I recently published two free Ethereum Uniswap V3 BTC/ETH datasets on Kaggle for researchers, quants, data scientists, and anyone studying DEX market structure.

These are not just price CSVs. The datasets include multiple research layers built from Ethereum mainnet data:

• raw Uniswap V3 logs
• decoded / normalized swaps
• canonical 1-minute OHLCV bars
• Mint, Burn, Collect liquidity events
• Flash events
• pool initialization data
• pool registry metadata
• daily archive-state snapshots

The pool universe covers 24 Uniswap V3 BTC/ETH-related pools:

• WBTC/USDC
• WBTC/USDT
• WBTC/WETH
• WETH/USDC
• WETH/USDT
• WETH/DAI

Across the major fee tiers:

• 0.01%
• 0.05%
• 0.30%
• 1.00%

The 2021 Kaggle sample covers 2021-05-04 to 2021-12-31 and includes about:

• 2.98M raw logs
• 2.78M normalized swaps
• 1.17M canonical 1-minute bars
• 288K liquidity events
• daily pool state snapshots

The June 2026 sample covers 2026-06-01 to 2026-06-30 and includes about:

• 1.61M raw logs
• 1.57M normalized swaps
• 329K canonical 1-minute bars
• 76K liquidity events
• daily pool state snapshots

Possible research ideas:

• BTC/ETH DEX microstructure
• Uniswap V3 liquidity behavior
• fee tier comparison
• pool-level volume and spread behavior
• swap flow and buy/sell imbalance
• LP activity around volatility regimes
• comparing 2021 Uniswap V3 launch-era behavior vs 2026 mature-market behavior

I also included starter notebooks so people can quickly inspect the Parquet files and start exploring without building a full Ethereum indexer.

The public Kaggle datasets are free samples. I also maintain a larger validated archive covering 2021-05-04 to 2026-06-30 with the same research layers. If any researchers, teams, funds, or data builders need the full historical range or custom extracts, feel free to reach out through Kaggle.

Hope this helps anyone working on DeFi data, market microstructure, or crypto time-series research.

2021 sample: https://www.kaggle.com/datasets/marvingozo/ethereum-uniswap-v3-btceth-2021-free-sample

June 2026 sample: https://www.kaggle.com/datasets/marvingozo/ethereum-uniswap-v3-btceth-june-2026

Getting good data is a big hurdle for retail investors. Reliable return histories are often locked behind thousand dollar a year subscriptions. But you can get a lot for free.

I put together a small return dataset covering developed-market stocks, sovereign bonds, interest rates, and currencies.

The goal is to consolidate the kinds of return series that are useful for testing global asset allocation strategies, especially those involving foreign equity, sovereign bonds, currency hedging, and excess returns.

The dataset includes 50+ years of coverage across several files. All available for free. Check it out!

https://github.com/birjusuketupatel/ReturnDataFiles/tree/main

Note: Reposting bc the mods removed post on original subreddit.

hi everyone

im trying to get access to the hirid dataset for a machine learning project but im stuck at the citi course requirement because my organization isnt listed in the available options

has anyone run into this before or knows how to proceed in this situation any help would be really appreciated

thanks in advance

Hi, so I am searching for any freely available dataset that would have information on websites using email services from third-parties.
BuiltWith provides that, like which websites are actively using Brevo, Klaviyo or MailChimp etc, but they are too expensive.
Thanks.

Hey everyone,

As a side project, I got really frustrated trying to navigate the different government business registries across the Baltics whenever I needed to check a company’s status, VAT number, or employee count.

To solve this, I downloaded all the raw open data from the Lithuanian, Latvian, and Estonian registries and built a unified, lightning-fast search engine and API on top of it.

Link: https://www.balticdata.eu

Right now, the API is completely free and open for developers to use. You can instantly search by name, registration code, or filter by active/liquidated status.

I’d love for you to try it out and let me know if it’s useful or if you find any bugs!

I’ve been trying to download project information for a specific year from the NIH Exporter tool (https://reporter.nih.gov/exporter/projects) and any file larger than 10Mb just times out every time. I tried downloading from the browser, from a console using bash tools, nothing works. There is no scheduled eRA maintenance going on. Anyone knows of any tricks for this? Has anyone tried to download project data recently?

I’ve been trying to access the ICMR Young Diabetes Registry dataset for my research. I submitted the official data request months ago but haven’t received any response. I also reached out directly to one of the researchers involved, but unfortunately I still haven’t heard back.

Has anyone here successfully obtained this dataset? Is there another contact person, process, or institution I should approach? Any advice would be greatly appreciated.

