OSSDIP: Open Source Secure Data Infrastructure and Processes Supporting Data Visiting

Data protection legislation in Africa and pathways for enhancing compliance in big data health research

BACKGROUND

The increasing availability of large volumes of personal data from diverse sources such as electronic health records, research programmes, commercial genetic testing, national health surveys and wearable devices presents significant opportunities for advancing public health, disease surveillance, personalized medicine and scientific research and innovation. However, this potential is hampered by a lack of clarity related to the processing and sharing of personal health data, particularly across varying national regulatory frameworks. This often leaves researcher stakeholders uncertain about how to navigate issues around secondary data use, repurposing data for different research objectives and cross-border data sharing.

METHOD

We analysed 37 data protection legislation across Africa to identify key principles and requirements for processing and sharing of personal health and genetic data in scientific research. On the basis of this analysis, we propose strategies that data science research initiatives in Africa can implement to ensure compliance with data protection laws while effectively reusing and sharing personal data for health research and scientific innovation.

RESULTS

In many African countries, health and genetic data are categorized as sensitive and subject to stricter protection. Key principles guiding the processing of personal data include confidentiality, non-discrimination, transparency, storage limitation, legitimacy, purpose specification, integrity, fairness, non-excessiveness, accountability and data minimality. The rights of data subjects include the right to be informed, the right of access, the right to rectification, the right to erasure/deletion of data, the right to restrict processing, the right to data portability and the right to seek compensation. Consent and adequacy assessments were the most common legal grounds for cross-border data transfers. However, considerable variation exists in legal requirements for data transfer across countries, potentially creating barriers to collaborative health research across Africa.

CONCLUSIONS

We propose several strategies that data science research initiatives can adopt to align with data protection laws. These include developing a standardized module for safe data flows, using trusted data environments to minimize cross-border transfers, implementing dynamic consent mechanisms to comply with consent specificity and data subject rights and establishing codes of conduct to govern the secondary use of personal data for health research and innovation.

PHIRI: lessons for an extensive reuse of sensitive data in federated health research

BACKGROUND

The extensive and continuous reuse of sensitive health data could enhance the role of population health research on public decisions. This paper describes the design principles and the different building blocks that have supported the implementation and deployment of Population Health Information Research Infrastructure (PHIRI), the strengths and challenges of the approach and some future developments.

METHODS

The design and implementation of PHIRI have been developed upon: (i) the data visiting principle-data does not move but code moves; (ii) the orchestration of the research question throughout a workflow that ensured legal, organizational, semantic and technological interoperability and (iii) a 'master-worker' federated computational architecture that supported the development of four uses cases.

RESULTS

Nine participants nodes and 28 Euro-Peristat members completed the deployment of the infrastructure according to the expected outputs. As a consequence, each use case produced and published their own common data model, the analytical pipeline and the corresponding research outputs. All the digital objects were developed and published according to Open Science and FAIR principles.

CONCLUSION

PHIRI has successfully supported the development of four use cases in a federated manner, overcoming limitations for the reuse of sensitive health data and providing a methodology to achieve interoperability in multiple research nodes.

"Be sustainable": EOSC-Life recommendations for implementation of FAIR principles in life science data handling

The main goals and challenges for the life science communities in the Open Science framework are to increase reuse and sustainability of data resources, software tools, and workflows, especially in large-scale data-driven research and computational analyses. Here, we present key findings, procedures, effective measures and recommendations for generating and establishing sustainable life science resources based on the collaborative, cross-disciplinary work done within the EOSC-Life (European Open Science Cloud for Life Sciences) consortium. Bringing together 13 European life science research infrastructures, it has laid the foundation for an open, digital space to support biological and medical research. Using lessons learned from 27 selected projects, we describe the organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the life sciences. We show how EOSC-Life provides a model for sustainable data management according to FAIR (findability, accessibility, interoperability, and reusability) principles, including solutions for sensitive- and industry-related resources, by means of cross-disciplinary training and best practices sharing. Finally, we illustrate how data harmonisation and collaborative work facilitate interoperability of tools, data, solutions and lead to a better understanding of concepts, semantics and functionalities in the life sciences.

