"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.

Sharing sensitive data in life sciences: an overview of centralized and federated approaches.

Biomedical data are generated and collected from various sources, including medical imaging, laboratory tests and genome sequencing. Sharing these data for research can help address unmet health needs, contribute to scientific breakthroughs, accelerate the development of more effective treatments and inform public health policy. Due to the potential sensitivity of such data, however, privacy concerns have led to policies that restrict data sharing. In addition, sharing sensitive data requires a secure and robust infrastructure with appropriate storage solutions. Here, we examine and compare the centralized and federated data sharing models through the prism of five large-scale and real-world use cases of strategic significance within the European data sharing landscape: the French Health Data Hub, the BBMRI-ERIC Colorectal Cancer Cohort, the federated European Genome-phenome Archive, the Observational Medical Outcomes Partnership/OHDSI network and the EBRAINS Medical Informatics Platform. Our analysis indicates that centralized models facilitate data linkage, harmonization and interoperability, while federated models facilitate scaling up and legal compliance, as the data typically reside on the data generator's premises, allowing for better control of how data are shared. This comparative study thus offers guidance on the selection of the most appropriate sharing strategy for sensitive datasets and provides key insights for informed decision-making in data sharing efforts.

Leveraging European infrastructures to access 1 million human genomes by 2022.

Human genomics is undergoing a step change from being a predominantly research-driven activity to one driven through health care as many countries in Europe now have nascent precision medicine programmes. To maximize the value of the genomic data generated, these data will need to be shared between institutions and across countries. In recognition of this challenge, 21 European countries recently signed a declaration to transnationally share data on at least 1 million human genomes by 2022. In this Roadmap, we identify the challenges of data sharing across borders and demonstrate that European research infrastructures are well-positioned to support the rapid implementation of widespread genomic data access.

Author Correction: Leveraging European infrastructures to access 1 million human genomes by 2022.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Biomarker Research and Development for Coronavirus Disease 2019 (COVID-19): European Medical Research Infrastructures Call for Global Coordination.

An effective response to the coronavirus disease 2019 (COVID-19) pandemic requires a better understanding of the biology of the infection and the identification of validated biomarker profiles that would increase the availability, accuracy, and speed of COVID-19 testing. Here, we describe the strategic objectives and action lines of the European Alliance of Medical Research Infrastructures (AMRI), established to improve the research process and tackle challenges related to diagnostic tests and biomarker development. Recommendations include: the creation of a European taskforce for validation of novel diagnostic products, the definition and promotion of criteria for COVID-19 samples biobanking, the identification and validation of biomarkers as clinical endpoints for clinical trials, and the definition of immune biomarker signatures at different stages of the disease. An effective management of the COVID-19 pandemic is possible only if there is a high level of knowledge and coordination between the public and private sectors within a robust quality framework.

The CORBEL matrix on informed consent in clinical studies: a multidisciplinary approach of Research Infrastructures Building Enduring Life-science Services.

BACKGROUND: Informed consent forms for clinical research are several and variable at international, national and local levels. According to the literature, they are often unclear and poorly understood by participants. Within the H2020 project CORBEL-Coordinated Research Infrastructures Building Enduring Life-science Services-clinical researchers, researchers in ethical, social, and legal issues, experts in planning and management of clinical studies, clinicians, researchers in citizen involvement and public engagement worked together to provide a minimum set of requirements for informed consent in clinical studies. METHODS: The template was based on a literature review including systematic reviews and guidelines searched on PubMed, Embase, Cochrane Library, NICE, SIGN, GIN, and Clearinghouse databases, and on comparison of templates gathered through an extensive search on the websites of research institutes, national and international agencies, and international initiatives. We discussed the draft versions step-by-step and then we referred to it as the "matrix" to underline its modular character and indicate that it allows adaptation to the context in which it will be used. The matrix was revised by representatives of two international patient groups. RESULTS: The matrix covers the process of ensuring that the appropriate information, context and setting are provided so that the participant can give truly informed consent. It addresses the key topics and proposes wording on how to clarify the meaning of placebo and of non-inferiority studies, the importance of individual participants' data sharing, and the impossibility of knowing in advance how the data might be used in future studies. Finally, it presents general suggestions on wording, format, and length of the information sheet. CONCLUSIONS: The matrix underlines the importance of improving the process of communication, its proper conditions (space, time, setting), and addresses the participants' lack of knowledge on how clinical research is conducted. It can be easily applied to a specific setting and could be a useful tool to identify the appropriate informed consent format for any study. The matrix is mainly intended to support multicentre interventional randomized clinical studies, but several suggestions also apply to non-interventional research.

