Committee on Data of the International Science Council

Interim report of the European Commission Expert Group on Turning FAIR Data into reality. The Group has a remit to provide recommendations, indicators and input on the financing of activities required to turn FAIR data into reality at an EU, Member State and international level. A FAIR Data Action Plan has also been proposed. See https://doi.org/10.5281/zenodo.1285290 The interim report will be formally released at the EOSC Summit on 11 June 2018 in Brussels, where a workshop will be run to consult on the recommendations and Action Plan. The report will be open for comments via a stakeholder consultation in June-August 2018. The FAIR Data Expert Group was also asked to contribute to the evaluation of the Horizon 2020 Data Management Plan template and future revisions in light of harmonisation with funders across the EU, including the development of additional sector/ discipline specific guidance (if desired). A separate report was published on this in Spring 2018. See https://doi.org/10.5281/zenodo.1120245

International audience

The COVID-19 pandemic has spurred the use of AI and DS innovations in data collection and aggregation. Extensive data on many aspects of the COVID-19 has been collected and used to optimize public health response to the pandemic and to manage the recovery of patients in Sub-Saharan Africa. However, there is no standard mechanism for collecting, documenting and disseminating COVID-19 related data or metadata, which makes the use and reuse a challenge. INSPIRE utilizes the Observational Medical Outcomes Partnership (OMOP) as the Common Data Model (CDM) implemented in the cloud as a Platform as a Service (PaaS) for COVID-19 data. The INSPIRE PaaS for COVID-19 data leverages the cloud gateway for both individual research organizations and for data networks. Individual research institutions may choose to use the PaaS to access the FAIR data management, data analysis and data sharing capabilities which come with the OMOP CDM. Network data hubs may be interested in harmonizing data across localities using the CDM conditioned by the data ownership and data sharing agreements available under OMOP's federated model. The INSPIRE platform for evaluation of COVID-19 Harmonized data (PEACH) harmonizes data from Kenya and Malawi. Data sharing platforms must remain trusted digital spaces that protect human rights and foster citizens' participation is vital in an era where information overload from the internet exists. The channel for sharing data between localities is included in the PaaS and is based on data sharing agreements provided by the data producer. This allows the data producers to retain control over how their data are used, which can be further protected through the use of the federated CDM. Federated regional OMOP-CDM are based on the PaaS instances and analysis workbenches in INSPIRE-PEACH with harmonized analysis powered by the AI technologies in OMOP. These AI technologies can be used to discover and evaluate pathways that COVID-19 cohorts take through public health interventions and treatments. By using both the data mapping and terminology mapping, we construct ETLs that populate the data and/or metadata elements of the CDM, making the hub both a central model and a distributed model.

An interim FAIR Data Action Plan developed by the European Commission Expert Group on Turning FAIR Data into reality. The Group has a remit to provide recommendations, indicators and input on the financing of activities required to turn FAIR data into reality at an EU, Member State and international level. See the full interim report at https://doi.org/10.5281/zenodo.1285272 The FAIR Data Action Plan is intended to act as a rubric for Member States and research communities to develop context-specific plans on how to implement FAIR data in their countries and/or research areas. A workshop at the EOSC Summit on 11 June 2018 will begin this process. The interim report will be open for comments via a stakeholder consultation in June-August 2018. The FAIR Data Expert Group was also asked to contribute to the evaluation of the Horizon 2020 Data Management Plan template and future revisions in light of harmonisation with funders across the EU, including the development of additional sector/ discipline specific guidance (if desired). A separate report was published on this in Spring 2018. See https://doi.org/10.5281/zenodo.1120245

