding stage estimate (pre-seed through growth) and geography

Collected from public GitHub events only. No private repositories.

The research finding this backs: in a panel of 219 confirmed fundraises (SSRN preprint), a composite of commit velocity and contributor diversity preceded fundraise announcements by 21 to 47 days (median 31), a 3.4x lift over baseline. The methodology page has the full definition; the preprint is at papers.ssrn.com, abstract 6606558.

Get the data (free, no API key):

• JSON: signals.gitdealflow.com/api/signals.json
• Catalog with CSV + JSON exports and field docs: gitdealflow.com/datasets
• Methodology: signals.gitdealflow.com/methodology

License: CC BY 4.0 (attribute as 'Source: GitDealFlow, CC BY 4.0').

One caveat worth knowing: velocity-change % saturates at +999% on the biggest jumps, so for top movers rely on the absolute commit counts rather than the percentage.

Happy to answer questions about the pipeline or the caveats.

Disclosure first, because rule 1: I used this dataset to build a free iOS app, so I am not a neutral party here. Posting it because the dataset is genuinely good and I could not find it discussed on this sub.

Italian law requires every fuel station operator to file its prices with the Ministry of Enterprise, and the Ministry republishes the whole lot daily under IODL 2.0. Around 23,955 stations and roughly 93,000 prices, split across a price file and a station registry that carries address, brand and a self service flag.

The part I find unusual is that every individual price carries the timestamp of when that operator filed it. Most national fuel feeds I have looked at hand you a number and tell you nothing about its age. Here you can actually measure it: the median price is one day old, 71.8% are under 24 hours, 93.3% under three days, and 0.6% are more than a month old. That last sliver matters more than its size suggests, because pump prices drift upward, so a station that quietly stops filing keeps an old low number and floats straight to the top of any cheapest-first sort.

One warning if you go to parse it. The file is pipe delimited and does not escape pipes, so 106 rows carry a literal pipe inside a field. There are also 2,466 unbalanced double quotes sitting in company names, which means any reader treating the quote character as an enclosure will silently merge rows and hand you no error at all. I gave up and split on raw pipes without touching quotes. Worth knowing that the values are not sanity checked either: diesel gets filed at €0.123 and at €8.888, about 74 rows a day.

Does anyone know of another country publishing fuel prices at station level with a per-station filing timestamp? I have been through the French and Spanish feeds and both are day resolution at best, which is enough to sort but not enough to tell a driver whether to trust a number.

Source: https://www.mimit.gov.it/it/open-data/elenco-dataset/carburanti-prezzi-praticati-e-anagrafica-degli-impianti

(The app is Riserva, if it matters for the disclosure. Italy only.)

Not sure if this is the right sub-reddit, but I'm wondering if anyone has knowledge in actual trend within AI companies, especially the bigger labs. No doubt they still have agreement to collect and buy new data for training their models, but is the trend going down?

Similarly for alignement, post training and fine tuning are they actually buying? Or is there a shift towards internalizing the capabilities? I'm seeing big labs hiring for synthetic data generation, sometimes even data annotations... or the other hand I'm also seeing startups getting traction by focusing more the infra for doing RL, alignement etc.. than the data itself (they call it "AI gym" or "world model").

What are you thoughts on this and where do you think the "traditional" data-selling industry is going?

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I wanna work on multimodel cancer prediction or prognosis
How can i find datasets for this
I looked on NCI it looks hard for me and image sections are very large sizes
Can you guys suggest me

Hey guys!

Last year, I competed in my first science fair. However, I realised that finding out whether my idea had already been done was weirdly hard. ISEF's official abstract database has the projects, but it's keyword search, so if you don't guess the exact words the abstract used, you get nothing.

So I built Fairfolio: every ISEF project from 2014–2026 plus CWSF 2026, 17,911 projects, searchable by meaning (semantic embeddings) instead of keywords.

example: search "teaching a machine to walk without falling over" on keyword search and you get zero results. Semantic search returns the quadruped and hexapod locomotion projects, because it matches the idea rather than the wording.

Current features:

- Filter by fair, year, category, award

- "Related projects" on every page, by embedding similarity

- Save projects to folders

- Every record links back to its original source

It's free, no account needed to browse. Data is from public ISEF abstracts and CWSF's public project listings.

