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

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

Exchanging words: Engaging the challenges of sharing qualitative research data.

In January 2023, a new NIH policy on data sharing went into effect. The policy applies to both quantitative and qualitative research (QR) data such as data from interviews or focus groups. QR data are often sensitive and difficult to deidentify, and thus have rarely been shared in the United States. Over the past 5 y, our research team has engaged stakeholders on QR data sharing, developed software to support data deidentification, produced guidance, and collaborated with the ICPSR data repository to pilot the deposit of 30 QR datasets. In this perspective article, we share important lessons learned by addressing eight clusters of questions on issues such as where, when, and what to share; how to deidentify data and support high-quality secondary use; budgeting for data sharing; and the permissions needed to share data. We also offer a brief assessment of the state of preparedness of data repositories, QR journals, and QR textbooks to support data sharing. While QR data sharing could yield important benefits to the research community, we quickly need to develop enforceable standards, expertise, and resources to support responsible QR data sharing. Absent these resources, we risk violating participant confidentiality and wasting a significant amount of time and funding on data that are not useful for either secondary use or data transparency and verification.

Big data and experimental biology: the complementary roles of hypothesis-led and blue skies research.

JEB has broadened its scope to include non-hypothesis-led research. In this Perspective, based on our lab's lived experience, I argue that this is excellent news, because truly novel insights can occur from 'blue skies' idea-led experiments. Hypothesis-led and hypothesis-free experimentation are not philosophically antagonistic; rather, the latter can provide a short-cut to an unbiased view of organism function, and is intrinsically hypothesis generating. Insights derived from hypothesis-free research are commonly obtained by the generation and analysis of big datasets - for example, by genetic screens - or from omics-led approaches (notably transcriptomics). Furthermore, meta-analyses of existing datasets can also provide a lower-cost means to formulating new hypotheses, specifically if researchers take advantage of the FAIR principles (findability, accessibility, interoperability and reusability) to access relevant, publicly available datasets. The broadened scope will thus bring new, original work and novel insights to our journal, by expanding the range of fundamental questions that can be asked.

Setting up an institutional OMERO environment for bioimage data: Perspectives from both facility staff and users.

Modern bioimaging core facilities at research institutions are essential for managing and maintaining high-end instruments, providing training and support for researchers in experimental design, image acquisition and data analysis. An important task for these facilities is the professional management of complex multidimensional bioimaging data, which are often produced in large quantity and very different file formats. This article details the process that led to successfully implementing the OME Remote Objects system (OMERO) for bioimage-specific research data management (RDM) at the Core Facility Cellular Imaging (CFCI) at the Technische Universität Dresden (TU Dresden). Ensuring compliance with the FAIR (findable, accessible, interoperable, reusable) principles, we outline here the challenges that we faced in adapting data handling and storage to a new RDM system. These challenges included the introduction of a standardised group-specific naming convention, metadata curation with tagging and Key-Value pairs, and integration of existing image processing workflows. By sharing our experiences, this article aims to provide insights and recommendations for both individual researchers and educational institutions intending to implement OMERO as a management system for bioimaging data. We showcase how tailored decisions and structured approaches lead to successful outcomes in RDM practices.

Desiderata for discoverability and FAIR adoption of health data hubs.

BACKGROUND: The future European Health Research and Innovation Cloud (HRIC), as fundamental part of the European Health Data Space (EHDS), will promote the secondary use of data and the capabilities to push the boundaries of health research within an ethical and legally compliant framework that reinforces the trust of patients and citizens. OBJECTIVE: This study aimed to analyse health data management mechanisms in Europe to determine their alignment with FAIR principles and data discovery generating best. practices for new data hubs joining the HRIC ecosystem. In this line, the compliance of health data hubs with FAIR principles and data discovery were assessed, and a set of best practices for health data hubs was concluded. METHODS: A survey was conducted in January 2022, involving 99 representative health data hubs from multiple countries, and 42 responses were obtained in June 2022. Stratification methods were employed to cover different levels of granularity. The survey data was analysed to assess compliance with FAIR and data discovery principles. The study started with a general analysis of survey responses, followed by the creation of specific profiles based on three categories: organization type, function, and level of data aggregation. RESULTS: The study produced specific best practices for data hubs regarding the adoption of FAIR principles and data discoverability. It also provided an overview of the survey study and specific profiles derived from category analysis, considering different types of data hubs. CONCLUSIONS: The study concluded that a significant number of health data hubs in Europe did not fully comply with FAIR and data discovery principles. However, the study identified specific best practices that can guide new data hubs in adhering to these principles. The study highlighted the importance of aligning health data management mechanisms with FAIR principles to enhance interoperability and reusability in the future HRIC.

