I want to get a dataset of all flights to Maldives (Velana International Airport) from 2009 onwards, however flightradar24 api is behind $900 bill for historic data. Any resources regarding this would be very helpful to me. Thank you

We’re working on a new feature to import slate data directly into existing projects for our VFX Database.

In setting up the auto-detect system that matches different headings from different systems (like setellite, filemaker, zoelog) we need samples.

Hoping someone out there would be willing to share specifically a Zoelog export, CSV, PDF & JSON but we will take any of them. CSV is the most important as that will likely be the supported file type.

Ideally a populated one, the more the better incase there are any differences in the workflows so we can try to make the system as universal as possible

Cheers!

can share them to [support@wranglervfx.com](mailto:support@wranglervfx.com) or just dm me here

Hey guys, I’m looking for a dataset that has Indian names along with context in English. Something like Call Rahul tonight, Let me inform Ajay about the party, Anaya is yet to arrive at the venue etc.

It’s for a project of mine. Any dataset recommendations?
I couldn’t find anything like this on the internet

Looking for multi-speaker audio where speakers switch between English and Arabic (any dialect, Gulf preferred) within the same conversation, with reference transcripts, ideally with speaker labels and timestamps. It’s for evaluating ASR and meeting-transcription quality. Already aware of ESCWA.CS, Mixat and ArzEn. Any others, including licensed or paid ones?
If not of Arabic, any other language combination is fine.

I’ve been building a Polymarket dataset for the cases where a candle or a periodic snapshot isn’t enough. If you’re testing short-window signals or execution, the useful question is often what was actually resting on the bid and ask when the decision was made.

The paid catalog has event-level order-book updates, top-of-book quotes, up to 25 levels per side, and a trade tape where available. The rows carry Polymarket’s source timestamp when present, our receive timestamp, market/token join keys and gap-audit information. Coverage is per market and day, so check the catalog for the particular series you need. The earliest buyable order-book day is 2026-02-21; feed trade tape starts 2026-04-13. Raw inbound websocket messages are available only from our own capture beginning 2026-05-11.

There is a no-sign-up sample from the BTC Up or Down 4h series on 2026-08-19: a full UTC day across 13 markets/26 tokens, with L1, L2 and trades in Parquet or CSV, plus one hour of raw websocket messages. The page shows the schema and download links: https://tickfoundry.com/samples

If you’re working on a backtest, I’d be interested in what matters most to you: quote lifetime, depth at a specific timestamp, or checking whether a data gap overlaps your signal window?

Disclosure: I run TickFoundry, which sells the historical dataset. The linked sample is free to download.

Free dataset: the 250,000 companies AI engines see best, ranked by AI visibility score.

The score (0 to 10) measures how well AI answer engines such as ChatGPT, Claude, Gemini, Perplexity and Meta AI can find, read and cite a company’s website. This file is the top of the Engagemii index, which covers 70M+ companies.

Columns (all filled on every row): rank, domain, companyName, website, aiVisibilityScore, scoredOn, engagemiiProfile (link to the company’s page with its score breakdown).

250,000 rows, 32 MB CSV (7 MB gzipped). Release 2026-09-30. The next 250,000 are on the way.

Kaggle (direct download): https://www.kaggle.com/datasets/gregorypellitteri/ai-visibility-top-250000 Hugging Face: https://huggingface.co/datasets/Engagemii/ai-visibility-top-250000 Source page: https://engagemii.com/aeo/data/top-250000

License: CC BY 4.0. Use it, share it and build on it, including commercially, with credit to Engagemii. No PII: company names, domains and scores only.

Disclosure: I run Engagemii.

I compiled official name counts from the US (SSA), England & Wales (ONS), Scotland (NRS), Northern Ireland (NISRA), France (Insee), Canada (StatCan), Ireland (CSO), Spain (INE), Norway (SSB), Austria (Statistik Austria), Poland (PESEL) and Switzerland (BFS) into one table. One row per name that appears in at least two of these countries with 100+ records each. For each country: female count, male count and female share. Summary columns say whether the name flips (≥70% girls in one country and ≥70% boys in another) and where. - 14,174 rows, ~1.3 MB - 479 names flip; France vs. the US is the most common pair (223), then Switzerland vs. the US (99) - Only sources with real counts; Spain, Poland and Switzerland count residents, the others births - Accents are removed before matching (so Ríadh and Riadh share a row) Download and method: https://namegender.com/research/names-that-change-gender-by-country Direct CSV: https://namegender.com/datasets/names-gender-by-country.csv Licence CC BY 4.0, with the underlying sources credited on the page. I run a name-to-gender API (NameGender), which is why I built this; the file is free and needs no account. 

