Extending the Minimum Information About BIobank Data Sharing Terminology to Describe Samples, Sample Donors, and Events

Biobanks and data interoperability in Latin America: engendering high-quality evidence for the global research ecosystem

Currently, each biobank in Latin America operates with its own set of standards for database creation and management, resulting in a lack of regional and international interoperability. Furthermore, regulations concerning data protection, curation, and the transfer of biological samples and associated data vary significantly from country to country, by complicating efforts to create a unified data-sharing platform. To address these challenges, Latin America should promote the development of an integrated regional network of biobanks to generate high-quality evidence within the global research ecosystem. This initiative will combine regulatory science—focused on interoperability standards across semantic, technical, legal, and organizational dimensions—and meta-science, which assesses the quality of scientific practice. Evidence indicates that harmonized standards in biobanks lead to higher-quality, more reliable data, thereby facilitating the reproducibility of scientific studies. This paper aims to identify and address existing regulatory, policy, and infrastructure gaps in Latin America to establish harmonized interoperability criteria essential for reproducing biomedical studies. Additionally, it seeks to propose minimum standards for regulating biobank networks, which will promote the development of medical products on a global scale, thereby engendering high quality evidence for the global research ecosystem and enhancing Latin America’s integration into it.

Data Governance and Distribution of Biobank: A Case from a Chinese Cancer Hospital

Objectives: To facilitate the regionalization, specialization, and digitization of biobanks, three issues regarding data collection and application must be addressed (1) integration and distribution of data governance, (2) efficiency and efficacy of data governance, and (3) sustainability of data governance. Methods: We collaborated with stakeholders to identify priorities and assess infrastructure needs through the continuous evaluation and analysis of projects. We developed data management solutions, catalogs, and data models to optimize and support data collection, distribution, and application. Furthermore, ontologies were used to facilitate data integration from multiple sources, and Minimum Information About BIobank Data Sharing (MIABIS) was defined as accessible to all patients. To enhance data integrity, we conducted retrospective and prospective follow-up studies. Results: We completed infrastructure upgrades to match technical solutions and research demands. An information management software with six primary functional divisions was developed for data governance. We optimized the database structure and changed the biospecimen accumulation model from biospecimen-based to patient-centered and service-oriented. Subsequently, we specified 85 attributes of MIABIS to describe the biobank contents. A dual-pillar approach was adopted to expand the biobank's data in collaboration with other institutions, and MIABIS served as a bridge for both vertical and horizontal networks. From 2003 to 2021, we collected a total of 156,997 patient biospecimens/data from 20 cancer types, matching 53,113 cases from follow-up surveys. In addition, we supplied more than 40,000 biospecimens/data points for above 300 scientific research projects. Conclusions: An appropriate information platform for a biobank is fundamental to data collection, distribution, and application, particularly in the context of data-intensive research. We implemented a standardized scientific data structure to fulfill the research requirements. The sustainable development of a biobank depends on a scientific, standardized, and service-oriented data governance approach, along with the efficient utilization of emerging technologies.

Applying Findable, Accessible, Interoperable, and Reusable Principles to Biospecimens and Biobanks

The importance of stimulating greater sharing of data for use and reuse in health research is widely recognized. To this end, the findable, accessible, interoperable, and reusable (FAIR) principles for data have been developed and widely accepted in the research community. Research biospecimens are a resource that leads to much of this health research data but are also a form of data. Therefore, the FAIR principles should apply to biospecimens. Nevertheless, there is a widespread problem of not sharing biospecimen resources that is clearly visible within the research arena. The impacts of this are likely to include diversion of precious research funds into compiling duplicate biospecimen cohorts, detraction from research productivity as researchers compete for and create duplicate resources, and deterrence of attempts to assess research reproducibility. This article explores some of the barriers that may limit availability of FAIR biospecimens. These barriers relate to the type of biospecimen collections and the characteristics of the custodians that influence their intention and interest in sharing. Barriers also relate to the ethical, legal, and social issues concerning collections, the research context of the collections, and cost and expertise involved in repurposing collections to enable sharing. Several solutions to increase sharing are identified. Some have recently been implemented, including enhancing biospecimen locators with tools to guide researchers and facilitating transfer of research collections to centralized biobank infrastructures at the conclusion of projects. New proposed solutions include improving search capabilities within publication databases, and introduction of evidence-based justifications for all new collections into peer-reviewed grant competition processes. It is recognized that there are both scientific factors and practical reasons that can impose limits to sharing biospecimens. However, funding availability, productivity, and progress in health research all stand to benefit from improved sharing of research biospecimen collections.

