r/datasets

I've been running unfairgaps-os for a while - MIT repo with 4 pipelines that mine court filings, regulatory fines, and enforcement data to find business pain points. B2B angle: what industry-level problem is documented in lawsuits worth solving with a SaaS.

Wanted to extend it to individual professionals. Started off thinking the same court-records approach would work - just narrow it from "construction in US" to "lawyers in US." It didn't. Lawyers don't get sued over the fact that calculating filing fees per court is tedious. Accountants don't get fined because reconciling trust accounts is annoying. The pain a working professional feels every Tuesday isn't in court records - it's in the regulation that says "you must file form X by date Y or pay penalty Z" plus the daily grind of actually doing that.

So I switched approach. Two-stage pipeline:

Stage 1 is WebSearch - 7 targeted queries pulling regulatory facts from .gov, law.cornell.edu, BLS, and professional association sites. Daily routine + documents, regulations + licensing, software they use, jargon, career levels + fears, professional communities, labor market. Output is a structured JSON profile with ~30 specific facts and source URLs per profession.

Stage 2 hands the profile to Opus 4.7 with a deductive prompt and no web access. Given the regulation and daily routine, infer 8-15 specific recurring tasks that would be painful and produce a structured spec for the AI tool that would solve each one.

Loaded 130 US profession profiles into the repo. Ran stage 2 on 25 of them to seed.

Here's the full output from one run - auto detailers in the US - so you can see what actually comes out:

1. Price a detail job profitably (cost-plus, not guess) - calculator
2. Quarterly estimated tax + self-employment tax calculation - calculator
3. EPA stormwater compliance checklist (avoid wash-water Clean Water Act fines) - checklist
4. California Car Wash and Polishing Act registration + bond compliance - checklist
5. Vehicle intake / pre-inspection form (protect against damage claims) - template
6. Ceramic coating warranty + service agreement template - template
7. Sales tax on detailing services - state-by-state lookup - reference
8. Mobile detailer route optimization + travel cost recovery - calculator
9. Chemical inventory + reorder + PFAS compliance tracker - checklist
10. Paint correction estimate from photos + paint depth gauge - advisor
11. Winter cash flow + slow-season pricing strategy - advisor
12. Damage claim response (customer alleges scratches/damage) - checklist
13. IDA Certified Detailer (CD/SV-CD) exam prep + study tracker - reference

The first one is the most obviously buildable. Most detailers eyeball pricing and undercut by 25% because they don't run a real cost-plus formula. The actual output JSON includes the formula (labor + chemicals + the 2026 IRS $0.67/mile rate + 15.3% SE tax + monthly overhead allocation), inputs (10 of them including services list and target margin), and outputs (minimum profitable price, recommended price with margin, breakdown, tax set-aside). That's a $19/mo SaaS already specced out.

Number 3 is the scariest. EPA Clean Water Act civil penalty is $64,618 per day per violation if you dump wash water in a storm drain. EPA has literally put mobile detailers out of business for this. The output is a 12-step compliance procedure with warnings (biodegradable soap is NOT a defense) and citations (33 USC 1311, 40 CFR 122.26).

Each of the 13 has a structured spec like that. Not platitudes, buildable tools.

Honest framing: this isn't a problem interview. It's a discovery funnel. The pains are inferred from regulation + daily routine, not from real users complaining. You'd use this to sift 130 professions in an afternoon, pick 5-10 candidates that sound viable, then spend a week on real customer development to validate. Beats brainstorming SaaS ideas with your roommate.

Repo: https://github.com/AyanbekDos/unfairgaps-os Direct link to the auto-detailer output: https://github.com/AyanbekDos/unfairgaps-os/blob/main/data/professions/us/pains/us-auto-detailers.json

105 profiles still need stage 2 run on them. Takes ~5 min of LLM time each.

tldr: open-source repo finds AI tool ideas per profession by reading regulations instead of guessing. 13 specific ideas with formulas + citations for auto detailers as a real example.

Full disclosure [self-promotion]: I'm the solo builder. Happy to answer questions about the data, methodology, or entity resolution approach.