Towards Machine-FAIR: Representing software and datasets to facilitate reuse and scientific discovery by machines.

OBJECTIVE: To use software, datasets, and data formats in the domain of Infectious Disease Epidemiology as a test collection to evaluate a novel M1 use case, which we introduce in this paper. M1 is a machine that upon receipt of a new digital object of research exhaustively finds all valid compositions of it with existing objects. METHOD: We implemented a data-format-matching-only M1 using exhaustive search, which we refer to as M1DFM. We then ran M1DFM on the test collection and used error analysis to identify needed semantic constraints. RESULTS: Precision of M1DFM search was 61.7%. Error analysis identified needed semantic constraints and needed changes in handling of data services. Most semantic constraints were simple, but one data format was sufficiently complex to be practically impossible to represent semantic constraints over, from which we conclude limitatively that software developers will have to meet the machines halfway by engineering software whose inputs are sufficiently simple that their semantic constraints can be represented, akin to the simple APIs of services. We summarize these insights as M1-FAIR guiding principles for composability and suggest a roadmap for progressively capable devices in the service of reuse and accelerated scientific discovery. CONCLUSION: Algorithmic search of digital repositories for valid workflow compositions has potential to accelerate scientific discovery but requires a scalable solution to the problem of knowledge acquisition about semantic constraints on software inputs. Additionally, practical limitations on the logical complexity of semantic constraints must be respected, which has implications for the design of software.

Data sharing considerations to maximize the use of pathogen biological and genomics resources data for public health.

Public sector data associated with health are a highly valuable resource with multiple potential end-users, from health practitioners, researchers, public bodies, policy makers, and industry. Data for infectious disease agents are used for epidemiological investigations, disease tracking and assessing emerging biological threats. Yet, there are challenges in collating and re-using it. Data may be derived from multiple sources, generated and collected for different purposes. While public sector data should be open access, providers from public health settings or from agriculture, food, or environment sources have sensitivity criteria to meet with ethical restrictions in how the data can be reused. Yet, sharable datasets need to describe the pathogens with sufficient contextual metadata for maximal utility, e.g. associated disease or disease potential and the pathogen source. As data comprise the physical resources of pathogen collections and potentially associated sequences, there is an added emerging technical issue of integration of omics 'big data'. Thus, there is a need to identify suitable means to integrate and safely access diverse data for pathogens. Established genomics alliances and platforms interpret and meet the challenges in different ways depending on their own context. Nonetheless, their templates and frameworks provide a solution for adaption to pathogen datasets.

Implementing FAIR data management within the German Network for Bioinformatics Infrastructure (de.NBI) exemplified by selected use cases.

This article describes some use case studies and self-assessments of FAIR status of de.NBI services to illustrate the challenges and requirements for the definition of the needs of adhering to the FAIR (findable, accessible, interoperable and reusable) data principles in a large distributed bioinformatics infrastructure. We address the challenge of heterogeneity of wet lab technologies, data, metadata, software, computational workflows and the levels of implementation and monitoring of FAIR principles within the different bioinformatics sub-disciplines joint in de.NBI. On the one hand, this broad service landscape and the excellent network of experts are a strong basis for the development of useful research data management plans. On the other hand, the large number of tools and techniques maintained by distributed teams renders FAIR compliance challenging.

Evaluating LC-HRMS metabolomics data processing software using FAIR principles for research software.

