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<p>The scientific research community stands at a critical juncture in its approach to data management. While we have successfully transitioned from manual to computer-controlled experiments across diverse fields (from tribology to materials science, from chemistry to bioengineering), our data practices have not kept pace with the exponential growth in data generation and the emergence of artificial intelligence. This paper explores why investing time in understanding modern data architectures is essential, not just as another publication requirement, but also as the foundation for accelerated collaborative discovery within our organizations and across the community. We examine the current landscape through recent survey data, explain the fundamental concepts behind FAIR (<Emphasis Type="Underline">F</Emphasis>indable, <Emphasis Type="Underline">A</Emphasis>ccessible, <Emphasis Type="Underline">I</Emphasis>nteroperable, and <Emphasis Type="Underline">R</Emphasis>eusable) data principles with the technical depth needed to assess true FAIR-ness, and present FAIR digital twins of scientific work as one concrete implementation that can transform isolated data into interconnected knowledge networks. Just as computer-controlled instruments revolutionized our experimental capabilities, modern data architectures promise to be the next enabling technology that amplifies our collective scientific output. While we use tribology as our primary field of interest, the principles apply equally to any experimental science dealing with multi-dimensional, context-dependent data.</p>

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From Data Islands to Knowledge Networks in Tribology: Understanding the Modern Data Architecture

  • 期刊论文

  • 发表日期：

    2026年7月24日

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  • Nick Garabedian,
  • Wilfred T. Tysoe

摘要

The scientific research community stands at a critical juncture in its approach to data management. While we have successfully transitioned from manual to computer-controlled experiments across diverse fields (from tribology to materials science, from chemistry to bioengineering), our data practices have not kept pace with the exponential growth in data generation and the emergence of artificial intelligence. This paper explores why investing time in understanding modern data architectures is essential, not just as another publication requirement, but also as the foundation for accelerated collaborative discovery within our organizations and across the community. We examine the current landscape through recent survey data, explain the fundamental concepts behind FAIR (Findable, Accessible, Interoperable, and Reusable) data principles with the technical depth needed to assess true FAIR-ness, and present FAIR digital twins of scientific work as one concrete implementation that can transform isolated data into interconnected knowledge networks. Just as computer-controlled instruments revolutionized our experimental capabilities, modern data architectures promise to be the next enabling technology that amplifies our collective scientific output. While we use tribology as our primary field of interest, the principles apply equally to any experimental science dealing with multi-dimensional, context-dependent data.