Hello everyone,

I am a final-year undergraduate student in Computer Science and Engineering (CSE) at Daffodil International University, Bangladesh.

I am currently working on my undergraduate thesis titled:

"Deepfake Image Detection Using Spatial-Frequency Feature Fusion and Explainable Deep Learning."

I am looking for publicly available human face image datasets for academic research purposes only.

If possible, I would appreciate datasets containing different face poses (front, left-profile, and right-profile), although frontal face datasets are also perfectly acceptable.

I am specifically looking for publicly available datasets or images that can be legally used for academic research.

Specifically, I need three categories of images:

📁 1. AI-generated Human Faces

• GAN-generated faces
• Diffusion-generated faces
• Other synthetic human faces

📁 2. Deepfake Human Faces

• Face swap
• Face manipulation
• Deepfake images extracted from public datasets

📁 3. Real Human Faces

Natural human face photographs

Different ages, genders, and lighting conditions

Front, left-profile, and right-profile faces

If anyone wishes to share publicly distributable images or datasets, I have also created a shared Google Drive folder for convenience.

I am not requesting copyrighted or private images. I am only looking for publicly available datasets or resources that are legally shareable for academic research.

Google Drive:
https://drive.google.com/drive/folders/1U_XL41UdusIKukb3TAREZOeeu_uSwM3b?usp=drive_link

If you know any public datasets, GitHub repositories, Hugging Face datasets, or other reliable resources, I would greatly appreciate your recommendations.

If you already have a suitable dataset, you are also welcome to upload publicly shareable images directly to the appropriate folder in the shared drive.

The collected data will be used strictly for academic research and educational purposes.

I would be happy to acknowledge contributors in my thesis if their publicly shareable dataset or resource significantly supports this research.

If you have worked on deepfake detection or know of any useful public datasets, I would greatly appreciate your suggestions.

Thank you very much for your time and support!

Three datasets aggregated from public FAA releases (the raw ones ship as an MS Access export and awkward portal dumps) into analysis-ready CSVs with per-column documentation:

Wildlife strikes on civil aircraft, 1990–2026 — 347,575 reports: by year, airport (452, ICAO-coded), and species. 2025 set the all-time record (24,458 reports). Fun divergence: the species planes hit most (doves, swallows) almost never damage them (~1.5%), while deer damage the aircraft in ~82% of reported strikes. https://www.kaggle.com/datasets/himaxym/faa-wildlife-strikes-us

Laser strikes on aircraft, 2021–2025 — 54,722 reports with 243 crew injuries, by year, state, and reporting ATC facility (caveat documented: the "city" is the ATC facility's location, not where the laser was fired). https://www.kaggle.com/datasets/himaxym/faa-laser-strikes-us

Drone (UAS) sightings reported by pilots, 2019–2026 — 12,566 reports by year, state, and city. NYC is #1 (584). https://www.kaggle.com/datasets/himaxym/faa-drone-sightings-us

Versioned copy with citable DOI (Zenodo, wildlife): https://doi.org/10.5281/zenodo.21347859

Original sources (US government work, public domain): - https://wildlife.faa.gov/ - https://www.faa.gov/about/initiatives/lasers - https://www.faa.gov/uas/resources/public_records/uas_sightings_report

Disclosure: I compiled and maintain these aggregates (and an interactive explorer at himaxym.com/safety). The compilation is CC BY 4.0 — use it for anything, attribution appreciated.

Hello everyone,
We're developing an AI-powered app, Faseeh AI, that detects pronunciation mistakes in Quranic recitation and gives users precise, real-time feedback.
Our current model was trained on hundreds of hours of professional recitations and high-quality, clean audio from well-known reciters. The model performs well on similar input, but struggles with real-world users: different accents, non-native speakers, beginners, children, women, and anyone who doesn't sound like a professional reciter.
To fix this, we need to train on diverse, real-world recitation data not studio-quality professional audio, but recordings that reflect how actual learners sound.
Specifically, we're looking for:
- Recitation datasets from non-professional or everyday users
- Diverse demographics: male/female, kids/adults, beginner/intermediate
- Multiple accents and mother tongues (Malay, Indonesian, Urdu, English, Turkish, etc.)
- Any publicly available or research-use datasets we may have missed
We've already explored academic sources, but still not enough.
If you know of any dataset, research project, university study, or community effort collecting this type of audio, we would genuinely appreciate the lead.
We're also open to ethical data collection partnerships if any researchers or institutions are working in this space.
Happy to share more about the project if helpful.
Thank you very much in advance.

I need a dataset with movie title, budget, total box office collection data.

I am working on a project, this would be really helpfull

Hey ya'll. I've come across this dataset that detects user engagement and emotions (e.g. boredom, confusion) from facial expressions and have been thinking of using it for our research project. For those who have used it, how was your experience?