BBMRI-ERIC: the novel gateway to biobanks. From humans to humans.

BBMRI-ERIC, the Biobanking and BioMolecular Resources Research Infrastructure-European Research Infrastructure Consortium, is a new form of umbrella organization for biobanking in Europe. For rare and common diseases alike, it aims at providing fair access to quality-controlled human biological samples and associated biomedical and biomolecular data. Such access enables basic mechanisms underlying diseases to be studied, which is indispensable for the development of new biomarkers and drugs. In the context of the European Research Area (ERA), biobanks, which were identified as a particular European strength, contribute to Europe's cohesion policy through capacity-building in the BBMRI-ERIC member countries.

Unlocking the potential of big data and AI in medicine: insights from biobanking.

Big data and artificial intelligence are key elements in the medical field as they are expected to improve accuracy and efficiency in diagnosis and treatment, particularly in identifying biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. These applications belong to current research practice that is data-intensive. While the combination of imaging, pathological, genomic, and clinical data is needed to train algorithms to realize the full potential of these technologies, biobanks often serve as crucial infrastructures for data-sharing and data flows. In this paper, we argue that the 'data turn' in the life sciences has increasingly re-structured major infrastructures, which often were created for biological samples and associated data, as predominantly data infrastructures. These have evolved and diversified over time in terms of tackling relevant issues such as harmonization and standardization, but also consent practices and risk assessment. In line with the datafication, an increased use of AI-based technologies marks the current developments at the forefront of the big data research in life science and medicine that engender new issues and concerns along with opportunities. At a time when secure health data environments, such as European Health Data Space, are in the making, we argue that such meta-infrastructures can benefit both from the experience and evolution of biobanking, but also the current state of affairs in AI in medicine, regarding good governance, the social aspects and practices, as well as critical thinking about data practices, which can contribute to trustworthiness of such meta-infrastructures.

Risk mapping for better governance in biobanking: the case of biobank.cy.

Introduction: Risk governance is central for the successful and ethical operation of biobanks and the continued social license for being custodians of samples and data. Risks in biobanking are often framed as risks for participants, whereas the biobank's risks are often considered as technical ones. Risk governance relies on identifying, assessing, mitigating and communicating all risks based on technical and standardized procedures. However, within such processes, biobank staff are often involved tangentially. In this study, the aim has been to conduct a risk mapping exercise bringing biobank staff as key actors into the process, making better sense of emerging structure of biobanks. Methods: Based on the qualitative research method of situational analysis as well as the card-based discussion and stakeholder engagement processes, risk mapping was conducted at the biobank setting as an interactive engagement exercise. The analyzed material comprises mainly of moderated group discussions. Results: The findings from the risk mapping activity are framed through an organismic metaphor: the biobank as a growing, living organism in a changing environment, where trust and sustainability are cross-cutting elements in making sense of the risks. Focusing on the situatedness of the dynamics within biobanking activity highlights the importance of prioritizing relations at the core of risk governance and promoting ethicality in the biobanking process by expanding the repertoire of considered risks. Conclusion: With the organismic metaphor, the research brings the diverse group of biobank staff to the central stage for risk governance, highlighting how accounting for such diversity and interdependencies at the biobank setting is a prerequisite for an adaptive risk governance.

