Hi everyone,
I’ve been working on a skin condition detection project using CNNs, with 5 classes — Wrinkles, Hyperpigmentation, Blackheads, Acne, and Open Pores.
I’ve collected around 3,000 images per class from various open sources and uploaded them to Google Drive for model training.

Now that I’ve trained and saved my model weights, I’m planning to delete the dataset from Drive to save space. But since I worked really hard to collect and clean it, I don’t want it to go to waste.

Can I upload the dataset to Kaggle Datasets for free and reference it in my GitHub project for future users?
Or is there a better alternative for sharing it publicly with proper licensing and access?

Any advice or experience sharing datasets like this would be super helpful.

Hey everyone,

I’m working on a small project related to website characterization and categorization — basically classifying domains into types like E-commerce, News, Social Media, Adult, etc.

I’ve heard that OpenDNS (now Cisco Umbrella) has a large Domain Tagging dataset where domains are categorized by the community. I’d love to use it (or even a subset) as part of my training or benchmarking data.

However, I can’t find any public dataset download or API endpoint that provides the full tagged domain list — only individual lookups or some small sample lists.

Does anyone know if:

• Is there a public mirror, dump, or archive of the OpenDNS domain tagging data?
• Or maybe a similar open alternative dataset with website categories that can be used for machine learning/research purposes?

I’ve already checked the official OpenDNS community site and Cisco forums, but I didn’t see a bulk export option.
Any pointers, mirrors, or even partial exports would be amazing.

Thanks in advance!

OpenDNS Link: https://community.opendns.com/domaintagging/

I am looking forward to make a dream interpreter so I need a Dream dataset. So if anyone knows something about it. Plus get me the dataset I am looking forward for the reply from the ambitious people in our community.

I’m working on a computer vision project for solar panel defect detection and localization. Specifically, I need datasets where defects are annotated with bounding boxes so the model can learn to detect where the problem is, not just classify the image as faulty or normal. I want to download the data and work locally, and I don’t want to use any online platforms for training.

Bonjour à tous,

Je développe une application mobile (Expo / React Native + backend Flask) où il est affiché les prix des stations carburants.

Je consomme déjà le jeu de données officiel [Prix des carburants en temps réel]() disponible sur data.gouv.fr, qui fournit les identifiants, adresses, coordonnées GPS et prix.

Problème : ce flux ne contient pas systématiquement le nom commercial (enseigne) des stations (ex : TotalEnergies, Leclerc, Intermarché, Carrefour Market…).

Je cherche une solution légale et durable, sans scraping, pour associer chaque station à son enseigne.
Le but est d’afficher dans l’application :

• le nom de la station,
• son adresse complète,

• les prix actualisés des carburants.

• Existe-t-il un jeu de données officiel (CSV / JSON / API) qui relie les identifiants de stations (id, adresse, cp, ville) à leur enseigne / nom commercial ? → Si oui, pouvez-vous indiquer le lien exact ou le nom du dataset ?

• Si ce jeu n’est pas public :

  • savez-vous quel organisme / contact (DGEC, Ministère, etc.) gère la donnée ?
  • et comment leur demander une autorisation de réutilisation des champs “enseigne” ?

• Connaissez-vous une source alternative légale (par exemple open data régionaux, INSEE, ou bases professionnelles) pour obtenir les enseignes correspondantes ?

• Côté technique : recommandez-vous de précharger ces correspondances côté serveur (ex : table SQLite ou CSV importé) afin d’éviter tout appel excessif ou scraping client ?

• Enfin, si quelqu’un a déjà fusionné ces données (via ID, adresse ou géolocalisation), je serais très intéressé par :

  • un exemple de correspondance (quelques lignes de CSV anonymisées),
  • ou une méthode de matching fiable à reproduire.

Contraintes

• Pas de scraping du site officiel (prix-carburants.gouv.fr)
• L’application sera publiée sur App Store / Play Store, donc la source doit être officielle, publique et réutilisable (licence ouverte).

Exemple du besoin:

Je souhaite obtenir une structure de données de ce type :

{ "id_station": "12345678", "enseigne": "TotalEnergies", "adresse": "4 Rue Étienne Kernours", "ville": "Douarnenez", "prix_gazole": 1.622, "prix_sp98": 1.739 } 

Merci d’avance pour toute aide, piste ou contact !

Cordialement,

Tom

All-Party Parliamentary Groups (APPGs) are informal cross-party groups within the UK Parliament. APPGs exist to examine particular topics or causes, for example, small modular reactors, blood cancer, and Saudi Arabia.