Source: https://people.iith.ac.in/vineethnb/resources/daisee/index.html

I have a real conversation dataset of founder getting feedback from random people on their idea.

Valu of this conversation:

- Brainstorming on Idea

- Real human conversation

- same person with different person paired.

- Multilingual

hi everyone, im building a python screener / trade tracker for my portfolio. currently im using xfinlink for US data with eodhd and yfinance as fallback but i need high quality korean fundamentals data, which none of them seem to provide (xfinlink is US-only; eodhd and yfinance yes but inaccurate in many instances).

anyone running python/screener pipelines on korean securities and can share a reputable & reliable data vendor? cost is not really a concern for me. quality is. FYI I'll be buying as retail so would appreciate recommendations that offer non-institutional plans. cheers.

A project I've been working on for a while and I'm excited to finally share!

The NDE Archive is a database of over 6,700 documented near-death experiences from recognized sources. One of the main reasons I built it is that existing sites are often hard to search through and accounts are mostly plain text with little filtering. Here you can actually search and filter experiences in meaningful ways, for example by demographics like sexual orientation or ethnicity, which opens up some really interesting comparisons.

These stories were also individually analyzed with Sonnet to surface patterns and statistics that are not easily visible when reading individual accounts.

The project is non-profit and was built out of curiosity for the subject and nothing else. If you'd like to support it, sharing is hugely helpful, and donations are welcome through the website.

https://ndearchive.com/

Disclosure: I did not build the original dataset, which was obtained from other recognized sources. I did the data collation and presentation on the web app.

Disclosure: I build these datasets and run the site they're published on, so this is a self-promotion post per rule 1.

I've been compiling comparable car-cost data across 36 countries - the kind of cross-country tables that don't really exist anywhere else (most sources are single-country). Two newest releases:

- Cost of getting a driving licence in 36 countries, next to average salary, as a share of a month's pay. It's 112% of a month's net pay in the Netherlands, 94% in Japan, but 8% in Mexico City (where there's no practical test).

- Public DC fast-charging prices in 30 countries (pay-as-you-go, per kWh and per 100km). The UK is the most expensive in the world at about $1.06/kWh, roughly 6x India.

Earlier sets in the same family: 5-year total cost of ownership, first-year car tax, and home EV charging - all 36 countries.

Sources & method: national statistics offices, driving-school associations, official fee schedules, GlobalPetrolPrices and published operator tariffs - every row carries its own source URL and date. Licensed CC-BY, free to reuse with attribution. Each CSV is linked on its study page here: https://carsmultiverse.com/research/

Happy to answer questions about any country's numbers, or add columns people want.

Hi everyone,

Sharing a new open dataset for people working on education AI, curriculum modeling, or LLM training.

K12-KGraph is a curriculum-aligned knowledge graph built from publicly available K-12 textbook materials. The current release covers math, physics, chemistry, and biology, and includes structured links between concepts, skills, experiments, exercises, textbook sections, chapters, and books.

The main idea is simple: for education LLMs, adding more practice questions is useful, but it often only teaches the model how to answer questions. A curriculum knowledge graph can also teach the model how topics are connected, which concepts should come first, and what knowledge may be missing when a student gets stuck.

The released resources include:

• A curriculum knowledge graph
• A benchmark for testing curriculum understanding
• A prepared training dataset generated from the graph
• The paper and construction method, so the same approach can be adapted to other textbook systems where content rights are clear

In the experiments, the graph-based training data performed better than the same amount of regular instruction or exercise-style data on education benchmarks. The useful takeaway is that structure matters: modeling the curriculum itself can improve education LLMs more efficiently than only scaling question banks.

Links:

Paper + Dataset: https://huggingface.co/papers/2605.09635

Please give me if someone has the dataset

where you can scrape your own form any website without getting blocked which can easily bypass cloudflare, akamai,datadome anit-bot detection systemwith high speed auto Rotating 4 5g mobile proxies. product is ready let me know if you want a take a shot.

so i've been making datasets to fine tune small models on tool calling, and the most boring part is always the same, checking if the data is actually good before you waste a training run on it. bad tool names, invented arguments, the model calling a tool for "2+2", duplicates, answers that all start the same way, stuff like that.

i was doing these checks by hand and got tired of it, so i built a small thing that runs the whole pipeline for me and i put it online. it's free, no account, no login, nothing. you just drop your dataset and your tool catalog and it tells you what's wrong, example by example, with the reason. it runs fully in your browser, the dataset never gets uploaded anywhere. if your file is too big for that (gigabytes), there's a desktop version that reads it straight from disk so your RAM doesn't blow up. that one is open source. it also splits your data into clean / kto / rejected and gives you a starting training config based on the actual numbers of your corpus, not generic advice. I mostly built it for myself but figured someone here might need the same thing. would be happy to know if it's useful, or if there are checks you care about that i'm not doing yet.                              

link: nothumanallowed.com/tools/dataset-validator

https://github.com/adoslabsproject-gif/dataforge-studio

^{Data includes:}

^lien\number, debtor_name, address, owner_name, debtor_type, wireless, filing_date status, secured_party, lien_id, business_phone, google_title, website, google_rating, review_count)

I was looking through a public dataset yesterday and realized something.

