Over the past few months, we’ve been building a structured POI data marketplace at Agenty.

https://agenty.com/marketplace

We now have datasets covering 3,000+ brands across 12 countries, with 10M+ POI records.

The datasets include things like:

1. Store/location name and address
2. Latitude & longitude
3. Phone numbers and websites
4. Opening hours
5. Brand and location metadata
6. Other useful POI attributes

The goal is to make it easier for AI agents, researchers, data teams, and developers to get clean location data without having to build and maintain scrapers for every brand themselves.

Some of the datasets cover large retail, restaurant, automotive, hospitality and other brand networks.

We’re putting the marketplace together and would love feedback from people who actually work with POI/location data:

What brands or types of POI datasets would be most useful to you?

Disclaimer/Disclosure: I’m the founder of Agenty, so I’m obviously biased here. Sharing this because we’ve spent a lot of time building the datasets and would genuinely like feedback from the community.

I recently completed a structured dataset of 7,241 renewable-energy installers, suppliers and related specialist businesses across the UK, covering Solar PV, heat pumps, battery storage, EV charging, solar thermal, biomass, wind and other renewable technologies.

Disclosure: I built this dataset and it is sold commercially through my company, Bitrixa Limited.

The dataset was assembled and enriched from multiple public and official sources rather than being a direct export of a single directory.

Some of the structured fields include:

• Company / trading name
• Installer / supplier classification
• Primary technology
• Other technologies and services
• Region and location
• Website
• MCS directory match
• MCS technologies
• Companies House number where confidently matched
• Company status / company information
• Accreditation and supporting public-source information

A few findings from the completed dataset:

• 7,241 deduplicated businesses
• 5,878 classified as installers
• 1,157 installer + supplier businesses
• 206 supplier-only businesses
• 5,443 matched to MCS directory information
• 6,275 matched to Companies House numbers
• 6,671 businesses with websites identified
• 3,605 businesses classified primarily as multi-technology

One result I found particularly interesting was how common multi-technology businesses are.

Nearly half of the dataset is primarily classified as Multi-Technology, rather than purely Solar PV or Heat Pumps.

For example, a solar installer may also offer battery storage, EV charging and heat pumps, which makes a simple one-category directory much less useful for analysing the market.

Another interesting difference appears in MCS matching.

Of the businesses primarily classified as Heat Pumps, nearly all were matched to MCS directory information, while the Solar PV group had a much lower match rate. This should not be interpreted as the percentage of certified solar installers nationally because the solar segment also includes suppliers and businesses identified through other sources.

This is therefore not intended to be an official census of every renewable-energy company in the UK. It is a research-enriched snapshot compiled from multiple public and official sources.

The full dataset is available commercially here:

https://www.bitrixa.com/product/uk-renewable-energy-installer-database/

I have also published an analysis of the dataset and methodology here:

https://www.bitrixa.com/uk-renewable-energy-installer-market-2026/

I’d be interested in feedback from people who work with energy, climate, business or company datasets.

In particular, what additional fields would make a dataset like this more useful for analysis rather than just company discovery?

Some things I’m considering for future versions are:

• installation technology combinations
• company age bands
• regional installer density
• richer accreditation data
• Companies House financial indicators
• geographic coordinates
• historical changes between releases

Does anyone here have an active IEEE DataPort subscription? 😭

I’m currently doing my thesis proposal and I need 2 datasets from IEEE DataPort, but I don’t have a subscription and the datasets are unfortunately not freely downloadable.

I don’t need your account/login or anything like that. I just need someone who has access to download the two datasets for me and send them over.

I know this is a long shot but I’m desperate at this point lol 😭🙏 If anyone can help, please DM me and I’ll send you the links. I’d really really appreciate it!!🫶

We ran a small experiment and we’re releasing everything so people can poke holes in it or rerun it.

We asked ChatGPT (web search) and Gemini (grounding) ten “best X for a small business” questions, 3 times each, same day. For each answer we extracted the set of businesses it recommended, then measured overlap across the 3 identical runs (mean pairwise Jaccard).

Result: ~69.5% overlap overall, so roughly a third of the recommended businesses change between identical asks. ChatGPT held steadier (87%) than Gemini (52%). The top 1-3 names stayed locked every run; the lower slots rotated.

Caveats up front: it’s 10 questions, 3 runs, 2 engines, one day. Directional, not definitive, no confidence intervals. We’re releasing it precisely because it’s small, so anyone can rerun with more questions. Extraction was validated (all 290 names appear verbatim in their source answers). Conducted by a company (Pressfront) with an interest in the topic, which is exactly why the full method and every response are public.

