Fair Data Governance in Natural Product Drug Discovery: Ownership, Benefit Sharing, Indigenous Knowledge Protection, Transparency, and Reuse Ethics

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Data-intensive natural product drug discovery increasingly depends on heterogeneous records that connect biological materials, chemical structures, species sources, bioactivity evidence, traditional knowledge, repository metadata, and computational derivatives. This growth creates a governance challenge because data may move from field collection and community knowledge contexts into databases, machine-learning pipelines, and commercial development pathways without carrying forward adequate information about authority, benefit sharing, sensitivity, permission, or reuse conditions. Narrow ownership models are insufficient because physical sample possession, data custody, legal control, intellectual property, Indigenous knowledge authority, and ethical legitimacy are distinct governance questions; similarly, open-data reasoning is insufficient because accessibility does not automatically authorize unrestricted reuse. This article develops an original conceptual governance framework for fair data stewardship in natural product drug discovery through synthesis of literature on natural product data infrastructures, access and benefit sharing, Indigenous data governance, FAIR and CARE principles, provenance, transparency, secondary reuse ethics, AI data documentation, and accountable data stewardship. The framework integrates five central governance domains: ownership and authority, benefit sharing, Indigenous knowledge protection, transparency, and reuse ethics. It proposes a provenance-sensitive and authority-aware decision architecture in which data origin, knowledge provenance, community involvement, permission status, commercial potential, downstream transfer, AI reuse, and unresolved uncertainty alter the governance state assigned to a proposed reuse. The principal contribution is a structured model that distinguishes Authorized Fair Reuse, Conditional Reuse with Governance Obligations, Restricted or Controlled Reuse, Governance-Insufficient Hold, and Do Not Reuse. The framework is intended to support research design, repository policy, institutional governance, and responsible computational reuse, but it is proposed as a conceptual governance architecture rather than a legally binding, empirically validated, or community-endorsed system.