Post-identifiability in changing sociotechnological genomic data environments.

Data practices in biomedical research often rely on standards that build on normative assumptions regarding privacy and involve 'ethics work.' In an increasingly datafied research environment, identifiability gains a new temporal and spatial dimension, especially in regard to genomic data. In this paper, we analyze how genomic identifiability is considered as a specific data issue in a recent controversial case: publication of the genome sequence of the HeLa cell line. Considering developments in the sociotechnological and data environment, such as big data, biomedical, recreational, and research uses of genomics, our analysis highlights what it means to be (re-)identifiable in the postgenomic era. By showing how the risk of genomic identifiability is not a specificity of the HeLa controversy, but rather a systematic data issue, we argue that a new conceptualization is needed. With the notion of post-identifiability as a sociotechnological situation, we show how past assumptions and ideas about future possibilities come together in the case of genomic identifiability. We conclude by discussing how kinship, temporality, and openness are subject to renewed negotiations along with the changing understandings and expectations of identifiability and status of genomic data.

Ethical layering in AI-driven polygenic risk scores-New complexities, new challenges.

Researchers aim to develop polygenic risk scores as a tool to prevent and more effectively treat serious diseases, disorders and conditions such as breast cancer, type 2 diabetes mellitus and coronary heart disease. Recently, machine learning techniques, in particular deep neural networks, have been increasingly developed to create polygenic risk scores using electronic health records as well as genomic and other health data. While the use of artificial intelligence for polygenic risk scores may enable greater accuracy, performance and prediction, it also presents a range of increasingly complex ethical challenges. The ethical and social issues of many polygenic risk score applications in medicine have been widely discussed. However, in the literature and in practice, the ethical implications of their confluence with the use of artificial intelligence have not yet been sufficiently considered. Based on a comprehensive review of the existing literature, we argue that this stands in need of urgent consideration for research and subsequent translation into the clinical setting. Considering the many ethical layers involved, we will first give a brief overview of the development of artificial intelligence-driven polygenic risk scores, associated ethical and social implications, challenges in artificial intelligence ethics, and finally, explore potential complexities of polygenic risk scores driven by artificial intelligence. We point out emerging complexity regarding fairness, challenges in building trust, explaining and understanding artificial intelligence and polygenic risk scores as well as regulatory uncertainties and further challenges. We strongly advocate taking a proactive approach to embedding ethics in research and implementation processes for polygenic risk scores driven by artificial intelligence.

Life course of retrospective harmonization initiatives: key elements to consider.

Optimizing research on the developmental origins of health and disease (DOHaD) involves implementing initiatives maximizing the use of the available cohort study data; achieving sufficient statistical power to support subgroup analysis; and using participant data presenting adequate follow-up and exposure heterogeneity. It also involves being able to undertake comparison, cross-validation, or replication across data sets. To answer these requirements, cohort study data need to be findable, accessible, interoperable, and reusable (FAIR), and more particularly, it often needs to be harmonized. Harmonization is required to achieve or improve comparability of the putatively equivalent measures collected by different studies on different individuals. Although the characteristics of the research initiatives generating and using harmonized data vary extensively, all are confronted by similar issues. Having to collate, understand, process, host, and co-analyze data from individual cohort studies is particularly challenging. The scientific success and timely management of projects can be facilitated by an ensemble of factors. The current document provides an overview of the 'life course' of research projects requiring harmonization of existing data and highlights key elements to be considered from the inception to the end of the project.

Paediatric biobanking for health: The ethical, legal, and societal landscape.