Raw JSON, CSVs, the analysis script, and a preprint (DOI: 10.5281/zenodo.22738861) are in the repo, CC BY 4.0.

https://github.com/jjoseph18/ai-recommendation-consistency

Disclosure: I created this dataset through PoolForge.

It contains 612 NHL skaters with at least 40 regular-season games, scored under two systems: goals + assists only, and a weighted-points setup that also rewards shots, hits, blocks and penalty minutes.

The CSV includes the underlying scoring inputs, calculated totals, both rankings and rank changes. The documentation explains eligibility, scoring weights and tied-rank handling.

It could be useful for practicing ranking analysis, sensitivity analysis or sports-data visualization.

This is historical analysis—not projections or a categories-league ranking. The dataset is available under CC BY 4.0.

Dataset DOI: 10.5281/zenodo.22730472 ( https://zenodo.org/records/22730472 )

Corrections and independent reproductions are welcome.

Me and my college professor are working on an academic ML project involving dental panoramic radiographs (OPGs).

We need roughly 60–120 de-identified adult OPGs across six categories:

• Healthy
• Caries
• Impacted teeth
• Infection
• Fractured teeth
• Broken-down crown/root (BDC/BDR)

The technical ML part isn’t really our biggest problem right now.

Getting a legitimate dataset is. 😭

We can’t just scrape random dental X-rays from Google, and we obviously don’t want to handle identifiable patient data. Our current idea is to collaborate with dentists/oral radiologists/dental colleges/clinics who can provide appropriately anonymized OPGs, with institutional documentation from our supervising professor.

We’re also planning to have the diagnostic class and affected tooth number recorded using FDI notation.

We’ve made a small expression-of-interest form for dental professionals, but before we start contacting clinics, I want to know:

For those who have worked on medical/dental AI projects:

1. How did you actually obtain your dataset?
2. Did you need IEC/IRB/ethics committee approval before approaching hospitals/clinics?
3. Is a professor’s collaboration letter enough to initially approach a dental hospital, or do institutions usually require additional documentation?
4. Is it better to approach dental colleges/hospitals rather than individual private dentists?
5. How do you handle anonymisation of DICOM/OPG files properly?
6. How do you get reliable diagnostic labels — treating dentist, oral radiologist, or multiple annotators?
7. If a dentist contributes images/annotations, how should authorship/acknowledgement be handled?
8. Are there existing public OPG datasets that would make more sense for a student research project?

We’re not looking to bypass ethics/privacy requirements — we want to do this properly from the beginning.

If you’ve actually collected medical imaging data for an academic ML project, I’d really appreciate hearing how you went about it.

Especially interested in the practical part: Who did you contact first, what documents did you need, and what made an institution actually agree to collaborate?

Looking for a dataset for a govt policy research project, i need a mainly india dataset, and other countries are also okay. Any suggestions or direct datasets needed? Thanks

Sharing a dataset collection I’ve been putting together.

OpenOmicsBench v1 has 12 bulk RNA-seq benchmark datasets derived from seven Expression Atlas studies across human, mouse and Arabidopsis.

Each contains counts, sample metadata, the experimental design, source/provenance information and a compact version intended for examples or software testing. I also keep validation results against the corresponding full matrix rather than assuming the smaller matrix preserves the original behaviour.

Everything is openly available and versioned, and the collection can either be installed from PyPI or obtained through GitHub/Zenodo.

I’d be interested in suggestions for other public RNA-seq study designs worth representing in future versions.

GitHub: https://github.com/vxxqv/openomicsbench

PyPI: https://pypi.org/project/openomicsbench/

Zenodo: https://doi.org/10.5281/zenodo.22679414

I build a B2B data tool (AtlasForgeX) and this week I needed to know how many companies each registry actually added in August 2026. All five sources below are free and need no key. Each one had a catch that gave me a wrong number on the first pass.

Finland: PRH YTJ API v3 (https://avoindata.prh.fi/opendata-ytj-api/v3/companies with registrationDateStart and registrationDateEnd) August returned 1,861 companies, but only 1,637 had their business ID registered in August. The date filter also returns older companies that had some other registry event that month, so filter on businessId.registrationDate yourself. Nearly all are limited companies (1,598 osakeyhtiö). Sole traders do not appear. The same query for August 2025 gives only 971, which I do not believe, so I am not comparing years with it.