Introduction: Population health data integration remains a critical challenge in low- and middle-income countries (LMIC), hindering the generation of actionable insights to inform policy and decision-making. This paper proposes a pan-African, Findable, Accessible, Interoperable, and Reusable (FAIR) research architecture and infrastructure named the INSPIRE datahub. This cloud-based Platform-as-a-Service (PaaS) and on-premises setup aims to enhance the discovery, integration, and analysis of clinical, population-based surveys, and other health data sources. Methods: The INSPIRE datahub, part of the Implementation Network for Sharing Population Information from Research Entities (INSPIRE), employs the Observational Health Data Sciences and Informatics (OHDSI) open-source stack of tools and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) to harmonise data from African longitudinal population studies. Operating on Microsoft Azure and Amazon Web Services cloud platforms, and on on-premises servers, the architecture offers adaptability and scalability for other cloud providers and technology infrastructure. The OHDSI-based tools enable a comprehensive suite of services for data pipeline development, profiling, mapping, extraction, transformation, loading, documentation, anonymization, and analysis. Results: The INSPIRE datahub's "On-ramp" services facilitate the integration of data and metadata from diverse sources into the OMOP CDM. The datahub supports the implementation of OMOP CDM across data producers, harmonizing source data semantically with standard vocabularies and structurally conforming to OMOP table structures. Leveraging OHDSI tools, the datahub performs quality assessment and analysis of the transformed data. It ensures FAIR data by establishing metadata flows, capturing provenance throughout the ETL processes, and providing accessible metadata for potential users. The ETL provenance is documented in a machine- and human-readable Implementation Guide (IG), enhancing transparency and usability. Conclusion: The pan-African INSPIRE datahub presents a scalable and systematic solution for integrating health data in LMICs. By adhering to FAIR principles and leveraging established standards like OMOP CDM, this architecture addresses the current gap in generating evidence to support policy and decision-making for improving the well-being of LMIC populations. The federated research network provisions allow data producers to maintain control over their data, fostering collaboration while respecting data privacy and security concerns. A use-case demonstrated the pipeline using OHDSI and other open-source tools.

Basic funding of data infrastructure may not keep pace with increasing costs. There is a need, therefore, to consider alternative cost recovery options and a diversification of revenue streams. In short: who will pay for public access to research data? The RDA/WDS Interest Group Publishing Data Cost Recovery for Data Centres aims to contribute to strategic thinking on cost recovery by conducting research to understand current and possible cost recovery strategies for data centres.

Report on the completed FAIR Implementation Profiles completed by project Case Studies in 2022. Project Deliverable D2.1 for EC WIDERA-funded project "WorldFAIR: Global cooperation on FAIR data policy and practice". This report gives a brief overview of the experience of the WorldFAIR project in using FAIR Implementation Profiles (FIPs). It describes the WorldFAIR project, its objectives and its rich set of Case Studies; and it introduces FIPs as a methodology for listing the FAIR implementation decisions made by a given community of practice. Subsequently, the report gives an overview of the initial feedback and findings from the Case Studies, and considers a number of issues and points of discussion that emerged from this exercise. Finally, and most importantly, we describe how we think the experience of using FIPs will assist each Case Study in its work to implement FAIR, and will assist the project as a whole in the development of two key outputs: the Cross-Domain Interoperability Framework (CDIF), and domain-sensitive recommendations for FAIR assessment. We hope this report will be of interest to data experts who want to find out more about the WorldFAIR project, its remarkable and diverse array of Case Studies, and about FIPs. It is important to stress that this report does not set out to give a comprehensive appraisal of the FIPs approach and could not do so. All the WorldFAIR Case Studies have developed an initial FIP, but the process of reflection on practice will continue throughout the project. Each Case Study will complete at least one further FIP, and in some cases more than one, towards the end of the project and this will enrich our understanding of the utility of the approach. At that stage, we intend to be able to incorporate some robust prospective and aspirational considerations, and we need to consider how best to represent this in the FIPs. As noted above, the final section of this report looks forward to the development of the Cross-Domain Interoperability Framework (CDIF), and domain-sensitive recommendations for FAIR assessment. On both these counts, we consider that the FIPs approach has helped considerably: For the CDIF, through helping refine our initial functional analysis of the requirements for cross-domain FAIR, and—as predicted—helping identify some candidate cross-domain standards. For the FAIR assessment recommendations, through demonstrating that the FIPs can provide an empirical basis for such recommendations, reflecting both the current practice, and the aspirations of a given community or research domain. Please visit WorldFAIR online at http://worldfair-project.eu. WorldFAIR is funded by the EC HORIZON-WIDERA-2021-ERA-01-41 Coordination and Support Action under Grant Agreement No. 101058393.

