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# Introduction  - Current practices for developing Materials Data Analytics Toolsets are highly localized within a few individual groups resulting in major inefficiency (unnecessary duplication of codes, inadequate verification and validation of multiple instantiations of code, not engaging the right talent for the right task, etc.)   - Community development and sharing of code repositories has been successful in certain science communities. Advantages of this approach include increased e-teaming and e-collaborations, vastly improved code hygiene, promotion of open science, rapid verification and validation, and a dramatic increase in overall productivity. All of these are key ingredients for realizing a major bump (scale-up) in innovation in sciences and technology  - Community development of a materials data analytics tool-set toolset  can significantly change the landscape of materials innovation through the emergence and adoption of  theemerging  cross-disciplinary field of Materials Informatics and address Informatics; help realize  the critical needs vision  outlined in MGI and ICME. This is probably the only practical way to get materials scientists and computer scientists to establish meaningful, mutually beneficial, and highly productive collaborations. Current efforts in materials science are: ##DFT/First Priniciples   - The Materials Project is currently powered by pyMatgen \cite{Ong_2013} - Integrated tools in python from first principles (DFT and MD) for thermodynamic and electrical quantities with applications in functional materials.