# Data visiting governance: a conceptual framework

**Authors:** Donrich Thaldar

PMC · DOI: 10.1186/s40246-025-00864-0 · Human Genomics · 2025-12-04

## TL;DR

This paper introduces a framework for governing data visiting in genomics to address privacy and legal challenges while enabling secure data sharing.

## Contribution

The novel contribution is the Seven-Dimensional Data Visiting Framework (7D-DVF), a governance tool for genomics data sharing.

## Key findings

- The 7D-DVF framework includes seven adjustable dimensions for governing data visiting systems.
- The framework supports proportional and context-sensitive configurations balancing privacy and legal compliance.
- The 7D-DVF is illustrated through real-world scenarios like Indigenous data sovereignty and federated AI training.

## Abstract

As genomic research scales globally, legal constraints such as data localization provisions in data privacy and other laws and ethical imperatives around privacy and sovereignty increasingly challenge traditional models of data sharing. Data visiting, where analysis occurs within the provider’s computing environment without moving the data, offers a promising alternative, yet its governance remains underdeveloped. This article introduces the Seven-Dimensional Data Visiting Framework (7D-DVF), a structured tool for designing, assessing, and regulating data visiting systems in genomics. Building on the Global Alliance for Genomics and Health (GA4GH) data sharing lexicon, the framework disaggregates data visiting into seven adjustable dimensions: researcher autonomy, data location, data visibility, nature of the shared data, output governance, trust and control model, and auditability and traceability. Each dimension operates as a governance lever, enabling proportional, context-sensitive configurations that balance privacy, utility, and legal compliance. The article illustrates how the 7D-DVF can guide practical implementation through checklists and real-world scenarios, including institutional data control, Indigenous data sovereignty, and federated AI model training. By shifting genomic governance from reactive compliance to design-based stewardship, the 7D-DVF equips stakeholders to operationalize secure, lawful, and future-ready data sharing practices.

## Full-text entities

- **Diseases:** Parkinson's disease (MESH:D010300), TREs (MESH:D014947), balkanization (MESH:D001449), Parkinson (MESH:D010302), rare disease (MESH:D035583), Crohn's (MESH:D003424), COVID-19 (MESH:D000086382)
- **Chemicals:** DUAs (-)
- **Species:** Homo sapiens (human, species) [taxon 9606]

## Full text

_Full body text omitted from this summary view._ Fetch the complete paper as Markdown: https://tomesphere.com/paper/PMC12802004/full.md

## Figures

1 figure with captions in the complete paper: https://tomesphere.com/paper/PMC12802004/full.md

## References

7 references — full list in the complete paper: https://tomesphere.com/paper/PMC12802004/full.md

---
Source: https://tomesphere.com/paper/PMC12802004