National Research Data Research Articles

A roadmap for a middleware as a federation service for integrative data retrieval of agricultural data.

Agriculture is confronted with several challenges such as climate change, the loss of biodiversity and stagnating productivity. The massive increasing amount of data and new digital technologies promise to overcome them, but they necessitate careful data integration and data management to make them usable. The FAIRagro consortium is part of the National Research Data Infrastructure (NFDI) in Germany and will develop FAIR compliant infrastructure services for the agrosystems science community, which will be integrated in the existing research data infrastructure service landscape. Here we present the initial steps of designing and implementing the FAIRagro middleware infrastructure to connect existing data infrastructures. The middleware will feature services for the seamless data integration across diverse infrastructures. Data and metadata are streamlined for research in agrosystems science by downstream processing in the central FAIRagro Search and Inventory Portal and the data integration and analysis workflow system "SciWIn".

Using Health IT Standards to Facilitate Metadata Exchange Between Research Data Management Systems.

The National Research Data Infrastructure for Personal Health Data (NFDI4Health) uses Local Data Hubs (LDHs) to manage locally research studies, documents and sensitive personal data to support controlled data sharing. While research data management (RDM) systems facilitate the storage and preparation of data and metadata as well as organizational access, they often lack support for interoperability standards of the application domain. To support the exchange with external registries of research studies, we chose 17 attributes to characterize the most relevant aspects of clinical trials (in the following named "metadata profile"). We implemented the metadata profile in the RDM system FAIRDOM SEEK using core attributes and SEEK's extended metadata feature and created a mapping conforming to the Health Level 7 Fast Healthcare Interoperability Resources (FHIR) standard version R4. Finally, we implemented a prototype application interface for exports in FHIR-JSON format. We plan to extend the interface to serve central registries and support specific FHIR Implementation Guides from various use cases.

How Data Infrastructure Deals with Bias Problems in Medical Imaging.

The paper discusses biases in medical imaging analysis, particularly focusing on the challenges posed by the development of machine learning algorithms and generative models. It introduces a taxonomy of bias problems and addresses them through a data infrastructure initiative: the PADME (Platform for Analytics and Distributed Machine-Learning for Enterprises), which is a part of the National Research Data Infrastructure for Personal Health Data (NFDI4Health) project. The PADME facilitates the structuring and sharing of health data while ensuring privacy and adherence to FAIR principles. The paper presents experimental results that show that generative methods can be effective in data augmentation. Complying with PADME infrastructure, this work proposes a solution framework to deal with bias in the different data stations and preserve privacy when transferring images. It highlights the importance of standardized data infrastructure in mitigating biases and promoting FAIR, reusable, and privacy-preserving research environments in healthcare.

Towards an Interoperability Landscape for a National Research Data Infrastructure for Personal Health Data

The German initiative “National Research Data Infrastructure for Personal Health Data” (NFDI4Health) focuses on research data management in health research. It aims to foster and develop harmonized informatics standards for public health, epidemiological studies, and clinical trials, facilitating access to relevant data and metadata standards. This publication lists syntactic and semantic data standards of potential use for NFDI4Health and beyond, based on interdisciplinary meetings and workshops, mappings of study questionnaires and the NFDI4Health metadata schema, and literature search. Included are 7 syntactic, 32 semantic and 9 combined syntactic and semantic standards. In addition, 101 ISO Standards from ISO/TC 215 Health Informatics and ISO/TC 276 Biotechnology could be identified as being potentially relevant. The work emphasizes the utilization of standards for epidemiological and health research data ensuring interoperability as well as the compatibility to NFDI4Health, its use cases, and to (inter-)national efforts within these sectors. The goal is to foster collaborative and inter-sectoral work in health research and initiate a debate around the potential of using common standards.

Search and Harvesting across NFDI Consortia – Gaps and Challenges

Search and harvesting use cases on harmonised metadata play an important role in several activities on National Research Data Infrastructures (NFDI). The working group Search and Harvesting of the NFDI section (meta)data, terminologies and provenance works on a common understanding of user needs (for search) and service requirements (for harvesting), analysis of the data sources landscape, and recommendations concerning common and specific needs, e.g., for spatial or sensitive data. Here, we present search and harvesting gaps and challenges across NFDI consortia and beyond, which were identified and structured in the Search and Harvesting Working Group, and the recommendations for the NFDI we derive from them. Our goal is to foster a common vision for search and harvesting in the NFDI.