Biobank Digitalization: From Data Acquisition to Efficient Use

Biobanks are involved in a broad range of studies, including both basic and clinical research, so their functions and roles are evolving. Digital biobanks have emerged due to digitalization in this field; however, it also entails an increasing number of ethical and legal issues, in particular those related to the protection of donor data and potential commercial applications. The development of biobanks and the size of stored datasets lay the groundwork for proceeding to digital biobanks that intensely employ artificial intelligence tools. Digital biobanks can simplify the search for and access to biological specimens, thus contributing to the conduction of research and creating new collaborations. They are becoming an increasingly important tool for personalized medicine and an individualized approach to disease treatment, contributing to the elaboration of more accurate diagnostic methods and the development of innovative therapeutic strategies.

Data Management in Biobanking: Strategies, Challenges, and Future Directions

Biobanking plays a pivotal role in biomedical research by providing standardized processing, precise storing, and management of biological sample collections along with the associated data. Effective data management is a prerequisite to ensure the integrity, quality, and accessibility of these resources. This review provides a current landscape of data management in biobanking, discussing key challenges, existing strategies, and potential future directions. We explore multiple aspects of data management, including data collection, storage, curation, sharing, and ethical considerations. By examining the evolving technologies and methodologies in biobanking, we aim to provide insights into addressing the complexities and maximizing the utility of biobank data for research and clinical applications.

The sample locator: A federated search tool for biosamples and associated data in Europe using HL7 FHIR

BACKGROUND

This study outlines the development of a highly interoperable federated IT infrastructure for academic biobanks located at the major university hospital sites across Germany. High-quality biosamples linked to clinical data, stored in biobanks are essential for biomedical research. We aimed to facilitate the findability of these biosamples and their associated data. Networks of biobanks provide access to even larger pools of samples and data even from rare diseases and small disease subgroups. The German Biobank Alliance (GBA) established in 2017 under the umbrella of the German Biobank Node (GBN), has taken on the mission of a federated data discovery service to make biosamples and associated data available to researchers across Germany and Europe.

METHODS

In this context, we identified the requirements of researchers seeking human biosamples from biobanks and the needs of biobanks for data sovereignty over their samples and data in conjunction with the sample donor's consent. Based on this, we developed a highly interoperable federated IT infrastructure using standards such as Fast Healthcare Interoperability Resources (HL7 FHIR) and Clinical Quality Language (CQL).

RESULTS

The infrastructure comprises two major components enabling federated real-time access to biosample metadata, allowing privacy-compliant queries and subsequent project requests. It has been in use since 2019, connecting 16 German academic biobanks, with additional European biobanks joining. In production since 2019 it has run 4941 queries over the span of one year on more than 900,000 biosamples collected from more than 170,000 donors.

CONCLUSION

This infrastructure enhances the visibility and accessibility of biosamples for research, addressing the growing demand for human biosamples and associated data in research. It also underscores the need for improvements in processes beyond IT infrastructure, aiming to advance biomedical research and similar infrastructure development in other fields.

Update of the Minimum Information About BIobank Data Sharing (MIABIS) Core Terminology to the 3rd Version

Introduction: The Minimum Information About BIobank Data Sharing (MIABIS) is a biobank-specific terminology enabling the sharing of biobank-related data for different purposes across a wide range of database implementations. After 4 years in use and with the first version of the individual-level MIABIS component Sample, Sample donor, and Event, it was necessary to revise the terminology, especially to include biobanks that work more in the data domain than with samples. Materials & Methods: Nine use-cases representing different types of biobanks, studies, and networks participated in the development work. They represent types of data, specific sample types, or levels of organization that were not included earlier in MIABIS. To support our revision of the Biobank entity, we conducted a survey of European biobanks to chart the services they provide. An important stakeholder group for biobanks include researchers as the main users of biobanks. To be able to render MIABIS more researcher-friendly, we collected different sample/data requests to analyze the terminology adjustment needs in detail. During the update process, the Core terminology was iteratively reviewed by a large group of experts until a consensus was reached. Results: With this update, MIABIS was adjusted to encompass data-driven biobanks and to include data collections, while also describing the services and capabilities biobanks offer to their users, besides the retrospective samples. The terminology was also extended to accommodate sample and data collections of nonhuman origin. Additionally, a set of organizational attributes was compiled to describe networks. Discussion: The usability of MIABIS Core v3 was increased by extending it to cover more topics of the biobanking domain. Additionally, the focus was on a more general terminology and harmonization of attributes with the individual-level entities Sample, Sample donor, and Event to keep the overall terminology minimal. With this work, the internal semantics of the MIABIS terminology was improved.