Norway: Enhetsregisteret API (https://data.brreg.no/enhetsregisteret/api/enheter with fraRegistreringsdatoEnhetsregisteret) August returned 6,782 units. That includes 323 bankruptcy estates (KBO), 269 associations and 241 foreign units. Business forms come to 5,806, of which 2,589 AS and 2,977 sole proprietorships (ENK). The counts have no lag, but for September 1-12, 1,850 of 3,371 business units had no industry code yet.

Sweden: Bolagsverket (monthly statistics file ftgstat_oppna.csv, event 1 is a new registration, plus the bulk company file from https://bolagsverket.se/apierochoppnadata/hamtaforetagsinformation/nedladdningsbarafiler.2517.html) The monthly statistics say 4,215 new registrations in August, 3,439 of them AB. In the bulk file 3,175 ABs were registered in August, and 770 of those match the names and descriptions of shelf company providers (323 at one address in Växjö), meaning companies registered to be sold later. August 2025 has only 13 by the same match, because shelf companies are renamed once sold. The match is a heuristic, but it is big enough to skew AB growth and city rankings.

Estonia: e-Business Register open data (https://avaandmed.ariregister.rik.ee/, daily files) August: 2,384 new entities, 2,066 of them OÜ. Deleted companies are not in the file, so older months shrink over time.

Netherlands: KVK open dataset (https://www.kvk.nl/producten-bestellen/kvk-handelsregister-open-data-set/, CC BY 4.0) Only BV and NV, no names, a start date instead of a registration date and a two-digit postcode. August: 5,684 BVs, and 44% of them carry the head-office or financial-holding activity codes (70102 and 64210). Quarter-ends spike: March had 12,538 and June 12,357.

Not possible without an account: Denmark (CVR data needs access, and Statistics Denmark publishes only an index) and Germany (the Handelsregister allows 60 lookups per hour, Destatis publishes half-years).

One pattern held in the four countries where I could read industries: programming and management consulting were the top two categories of new companies in Finland, Norway, Sweden and Estonia.

Hi, everyone.
I’ve been looking into dictionary data for a language-learning app I’m building, and I’ve run into something surprising.

There are tons of dictionary apps, browser extensions, reading tools, and language-learning apps that support many languages.

But when I actually try to find good open dictionary data myself, it’s surprisingly difficult.

Even for something as common as English -> Chinese, it’s hard to find a dataset that has all of these:

• headwords
• translations
• part of speech
• multiple senses
• example sentences
• a clear license that allows reuse

I’ve found things like:

• Wiktionary / Wiktextract / Kaikki
• WordNet / Open Multilingual WordNet
• ECDICT for English–Chinese
• CC-CEDICT for Chinese–English
• Tatoeba for bilingual example sentences
• FreeDict
• various language-specific projects

But they all seem to cover different pieces of the puzzle, and the quality and coverage vary quite a bit.

Meanwhile, I regularly see relatively small dictionary apps or browser extensions claiming to support English, Chinese, Japanese, Korean, French, German, Spanish, Italian, Russian, Arabic, etc.

So I’m genuinely curious:
Where does all of that dictionary data usually come from?

I’m especially interested in hearing from anyone who has actually built a dictionary app.

What data sources did you use, and how did you handle licensing?

Btw, I know Cambridge, Oxford, and other major dictionaries offer paid APIs or data licensing, but they’re quite expensive. I assume many smaller dictionary apps probably use their own databases or combine multiple data sources somehow.

Any pointers would be greatly appreciated. Thanks!!!

What does poverty actually look like beyond a single income number?

For millions of rural households, poverty is not simply the absence of income but rather a web of constraints involving education, productive assets, access to finance, consumption, debt, and the ability to withstand economic shocks.

This dataset I made shows how rural households earn, consume, borrow, save, spend, insure and get by, using data from 4,184 rural households in Tamil Nadu.

https://www.kaggle.com/datasets/ouiouinonoui/kshetriyagramin-financialservices-tamilnadu-survey

This analysis examines how household composition, schooling, housing, land ownership, enterprise activity, savings, insurance and credit interact with economic outcomes.

https://www.kaggle.com/code/ouiouinonoui/visualizing-the-economics-of-rural-poverty

Every US healthcare provider has an NPI, and CMS publishes the whole registry as a bulk file. It’s public, but it ships as a multi-million-row monster with taxonomy codes instead of job titles, no category labels and no dedupe, so most people bounce off it.

I mapped the taxonomy codes to 290 plain-English profession labels across 14 sectors, normalized the addresses and deduped it. 9,270,029 rows. Per row: name, full mailing address, profession, the federal source it came from, and the date it was last rebuilt.