Biobanks play a central role in pediatric translational research, which deals primarily with genetic data from sample-based research. However, participation of children in biobanking has received only limited attention in the literature, even though research in general and in clinical trials in particular have a long history in involving minors. So, we resolved to explore specific challenging ethical, legal, and societal issues (ELSI) in the current pediatric biobanking landscape to propose a way forward for biobanking with children as partners in research. Methodologically, we first established the accessibility and utilization of pediatric biobanks, mainly in Europe. This was supported by a literature review related to children's participation, taking into account not only academic papers but also relevant guidelines and best-practices. Our findings are discussed under five themes: general vulnerability; ethical issues-balancing risks and benefits, right to an open future, return of results including secondary findings; legal issues-capacity and legal majority; societal issues-public awareness and empowerment; and responsible research with children. Ultimately, we observed an on-going shift from the parents'/guardians' consent being a sine-qua-non condition to the positive minor's agreement: confirming that the minor is the participant, not the parent(s)/guardian(s). This ethical rethinking is paving the way toward age-appropriate, dynamic and participatory models of involving minors in decision-making. However, we identified a requirement for dynamic tools to assess maturity, a lack of co-produced engagement tools and paucity of shared best practices. We highlight the need to provide empowerment and capability settings to support researchers and biobankers, and back this with practical examples. In conclusion, equipping children and adults with appropriate tools, and ensuring children's participation is at the forefront of responsible pediatric biobanking, is an ethical obligation, and a cornerstone for research integrity.

You Can't Have AI Both Ways: Balancing Health Data Privacy and Access Fairly.

Artificial intelligence (AI) in healthcare promises to make healthcare safer, more accurate, and more cost-effective. Public and private actors have been investing significant amounts of resources into the field. However, to benefit from data-intensive medicine, particularly from AI technologies, one must first and foremost have access to data. It has been previously argued that the conventionally used "consent or anonymize approach" undermines data-intensive medicine, and worse, may ultimately harm patients. Yet, this is still a dominant approach in European countries and framed as an either-or choice. In this paper, we contrast the different data governance approaches in the EU and their advantages and disadvantages in the context of healthcare AI. We detail the ethical trade-offs inherent to data-intensive medicine, particularly the balancing of data privacy and data access, and the subsequent prioritization between AI and other effective health interventions. If countries wish to allocate resources to AI, they also need to make corresponding efforts to improve (secure) data access. We conclude that it is unethical to invest significant amounts of public funds into AI development whilst at the same time limiting data access through strict privacy measures, as this constitutes a waste of public resources. The "AI revolution" in healthcare can only realise its full potential if a fair, inclusive engagement process spells out the values underlying (trans) national data governance policies and their impact on AI development, and priorities are set accordingly.

An iterative and interdisciplinary categorisation process towards FAIRer digital resources for sensitive life-sciences data.

For life science infrastructures, sensitive data generate an additional layer of complexity. Cross-domain categorisation and discovery of digital resources related to sensitive data presents major interoperability challenges. To support this FAIRification process, a toolbox demonstrator aiming at support for discovery of digital objects related to sensitive data (e.g., regulations, guidelines, best practice, tools) has been developed. The toolbox is based upon a categorisation system developed and harmonised across a cluster of 6 life science research infrastructures. Three different versions were built, tested by subsequent pilot studies, finally leading to a system with 7 main categories (sensitive data type, resource type, research field, data type, stage in data sharing life cycle, geographical scope, specific topics). 109 resources attached with the tags in pilot study 3 were used as the initial content for the toolbox demonstrator, a software tool allowing searching of digital objects linked to sensitive data with filtering based upon the categorisation system. Important next steps are a broad evaluation of the usability and user-friendliness of the toolbox, extension to more resources, broader adoption by different life-science communities, and a long-term vision for maintenance and sustainability.

Biobanking and risk assessment: a comprehensive typology of risks for an adaptive risk governance.

