Inspiration
The current landscape of data aggregation for ML models relies heavily on centralized platforms, such as Roboflow and Kaggle. This causes an overreliance on invalidated human-volunteered data. Billions of dollars worth of information is unused, resulting in unnecessary inefficiencies and challenges in the data engineering process. With this in mind, we wanted to create a solution.
What it does
1. Data Contribution and Governance DAG operates as a decentralized and autonomous organization (DAO) governed by smart contracts and consensus mechanisms within a blockchain network. DAG also supports data annotation and enrichment activities, as users can participate in annotating and adding value to the shared datasets. Annotation involves labeling, tagging, or categorizing data, which is increasingly valuable for machine learning, AI, and research purposes.
2. Micropayments in Cryptocurrency In return for adding datasets to DAG, users receive micropayments in the form of cryptocurrency. These micropayments act as incentives for users to share their data with the community and ensure that contributors are compensated based on factors such as the quality and usefulness of their data.
3. Data Quality Control The community of users actively participates in data validation and quality assessment. This can involve data curation, data cleaning, and verification processes. By identifying and reporting data quality issues or errors, our platform encourages everyone to actively participate in maintaining data integrity.
How we built it
DAG was used building Next.js, MongoDB, Cohere, Tailwind CSS, Flow, React, Syro, and Soroban.
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