The FAIR Guiding Principles for scientific data management and stewardship were introduced to improve the infrastructure supporting the reuse of scholarly data. These principles, developed by a diverse group of stakeholders including academia, industry, funding agencies, and publishers, aim to enhance the reusability of data by both humans and machines. The principles emphasize Findability, Accessibility, Interoperability, and Reusability (FAIR), ensuring that data can be easily discovered, accessed, integrated, and reused. The principles apply not only to data but also to algorithms, tools, and workflows that generate data. The article discusses the importance of good data management and stewardship in facilitating knowledge discovery and innovation. It highlights the challenges faced by researchers in finding and reusing data, particularly in the absence of specialized repositories. The FAIR principles provide a framework for addressing these challenges by ensuring that data is well-structured, accessible, and interoperable. The principles are supported by various examples, including Dataverse, FAIRDOM, ISA, and Open PHACTS, which demonstrate how the FAIR principles can be applied in practice. The article also discusses the significance of machine-actionable data, which allows computational agents to autonomously find, access, and process data. The FAIR principles are not a specific technology or standard but rather a guide for data producers and stewards to ensure that their data is FAIR. The principles are designed to be applied in any combination and incrementally, as data publishing environments evolve. The article emphasizes the need for all stakeholders, including researchers, repositories, and funding agencies, to adopt the FAIR principles to ensure that data is properly managed and stewarded. The FAIR principles are a prerequisite for proper data management and stewardship, ensuring that data is Findable, Accessible, Interoperable, and Reusable. The principles are supported by a range of initiatives and projects, including the Data Citation Implementation Group of Force11 and the Skunkworks group, which are working to implement the FAIR principles in practice. The article concludes by calling on all data producers and publishers to adopt the FAIR principles and actively participate in the FAIR initiative to ensure that data is properly managed and stewarded.The FAIR Guiding Principles for scientific data management and stewardship were introduced to improve the infrastructure supporting the reuse of scholarly data. These principles, developed by a diverse group of stakeholders including academia, industry, funding agencies, and publishers, aim to enhance the reusability of data by both humans and machines. The principles emphasize Findability, Accessibility, Interoperability, and Reusability (FAIR), ensuring that data can be easily discovered, accessed, integrated, and reused. The principles apply not only to data but also to algorithms, tools, and workflows that generate data. The article discusses the importance of good data management and stewardship in facilitating knowledge discovery and innovation. It highlights the challenges faced by researchers in finding and reusing data, particularly in the absence of specialized repositories. The FAIR principles provide a framework for addressing these challenges by ensuring that data is well-structured, accessible, and interoperable. The principles are supported by various examples, including Dataverse, FAIRDOM, ISA, and Open PHACTS, which demonstrate how the FAIR principles can be applied in practice. The article also discusses the significance of machine-actionable data, which allows computational agents to autonomously find, access, and process data. The FAIR principles are not a specific technology or standard but rather a guide for data producers and stewards to ensure that their data is FAIR. The principles are designed to be applied in any combination and incrementally, as data publishing environments evolve. The article emphasizes the need for all stakeholders, including researchers, repositories, and funding agencies, to adopt the FAIR principles to ensure that data is properly managed and stewarded. The FAIR principles are a prerequisite for proper data management and stewardship, ensuring that data is Findable, Accessible, Interoperable, and Reusable. The principles are supported by a range of initiatives and projects, including the Data Citation Implementation Group of Force11 and the Skunkworks group, which are working to implement the FAIR principles in practice. The article concludes by calling on all data producers and publishers to adopt the FAIR principles and actively participate in the FAIR initiative to ensure that data is properly managed and stewarded.