Hi all,

I’m looking to partner with researchers/teams who need support creating synthetic tabular datasets — realistic, privacy-compliant (HIPAA/GDPR) and tailored to research needs.

I can help expanding “small” samples, ensuring data safety for machine learning and artificial intelligence prototyping, and supporting academic or applied research.

If you or your group could use this kind of support, let’s connect!

I’m also interested in participating in initiatives aimed at promoting health and biomedical research. I possess expertise in developing high-quality, privacy-preserving synthetic datasets that can be utilized for educational purposes. I would be more than willing to contribute my skills and knowledge to these efforts, even if it means providing my services for free.

I’ve been working on a library that approximates geometric shapes (circle, ellipse, triangle, square, pentagon, hexagon, oriented bounding box) from a sequence of 2D points.

• Given a list of (x, y) points, it tries to fit the best-matching shape.
  
• Example use case: hand-drawn sketches, geometric recognition, shape fitting in graphics/vision tasks.
  

I’d like to test and improve the library using real-world or benchmark datasets. Ideally something like:

• Point sequences or stroke data (like hand-drawn shapes).
  
• Annotated datasets where the intended shape is known.
  
• Noisy samples that simulate real drawing or sensor data.
  

Library for context: https://github.com/sarimmehdi/Compose-Shape-Fitter

Does anyone know of existing datasets I could use for this?

Basically I'm trying to create a huge data set(probably with about 1t tokens, of good quality code). Disclaimer: this code will be generated by qwen 3 coder 480b, which I'll run locally(Yes I can do that). The data set will have a lot of programming languages, I'll prolly make it on every possible one. For api requests, you will be able to specify the Programming language, the type of the code(debugging, algorithms, library usage, and snippets). After the api request, you will get a json file with what you asked for in the request, which will be randomly chosen, but you will not be able to get the same code twice. But if you need to get the same code, you can send a reset request with you api key, which will clear the data, about the asked data.

Hola a todos,

Hace un tiempo me tocó arrancar un proyecto que empezó como algo muy chico: una base de datos de productos con códigos de barras para kioscos y pequeños negocios en Argentina. En su momento me la robaron y la empezaron a revender en MercadoLibre, así que decidí rehacer todo desde cero, pero esta vez con scraping, normalización de descripciones y un poco de IA para ordenar categorías.

Hoy tengo un dataset con más de 120.000 productos que incluye códigos de barras EAN-13 reales, descripciones normalizadas y categorías básicas (actualmente estoy investigando cómo puedo usar ia para clasificar todo con rubro y subrubro). Lo tengo en formato CSV y lo estoy usando en un buscador web que armé, pero la base como tal puede servir para distintos fines: cargar catálogos masivos en sistemas POS, stock, e-commerce, o incluso entrenar modelos de NLP aplicados a productos de consumo masivo.
Un ejemplo de cómo se ve cada registro:

7790070410120, Arroz Gallo Oro 1kg

7790895000860, Coca Cola Regular 1.5L

7791234567890, Shampoo Sedal Ceramidas 400ml

Lo que me interesa saber es si un dataset así puede tener utilidad también fuera de Argentina o LatAm. ¿Ven que pueda servir para la comunidad en general? ¿Qué cosas agregarían para que sea más útil, por ejemplo precios, jerarquía de categorías más detallada, marcas, etc.?

Si a alguien le interesa, puedo compartir un CSV reducido de 500 filas para que lo prueben.

Gracias por leer, y abierto a feedback.

I have about 1k hours of nature video footage that I have originally taking from mountains around the world. Is there a place online like a marketplace where I can sell this for AI/LLM training?

Hi everyone, I’m working on a research project where I need a time-series dataset structured similarly to the waveform attached—basically a signal with repeatable cycles marked by distinct peaks and troughs (like systolic and diastolic phases). There may also be false positives or noise in the signal.

I'm not necessarily looking for physiological heartbeat data—just any dataset that behaves similarly enough to allow me to prototype my labeling pipeline (e.g., finding cycles, handling noise artifacts).

Key requirements:

• Time-series data with clear, repeated peaks and dips (like systole & diastole).
• Presence of noise or spurious peaks for robustness testing.
• Ideally available in a simple, accessible format (e.g., CSV).

If you know of any open-source datasets (Kaggle, UCI, PhysioNet, or others) that fit the bill, please share! A second-best option for more general signals (not biological) is also welcome if they mimic this structure.

I’d love to get started ASAP—thanks so much in advance!

photos 1

photo 2

https://nomas.fyi/research/apiDocs

It is a compiled + deduped version from SEC data source. So feel free to play around! and I have visualized the SEC data for front-end, feel free to play around it as well

Any feedback is welcome!