Biobanks act as the custodians for the access to and responsible use of human biological samples and related data that have been generously donated by individuals to serve the public interest and scientific advances in the health research realm. Risk assessment has become a daily practice for biobanks and has been discussed from different perspectives. This paper aims to provide a literature review on risk assessment in order to put together a comprehensive typology of diverse risks biobanks could potentially face. Methodologically set as a typology, the conceptual approach used in this paper is based on the interdisciplinary analysis of scientific literature, the relevant ethical and legal instruments and practices in biobanking to identify how risks are assessed, considered and mitigated. Through an interdisciplinary mapping exercise, we have produced a typology of potential risks in biobanking, taking into consideration the perspectives of different stakeholders, such as institutional actors and publics, including participants and representative organizations. With this approach, we have identified the following risk types: economic, infrastructural, institutional, research community risks and participant's risks. The paper concludes by highlighting the necessity of an adaptive risk governance as an integral part of good governance in biobanking. In this regard, it contributes to sustainability in biobanking by assisting in the design of relevant risk management practices, where they are not already in place or require an update. The typology is intended to be useful from the early stages of establishing such a complex and multileveled biomedical infrastructure as well as to provide a catalogue of risks for improving the risk management practices already in place.

An agenda-setting paper on data sharing platforms: euCanSHare workshop.

Various data sharing platforms are being developed to enhance the sharing of cohort data by addressing the fragmented state of data storage and access systems. However, policy challenges in several domains remain unresolved. The euCanSHare workshop was organized to identify and discuss these challenges and to set the future research agenda. Concerns over the multiplicity and long-term sustainability of platforms, lack of resources, access of commercial parties to medical data, credit and recognition mechanisms in academia and the organization of data access committees are outlined. Within these areas, solutions need to be devised to ensure an optimal functioning of platforms.

BBMRI-ERIC's contributions to research and knowledge exchange on COVID-19.

During the COVID-19 pandemic, the European biobanking infrastructure is in a unique position to preserve valuable biological material complemented with detailed data for future research purposes. Biobanks can be either integrated into healthcare, where preservation of the biological material is a fork in clinical routine diagnostics and medical treatment processes or they can also host prospective cohorts or material related to clinical trials. The paper discussed objectives of BBMRI-ERIC, the European research infrastructure established to facilitate access to quality-defined biological materials and data for research purposes, with respect to the COVID-19 crisis: (a) to collect information on available European as well as non-European COVID-19-relevant biobanking resources in BBMRI-ERIC Directory and to facilitate access to these via BBMRI-ERIC Negotiator platform; (b) to help harmonizing guidelines on how data and biological material is to be collected to maximize utility for future research, including large-scale data processing in artificial intelligence, by participating in activities such as COVID-19 Host Genetics Initiative; (c) to minimize risks for all involved parties dealing with (potentially) infectious material by developing recommendations and guidelines; (d) to provide a European-wide platform of exchange in relation to ethical, legal, and societal issues (ELSI) specific to the collection of biological material and data during the COVID-19 pandemic.

Quality Matters: A Global Discussion in Qatar.

The International Biobanking Conference titled "Quality Matters: A Global Discussion in Qatar" was held on March 25-27, 2019, in the vibrant city of Doha, Qatar. The 3-day event was organized and hosted by the Qatar Biobank (QBB) and the European, Middle Eastern and African Society for Biopreservation and Biobanking (ESBB), with supporting collaboration from the International Society for Biological and Environmental Repositories (ISBER) and the Biobanking and BioMolecular Resources Research Infrastructure-European Research Infrastructure Consortium (BBMRI-ERIC). The aim was to highlight the role of biobanking in medical research and advancing health care, as well as improving clinical outcomes. The conference convened experts from across the globe to discuss continuing efforts to harmonize biobanking-related processes to achieve high-quality standards and to support international advancements in medical research for our diverse populations. The scientific agenda drew more than 1000 scientists, researchers, industry experts, and health professionals from five continents. The conference focused on the quality aspect of biobanking through seven sessions over 3 days. Researchers, scientists, and experts from around the world were invited to present, and included special presentations from QBB demonstrating their standing as a leading clinical biobank innovator in support of population and genomic medicine. The 3-day conference concluded with a session on Best Practices and Standards, a topic much in discussion with today's context.