BACKGROUND: Liquid chromatography-high resolution mass spectrometry (LC-HRMS) is a popular approach for metabolomics data acquisition and requires many data processing software tools. The FAIR Principles - Findability, Accessibility, Interoperability, and Reusability - were proposed to promote open science and reusable data management, and to maximize the benefit obtained from contemporary and formal scholarly digital publishing. More recently, the FAIR principles were extended to include Research Software (FAIR4RS). AIM OF REVIEW: This study facilitates open science in metabolomics by providing an implementation solution for adopting FAIR4RS in the LC-HRMS metabolomics data processing software. We believe our evaluation guidelines and results can help improve the FAIRness of research software. KEY SCIENTIFIC CONCEPTS OF REVIEW: We evaluated 124 LC-HRMS metabolomics data processing software obtained from a systematic review and selected 61 software for detailed evaluation using FAIR4RS-related criteria, which were extracted from the literature along with internal discussions. We assigned each criterion one or more FAIR4RS categories through discussion. The minimum, median, and maximum percentages of criteria fulfillment of software were 21.6%, 47.7%, and 71.8%. Statistical analysis revealed no significant improvement in FAIRness over time. We identified four criteria covering multiple FAIR4RS categories but had a low %fulfillment: (1) No software had semantic annotation of key information; (2) only 6.3% of evaluated software were registered to Zenodo and received DOIs; (3) only 14.5% of selected software had official software containerization or virtual machine; (4) only 16.7% of evaluated software had a fully documented functions in code. According to the results, we discussed improvement strategies and future directions.

Challenges and opportunities for bioimage analysis core-facilities.

Recent advances in microscopy imaging and image analysis motivate more and more institutes worldwide to establish dedicated core-facilities for bioimage analysis. To maximise the benefits research groups at these institutes gain from their core-facilities, they should be established to fit well into their respective environment. In this article, we introduce common collaborator requests and corresponding potential services core-facilities can offer. We also discuss potential competing interests between the targeted missions and implementations of services to guide decision makers and core-facility founders to circumvent common pitfalls.

Can OpenEHR, ISO 13606, and HL7 FHIR Work Together? An Agnostic Approach for the Selection and Application of Electronic Health Record Standards to the Next-Generation Health Data Spaces.

In order to maximize the value of electronic health records (EHRs) for both health care and secondary use, it is necessary for the data to be interoperable and reusable without loss of the original meaning and context, in accordance with the findable, accessible, interoperable, and reusable (FAIR) principles. To achieve this, it is essential for health data platforms to incorporate standards that facilitate addressing needs such as formal modeling of clinical knowledge (health domain concepts) as well as the harmonized persistence, query, and exchange of data across different information systems and organizations. However, the selection of these specifications has not been consistent across the different health data initiatives, often applying standards to address needs for which they were not originally designed. This issue is essential in the current scenario of implementing the European Health Data Space, which advocates harmonization, interoperability, and reuse of data without regulating the specific standards to be applied for this purpose. Therefore, this viewpoint aims to establish a coherent, agnostic, and homogeneous framework for the use of the most impactful EHR standards in the new-generation health data spaces: OpenEHR, International Organization for Standardization (ISO) 13606, and Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR). Thus, a panel of EHR standards experts has discussed several critical points to reach a consensus that will serve decision-making teams in health data platform projects who may not be experts in these EHR standards. It was concluded that these specifications possess different capabilities related to modeling, flexibility, and implementation resources. Because of this, in the design of future data platforms, these standards must be applied based on the specific needs they were designed for, being likewise fully compatible with their combined functional and technical implementation.

A Data Transformation Methodology to Create Findable, Accessible, Interoperable, and Reusable Health Data: Software Design, Development, and Evaluation Study.

BACKGROUND: Sharing health data is challenging because of several technical, ethical, and regulatory issues. The Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles have been conceptualized to enable data interoperability. Many studies provide implementation guidelines, assessment metrics, and software to achieve FAIR-compliant data, especially for health data sets. Health Level 7 (HL7) Fast Healthcare Interoperability Resources (FHIR) is a health data content modeling and exchange standard. OBJECTIVE: Our goal was to devise a new methodology to extract, transform, and load existing health data sets into HL7 FHIR repositories in line with FAIR principles, develop a Data Curation Tool to implement the methodology, and evaluate it on health data sets from 2 different but complementary institutions. We aimed to increase the level of compliance with FAIR principles of existing health data sets through standardization and facilitate health data sharing by eliminating the associated technical barriers. METHODS: Our approach automatically processes the capabilities of a given FHIR end point and directs the user while configuring mappings according to the rules enforced by FHIR profile definitions. Code system mappings can be configured for terminology translations through automatic use of FHIR resources. The validity of the created FHIR resources can be automatically checked, and the software does not allow invalid resources to be persisted. At each stage of our data transformation methodology, we used particular FHIR-based techniques so that the resulting data set could be evaluated as FAIR. We performed a data-centric evaluation of our methodology on health data sets from 2 different institutions. RESULTS: Through an intuitive graphical user interface, users are prompted to configure the mappings into FHIR resource types with respect to the restrictions of selected profiles. Once the mappings are developed, our approach can syntactically and semantically transform existing health data sets into HL7 FHIR without loss of data utility according to our privacy-concerned criteria. In addition to the mapped resource types, behind the scenes, we create additional FHIR resources to satisfy several FAIR criteria. According to the data maturity indicators and evaluation methods of the FAIR Data Maturity Model, we achieved the maximum level (level 5) for being Findable, Accessible, and Interoperable and level 3 for being Reusable. CONCLUSIONS: We developed and extensively evaluated our data transformation approach to unlock the value of existing health data residing in disparate data silos to make them available for sharing according to the FAIR principles. We showed that our method can successfully transform existing health data sets into HL7 FHIR without loss of data utility, and the result is FAIR in terms of the FAIR Data Maturity Model. We support institutional migration to HL7 FHIR, which not only leads to FAIR data sharing but also eases the integration with different research networks.

