For anyone who’s used ASRS Database Online: it has great narratives, but they’re painful to search. We indexed ~18.5k recent reports so an agent can run natural-language searches over them and cite each report by ACN.

Caveats: reports are voluntary and de-identified, NASA doesn’t verify them, and counts aren’t prevalence. It’s not an official NASA product and not for enforcement.

Endpoint: https://nasa-asrs.mcp.kapa.ai
Details: https://www.kapa.ai/indexes/nasa-asrs (made by kapa.ai, where I work)

Happy to hear what other government datasets deserve this treatment.

Help finding dataset

Hey guys, I am currently working on a project using yoloe 26, in which i am trying to build a pipeline to find the number of different screws available in the picture, with count of each. So as far I searched, I couldnt get a proper labelled dataset to train the yoloe model, and that too I am focusing mainly on tiny screws used in the electronics like smart phones, smart watches, watches, microphone, ear buds, laptops.

So help me if you find a labeled dataset for this vision.

Thanks for reading.

Im building an option trading bot and I need a fresh dataset to test on, buying them is a bit expensi was wondering if anyone has them?

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I have a ~59 GB Parquet dataset stored in Google Drive, and I want to use it for ML training on Kaggle GPUs.

My folder structure is:

My Drive/

└── DATASET/

└── data/

└── processed/

└── era5_land_preprocessed/

├── train/ → 60 Parquet files → 44.34 GB

├── validation/ → 12 Parquet files → 8.83 GB

└── test/ → 8 Parquet files → 5.82 GB

Total: 80 Parquet files, ~58.99 GB

I don’t want to download the dataset to my local computer. I was planning to use Google Colab + Kaggle API to transfer the files directly from Google Drive to a Kaggle Dataset.

My questions are:

1. Is uploading a ~59 GB dataset to Kaggle this way reliable?

2. Is Google Drive → Colab → Kaggle API the best approach?

3. Should I create one Kaggle dataset containing “train/”, “validation/”, and “test/”, or split them into multiple Kaggle datasets?

4. Are there any problems with uploading large Parquet files to Kaggle?

5. Is there a better/faster method for transferring this amount of data to Kaggle?

The dataset will be used for climate/weather ML forecasting, and I need to train models using Kaggle’s GPUs.

Any advice from people who have handled 50+ GB datasets on Kaggle would be appreciated.

I was talking to a massive global facilities provider that covers a huge footprint of physical operations (management, cleaning, warehouses, driving, and even construction). They’ve heard a lot about modern data platforms and robotics companies spending heavily to acquire real-world video data, and they naturally sit on a ton of it from their day-to-day work. Right now, they have roughly 2 million hours of footage they could make available—and if the demand is real, they can easily invest to capture significantly more across their sites.

To be clear, they have zero plans to build models themselves. They strictly want to understand the market and monetization channels:

• How data is valued, and whether in-house analytics or curation is worth the effort over raw footage
• What specific types of facility footage are actually in demand
• Where this data can be brought to market or listed
• What the standard commercial process looks like to license and sell it

Sharing a data source I maintain for pulling structured Google Maps place records in bulk.

What you get per goole maps place:

business name, full address, latitude/longitude, phone, website, rating and review count, category, and price level. An optional Rich Details mode adds opening hours, category types, a short description, a photo thumbnail, and the Google Place ID.

How you query google maps place records:

give it search terms like “hardware stores in Denver, CO” or “dentists in 90210”, set a max per term, and it pages Google Maps until it reaches that count. Output is one JSON row per place, ready for a dataframe or a map layer.

Coverage and format:

results arrive in pages of about 20, and a term returns as many places as Maps will surface for it. It reads Google Maps through a structured search API rather than a headless browser, so rows stay consistent across large pulls.

A few limits to keep in mind:

no emails or social profiles, so it is raw place data, not contact enrichment. Standard rows omit hours, photos, and Place ID unless Rich Details is on.

Pricing is per place returned, with a standard tier and a rich-detail tier.

Disclosure: this is my own Actor on Apify, so the link is a paid product.

