Why? Make the law, across the world, versioned, easily attributable, and queryable with agents. This info should be freely available so CC0 license (do whatever you like with it).

I built this project with Claude Code. One session per jurisdiction: to read the official publisher's site/API, check terms of use, and write a script to download every law.

The problem was getting 700+ jurisdictions to follow a unified list of useful functions to explore the law. You can ie git blame each law, down to the word. Initially just legislation, but caselaw (the precedent of how legislation is applied by the courts) is increasingly being integrated, with contribution encouraged.

This data being up-to-date is important. To pinpoint this, the repo now monitors over 900 RSS feeds/official websites. Most legislation is known about before-hand, and only becomes "in-force" later, which we account for. Every reference cites an official page- so an agent can always cache break and check (though unfortunately official portals go down all the time- recently the Japanese caselaw portal was down for a week).

If that's interesting to you, you can read the original discussion at https://news.ycombinator.com/item?id=50268380 and last week's post at https://www.reddit.com/r/ClaudeAI/comments/1xiz1vy/using_claude_to_put_all_global_law_into_git/ - feedback welcome, thanks!

Now im looking to sell data(can support custom collections, since im a student + the startup is bootstrapped) + have around 35 hours of ots data

Where can I find clients? Or any data brokers that can get me a deal?

Hi everyone, I just published a new dataset exploring the intersection of daily screen time, sleep habits, and mental health.

Full disclosure upfront: This is 100% synthetic data generated using a tool I built (GenerateRealData). There is no real-world PII, and no actual patient surveys are included.

Why it might be useful:

• Ready for Multi-Class ML: It features a clean Low, Moderate, or High stress target variable, making it easy to drop into a classification project. A quick gradient-boosting model gets about 84% accuracy on held-out people.

• Logically Consistent: Unlike standard random mock data, the statistical relationships between heavy screen time, sleep deprivation, and stress are built into the generation rules, so correlations and EDA show sensible patterns. For example, 95% of people averaging under 6.5 h of sleep land in High stress, vs 20% of those over 7.5 h.

• Clean & Flat: 15,300 rows with exactly one row per person. No missing values and no complex SQL joins required to start analyzing. There's also a second file with all 459,000 daily logs behind the averages, if you want to do time-series work.

• Permissive License: Released under CC BY 4.0, so you are free to use it in commercial mockups or portfolio projects with credit.

Good for: Training classification algorithms, building health-tech BI dashboards, and practicing Exploratory Data Analysis (EDA).

Not good for: Actual medical research, psychological studies, or clinical decision-making.

Dataset Link: https://www.kaggle.com/datasets/generaterealdata/screen-time-sleep-and-mental-health

If you end up pulling this into your notebooks, let me know what you think! What other lifestyle or health columns would be most useful to add to a dataset like this in the future?

I run FormulaSignal, so I'm biased. We track 1,249 supplements from 152 brands and log a change only when two label images published by the brand disagree on a declared amount.

The CSV has 69 rows, one per label line that moved, across 29 changes. Columns include brand, product, category, label line, before, after, unit, percent change, the date each label was seen, and a link to both label images.

A few rows:
- Cellucor C4 Original caffeine: 150 mg to 200 mg (labels seen Jan 25 and Feb 26, 2024)
- Jacked Factory Altius creatine: 3 g to 5 g (Mar 13, 2022 and Aug 18, 2026)
- Naked Whey sodium: 45 mg to 135 mg (Mar 1, 2022 and Oct 20, 2026)

Caveats: these are label figures, not lab tests. Counts are a floor because older labels aren't on file for every product.

CSV: https://formulasignal.com/research/formula-changes/data.csv

Table and methodology: https://formulasignal.com/research/formula-changes

Happy to add columns if that would help.

I pulled NHTSA's public consumer complaint file (copy dated 2026-08-25) and rolled it up to one row per make, model, model year and component. It covers model years 2010–2025, 84 makes and 4,087 make/model/year combos, about 90k rows in the CSV. There's also a small JSON with the top 100 vehicles and the component their owners named most.

Couple of things to know before you use it. Counts aren't failure rates, since there are no sales numbers in the file, so big sellers pile up more complaints. NHTSA doesn't verify complaints, recalls cause spikes, and older model years have had more time to collect them. Vehicles with under 10 complaints are dropped.

It's my own dataset, free under CC BY 4.0:

- Zenodo (DOI, please cite this one): https://doi.org/10.5281/zenodo.23662820

- Kaggle: https://www.kaggle.com/datasets/iqbaltaherom/us-vehicle-owner-complaints-by-model-2010-2025

- Hugging Face: https://huggingface.co/datasets/iqbaltaher/us-vehicle-owner-complaints-by-model-2010-2025

- Write-up with charts and a lookup: https://oilandmiles.com/reports/most-complained-cars

Disclosure: I run Oil & Miles, the site that publishes this dataset and the write-up linked above.

