here is the preview https://huggingface.co/datasets/Lucasautomatekc/Cybersecurity_RAG_Knowledge_Graph-25-Topics-75-Articles-200-Chunks

I am trying to see if this is something people actually want – I had an idea that some how lead to me looking into selling data sets – total beginner so I’m seeing if there is a certain structure or format folks prefer… I have the data through my web pages, its all clean and enterprise ready for LLM or whatever people need it for…

Honestly – I have no clue what I’m doing, so feedback would be appreciated to even see if I’m going down the right path… yes this is a preview, I have the full set for sale but again have no idea what I’m dong LOL.

Some how AI lead me here, depending on if this the content is actually sellable, I may never follow robots blindly again, or… I will make it my life mission to praise the bots!

Thanks all!

I built and maintain this dataset — linking to my own Kaggle.

The European Commission publishes weekly pump prices for all 27 EU member states going back to January 2005 — but only as a messy 200-column Excel file with multiple header rows and prices per 1000 litres. I cleaned it into one flat CSV, one row per country per week per fuel type.

Covers petrol 95, diesel, heating oil, fuel oil and LPG across all 27 EU member states + UK.

A few things the data shows right now:

• Irish diesel is rising 5.7x faster during the Iran war than it did during the Ukraine war
• Netherlands and Denmark are the most expensive countries for diesel at €2.58 and €2.56/litre
• Malta is by far the cheapest at €1.21/litre — government price controls
• The 2022 Ukraine war spike is visible across every country in the heatmap simultaneously

Free dataset on Kaggle | Analysis notebook | Hugging Face mirror

Updated every Wednesday when the EC releases new data. Next update April 15th.

Happy to pull specific country numbers if anyone wants them.

Been thinking about a failure mode that feels more like a dataset problem than a tooling problem:

the retrieval stack is available
the tool is wired
the docs are there

but the model still answers from memory on requests that clearly depend on current information.

So the issue is not always “bad search.”
A lot of the time it is the trigger decision:
when should the model actually check, and when should it not?

I’ve been looking at a Lane 07 style setup for this where the supervision signal is explicit:

• needs_search: true when freshness matters
• needs_search: false when model knowledge is enough

Example row:

{ "sample_id": "lane_07_search_triggering_en_00000008", "needs_search": true, "assistant_response": "This is best answered with a quick lookup for current data. If you want me to verify it, I can." } 

What I like about this framing is that it does not just teach “retrieve more.”
It teaches both sides of the boundary:

• when to trigger
• when to hold back

That seems useful because bad gating hurts in both directions:

• over-triggering adds latency and cost
• under-triggering gives stale but confident answers

I’m experimenting with dataset structures for this kind of retrieval judgment and I think it is an underrated training target compared with just improving retrieval quality itself.

Curious how others here would structure it:

• binary needs_search
• richer labels
• classifier-style trigger data
• conversational SFT rows
• hybrid setup

Would love to hear if anyone else is working on datasets for this boundary.

Hey, I am looking for sources that provide some dataset(s) of Battery energy storage system (BESS) from Supervisory Control and Data Acquisition (SCADA).

Do you have any clue to where I can find it, or something similar?

I found Wind Turbines, but it’s not enough.

I built and released a free dataset of 12,137 active Texas residential listings for 2026 — structured features (price, sqft, beds, baths, garage, year built) plus NLP-ready listing descriptions with PII redacted. Texas is the #1 volume real estate market in the US and there was nothing clean like this on Kaggle.

Hi,

Does anyone know how to access to BankFocus Orbis Data? I want to use banks level data from different countries for my studies but unfortunately my university doesn’t give access to it. Is there another way to do it?

And another question is if I access to it, is data ordered in a nice way like IMF or World Bank.

Thanks,

​

Working on a GIS project mapping industrial facilities in Russia.

