A5103172054- Research Data Management Practices
- Scientific Computing and Data Management
- Big Data and Business Intelligence
- Data Analysis with R
- Data Quality and Management
There is an urgent need to improve the infrastructure supporting reuse of scholarly data. A diverse set stakeholders-representing academia, industry, funding agencies, and publishers-have come together design jointly endorse a concise measureable principles that we refer as FAIR Data Principles. The intent these may act guideline for those wishing enhance reusability their data holdings. Distinct from peer initiatives focus on human scholar, Principles put specific emphasis enhancing ability...
The industry sector is a very large producer and consumer of data, many companies traditionally focused on production or manufacturing are now relying the analysis amounts data to develop new products services. As sources needed distributed outside company, FAIR will have major impact, both by reducing existing internal silos enabling efficient integration with external (public commercial) data. Many still in early phases “FAIRification”, providing opportunities for SMEs academics apply...
Although all the technical components supporting fully orchestrated Digital Twins (DT) currently exist, what remains missing is a conceptual clarification and analysis of more generalized concept DT that made FAIR, is, universally machine actionable. This methodological overview first step toward this clarification. We present review previously developed semantic artifacts how they may be used to compose higher-order data model referred here as FAIR Twin (FDT). propose an architectural...