This report lays out the set-up of the FAIR core competence centre, including initial knowledge base design and tools, communications infrastructure, defined responsibilities, and expectations on service levels. The document focuses on the design and functionality of the competence centre and how it will meet the needs of its user base.

Preface: Status of the Document The first draft of this strategy for the African Open Science Platform (AOSP) was developed as a result of an expert group meeting held in Pretoria in March 2018. It then formed the discussion document for a stakeholder meeting held in Pretoria on 3-4 September 2018. Resulting amendments have been incorporated in this strategy for the Platform, which will be launched at the Science Forum South Africa in December 2018. The March and September meetings involved a wide range of representatives from scientific bodies from across Africa, together with representatives of international bodies including UNESCO, the International Science Council (ISC), the Research Data Alliance (RDA) and the ISC Committee on Data (CODATA). Summary: Open Science and the digital revolution: the imperative for action The reality and potential of the modern storm of digital data together with pervasive communication have profound implications for society, the economy and for science. No state should fail to adapt its national intellectual infrastructure to exploit the bene ts and minimise the risks this technology creates. Open Science is a vital enabler: in maintaining the rigour and reliability of science; in creatively integrating diverse data resources to address complex modern challenges; in open innovation and in engaging with other societal actors as knowledge partners in tackling shared problems. It is fundamental to realisation of the SDGs. The challenge for Africa. National science systems worldwide are struggling to adapt to this new paradigm. The alternatives are to do so or risk stagnating in a scientific backwater, isolated from creative streams of social, cultural and economic opportunity. Africa should adapt, but in its own way, and as a leader not a follower, with its own broader, more societally-engaged priorities. It should seize the challenge with boldness and resolution by creating an African Open Science Platform, with the potential to be a powerful lever of social, cultural and scientific vitality and of economic development. The African Open Science Platform. The Platform’s mission is to put African scientists at the cutting edge of contemporary, data-intensive science as a fundamental resource for a modern society. Its building blocks are: ► a federated hardware, communications and software infrastructure, including policies and enabling practices to support open science in the digital era; ► a network of excellence in open science that supports scientists and other societal actors in accumulating and using modern data resources to maximise scientific, social and economic benefit. These objectives will be realised through six related strands of activity: Strand 1: A federated network of computational facilities and services. Strand 2: Software tools and advice on policies and practices of research data management. Strand 3: A Data Science and AI Institute at the cutting edge of data analytics. Strand 4: Priority application programmes: e.g. cities, disease, biosphere, agriculture. Strand 5: A Network for Education and Skills in data and information. Strand 6: A Network for Open Science Access and Dialogue. The document also outlines the proposed governance, membership and management structure of the Platform, the approach to initial funding, immediate priorities and targets for 3-5 year horizons. As well as the full vision and strategy document, we present here a shorter, four-page leaflet on the initiative.

This document presents a draft strategy and makes the scientific case for the African Open Science Platform (AOSP). It is based on an expert group meeting held in Pretoria on 27-28 March 2018. Its purpose is to act as a framework for detailed, work on the creation of the Platform and as a basis for discussion at a stakeholder meeting to be held on 3-4 September 2018, which will lead to a definitive strategy for implementation from 2019. Expert group members at the March meeting were drawn from the following organisations: African Academy of Sciences (AAS), Academy of Science of South Africa (ASSAf), Committee on Data for Science and Technology (CODATA), International Council for Science (ICSU), National Research and Education Networks (NRENS), Research Data Alliance (RDA), South African Department of Science & Technology (DST) and National Research Foundation (NRF), Square Kilometre Array (SKA), UNESCO. The African Open Science Platform. The Platform’s mission is to put African scientists at the cutting edge of contemporary, data-intensive science as a fundamental resource for a modern society. Its building blocks are: a federated hardware, communications and software infrastructure, including policies and enabling practices, to support Open Science in the digital era; a network of excellence in Open Science that supports scientists and other societal actors in accumulating and using modern data resources to maximise scientific, social and economic benefit. These objectives will be realised through seven related strands of activity: Strand 0: Register and portal for African and related international data collections & services. Strand 1: A federated network of computational facilities and services. Strand 2: Software tools and advice on policies & practices of research data management. Strand 3: A Data Science Institute at the cutting edge of data analytics and AI. Strand 4: Priority application programmes: e.g. cities, disease, biosphere, agriculture. Strand 5: A Network for Education and Skills in data and information. Strand 6: A Network for Open Science Access and Dialogue. The document also outlines the proposed governance, membership and management structure of the Platform, the approach to initial funding and the milestones in building up to the launch. The case for Open Science is based on the profound implications for society and for science, of the digital revolution and of the storm of data that it has unleashed and of the pervasive and novel means of communication that it has enabled. No state should fail to recognise this potential or to adapt their national intellectual infrastructure in exploiting benefits and minimising risks. Open Science is a vital enabler in maintaining the rigour and reliability of science; in creatively integrating diverse data resources to address complex modern challenges; in open innovation and in engaging with other societal actors as knowledge partners in tackling shared problems. It is fundamental to realisation of the Sustainable Development Goals. National science systems worldwide are struggling to adapt to this new paradigm. The alternatives are to do so or risk stagnating in a scientific backwater, isolated from creative streams of social, cultural and economic opportunity. Africa should adapt and capitalise on the opportunities, but in its own way, and as a leader not a follower, with broader, more societally-engaged priorities. It should seize the challenge with boldness and resolution.

