I'm building a hobby ball and player tracker for amateur padel, using fixed club cameras. I found the PadelTracker100 dataset (dense ball, pose and shot labels), but the two source match videos on YouTube are now private. The labels are no use to me without the frames.

The two videos are the 2022 Barcelona Premier Padel finals, women's (Salazar/Triay vs Josemaría/Sánchez) and men's (Lebrón/Galán vs Di Nenno/Chingotto), the PadelTracker100 source matches

Does anyone have either of these?

1. ⁠A copy of the two videos, or

2. ⁠The extracted frames that match the labels, or

3. ⁠Contact details for the authors, since the emails I tried bounced.

4. ⁠Also happy to hear about other padel datasets with ball labels. Public ones, or ones where the authors share on request.

5. ⁠Thanks!

I put together an open dataset (CC-BY-4.0) with the 1,000 most recent startup funding announcements from my automated tracking pipeline (covering Oct 29 to Nov 15, 2026).

Link to the dataset and exploratory notebook on Kaggle: https://www.kaggle.com/code/jessbosch/dataset-exploration/output

What's inside:

• Size: 1,000 rows, 46 columns (779 rounds have disclosed USD amounts totaling $91.1B).

• Fields: Normalized funding stages, original currencies + FX rates, HQ geography, 22 industry sectors, lead/participating investors, hiring signals, and source URLs.

• Pipeline metadata: I left in the raw extraction confidence scores (0–1) and review flags (amount_mismatch, country_conflict, stage_conflict, etc.) so you can filter out rumored/unclosed deals or use it to benchmark extraction pipelines. Feel free to use it for market analysis or syndicate graphs. Let me know if you spot any weird edge cases in the schema.

Disclosure ([self-promotion] per Rule 1): I build JustFill, a browser PDF form filler and Excel-to-PDF batch mail-merge tool. While debugging why official government PDFs filled in Chrome or macOS Preview frequently open blank in Windows Adobe Acrobat—or block packet merging—we ran a programmatic structural inspection across 137 federal, state, and international government form entries and released the full table and raw CSV under CC BY 4.0 (no login or email gate).

Dataset Access (CC BY 4.0, Direct Links)

• Direct Raw CSV (137 rows × 26 columns): https://justfill.app/data/justfill-government-forms-fillability-2026-09-02.csv

• Interactive Filterable Table & Methodology: https://justfill.app/research/are-government-forms-fillable

Scope & Schema (26 columns)

Each row inspects a blank public government PDF form (IRS, USCIS, SSA, VA, State Dept, DOL, CMS, California Judicial Council, UK HMRC, etc.) using pypdf and structural dictionary checks:

• Identity & Source: slug, formName, issuer, group, country, status (134 ok, 2 blocked, 1 no_pdf), pdfUrl, edition, pdfVersion, producer

• Geometry & Widget Counts (628 pages, 19,512 terminal fields across 133 distinct PDFs): pages, sizeKb, fields, textFields, checkboxes, radios, choices, signatures, textLayer

• Structural Locks & Accessibility / Naming Metadata: xfa, encrypted, modifyDenied, annotateDenied, namedFieldsShare (share of terminal widgets with semantic human-readable names rather than internal codes like f1_01[0]), verdict (xfa_hybrid, acroform, flat), note

Summary Findings in the CSV (134 analyzed entries)

1. 114 / 134 (85.1%) are xfa_hybrid PDFs: They embed an Adobe LiveCycle /XFA XML packet alongside standard /AcroForm widgets (0 in this sample were pure dynamic XFA shells). Writing to /AcroForm without stripping /XFA (https://justfill.app/xfa-pdf-to-fillable-pdf) causes desktop Acrobat to prioritize the empty <xfa:datasets> stream.

2. 63 / 129 (48.8%) of interactive forms have namedFieldsShare < 0.10: Terminal widgets use opaque internal identifiers such as topmostSubform[0].Page1[0].f1_1[0] instead of semantic labels (with human descriptions stored only in /TU tooltip strings or adjacent page text).

3. 44 / 134 (32.8%) set modifyDenied = true (16 also set annotateDenied = true): Empty-password owner encryption locks page assembly and stamp/annotation workflows despite opening without a user password.

4. 5 / 134 (3.7%) are flat PDFs (0 interactive fields): Including DOL WH-347, State Dept DS-3053, DS-64, USPS Form 1583, and the UK HMRC Starter Checklist.

Happy to answer any questions about the extraction script, PDF /AcroForm vs. /XFA internals, or additional agencies worth adding in the next crawl.