Source: Google Maps Places Scraper on Apify

This might be too specific, but I’m looking for a dataset focused around the effects of proper workout forms. I would want it to include things like proper range of motion for common workouts (bicep curls, split squats, generally workouts that almost everyone does), angle of your limbs during the workout, general exercise posture. I want the data to (hopefully) include how much more efficient proper workout form is than flawed forms (Improper angle of limbs, not full range of motion, etc.), estimated reduction of injury, stability increases, anything that shows the benefits of a proper form.

I’m looking to create a model trained on this data that can generate a wireframe of a human figure that can help the user adjust their form, but I don’t have much data to work on.

I’m still pretty new to academic research so I’m really sorry if I’m not being specific enough.

Honestly, any datasets about workout forms or even just the most efficient workouts that grow muscles the best would help me.

Thank you!

I’m sharing a data source I maintain. Google Flights has no official API, so this pulls its search results as structured JSON: per-itinerary rows (route, carrier, price, departure and arrival times, stops, duration), plus Google’s price insight for each search (lowest price, whether the current fare is low/typical/high, and the typical price range).

Fields you can pass: departure_id, arrival_id, outbound_date, return_date, multi_city_json, max_price, max_stops, airlines, and a few others.

Output splits into Google’s “best flights” group and the alternatives, with airport metadata and optional resolved booking links.

This data source is really good for building a fare-history dataset (you can schedule one route/date and log daily) and route-pricing analysis.

Limits: it reads what Google shows at request time, and price insight only appears on routes where Google itself shows it. However, I have an uncleaned dataset I’m working on sharing as well, of more historical data.

This is my own paid data source (pay per page of results, no subscription). Source: Google Flights API

We pooled every gambling-harm indicator that a US state, DC or a national body actually publishes into machine-readable files, because none of it existed in anything but PDFs.

Five CSVs, all direct download, no registration:

– Gambling Harm Index (51 rows): what each state publishes about gambling harm + NCPG’s helpline contacts per state for 2024/2025. Headline: 32 of 51 jurisdictions publish no recurring, current count; 18 publish nothing at all.
– Sportsbook Responsible-Gambling Tool Matrix (14 operators): deposit/loss/time limits, cool-off durations, self-exclusion terms, account-closure path — every cell has the operator’s own source URL. 47 of 154 core cells are “not published.”
– Gambling Recovery Glossary (125 terms), with stigma-preferred phrasing and a source per term (32 marked unsourced rather than faked).
– Quit-Gambling App Panel (107 iOS apps, 63 gambling-specific): release dates, update recency, rating counts.
– State + multi-state self-exclusion programs (12 rows — partial, says so).

Repo: https://github.com/betttr-data/gambling-harm-data
Landing page + schema.org Dataset markup: https://betttr.net/data/
DOI: https://doi.org/10.5281/zenodo.22857767
License: CC BY 4.0. Frictionless datapackage.json + a column dictionary per file.

Method notes: nothing estimated or imputed; “not published” is recorded as a finding, not filled in; no composite score or ranking, because the state-published figures aren’t comparable. Monthly refresh on the 15th for the index and app panel, quarterly for the operator matrix.

Disclosure: produced by Betttr LLC, which makes a gambling-recovery app. No operator, affiliate or sportsbook money. Our own app is a row in the app panel, counted by the same rule as everyone else’s. Corrections: support@betttr.net or open an issue.

Together with OpenSky Network I published a 700 million record dataset of every plane flying world-wide on March 1 2026, one full day of telemetry. The data is public and free for private and research use, and you read it straight from pandas or Spark with Apache OpenSharing.

The OpenSky data is static, it’s hosted on Databricks Marketplace and part of this free tutorial on Databricks Genie which comes with some cool avionics animations.

What is in the data?

• Around 700 million avionic state points
• One full day; every plane flying on March 1st, 2026
• Per aircraft: position (lat, lon), altitude, callsign and the ICAO aircraft id
• Served as a table you query from pandas, Spark and other clients

How the OpenSharing works

It runs on OpenSharing, the open protocol (Linux Foundation, Apache 2.0), so to read it you:

1. Grab a small credential file
2. Point the delta-sharing client at it
3. Load the table

No vendor, cloud, or platform lock-in, no data copy, no vendor SDK.