I built FastDOL, a platform that links federal workplace enforcement records across agencies into a single employer profile. The government publishes this data, but each agency has its own database, its own identifiers, and its own terrible search UI.

The cross-agency dataset links enforcement records from OSHA, WHD, MSHA, EPA, EEOC, OFCCP, OFLC, and others at the employer level with parent-company rollup. The interesting finding: employers cited by 3+ agencies have a 3.4x higher worker fatality rate than employers cited by 1-2 agencies.

Four open datasets available so far, all CC BY 4.0:

• Cross-Agency Federal Violations by Employer (~2.3M rows)
• OSHA Construction Enforcement by Employer (377K rows)
• OSHA Citations Q1 2026 (28,827 rows, citation-level)
• WHD Wage Theft Enforcement Actions by Employer

All hosted on Hugging Face, Kaggle, and Zenodo with DOIs. Full schema, methodology, and BibTeX on the canonical pages: https://www.fastdol.com/datasets

Built this over the past few weeks as part of a multilingual research project. Figured I'd share it here. Check it out!

What: ~9.8M web documents across 11 languages — hi, bn, ta, te, mr, gu, kn, ml, pa, ur, en. ~8.4B tokens. CC0 license.

🤗 https://huggingface.co/datasets/AM0908/indic-hplt-v1

PreSeedVCList covers 390 venture capital firms actively writing pre-seed checks, with data on firm websites, investment stages, sectors, office locations, and portfolio links, structured from recent funding activity and updated monthly at https://preseedvclist.com.

I’m building a lead enrichment pipeline for my friend in Canada and hitting a wall. Looking for advice from anyone who’s done similar work.

The data I have:

•3,800 licensed property managers from Ontario’s official CMRAO registry  
•Name only — no employer, no domain, no address  
•These are real licensed professionals, not residential contacts  

What I’ve already tested (with results):
• Apollo.io free tier → blocked on Search API, needs paid plan
• Hunter.io → needs company domain to work, useless without it
• PeopleDataLabs → blocked signup, requires work email
• Prospeo → B2B only, 0% hit on Canadian residential-style data
• Spokeo/BeenVerified → US database only, no Canada coverage
• Canada411 via Apify → works but returns phone numbers only, no emails

What I’m trying to figure out:

1.Is Apollo Basic ($49) actually worth it for Canadian property managers? Has anyone tested it for Canada specifically?  
2.Is there any people-search or enrichment tool with decent Canadian professional coverage?  
3.Has anyone successfully enriched name-only Canadian professional contacts at scale?

What I’ve already ruled out:

•US-only people search tools (Spokeo, BeenVerified, TruthFinder)  
•Tools that need a company domain as input  
•Residential Canadian data (confirmed it basically doesn’t exist)

These are licensed professionals so they should have LinkedIn profiles and company affiliations — just need the right tool to match name → email efficiently.

Any real-world experience appreciated. Happy to share results once I find something that works.

Hello, I built a validation dataset of real and generated images (with a vanilla SDXL+InstantID architecture). I'm running low on AWS credits/have a low budget, but I want to benchmark the performance detection models against it. Can anyone recommend open-source detection models that I can test?

I know there is a mix of ones created by universities and made by members of the open source community, but any opinions on which 4-5 I should test would be greatly appreciated.

Disclosure: I’m on the Abliteration team.

We just shipped a training-data generator for people who need specific examples rather than another generic public dataset.

You describe the examples you want and it generates structured synthetic data. If the dataset needs current or real-world facts, you can turn on web search. Exports are live for Hugging Face, Kaggle, S3, and OpenAI.

The first use cases we built around are classifier and eval datasets for trust and safety: grooming detection, harassment detection, security research evals, jailbreak and edge-case sets, and similar work where teams need examples that general-purpose models often refuse to generate.

I marked this as synthetic and paid because the outputs are generated and this is a commercial tool.

Product: https://abliteration.ai/

Synthetic data page: https://abliteration.ai/use-cases/synthetic-data

Launch video: https://x.com/abliteration_ai/status/2054675554138194178

For people who curate datasets: what export format or per-row provenance metadata do you usually need before a generated dataset is usable?