Data Model for the Comprehensive Management of Biobanks and Its Contribution to Personalized Medicine

Biobanks are infrastructures essential for research involving multi-disciplinary teams and an increasing number of stakeholders. In the field of personalized medicine, biobanks play a key role through the provision of well-characterized and annotated samples protecting at the same time the right of donors. The Andalusian Public Health System Biobank (SSPA Biobank) has implemented a global information management system made up of different modules that allow for the recording, traceability and monitoring of all the information associated with the biobank operations. The data model, designed in a standardized and normalized way according to international initiatives on data harmonization, integrates the information necessary to guarantee the quality of results from research, benefiting researchers, clinicians and donors.

Biobanks in chronic disease management: A comprehensive review of strategies, challenges, and future directions

Biobanks, through the collection and storage of patient blood, tissue, genomic, and other biological samples, provide unique and rich resources for the research and management of chronic diseases such as cardiovascular diseases, diabetes, and cancer. These samples contain valuable cellular and molecular level information that can be utilized to decipher the pathogenesis of diseases, guide the development of novel diagnostic technologies, treatment methods, and personalized medical strategies. This article first outlines the historical evolution of biobanks, their classification, and the impact of technological advancements. Subsequently, it elaborates on the significant role of biobanks in revealing molecular biomarkers of chronic diseases, promoting the translation of basic research to clinical applications, and achieving individualized treatment and management. Additionally, challenges such as standardization of sample processing, information privacy, and security are discussed. Finally, from the perspectives of policy support, regulatory improvement, and public participation, this article provides a forecast on the future development directions of biobanks and strategies to address challenges, aiming to safeguard and enhance their unique advantages in supporting chronic disease prevention and treatment.

Standardizing digital biobanks: integrating imaging, genomic, and clinical data for precision medicine

Advancements in data acquisition and computational methods are generating a large amount of heterogeneous biomedical data from diagnostic domains such as clinical imaging, pathology, and next-generation sequencing (NGS), which help characterize individual differences in patients. However, this information needs to be available and suitable to promote and support scientific research and technological development, supporting the effective adoption of the precision medicine approach in clinical practice. Digital biobanks can catalyze this process, facilitating the sharing of curated and standardized imaging data, clinical, pathological and molecular data, crucial to enable the development of a comprehensive and personalized data-driven diagnostic approach in disease management and fostering the development of computational predictive models. This work aims to frame this perspective, first by evaluating the state of standardization of individual diagnostic domains and then by identifying challenges and proposing a possible solution towards an integrative approach that can guarantee the suitability of information that can be shared through a digital biobank. Our analysis of the state of the art shows the presence and use of reference standards in biobanks and, generally, digital repositories for each specific domain. Despite this, standardization to guarantee the integration and reproducibility of the numerical descriptors generated by each domain, e.g. radiomic, pathomic and -omic features, is still an open challenge. Based on specific use cases and scenarios, an integration model, based on the JSON format, is proposed that can help address this problem. Ultimately, this work shows how, with specific standardization and promotion efforts, the digital biobank model can become an enabling technology for the comprehensive study of diseases and the effective development of data-driven technologies at the service of precision medicine.

Unlocking the potential of big data and AI in medicine: insights from biobanking

Big data and artificial intelligence are key elements in the medical field as they are expected to improve accuracy and efficiency in diagnosis and treatment, particularly in identifying biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. These applications belong to current research practice that is data-intensive. While the combination of imaging, pathological, genomic, and clinical data is needed to train algorithms to realize the full potential of these technologies, biobanks often serve as crucial infrastructures for data-sharing and data flows. In this paper, we argue that the 'data turn' in the life sciences has increasingly re-structured major infrastructures, which often were created for biological samples and associated data, as predominantly data infrastructures. These have evolved and diversified over time in terms of tackling relevant issues such as harmonization and standardization, but also consent practices and risk assessment. In line with the datafication, an increased use of AI-based technologies marks the current developments at the forefront of the big data research in life science and medicine that engender new issues and concerns along with opportunities. At a time when secure health data environments, such as European Health Data Space, are in the making, we argue that such meta-infrastructures can benefit both from the experience and evolution of biobanking, but also the current state of affairs in AI in medicine, regarding good governance, the social aspects and practices, as well as critical thinking about data practices, which can contribute to trustworthiness of such meta-infrastructures.

