FAIR+E pathogen data for surveillance and research: lessons from COVID-19.

The COVID-19 pandemic has exemplified the importance of interoperable and equitable data sharing for global surveillance and to support research. While many challenges could be overcome, at least in some countries, many hurdles within the organizational, scientific, technical and cultural realms still remain to be tackled to be prepared for future threats. We propose to (i) continue supporting global efforts that have proven to be efficient and trustworthy toward addressing challenges in pathogen molecular data sharing; (ii) establish a distributed network of Pathogen Data Platforms to (a) ensure high quality data, metadata standardization and data analysis, (b) perform data brokering on behalf of data providers both for research and surveillance, (c) foster capacity building and continuous improvements, also for pandemic preparedness; (iii) establish an International One Health Pathogens Portal, connecting pathogen data isolated from various sources (human, animal, food, environment), in a truly One Health approach and following FAIR principles. To address these challenging endeavors, we have started an ELIXIR Focus Group where we invite all interested experts to join in a concerted, expert-driven effort toward sustaining and ensuring high-quality data for global surveillance and research.

eTRANSAFE: Building a sustainable framework to share reproducible drug safety knowledge with the public domain.

Integrative drug safety research in translational health informatics has rapidly evolved and included data that are drawn in from many resources, combining diverse data that are either reused from (curated) repositories, or newly generated at source. Each resource is mandated by different sets of metadata rules that are imposed on the incoming data. Combination of the data cannot be readily achieved without interference of data stewardship and the top-down policy guidelines that supervise and inform the process for data combination to aid meaningful interpretation and analysis of such data. The eTRANSAFE Consortium's effort to drive integrative drug safety research at a large scale hereby present the lessons learnt and the proposal of solution at the guidelines in practice at this Innovative Medicines Initiative (IMI) project. Recommendations in these guidelines were compiled from feedback received from key stakeholders in regulatory agencies, EFPIA companies, and academic partners. The research reproducibility guidelines presented in this study lay the foundation for a comprehensive data sharing and knowledge management plans accounting for research data management in the drug safety space - FAIR data sharing guidelines, and the model verification guidelines as generic deliverables that best practices that can be reused by other scientific community members at large. FAIR data sharing is a dynamic landscape that rapidly evolves with fast-paced technology advancements. The research reproducibility in drug safety guidelines introduced in this study provides a reusable framework that can be adopted by other research communities that aim to integrate public and private data in biomedical research space.

The COVID-19 Data Portal: accelerating SARS-CoV-2 and COVID-19 research through rapid open access data sharing.

The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic will be remembered as one of the defining events of the 21st century. The rapid global outbreak has had significant impacts on human society and is already responsible for millions of deaths. Understanding and tackling the impact of the virus has required a worldwide mobilisation and coordination of scientific research. The COVID-19 Data Portal (https://www.covid19dataportal.org/) was first released as part of the European COVID-19 Data Platform, on April 20th 2020 to facilitate rapid and open data sharing and analysis, to accelerate global SARS-CoV-2 and COVID-19 research. The COVID-19 Data Portal has fortnightly feature releases to continue to add new data types, search options, visualisations and improvements based on user feedback and research. The open datasets and intuitive suite of search, identification and download services, represent a truly FAIR (Findable, Accessible, Interoperable and Reusable) resource that enables researchers to easily identify and quickly obtain the key datasets needed for their COVID-19 research.

The eTRANSAFE Project on Translational Safety Assessment through Integrative Knowledge Management: Achievements and Perspectives.

eTRANSAFE is a research project funded within the Innovative Medicines Initiative (IMI), which aims at developing integrated databases and computational tools (the eTRANSAFE ToxHub) that support the translational safety assessment of new drugs by using legacy data provided by the pharmaceutical companies that participate in the project. The project objectives include the development of databases containing preclinical and clinical data, computational systems for translational analysis including tools for data query, analysis and visualization, as well as computational models to explain and predict drug safety events.

ELIXIR-EXCELERATE: establishing Europe's data infrastructure for the life science research of the future.

A new inter-governmental research infrastructure, ELIXIR, aims to unify bioinformatics resources and life science data across Europe, thereby facilitating their mining and (re-)use.

Connecting data, tools and people across Europe: ELIXIR's response to the COVID-19 pandemic.

ELIXIR, the European research infrastructure for life science data, provides open access to data, tools and workflows in the response to the COVID-19 pandemic. ELIXIR's 23 nodes have reacted swiftly to support researchers in their combined efforts against the pandemic setting out three joint priorities: 1. Connecting national COVID-19 data platforms to create federated European COVID-19 Data Spaces; 2. Fostering good data management to make COVID-19 data open, FAIR and reusable over the long term; 3. Providing open tools, workflows and computational resources to drive reproducible and collaborative science. ELIXIR's strategy is based on the support given by our national nodes - collectively spanning over 200 institutes - to research projects and on partnering with community initiatives to drive development and adoption of good data practice and community driven standards. ELIXIR Nodes provide support activities locally and internationally, from provisioning compute capabilities to helping collect viral sequence data from hospitals. Some Nodes have prioritised access to their national cloud and compute facilities for all COVID-19 research projects, while others have developed tools to search, access and share all data related to the pandemic in a national healthcare setting.