Looking for datasets covering active, large-scale industrial sites (operating, not abandoned), such as:

– manufacturing plants / заводы

– mining sites (open-pit карьеры, underground шахты)

– металлургия (steel mills, smelters)

– oil & gas infrastructure (refineries, GPPs, terminals)

– chemical / fertilizer plants

– power generation (ТЭЦ, ГЭС, АЭС)

– major industrial zones / hubs

Need:

– points (addresses or lat/long) OR

– polygons / site boundaries

Bonus:

Regional data, industry classification, operator/company

Formats:

CSV, GeoJSON, SHP, etc.

Interested in publicly available datasets like:

– OSM data

– corporate reports

– aggregated/open GIS datasets

Prefer datasets with regional breakdown (especially Ural region), but country-wide data is also useful.

Upload a CSV, describe what you want for each column, and it runs a live web search, verifies the result, and returns it with a source link.

Good for: company info, domain checks, competitor pricing, tech stacks, open job listings, anything that lives on the public web but is annoying to pull at scale.

100 cells free, no credit card: ampledata.io

Happy to answer questions.

We’ve been working on conversational speech datasets recently and wanted to share a sample to get feedback from this community.

This is focused on real conversational behaviour rather than clean, scripted dialogue.

What it includes:

• multi-speaker conversations
• natural interruptions and overlapping speech
• code-switching (Hindi + English, Hinglish)
• context-driven interactions (not isolated utterances)
• speaker variability (accent, pace, fluency)

Languages covered in the sample:

• Indian English
• Hindi
• Hinglish
• Punjabi
• Marwadi

We’ve tried to keep the structure usable for training and evaluation, with metadata around speakers, turns, and context.

Still early, and would genuinely appreciate feedback on:

• dataset structure
• missing edge cases
• what would make this more useful in real pipelines

Happy to share access if anyone wants to take a closer look.

WIP My attempt at pulling useful information out of a large dataset.

High-level loop:

artifact -> frontier -> bounded claim -> probe family -> evidence bundle -> route -> reasoner -> confidence update -> next-evidence request -> validation -> frontier advance

What it is trying to do is take stage-1 corpus outputs and turn them into a controlled evidence loop: pick one bounded question, gather support or contradiction around it, decide what evidence to ask for next, and only move forward when the result holds up.

This HF link is one visible slice of the larger system, not the whole thing. Here it shows repeated bridge and contrast structure plus one stable rare finding across follow-up validation.

partial artifact, more on the link below

“cluster_ids”: [ 406 ], “member_count”: 1, “observation_count”: 3, “source_bundle_ids”: [ “tier1_bundle_mixed_region_0410c17c2515bf49” ], “source_result_ids”: [ “finding:406” ], “primary_terms”: [ “anisotropy”, “coherent”, “high-confidence”, “rare”, “centered”, “gmims-related”, “text”, “magnetic”, “medium”, “film” ], “supporting_results”: [ { “result_type”: “finding”, “cluster_ids”: [ 406 ], “bundle_id”: “tier1_bundle_mixed_region_0410c17c2515bf49”, “summary”: “Cluster 406 is a coherent, high-confidence rare cluster centered on GMIMS-related text about anisotropy, magnetic medium, and film/samples, with 22 rows and a strong mean probability of 0.814.”, “confidence”: 0.76608, “support_cycles”: 3, “next_probe_name”: “neighbor_cluster_comparison”, “next_probe_cluster_ids”: [ 406 ], “next_probe_reason”: “The card lists centroid neighbors 285 and 282 with very high cosine similarity, making them the most direct follow-up for checking whether the GMIMS/anisotropy pattern extends to adjacent clusters.” } ],

link to hf with artifacts : https://huggingface.co/datasets/cjc0013/reasoningovercorpusartifacts/tree/main

link to previous dataset post : https://old.reddit.com/r/datasets/comments/1sej8ro/fused_patent_arxiv_clustering_dataset_9m_raw_388m/

Hiii,
https://archive.ics.uci.edu

it appreciate any help x I need a data set for a course and I done wanna the professor beforehand

thank you x

the very cool Kyoto cherry datasets is a bit orphaned right now. our world in data and others are looking for someone in the area who is an expert to help it.