BiodivPortal: Enabling Semantic Services for Biodiversity within the German National Research Data Infrastructure

BiodivPortal: Enabling Semantic Services for Biodiversity within the German National Research Data Infrastructure

Making Epidemiological and Clinical Studies FAIR Using the Example of COVID-19

FAIRification of personal health data is of utmost importance to improve health research and political as well as medical decision-making, which ultimately contributes to a better health of the general population. Despite the many advances in information technology, several obstacles such as interoperability problems remain and relevant research on the health topic of interest is likely to be missed out due to time-consuming search and access processes. A recent example is the COVID-19 pandemic, where a better understanding of the virus’ transmission dynamics as well as preventive and therapeutic options would have improved public health and medical decision-making. Consequently, the NFDI4Health Task Force COVID-19 was established to foster the FAIRification of German COVID-19 studies.This paper describes the various steps that have been taken to create low barrier workflows for scientists in finding and accessing German COVID-19 research. It provides an overview on the building blocks for FAIR health research within the Task Force COVID-19 and how this initial work was subsequently expanded by the German consortium National Research Data Infrastructure for Personal Health Data (NFDI4Health) to cover a wider range of studies and research areas in epidemiological, public health and clinical research. Lessons learned from the Task Force helped to improve the respective tasks of NFDI4Health.

Federated Heterogeneous Compute and Storage Infrastructure for the PUNCH4NFDI Consortium

PUNCH4NFDI, funded by the Germany Research Foundation initially for five years, is a diverse consortium of particle, astro-, astroparticle, hadron and nuclear physics embedded in the National Research Data Infrastructure initiative. In order to provide seamless and federated access to the huge variety of compute and storage systems provided by the participating communities covering their very diverse needs, the Compute4PUNCH and Storage4PUNCH concepts have been developed. Both concepts comprise state-of-the-art technologies such as a token-based AAI for standardized access to compute and storage resources. The community supplied heterogeneous HPC, HTC and Cloud compute resources are dynamically and transparently integrated into one federated HTCondorbased overlay batch system using the COBalD/TARDIS resource meta-scheduler. Traditional login nodes and a JupyterHub provide entry points into the entire landscape of available compute resources, while container technologies and the CERN Virtual Machine File System (CVMFS) ensure a scalable provisioning of community-specific software environments. In Storage4PUNCH, community supplied storage systems mainly based on dCache or XRootD technology are being federated in a common infrastructure employing methods that are well established in the wider HEP community. Furthermore existing technologies for caching as well as metadata handling are being evaluated with the aim for a deeper integration. The combined Compute4PUNCH and Storage4PUNCH environment will allow a large variety of researchers to carry out resource-demanding analysis tasks. In this contribution we will present the Compute4PUNCH and Storage4PUNCH concepts, the current status of the developments as well as first experiences with scientific applications being executed on the available prototypes.

Chronic disease outcome metadata from German observational studies – public availability and FAIR principles

Metadata from epidemiological studies, including chronic disease outcome metadata (CDOM), are important to be findable to allow interpretability and reusability. We propose a comprehensive metadata schema and used it to assess public availability and findability of CDOM from German population-based observational studies participating in the consortium National Research Data Infrastructure for Personal Health Data (NFDI4Health). Additionally, principal investigators from the included studies completed a checklist evaluating consistency with FAIR principles (Findability, Accessibility, Interoperability, Reusability) within their studies. Overall, six of sixteen studies had complete publicly available CDOM. The most frequent CDOM source was scientific publications and the most frequently missing metadata were availability of codes of the International Classification of Diseases, Tenth Revision (ICD-10). Principal investigators’ main perceived barriers for consistency with FAIR principles were limited human and financial resources. Our results reveal that CDOM from German population-based studies have incomplete availability and limited findability. There is a need to make CDOM publicly available in searchable platforms or metadata catalogues to improve their FAIRness, which requires human and financial resources.