While APPGs can provide useful forums for bringing together stakeholders and advancing policy discussions, there have been instances of impropriety, and the groups have faced criticism for potential conflicts of interest and undue influence from external bodies.

I have pulled data from Parliament’s register of APPGs (individual webpages / single PDF) into a JSON object for easy interrogation. Each APPG entry lists a chair, a secretariat, sources of funding, and so on.

How many APPGs are there on cancer; which political party chairs the most APPGs; how many donations do they receive?

Click HERE to view the dataset on Kaggle.

Hello, I am looking for a large pokemon image dataset (with names) that includes ALL 1025 (+ alternate forms) pokemon and their shiny variations.

Trying to find a dataset on test scores for the last few years in order to compare them with when generative AI started having a boom and being used by students, to see if it’s effects have worsened the current education efforts of schooling.

I’m in college right now and I need an “unclean/untidy” dataset. One that has a bunch of missing values, poor formatting, duplicate entries, etc., is there a website I can go to that gives data like this? I hope to get into the renewable energy field, so data covering that topic would be exactly what I’m looking for, but any website that has this sort of this would help me.

Thanks in advance

hi guys, for a project i need a large dataset that’s uncleaned so that i can show i can clean it and make visualizations and draw analysis from it. if anyone can help please reach out thank you so much.

Can anyone help with some resource which has a full case study that I can work on and if possible there is a solution that I can compare with. The solution part is not a must. Just looking for a case study to try my hands on. Thanks

No logins, no paywalls—just links to stuff that’s (supposed to be) freely available. Some are solid, some not so much. Still interesting to see how scattered this space is.

Here’s the link: Free and Open Databases Directory

My name is Jason Baumgartner and I am the founder of Pushshift. I have been dealing with some health issues but hopefully my eye surgery will be coming up soon. I developed PSCs (posterior subcapular cataracts) from late onset Diabetes.

I have been working lately to bring more amazing APIs and tools to the research community including making available a large amount of datasets containing YouTube data and many other social media datasets.

Currently I have collected around 15 billion Youtube comments and billions of YouTube channel metadata and video metadata.

My goal, once my surgery is completed and my eyes heal is to get back into the community and invite others who love data to work with all this data.

I greatly appreciate everyone who donates or spreads the word about my gofundme.

I will be providing updates over time, but if you want to reach out to me, please use the email in my Reddit profile (the gmail one).

I want to thank all of the datasets moderators for assisting me during this challenging period in my life.

I am very excited to get back into the saddle and pursuing my biggest passion – data science and datasets.

I no longer control the Pushshift domain bit I will be sharing a new name soon and letting everyone know what’s been happening over the past 2 years.

Thanks again and I will try to respond to as many emails as possible.

You can find the link to my gofundme in my Reddit profile or my post in /r/pushshift.

Feel free to ask questions in this post and I will try to answer as soon as possible. Also, if you have any questions about specific social media data that you are interested in, I would be happy to clarify what data I currently have and what is on the roadmap in the future. It would be very helpful to see what data sources people are interested in!

Hey there, I’m currently trying to start my first SaaS and I’m searching for a genuinly painful problem to create a solution. Need your help. Got a quick minute to help me?
I’m specifically interested in things that are taking your time, money, or effort. Would be great if you tell me the story.

If you work with Excel files regularly, I wanted to share something that’s been a game-changer for me: Tideon AI — an AI-powered platform that lets you chat with your datasets instantly.

Instead of manually digging through spreadsheets, you can:

• Upload Excel files and ask questions in plain English
• Get instant insights without writing formulas

Would love to hear if this helps anyone here streamline their workflow!

Link: https://tideon.ai

Hi Community, So I have a task to fine tune Llama 3.1 model on fraud detection dataset. Ask is simple, anyone here knows what the best datasets that can be utilized for this task are. What is the best known model SOTA for fraud detection in the market so far.

Paid: 60% off everything before Nov-10 shutdown.

I’m working on an ML project related to medicine access inequality. For that, I need a dataset showing disease prevalence(or incidence rates) by country I’ve already looked into WHO, healthdata.org etc. but most of what I’ve found is either aggregated by disease type or missing country-level granularity. Please give some guidance on how to get this data, thank you.

Hi everyone, first time building a dataset. This is a v0.1, about 100 scans of book pages (both single and double-page per scan). The books are in the public domain. The intended use is for anyone looking to do image-to-text software work.