[Information support for the bioresource collection: a biological information system «NeuroOnc»]

The manuscript is devoted to development of information support system for a bioresource collection - biological information system «NeuroOnc». Architecture and main functions of system are presented. This system was formed in the project «Development of bioresource collection of tumors of the human nervous system with molecular genetic certification for personalized treatment of patients with neuro-oncological diseases». The purpose of this project was not only formation of bioresource collection, but also development of various molecular genetic methods for analysis of biospecimens in context of clinical researches. Biological information systems created to support the work of bioresource collections in hospitals should become a natural part of information infrastructure. Information support of bioresource collections cannot imply only «warehouse» functions. This system should have tools to support various scientific and clinical researches. Biological information systems can sometimes expand medical information systems but remain sufficiently autonomous. It is advisable to develop biological information systems in large specialized companies that can support their products for many years.

Data infrastructures for AI in medical imaging: a report on the experiences of five EU projects

Artificial intelligence (AI) is transforming the field of medical imaging and has the potential to bring medicine from the era of 'sick-care' to the era of healthcare and prevention. The development of AI requires access to large, complete, and harmonized real-world datasets, representative of the population, and disease diversity. However, to date, efforts are fragmented, based on single-institution, size-limited, and annotation-limited datasets. Available public datasets (e.g., The Cancer Imaging Archive, TCIA, USA) are limited in scope, making model generalizability really difficult. In this direction, five European Union projects are currently working on the development of big data infrastructures that will enable European, ethically and General Data Protection Regulation-compliant, quality-controlled, cancer-related, medical imaging platforms, in which both large-scale data and AI algorithms will coexist. The vision is to create sustainable AI cloud-based platforms for the development, implementation, verification, and validation of trustable, usable, and reliable AI models for addressing specific unmet needs regarding cancer care provision. In this paper, we present an overview of the development efforts highlighting challenges and approaches selected providing valuable feedback to future attempts in the area.Key points• Artificial intelligence models for health imaging require access to large amounts of harmonized imaging data and metadata.• Main infrastructures adopted either collect centrally anonymized data or enable access to pseudonymized distributed data.• Developing a common data model for storing all relevant information is a challenge.• Trust of data providers in data sharing initiatives is essential.• An online European Union meta-tool-repository is a necessity minimizing effort duplication for the various projects in the area.

Digital public health leadership in the global fight for health security

The COVID-19 pandemic highlighted the need to prioritise mature digital health and data governance at both national and supranational levels to guarantee future health security. The Riyadh Declaration on Digital Health was a call to action to create the infrastructure needed to share effective digital health evidence-based practices and high-quality, real-time data locally and globally to provide actionable information to more health systems and countries. The declaration proposed nine key recommendations for data and digital health that need to be adopted by the global health community to address future pandemics and health threats. Here, we expand on each recommendation and provide an evidence-based roadmap for their implementation. This policy document serves as a resource and toolkit that all stakeholders in digital health and disaster preparedness can follow to develop digital infrastructure and protocols in readiness for future health threats through robust digital public health leadership.

Biobanking in dentistry: A review

Biobanks are not-for-profit services for the collection, processing, storage and distribution of biological samples and data for research and diagnostic purposes. In dentistry, biological materials and data obtained from questionnaires investigating oral conditions can be stored and used for large-scale studies on oral and systemic diseases. To give some examples: gene expression microarrays obtained on biobanked specimens were used in the identification of genetic alterations in oral cancer; efforts to identify genetic mechanisms behind dental caries have been based on an integrative analysis of transcriptome-wide associations and messenger RNA expression. One of the largest studies on facial pain was conducted using Biobank data. Cryopreservation of dental pulp stem cells is a common practice in tooth biobanks. With the exception of teeth and pulp, also leftover oral soft and hard tissues may represent a source of healthy samples that has rarely been exploited as yet. While biobanks are increasingly attracting the attention of the scientific community and becoming economically sustainable, a systematic approach to this resource in dentistry seems to be lacking. This review illustrates the applications of biobanking in dentistry, describing biobanked pathological and healthy samples and data, and discussing future developments.

