Waste Not, Want Not: How to Make Your Data Futureproof Through Good Data Sharing Practices

Scientific progress relies on the generation, validation, and reuse of research data, yet standard practices and cultural, legal, and technological challenges have long limited data sharing. In the 21^st century, growing volumes of data, higher transparency requirements, and concerns about reproducibility have pushed research data management to the forefront. This manuscript brings together three perspectives to provide an extensive overview of data sharing: theoretical foundations, ethical and normative frameworks, and practical implementation. First, it discusses the way research data differs across fields and formats, the distinction between primary and secondary data, and how metadata helps ensure data can be reused. It emphasizes how open data fosters transparency, reproducibility, accountability, and innovation, while also acknowledging that research data has historically been viewed as private intellectual property. Second, it explores the emergence of principles and ethical standards designed to enhance data quality and promote responsible use. Documentation standards, data management plans, and sharing of code and workflows have helped the FAIR (Findability, Accessibility, Interoperability, and Reusability) principles become a cornerstone for data sharing. Regulatory frameworks, such as the General Data Protection Regulation (GDPR) and California Consumer Privacy Act (CCPA), as well as mechanisms such as de-identification and Data Trusts, address legal and ethical issues, including privacy protection, licensing, and data governance. Finally, the third major topic discusses how these principles are implemented through infrastructure, incentives, and new technologies. It addresses the significance of cultural change and recognition systems, the impact of policies by journals and funders, and the role of repositories in preservation and interoperability. It also emphasizes the emergence of novel trends, such as artificial intelligence-driven metadata generation, blockchain-based provenance, executable workflows, and privacy-preserving computation, all of which are redefining the concept of responsible and scalable data sharing. By connecting conceptual, ethical, and practical dimensions, the manuscript outlines both current challenges and realistic pathways toward transparent, collaborative, and future-oriented research. © 2025 Wiley Periodicals LLC.