Why it’s cool

• It is a real large dataset, not a toy CSV, so it makes a great source for your own Spark and pandas examples, demos and benchmarks.
• Real data means it’s dirty. There are outliers. Some sensors sent test data. Yes, that’s cool!
• It’s fabulous data for animations, visualization, geo apps.
• The data is free, and OpenSharing just hands you a plain table, so you can process it almost anywhere: a laptop with pandas, a Spark cluster, or any other engine.
• This is a static dataset. We have no intention to update it at all (the titanic dataset wasn’t updated either). Using Apache SDP, you can get the current avionics data as a data stream. I published the tutorial in this reddit posting on /dataengineering.

The tutorial

I built a small free tutorial around it, fully open source. The step that reads the shared data is just pandas in VS Code, no Spark, so anyone can replicate it for free.

Why I care (and you should too)

Databricks created a lot of the open source projects you know, Spark, Delta Lake, MLflow and Delta Sharing, and I like showing how well it also plugs into the wider open source ecosystem.

This is one of a series of small, free tutorials based on that OpenSky dataset. All of these tutorials run on Free Edition (no cost, no credit card, sign-up with Gmail etc).

This section focuses on open source data sharing. Reading a shared dataset with nothing but pandas is a good example 🙂

• Check out this tutorial step with OpenSharing, VS Code and Pandas
• I published the full guide as a Databricks Community Tech blog which entirely runs on Databricks Free Edition (no credit card required!): Tutorial: Genie for Data Engineers and Data Scientists on Databricks Free Edition

Hi, I’m working on a project that is kind of a RAG system for methods and algorithms, and I’m having a hard time building the database for it. I’ve tried downloading blogs and research papers from the internet, but I’ve only been able to find a few thousand documents. I’m looking for a real dataset to build a proper knowledge base. Any suggestions?

I run AI Undetectable, an AI humanizer. I’m sharing the recorded detector scores from a small repeatability check, not a product ranking.

Six unchanged AI-written texts were checked on July 3 and July 11, 2026, using ZeroGPT and aidetector.com. That’s 24 readings: six texts, two detectors, two dates.

The JSON’s historicalControls section has a topic ID and four scores per row. ZeroGPT’s scores stayed unchanged for all six; aidetector.com’s changed in both directions. One went from 28% to 73%, another from 19% to 3%.

Direct JSON, no signup: https://aiundetectable.com/research/ai-humanizer-benchmark-2026.json

Methods and limitations: https://aiundetectable.com/ai-humanizer-benchmark

This is only six texts, with no human-written control group. The file contains scores, not the original texts. It can’t establish false-positive rates or explain why scores changed. The separate holdout results in the same file are aggregate summaries, not 32 individual records.

AI helped prepare this post.

I run a food recall site and built this feed for it. Figured it might be useful here.

It pulls from openFDA enforcement reports, FDA press releases, and USDA FSIS, then merges the duplicates so each recall shows up once. Records include product, brand, hazard, allergens, affected states, lot codes and UPCs when they’re listed, plus a plain-English “what to do.”

The reason for merging: openFDA is the clean structured source, but it trails the actual recall by a median of 35 days (across 441 records). Press releases come out the same day but they’re free text, so I parse them and flag the hazard class as inferred.

All active recalls (284 right now): https://dailyfoodrecalls.com/api/recalls.json 20 newest: https://dailyfoodrecalls.com/api/recalls-latest.json Docs: https://dailyfoodrecalls.com/api/

No key, CORS is on, CC BY 4.0. Current recalls only, no history. USDA updates once a day, FDA more often.

If you spot anything wrong, let me know.

The list has around 5,000 important words, chosen and ordered based on a huge amount of evidence – over 13.6 billion words – from five different sources. The way these words were selected and ranked is clearly explained, and the tools used to do this can be easily repeated and verified.

It’s available for anyone to use and it’s totally free, thanks to the CC BY 4.0 license.