A novel approach for standardizing clinical laboratory categorical test results using machine learning and string distance similarity

Standardizing clinical laboratory test results is critical for conducting clinical data science research and analysis. However, standardized data processing tools and guidelines are inadequate. In this paper, a novel approach for standardizing categorical test results based on supervised machine learning and the Jaro-Winkler similarity algorithm is proposed. A supervised machine learning model is used in this approach for scalable categorization of the test results into predefined groups or clusters, while Jaro-Winkler similarity is used to map text terms into standard clinical terms within these corresponding groups. The proposed method is applied to 75062 test results from two private hospitals in Bangladesh. The Support Vector Classification algorithm with a linear kernel has a classification accuracy of 98%, which is better than the Random Forest algorithm when categorizing test results. The experiment results show that Jaro-Winkler similarity achieves a remarkable 99.93% success rate in the test result standardization for the majority of groups with manual validation. The proposed method outperforms previous studies that concentrated on standardizing test results using rule-based classifiers on a smaller number of groups and distance similarities such as Cosine similarity or Levenshtein distance. Furthermore, when applied to the publicly available MIMIC-III dataset, our approach also performs excellently. All these findings show that the proposed standardization technique can be very beneficial for clinical big data research, particularly for national clinical research data hubs in low- and middle-income countries.

Racism Data in Australia: A Review of Quantitative Studies and Directions for Future Research

ABSTRACT There are growing public discussions about racism in Australia with renewed government commitment to addressing it. Robust evidence and high-quality data are important for informing anti-racism. However, current data have serious limitations that impact our knowledge about the nature, prevalence and impact of racism in Australia. To examine the state and limitations of data on racism in Australia, we conducted a stocktake review of quantitative racism data collected nationally until July 2022. This article reports on 32 survey-based research studies and six ongoing organisational reporting initiatives. We organise and classify existing data based on study designs and participant characteristics, as well as the settings, targets, perpetrators, responses to and effects of racism. We identify data gaps and recommend how they may be bridged. First, we recommend further analysis of existing, under-utilised data, to address outstanding questions about perpetrators’ demographics, priority localities, and the health and socio-economic outcomes of racism. Second, we recommend new data collection on emerging settings where racism occurs, under-explored forms, cohorts experiencing racism, and responses to racism. We propose this study as a foundation for a national anti-racism research agenda and data management plan in Australia, and as a template for stocktakes in other countries.

How to Make Outpatient Healthcare Data in Germany Available for Research in the Dynamic Course of Digital Transformation.

There is increasing interest on re-use of outpatient healthcare data for research, as most medical diagnosis and treatment is provided in the ambulatory sector. One of the early projects to bring primary data from German ambulatory care into clinical research technically, organizationally and in compliance with legal demands has been the RADAR project, that is based on a broad consent and has used the then available practice information system's interfaces to extract and transfer data to a research repository. In course of the digital transformation of the German healthcare system, former standards are abandoned and new interoperability standards, interfaces and regulations on secondary use of patient data are defined, however with slow adoption by Health-IT systems. Therefore, it is of importance for all initiatives that aim at using ambulatory healthcare data for research, how to access this data in an efficient and effective way. Currently defined healthcare standards are compared regarding coverage of data relevant for research as defined by the RADAR project. We compare four architectural options to access ambulatory health data through different components of healthcare and health research data infrastructures along the technical, organizational and regulatory conditions, the timetable of dissemination and the researcher's perspective. A high-level comparison showed a high degree of semantic overlap in the information models used. Electronic patient records and practice information systems are alternative data sources for ambulatory health data - but differ strongly in data richness and accessibility. Considering the compared dimensions of architectural routes to access health data for secondary research use we conclude that data extraction from practice information systems is currently the most promising way due to data availability on a mid-term perspective. Integration of routine data into the national research data infrastructures might be enforced by convergence of to date different information models.