Position of the AI for Health Imaging (AI4HI) network on metadata models for imaging biobanks

A huge amount of imaging data is becoming available worldwide and an incredible range of possible improvements can be provided by artificial intelligence algorithms in clinical care for diagnosis and decision support. In this context, it has become essential to properly manage and handle these medical images and to define which metadata have to be considered, in order for the images to provide their full potential. Metadata are additional data associated with the images, which provide a complete description of the image acquisition, curation, analysis, and of the relevant clinical variables associated with the images. Currently, several data models are available to describe one or more subcategories of metadata, but a unique, common, and standard data model capable of fully representing the heterogeneity of medical metadata has not been yet developed. This paper reports the state of the art on metadata models for medical imaging, the current limitations and further developments, and describes the strategy adopted by the Horizon 2020 "AI for Health Imaging" projects, which are all dedicated to the creation of imaging biobanks.

Evaluating and Extending the Informed Consent Ontology for Representing Permissions from the Clinical Domain

The purpose of this study was to evaluate, revise, and extend the Informed Consent Ontology (ICO) for expressing clinical permissions, including reuse of residual clinical biospecimens and health data. This study followed a formative evaluation design and used a bottom-up modeling approach. Data were collected from the literature on US federal regulations and a study of clinical consent forms. Eleven federal regulations and fifteen permission-sentences from clinical consent forms were iteratively modeled to identify entities and their relationships, followed by community reflection and negotiation based on a series of predetermined evaluation questions. ICO included fifty-two classes and twelve object properties necessary when modeling, demonstrating appropriateness of extending ICO for the clinical domain. Twenty-six additional classes were imported into ICO from other ontologies, and twelve new classes were recommended for development. This work addresses a critical gap in formally representing permissions clinical permissions, including reuse of residual clinical biospecimens and health data. It makes missing content available to the OBO Foundry, enabling use alongside other widely-adopted biomedical ontologies. ICO serves as a machine-interpretable and interoperable tool for responsible reuse of residual clinical biospecimens and health data at scale.

The Availability of Human Biospecimens to Support Biomarker Research

Preserved biospecimens held in biobank inventories and clinical archives are important resources for biomarker research. Recent advances in technologies have led to an increase in use of clinical archives in particular, in order to study retrospective cohorts and to generate data relevant to tissue biomarkers. This raises the question of whether the current sizes of biobank inventories are appropriate to meet the demands of biomarker research. This commentary discusses this question by considering data concerning overall biobank and biospecimen numbers to estimate current biospecimen supply and use. The data suggests that biospecimen supply exceeds current demand. Therefore, it may be important for individual biobanks to reassess the targets for their inventories, consider culling unused portions of these inventories, and shift resources towards providing prospective custom biobanking services.

Integrating Biological and Radiological Data in a Structured Repository: a Data Model Applied to the COSMOS Case Study

Integrating the information coming from biological samples with digital data, such as medical images, has gained prominence with the advent of precision medicine. Research in this field faces an ever-increasing amount of data to manage and, as a consequence, the need to structure these data in a functional and standardized fashion to promote and facilitate cooperation among institutions. Inspired by the Minimum Information About BIobank data Sharing (MIABIS), we propose an extended data model which aims to standardize data collections where both biological and digital samples are involved. In the proposed model, strong emphasis is given to the cause-effect relationships among factors as these are frequently encountered in clinical workflows. To test the data model in a realistic context, we consider the Continuous Observation of SMOking Subjects (COSMOS) dataset as case study, consisting of 10 consecutive years of lung cancer screening and follow-up on more than 5000 subjects. The structure of the COSMOS database, implemented to facilitate the process of data retrieval, is therefore presented along with a description of data that we hope to share in a public repository for lung cancer screening research.