Missed my own release date by about a week, but such is the life of a solo dev.

Anyone else had a speech system that works fine on clean audio and falls apart once it’s been through a phone line?

I built a 1:1 comparison dataset for isolating codec, transmission and presentation effects on speech. The same 5,992 base clips go through every one of 101 conditions, so any difference between conditions comes from the condition, not the speaker or the recording. It’s free to use; hopefully it’s useful for voice and speech projects people are working on.

The main thing I wanted was matched sample-rate controls. Narrowband codecs decode at 8 kHz, so if you compare clean wideband audio against codec output, you can’t tell whether your model or extractor broke because of the codec or just because the bandwidth dropped. Every codec condition here has an unencoded resample control alongside it (8, 16, 22, 44 and 48 kHz). That lets you check whether it’s the channel, the resampling or the presentation causing problems. (In a small pilot, the 8 kHz resample alone accounted for much of the apparent “codec effect” on some features, but not all of it.) I couldn’t find many public codec or robustness sets that ship controls like this, or per-clip generation logs, so I included both.

Base pool: 5,992 bona fide mono 16-bit clips.

– 2,992 AMI headset clips (native 16 kHz)

– 1,500 VCTK mic1 + 1,500 VCTK mic2 clips (native 48 kHz), including 1,485 exact dual-mic pairs

– VCTK portion is gender-balanced (750 male / 750 female)

PACC-T (Telecoms): 20.7 GB, 305,592 FLAC files, 51 conditions

– 34 speech/audio codecs: G.711, G.722, G.726, GSM, AMR-NB, AMR-WB, EVS (adaptive/fixed/no DTX), Opus (auto/CELT), LC3, Speex, iLBC, Codec2 (700/1300 bps), MP3, AAC

– 12 tandem codec chains

– 5 resample controls (8k, 16k, 22k, 44k, 48k)

PACC-P (Presentation): 34.3 GB, 299,600 FLAC files, 50 conditions

– Additive noise, 6-talker babble, and music (vocal and instrumental) at 5 calibrated SNRs (0–20 dB)

– Simulated rooms (pyroomacoustics, with cached RIRs for bit-exact recreation) and echoes

– Bandpass/lowpass filters

– PSOLA vs resampled pitch shifts, autotune, tempo shifts

– Compound cafe + codec scenes

Every condition downloads individually as its own tarball (0.18–1.15 GB). There’s also a small sample tarball for each set (pacc-t_sample.tar / pacc-p_sample.tar) if you just want to look first. Everything ships with full SHA-256 manifests and a per-clip params.csv, so any clip can be traced back to exactly how it was made.

Licence: CC-BY-4.0

PACC-T: zenodo.org/records/23026393

PACC-P: zenodo.org/records/23026395

Happy to answer questions, and bug reports welcome.

Hey everyone,

I wanted to share a dataset I published for anyone working on low-resource NLP, tokenization, or language modeling for Haitian Creole (Kreyòl ayisyen): the Haitian Creole Word Frequency dataset (haitian-creole-word-freq), now live on both Hugging Face and Kaggle.

Overview

This is a word frequency list for Haitian Creole built from the Carnegie Mellon University (CMU) Haitian newswire corpus. It contains 17,947 unique lowercase words with their occurrence counts, sorted in descending order by count.

Links

• Hugging Face: https://huggingface.co/datasets/Speedk4011/haitian-creole-word-freq
• Kaggle: https://www.kaggle.com/datasets/spdk4011/haitian-creole-word-freq

Potential Use Cases

• Stopword Candidates: Extracting function words from the top of the frequency list (te, yo, nan, yon, li, pou, ki).
• Tokenizer Customization: Fine-tuning or building BPE/WordPiece vocabularies for low-resource LLMs.
• Spellcheck and Auto-correct: Prioritizing candidate suggestions by word popularity.
• Vocabulary Membership Tests and Orthographic Checks: Testing if a token is spelled like a Haitian Creole word or checking lexical presence.
• Lexicography and Language Learning: Extracting core vocabulary lists based on news text.
• Language Identification and N-gram Models: Statistical language modeling for text classification pipelines.
• Testing Fixtures: Generating reproducible data inputs for unit testing Haitian Creole NLP pipelines.

