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JAMES A WARREN DIRECTOR, MATERIALS GENOME PROGRAM MATERIAL MEASUREMENT LABORATORY NATIONAL INSTITUTE OF STANDARDS AND TECHNOLOGY EXECUTIVE SECRETARY, NSTC SUBCOMMITTEE ON MGI MATERIALS CHALLENGES MGI FOR SCIDATACON QUESTIONS ▸ What is your discipline culture with regard to data sharing? ▸ What are the science drivers for building your discipline interoperability infrastructure? ▸ How is your discipline organized to build interoperability infrastructure? ▸ What are the lessons learned from building your discipline interoperability infrastructure? ▸ Which part of your discipline interoperability infrastructure could be made generic for other disciplines? ▸ WHAT IS YOUR DISCIPLINE?? MGI FOR SCIDATACON WHAT IS THE DISCIPLINE? ▸ Materials Research is about making stuff (transistors, LED, titanium turbine blades, sporting goods…) ▸ Processing (Recipe) ▸ Structure (Souffle or Mess?) ▸ Properties (Fluffy, Delicious vs. Rocklike Charcoal) ▸ Challenge is to design new materials from existing knowledge without resorting to raw “Edisonian” repetition. Models Data The MGI Approach Creating and Capturing Knowledge of Materials Experiment Quantum Nano Micro Macro Simulation Materials w/ Targeted Properties MGI FOR SCIDATACON WHAT IS YOUR DISCIPLINE CULTURE WITH RESPECT TO DATA SHARING? ▸ Better than it was 4 years ago! ▸ Usual incentive challenges ▸ Current reward model for high impact pubs ▸ little reward for data/code sharing ▸ Software community better, but commercial codes are a lucrative business model, so there is a natural tension ▸ Younger researchers seem more inclined to accept the new paradigm MGI FOR SCIDATACON WHAT ARE THE SCIENCE DRIVERS FOR BUILDING YOUR DISCIPLINE INTEROPERABILITY INFRASTRUCTURE? ▸ High value data from beam lines (neutrons and photons), and supercomputers) ▸ Need for better, multi-scale models ▸ Opportunities for long-tail “big data” research are intriguing Astronomy vs. Material Measurement Measurement Type Material synthesis and processing history Position in the sky e.g. e.g. Measurement Type Singular data models Modular data models 7 MGI FOR SCIDATACON WHAT ARE THE LESSONS LEARNED FROM BUILDING YOUR DISCIPLINE INTEROPERABILITY INFRASTRUCTURE? ▸ Change is hard ▸ Need for tools is self-evident (now) ▸ Standards are risky ▸ The NIST MGI Program is taking a very careful approach to standards for data representation. ▸ There is a long track record of failure for most of the space ▸ exceptions for highly structured data (such as crystallography and molecular structure) ▸ Strong belief that this should be top-down (this is counter-intuitive to some) MGI FOR SCIDATACON WHICH PART OF YOUR DISCIPLINE INTEROPERABILITY INFRASTRUCTURE COULD BE MADE GENERIC FOR OTHER DISCIPLINES? ▸ We (a collaboration between several elements of NIST) are developing ▸ Repositories ▸ A data curation system ▸ A registry infrastructure ▸ Others are building “platforms” to capture research workflows and data (Materials Commons, ICE) ▸ NLP techniques?