GBIF-Compliant Data Pipeline for the Management and Publication of a Global Taxonomic Reference List of Pests in Natural History Collections

There is a growing demand for monitoring pests in natural history collections (NHCs) and establishing integrated pest management (IPM) solutions (Crossman and Ryde 2022). In this context, up-to-date taxonomic reference lists and controlled vocabularies following standard schemes are crucial and facilitate recording organisms detected in collections. The data pipeline described here results in the publication of a taxon reference list based on information from online resources and standard IPM literature. Most of the over 140 pest taxa on species level and above are insects, the rest belong to other animal groups and fungi. The complete taxon names, synonyms, English and German common names, and the hierarchical classification (parent-child relationships) are organised in a client-server installation of DiversityTaxonNames (DTN) at the Bavarian Natural History Collections (SNSB). DTN is a Microsoft Structured Query Language (MS SQL) database tool of the Diversity Workbench (DWB) framework with a published Entity Relation (ER) diagram (Hagedorn et al. 2019). The management is done using the Global Biodiversity Information Facility (GBIF) backbone taxonomy as external name resource, with linkage to the respective Wikidata Q item ID as a external persistent identifier (PID). Moreover, information on pest occurrence in NHCs is given, distinguishing the Consortium of European Taxonomic Facilities (CETAF) major NHC collection types affected (i.e., heritage sciences, life sciences and earth sciences) and the object categories, e.g., natural objects/specimens damaged. The data management in DTN enables the long-running curation, done by list curators. The generic data pipeline for the management and publication of a Global Taxonomic Reference List of Pests in NHCs is based on the DTN taxon lists concept and architecture and described under About "Taxon list of pest organisms for IPM at natural history collections compiled at the SNSB". It includes four steps (A–D) with significant results for best practices of data processing (Fig. 1). A. The data is managed and processed for publication by list curators in the database DiversityTaxonNames (DTN). As a result, the list can be kept up-to-date and is—without transformation—ready to be used for IPM solutions at any NHC with a DiversityCollection installation and as part of the DWB cloud services. B. The up-to-date data is publicly available via the DTN REST Webservice for Taxon Lists with machine-readable Application Programming Interface (API). As a result, the dynamic list publication service can be used as a reference backbone for establishing IPM solutions for pest monitoring at any NHC. C. The data is provided via the GBIF checklist data publication pipeline of the SNSB through GBIF validation tools and Darwin Core Archive in DwC-A (zip format) for GBIF. As a result, the checklist information becomes part of the GBIF network with GBIF ChecklistBank and GBIF Global Taxonomy. This ensures future compliance of data with the Findability, Accessibility, Interoperability, and Reuse (FAIR) guiding principles. D. The DTN REST Web service for Taxon Lists (currently 60 lists) is registered and accessible through the German Federation for Biological Data (GFBio) Terminology service. As a result, the lists with external PIDs and other information are available as a service (see DTN lists overview). In the upcoming Research Data Commons of the German National Research Data Infrastructure (NFDI) Initiative (Diepenbroek et al. 2021), it will be part of a standardized layer of APIs with an agreed interface scheme for improved accessibility. The provided tools, API and data are part of the upcoming NFDI4Biodiversity service portfolio. Future scenarios include the use of the list items and properties as classes for diagnosis purposes with DiversityNaviKey (Triebel et al. 2021) including the publication of images for identifying pests.

Pathways Between National and European Research Infrastructures: A Humanities’ Perspective

In this poster we will discuss challenges, requirements and good practices for creating synergies and inter-connections between national and international research and research data infrastructures from the perspective of the humanities. Stakeholders of the NFDI consortium Text+, of the German national node of DARIAH, of the SSH Open Cluster and of related EOSC projects are presenting benefits and outcomes of these structures and their linkages for the actual research data management and for the researchers. How are research data repositories connected and researchers guided to all the various service offers? With an example about the tool registry in the SSH Open Marketplace reused by Text+ and connected to EOSC, we illustrate the overall picture of these infrastructures and their impact in practice.

Building the Next Generation of Data Savvy Biomedical Researchers

Modern research data management in biomedicine requires data literacy skills. The NFDI4Health consortium, the national research data infrastructure for personal health data, addresses this need with several different training concepts for clinical and epidemiological scientists. Both the institutional anchorage of the training and the thematic focus vary and can be assembled from a modular system as required. The aim is to enable multipliers to adapt and use the training modules through open educational resources. In addition, a competency profile for data stewards is under development to support the choice of required training in research institutions. The results will be fed into the NFDI “Education & Training” Section and will contribute to the Data Literacy Alliance in the future.