The ELIXIR Core Data Resources: fundamental infrastructure for the life sciences.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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.

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.

Registered access: authorizing data access.

The Global Alliance for Genomics and Health (GA4GH) proposes a data access policy model-"registered access"-to increase and improve access to data requiring an agreement to basic terms and conditions, such as the use of DNA sequence and health data in research. A registered access policy would enable a range of categories of users to gain access, starting with researchers and clinical care professionals. It would also facilitate general use and reuse of data but within the bounds of consent restrictions and other ethical obligations. In piloting registered access with the Scientific Demonstration data sharing projects of GA4GH, we provide additional ethics, policy and technical guidance to facilitate the implementation of this access model in an international setting.

General guidelines for biomedical software development.

Most bioinformatics tools available today were not written by professional software developers, but by people that wanted to solve their own problems, using computational solutions and spending the minimum time and effort possible, since these were just the means to an end. Consequently, a vast number of software applications are currently available, hindering the task of identifying the utility and quality of each. At the same time, this situation has hindered regular adoption of these tools in clinical practice. Typically, they are not sufficiently developed to be used by most clinical researchers and practitioners. To address these issues, it is necessary to re-think how biomedical applications are built and adopt new strategies that ensure quality, efficiency, robustness, correctness and reusability of software components. We also need to engage end-users during the development process to ensure that applications fit their needs. In this review, we present a set of guidelines to support biomedical software development, with an explanation of how they can be implemented and what kind of open-source tools can be used for each specific topic.

Identifiers for the 21st century: How to design, provision, and reuse persistent identifiers to maximize utility and impact of life science data.

In many disciplines, data are highly decentralized across thousands of online databases (repositories, registries, and knowledgebases). Wringing value from such databases depends on the discipline of data science and on the humble bricks and mortar that make integration possible; identifiers are a core component of this integration infrastructure. Drawing on our experience and on work by other groups, we outline 10 lessons we have learned about the identifier qualities and best practices that facilitate large-scale data integration. Specifically, we propose actions that identifier practitioners (database providers) should take in the design, provision and reuse of identifiers. We also outline the important considerations for those referencing identifiers in various circumstances, including by authors and data generators. While the importance and relevance of each lesson will vary by context, there is a need for increased awareness about how to avoid and manage common identifier problems, especially those related to persistence and web-accessibility/resolvability. We focus strongly on web-based identifiers in the life sciences; however, the principles are broadly relevant to other disciplines.

Making sense of big data in health research: Towards an EU action plan.

Medicine and healthcare are undergoing profound changes. Whole-genome sequencing and high-resolution imaging technologies are key drivers of this rapid and crucial transformation. Technological innovation combined with automation and miniaturization has triggered an explosion in data production that will soon reach exabyte proportions. How are we going to deal with this exponential increase in data production? The potential of "big data" for improving health is enormous but, at the same time, we face a wide range of challenges to overcome urgently. Europe is very proud of its cultural diversity; however, exploitation of the data made available through advances in genomic medicine, imaging, and a wide range of mobile health applications or connected devices is hampered by numerous historical, technical, legal, and political barriers. European health systems and databases are diverse and fragmented. There is a lack of harmonization of data formats, processing, analysis, and data transfer, which leads to incompatibilities and lost opportunities. Legal frameworks for data sharing are evolving. Clinicians, researchers, and citizens need improved methods, tools, and training to generate, analyze, and query data effectively. Addressing these barriers will contribute to creating the European Single Market for health, which will improve health and healthcare for all Europeans.

Patterns of database citation in articles and patents indicate long-term scientific and industry value of biological data resources.

Data from open access biomolecular data resources, such as the European Nucleotide Archive and the Protein Data Bank are extensively reused within life science research for comparative studies, method development and to derive new scientific insights. Indicators that estimate the extent and utility of such secondary use of research data need to reflect this complex and highly variable data usage. By linking open access scientific literature, via Europe PubMedCentral, to the metadata in biological data resources we separate data citations associated with a deposition statement from citations that capture the subsequent, long-term, reuse of data in academia and industry.  We extend this analysis to begin to investigate citations of biomolecular resources in patent documents. We find citations in more than 8,000 patents from 2014, demonstrating substantial use and an important role for data resources in defining biological concepts in granted patents to both academic and industrial innovators. Combined together our results indicate that the citation patterns in biomedical literature and patents vary, not only due to citation practice but also according to the data resource cited. The results guard against the use of simple metrics such as citation counts and show that indicators of data use must not only take into account citations within the biomedical literature but also include reuse of data in industry and other parts of society by including patents and other scientific and technical documents such as guidelines, reports and grant applications.