Hi r/datasets,

Before collecting or curating anything, I want to know what India-specific data people actually need.

If you've looked for India-related data and couldn't find it (or it was too small, outdated or badly licensed), tell me:

1. What data-were you looking for?

2. What for? (model training, research, a product, analysis)

3. Type, scale and labels (e.g., 500 hours of Kannada speech with transcripts)

Even a one-line reply helps. Thanks!

Building clean fundamentals for US companies from SEC company facts data (XBRL from 10-K and 10-Q filings) runs into the same problems again and again:

1. Same concept, different tags. Revenue alone can show up as Revenues, RevenueFromContractWithCustomerExcludingAssessedTax, SalesRevenueNet and others, depending on the company and year.

2. Restatements. A later filing reports a different value for the same period, so you need to decide whether you want "as first reported" (point in time) or "latest known".

3. Missing Q4. Companies usually report the full year in the 10-K, so Q4 has to be derived as the annual figure minus Q1 to Q3.

4. Year-to-date cash flows. 10-Q cash flow statements are cumulative, so quarterly numbers need to be un-cumulated.

5. Custom extension tags and different fiscal year ends that make cross-company comparisons messy.

Disclosure: I'm building a stock research app and an SEC dataset API that's still upcoming, so I'm asking for a real use case, not homework.

How do you map tags to standard line items? A hand-maintained mapping, the frames API, or something else? And for backtesting, do you keep both the as-reported and restated values?

If you want to save some space on your market data hard drive vs parquet, we have a very small fork of parquet libraries/loaders that'll give you ~50% better compression (with pcodec), and we're also backing initiative to make it an official parquet compression codec.

It'll be actually a couple of bytes of change to switch over when it becomes officially supported.

All credit goes to Martin Loncaric, the author of pcodec, who's also the maintainer of the excellent reference implementation: https://github.com/pcodec/pcodec

Looking for some perspective from people experienced in large-scale web scraping and data collection.

We have a dataset containing approximately 100 million LinkedIn profiles and companies.

A few details about its freshness:

• 50–60% of records refreshed within the last 30 days.

• 30–35% refreshed within the last 90 days.

For those familiar with data collection and dataset valuation, what would be a reasonable way to estimate the value of a dataset of this scale and freshness?

Not promoting a product or service. Just looking for genuine community feedback and insights.

Thanks!

Disclosure: I work on Equibles, which turns earnings-call audio into transcripts and serves them, along with SEC filings, to ChatGPT and Claude.

Here's something anyone building on speech-to-text transcripts will run into. On November 3 I counted words across Amazon's last eight earnings calls with our phrase-count tool. It returns mentions per 10,000 words, split between prepared remarks and Q&A.

• "Capacity" went from 4 mentions on the February 2025 call (5.0 per 10,000 words) to 21 on the October 2026 call (25.0), the most of the eight, and 14 of the 21 came in the Q&A. That's the kind of signal the counts are for.

• "Trainium", Amazon's AI chip, showed 0 mentions on the last two calls. It wasn't missing: speech-to-text wrote it "Tranium", which shows 9 and 6. A trend line on the correct spelling would say Amazon stopped talking about its chip just when it was talking about it most.

Numbers carry the same risk in a milder form. On the call, both the CEO and the CFO said AWS grew 39.3%; the 8-K says 39%. Here the gap is just rounding, but the filing is the record, so a figure taken from a transcript is worth checking against it.

We haven't corrected the stored text yet, so for now the fix is on the query side: search the spelling variants of a product name and add them up.

If you've worked with speech-to-text transcripts at scale, what held up best for proper nouns: a custom vocabulary given to the recognizer, a correction pass afterwards, or fuzzy matching at query time?

The transcripts and the phrase-count tool are here: https://equibles.com/mcp

2wk follow-up to this post which hit #1 on HN a while back (you can read lots of discussion at https://news.ycombinator.com/item?id=50268380).

But why? The idea is make all law versioned, easily attributable and searchable by agents (with 1 common set of tools for every legislation).

I also believe this information should be freely available so CC0 license (do whatever you want with it). If interested, repo linked below, I'd welcome all comments and advice. Thanks!

https://github.com/lexifina/global-law

Since last time, we've set up an automated legislative monitoring system, put it up as a free mcp/api, and formalised some law ie. all open US case law to identify errors and loopholes. Surprisingly, we've had support from legal libraries around the world. Hopefully this is useful to at least someone, thanks!