Background: Longitudinal studies are essential for understanding the progression of mental health disorders over time, but combining data collected through different methods to assess conditions like depression, anxiety, and psychosis presents significant challenges. This study presents a mapping technique allowing for the conversion of diverse longitudinal data into a standardized staging database, leveraging the Data Documentation Initiative (DDI) Lifecycle and the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) standards to ensure consistency and compatibility across datasets. Methods: The "INSPIRE" project integrates longitudinal data from African studies into a staging database using metadata documentation standards structured with a snowflake schema. This facilitates the development of Extraction, Transformation, and Loading (ETL) scripts for integrating data into OMOP CDM. The staging database schema is designed to capture the dynamic nature of longitudinal studies, including changes in research protocols and the use of different instruments across data collection waves. Results: Utilizing this mapping method, we streamlined the data migration process to the staging database, enabling subsequent integration into the OMOP CDM. Adherence to metadata standards ensures data quality, promotes interoperability, and expands opportunities for data sharing in mental health research. Conclusion: The staging database serves as an innovative tool in managing longitudinal mental health data, going beyond simple data hosting to act as a comprehensive study descriptor. It provides detailed insights into each study stage and establishes a data science foundation for standardizing and integrating the data into OMOP CDM.

Data management plans (DMPs) are recognised as an important element of good practice in research management, including by the European Commission and Science Europe. Especially since the beginning of the EC Horizon 2020 programme, funders at national and international level expect research grant holders to complete a DMP demonstrating they have planned how data will be managed from the outset of a research project. Research Producing Organisations (RPOs) are expected to play their part, to help their researchers in producing data that is FAIR, and in depositing it in a trustworthy repository that can keep it in FAIR condition. And in some cases including the EC Horizon Europe programme, there is a need for DMPs to cover all research outputs (data, code, models, samples etc.), to be updated throughout the project, and ultimately made available as a project deliverable. ACME-FAIR is a 7-part guide developed in the FAIRsFAIR project, whose main purpose is to help managers of Research Data Management and related professional services to self-assess how they are enabling researchers, and the professional staff who support them, to put the FAIR data principles into practice (for short we refer to this as ‘FAIR-enabling practice’). This part addresses the key issue of Supporting data management planning. The guide aims to help Research Performing Organisations assess their own needs to support DMPs, taking into account what they currently have in place and where improvements may be needed.

This report advances the establishment of a FAIR Competence Centre as outlined in the previous two reports from WP6 of FAIRsFAIR, D6.1 “Overview of needs for Competence Centre” and D.6.2 “Initial core competence centre structure”, part of FAIRsFAIR WP6 deliverables which is concerned with the development of a competence centre as a model of engagement and support for research communities. Whilst the aforementioned reports focused, the first on the analysis of the landscape of available competence centres, and the second the set-up of the FAIR core competence centre, the present deliverable’s emphasis is put on the description of operations of the core competence centre, including initiatives aiming to identify synergies and areas of harmonisation that are required to support knowledge base development. This is the draft version of the deliverable not yet approved by the European Commission.