It’s always better to use real world data sources for your data projects, it stands out better to recruiters and potential employers.

I made a super short vid covering some good completely free resources to find real datasets.

https://vm.tiktok.com/ZNR4m3w9W/

I’m a staff level data engineer so let me know if you guys have any other questions, happy to help and answer.

One failure mode I keep seeing in assistants with retrieval is this:

the search path exists
the tool is available
the orchestration is wired

but the model still answers from memory on requests that clearly depend on current information.

So the failure is not always retrieval quality itself.
A lot of the time it is the trigger decision.

That got me interested in treating this as a dataset problem rather than only a prompting or orchestration problem.

We’ve been working on a Lane 07 style dataset focused on search triggering, where the supervision target is the boundary between:

• requests that should trigger retrieval
• requests that should stay on general knowledge

Each row is built to teach that judgment explicitly.

Example row:

{ "sample_id": "lane_07_search_triggering_en_00000008", "needs_search": true, "assistant_response": "This is best answered with a quick lookup for current data. If you want me to verify it, I can." } 

What I find important here is that the dataset is not just teaching “search more.”

It teaches both sides:

• when retrieval is actually required
• when retrieval is unnecessary and just adds latency / cost

That matters because bad gating hurts in both directions:

• over-triggering makes the system slower and more expensive
• under-triggering gives you stale but confident answers

For me, the interesting dataset question is:
how do you represent retrieval judgment as a trainable supervision signal instead of leaving it to prompt heuristics?

A few things I’m curious about from others working on datasets or fine-tuning:

• Would you model this as binary needs_search, or something richer?
• How much do you rely on explicit freshness words like “latest” vs implicit freshness cases like booking, availability, status, schedules?
• Have you seen better results from classifier-style data, SFT conversational rows, or hybrid setups?

Would love to hear how others are structuring retrieval-trigger data, if you’re building similar datasets.

A few weeks ago I released a synthetic sleep health dataset on Kaggle and it took off faster than I expected. Sharing it here in case anyone finds it useful.

What’s in it:

– 100,000 records, 32 features, 3 prediction targets

– Sleep architecture: REM %, deep sleep %, latency, wake episodes

– Lifestyle: caffeine, alcohol, screen time, exercise, steps

– Psychological: stress score, chronotype, mental health condition

– Demographics: 12 occupations, 15 countries, ages 18-69

Three ML targets:

– cognitive_performance_score- regression (0–100)

– sleep_disorder_risk – multiclass (Healthy / Mild / Moderate / Severe)

– felt_rested – binary classification

One finding that surprised people:

Lawyers average 5.74 hrs of sleep and 7.3/10 stress. Retired individuals average 8.03 hrs and 2.6/10 stress. That 2.13-hour gap shows up clearly in every model – occupation is the strongest predictor of sleep health in the entire dataset.

All distributions are calibrated against CDC, Sleep Foundation, and Frontiers in Sleep research. Correlations match peer-reviewed values (e.g. stress vs quality r=-0.64).

Link in profile if you want to check it out. Happy to answer questions about how it was built.

Hello all,

I’m sure that this is a noob question, but how would I go about finding this dataset so that I can use it? I’ve tried my usual googling around, but can’t seem to find a way to access the dataset itself, other than for a few python questions labeled as “TeX Source” in the top right-hand side of the webpage provided.

Alternatively, is there another dataset that anyone knows about that has heaps of Java source code written by AI?

Thanks!

The Irish Property Price Register is public data but only accessible

through a slow paginated search with no bulk download. I wrote a Python

script to pull the entire register into one flat CSV.

778,508 rows covering every recorded residential sale in Ireland since 2010.