A few things I learned doing it:

• The state column has 124 distinct values, not 50. Military post codes (AE/AP/AA), foreign provinces, and genuine junk like “ZZ” and “–” with one row each. 99.947% are real US codes.
• “Medical (Other)” isn’t an unclassified bucket — it’s 258 real professions, led by Nurse Practitioner (498k), Pharmacist (326k) and Registered Nurse (296k).
• Ratings exist on only ~33,000 of 9.5M rows (CMS-rated facilities only), so they’re useless as a feature. I stopped advertising them.

Source files: NPPES/NPI, CMS, plus NCES for schools and FDIC for banks. All US government works, so public domain under 17 U.S.C. §105.

Happy to send a free 1,000-row sample to anyone who wants to look at the structure — comment or DM. Sourcing writeup is at procureadjacent.com/data-sourcing if you want the provenance detail.

I’ve published a structured transcript dataset of Pinga-Fogo, two live TV interviews with Brazilian medium Chico Xavier, broadcast by TV Tupi in 1971.

Together, the two programs amount to roughly 6 hours of material and are among the longest surviving recordings of Chico Xavier answering questions live before a panel of journalists.

Dataset:

https://huggingface.co/datasets/ia-espirita/pinga-fogo-chico-xavier

What’s in it

345 speech turns, including 115 answers by Chico Xavier.

Available in JSONL, Parquet, and combined CSV. Each turn includes speaker, turn type, topic label, timestamps, transcript text, source audio, and review flags.

Portuguese only.

How it was made

Internet Archive audio → Whisper large-v3 → LLM-assisted segmentation and labeling.

The LLM did not rewrite the transcript. It only worked with segment indices, boundaries, speaker/turn classification, and topic labels.

Every word in the transcript comes directly from Whisper output.

Known limitations

This is still unreviewed ASR from degraded 1971 recordings, so there are transcription errors, especially in proper names and numbers.

Every record is marked:

revisado_por_humano: false

If you want to quote something, check the original audio first. Timestamps are included for that purpose.

The date of the second program is inconsistent across historical sources, so I recorded only December 1971 rather than forcing an exact day.

Speaker attribution is also left as desconhecido whenever I couldn’t identify someone confidently.

Why I made it

I’m building a retrieval system over historical Spiritist literature and primary sources, and I couldn’t find a structured, timestamped version of Pinga-Fogo anywhere.

Potential uses include pt-BR ASR benchmarking, speaker-turn segmentation, diarization, information retrieval, and long-form QA.

Human review is the obvious next step.

Corrections and PRs are very welcome.

I’m starting my PhD and I’ve been thinking quite a bit about what I actually want to spend the next few years working on.

My background is mainly in computer vision and deep learning, although I’ve worked on quite a few broader ML projects as well. Most of my work has been research-oriented: training and evaluating models, running experiments and ablations, comparing architectures, benchmarking methods, and working on papers/scientific contributions.

What I’d really like to do now is work on more real problems with real data, rather than picking another benchmark and trying to squeeze another 0.x% out of it.

So I thought I’d ask here:

Does anyone have an interesting dataset or real-world problem that they think is underexplored from an ML/CV perspective?

I’m particularly interested in situations where someone has:

• collected an interesting dataset but doesn’t have the ML/CV background to fully explore it
• a domain-specific problem where existing models don’t work particularly well
• data that hasn’t really been benchmarked with modern deep learning methods
• an interesting detection, segmentation, classification, tracking, multimodal, remote sensing, medical imaging, etc. problem
• an existing research project where another person who can handle the experimental/ML side would be useful

I’m not really looking for a Kaggle-style project just for the sake of training a model. I’d much rather find something where there is an actual research question behind it and where careful experiments could potentially produce something scientifically useful.

On my side, I can contribute with things like model development/training, PyTorch pipelines, baselines, experiment design, ablations, evaluation, literature review and research writing.

I’m also completely open to learning a new application domain if the problem and dataset are interesting. In fact, that’s partly what I’m looking for — collaborating with people who understand a domain much better than I do, while I contribute the ML/research side.

If you have something sitting around that you’ve always thought “someone should really try ML on this properly”, I’d genuinely be interested in hearing about it.

Feel free to comment or DM me. Even if it doesn’t turn into a collaboration, I’d be interested in seeing what kinds of real datasets/problems people here are working with.

I’ve been trying to find a good dataset over the internet which is freely available . The main issue I want to tackle is reduce the number of false positives such as reflections , headlights, yellow/red coloured objects. Initially I was looking for video datasets with enough negative class videos since I wanted to add a temporal analysis layer in the system, but didn’t find much . Had less videos or with the same background, which didn’t allow my model to generalize well .

