datadon | Devpost

adding medical test results
list of researches

Inspiration

Have you ever taken a blood test or made an x-ray? You definitely have. But have you thought that your results might be useful not only for you, but for scientists, developers and other people? My friend once wanted to make a research so he needed to collect data, but that took him ages to get 30 results of EEG from the hospital he was working at because of bureaucracy. He also had to ask his friends and family to pass EEG so there was enough data for his project. The whole collecting data process took over a year.

What it does

We match people who need and people who have. You can upload any file with the results of the test to our platform and it would be stored there. When a person who needs your data, files the new research form, your data will be accessible for him. Depersonalized, of course.

How we built it

With blood, tears, AWS S3, AWS Cognito, AWS RDS, nginx, postgreSQL, react, secrets manager, terraform, RDS, API Gateway, Go, Typescript, bash.

Challenges we ran into

Too many things we wanted to do. Didn't settle mvp when started.

Accomplishments that we're proud of

Making 19 methods (when i am writing this, they all work!!!!!!), strong frontend, powerful backend and the idea that we may easy someone's life as soon as we launch.

What we learned

We need a consultant to launch this project succesfully.

What's next for datadon

Stage 1: Meet with researchers and users. Search for clients among large research institutes or group of interests. In this step, we can get monetisation from researchers and approbate our method and ideas. We can prove our assumptions about market size.

Stage 2: Improve the system with requests of customers to clarify the researchers needs. Find ways to optimise interaction with users. We can prove our hypotheses about the motivation of data-donors and increase our gross revenue.

Stage 3: Add a data collection and monitoring platform for users depending on step 2 insights. This allows us to increase the amount of data and make new additional value to data by describing and recognising them. In this step we can establish us as integrated data provider.

Stage 4: Integrate AI methods to our system. This is hard to deploy to production any ML especially for researches. But we can help. We can provide platform for run ML models and perform inferences on user data. Researcher gets approbation, users get analyze by raw but new methos, we get some comission.