Impact of structural biologists and the Protein Data Bank on small-molecule drug discovery and development.

The Protein Data Bank (PDB) is an international core data resource central to fundamental biology, biomedicine, bioenergy, and biotechnology/bioengineering. Now celebrating its 50th anniversary, the PDB houses >175,000 experimentally determined atomic structures of proteins, nucleic acids, and their complexes with one another and small molecules and drugs. The importance of three-dimensional (3D) biostructure information for research and education obtains from the intimate link between molecular form and function evident throughout biology. Among the most prolific consumers of PDB data are biomedical researchers, who rely on the open access resource as the authoritative source of well-validated, expertly curated biostructures. This review recounts how the PDB grew from just seven protein structures to contain more than 49,000 structures of human proteins that have proven critical for understanding their roles in human health and disease. It then describes how these structures are used in academe and industry to validate drug targets, assess target druggability, characterize how tool compounds and other small-molecules bind to drug targets, guide medicinal chemistry optimization of binding affinity and selectivity, and overcome challenges during preclinical drug development. Three case studies drawn from oncology exemplify how structural biologists and open access to PDB structures impacted recent regulatory approvals of antineoplastic drugs.

How often do cancer researchers make their data and code available and what factors are associated with sharing?

BACKGROUND: Various stakeholders are calling for increased availability of data and code from cancer research. However, it is unclear how commonly these products are shared, and what factors are associated with sharing. Our objective was to evaluate how frequently oncology researchers make data and code available and explore factors associated with sharing. METHODS: A cross-sectional analysis of a random sample of 306 cancer-related articles indexed in PubMed in 2019 which studied research subjects with a cancer diagnosis was performed. All articles were independently screened for eligibility by two authors. Outcomes of interest included the prevalence of affirmative sharing declarations and the rate with which declarations connected to data complying with key FAIR principles (e.g. posted to a recognised repository, assigned an identifier, data license outlined, non-proprietary formatting). We also investigated associations between sharing rates and several journal characteristics (e.g. sharing policies, publication models), study characteristics (e.g. cancer rarity, study design), open science practices (e.g. pre-registration, pre-printing) and subsequent citation rates between 2020 and 2021. RESULTS: One in five studies declared data were publicly available (59/306, 19%, 95% CI: 15-24%). However, when data availability was investigated this percentage dropped to 16% (49/306, 95% CI: 12-20%), and then to less than 1% (1/306, 95% CI: 0-2%) when data were checked for compliance with key FAIR principles. While only 4% of articles that used inferential statistics reported code to be available (10/274, 95% CI: 2-6%), the odds of reporting code to be available were 5.6 times higher for researchers who shared data. Compliance with mandatory data and code sharing policies was observed in 48% (14/29) and 0% (0/6) of articles, respectively. However, 88% of articles (45/51) included data availability statements when required. Policies that encouraged data sharing did not appear to be any more effective than not having a policy at all. The only factors associated with higher rates of data sharing were studying rare cancers and using publicly available data to complement original research. CONCLUSIONS: Data and code sharing in oncology occurs infrequently, and at a lower rate than would be expected given the prevalence of mandatory sharing policies. There is also a large gap between those declaring data to be available, and those archiving data in a way that facilitates its reuse. We encourage journals to actively check compliance with sharing policies, and researchers consult community-accepted guidelines when archiving the products of their research.