Emerging FAIR Ecosystem(s): A Practical Perspective

There is broad acceptance that FAIR data (Wilkinson et al. 2016) reuse is desirable, with considerable interest and energy being devoted to its realization, but many questions remain on the part of prospective implementers. Fundamental to explaining how best to implement FAIR is an overview of how the many FAIR models (and the technologies that support them) fit together into a coherent "FAIR ecosystem". How do FAIR Implementation Profiles (FIPs) (Schultes et al. 2020) relate to FAIR Data Points (FDPs) (Bonino da Silva Santo et al. 2022), and how do these relate to the concept of FAIR Digital Objects (FDOs) (Hudson et al. 2018)? What is their relationship to other diverse FAIR resources and digital assets (metadata, datasets, repositories, and the complex web of services that run them)? How are these novel and legacy systems intended to interoperate? These questions are often encountered by those involved in the growing number of projects looking at FAIR implementation (ENVRI-FAIR 2019, CODATA 2022, SciDataCon 2022, Seoul Korea 2022b, Health-Holland Project 2021, Health-Holland Project 2022, Swiss Personalized Health Network 2022, Maxwell 2021, VODAN 2020).

Such an overview could also inform the development of specifications for the different models involved in a FAIR ecosystem, such as FIPs, FDPs, and the description of digital resources (data and services) at various levels. With an agreed picture of the FAIR-reuse ecosystem, the points of contact and "hand-off" would be easier to describe and coordinate.

This presentation looks at questions from FAIR implementation across various settings, and proposes a view of the overall ecosystem which could be agreed and communicated to prospective implementers. It suggests the relationship between various artefacts being discussed in the FAIR community today (FIPs, FDPs, FDOs, and other digital assets) and looks at how these can be connected to the business layer to support the development of services and applications within the envisioned FAIR ecosystem. Notably, this includes how the Cross-Domain Interoperability Framework (CDIF) being developed through the WorldFAIR project can connect to the underlying FAIR ecosystem in practical terms (Weise et al. 2022).

The presentation will address high-level considerations around the major technology components of a FAIR ecosystem, their roles within a range of common user scenarios (often having unavoidable legacy technology), and their relationship to each other and to the set of models needed to provide practical services for FAIR interoperation at the business level.

Three basic scenarios are examined, in order to understand the practical requirements of different communities. The first scenario is one which has received a good deal of attention during initial efforts to implement the FAIR principles, a domain or user community without a strong pre-existing culture of data sharing and resume, wishes to become FAIR. The second scenario is one where a community with a strong existing culture of data sharing and reuse is looking to integrate its current approaches with those advocated by the FAIR community. The third scenario looks at FAIR from the perspective of the implementer of FAIR services from an “industrial” perspective: how does FAIR provide the kind of market which is needed to support full-scale services and application development?

Each of these scenarios provide valid, but different views of what it will take to implement the FAIR in practical terms. In order to understand them, we can draw parallels with other large-scale data-sharing efforts in other communities - the Internet and the Web itself can be understood as a useful example of how vision, standards, and implementation combine to provide successful infrastructure at this scale. Indeed, the concept of Digital Objects (and now FAIR Digital Objects) has its roots in this analogy (Kahn and Wilensky 2006). Other, smaller examples also exist, which focus more specifically on the exchange of data and metadata: for example the Statistical Data and Metadata Exchange (SDMX) Initiative (SDMX community 2022) or the emerging Cross-Domain Interoperability Framework (SciDataCon 2022, Seoul Korea 2022a). Although implemented within targeted communities, these efforts exchange a wide range of data and metadata not entirely dissimilar to what is envisioned in FAIR. There is currently no single exact parallel for FAIR ecosystems, but there are examples from which we can learn in terms of making large-scale data reuse a practical reality.

Core to these is a vision of all of the component pieces, and how they can act in concert to provide a scalable infrastructure which will address the needs of the many different communities of users. Such a common vision may be implicitly agreed among those working on FAIR implementation today, but in the interests of clear communication, it is time to document it - and in keeping with FAIR, this documentation should be itself machine-actionable. As we move toward the specification of the many components of the FAIR ecosystem, it seems only common sense to have an agreed roadmap.