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  • FAIR原則実装の鍵：R Dデータ「メタデータ付与」ルールと検索 . . .
    FAIR原則は、研究データを「見つけられる（Findable）」「アクセスできる（Accessible）」「相互運用できる（Interoperable）」「再利用できる（Reusable）」状態にするための国際的なガイドラインです。 この原則を実装することは、R Dデータの価値を最大化し、研究の再現性向上と国際連携を促進するための結論的なアプローチと言えます。 特に、データが「死蔵」される最大の原因は、そもそも見つけられないことにあります。 そのため、FAIR原則の中でも「Findable（検索性）」の達成が、データ活用の第一歩として最も重要視されています。
  • A FAIR-Decide framework for pharmaceutical R D: FAIR . . .
    We describe the ‘FAIR-Decide’ framework, which aims to guide decision-making on the retrospective FAIRification of existing datasets by using business analysis techniques to estimate costs and expected benefits
  • FAIR Data Principles for Life Sciences: Implementation Guide
    This guide covers practical implementation of FAIR data principles (Findable, Accessible, Interoperable, Reusable) for life sciences organisations, framing FAIR as the essential foundation for scaling AI and machine learning in R D, clinical operations, and commercial systems
  • Unlocking the ROI of FAIR Data in Pharma and Life Sciences . . .
    The video explains how applying the **FAIR data principles—Findable, Accessible, Interoperable, and Reusable—can unlock measurable return on investment (ROI) for pharmaceutical and life sciences organizations by making data easier to discover, integrate, and reuse across teams and systems
  • Digital evolution: Novo Nordisk’s shift to ontology-based data . . .
    In Novo Nordisk R ED, we are implementing a “FAIR@source and scale” approach to data management The FAIR@source strategy aims to expedite the response time to scientific queries while ensuring factual context and reducing time-to-value
  • A FAIR-Decide framework for pharmaceutical R D: FAIR . . .
    Case studies on FAIR implementation by two pharmaceutical companies reported the successful reuse of data and associated metadata, and highlighted the importance of data quality assurance alongside the implementation of the FAIR principles [10]
  • Implementing FAIR Data Principles Delivers Value in . . .
    If your organization is looking to optimize data management and maximize R D efficiency, now is the time to embrace FAIR At Texium Solutions, we specialize in helping pharmaceutical companies implement FAIR data strategies, ensuring seamless data integration and maximizing research potential
  • Accelerating Vaccine R D with FAIR Data and Digital Lab . . .
    This case study highlights how Excelra partnered with a leading European pharmaceutical company to accelerate vaccine R D through FAIR data principles in pharma, ELN LIMS consulting, and scientific informatics-driven digital transformation
  • FAIR Data in Pharma R D | Flywheel
    Unite imaging and associated data across your enterprise with a FAIR-friendly data framework that helps you standardize how data is captured and shared With Flywheel, your drug discovery and AI development is scalable, reproducible and efficient
  • FAIR Data Principles and Use Cases in Pharma - OXFORD . . .
    FAIR data principles aim to standardise and enhance the management of scientific data through unique identifiers and standardised protocols The gradual implementation of these principles can improve research efficiency, facilitating better data utilisation by both humans and machines
  • FAIR data management: what does it mean for drug discovery?
    In this mini-review, we elucidate the utility of FAIR data management across the drug discovery pipeline and assess the impact such FAIR data has made on the drug development process





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