Information technology and data science development stimulate transformation in many fields of scientific knowledge.In recent years,a large number of specialised systems for information and knowledge management have b...Information technology and data science development stimulate transformation in many fields of scientific knowledge.In recent years,a large number of specialised systems for information and knowledge management have been created in materials science.However,the development and deployment of open adaptive systems for research support in materials science based on the acquisition,storage,and processing of different types of information remains unsolved.We propose MatInf-an extensible,open-source solution for research digitalisation in materials science based on an adaptive,flexible information management systemfor heterogeneous data sources.MatInf can be easily adapted to any materials science laboratory and is especially useful for collaborative projects between several labs.As an example,we demonstrate its application in high-throughput experimentation.展开更多
基金supported by the Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)Project-ID 388390466-TRR 247(subproject INF)funding from Deutsche Forschungsgemeinschaft(DFG,German Research Foundation)-CRC 1625,project number 506711657,subproject INF.
文摘Information technology and data science development stimulate transformation in many fields of scientific knowledge.In recent years,a large number of specialised systems for information and knowledge management have been created in materials science.However,the development and deployment of open adaptive systems for research support in materials science based on the acquisition,storage,and processing of different types of information remains unsolved.We propose MatInf-an extensible,open-source solution for research digitalisation in materials science based on an adaptive,flexible information management systemfor heterogeneous data sources.MatInf can be easily adapted to any materials science laboratory and is especially useful for collaborative projects between several labs.As an example,we demonstrate its application in high-throughput experimentation.