Laying the groundwork for the Biobank of Rare Malignant Neoplasms at the service of the Hellenic Network of Precision Medicine on Cancer

Biobanks constitute an integral part of precision medicine. They provide a repository of biospecimens that may be used to elucidate the pathophysiology, support diagnoses, and guide the treatment of diseases. The pilot biobank of rare malignant neoplasms has been established in the context of the Hellenic Network of Precision Medicine on Cancer and aims to enhance future clinical and/or research studies in Greece by collecting, processing, and storing rare malignant neoplasm samples with associated data. The biobank currently comprises 553 samples; 384 samples of hematopoietic and lymphoid tissue malignancies, 72 samples of pediatric brain tumors and 97 samples of malignant skin neoplasms. In this article, sample collections and their individual significance in clinical research are described in detail along with computational methods developed specifically for this project. A concise review of the Greek biobanking landscape is also delineated, in addition to recommended technologies, methodologies and protocols that were integrated during the creation of the biobank. This project is expected to re‑enforce current clinical and research studies, introduce advances in clinical and genetic research and potentially aid in future targeted drug discovery. It is our belief that the future of medical research is entwined with accessible, effective, and ethical biobanking and that our project will facilitate research planning in the '‑omic' era by contributing high‑quality samples along with their associated data.

Human exposome assessment platform

The Human Exposome Assessment Platform (HEAP) is a research resource for the integrated and efficient management and analysis of human exposome data. The project will provide the complete workflow for obtaining exposome actionable knowledge from population-based cohorts. HEAP is a state-of-the-science service composed of computational resources from partner institutions, accessed through a software framework that provides the world's fastest Hadoop platform for data warehousing and applied artificial intelligence (AI). The software, will provide a decision support system for researchers and policymakers. All the data managed and processed by HEAP, together with the analysis pipelines, will be available for future research. In addition, the platform enables adding new data and analysis pipelines. HEAP's final product can be deployed in multiple instances to create a network of shareable and reusable knowledge on the impact of exposures on public health.

The journey to establishing an IT-infrastructure within the German Biobank Alliance

Background

Biobanks ensure the long-term storage and accessibility of biospecimens and corresponding data sets. Thus, they form the foundation for many research projects which may contribute to improving medical care. With the establishment of the German Biobank Node and Alliance, expertise in biobanking is bundled and strengthened. An important component within this research infrastructure is the set-up of an information technology (IT) network for allowing feasibility requests across individual biobanks.

Objective

We aim to describe relevant aspects that have shaped the journey to interconnect biobanks, to enhance their visibility within the research-community, to harmonize data, and to enable feasibility searches to support access to available data and biosamples.

Methods

To achieve this task, we resorted to a wide variety of methods: we ran a requirement analysis, decided on the mode of operation for the federated team of IT-developers and on the development approach itself, took related national and international initiatives into account, and concluded with evaluations of the developed software artefacts and the operation of the entire chain of applications.

Results

We drew an IT framework including all heterogeneous data aspects derived from our requirement analysis and developed a comprehensive IT infrastructure. The successful implementation benefited from a smooth interaction of a federated IT team distributed across all participating sites that was even able to manage a major technology change mid-project. Authentication and project management services from associated partners could be integrated and the graphic user interface for an intuitive search tool for biospecimens was designed iteratively. The developed code is open source to ensure sustainability and the local implementation is concluded and functioning. The evaluation of the components was positive.

Conclusions

The entire project had given ample opportunity for challenges, predictable and unpredictable—from the mode of operation to changing some of the initial ideas. We learned our lessons concerning personnel, budget planning and technical as well as manual monitoring as well as some requirements arising only during the process of the project. Nevertheless, we can here report a success story of a network infrastructure, highly agile and much easier in local installation than initially anticipated.

[Making COVID-19 research data more accessible-building a nationwide information infrastructure]

Public-Health-Forschung, epidemiologische und klinische Studien sind erforderlich, um die COVID-19-Pandemie besser zu verstehen und geeignete Maßnahmen zu ergreifen. Daher wurden auch in Deutschland zahlreiche Forschungsprojekte initiiert. Zum heutigen Zeitpunkt ist es ob der Fülle an Informationen jedoch kaum noch möglich, einen Überblick über die vielfältigen Forschungsaktivitäten und deren Ergebnisse zu erhalten. Im Rahmen der Initiative „Nationale Forschungsdateninfrastruktur für personenbezogene Gesundheitsdaten“ (NFDI4Health) schafft die „Task Force COVID-19“ einen leichteren Zugang zu SARS-CoV-2- und COVID-19-bezogenen klinischen, epidemiologischen und Public-Health-Forschungsdaten. Dabei werden die sogenannten FAIR-Prinzipien (Findable, Accessible, Interoperable, Reusable) berücksichtigt, die eine schnellere Kommunikation von Ergebnissen befördern sollen. Zu den wesentlichen Arbeitsinhalten der Taskforce gehören die Erstellung eines Studienportals mit Metadaten, Erhebungsinstrumenten, Studiendokumenten, Studienergebnissen und Veröffentlichungen sowie einer Suchmaschine für Preprint-Publikationen. Weitere Inhalte sind ein Konzept zur Verknüpfung von Forschungs- und Routinedaten, Services zum verbesserten Umgang mit Bilddaten und die Anwendung standardisierter Analyseroutinen für harmonisierte Qualitätsbewertungen. Die im Aufbau befindliche Infrastruktur erleichtert die Auffindbarkeit von und den Umgang mit deutscher COVID-19-Forschung. Die im Rahmen der NFDI4Health Task Force COVID-19 begonnenen Entwicklungen sind für weitere Forschungsthemen nachnutzbar, da die adressierten Herausforderungen generisch für die Auffindbarkeit von und den Umgang mit Forschungsdaten sind.

