Data Set Lifecycle

Fair Launch and Adaptive Rewards

Our aim is to create a fair launch for data sets that aligns rewards with the risks and efforts of contributors throughout the data set's lifecycle. This approach recognizes that data set development is a fluid process with overlapping phases and dynamic value creation.

The core idea is to adequately compensate early adopters for their pioneering efforts while maintaining incentives for long-term participation. This ensures a balance between rewarding risk-takers and sustaining ongoing contributions.

Early Data Phase

A small group of environmental scientists and tech enthusiasts start contributing air quality data from their personal sensors in various cities. The data is sparse and of varying quality.

• Rewards: High token rewards to compensate for the risks and uncertainties.

• Validation: Mostly manual checks by experts.

• Contributors: Primarily passionate individuals and researchers in the field.

• Value: Limited, mainly used for small-scale research projects.

Example scenario: A climate scientist in New York sets up a DIY air quality sensor on her balcony and contributes daily readings. Despite occasional data gaps due to equipment issues, she receives substantial token rewards for being an early adopter.

Middle Data Phase

The project gains traction. More people join, including citizen scientists and some local government agencies.

• Rewards: Moderate but consistent token rewards.

• Validation: Experts use AI to speed up the validation of data but still manually spot check.

• Contributors: A broader range of participants, including hobbyists and professionals.

• Value: Growing interest from researchers and some policy makers.

Example scenario: A network of schools in London implements a program where students set up and maintain air quality sensors as part of their science curriculum. The data, while not always perfect, provides valuable insights into local air quality trends.

Late Data Phase

The data set is well-established and widely recognized as a valuable resource.

• Rewards: Lower but stable token rewards, supplemented by other incentives.

• Validation: Mostly automated with AI, minimal human intervention needed.

• Contributors: Wide range, from individuals to large organizations.

• Value: High, used by researchers, policy makers, and businesses globally.

Example scenario: A major ride-sharing company equips its entire fleet with air quality sensors, contributing massive amounts of data across multiple cities. This data is used by urban planners, health researchers, and even real estate developers to make informed decisions about city development and public health initiatives.

In each phase, the reward structure and validation methods adapt to the changing needs and value of the data set, maintaining fairness while encouraging continued growth and improvement.

	Early Stage	Middle Stage	End Stage
Risk	High	Medium	Low
Goals	Test interest	Grow quickly	Maintain data
Reward	High	Medium	Low
Reward Rate	Exponential	Linear	Sub-linear
Validation	Manual	Manual + AI	AI
Size	<100	100-1000	1000+
Liquidity	No Illiquidity	Low liquidity	Liquid
User Type	True believer	General Users	Opportunists