Appendix: International frameworks for data sharing principles

This appendix sets out details of the international frameworks that can be used as guidance when defining principles around how data should be shared.

The Five Safes Framework

Developed by: Office for National Statistics, UK

Description: An internationally recognised approach to managing risk from sharing data

Framework component	Key characteristic	Mechanisms to address this in data sharing processes
Safe projects	Use of the data is legal, ethical and the project is expected to deliver public benefit	Recruit legal and regulatory expertise Conduct data ethics review processes such as using the Data Ethics Canvas
Safe people	Stakeholders have the knowledge, skills and incentives to act in accordance with required standards of behaviour	Partnership agreements define standards
Safe data	Data has been treated appropriately to minimise the potential for identification of individuals or organisations	Accountability and review mechanisms ensure responsibilities
Safe settings	There are practical controls on the way the data is stored and accessed, both technologically and physically	Data systems infrastructure includes access and control permission policies and security technologies
Safe outputs	Before final release and use, a final check is undertaken to minimise risk when releasing data publicly or to partners	Clear communication with all stakeholders

FAIR Framework

Developed by: A scientific community arising out of a workshop organised by Barend Mons in collaboration with, and co-sponsored by, the Lorentz center, The Dutch Techcenter for the Life Sciences and the Netherlands eScience Center. The principles and themes were the result of significant voluntary contributions and participation of scientists working in the Force11, BD2K and ELIXIR communities.

Description: A framework that ensures that data is Findable, Accessible, Interoperable and Reusable. The principles emphasise machine-actionability (that is the capacity of computational systems to find, access, interoperate and reuse data with no or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in volume, complexity and creation speed of data.

Framework component	Key characteristic	Mechanisms to address this in data sharing processes
Findable	Making it easy to find datasets by using metadata and machine-readable formats	Data is described using metadata
Accessible	Describing how data can be accessed and what authorisation is required	Common platforms and technology are used
Interoperable	Making sure data can be used in a variety of systems including for processing, analysis and storage	Common platforms and technology are used
Reusable	Making sure data is well-described and able to be compared and combined with other datasets	Data licences and standards are used

CARE Framework

Developed by: Global Indigenous Data Alliance

Description: The CARE Principles for Indigenous Data Governance are people and purpose-oriented, reflecting the crucial role of data in advancing Indigenous innovation and self-determination. These principles complement the existing FAIR principles encouraging open and other data movements to consider both people and purpose in their advocacy and pursuits.

Framework component	Key characteristic	Mechanisms to address this in data sharing processes
Collective benefit	Data ecosystems shall be designed and function in ways that enable Indigenous Peoples to derive benefit from the data	Clear communication with all stakeholders
Authority to control	Indigenous Peoples’ rights and interests in Indigenous data must be recognised and their authority to control such data be empowered. Indigenous data governance enables Indigenous Peoples and governing bodies to determine how Indigenous Peoples, as well as Indigenous lands, territories, resources, knowledge and geographical indicators, are represented and identified within data	Data sharing agreements are in place
Responsibility	Those working with Indigenous data have a responsibility to share how those data are used to support Indigenous Peoples’ self-determination and collective benefit. Accountability requires meaningful and openly available evidence of these efforts and the benefits accruing to Indigenous Peoples.	Clear communication with all stakeholders
Ethics	Indigenous Peoples’ rights and wellbeing should be the primary concern at all stages of the data life cycle and across the data ecosystem.	Data ethics processes are used such as the Data Ethics Canvas

WHO data principles

Developed by: The World Health Organization (WHO)

Description: The data principles of WHO provide a foundation for reaffirming trust in WHO’s information and evidence on public health, on an ongoing basis. The five principles are designed to provide a framework for data governance for WHO. The principles are intended primarily for use by WHO staff across all parts of the Organization in order to help define the values and standards that govern how data that flows into, across and out of WHO is collected, processed, shared and used. These principles are made publicly available so that they may be used and referred to by Member States and non-state actors collaborating with WHO.

Framework component	Key characteristic	Mechanisms to address this in data sharing processes
Treat data as a public good	WHO shall make every effort to release data publicly and to share when safe and ethical to do so. Unless there is a legitimate justification to the contrary, WHO shall make data open and accessible to the public in line with data being a public good	Provide clear guidance Ensure transparency
Uphold trust in data	WHO shall uphold the trust placed in it by Member States when the Organization processes data that Member States have shared with it and placed under WHO’s control.	Provide impartial and inclusive consultation Secure storage and processing Apply human rights and the right to privacy
Support data and health information systems capacity	WHO shall support Member States’ capacity-building activities, aiming for sustainability and sharing of best practices wherever it can.	Respond to requests for support Advance evidence-based decision-making by focusing on sustainable health information management systems (HIMS) and digital development systems Align with nationally owned monitoring and evaluation processes, structures and budgets
Be a responsible data manager and steward	WHO will ensure that all data made available to it are processed, maintained, analysed, disseminated and used in accordance with international standards and best practices in health data management.	Apply international scientific data standards Maintain and strengthen partnerships with relevant stakeholders Strengthen the quality of SDG monitoring efforts Adapt to specific contexts
Strive to fill public health data gaps	WHO will support Member States to fill data gaps in public health data, using empirical data collection and predictive, transparent and coherent modelling methods with proven validity.	Use transparent models and methods