517 rows of trading-strategy backtest results (stocks, ETFs, crypto; 31 families): annualized excess return vs buy-and-hold, p-value and multiple-testing (Benjamini-Hochberg) q-value. CC BY 4.0, JSON, no signup. I built this; it includes the negative results (0 of 517 passed the correction).

Disclosure: I run Tickfloor, which sells a paid pass (backtester, daily board, alerts, course). The dataset and write-ups are free.

Data, method and code on GitHub

What’s in the repo:

• data/strategy-results.json: 517 rows, one per strategy, with ID, family, asset class, annualized excess return versus buy-and-hold, unadjusted p-value, Benjamini-Hochberg adjusted q-value and a pre-correction verdict column (ignore it; it is not a pass after correction).
• research/hunt2/ledger.jsonl: run-level records, including trade counts, returns, drawdowns and source/data/harness hashes.
• The strategy rules and backtest harness, including trading-cost assumptions, plus cost-sensitivity and random-control results.
• The 29 September 2026 robustness and holdout reports: 20 candidates stress-tested, 14 passed that battery, the two whose holdout window earlier runs had not read were run once on it, neither confirmed.

I coded the rules and ran them through a shared harness on discovery data before 15 March 2025. It charges costs when positions or weights change and uses a one-sided block-bootstrap test of excess returns. The benchmark labelled buy-and-hold is an equal-weight holding of the same assets, rebalanced daily, with no costs charged to the benchmark.

Of the 517 strategies, 87 finished ahead on the return estimate; eight had p < 0.05 before correction. None met the BH q < 0.10 threshold across the full family of 598 tests, including reruns. The 517-row file shows each strategy’s latest run.

The holdout exam used return days from 15 March 2025 to 27 September 2026. Low-BTC-beta crypto finished +11.52 percentage points per year above the benchmark, but p = 0.3208 missed the per-candidate threshold of 0.025. Scaled crypto trend finished 6.87 points below it. Neither confirmed.

Limits: the work is mainly on daily bars and does not establish intraday execution performance. The stock universe has survivorship bias, and the fills and cost model simplify live trading. Raw vendor price bars aren’t included, so you can re-analyse the results but not re-run the backtests.

Data and write-ups: CC BY 4.0, with attribution to Tickfloor. Code: MIT. These findings apply to the tested rules, assets and periods.

does anyone have a legitimate spectral dataset for HER(preferably containing binding energy in eV and count/s) with the source and permission to use it

After a bunch of feedback from you all we’ve launched the second iteration of our Deribit Bitcoin data API.

We’ve included some of the other major cryptos too.

Why we built this?

Tardis is simply too expensive for individual quants like us, so we’re building an alternative.

i run a small daily size game and published the raw guesses from 17 to 26 sep 2026: 50 rounds, 8,311 guesses, no device ids, with a readme.

the round that surprised me most: the hindenburg. 181 of 184 players made it too short, typical guess 46 m against a real 245 m. i wouldn’t have done better myself. across the 50 rounds, the two biggest misses were both airships.

it’s real measured data from players, not synthetic. self-selected players though, not a representative sample.

csv and write-up: https://roughly.is/blog/hindenburg-guessed-too-small.html?s=reddit-datasets

(i made the game, so not neutral)

Hello Reddit. I wanted to ask if anyone had a good website that I would be able to find a list of runners average heart rate while running a 5k? I’m sort of incompetent so I can’t decipher any coding. Every site I have found either needed me to download it or pay for it. All I need is around 10 runners data.
Please help me.

I’m working on a research project and need to build a dataset from recent social media data — mainly text like posts, comments, captions, etc.

I’ve been checking out different platforms and this is what I’ve found so far:

• YouTube → comments are available through the official API
• Reddit → seems possible, but I’m a bit confused about the current access/API situation
• X/Twitter → official access looks paid
• Instagram → came across Apify and a few similar scraping services
• Facebook → same, there are some third-party options

I also checked Apify just to get an idea of the cost. Depending on the platform/actor, it seems to range from relatively cheap to pretty expensive. But I don’t have a fixed dataset size yet, so I’m not really committed to Apify or any particular platform.

Basically, I’m trying to figure out:

• What’s the easiest way to collect recent public data from these platforms?
• Are there any good free/low-cost alternatives?
• Is Apify actually worth using for something like this?
• Are there any APIs/tools/datasets I might be missing?
• If you’ve built a similar dataset recently, what approach did you use?

The data would only be from public sources, and this is for research/academic use.

Would really appreciate some pointers from people who have actually dealt with this recently.

