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

Area of Science:

• * Research data management and sharing across diverse scientific disciplines.
• * Understanding the nuances of primary vs. secondary data and the role of metadata.

Background:

• * Historical limitations in research data sharing due to cultural, legal, and technological barriers.
• * Increasing demand for transparency, reproducibility, and data reuse in the 21st century.
• * The evolution of research data management as a critical scientific practice.

Purpose of the Study:

• * To provide an extensive overview of research data sharing from theoretical, ethical, and practical perspectives.
• * To explore the principles, frameworks, and technologies enabling effective data sharing.
• * To identify current challenges and propose pathways for transparent and collaborative research.

Main Methods:

• * Synthesis of theoretical foundations of data sharing, including data types and metadata.
• * Analysis of ethical and normative frameworks, such as FAIR principles and privacy regulations (GDPR, CCPA).
• * Examination of practical implementation through infrastructure, incentives, policies, and emerging technologies.

Main Results:

• * Open data promotes transparency, reproducibility, accountability, and innovation.
• * FAIR (Findability, Accessibility, Interoperability, Reusability) principles are central to data sharing.
• * Emerging technologies like AI, blockchain, and privacy-preserving computation are transforming data sharing.

Conclusions:

• * Connecting conceptual, ethical, and practical dimensions is crucial for advancing data sharing.
• * Addressing challenges requires cultural shifts, supportive policies, and robust infrastructure.
• * The manuscript outlines realistic pathways toward transparent, collaborative, and future-oriented scientific research.