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Datasets are quickly becoming one of the most valuable resources for the future, especially in fields like medicine and law. But right now, getting access to those datasets from hospitals or legal firms is nearly impossible because of privacy concerns.
So, what if people could share their own data, safely and anonymously, through an app, and the community helps verify what’s real? Their personal identity would never be revealed, and they could even earn rewards just by posting once in a while.
Why it Matters:
Building datasets is hard and very resource-heavy. But if thousands of people contribute small pieces of data, together it becomes much easier and more powerful. Verified, community-driven datasets would also be far more trustworthy.
How the App Works:
• Each entry has two forms: unstructured (story-style, in your own words) and structured (fields like category, date, treatment, outcome, etc.). Both are useful for training AI and research.
• Users verify posts with a Verify button. Because fake or low-quality data in medical or legal domains is dangerous, posts that receive more verifications rise into the official dataset. Because posts are public, the community can spot fakes. The more verifies an entry gets, the higher priority that data gets.
• Users earn points for posting, verifying, and reposting (editing someone else’s entry to reflect their own experience).
• Security: No phone number, no email. Instead, when you create an account, the app generates a unique 25-digit private key — this becomes your permanent identity. You also get a changeable username and a device-specific 4-digit PIN for easy logins.
These exclusive, high-quality datasets could become valuable to researchers and even LLM companies. Contributors who add the most verified data would share in the rewards.
Gaps in Current Solutions:
Data Quality & Reliability → A lot of datasets today suffer from noise and fake entries. By introducing a user-driven verification system, this app ensures that only trustworthy, validated information makes it into the dataset.
Unstructured Data for NLP → Current solutions often ignore the value of casual, unstructured text. This app captures both structured and unstructured data, which can be used to train NLP models to better understand real human language, slang, and context — especially in sensitive fields like medical and legal.
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