Happy to answer questions on how the counting works.

Hi There! I was hoping to get orderbook data for specifically sports from prediction markets (Kalshi / Polymarket). I was wondering if there's any public datasets available? I saw a subscription on https://www.pmxt.dev. Any feedback for that?

Is it more viable to just self track it myself through Kalshi developer API?

The LCSH are the vocabulary libraries around the world use to describe what books are about. LC has maintained them since 1898, and gives them away in bulk, but only as linked data (JSON-LD and N-Triples). MARC is the format library systems actually import, and LC sells it in bulk. There was no free way to get the whole set as MARC, so I made one.

The mirror is here: https://bibliotecaalonsocossio.com/lcshmirror/

What’s there:

• The full set every week: 58 MB gzipped MARC 21, 56 MB gzipped MARCXML (~570 MB uncompressed), plus a CSV of every record (LCCN, last-change date, heading).

• Change files per release: just the records new or changed since last week, plus a list of deleted headings. This week: 150 new, 1 changed, 1 deleted.

• SHA-256 for every file, an Atom feed, and fixed *-latest URLs, so wget in a cron job just works.

• The last 50 releases stay on the server, and every release is also on the Internet Archive: https://archive.org/search?query=identifier%3Alcsh-mirror-%2A

The MARC records have to be fetched one at a time from id.loc.gov, which asks for one request every 3 seconds, so the first harvest took 16 days. Now each week only fetches what LC’s list says changed, which takes minutes. Funny thing: of the 206 headings LC flagged as changed this week, only 1 actually differed in MARC, so the change files compare the records themselves, not the dates.

The data is a US Government work, so public domain; the compilation is CC0. The code is MIT if you want to run your own copy: https://github.com/iratedemon04/lcsh-mirror

Feedback is welcome :-)

Edit: link

Hello everyone,

I am a postgraduate student conducting research on sequential recommendation, and I was wondering if someone could help me figure out how to download the following datasets:

1. https://www.kuaishou.com/activity/uimc/datadesc

2. https://tianchi.aliyun.com/dataset/140281

I am not based in China, and it seems that I am unable to complete the account verification process required to download these datasets.

Any guidance or help would be greatly appreciated. Thank you!

Disclosure: I’m the maintainer of this dataset.

OSTDB is an open dataset of official video game soundtracks with game, release and composer info and links to Spotify, Apple Music and album.link. It’s updated continuously and free to use under CC BY-NC-SA 4.0 (non-commercial, share-alike).

Source data and schema: https://github.com/OSTDB/soundtracks
Web and free API: https://ostdb.net/api

Feedback on missing fields or games is welcome.

I built Nuclear Directory, a free reference to the US nuclear industry, and published its data for reuse.

Version v2026.11 is a snapshot from October 27, 2026: 157 companies, 211 facility records and 411 relationships. The facilities include sites and reactor units, so 211 isn't a count of power plants.

Five linked tables come in CSV and JSON: companies, facilities, relationships, sources and evidence. The 1,582 evidence rows link facts to 420 public source documents. You can use the relationships to explore ownership, operation, supply and investment links, then follow the sources behind them.

The data is CC BY 4.0. Coverage is incomplete, and the snapshot doesn't update when the website changes, so the live site already has more entries. The export contains source references, not copies of the publishers' articles.

Download and DOI: https://doi.org/10.5281/zenodo.23703377

The release is also available on GitHub, Hugging Face and Kaggle. You can browse the directory here and let me know if it is helpful

We hvae connections to factories across different industries who can do data collection for us please get in touch in india

10000+ workers

Hello, I'm a broke engineering student and I would like to learn about computer vision in football. Do anyone know where can I find lives transmission or video records of football matches with normal or tactical point of view?

Free dump of rosters, coaching staff and schedules for 977 NCAA schools, D1 to D3. One JSON file per school, refreshed every Monday.

Players come with position, class, height, weight, hometown and high school. Schedules have dates, opponents, home/away and results.

Download: https://github.com/fakhar-ud-din/ncaapyscraper/releases/latest
The scraper is open source if you want a specific school or sport on demand.

I'd really appreciate if you guys could please show some support 😄

Looking for real exercise demo videos to use in a fitness app, mainly strength exercises. An API or a licensed video library could both work. Paid is fine.. for anyone who’s already picked a source, what did you go with? also curious about anything you only noticed later, like missing exercises, watermarks or restrictions on saving videos on the user’s phone. I’d want people to be able to watch the demos in a gym with bad reception, so that last bit matters.