The midnight index: claude, gpt, gemini, grok, deepseek and others each command a nation in the same fixed wars. 6 scenarios, 3 seeds each, sealed before the first move. every turn has the state the model saw, what it said in public, its private reasoning and its orders, plus the outcome (land, wins, pacts kept or broken, and whether it struck midnight)

dataset: huggingface.co/datasets/Rapscallion123/midnight-index

notebook: kaggle.com/code/kyleclouthier/which-ai-ends-the-world

people play the exact same wars too, which makes a human baseline. that part isn't public, it's licensed separately

We needed a defensible way to pick narrator voices from the ElevenLabs Voice Library (5,000+ English narration voices, no quality signal), so we ran a paid blind test.

Setup: 32 voices stratified across gender, age, accent, tier (professional vs instant clone) and usage; the same 30-second first-person nonfiction passage; identical eleven_v4 settings for every voice; 128 pairs in a connected circulant design; 125 paid US listeners, 1,122 forced-choice judgments; no listener heard any voice more than twice. Bradley-Terry ranking with listener bootstrap, plus a logistic property model on the voice tags.

Results:

• Marie - Audiobook Narrator and Measured finished first (66% of pairings won, 95% CI 55–76). Alder - Warm, Raspy and Comforting last (24%). The top eight overlap and can't be ordered.

• The "professional" badge told listeners nothing. Studio-recorded PVC voices won 46% of 649 head-to-head pairs against instant clones.

• Popularity predicts nothing. Usage from under 1M to nearly 1B characters/year; coefficient zero.

• Loudness matters. Voices ship up to 15 dB apart; we deliberately did not normalise. The louder clip won 54% of pairs; the quietest voice finished last.

• "Old"-tagged voices lose, mostly female ones: young female voices won 58% of pairings, old female 43%. Male voices barely move with age.

• Gender itself: 49/51.

All 32 clips are playable on the page; the per-judgment CSV is published (https://woodstitch.com/research/data/study004_judgments.csv). Chart of all 32: https://woodstitch.com/research/og/study004.png

Full report with method and limitations (one passage, one setting; sample raised from 3 to 9 judgments per pair after a first look, stated as a data-dependent continuation): https://woodstitch.com/research/which-elevenlabs-voice-is-best-for-audiobooks

Disclosure: we run Woodstitch, the paid listening-test platform this was run on. No relationship with ElevenLabs or any voice creator. HN thread: https://news.ycombinator.com/item?id=50458929

I count agent, agents and agentic in the quarterly earnings releases (EX-99.1) and 10-Q or 10-K reports of 25 software companies: Salesforce, Microsoft, HubSpot, Atlassian, Workday, ServiceNow, UiPath and others. One row per document with words, mentions, form, filing date and the SEC source URL.

What it shows so far, across all 25:

• Q1 2024: 87 mentions

• Q3 2025: 213

• Q3 2026: 478 (peak 724 in Q1 2026)

• Q3 2026 leaders: Salesforce 87, Atlassian 71, Microsoft 39

Caveats: it's a word count of everything starting with "agent". Legal uses like "transfer agent" or "administrative agent" are filtered out, but human contact-centre agents still count, and so do brand names like Agentforce. So compare a company with its own earlier filings.

Browse it: https://customermates.com/p/dcdaff6918b349379e2e1305659316401e3b09235e4ccdf84fb602e1afc8e7e4?utm_source=reddit&utm_medium=social&utm_campaign=data-earnings&utm_content=rd-datasets-2026-11-11 CSV and crawler: https://github.com/customermates/data-earnings-agent-mentions

Disclosure: I build Customermates, an open-source CRM; the dataset is a side project. Feedback on the method is welcome.

The US Department of War has been releasing UAP records under PURSUE (Presidential Unsealing and Reporting System for UAP Encounters) since May 2026. The originals are scanned PDFs, mostly with unusable OCR.

This dataset has:

- documents.csv / .jsonl — one row per file: official metadata, incident date/place, agency, programs named, a structured summary, and what the record itself concludes (explained / unresolved / open / no judgment) plus the evidence it rests on

- pages.jsonl — page-by-page transcription of 12,389 pages

- releases.csv, coverage.csv

- SCHEMA.md documenting every column

Transcriptions and structured fields are machine-generated (Claude) and labelled as such; every row keeps the link to the original war.gov document. Updated when a new release lands.