The scans are in a .jpg format, with a PDF with the whole collection.

I have also included 2 .txt files:

1)”raw” (aka not corrected for halluciations, artifacts, etc.) .txt file for anyone looking to do a check. The file is in Markdown.

2) A “corrected” .txt file, where the hallucinations, artifacts, errors, etc. were manually corrected. This file is in .txt, not Markdown.

Looking for feedback if this is useful, how to make a dataset like this better, etc.

Kaggle: https://www.kaggle.com/datasets/booksofjeremiah/serbian-cyrillic-script-printed

Huggingface: https://huggingface.co/datasets/Books-of-Jeremiah/raw-OCR-serbian-cyrillic

Any feedback on whether the set is useful for other use cases or how it can be made better is appreciated!

Looking for a dataset containing text from radio messages generated by firefighters at incidents. I can’t find anything, and my next step is to feed audio databases into a transcriber and create my own.

I’ve been experimenting with data-efficient LLM training as part of a project I’m calling Oren, focused on entropy-based dataset filtering.

The philosophy behind this emerged from knowledge distillation pipelines, where student models basically inherit the same limitations of intelligence as the teacher models have. Thus, the goal of Oren is to change LLM training completely – from the current frontier approach of rapidly upscaling in compute and GPU hours to a new strategy: optimizing training datasets for smaller, smarter models.

The experimentation setup: two identical 100M-parameter language models.

• Model A: trained on 700M raw tokens
• Model B: trained on the top 70% of samples (500M tokens) selected via entropy-based filtering

Result: Model B matched Model A in performance, while using 30% less data, time, and compute. No architecture or hyperparameter changes.

Open-source models:

🤗 Model A – Raw (700M tokens)

🤗 Model B – Filtered (500M tokens)

Full documentation:

👾GitHub Repository

I’d love feedback, especially on how to generalize this into a reusable pipeline that can be directly applied onto LLMs before training and/or fine-tuning–I’m currently thinking of a multi-agent system, with each agent being a SLM trained on a subdomain (i.e., coding, math, science), each with their own scoring metrics. Would love feedback from anyone here who has tried entropy or loss-based filtering and possibly even scaled it

i have scraped the fm23 data and got the 90k+ player information hope its helpful for u if u like it upvote on the kaggle and here too

more information on the kaggle website

thanks for reading this

Hello, For a personal learning project in data visualization I am looking for the most up-to-date database possible containing all the models of new vehicles sold in France and europa with car characteristics and recommended official price. Ideally, this database would contain the data of the last 2 to 5 years. I want to be able to plot EV car price per kilometer and buying price vs autonomy etc. thank you in advance it is my first Reddit post

hello everyone.

I’m building a synthetic dataset from our ~200MB private codebase to fine-tune a 120B parameter GPT-OSS LLM using QLoRA. The model will be used for bug fixing, new code/config generation.

Codebase specifics:

• Primarily C# with extensive JSON/YAML configs (with common patterns)
• Good documentation & comments exist throughout
• Total size: ~200MB of code/config files

My plan:

1. Use tree-sitter to parse C# and extract methods/functions with their docstrings
2. Parse JSON/YAML files to identify configuration patterns
3. Generate synthetic prompts using existing docstrings + maybe light LLM augmentation
4. Format as JSONL with prompt-completion pairs
5. Train using QLoRA for efficiency

Specific questions:

1. Parsing with existing docs: Since I have good comments/docstrings, should I primarily use those as prompts rather than generating synthetic ones? Or combine both?
2. Bug-fixing specific data: How would you structure training examples for bug fixing? Should I create “broken code -> fixed code” pairs, or “bug report -> fix” pairs?
3. Configuration generation: For JSON/YAML, what’s the best way to create training examples? Show partial configs and train to complete them?
4. Scale considerations: For a 200MB codebase targeting a 120B model with LoRA – what’s a realistic expected dataset size? Thousands or tens of thousands of examples?
5. Tooling recommendations: Are there any code-specific dataset tools that work particularly well with documented codebases?

Any experiences with similar code-to-dataset pipelines would be incredibly valuable! especially from those who’ve worked with C# codebases or configuration generation.

Hi guys I want help finding diseased plant images with it’s metadata specifically it’s geolocation and timestamps for a research based project please help me out.

I am building a scene classification AI, and I was wondering where I could find a dataset that contains a bunch of different images from a certain room. For example, I would want a lot of images of different kitchens.