Scholarlink

The digital transformation of life science R&D is revolutionizing laboratory operations through integrated informatics solutions. This chapter explores the role of Laboratory Information Management Systems (LIMS)Laboratory Information Management Systems (LIMS), Electronic Laboratory Notebooks (ELN), analyticsAnalytics, and semantic searchSemantic search in creating a data-driven digital lab. By leveraging FAIRFAIR (Findable, Accessible, Interoperable, and Reusable) data principles, knowledge graphs, and ontology-based data managementOntology-based data management, modern laboratories enhance data integrityData integrity, interoperability, and automation. We discuss how AI and machine learning (ML)AI and machine learning (ML) enable advanced analyticsAnalytics, driving insights from high-throughput experiments while ensuring compliance with GxP, ALCOA+, and FAIRFAIR standards. The chapter also introduces semantic technologies that power concept-based search, retrieval-augmented generation (RAG), and predictive analyticsAnalytics, transforming knowledge management in R&D. A structured, interconnected scientific data platform ensures that FAIRFAIR principles are embedded in data workflows, facilitating efficient decision-making, reproducibility, and collaboration while reducing research inefficiencies. Integrating LIMSLaboratory Information Management Systems (LIMS), ELNElectronic Laboratory Notebooks (ELN), and AI-driven analyticsAnalytics within a semantic knowledge framework enables seamless data flow from experiment design to final analysis. By embracing automation, ontologies, and machine learning, laboratories can transition toward an intelligent, FAIRFAIR-compliant R&D ecosystem, ensuring sustained innovation and regulatory compliance. This chapter provides a comprehensive roadmap for implementing digital laboratory strategies, positioning organizations at the forefront of data-centric scientific research.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

The Digital Lab of the Future, Laboratory Information Systems, Analytics, and AI in Life Science R&D

  • 章节

  • 发表日期：

    2026年

查看全文
  • Verena Mertes,
  • Sascha Losko,
  • Denise Bell,
  • Panchali Roychoudhury,
  • Klaus Heumann

摘要

The digital transformation of life science R&D is revolutionizing laboratory operations through integrated informatics solutions. This chapter explores the role of Laboratory Information Management Systems (LIMS)Laboratory Information Management Systems (LIMS), Electronic Laboratory Notebooks (ELN), analyticsAnalytics, and semantic searchSemantic search in creating a data-driven digital lab. By leveraging FAIRFAIR (Findable, Accessible, Interoperable, and Reusable) data principles, knowledge graphs, and ontology-based data managementOntology-based data management, modern laboratories enhance data integrityData integrity, interoperability, and automation. We discuss how AI and machine learning (ML)AI and machine learning (ML) enable advanced analyticsAnalytics, driving insights from high-throughput experiments while ensuring compliance with GxP, ALCOA+, and FAIRFAIR standards. The chapter also introduces semantic technologies that power concept-based search, retrieval-augmented generation (RAG), and predictive analyticsAnalytics, transforming knowledge management in R&D. A structured, interconnected scientific data platform ensures that FAIRFAIR principles are embedded in data workflows, facilitating efficient decision-making, reproducibility, and collaboration while reducing research inefficiencies. Integrating LIMSLaboratory Information Management Systems (LIMS), ELNElectronic Laboratory Notebooks (ELN), and AI-driven analyticsAnalytics within a semantic knowledge framework enables seamless data flow from experiment design to final analysis. By embracing automation, ontologies, and machine learning, laboratories can transition toward an intelligent, FAIRFAIR-compliant R&D ecosystem, ensuring sustained innovation and regulatory compliance. This chapter provides a comprehensive roadmap for implementing digital laboratory strategies, positioning organizations at the forefront of data-centric scientific research.