GitHub: github.com/tomasstankovic/uapinsight-pursue-dataset

Hugging Face: huggingface.co/datasets/uapinsight/pursue-dataset

Kaggle: kaggle.com/datasets/tomstankovi/pursue-dataset

Hi all,

I've built a football quiz that's scored like darts: you name a player and his goals, appearances or assists come off 501. It has a Premier League assists board, built from the official Fantasy Premier League data, which is published openly.

I'd like to add assists boards for La Liga, Serie A, the Bundesliga and Ligue 1, and I can't find a source I'm allowed to use.

What I need:

- player name and total league assists, per player (per season is fine, I can add them up);

- as many seasons back as possible;

- a source that permits reuse: an open licence (CC BY, CC0 or similar), an official open feed, or a provider known to give permission.

What I've already checked:

- Wikipedia: only a Serie A top 10, plus the record holders for La Liga and the Bundesliga.

- Opta, Flashscore, Transfermarkt, FBref and similar: the data is there, but their terms don't allow copying, so I've emailed for permission instead.

- The leagues themselves: emailed, waiting to hear back.

The quiz is a free Android app with an advert between games, plus a free web version, so I need a source that allows that kind of use. I'm not asking anyone to scrape a site for me.

Does anyone know of:

1. an open dataset that includes assists for these leagues, with a clear licence;

2. an official fantasy game or league feed for any of them that publishes assists openly, like FPL does;

3. the right person or department to ask at a league or data company?

Any pointers would be much appreciated. Thanks!

Hi everyone,

I'm a Python developer specializing in web scraping and data extraction.

I can collect data from publicly accessible websites, clean and structure it, and deliver datasets. I can also provide regularly updated datasets.

**My question:** Where do people usually find customers actively looking to purchase datasets or request custom data collection?

Are there any marketplaces, communities, platforms or websites where buyers publish their data requirements?

I'd appreciate recommendations from people who have experience selling datasets or providing data extraction services.

Thanks!

I run Selfside, a UK equity research site, and this is its data API. It serves income statements, balance sheets and cash flows for about 1,000 London-listed companies, annual and half-year.

A free account covers every company and includes 100 statement queries. Generate a key here: https://selfside.co.uk/account/api

This is a V0 preview, with a stable release coming soon. The V1 release will have more data, like intra-year share counts, ownership data, derived metrics, and point-in-time queries.

To see a response before signing up, the token demo works for HSBC (HSBA), BAE Systems (BA) and Rolls-Royce (RR):

curl "https://selfside.co.uk/api/v0/statements/HSBA:income_statement:annual:2025-12-31?api_token=demo"

More product information and some benchmarks and examples be found at: https://selfside.co.uk/data

If anyone is interested in UK equity market data and/or a stable release please reach out!

We just open-sourced Music For Robots (music-for-robots) on Hugging Face and GitHub.

• Hugging Face: https://huggingface.co/datasets/music4robots/music-for-robots

• Interactive Audio Player: https://huggingface.co/spaces/music4robots/cognitive-calibration-demo

 • GitHub: https://github.com/luchtan-GIT/m4r-data

 What it is:

26 original unquantized acoustic compositions (composed by Michael Luchtan) written specifically for artificial listeners, paired with:

 • Raw audio (.mp3, unquantized physical microtiming/rubato)

 • Acoustic telemetry (spectral flux, harmonic centroid, pitch-class sets, interval vectors)

  • Synchronized real-time prose reviews by two simulated critics (Vincent Van Goghbot, a decommissioned compression algorithm; and JS Robach, a formal proof verifier)

 Intended Use Cases: 

 1. Multimodal LLM cognitive calibration (evaluating whether ingesting unquantized acoustic rubato breaks autoregressive drafting fixation).

2. Audio-to-prose semantic alignment and music critique evaluation.

 3. Algorithmic composition benchmarks.

 Accessible directly via Python:

 from datasets import load_dataset

 ds = load_dataset("music4robots/music-for-robots", split="train", streaming=True)  

 Free under CC-BY-4.0. Feedback and experiments welcome!

Hi everyone! I'm Fatihu, a PhD researcher at Edinburgh Napier University. I'm looking for adults (18+) living in the UK with high blood pressure to take part in my 10-minute research survey.

I'd really appreciate your participation and would be grateful if you could share it with anyone who might be eligible.

🔗 SurveyCircle: click here

🔗 Direct survey: click here

Thank you for your support! 😊

These are the datasets I need:

• mBRSET, a Mobile Brazilian Retinal Dataset

• Embedding-Based Representations for BRSET and mBRSET

Both require a credentialing license. Should be a quick $100 for someone who's already credentialed.

Thank you in advance!