Hi!

Does anyone know how or have a script to post ads automatically? I’ve made an app where I take photos of car tires, input some info, and then it creates a full ad. I just want to post that on Kijiji and Facebook but have it automated cause I don’t want to do that for 100+ sets. Kijiji doesn’t have an open API and I’ve been getting blocked by HTTPS and all kijiji’s protection. Haven’t tried for Facebook yet but I’m not a seasoned coder and chatgpt hasn’t helped me at all

Okay so we're talking about the Twitter feed of the Sesame Street character Count Von Count. https://x.com/CountVonCount On May 2, 2012, he tweeted simply https://x.com/CountVonCount/status/197685573325029379 "One!", and over the past 13 years he has made it to "Five thousand three hundred twenty-eight!" I need the date and time that each tweet was posted, plus how many likes and retweets each post had. This contains some interesting data, for example each tweet was originally just posted randomly (no pattern to the time), and then at some point tweets began to be scheduled x hours in advance (the minutes past the hour are noticeably identical for a while until the poster forgot to schedule any and they needed yo start with a new random time). Also, the likes and retweets are mostly a simple function of how many followers the account had at the time they were posted, with some exceptions. There have been situations where someone has retweeted a certain number when it became newsworthy (for instance on election night 2020 someone retweeted the number of electoral votes Joe Biden had when he clinched the presidency and got the tweet a bunch of likes). And the round numbers and the funny numbers (69 and 420) show higher than expected "like" nnumbers. I was collecting data by hand but I realized by not getting it all at once i might be skewing the data. I have used Selenium before to scrap data from websites, but I don't know if that will work for x.com . I also don't want to pay for API key usage for anything so frivolous. Does anyone have any ideas?

Hi everyone,

We just released MMP-2K, the first large-scale benchmark dataset for Macro Photography Image Quality Assessment (IQA). (PLEASE GIVE US A STAR IN GITHUB)

What’s inside:

• ✅ 2,000 macro photos (captured under diverse settings)
• ✅ Human MOS (Mean Opinion Score) quality ratings
• ✅ Multi-dimensional distortion labels (blur, noise, color, artifacts, etc.)

Why it matters:

• Current state-of-the-art IQA models perform well on natural images, but collapse on macro photography.
• MMP-2K reveals new challenges for IQA and opens a new research frontier.

Resources:

• 📄 Paper (ICIP 2025)
• 💾 Dataset & Code (GitHub)

I’d love to hear your thoughts:
👉 How would you approach IQA for macro photos?
👉 Do you think existing deep IQA models can adapt to this domain?

Thanks, and happy to answer any questions!

Hi All, I heard back from a couple companies and effectively all of them, including ones like Everbridge effectively said “Thanks, xxx, I don't think we'd be able to effectively consume that volume of RSS feeds at this time. If things change in the future, Xxx or I will reach out.”, now the thing is I don’t have the infrastructure to handle this data at all, would anyone want this data, like if I put it up on Kaggle or HF would anyone make something of it? I’m debating putting the data on kaggle or taking suggestions for an open source project, any help would be appreciated.

Please help

Went through the hassle of compiling data from near every free (and some paid) real estate resources to have (probably) the most comprehensive dataset of its kind. Currently its being displayed in a tool I built, but the MO is to make this data free and accessible to anybody who wants it.

For most of the zip codes in the USA (about 25k, accounting for ~90% of the population), I have:

1. home prices (average, median, valuation) -- broken down by bedroom
2. rent prices -- by bedroom
3. listing counts, days on market, etc, y/y%
4. mortgage data (originations, first lien, second lien, debt to income, etc.)
5. affordability metrics, mortgage cost
6. basic demographics (age, college, poverty, race / ethnicity)

Once you're in the dashboard and select a given area (ie: Chicago metro), there's a table view in the bottom left corner and you can download the export the data for that metro.

I"m working on setting up an S3 bucket to host the data (including the historical datasets too), but wanted to give a preview (and open myself up to any comments / requests) before I start including it there.

I want a free pool tool which can add pictures and videos

Hey everyone, I’m doing a university research project on making text labeling less painful.
Instead of labeling everything, we’re testing an Active Learning strategy that picks the most useful items next.
I’d love to ask 5 quick questions from anyone who has labeled or managed datasets:
– What makes labeling worth it?
– What slows you down?
– What’s a big “don’t do”?
– Any dataset/privacy rules you’ve faced?
– How much can you label per week without burning out?

Totally academic, no tools or sales. Just trying to reflect real labeling experiences

Repo: https://github.com/Datalore-ai/datalore-localgen-cli

Hi everyone,

During my internship I built a small terminal tool that could generate fine tuning datasets from real world data using deep research. I later open sourced it and recently built a version that works fully offline on local files like PDFs DOCX TXT or even JPGs.