Towards a Research Data Commons in the German National Research Data Infrastructure NFDI: Vision, Governance, Architecture

The concept of a "Research Data Commons" (RDC) established itself as an infrastructure ecosystem for science based on open standards and federated resources to facilitate the sharing of research data and services. The consortia of the German National Research Data Infrastructure (NFDI) have identified the collaborative provisioning of resources and services to be of key importance for a functioning and efficient RDC and are leveraging different corresponding measures to establish a sustainable concept in line with international developments.

NFDI4Health Local Data Hubs for Finding and Accessing Health Data

To support federated data structuring and sharing for sensitive health data from clinical trial, epidemiological and public health studies in the context of the German National Research Data Infrastructure for Personal Health Data (NFDI4Health), we have developed Local Data Hubs (LDHs) based on the FAIRDOM-SEEK platform. Those LDHs connect to the German Central Health Study Hub (CSH) to make the health data searchable and findable. This decentralised approach supports researchers to make health studies with their data FAIR (Findable, Accessible, Interoperable and Reusable), and at the same time fully preserves data protection for sensitive data.

Establishing Adaptive Governance in NFDI Consortia:

There is widespread support for patient and public involvement (PPI) as an ethical requirement in biomedical research and policy development [1, 2], including research using personal health data, such as genomic data [3]. Sharing genomic and other personal health data is important for progress in health research [4], but poses ethical, legal and societal challenges for the governance of research and the institutions that conduct and support it. These challenges arise in part from the varied interests of the stakeholders involved, including patients, researchers, and funders [5]. In recent years, there has been a rise in collaborations between research institutions that conduct joint research programs. In these research consortia, the objectives and stakeholders in research are multiplied and more varied, making governance even more complex [6]. There has been debate about strengths and weaknesses of different forms of governance of research, with proponents of adaptive governance highlighting its responsiveness and flexibility to evolving goals and needs of stakeholders [5, 7]. However, there are no set standards for involving patients in the governance of health research. Moreover, as it is a highly context-specific process, cultural, legal and social contexts of the individual institutions or consortia must be considered. Within the German National Research Data Infrastructure (NFDI), there are consortia supporting research with personal health data, one of which is the German Human Genome-Phenome Archive (GHGA) [8]. We describe GHGA’s PaGODA Project (Patient Involvement in the Governance of an Omics Data Archive), whose goals are to gather patients’ views for their involvement in the governance of GHGA by conducting deliberative forums, and to implement these views. We focus here on the process of translating the findings from the forums into the GHGA governance policy, and identify procedural factors that were important for the process.

Leveraging Terminology Services for FAIR Semantic Data Integration Across NFDI Domains

The National Research Data Infrastructure (NFDI) strives to develop FAIR research data and data services for major scientific disciplines, using terminologies as a key factor for semantic annotations and semantic interoperability of data. Several NFDI consortia provide domain-specific terminologies through Terminology services or registries, offering access, search capabilities, visualization, and downloads. Prioritizing user-friendly access, terminology services seamlessly integrate semantic concepts into applications, often operating in the background to enable smooth semantic annotation and data interoperability. We present exemplary fields of application from selected disciplines and how terminology services support semantic search, user experience, annotation workflows, terminology curation and design.

Connecting the Dots

The Helmholtz Centers operate scientific-technical infrastructures that produce a high volume of digital research data, making Helmholtz a hub for expertise in research data. With the rapid digital transformation and growing volume of data, Helmholtz has implemented relevant policies and engaged in NFDI in various ways to manage and use research data effectively. The Helmholtz Information & Data Science Incubator, in particular the HMC and HIFIS platforms, contribute to these networking activities. The Helmholtz Open Science Office organized several internal forums for Helmholtz members to discuss NFDI, their findings will be presented at the Conference on Research Data Infrastructure (CoRDI). Helmholtz' multifaceted commitment to research data infrastructure is closely related to NFDI activities. The presentation at CoRDI will demonstrate how a large national research data ecosystem can be successfully connected to the NFDI network, highlighting opportunities for future collaboration.