Opportunities and Challenges in High-Quality Contemporary Data Collection in Traumatic Brain Injury: The CENTER-TBI Experience.

Strong evidence in support of guidelines for traumatic brain injury (TBI) is lacking. Large-scale observational studies may offer a complementary source of evidence to clinical trials to improve the care and outcome for patients with TBI. They are, however, challenging to execute. In this review, we aim to characterize opportunities and challenges of large-scale collaborative research in neurotrauma. We use the setup and conduct of Collaborative European Neurotrauma Effectiveness Research in TBI (CENTER-TBI) as an illustrative example. We highlight the importance of building a team and of developing a network for younger researchers, thus investing toward the future. We involved investigators early in the design phase and recognized their efforts in a group contributor list on all publications. We found, however, that translation to academic credits often failed, and we suggest that the current system of academic credits be critically appraised. We found substantial variability in consent procedures for participant enrollment within and between countries. Overall, obtaining approvals typically required 4-6 months, with outliers up to 18 months. Research costs varied considerably across Europe and should be defined by center. We substantially underestimated costs of data curation, and we suggest that 15-20% of the budget be reserved for this purpose. Streamlining analyses and accommodating external research proposals demanded a structured approach. We implemented a systematic inventory of study plans and found this effective in maintaining oversight and in promoting collaboration between research groups. Ensuring good use of the data was a prominent feature in the review of external proposals. Multiple interactions occurred with industrial partners, mainly related to biomarkers and neuroimaging, and resulted in various formal collaborations, substantially extending the scope of CENTER-TBI. Overall, CENTER-TBI has been productive, with over 250 international peer-reviewed publications. We have ensured mechanisms to maintain the infrastructure and continued analyses. We see potential for individual patient data meta-analyses in connection to other large-scale projects. Our collaboration with Transforming Research and Clinical Knowledge in TBI (TRACK-TBI) has taught us that although standardized data collection and coding according to common data elements can facilitate such meta-analyses, further data harmonization is required for meaningful results. Both CENTER-TBI and TRACK-TBI have demonstrated the complexity of the conduct of large-scale collaborative studies that produce high-quality science and new insights.

[Linkage of claims data with data from epidemiological cancer registries: possibilities and limitations in the German federal states]. / Verknüpfung von Abrechnungsdaten gesetzlicher Krankenkassen mit Daten epidemiologischer Krebsregister: länderspezifische Möglichkeiten und Limitationen.

BACKGROUND: In recent years, there has been an increasing demand for the reuse of research data in accordance with the so-called FAIR principles. This would allow researchers to conduct projects on a broader data basis and to investigate new research questions by linking different data sources. OBJECTIVES: We explored if nationwide linking of claims data from statutory health insurances (SHI) with data from population-based cancer registries can be used to obtain additional information on cancer that is missing in claims data and to assess the validity of SHI tumour diagnoses. This paper focuses on describing the specific requirements of German federal states for such data linkage. MATERIALS AND METHODS: The Pharmacoepidemiological Research Database GePaRD at the Leibniz Institute for Prevention Research and Epidemiology - BIPS and six cancer registries were used as data sources. The logistically complex direct linkage was compared with a less complex indirect linkage. For this purpose, permission had to be obtained for GePaRD and for each cancer registry from the respective responsible authority. RESULTS: Regarding the linkage of cancer registry data with GePaRD, the cancer registries showed profound differences in the modalities for data provision, ranging from a complete rejection to an uncomplicated implementation of linkage procedures. DISCUSSION: In Germany, a consistent legal framework is needed to adequately enable the reuse and record linkage of personal health data for research purposes according to the FAIR principles. The new law on the consolidation of cancer registry data could provide a remedy regarding the linkage of cancer registry data with other data sources.

A Second Look at FAIR in Proteomic Investigations.