In this policy brief the WorldFAIR project makes seven policy recommendations relevant to EOSC. Evidence and analysis is presented for each recommendation. The Policy Brief and recommendations draw on project deliverables and discussions held at workshops including project participants and wider stakeholders. The policy brief also includes a short report on progress made by the project. Visit WorldFAIR online at http://worldfair-project.eu. WorldFAIR is funded by the EC HORIZON-WIDERA-2021-ERA-01-41 Coordination and Support Action under Grant Agreement No. 101058393.

The Cross-Domain Interoperability Framework (CDIF) is an emerging idea for a set of guidelines around domain-agnostic standards for supporting the implementation of interoperability and reusability of FAIR data, especially across domain- and institutional boundaries. The CDIF aims to provide a list of standards in a range of functional roles to support the next level of interoperability, but giving domains the “lingua franca” against which to map domain-specific standards and ontologies. Such standards as Schema.org, DCAT, SKOS, PROV-O, DDI-CDI, and I-ADOPT are being suggested. The functional roles include the description of units and measurements, the relation of variables to concepts and consistent descriptions of data structure. The work around the CDIF is based on practical use-cases, and has been slowly growing out of various activities conducted under CODATA’s Decadal Programme 'Making Data Work...' and in partnership with the DDI Alliance at a series of Dagstuhl Workshops and the development of DDI’s Cross-Domain Integration specification. It is still in early stages, but reflects an emerging consensus among many of the participants in this endeavour. WorldFAIR provides an opportunity to take this work forward and to test and refine it with the WorldFAIR case studies and on the basis of information gathered through the FIPs exercises. This collection contains preparatory, working documents used as input to workshops and meeting on the topic. The Cross-Domain Interoperability Framework: A Proposed Lingua Franca for FAIR Data Reuse (Discussion Draft), 15 August 2022, was prepared as input to the 2022 Dagstuhl Workshop. The Cross-Domain Interoperability Framework (CDIF): Current Status, 14 December 2022, summarised current thinking as input to the first meeting of the CDIF Working Group and Advisory Group, convened in support of this work under the WorldFAIR Project.

The existence of FAIR-aligned and harmonised data policies across various stakeholders such as funding bodies, publishers and Research Performing Organisations (RPOs) is crucial for ensuring that we can progress from a vision of the European Open Science Cloud (EOSC) to it becoming a fully functioning reality. As noted in the Turning FAIR into Reality report and action plan, policies define and regulate various components of a FAIR ecosystem and the relationships between them. Indeed, policies are a cross-cutting theme in Turning FAIR into Reality (TFiR) and are reflected in many of the priority and supporting actions presented in the action plan. This guide aims to help Research Performing Organisations to assess the data policy framework currently in place and to consider where possible improvements may be needed. To complement the guide, a FAIRsFAIR policy support checklist is available. This aims to help RPOs to consider the content of their data policy, and how they might better align it with the FAIR Principles. ACME-FAIR is a 7-part guide developed in the FAIRsFAIR project, whose main purpose is to help managers of Research Data Management and related professional services to self-assess how they are enabling researchers, and the professional staff who support them, to put the FAIR data principles into practice (for short we refer to this as ‘FAIR-enabling practice’). This part addresses the key issue of Defining the FAIR data policy environment.

In June 2017, the International Council for Science (ICSU) and its Committee on Data for Science and Technology (CODATA) brought together international scientific unions and associations of ICSU and the International Social Science Council (ISSC) that have made major strides in this area of work, as well as other organisations that curate standards and vocabularies for particular disciplines. The objective of the meeting was to develop an action plan to realise the full potential of the data science, technologies, and infrastructures currently being created by specific disciplinary groups and expand those efforts on an inter- and trans-disciplinary basis. The meeting identified key opportunities of the digital revolution and how they can be achieved. Priorities for action include: the need for examples of the benefits that have already been realised by specific disciplinary groups and inter- and trans-disciplinary projects; the need to extend activities to disciplinary fields that have not yet developed strategies , for developing interoperable vocabularies, standards and models, and for the creation of effective “information communities”; there must be a major effort to achieve interoperability within and between disciplines, without this, the national and regional initiatives to create cloud or platform technologies designed to provide services to support data priorities will fall far short of their potential; international scientific unions and associations, and the international councils of which they are members, are uniquely qualified for this task, and their engagement is essential if its promise is to realised; there is a need to develop a flagship programme on one or more major global challenge themes to develop, demonstrate and apply the methods of linking and integrating data from across the disciplines in the production and use of actionable knowledge. Such a programme will entail a long-term, decadal commitment. It will convene and support the scientific members of ICSU and ISSC, serve as a mechanism for their engagement with relevant international research initiatives, significantly strengthen their data capacities and relate to the priorities of research funding bodies such as the Belmont Forum. The immediate next step was a major ICSU-CODATA workshop in November 2017 to bring together the full range of scientific international unions and associations with organisations working on complex global problems to sharpen the design of the flagship project and create the international, multi-disciplinary data community needed to convert these opportunities into solutions.

