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Different disciplines have vastly different ways of organising research activities, research efforts take place at a variety of scales and use a huge range of different methodologies and workflows. These silos can slow down or hinder research on topical issues like pandemics where social science can complement biomedical approaches. The challenge is to bridge the scientific domains by trying to bring together related biomedical and social science data. The BY-COVID project explores this based on a use case between the resources of the Consortium of European Social Science Data Archive (CESSDA) and the European Clinical Research Infrastructure Network (ECRIN). The work will include mapping of the DDI-based CESSDA metadata model and the ECRIN Metadata Schema for Clinical Research Data Objects, and analysis of used vocabularies and how the terms are used in different domains. This presentation will summarise findings and discuss possible areas of convergence and how the findings could be applied in the CESSDA Data Catalogue (CDC) and the Clinical Research Metadata Repository (MDR), and fed into the wider metadata discussions and EOSC debates.

This document is the first draft of the Strategic Agenda to towards the construction of the Health Research and Innovation Cloud (HRIC). The HRIC is intended to be one of the cornerstones of European Health Data Space, specifically on its incarnation devoted to secondary use of health data, building on top of existing initiatives such as the European Open Science Cloud (EOSC) building blocks. NOTE:thisdraft has not been approved by the European Commision and reflects only the opinions of the HealthyCloud Consortium.

The digitisation of healthcare has brought new opportunities to complement and enhance the data traditionally utilized in regulatory decision-making. According to the EMA, real world evidence (RWE) has been defined as the information derived from analysis of routinely collected real world data (RWD) relating to a patient’s health status or the delivery of healthcare from a variety of sources other than traditional clinical trials. Before fostering the enormous potential presented by the use of routinely collected RWD (e.g. electronic health records, medical claims, insurance data etc.) several challenges need to be addressed: operational, technical, methodological and ELSI. Reusing RWD for research purposes in Europe and especially in a crossborder manner is hampered by the fact that health databases and registries are not easily discoverable and, even when they are, understanding what data they contain and their suitability for addressing a specific research question remains not trivial due to the lack of detailed data catalogues with adequate metadata (especially in English). The present report is entitled “D4.5 Public database inventorying the national health databases and registries and describing their access procedures for reuse for research purposes”. As the title indicates, the report delivers an inventory of national health databases and registries covering 15 European countries: Austria, Czech Republic, France, Germany, Hungary, Ireland, Italy, the Netherlands, Norway, Poland, Portugal, Slovakia, Spain, Sweden, Switzerland. For each country the reader can find information on the national healthcare system, a list of health databases and registries, their description (or links to websites where this description can be found) and information on data access for research purposes. Although this deliverable was initially conceived as a “public database” in the form of a website, the EOSC-Life WP4 partners agreed that this would be unnecessary as the European Health Information Portal1 is already playing this role. Instead, this report will become publicly available through Zenodo and disseminated to relevant stakeholders working on similar issues (including the actors behind the Health Information Portal) as a way to “join forces” and complement each other’s work instead of duplicating efforts. In summary, we conclude that the picture across Europe is diverse and at times patchy as the health databases and registries are subject to different governance and sustainability models but also to different local laws and access rules. Interestingly, there is still, on a European level, great debate around the terms “anonymisation”, “pseudonymisation” and “de-identification” and when data can be considered anonymised and as such exempted from the GDPR. Additionally, even when the current barriers of discoverability and accessibility (that are the main focus of this report) are lifted, there remains the major question of whether such data sources are suitable for research, as concerns around their quality, completeness and structure (or lack of) are still to be addressed.

Within the BY-COVID project(https://by-covid.org/), apilot study will be performed to evaluate a simple classification system for data sharing statements of registered clinical studies, characterising the degree of willingness for data sharing, initially and specifically within COVID-19 studies. The evaluation will be performed by 3 experts on a random sample of 200 studies with a data sharing statement in a trial registry, extracted using a search of the metadata repository (MDR, https://crmdr.org/) of ECRIN. The bilateral inter-observer variability between experts will be investigated. In case of disagreement between experts a consensus will be derived to serve as the ‘source of truth’ (so called “gold standard”) for further investigations exploring the use of semi-automatic classification algorithms. Subsequent to the data sharing statement categorisation, the intention is to contact the sponsors and / or principal investigators of those trials that appear likely to provide individual participant data (IPD) and ask whether they would be willing to share IPD in the context of the BY-COVID project.