GitHub: https://github.com/X-Trivle/LexiCore

I’m posting this to get some feedback from people who are into language, learning new languages, and studying words and how we use them.

If you have ideas for improvements, find issues in the data or methodology, or have suggestions for additional analyses/features, I’d be interested in hearing them

Hey everyone, sharing a structured multidimensional dataset covering 307 characters from the Sanskrit epic Mahabharata.

What is included: – 307 characters classified across the Pandava Coalition (90), Kaurava Host (153), and Neutral/allied kingdoms (64) – Breakdown by philosophical gunas: 135 Sattva (purity/virtue), 112 Rajas (passion/action), 60 Tamas (inertia/darkness) – Martial attributes, moral dilemmas, and ethical indices – Integration with TypeSafe AI System One decision primitives (Choice, Score, Noul)

Dataset link: https://huggingface.co/datasets/gnumanth/mahabharata-characters

Available in Parquet, tabular, and Croissant metadata under CC-BY-4.0. Feedback and ideas for benchmarks or visualizations are welcome!

I kept running into the same problem with congressional trading data: the disclosures are public, but the usable history is scattered across House and Senate systems, buried in PDFs, and full of amendments and inconsistent formatting.

I open-sourced both the dataset and the pipeline behind it.

The current snapshot contains 183,675 reported transaction rows from 10,809 official filings, covering 2012 through 2026. It includes:

• the original filing record and source URL
• each reported transaction row
• reconciled event versions for handling amendments
• the date a trade became publicly knowable, for point-in-time research
• reported value ranges, owner, chamber, asset type, and option details when available
• a snapshot receipt with row counts, freshness, file sizes, and checksums

The files are available as Parquet on Hugging Face. If you just want a local database, this downloads the snapshot and builds SQLite in one command:

npx congressional-disclosures@latest download --sqlite 

No API key, scraping setup, OCR service, or model credentials are needed for the hosted snapshot. The extraction pipeline is also open source if you want to inspect it, run it yourself, or contribute a fix.

The main caveat is that these are financial disclosure records, not brokerage statements. Amounts are reported as ranges, filings can arrive after the transaction date, and later amendments can change the record. The dataset preserves those distinctions instead of flattening them away. There is also a data-use notice worth reading before any commercial use.

Dataset: https://huggingface.co/datasets/austin-starks/congressional-stock-trades

Source and pipeline: https://github.com/austin-starks/congressional-disclosures

Data-use notice: https://huggingface.co/datasets/austin-starks/congressional-stock-trades/blob/main/LICENSE_DATA.md

Have you used congressional data in your trading research or trading strategies? What were your results? How did you collect the data?

i built a news sentiment tool and needed to know whether the thing actually works, so i collected four months of headlines and scored them with an llm. putting the archive out because a claim nobody can check is worth very little.

25,548 rows, 51 stocks and cryptocurrencies, 13 may to 19 september 2026. columns are ticker, timestamp, source, language, headline, url, sentiment, and which scoring model produced it.

things worth knowing before you use it

the rows are not homogeneous. there are three collection regimes and each row says which one it belongs to. until 16 august my relevance filter recognised profits in five languages and had no word for losses in any of them, so sentiment before that date is biased upward by construction. i kept those rows instead of quietly deleting them because you cannot judge what you cannot see.

seventeen tickers have under 100 rows and they are thin for two different reasons. some were never in the collection vocabulary at all. others are searched for but their name is an ordinary english word, near, cosmos, avalanche, optimism, and the relevance filter correctly throws most matches away. for those the honest sentence is not “little news about this asset”, it is “i cannot separate news about this asset from news that uses this word”.

only gdelt rows are included. i also hold ecb, esma and alpha vantage data but their licences differ and bundling three licences under one attribution line would force you to trust instead of check.

https://huggingface.co/datasets/cheruvo/financial-news-sentiment

Can anyone provide raw images/ scans of 7/12 documents? I need for a verification project. Thank you!