Columns: date_of_sale, address, county, eircode, price_eur,

not_full_market_price, vat_exclusive, description, property_size

Some findings from the data:

– National median went from €205k (2010) to €360k (2026)

– Laois prices rose 126% from 2010–2012 avg to 2020–2022 avg

– Dublin’s premium over rest of Ireland narrowed from 117% to 47%

– New builds went from 25% of market in 2010 to 24% in 2026,

but now cost €45k more than second-hand on average

– COVID barely dented prices — volumes collapsed but median held

[Dataset](https://www.kaggle.com/datasets/fionnhughes/property-price-register)

[Analysis notebook](https://www.kaggle.com/code/fionnhughes/property-price-analysis)

🔥 Cleaned Indian Liver Patient Dataset (ML Ready)

https://www.kaggle.com/datasets/shauryasrivastava01/liver-patient-dataset

• 583 patient records with real clinical biomarkers

• Binary classification (Liver Disease vs Healthy)

• Fully cleaned + preprocessed (no messy columns)

• Includes enzymes, bilirubin, proteins & demographic data

• Perfect for ML projects, EDA, and healthcare modeling

💡 Great for:

– Beginners learning classification

– Feature importance & SHAP analysis

– Bias & fairness studies in healthcare

🚀 Ready to plug into your ML pipeline!

Sharing an open dataset of real-world trash and debris with geo-tagged imagery across different environments.

Useful for:

• Waste / debris detection models
• Environmental monitoring
• Urban cleanliness analysis
• Smart city / cleanup planning

Dataset: https://huggingface.co/datasets/Outerview/global-trash-and-debris-index

Most existing waste datasets are small or staged — this is focused on real-world, in-the-wild data, which is still relatively limited in computer vision.

Would love feedback or ideas on how people would use this.

Sharing two open geotagged image datasets:

• Potholes: https://huggingface.co/datasets/Outerview/global-potholes-dataset
• Fire hydrants: https://huggingface.co/datasets/Outerview/fire-hydrants-dataset

Each dataset includes ground-level imagery with location metadata (latitude/longitude), along with additional attributes depending on the source.

Data is compiled from a mix of our own collection efforts and open mapping datasets, with a focus on real-world, observable infrastructure.

Potential use cases:

• computer vision training (object detection / classification)
• infrastructure analysis
• urban planning / maintenance modeling
• geospatial ML

Happy to answer questions or expand coverage if useful.

I can’t find any link to download this dataset

I want to practice my data preprocessing more. I looked at kaggle but its like 99% of them are already cleaned or atleast a litle bit messy. I want the raw data that actually happens alot in real work. Any advice would be great.

Thanks…

Been looking closely at speech datasets lately, and something feels off.

Most of what’s used to train models is way too clean.

No interruptions.
No overlap.
Hardly any code-switching.

But that’s not how people actually speak, especially in India.

Real conversations are messy. People switch between Hindi and English mid-sentence, talk over each other, drop context, pick it back up.

Feels like models aren’t failing because of architecture, but because the data doesn’t reflect reality.

Curious how others here are dealing with this.
Are you seeing the same gap in real-world performance?

Tell me

We’ve been running a weekly price index for the U.S. online cannabis market since December 2025. Today we’re open-sourcing the methodology, the SQL used to compute the index, and a sample dataset.

The index tracks average effective prices, discount rates, and discount depth across subcategories (Pre-Rolls, Cartridges, Gummies, etc.) relative to a fixed baseline week. It’s a straightforward avg-price-over-baseline calculation at the (category, subcategory) grain.

Repo: https://github.com/TheoV823/cannabis-price-index

Live index with full data: https://cannabisdealsus.com/cannabis-price-index/

Happy to answer questions about the approach or limitations.

Hi everyone,

I have a large xlsx vendor master list (~30k vendors).

Goal:

Add ONE column: “IT_Relevant” with values Yes / No.

Definition:

Yes = vendor provides software, hardware, IT services, consulting, cloud, infrastructure, etc.

No = clearly non‑IT (energy, hotel, law firm, logistics, etc.).

Accuracy does NOT need to be perfect – this is a first‑pass filter for sourcing analysis.

Question:

What is a practical way to do this at scale?

Can it be done easily? Basically, the companies should be researched (web) to decide if it is IT relevant or not. ChatGPT cannot handle that much data.

Thank you for your help.