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district0x is developing a standardized, open-source protocol for creating and managing decentralized marketplaces.Cybervein is developing a decentralized data sharing network. Their eventual goal is to create an universal “Internet of Data”.

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Company Description

district0x is developing a network of decentralized marketplaces and communities powered by Ethereum, Aragon, and IPFS. Marketplaces on the network, also known as districts, are built upon a standard open source framework, consisting of Ethereum smart contracts. Districts provide the core functionalities required to operate a decentralized marketplace. Users can post and search listings, rank sellers, and process payments directly on the platform. The network is powered by the Ethereum-based DNT token.

CyberVein is a distributed data network that enables the decentralized management of complex datasets on the blockchain itself. CyberVein's goal is to become a public network of immutable databases, on which information can easily be traded, interconnected, and transformed into structured knowledge.
CyberVein allows multiple parties to process distributed databases that are shared and immutable. CyberVein's databases can be connected, referenced, and moentized. This creates a marketplace for data that can benefit researchers, businesses, and others.
CyberVein's directed acylic graph (DAG) architecture improves upon existing blockchain networks, and enables a larger amount of data to be stored and transmitted by multiple parties. CyberVein has an innovative consensus algorithm called Proof of Contribution (PoC), which gives incentives to nodes to contribute storage resources. Their ledger system consists of a network of interconnected smart contracts that are designed to function independently, but can easily communicate and exchange information between the. CyberVein also uses a data oriented contracting language is a modified version of Solidity that is optimized for data monetization.
CyberVein's innovative twist on data management networks have the potential to be very useful in cases were datasets need to be shared or when data needs to be processed by several parties in parallel. Some examples include machine learning, IoT systems, and cross-industry research.

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