Finding the Value in Biobanks: Enhancing the CTRNet Locator

Biobanks are a critical piece of Research Infrastructure (RI). However, biobanks need to accept the reality of a life cycle for RIs. Until recently, strategies to sustain biobanks have been commonly focused on ways to maintain current operational models. However, sustaining biobanks as they exist today may be increasingly challenging in the face of the disruption in health and research priorities caused by the COVID-19 pandemic. In this opinion article, we review the current and emerging future drivers of biobank value for their researchers, institutions, and funders, highlighting utilization and impact of research performed using the biobank as key measures of future value. While biobanks can only indirectly influence the specific impact of the research performed, they can transform themselves to more actively redefine utilization to their advantage. Utilization means more than the balance of samples and data in versus out. Utilization means redirecting expertise to best support end users, and importantly, closing the operating gap between biobanks and their end users who seek to find the right biospecimens and data to pursue their research. We discuss the specific role of locators (those created by public investment) in closing this gap and the need for additional tools for researchers, before and subsequent to connecting with locators. For the former, we specifically propose that more support is needed to assist researchers in the decision as to how to best obtain biospecimens and navigate the options as to whether finding existing biospecimens and data held by a biobank is the optimal solution for a given project, or whether the optimal solution is either contracting with a biobank to collect samples or creating a new biobank. We believe this type of biospecimen navigator platform will help to maximize utilization of current biobank resources, and also promote the services and expertise in biobanks to better serve researchers' needs.

The Andalusian Registry of Donors for Biomedical Research: Five Years of History

The mission of the Andalusian Public Health System Biobank is to offer the best options for biological samples of human origin and associated clinical information, protecting the rights of citizens who donate their samples for research. Since the Andalusian Biobank provides high-quality biological samples of all types in a specified format, adapting the preanalytical phase according to the requirements of the research, prospective collection and distribution of samples are being prioritized in order to contribute to the sustainability of the Biobank. The Andalusian Registry of Donors for Biomedical Research is a tool for the recruitment of donors and the prospective collection of samples. Its operation is based on the informed consent of donors for their incorporation into the Registry and contact with possible donors under request from specific projects. An additional advantage of this unique initiative is to ensure that societal actors work together throughout the entire research process, establishing alliances with patient associations and groups to develop joint actions and promote biomedical research. Here, we describe the creation, ethical–legal aspects, management and results of the Andalusian Registry of Donors for Biomedical Research after five years of operation.

Opportunities and Risks for Research Biobanks in the COVID-19 Era and Beyond

The SARS-CoV-2 pandemic, which caused a global outbreak of COVID-19 disease, has been a crisis of extraordinary proportions, causing serious impacts for research and public health. Biobanks have played a key important role in understanding the disease and response. In our article we will highlight the opportunities and risks of biobanks during and after the pandemic. The different aspects of safety and sustainability have and will be the main challenges for biobanks. Furthermore, the role of biobanks in biomedical research and public health has been emphasized as well as opportunities that have arisen for their participation in research.

Biobanking for Cancer Biomarker Research: Issues and Solutions

Biomarkers are critical tools that underpin precision medicine. However there has been slow progress and frequent failure of biomarker development. The root causes are multifactorial. Here, we focus on the need for fast, efficient, and reliable access to quality biospecimens as a critical area that impacts biomarker development. We discuss the past history of biobanking and the evolution of biobanking processes relevant to the specific area of cancer biomarker development as an example, and describe some solutions that can improve this area, thus potentially accelerating biomarker research.