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Life Science (LS) communities must increase the sustainability of their data resources, software tools, and workflows, to allow their long-term re-use by the wider scientific community, especially in future applications involving machine-based analyses. Here, we describe key findings regarding sustainable LS resources, based on experiences from the EOSC-Life project. EOSC-Life involves 13 European LS Research Infrastructures. It lays the foundation for an open, digital and collaborative space for biological and medical research. We describe organisational, technical, financial and legal/ethical challenges that represent the main barriers to sustainability in the LS domain. Using lessons from 27 scientific projects selected via open calls, we demonstrate the efficiency of the EOSC-Life support model for sustainable FAIR data management and explore the complex sustainability needs for sensitive- and industry-related data resources. We describe cross-disciplinary sharing of best practices, and how this contributes to the sustainability of knowledge across communities, through building of training resources. We formulate a set of recommendations, focusing on working with experts, and communicating outcomes, to establish strong credibility and recognition of research projects. Furthermore, demonstrating and practising also permit a more sustainable adoption of reproducible research outputs. Recommended measures include sustainable training methodologies, as well as ensuring high quality metadata to drive the sustainable reusability of scientific objects. Responsibilities and roles to ensure sovereignty, sustainable services and tools must also be attributed. Finally, our analysis demonstrates how data harmonisation facilitates interoperability of tools, data, solutions and a better understanding of concepts, functionalities and semantics in the LSs., This research is mainly a product of the EOSC-Life European programme funding from the European Union's Horizon Europe research and innovation programme under grant agreement Nº824087. Complementary support was provided through EU funded project AgroServ (grant agreement Nº101058020), EU funded project BY-COVID (grant agreement Nº101046203), EU funded project DANUBIUS-IP (grant agreement Nº101079778), EU funded project EMPHASIS-GO (grant agreement Nº101079772), EU funded project FAIRplus (IMI grant agreement Nº802750), EU funded project FAIRsharing (Wellcome grant agreement Nº212930/Z/18/Z), EU funded project ISIDORe (grant agreement Nº101046133), EU funded project Precision Toxicology (grant agreement Nº965406), UKRI DASH (grant agreement NºMR/V038966/1). Special thanks to T. Biro and her radical collaboration team from Research Data Alliance who gave us great inspiration on how to lead this radical collaboration work.

In EOSC-Life WP14, the European Clinical Research Infrastructure Network (ECRIN) has partnered with the University of Oslo (UiO) in order to commonly design, develop, implement and operate a Clinical Research Repository for individual participant data (IPD) from COVID-19 clinical studies that is compliant with European regulations and in particular with the GDPR. The functionality of the repository is split between two main systems – the service for sensitive data (TSD) infrastructure managed by the UiO on the one hand and a Repository Management System (RMS) developed by ECRIN, on the other. The TSD is used for the secure storage, access and reuse of controlled-access data objects (e.g. datasets). The RMS is designed to support and record the workflows associated with managing the repository and its interactions with data object providers, users, and requesters. The first chapter of this report provides an introduction to the Clinical Research Repository, the actors involved, and the two main processes that it supports: the Data Transfer Process (when clinical study material is provided to the repository) and the Data Use Process (when clinical study material is accessed for secondary use). Terms around the operations of the repository such as “data object”, “provider” and “secondary user” are defined to facilitate the further reading of the report. The second chapter describes the steps followed for evaluating the usability and user satisfaction of the Clinical Research Repository. Usability is defined as “the extent to which a product can be used by specified users to achieve specified goals with effectiveness, efficiency, and satisfaction in a specified context of use”. User satisfaction is part of the usability of a system and can be defined as “a measure of how well a product or system meets the needs and expectations of its users, as well as how positively users feel about their overall experience with the product or system”. To evaluate the usability and user satisfaction of the repository, two workshops were organised on the 14th and 15th of March 2023 with a small group of potential users (n=13). As part of the workshops, the participants tested the alpha version of the repository and completed a usability and user satisfaction survey. Overall, the respondents of the survey appreciated the Clinical Research Repository as a tool to facilitate clinical research data sharing and reuse. Usability scores according to the UMUX-LITE and SUS standards are provided. The respondents also raised points for improvement that are being addressed for the beta version of the tool in order to increase its usability and user satisfaction. The third chapter details the requirements for the maintenance and sustainability of the Clinical Research Repository, taking into account the needs of both the RMS and the TSD. The cost estimations include needs in software, hardware, and personnel (e.g. for the repository manager, for developers, for legal support with contracts). In an effort to be precise, estimations are provided for a short-term, a mid-term and a longer-term period. Possible funding models for the repository are discussed. Beyond the EOSC-Life project, the repository’s short-term sustainability has been secured via follow-up grants; however, for the longer-term sustainability plan to be translated into a concrete reality, extensive discussions will be needed with relevant stakeholders following the public launch of the repository.&nbsp