I require immediate assistance with a Nordi/ ONE setup. Test branches are ready to run.debug scripts are necessary NOW. Immediate return of logs. At risk of losing raw data of a rogue gov project. Help please. Riverview florida

Original dataset: World Bank World Development Indicators — https://databank.worldbank.org/source/world-development-indicators

Official API documentation: https://datahelpdesk.worldbank.org/knowledgebase/topics/125589-developer-information

Disclosure: I’m the maker of Global Data Tracker, a free visual browser built with AI/Codex assistance. I’m sharing my own tool here; I didn’t collect the underlying World Bank data.

The site currently covers 20 measures across 217 countries and areas, with country histories, rankings, comparison tables and CSV exports. It’s intended as a quick way to explore a subset of WDI before working with the source data directly.

Two practical cautions when exploring it: ‘latest available’ values may refer to different years for different countries or measures; and a missing observation is not zero. The interface displays reference years and keeps missing values as missing. For longitudinal work, GDP in current US dollars also shouldn’t be treated as inflation-adjusted growth.

The site has a separate set of moving population/economic-output estimates. Those are calculated estimates, not additional measured observations in the WDI dataset. The historical charts use published figures.

I’d welcome feedback on whether the reference years, missing data and export context are clear enough to avoid misleading comparisons.

GlobalDataTracker.com

Disclaimer: I made this one.

It’s a curriculum graph across 11 subjects (maths, physics, chemistry, biology, earth science, CS, electronics, AI/robotics, mechanical, materials, aerospace). Each chapter has a level from 1 to 5, a short summary, a list of topics, and the chapters you need before it. About 2,400 prerequisite links in total, and a few hundred of them go between subjects, which was the part I cared about most.

Whole thing as one JSON file: https://mohamedmuneerm.github.io/knowledge-navigator/knowledge_base.json
Source files and the format: https://github.com/MohamedMuneerM/knowledge-navigator/tree/main/data
Licence is CC BY-SA 4.0.

How it was made: it started as my own study notes. I used AI to help expand and organise it, and each subject was checked against published curriculum standards and university course lists (there’s a report per subject in the repo listing the sources). It’s not expert reviewed yet, so corrections are very welcome.

There’s a free web app on top of it too, if that’s more useful: https://mohamedmuneerm.github.io/knowledge-navigator/

Section 25D expired on 31 December 2025, so a purchased residential system in

2026 receives no federal credit. Most published payback figures still include

the 30%, which makes them roughly three to five years too short. I recalculated

all 50 states without it.

Data: https://energycostmap.com/states.json

DOI: https://doi.org/10.5281/zenodo.22305313

Repo: https://github.com/chameerasampathkorea/us-solar-payback-by-state

Method: https://energycostmap.com/methodology/

Licence: CC BY 4.0

Fields per state: annual generation (NREL PVWatts v8), retail rate (EIA), export

factor and netting period from the serving utility’s tariff, installed cost,

incentives, simple and degradation-adjusted payback, 25-year net return, plus the

source URL and date the export terms were checked against.

Range: Hawaii 4.1 years to Alabama 24.5. Alabama is the outlier because Alabama

Power bills solar customers $5.41/kW/month — about $390 a year on a 6 kW system,

a charge a federal judge upheld in March 2026.

The field I would point at is nettingPeriod. Before a utility compensates

exported power it nets exports against imports — over a whole monthly bill in

some states, over fifteen-minute intervals in others. With monthly netting,

power exported at midday offsets power drawn at 7pm at the full retail rate.

Iowa and Oregon both credit exports at 100% of retail, but Iowa nets in

fifteen-minute intervals and Oregon monthly, and Oregon pays back faster despite

weaker irradiance. Almost no published comparison records this field.

Known limitations, also listed in the method: one utility modelled per state

(normally the largest); the retail rate is an EIA state average rather than a

specific tariff; self-consumption is assumed rather than measured; loan interest,

dealer fees, resale premium, leases, batteries and roof replacement are all

excluded.

Happy to answer questions on any state’s figure.

I have a large number of infographic images that i have built for a vendor but due to the price conflict deal failed, now i want to sell this dataset. If someone wants this data for their company or research can purchase. I can share the samples u want. This dataset was fully verified by the client and highly dense infographic images. if someone wants it we can discuss price, quantity and licensing.