The study described is planned as part of the Horizon 2020 funded project EOSC Future (https://eoscfuture.eu/). In Work Package 6, Task 6.3 of EOSC Future, Test Science Projects (TSP) are specified. This interview guide refers to the TSP “COVID-19 metadata findability and interoperability in EOSC” (short: META-COVID). The objective of this TSP is to develop a framework for a metadata model characterising the contextual metadata and especially the research approach followed in the domain represented by each research infrastructure (RI). Contextual metadata refers to data about the research process that generated the data, and / or objects that describe that activity and to data about the ‘inputs’ into the research process – grants, people, organisations, regulators, research infrastructures etc. In the TSP a specific focus should be given to the modelling of the artifact “research activity”. The intention is to apply an iterative process of consensus building amongst experts drawn from different disciplines in the life sciences and social sciences and humanities. As a first step semi-structured interviews with representatives of the participating RIs will be performed by the European Clinical Research Infrastructure Network (ECRIN) following this interview guide. The protocol of the study is also available in Zenodo and is to be read together with the interview guide:https://zenodo.org/record/7025319#.YwjjbHHP3ct&nbsp

The qualitative study described is planned as part of the Horizon 2020 funded project EOSC Future (https://eoscfuture.eu/). In Work Package 6, Task 6.3 of EOSC Future, Test Science Projects (TSP) are specified. Relevant to this protocol is the TSP “COVID-19 metadata findability and interoperability in EOSC” (short: META-COVID). The objective of this TSP is to develop a framework for a metadata model characterising the contextual metadata and especially the research approach followed in the domain represented by each research infrastructure (RI). The intention is to apply an iterative process of consensus building amongst experts drawn from different disciplines in the life sciences and social sciences and humanities. As a first step, semi-structured interviews of representatives of individual participating RIs will be performed by the European Clinical Research Infrastructure Network (ECRIN). The study protocol has been structured according to COREQ, the consolidated criteria for reporting qualitative research (checklist for interviews and focus groups; https://pubmed.ncbi.nlm.nih.gov/17872937/). The interview guide of the study is also available in Zenodo and is to be read together with the protocol document:https://zenodo.org/record/7025502#.YwjavHZBzcs&nbsp

The present report is focused on the technical implementation and validation of the COVID-19 clinical research repository developed within the EOSC-Life project. The first section describes the functionality of the repository, split between its two main systems - the TSD infrastructure on the one hand, operated by the University of Oslo, and the Repository Management System (RMS), developed by ECRIN, on the other hand. The RMS is designed to support and record the workflows associated with managing the repository and its interactions with providers, secondary users and requesters. Subsequently, the two systems and their interface to support the repository’s activity are described in detail, including the AAI infrastructure that will be used and shared by both the RMS and the TSD. Finally, the validation strategy that is currently applied to the RMS and to the new, repository-specific elements of the TSD is outlined.

Conducting clinical trials (CTs) in the paediatric population can be difficult due to ethical, regulatory, methodological, and commercial issues influencing many factors such as procedures, sample size, and company strategies. On one hand, the lack of evidence-based research and the unmet medical needs, and on the other hand, the low investment of pharma companies in paediatric research, are increasingly promoting the use of existing resources. Sharing CTs data offer many potential benefits without conducting new studies. Although strongly recommended, a few initiatives sharing CTs data in the paediatric field still exist. In the framework of Work Package 5 “Data coordinating center and data quality standards”, scouting of initiatives (such as platforms, projects, private and public initiatives of pharma industries or no-profit organization, consortium of Sponsors/Funders, databases of pharma industries, or no-profit organization, etc.) sharing CTs data, with a focus on paediatric data, will beperformed following a specific protocol.

This deliverable summarises the work of WP1 to deliver Findable, Accessible, Interoperable and Re-usable (FAIR) services in the context of EOSC-Life’s funding calls, using these to improve FAIR services, service uptake and to inform sustainable development and future use. We describe service delivery and development around the FAIR principles and present the funded projects which have driven our implementation of FAIR Services. We address sustainability and describe the processes used to engage the EOSC-Life funded projects, as well as future work.

This deliverable summarises the work of WP1 to deliver Findable, Accessible, Interoperable and Re-usable (FAIR) services in the context of EOSC-Life’s funding calls, using these to improve FAIR services, service uptake and to inform sustainable development and future use. We describe service delivery and development around the FAIR principles and present the funded projects which have driven our implementation of FAIR Services. We address sustainability and describe the processes used to engage the EOSC-Life funded projects, as well as future work.

The scope of the report is discussed, and clarified as an examination of data standards and the interoperability of data both within and between healthcare data (including that used for observational research) and interventional research data (largely from clinical trials). The data standards in use within healthcare data are examined from the perspectives of outcome measures, syntactic and transport standards, semantic standards and metadata standards. The same is then done for standards within interventional research. The practicalities of interoperability, in particular with respect to making healthcare data more like interventional research data, are explored – again under the headings of outcome measures, syntactic and transport standards, semantic standards and metadata standards. The major finding is of a current semantic incompatibility between healthcare and interventional research data standards – i.e. they tend to use different vocabularies and concepts. A conclusion summarises the report’s findings and discusses possible actions to improve data interoperability in the short, medium and long term.