This appendix sets out details of the international frameworks that can be used as guidance when defining principles around how data should be shared.
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Developed by: Office for National Statistics, UK
Description: An internationally recognised approach to managing risk from sharing data
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
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.
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
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.
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
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.
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