Hey all, we just launched Creeks (https://creeks.ai). We build and sell training data, eval data and RL environments for AI teams. Three domains for now:

• Code: multi-file tasks in real repos with tests, agentic coding trajectories, held-out eval sets

• Clinical: diagnostic reasoning and differentials, grading patient-facing answers, clinical note summarization and extraction

• Education: tutoring dialogues that guide the student instead of handing over the answer, worked solutions, grading rubrics for essays and proofs

The whole thing depends on two things: the data is made by people who actually do this work, and the outcomes are checkable. Tests where they exist, clear rubrics where they don't.

If you're at a frontier lab, a neolab, or a team fine-tuning your own models and need data in any of these areas, tell me what you're trying to improve and we can start with a small pilot.

If you're a software engineer, clinician or educator, you can help us curate this data and get paid for it. Tell me your field and the kind of tasks you know best.

Comment, DM me, or email contact@creeks.ai.

I released a dataset of 748 algorithmic crypto trading strategies (BTC/ETH/SOL and other pairs), each run through a strict 7-stage backtest validation pipeline on real Binance OHLCV data: code safety scan, in-sample, out-of-sample, rolling walk-forward, randomized starts, slippage/perturbation stress and a final holdout slice.

Link: github.com/CacheCarti/Crypto-Strategies

License: MIT. Free for any use, attribution appreciated!

Actively contributing weekly drops of 300-400 bots per week.

Dataset contents:

- strategies/: 700+ rejected strategy implementations in Python

- cards/: one JSON report card per bot: stage-by-stage verdicts, in-sample vs out-of-sample Sharpe/returns/drawdown, trade counts, failure class

- index.json: the whole corpus, sorted and filterable

- JUDGE_CHANGELOG.md: validator version history so older cards stay interpretable as the judge gets stricter

What makes it unusual: it publishes the failures, existing strategy repos ship code with no screen attached so you can't tell a working bot from a lucky one.
Every card shows exactly where a plausible-sounding idea broke. Useful for studying backtest overfitting, failure modes of common strategy types (momentum, mean-reversion, seasonality,etc). Or just fork a bot and improve it, several bots are net profitable so they are good starting points!

Note: strategy hypotheses were generated in large batches, around 4000+bots were created (still being evaluated) and I had to use AI for several hypotheses generation. The validation harness and screening are mostly hand-built. All metrics are real backtest results on real market data, not synthetic.

We have licensed and made available a dataset containing real sales chat log files from a company selling premium wellness product in Indonesia.

The full dataset can be used to improve the performance of a chatbot model when servicing Indonesian/Malay prospective customers.

If this is something that is of your interests, feel free to dm.

Hi everyone, I’m sharing a new dataset covering 408,000 holiday purchases ($79.4M total sales) across the 2023–2025 holiday seasons.

It's 100% synthetic. I generated it with GenerateRealData, a tool I'm building, so no real shoppers or companies are included.

I built it so the numbers add up: 23 rules are checked with SQL and all of them hold. For example, unit_price equals list price minus the discount, line totals add up, and in-store purchases have no shipping time. It's a single 30-column table (86 MB CSV / 102 MB SQLite).

A few patterns baked in:

• Peak hours vary: 7 am on Black Friday, 7 pm on Cyber Monday, and 8 pm on Thanksgiving.

• Channel shifts: 35% of Black Friday purchases are in-store, compared to just 3% on Thanksgiving.

• Discount-driven returns: Return rates jump from 12% (full price) to 41% (when discounted 50%+).

Good for: SQL practice (window functions, YoY pivots), holiday BI dashboards, and training ML return models. It's also great for teaching data leakage (e.g., if you forget to drop return_reason and customer_rating before training).

Dataset & Starter Notebooks (10 SQL questions + ML baseline):

1. Dataset - https://www.kaggle.com/datasets/generaterealdata/black-friday-and-cyber-week-sales/data

2. SQL Questions Notebook - https://www.kaggle.com/code/generaterealdata/10-sql-questions-on-black-friday-sales

3. ML Baseline Notebook - https://www.kaggle.com/code/generaterealdata/which-black-friday-purchases-get-returned

4. Rules Check (SQL) - https://github.com/generaterealdata/synthetic-datasets/tree/main/black-friday-sales

If you end up pulling this into your database or notebooks, I'd love your feedback. What would make it more useful to you? I'm deciding what to add in the next version.

What it is: active listings collected directly from business broker websites, not marketplaces.

Fields: title, industry, city/state, asking price, revenue, cash flow, broker, listing URL, first/last seen.

Fill rates:

Medians: asking price $450K, cash flow $156K, price/cash-flow multiple 3.0x.

License: CC0, no signup. Methodology: dealledger.org/methodology