Presented at iPres 2024.

The Global Open Science Cloud has the potential to advance the way scientific data and resources are shared and accessed, and how global collaboration happens. However, addressing the challenges associated with its creation and ensuring inclusivity, interoperability, data privacy, and sustainability are crucial for its success. The collaborative efforts of stakeholders from different disciplines, regions, and sectors will be essential in realising the vision of a truly global and open science platform. The achievements of GOSC so far, including successful collaborations, funded projects, and the development of a common reference framework, demonstrate its potential and progress towards its goals.

One of the key requirements for FAIR data reuse is that the user of a FAIR data resource understands the exact nature of the data. The FAIR principles talk about the kinds of metadata needed to describe data, but it is necessary for implementers to understand how these metadata can be provided, to effectively realise FAIR within their systems. This implementation guide describes the way all aspects of the data are made available for use, both within and from outside the INSPIRE Network community, using standard metadata to describe the data. This is an exploration of how generic standards can be used to express the agreed community metadata set. The INSPIRE platform supports network studies using population health data to stand up their own instances of a common data model called the OMOP CDM. The WorldFAIR project is an exploration to facilitate a better understanding of what is needed for data infrastructures to provide data in line with the FAIR principles within and across domains. The types of metadata used in INSPIRE are aligned as much as possible with existing and popular models common in the public health domain. Primary among these are the standards (and tools) coming from OHDSI (Observational Health Data Sciences and Informatics), notably their OMOP Common Data Model (CDM). This suite of products addresses the definition of specific concepts and their semantics, standard (primarily medical) classifications, and the mechanism for selecting data from among those available to produce a specific cohort for analysis. These standards are common within the public health domain internationally, and INSPIRE has chosen to use them to reduce the significant cost of developing tools for many aspects of data and metadata management and use. FAIR demands that we provide data in a useful way to those who may not be familiar with the community tools and standards used by INSPIRE. More generic standards are thus needed to support this broader community. It is significant that members of the OHDSI community have already looked at how Schema.org - developed and supported by many popular search engines, Google foremost among them - can be used in combination with the OHDSI OMOP CDM to describe data resources. Here, INSPIRE builds on that work to describe how INSPIRE data resources, specifically, can be documented in a way which will be maximally accessible to users both within the community and external to it. One critical part of the overall information set provided by standard FAIR metadata is a description of the experiment for which the data was used, and the protocol employed in the selection and analysis of the data. This aspect of the metadata description is a major focus of the implementation guide, and one for which Schema.org would seem to be well-suited. WorldFAIR WP (Work Package) 07 is one of eleven domain-specific case studies being undertaken by the WorldFAIR project, with the domain-specific practices being analysed across these domains in WP02. Early indications from WP02 suggest that Schema.org is one of the standards which will be recommended as part of the Cross-Domain Interoperability Framework (CDIF). This implementation guide contributes to an understanding of exactly how Schema.org fits into the description of domain data. While some open questions remain, the implementation guide has achieved its primary goal of showing how standards such as Schema.org can be used within the public health domain to provide a complete set of the information needed for FAIR data use across and within domain boundaries. Visit WorldFAIR online at http://worldfair-project.eu. WorldFAIR is funded by the EC HORIZON-WIDERA-2021-ERA-01-41 Coordination and Support Action under Grant Agreement No. 101058393.