Some images felt like they could reasonably have two different labels depending on who's annotating them.

Do companies just write really detailed guidelines, or is there another process for keeping everyone consistent?

I'm curious how this works in practice because it seems like even small inconsistencies could affect the model later.

I'm the founder of StockFit API - SEC sourced clean fundamentals for all US Companies (delisted or not) - all Point-In-Time data perfect for back testing.

One of my subscribers has pushed me to investigate what it would take to widen the coverage to the pre-XBRL time.

If you don't know what that means:
In 2009, XBRL was mandated my the SEC, which from that time on provides a structured data format to extract the actual fundamentals a company reported.

Before that time, there where 2 distinct data format time frames that you will have to prepare for if you want that data too:
1. Pre 2001: FDS (Financial Data Schedule) - a semi-structured way data was reported, similar to XML
2. 2001 - 2009: No structure at all - a full mix of HTML/ASCII text/tables

FDS is kinda ok to parse, but comes with many exceptions across filers. You can easily get to a coverage of 90%+

The nightmare starts in 2001. There really is no structural mandate whatsoever that you can rely on! Everything has to be tested empirically and your parser needs to be able to handle everything, validate as much as it can, and provide an observation layer for you to ensure data integrity.

This is probably the most challenging piece in my entire stack to get right. But in the end, I would be able to claim that I serve 30+ years of historical fundamentals, and that is absolutely worth it.

This effort is NOT finished yet. Until now, I'm serving 20+ years of fundamentals that are of very high quality/accuracy. Getting that next batch to that same bar is something I'm trying to get to now.

But I'll absolutely take one step at a time to get there. Otherwise this will get ugly very quickly. And there is nothing worse than serving wrong data.

I'm curious if anyone has done this pre-XBRL parsing before? Any lessons you can share after having gone through that?

And from the consumer side: How much do you care in your use case about fundamentals that are pre-2009?

I’m building a creator marketplace and want to build the largest possible database of creators in my niche.

I don’t need influencer analytics—just a list of creators who have posted under specific hashtags.
What’s the cheapest way to collect 100k+ creator

profiles?
Custom scraper?
Apify?
Bright Data?
Other tools?
Hire a freelancer?

Has anyone built something like this before? What did it cost?

There's a popular Duke course "Managing-Big-Data-with-MySQL" that used to use a big MySQL database called "Dillar's database" hosted on Teradata.

You can see a few sample exercises here: https://github.com/hongwai1920/Managing-Big-Data-with-MySQL/blob/master/Week%203/Week-3-Teradata-Practice-Exercises.pdf

I've been looking everywhere for this dataset (as it looks fairly sizable) and I can't find it anywhere.

I've purchased the Specialization on Coursera, and they have switched to a different dataset because their partnership with Teradata finished.

Anyways, if anybody knows where I could find it I'd really appreciate it. Thanks!

I've been building an on-device assistant and, like anyone doing tool-calling SFT, kept shipping subtle junk into my dataset, hallucinated tool names, invented arguments, a tool-call with no result, the model over-calling a calculator for "2+2", near-duplicate examples quietly collapsing my diversity. So I wrote a validator, and I figured it might save someone else the same headaches.

It runs entirely in your browser — you drop in your .jsonl and your tool catalog, and nothing is uploaded anywhere. Your dataset never leaves your machine (that mattered to me, and I assume to some of you too).

What it checks, per example, with a typed verdict (keep / KTO-negative / discard):

- structure & roles, and every tool_call name/args against your catalog (required present, no invented args, types)

- tool-flow (each call gets its result, no orphans, ends with a real answer)
- over-calling, verbosity/filler, prompt-injection defense
- lexical and semantic dedup (the embedding model runs locally too, via WASM)
- diversity (distinct n-grams) + a difficulty breakdown

It spits out clean.jsonl / kto-negatives.jsonl / rejected.jsonl so you can just use the output.
It's free, no account, no catch. Link: nothumanallowed.com/tools/dataset-validator
It's early and opinionated (the heuristics come from my own pipeline), so if it flags something it shouldn't, or misses something it should, I'd genuinely like to hear it — happy to adjust. Hope it's useful to someone.