(cross-discipline and internationally), or effectively (virtually no materials data meetings, standards groups, or dedicated funded programs). One of the greatest challenges that Big Data approaches can address is predicting the performance of real materials (gram to ton quantities) starting at the atomistic, nanometer, and/or micrometer level of description. As a result of the above considerations, decisions about materials usage are unnecessarily conservative, often based on older rather than newer materials R&D data, and not taking advantage of advances in modeling and simulations. Materials informatics is an area in which the new tools of data science can have major impact. Current Compute(System) None Solutions Storage Widely dispersed with many barriers to access Networking Virtually none Software Narrow approaches based on national programs (Japan, Korea, and China), applications (EU Nuclear program), proprietary solutions (Granta, etc.) Big Data Data Source Extremely distributed with data repositories existing Characteristics (distributed/centralized) only for a very few fundamental properties Volume (size) It is has been estimated (in the 1980s) that there were over 500,000 commercial materials made in the last fifty years. The last three decades has seen large growth in that number. Velocity Computer-designed and theoretically design (e.g. real time) materials (e.g., nanomaterials) are growing over time Variety Many data sets and virtually no standards for (multiple datasets, mashups mashup) Variability (rate of Materials are changing all the time, and new change) materials data are constantly being generated to describe the new materials Big Data Science Veracity (Robustness More complex material properties can require (collection, Issues) many (100s?) of independent variables to curation, describe accurately. Virtually no activity no analysis, exists that is trying to identify and systematize action) the collection of these variables to create robust data sets. Visualization Important for materials discovery. Potentially important to understand the dependency of properties on the many independent variables. Virtually unaddressed. Data Quality Except for fundamental data on the structural and thermal properties, data quality is poor or NIST Standard Practice unknown. See Munro’s Guide. Data Types Numbers, graphical, images Data Analytics Empirical and narrow in scope
(cross-discipline and internationally), or effectively (virtually no materials data meetings, standards groups, or dedicated funded programs). One of the greatest challenges that Big Data approaches can address is predicting the performance of real materials (gram to ton quantities) starting at the atomistic, nanometer, and/or micrometer level of description. As a result of the above considerations, decisions about materials usage are unnecessarily conservative, often based on older rather than newer materials R&D data, and not taking advantage of advances in modeling and simulations. Materials informatics is an area in which the new tools of data science can have major impact. Current Solutions Compute(System) None Storage Widely dispersed with many barriers to access Networking Virtually none Software Narrow approaches based on national programs (Japan, Korea, and China), applications (EU Nuclear program), proprietary solutions (Granta, etc.) Big Data Characteristics Data Source (distributed/centralized) Extremely distributed with data repositories existing only for a very few fundamental properties Volume (size) It is has been estimated (in the 1980s) that there were over 500,000 commercial materials made in the last fifty years. The last three decades has seen large growth in that number. Velocity (e.g. real time) Computer-designed and theoretically design materials (e.g., nanomaterials) are growing over time Variety (multiple datasets, mashup) Many data sets and virtually no standards for mashups Variability (rate of change) Materials are changing all the time, and new materials data are constantly being generated to describe the new materials Big Data Science (collection, curation, analysis, action) Veracity (Robustness Issues) More complex material properties can require many (100s?) of independent variables to describe accurately. Virtually no activity no exists that is trying to identify and systematize the collection of these variables to create robust data sets. Visualization Important for materials discovery. Potentially important to understand the dependency of properties on the many independent variables. Virtually unaddressed. Data Quality Except for fundamental data on the structural and thermal properties, data quality is poor or unknown. See Munro’s NIST Standard Practice Guide. Data Types Numbers, graphical, images Data Analytics Empirical and narrow in scope