Please let me know if you know of any other datasets that concerns type 1 and type 2 data and diabetes and pre-diabetes on indian population.

Been working on a football prediction project and couldn’t find a dataset that had

the actual context needed to model match outcomes — just raw results everywhere.

So I built one from scratch on top of the International Football Results dataset

by Mart Jürisoo (the well known one on Kaggle with 49,000+ matches going back to 1872).

What I added:

**Elo ratings** — built from scratch, updated after every single match across 150

years. Both teams’ ratings, their difference, and the expected win probability

going into each match.

**Rolling form** — win rate, goals scored, goals conceded, goal difference, clean

sheet rate, both-teams-scored rate, scoring rate, and win streak. Computed at

three lookback windows: last 5, last 10, and last 20 matches. For both teams.

**Head-to-head history** — based on the last 10 meetings between those two specific

teams. Some teams have persistent edges over specific opponents that their general

form doesn’t explain.

**Fatigue signals** — days since each team’s last match and the difference between

the two.

**Penalty reliance** — fraction of each team’s historical goals that came from

penalties, pulled from the goalscorer dataset.

**Shootout composure** — historical penalty shootout win rate for each team, from

the shootouts dataset.

**Tournament context** — World Cup, qualifier, friendly, neutral venue, competition

importance weight, confederation.

The thing I spent the most time on: every feature is computed in strict

chronological order using only data that existed before that match was played.

State updates happen after each row is recorded, never before. No lookahead,

no leakage anywhere in the 102 columns.

102 features total. 49,094 rows. result column (H/D/A) included as the label.

Drop date and result, plug into any classifier.

Dataset is fully documented with column descriptors for every feature.

Link: https://www.kaggle.com/datasets/kriishgulati/football-match-results-1872-2026-with-ml-features

Built on top of the original dataset by Mart Jürisoo — full credit and link

in the dataset description.

Hello, I recently built a website about CO2 emissions of private jets. I’m looking to expand on (pleasure) cruises but I’m unable to find a historical data set.

Can anyone help me get this data or know where to find it?

Here’s my project to give insights to what my goal is with the data.
[https://paperstraw.info/](https://paperstraw.info/)

Genuinely trying to understand this from people actually building.

If clean, licensed, fully rights-cleared video data existed at the volume and style you needed, would you use it instead of scraped data? And if not, what’s the actual blocker? Cost, availability, doesn’t matter to your legal team yet, something else?

Building in this space and would rather understand the real objection than guess at it.

Happy to go deeper in the comments

Hi,

I made a new sub that’s tangentially related to this one and want to advertise it but don’t want to break any rules. Can any mods tell me the rules?

Hello everyone, hope you are all doing well.I live in Brazil, where Polymarket is currently geoblocked (ironically, sports betting sites work completely fine here, go figure). I am looking to extract Polymarket data to incorporate into my predictive models. Prediction markets serve as an excellent proxy for public sentiment, such as forecasting the final outcome of a World Cup match.

I considered using a VPN, but I know Polymarket actively blocks them. Does anyone know of an alternative repository on GitHub, Hugging Face, Kaggle, or Google Cloud BigQuery that hosts historical Polymarket data (order books, transaction-level data, or market resolution history)?

Ideally, I am looking for structured formats like .csv, .parquet, or public SQL tables so I can bypass the local geo-restriction. Any leads or links to open-source data dumps would be highly appreciated.

I scraped the census data files and arranged them in a kaggle dataset. Also added a notebook for quick-start. There are attributes on demography and housing. Unfortunately the attributes are all in german, but I did not want to change the original data with half assed translations (LLMS will do a much better job in explaining what is what than I could anyways). I think this is a very neat geo dataset with interesting correlations.

I built NorthTick after getting tired of patchy Canadian market data. It is local Parquet files, not an API: - TSX/TSXV/CSE/NEO OHLCV - Daily data back to 1993 - Minute bars from 2020 - Ticker metadata included - Free sample available Site: https://northtick.ca Disclosure: I built it. 

I built API for SEC 13F data with 57 endpoints as a site project. The data collection and the collectors are running on AWS, so the resources can be expended. I am thinking to add more data and endpoints. It is for free if someone wants to try it out.

So I was trying to figure out what Android app to build next, and the first step was doing some market research. I wanted to see what apps were already out there, so I ended up creating this dataset.

It contains app data across the top 10 fastest-growing Android categories. If you’re planning to build an Android app or just want to analyze the market, feel free to use it.

It also includes my GitHub repo, so you can customize it and scrape whatever kind of app data you want.