The Horizon Europe project ISIDORe is dedicated to pandemic preparedness and responsiveness research. It brings together 17 Research Infrastructures (RIs) and networks to provide a broad range of services to infectious disease researchers. An efficient and structured treatment of data is central to ISIDORe’s aim to furnish seamless access to its multidisciplinary catalogue of services, and to ensure that users’ results are treated FAIRly. ISIDORe therefore requires a data management plan (DMP) covering both access management and research outputs, applicable over a broad range of disciplines, and compatible with the constraints and existing practices of its diverse partners. We undertook an iterative, step-by-step, process to build a community-approved living document, identifying good practices and processes, on the basis of use cases, presented as proof of concepts. International fora such as the RDA and EOSC, and primarily the BY-COVID project, furnished registries, tools and online data platforms, as well as standards, and the support of data scientists. Together, these elements provide a path for building an umbrella, FAIR-compliant DMP, aligned as fully as possible with FAIR principles, which could also be applied as a framework for data management harmonisation in other large-scale, challenge-driven projects. Finally, we discuss how data management and reuse can be further improved through the writing of realistic DMPs using 'DMP profiles' and, in the future, the contribution of an inter RIs data steward network, to produce a Community of Practice that could be integrated into planned trans-RI competence centres., Preprint added on zenodo with the autorisation of DSJ Editor

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.

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.

BY-COVID Work Package 3 is focused on services for the discovery and integration of COVID-19 data by delivering a flexible, tiered metadata discovery system across different domains, metadata standards, and maturity/robustness levels of data sources. This will enable the linking of FAIR data and metadata on SARS-CoV-2 and COVID-19, on other related viruses and diseases, and on socio-economic consequences, across research fields, from omics, clinical, and epidemiological research, to social sciences and humanities. Building on the metadata format developed in D3.1, in a series of work package meetings and a workshop, with participation from all other work packages, we have developed tools (example Validator), workflows (Semi-automated transfer of resource metadata from FAIRsharing to Covid-19 portal), and documentation and training (Training: Discoverability hackathon) to support the efficient integration of additional resources from a broad range of domains into the COVID-19 Data Portal as well as improved the end user facing COVID-19 Data Portal itself (Global search). This work establishes the basis for the further development of the COVID-19 Data Portal metadata discovery, and provides a path for integration of metadata from multi-domain partners in BY-COVID, as well as our ISIDORe sibling project, and other relevant external resources. To ensure smooth integration of partner provided metadata, we anticipate re-running our “Discoverability hackathon” in the future and will continue to evolve our metadata format and presentation of the COVID-19 Data Portal. We anticipate significant development and metadata modelling work for the use-case driven support of complex data sources in close collaboration with WPs 2, 4 and 5.&nbsp

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.