Proteomics is, by definition, comprehensive and large-scale, seeking to unravel ome-level protein features with phenotypic information on an entire system, an organ, cells, or organisms. This scope consistently involves and extends beyond single experiments. Multitudinous resources now exist to assist in making the results of proteomics experiments more findable, accessible, interoperable, and reusable (FAIR), yet many tools are awaiting to be adopted by our community. Here we highlight strategies for expanding the impact of proteomics data beyond single studies. We show how linking specific terminologies, identifiers, and text (words) can unify individual data points across a wide spectrum of studies and, more importantly, how this approach may potentially reveal novel relationships. In this effort, we explain how data sets and methods can be rendered more linkable and how this maximizes their value. We also include a discussion on how data linking strategies benefit stakeholders across the proteomics community and beyond.

How FAIR are plant sciences in the twenty-first century? The pressing need for reproducibility in plant ecology and evolution.

The need for open, reproducible science is of growing concern in the twenty-first century, with multiple initiatives like the widely supported FAIR principles advocating for data to be Findable, Accessible, Interoperable and Reusable. Plant ecological and evolutionary studies are not exempt from the need to ensure that the data upon which their findings are based are accessible and allow for replication in accordance with the FAIR principles. However, it is common that the collection and curation of herbarium specimens, a foundational aspect of studies involving plants, is neglected by authors. Without publicly available specimens, huge numbers of studies that rely on the field identification of plants are fundamentally not reproducible. We argue that the collection and public availability of herbarium specimens is not only good botanical practice but is also fundamental in ensuring that plant ecological and evolutionary studies are replicable, and thus scientifically sound. Data repositories that adhere to the FAIR principles must make sure that the original data are traceable to and re-examinable at their empirical source. In order to secure replicability, and adherence to the FAIR principles, substantial changes need to be brought about to restore the practice of collecting and curating specimens, to educate students of their importance, and to properly fund the herbaria which house them.

Collective knowledge: organizing research projects as a database of reusable components and portable workflows with common interfaces.

This article provides the motivation and overview of the Collective Knowledge Framework (CK or cKnowledge). The CK concept is to decompose research projects into reusable components that encapsulate research artifacts and provide unified application programming interfaces (APIs), command-line interfaces (CLIs), meta descriptions and common automation actions for related artifacts. The CK framework is used to organize and manage research projects as a database of such components. Inspired by the USB 'plug and play' approach for hardware, CK also helps to assemble portable workflows that can automatically plug in compatible components from different users and vendors (models, datasets, frameworks, compilers, tools). Such workflows can build and run algorithms on different platforms and environments in a unified way using the customizable CK program pipeline with software detection plugins and the automatic installation of missing packages. This article presents a number of industrial projects in which the modular CK approach was successfully validated in order to automate benchmarking, auto-tuning and co-design of efficient software and hardware for machine learning and artificial intelligence in terms of speed, accuracy, energy, size and various costs. The CK framework also helped to automate the artifact evaluation process at several computer science conferences as well as to make it easier to reproduce, compare and reuse research techniques from published papers, deploy them in production, and automatically adapt them to continuously changing datasets, models and systems. The long-term goal is to accelerate innovation by connecting researchers and practitioners to share and reuse all their knowledge, best practices, artifacts, workflows and experimental results in a common, portable and reproducible format at https://cKnowledge.io/. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.

[Making COVID-19 research data more accessible-building a nationwide information infrastructure]. / COVID-19-Forschungsdaten leichter zugänglich machen ­ Aufbau einer bundesweiten Informationsinfrastruktur.

Public health research and epidemiological and clinical studies are necessary to understand the COVID-19 pandemic and to take appropriate action. Therefore, since early 2020, numerous research projects have also been initiated in Germany. However, due to the large amount of information, it is currently difficult to get an overview of the diverse research activities and their results. Based on the "Federated research data infrastructure for personal health data" (NFDI4Health) initiative, the "COVID-19 task force" is able to create easier access to SARS-CoV-2- and COVID-19-related clinical, epidemiological, and public health research data. Therefore, the so-called FAIR data principles (findable, accessible, interoperable, reusable) are taken into account and should allow an expedited communication of results. The most essential work of the task force includes the generation of a study portal with metadata, selected instruments, other study documents, and study results as well as a search engine for preprint publications. Additional contents include a concept for the linkage between research and routine data, a service for an enhanced practice of image data, and the application of a standardized analysis routine for harmonized quality assessment. This infrastructure, currently being established, will facilitate the findability and handling of German COVID-19 research. The developments initiated in the context of the NFDI4Health COVID-19 task force are reusable for further research topics, as the challenges addressed are generic for the findability of and the handling with research data.