Data Tokenization
Description
Louise Borreani and Pat Rawson:
"Data tokenization is the process of converting data of any sort into tokens that can be securely transferred without revealing the original data. This process enhances “data security, privacy, and compliance while preventing unauthorized access and misuse.” Datatokens are a new Web3-native asset class, finding no equivalent in the traditional world where the technical access conditions of intellectual property (IP) are embedded into its asset form. All forms of IP, from “patents, datasets, or contractual agreements” are being wrapped inside tokens like IP-NFTS, “enabling easy transfer and collective ownership over such assets.” IP-NFTs are an increasingly popular token model implemented in the decentralized science community, having already proven their usefulness as investment instruments in the biotechnology and longevity sectors."
(https://mirror.xyz/ecofrontiers.eth/zkh2LoADInAgr7GLbXnsuUOEcwJKFE4GuUSYuYU22io)
Discussion
Ecological Datatokens
Louise Borreani and Pat Rawson:
"Ecological datatokens can be considered “green” in the sense that their possession, staking, or exchange on markets generates financial flows that incentivize a growing awareness and understanding of complex ecosystems. As mentioned in previous chapters, the observation of the biosphere and its constituent ecosystems is the first critical step towards effective intervention. Ecological data informing an ecosystem’s state is of paramount importance for ongoing management of the environment, used in ecological simulations and model development, climate models, verification of research results, meta-analysis, natural resource management, and education (among other use cases).
The market for ecological state data has grown significantly over the years, and many applications requiring high quality ecological data are being developed. New financial branches such as Spatial Finance that integrate “geospatial data with financial analysis and decision-making” and innovative AI algorithms/applications which have the direct aim of conserving or regenerating natural ecosystems are emerging. Example Web3 projects gathering environmental data include dClimate and the Open Climate database from Open Earth. The global environmental monitoring data sector reached several billion USD in 2020, and is expected to double by 2030.
Despite the overall appetite for ecological state data of various types—agricultural, environmental monitoring, weather and climate—available datasets remain limited and siloed across actors, territories, and industries. Web3 offers the possibility to collectivize large data sets to form so-called “data trust DAO[s]”, strengthening the control and market power that data producers have over their data. With this in mind, DAOs hold the potential to democratize the vast wealth afforded by biodiversity and other environmental scientific IP, further incentivizing adequate data collection."
(https://mirror.xyz/ecofrontiers.eth/zkh2LoADInAgr7GLbXnsuUOEcwJKFE4GuUSYuYU22io)