I'm also currently working on adding database APIs and public MCP access so yall can connect your AI agents for ideation purposes.

Currently still in its early stages which is why I'd appreciate it if some of you guys tested it out. Thanks! (fairfolio.org)

I pulled all 1.3 million federal contract records from the Government of

Canada's open data and looked at one question: who's holding contracts that

are about to come up for renewal?

A few things surprised me.

26,240 federal contracts are currently live, worth $125B. Of the ones that

report a bidder count, 75% drew one bid or none.

And roughly 63 contracts cross into the 12-month planning window every single

week — about $19B of contract value a year.

That last number is the interesting one. Agencies typically start recompete

planning 12–18 months before a contract ends. So the moment a contract crosses

that line is the moment it becomes worth a conversation. It's all public, but

nobody was watching for it.

So I built something that does. It's free, it updates itself monthly, and it

shows you which contracts are expiring, who holds them, what they're worth,

and how contested they were last time.

No signup needed to browse it. Link in the comments.

If you bid on federal work, I'd genuinely like to know whether this is useful

or whether I've missed something obvious.

I have a dataset of all contractor/home service and construction businesses in the US, Puerto Rico and Canada with up to date Google maps info. Would anyone be interested in seeing a sample?

I'm looking to build out 30 different use cases for 30 days where I pick up real data from multiple domains for eg - finance, technology, gaming etc. I want to run the data through a business reporting tool I came across and want to convince my peers/seniors in my firm that it's a great product.

My real question - which all domains should I focus on and where can I real messy data for each of those domains. Any help is appreciated. Thanks.

I run freehire, an open-source job aggregator that ingests postings from dozens of ATS platforms (Greenhouse, Lever, Ashby, Workday, etc.), normalizes them into one schema, and runs each through a facet pipeline.

Just exported the whole catalogue to Hugging Face: freehire-jobs — 6,041,471 postings.

Each row is a raw posting (title, company, description, URL, source, posted_at) plus derived facets:

- Dictionary-only (deterministic, no LLM): skills, seniority, category, work_mode, posting_language, employment_type, education_level, english_level, experience_years_min

- Dictionary-first, LLM-filled: countries, regions, cities

- LLM-only: salary_min/max, currency, period — pay is stated in every format imaginable, no dictionary handles that

Company rows carry their own facets too (industries, HQ country, size, YC batch/stage where applicable).

Format: 20 gzip JSONL shards, split by row range for easy streaming/resuming.

Backstory: I originally wanted to train a cheap classifier for seniority/category tagging instead of paying for an LLM call per job. Turned out the dictionary+LLM pipeline already in prod beats a from-scratch classifier by enough that finishing the classifier wasn't worth it — so I'm sharing the data instead.

Hey y'all,

I'm Luke, and I built Stackra for some context. Not selling a ding o dang thing here, just looking to see what this dataset is really capable of.

The corpus is 15 million sites pulled from HTTP, CommonCrawl, CrUX, and Overture.

I have a few thousand elements mapped out, like analytics, tools, vendors, link value, site category, schema, plugins, you name it. It runs back to 2024, and I've started putting some of this data user facing.

I find things like being able to identify that the market for funeral homes is dominated by two vendors, that I can identify site directories and their value/trajectory, that custom built websites pass CVW at a higher rate than WP, but rarely crack the top rankings without investor backing.

Weird little stuff like an emulator site that has been highjacked by porn/gambling sites. A popular listing tool that claims their links provide DR, but are explicitly UTM and nofollow. Pinpointing an ICP for a small business from clustering shared linked domains.

I'm curious to see what it can do, what would you want to ask?

Hi everyone, First time posting here. Can i get some datasets in excel to clean and analyze it? I'm just practicing it to enhance my skills. And also where can i get more datasets aside kaggle and creating from chatgpt? Thanks!

hey, working on a research thing where we estimate flood depth from traffic cameras using signs/poles as reference.

problem is i can find live flood cams (atxfloods, sunny day flooding, san diego cams, fl511 etc) but almost nothing archived where the road is actually flooded AND a sign/pole is clear enough to measure from.

if anyone’s seen a dataset, old webcam dumps, youtube clips, or even just a few screenshots like that, drop a link. would help a lot.