If anyone has suggestions about a good image/video dataset please tell me .

I found the MIVIA lab’s LFDN dataset perfect for my cause but I guess their website isn’t maintained anymore. So yea let me know if there’s another way to get their dataset.

I built https://nichedb.dev/ because I was using the same on multiple projects, most of it is free. some require free api keys, but I’m not paying for anything as far as sources go.

My goal was to provide a bot-friendly x402 price of $1/day to query up to 1k/minute, free users can get 100 queries/minute without being throttled.

What other sources should I add? I’m mostly looking for large datasets that are updated continuously and available free to download.

Disclosure: I work on StarTarot, a tarot/astrology site, and this dataset is published under its name.

One record per Rider-Waite-Smith (RWS) card, built only from two early-1900s texts (nothing from modern tarot sites or other datasets):

• Waite’s own upright and reversed meanings from Part III of The Pictorial Key to the Tarot (first published 1910), quoted verbatim from Wikisource’s validated transcription. Each line has a section reference like “Part III §2”. The wording is archaic in places (“bewrayment”, “the Querent, if male”) and it’s left as is.
• Golden Dawn (GD) titles and elemental, planetary, zodiacal and decan correspondences, normalized from the tables in Liber LXXVIII (The Equinox, vol. I no. VIII, 1912). Decans come with degree ranges.

Files: cards.jsonl (nested), cards.csv (flat), a README with the field guide and sources, and SHA-256 checksums. No card images, no model outputs, no personal data. The source texts are public domain; CC BY covers the selection and structure. And to be clear, it records what two old texts say. It doesn’t claim tarot predicts anything.

A few things that might trip you up:

• The Two of Cups has no reversed line in Waite’s main entry, so its reversed value (“Passion.”) comes from his short list of additional meanings (§4), and the section field says so.
• Strength is 8 and Justice is 11 (RWS order), attributed to Leo and Libra. Marseille and Thoth decks swap those two numbers, so don’t match cards across decks by number.
• Courts: mounted Knight = GD King (Fire), Queen = Water, RWS King = GD Prince (Air), Page = GD Princess (Earth). Some modern books swap King and Knight.
• No yes/no field. That’s a reading convention, and neither source gives one.

DOI (v1.0.0): https://doi.org/10.5281/zenodo.21381780

It’s also mirrored on Hugging Face and Kaggle; I can post those links if anyone prefers them.

One open question: Waite’s §4 has a second, shorter set of meanings for all 56 minor cards, and v1.0.0 only uses it to fill the Two of Cups gap. Worth adding as separate columns, or just noise?

I’ve been working on an agent that tracks leadership changes at mid-market SaaS companies and turns them into potential outbound timing signals.

The basic idea is pretty simple: when a company brings in a new sales leader, there’s often a period in the first few months when they’re reviewing the existing stack and deciding what needs to change. So the goal is to catch the hire early.

The data comes through the Coresignal API and then gets scored based on things like the role, company size, and how the person’s previous career moves line up. I’ve also been using their MCP during development, mostly for digging into career histories without having to build another query layer. n8n handles the workflow around it.

The part I’m still trying to get right is how much weight to give the secondary signals. Some combinations seem meaningful, while others don’t tell you much on their own.

For anyone who’s built something around leadership changes or intent signals, how did you handle the scoring without making the model too rigid?

Would somebody be interested in getting the full primary dealer data all the way back upto 1998 fully stitched?

Any sort of further inferences could be made based on that.

Currently the data is very chopped/missing across time eras on the NYFED website.

Bloomberg also doesn’t seem to have that data in the proper manner.

Disclosure: I’m the developer of the tool linked below.

GDELT (the Global Database of Events, Language, and Tone) is one of the largest open event datasets around: every reported event worldwide since 1979, tagged by actor, location, tone, and (via the Global Knowledge Graph) the article that reported it. The catch is actually getting the full thing: the official API caps you at ~250 rows per query and a 3-month window, and BigQuery’s free tier (1TB/month query, 10GB storage) doesn’t stretch to a full historical pull either. The raw bulk archive at data.gdeltproject.org is open, but it’s thousands of loose CSV/ZIP files with no sampling or joining logic of its own.

I built GdeltForge to close that gap: it downloads, checksums, converts to Parquet, and reproducibly samples the whole archive locally, and can cross-reference a sample back onto the Global Knowledge Graph. Check out the PyPI page, the GitHub Repo, the documentation, and especially the hands-on getting started notebook, runnable directly in Colab, no install needed. You can generate you own samples or even scan the entire data to create a dataset