WP4 is concerned with policies, specifications and tools for secure management of sensitive data for research purposes and addresses this in general policies and guidelines for sensitive data storage, processing, sharing and reuse for research purposes across Research Infrastructures (RIs). The particular objective of this deliverable is to establish the requirements for managing controlled access to sensitive data sets within the Life Science AAI (WP5) and to manage and analyse those sensitive data in clouds (WP7), in particular data coming from the life sciences Research Infrastructure (LS RI) demonstrators. These requirements may include GDPR-compliance, geographical location of the data centre, legal jurisdiction, institutional scope, etc. T7.3 works with the WP7 cloud providers to identify their compliance with these sensitive data handling requirements and T5.1 integrates the sensitive data access controls into WP5 so that a single authoritative source of access can be used across the project. This requirements-gathering draws on technical expertise from the LS RIs in WP4 and from WP5 and WP7 to ensure both the requirements and implementation routes are shared and capture existing national requirements to host/distribute sensitive data (i.e. health and healthcare data, biomedical research data, classified data) and the tools used within the LS RIs to manage controlled access to such sensitive data sets. Ultimately, this deliverable reports on the different sensitive data types that the LS RIs are working with and possibly hosting and providing access to. Regulations, best practices and standards that apply to all sensitive data types are collected. For completing this deliverable, a questionnaire on sensitive data was taken by each RI, based on the EOSC-Life toolbox data types1, demonstrating the utility of its categorisation system.

WP4 is concerned with policies, specifications and tools for secure management of sensitive data for research purposes and addresses this in general policies and guidelines for sensitive data storage, processing, sharing and reuse for research purposes across Research Infrastructures (RIs).The particular objective of this deliverable is to establish the requirements for managing controlled access to sensitive data sets within the Life Science AAI (WP5) and to manage and analyse those sensitive data in clouds (WP7), in particular data coming from the life sciences Research Infrastructure (LS RI) demonstrators. These requirements may include GDPR-compliance, geographical location of the data centre, legal jurisdiction, institutional scope, etc.T7.3 works with the WP7 cloud providers to identify their compliance with these sensitive data handling requirements and T5.1 integrates the sensitive data access controls into WP5 so that a single authoritative source of access can be used across the project. This requirements-gathering draws on technical expertise from the LS RIs in WP4 and from WP5 and WP7 to ensure both therequirements and implementation routes are shared and capture existing national requirements to host/distribute sensitive data (i.e. health and healthcare data, biomedical research data, classified data) and the tools used within the LS RIs to manage controlled access to such sensitive data sets.Ultimately, this deliverable reports on the different sensitive data types that the LS RIs are working with and possibly hosting and providing access to. Regulations, best practices and standards that apply to all sensitive data types are collected. For completing this deliverable, a questionnaire on sensitive data was taken by each RI, based on the EOSC-Life toolbox data types1, demonstrating the utility of its categorisation system.

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

This document was developed to provide a standard operating procedure (SOP) on clinical (secondary use) and preclinical in vivo data acquisition in order to create virtual cohorts for in-silico modelling. Considering the different requirements and processes involved in the acquisition of clinical vs. preclinical data, the document is split into 2 parts. Additionally, the SOP is designed for SIMCor partners as well as for the overall scientific community. Different countries and local organisations follow several different regulations and procedures for the acquisition and processing of data for research purposes. Therefore, this SOP provides general recommendations and comments, along with specific examples related to the SIMCor use cases: transcatheter aortic valve implantation (TAVI) for clinical data acquired at University College London (UCL) and Charité (CHA), and pulmonary artery pressure sensors (PAPS) for preclinical data acquisition., SIMCor (In-Silico testing and validation of Cardiovascular IMplantable devices) has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101017578.

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.

BY-COVID Work Package 3 is focused on services for the discovery and integration of COVID-19 data by delivering a flexible, tiered metadata discovery system across different domains, metadata standards, and maturity/robustness levels of data sources. This will enable the linking of FAIR data and metadata on SARS-CoV-2 and COVID-19, on other related viruses and diseases, and on socio-economic consequences, across research fields, from omics, clinical, and epidemiological research, to social sciences and humanities. In a series of work package meetings and a workshop, with participation from all other work packages, we have surveyed community metadata standards used by (potential) BY-COVID-19 portal resources (4.1), defined a flexible, three-tiered approach to metadata indexing in the COVID-19 portal (section 4.2), derived common metadata attributes for record level discovery (4.3) and established a workflow with FAIRsharing for resource level metadata capture and exchange (4.4). This work establishes the basis for the further development of the COVID-19 Portal metadata discovery and provides a path for integration of metadata from multi-domain partners in BY-COVID, as well as our ISIDORe sibling project, and relevant external resources. To ensure smooth integration of partner-provided metadata, we will run a technical workshop open to all partners, discussing workflows, metadata attributes and formats, and support tools., Contributor: Morris Swertz (UMCG/BBMRI)

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.