I shared this update a few days ago and it was really cool to see the response. It got around 50 stars and so many thoughtful suggestions. Really grateful to everyone who checked it out.

One suggestion that came up a lot was if it can handle multiple files at once. So I integrated that. Now you can just point it at a directory path and it will process everything inside extract text find relevant parts with semantic search apply your schema or instructions and output a clean dataset.

Another common request was around privacy like supporting local LLMs such as Ollama instead of relying only on external APIs. That is definitely something we want to explore next.

We are two students juggling college with this side project so sorry for the slow updates but every piece of feedback has been super motivating. Since it is open source contributions are very welcome and if anyone wants to jump in we would be really really grateful.

so i wanna scrape every business name registered on google in an entire city or state but scraping it directly through selenium does not seem like a good idea even with proxies so is there is any dataset like this for a city like Delhi so that i don't need to scrape entirety of google maps i need id to train a model for text classification any viable way i can do this?

I created a tool that extracts books and metadata from Project Gutenberg, the online repository for public domain books, with options for filtering by keyword, category, and language. It outputs structured JSON or CSV for analysis.

Repo link: Project Gutenberg Scraper.

Useful for NLP projects, training data, or text mining experiments.

Hi everyone,

I’m working on a project where I need a dataset that contains numbers (like 4–8 digit sequences, phone numbers, PINs, etc.) along with some measure of how easy they are to remember.

For example, numbers like 1234 or 7777 are obviously easier to recall than something like 9274, but I need structured data where each number has a "memorability" score (human-rated or algorithmically assigned).

I’ve been searching, but I haven’t found any existing dataset that directly covers this. Before I go ahead and build a synthetic dataset (based on repetition, patterns, palindromes, chunking, etc.), I wanted to check:

• Does such a dataset already exist in psychology, telecom, or cognitive science research?
• If not, has anyone here worked on generating similar "memorability" metrics for numbers?
• Any tips on crowdsourcing this kind of data (e.g., survey setups)?

Any leads or references would be super helpful

Thanks in advance!

Looking for affordable, reliable nationwide data for comps. Need both:

• Sold properties (6–12 months history: price, date, address, beds, baths, sqft, lot size, year built, type).
• Active listings (list price, DOM, beds/baths, sqft, property type, location).
• Nationwide coverage preferred (not just one MLS).
• Property details (beds, baths, sqft, lot size, year built, assessed value, taxes).
• API access so it can plug into an app.

Constraints:

• Budget: under $200/month.
• Not an agent → no direct MLS access.
• Needs to be consistent + credible for trend analysis.

If you’ve used a provider that balances accuracy, cost, and coverage, I’d love your recommendations.

Hello,
Kindly let me know where I can get low quality football datasets for player detection and analysis. I am working on optimizing a model for African grassroots football. Datasets on Kaggle are done on green astro turf pitches with good cameras and I want to optimize a model for low quality and low resource settings.

I've just released The Stack Processed V2, a carefully curated version of The Stack dataset optimized for training robust multi-language code models.

📊 Key Stats:

• 468GB of high-quality code
• 91.3% syntax validation rate (vs ~70% in raw Stack)
• ~10,000 files per language (perfectly balanced)
• 8 major languages: Python, JavaScript, Java, C++, Ruby, PHP, Swift, Shell
• Parquet format for 3x faster loading
• 271 downloads in first month

🎯 What Makes It Different:

Unlike raw scraped datasets that are heavily imbalanced (some languages have millions of files, others just thousands), this dataset ensures equal representation for each language. This prevents model bias toward overrepresented languages.

Processing Pipeline:

1. Syntax validation (removed 8.7% invalid code)
2. Deduplication
3. Quality scoring based on comments, structure, patterns
4. Balanced sampling to ~10k files per language
5. Optimized Parquet format

📈 Performance Impact:

Early testing shows models trained on this dataset achieve:

• +15% accuracy on syntax validation tasks
• +8% improvement on cross-language transfer
• 2x faster convergence compared to raw Stack

🔗 Resources:

• Dataset: https://huggingface.co/datasets/vinsblack/The_Stack_Processed-v2
• Interactive Demo: [Colab Notebook Link]
• License: Apache 2.0

💭 Use Cases:

Perfect for:

• Pre-training multi-language code models
• Fine-tuning for code completion
• Cross-language understanding research
• Educational purposes

Looking for feedback! What features would you like to see in v3? More languages? Different sampling strategies? Enterprise patterns focus?

Happy to answer any questions about the curation process or technical details.