Actually I’m doing a project in signal processing and I need datasets of following cats and dogs

Cat: Persian, Siamese, Bengal

Dog: Husky, Beagle, Labrador

Please dm if anyone has

I need their audio not image

I looked at the Geometry preview and the COCO depth JSONL for SenseNova-Vision-Corpus-50M before committing disk space. The preview parquet has 300 rows: 192 depth, 108 normal. Each row pairs a photo with a target map plus a prompt and reply. The COCO JSONL is about 32 MB and 77,870 lines of paths to COCO 2017 photos and generated depth maps. The prompt comes in seven wordings, and several warn it shouldn’t look like a segmentation mask.

The paper says another model, MoGe-2, generated or densified those depth and normal targets. Part of the boxes and OCR came out of the Rex-Omni labeling pipeline.

The release is 50 million examples, not 50 million unique images, since one photo can show up in multiple tasks. The archive of generated maps is about 8.7 TB in 406 parts, no photos inside. The JSONLs keep relative paths, so you get the photos from the 40-odd source datasets yourself, and some want an account or approval.

License is CC BY-NC 4.0 on the HF card, plus each source’s own terms.

Has anyone pulled a single folder like coco2017/depth out of the split archive without downloading all 406 parts?

Small Question
Am i feel right that , Most of people in data field ( data science, data engineering, analysis .. etc)
Just do not understand Math and statistics well ?! ( NOT all of them but most of them)

I am an honours student studying population sciences and I have to be efficient with SPSS in order to survive. It will be of great help if someone can provide me with legal and open based sources for practicing analyses.

Build vs buy for web data: scraping hours, proxy costs, maintenance burden versus one-time dataset prices. With concrete numbers from our own pipelines.

Sources compared

DIY scraping (Apify/Crawlee): Cheap at 1 run, expensive at maintenance forever.

Apify Store actors: Best for ongoing needs on popular targets.

Dataset marketplaces (Bright Data, ScrapeHero, CoreSignal): Cheapest for point-in-time analysis. Ask what QA was applied before you buy.

Our datasets: Verification layer is the product, not just rows.

What our data adds

• Worked example from our own pipeline, 2026-09-24: 100 Swiss Zefix companies with full register details, 56 seconds runtime, $0.0124 platform compute. That is $0.12 per 1,000 companies; the compute is cheap, the maintenance is the cost.
• Maintenance evidence from a 30-scraper sweep the same day: 25 of 30 delivered data. The 5 failures were 2 upstream outages (crt.sh HTTP 502, Lithuanian KRS HTTP 500), 1 target silently dropping our search filter server-side, and 2 anti-bot blocks (Indeed, Google Jobs). Two of the five failed silently: exit code 0 with zero results.
• Targets change markup and behavior more often than buyers expect. Silent failures mean a scraper can look healthy and return nothing, so recurring scraping needs monitoring or a maintainer either way.
• Break-even: one-time analysis favors buying; recurring favors scraping or maintained actors

Disclosure: I run vhsgreed.win and sell the related datasets and actors. Take my numbers accordingly.
https://www.vhsgreed.win/data/web-scraping-vs-buying-datasets-cost/

Web Tech Dataset

I always though we were lacking common jargon datasets on tech detection.

As an engineer I talk about fullstack frameworks or frontend frameworks, not about standalone technologies with low information about the architecture of the app.

Next.js, Vue, React SPA, rather than React Router, core-js, Strapi CDN, and so forth.

I put together a small, static World Bank snapshot for comparing internet access and GDP per capita across Brazil, Canada, China, Germany, India, and the United States from 2000–2024.

I built GlobalDataTracker.com, so this is self-promotion. The site is a companion for exploring country-level statistics interactively.

Source: World Bank World Development Indicators: IT.NET.USER.ZS (internet users, % of population) and NY.GDP.PCAP.CD (GDP per capita, current US$). The CSV has 150 country-year rows, 25 per country, with no missing values in these selected indicators in this snapshot, retrieved 2026-09-24. It is a static snapshot, not a live feed; the World Bank may revise historical values.

Original source/API: https://api.worldbank.org/v2/country/BRA;CAN;CHN;DEU;IND;USA/indicator/IT.NET.USER.ZS;NY.GDP.PCAP.CD?date=2000:2024&format=json&per_page=2000 CSV and indicator notes: https://github.com/ChessShark1000/world-bank-internet-income-snapshot GlobalDataTracker.com: https://globaldatatracker.com/

What comparison or context would make this more useful to people working with country data?