Peer to Peer Science: a new way of SenseMaking


The project is all open sourced. Code and documents Github

Documentation user setup, development philosophy and technical

The overview Architecture



Mapping Protocol

In total this a local AI (artificial intelligence) that is looking to make the most appropriate science computationally active, that is the best science theories working on your data, all the time. It does that by managing access to compute resources and 'scoring' their effectiveness given the local sensor data context while being given network wide feedback. In short this establishes the most applicable and computationally relevant science to be active and available to make sense of the World from.

Sensor Library

The primary function of the Sensor Library is to turn sensor data into a data asset. It provides sensor convenience by providing communication plumbing e.g bluetooth driver for multiple sensors and extracts the data, and in preparation for saving the data a merkle tree (and potentially) a proof of work process is undertaken to provide evidence of the datas provenance while economically disincentivising the creation of fraudulent data. A data API governs access to the data and an audit trail of entries are made on a blockchain.

Dapps API

Dapps provide a User Interface to the underlying science computations. For example, a sport science Dapp might pull out data relevant to swimmers.

Peer to Peer Networking

The two primary functions of the PtoP networking is a network AI that governs consensus building and keeps Mappers honest by controlling how new science is rolled out to the network. Random sampling/ random walk network techniques combined with per node random sampling technique create vast complexity, that is hard to game or corrupt and the act of trying will be computationally expensive thus provide an economic disencentive for Mappers to cheat.

Quick Guides for: