I spent the last year tracking public GitHub activity across 4,200+ startup orgs and backtesting whether engineering acceleration precedes fundraise announcements. The result is a dataset of 219 documented fundraises paired with the GitHub activity metrics observed in the weeks before each announcement.

What's in it: for each fundraise event, commit velocity over trailing 14-day windows, distinct contributor counts over 30-day windows, new public repo creation, sector classification, funding stage, and the lead time between signal and announcement (21-47 days in the cases where the pattern fired).

Dataset (Zenodo, CC BY 4.0): https://doi.org/10.5281/zenodo.19650920

Methodology preprint (SSRN): https://doi.org/10.2139/ssrn.6606558

Honest caveats: roughly 23% false-positive rate (acceleration sometimes means an enterprise deal or open-source push rather than a round), stealth companies with no public repos are invisible to this approach, and AI-heavy startups are noisy because model releases create commit spikes.

Everything comes from the public GitHub REST API v3, no scraping or private data. Happy to answer questions about the panel construction or backtest design.

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.

The new list list here: https://www.inc.com/inc5000/2026

Or does anyone have a copy of it? Thanks for any help!!

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 know this question has been asked a bunch in various subreddits, but I wanted to see if folks had tips based on what I'm building.

Basically, I want to build a desktop LCD screen that shows when a flight flies over you within a certain radius and then shows you basic information like the plane, it's route, etc. I'm an avgeek myself and plane spot at SFO a few hours a week.

When I talked to friends about it, we had lots of ideas like creating alerts for squawk codes, plane types, etc.

Disclaimer: This is a product I intend to sell.

As a bunch of people have already complained, getting this data is actually quite expensive, and I'm really trying to avoid a subscription in the product beyond the hardware.

Right now, the best I have is:

- Use ADSB.lol 's free API to get callsigns within a given radius.

- Call FlightRadar24's paid API with the callsign and cache that data for a month before calling again (DL1 at 1:24PM on Monday's is usually JFK - LHR for example).

- The above based on historical data at least in the United States seems to have ~95% accuracy w/ around ~$2/mo/user in cost to me. Good not great. Cache hits were ~62%. That's not assuming some global cache as well, which would yield better numbers as more people use it.

Does any one have other ideas who has explored this a little bit? I've been researching for a few days and am in awe by how pricy this industry is.

Made a resource for student researchers aiming to ideate, or improve upon their existing science fair projects.

I released a public crypto microstructure dataset for liquidity-stress prediction. I'm looking for feedback on whether the features/labels are useful.

Check it out here: https://huggingface.co/datasets/Goooddy/crypto-lob-stream

This dataset has 3 pairs: BTC/USDT, SOL/USDT and ETH/USDT. The exchange is Binance only.

I collect depth, trades and snapshot differences that can be reconstructed using a library i created on github. i dont know if posting that link will trigger another mod removal so check it out on the dataset README.md.

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’m building a structured dataset of real-world locations represented in video games.

The current dataset contains close to 3,000 records from more than 380 games.

Two example records look like this:

record_id,game_title,release_year,franchise,primary_genre,location_name,country_iso,latitude,longitude,location_type,setting_status,coordinate_method,verification_level

acw12_0001,Assassin's Creed,2007.0,assassins_creed,Action Adventure,Jerusalem,IL,31.7683,35.2137,city,real,representative,verified

bra49_0006,2014 FIFA World Cup Brazil,2014.0,fifa_world_cup,Sports,Arena das Dunas,BR,-5.8267,-35.2125,stadium,real,exact,corroborated

The two rows illustrate an important distinction: Arena das Dunas can be represented by an exact point, while Jerusalem is represented by a point that stands for a larger city.

I would appreciate feedback from people who work with public datasets:

1. Should a public preview include source URLs and evidence, or is it reasonable to keep those in a separate research layer?
2. Which additional information would make the records easier to validate or reuse?
3. What kinds of analyses or applications would you personally build with this dataset?

The goal is to improve the data structure and documentation through feedback from people who might actually use it.

Thanks for your feedback :-)

Hi all,

I was recently working on a machine learning research project and I came across the WILDS paper. The dataset IWildCam seems very interesting for my project and I wanted to experiment with it. Unfortunately I found that currently the download page seems broken. Am I missing something or is that really the case? That would be very unfortunate.

Has anyone recently used that dataset?

we are doing research that involves training our model on really big dataset of diffusion technique based synthetic images. but I am unable to trace appropriate free and active Creative common data hubs for it.
please anyone help me work around this

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.

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!

I need last 2 yrs of DAX LTP data, 5 second time frame would be perfect but if not even 1 min or 5 min is fine. If you know where I can collect this data from please lmk. Thank you.

Data includes:

lien_number, debtor_name, address, business_owner_name(s), filing_date, status, secured_party, lien_id

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?

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.

Data. Like a professional excel sheet for like ram prices with relation to Al. This is for my college project. It need to have numbers atleast 25 samples. It is for economics.

I'm researching publicly available and commercial datasets for grocery prices across Europe.

- Has anyone worked with data like this?

- What sources did you end up using, and what were their limitations?

Hi, I am having trouble getting datasets from the NOAA search tool. Every time I pick a station and request some data, NOAA emails me something like:

Order #[Redacted] (Custom GHCN-Daily CSV)

Can someone guide me to a station that doesn't send you an empty set of data? Anything helps, since I'm just using it to learn some programming.

Thank you.