The objective of this deliverable is to define and develop a framework for estimating the impact of in-silico methods and technologies on clinical and preclinical trials, laying the basis for the assessment of broader effects on patient safety, animal welfare and industry workflow. In the introductory part a status view on this area is given. The document starts with basic terminology and definitions related to in-silico approaches. Thereafter, existing standards, guidelines and frameworks relevant for in-silico trials are referenced. Examples and use cases of published in-silico trials are presented as well as existing tools to support in-silico trials described. The introductory part of the deliverable closes with a selection of projects and programs dedicated to in-silico approaches and relevant for SIMCor. The approach for executing an in-silico trial in SIMCor will be based on the methodology provided by Bodner and Kaul (Proceedings of the ASME 2021 Verification and Validation Symposium, V001T02A0012021) for in-silico clinical trials for medical devices. This framework heavily relies on ideas already developed for model verification, validation, and uncertainty quantification. The process starts with the computational model, which is developed upstream of the in-silico trial. The next step is to establish baseline credibility evidence with the goal that the model is ready to use for the in-silico trial. After this step, clinical trial planning starts with developing the in-silico trial protocol. Thereafter, the in-silico trial is designed as follows: a virtual cohort following the specifications of the human cohort is generated and a final model validation will be performed using human data. If the model is adequate the model predictions to the target population can be performed and the clinical endpoints can be evaluated. An important aspect in applying in-silico trials is the transfer from engineering outputs to clinical endpoints. To support this process, transfer functions based upon available data will be considered in SIMCor.

The objective of this deliverable is to define and develop a framework for estimating the impact of in-silico methods and technologies on clinical and preclinical trials, laying the basis for the assessment of broader effects on patient safety, animal welfare and industry workflow. In the introductory part a status view on this area is given. The document starts with basic terminology and definitions related to in-silico approaches. Thereafter, existing standards, guidelines and frameworks relevant for in-silico trials are referenced. Examples and use cases of published in-silico trials are presented as well as existing tools to support in-silico trials described. The introductory part of the deliverable closes with a selection of projects and programs dedicated to in-silico approaches and relevant for SIMCor. The approach for executing an in-silico trial in SIMCor will be based on the methodology provided by Bodner and Kaul (Proceedings of the ASME 2021 Verification and Validation Symposium, V001T02A0012021) for in-silico clinical trials for medical devices. This framework heavily relies on ideas already developed for model verification, validation, and uncertainty quantification. The process starts with the computational model, which is developed upstream of the in-silico trial. The next step is to establish baseline credibility evidence with the goal that the model is ready to use for the in-silico trial. After this step, clinical trial planning starts with developing the in-silico trial protocol. Thereafter, the in-silico trial is designed as follows: a virtual cohort following the specifications of the human cohort is generated and a final model validation will be performed using human data. If the model is adequate the model predictions to the target population can be performed and the clinical endpoints can be evaluated. An important aspect in applying in-silico trials is the transfer from engineering outputs to clinical endpoints. To support this process, transfer functions based upon available data will be considered in SIMCor., SIMCor (In-Silico testing and validation of Cardiovascular IMplantable devices) has received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 101017578.

The Horizon 2020 project EOSC-Life brings together the 13 Life Science ‘ESFRI’ research infrastructures to create an open, digital and collaborative space for biological and medical research. Sharing sensitive data is a specific challenge within EOSC-Life. For that purpose, a toolbox is developed, providing information to researchers who wish to share or use sensitive data. The authors developed a categorisation system designed to support consistent labelling and categorisation of the resources stored in the toolboxin terms most relevant to data sharing tasks and activities, so that they are available to users (e.g., as on screen filters) when searching the system. The poster describes the methods and main results of a pilot study set to evaluate the proposed categorisation system for the toolbox resources with respect to applicability, reliability, sustainability and consistency. The primary objective of the pilot study was to identify the main improvements that are necessary to arrive at an inter-community, well understood and commonly approved categorisation system.