Research Data Infrastructure 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".

Adapting the open-source Gen3 platform and kubernetes for the NIH HEAL IMPOWR and MIRHIQL clinical trial data commons: Customization, cloud transition, and optimization

ObjectiveThis study aims to provide the decision-making framework, strategies, and software used to successfully deploy the first combined chronic pain and opioid use data clinical trial data commons using the Gen3 platform. Materials and MethodsThe approach involved adapting the open-source Gen3 platform and Kubernetes for the needs of the NIH HEAL IMPOWR and MIRHIQL networks. Key steps included customizing the Gen3 architecture, transitioning from Amazon to Google Cloud, adapting data ingestion and harmonization processes, ensuring security and compliance for the Kubernetes environment, and optimizing performance and user experience. ResultsThe primary result was a fully operational IMPOWR data commons built on Gen3. Key features include a modular architecture supporting diverse clinical trial data types, automated processes for data management, fine-grained access control and auditing, and researcher-friendly interfaces for data exploration and analysis. DiscussionThe successful development of the Wake Forest IDEA-CC data commons represents a significant milestone for chronic pain and addiction research. Harmonized, FAIR data from diverse studies can be discovered in a secure, scalable repository. Challenges remain in long-term maintenance and governance, but the commons provides a foundation for accelerating scientific progress. Key lessons learned include the importance of engaging both technical and domain experts, the need for flexible yet robust infrastructure, and the value of building on established open-source platforms. ConclusionThe WF IDEA-CC Gen3 data commons demonstrates the feasibility and value of developing a shared data infrastructure for chronic pain and opioid use research. The lessons learned can inform similar efforts in other clinical domains.

Rapid Deployment of a Pseudonymization Service in a Distributed Research Data Infrastructure - Lessons Learned.

The SARS-CoV-2 pandemic highlighted the importance of fast, collaborative research in biomedicine. Within the ORCHESTRA consortium, we rapidly deployed a pseudonymization service with minimal training and maintenance efforts under time-critical conditions to support a complex, multi-site research project. Over two years, the service was deployed in 13 sites across 11 countries to register more than 10,000 study participants and 15,000 biosamples. In this work, we present lessons learned as part of this process. Most importantly, we learned that common challenges can be overcome by creatively utilizing widely available tools and that having a dedicated partner to manage software rollout and pre-configure software packages for each site fosters the effective implementation.

Harmonizing Microneurography Metadata with Local Data Hubs: A Concept.

This work aims to improve FAIR-ness of the microneurography research by integrating the local (meta)data to existing research data infrastructures. In the previous work, we developed an odML based solution for local metadata storage of microneurography data. However, this solution is limited to a narrow community. As a next step, we propose the integration into the Local Data Hubs, data-sharing services within NFDI4Health infrastructure. We outline a first concept, that streams chosen data from the established odMLtables GUI.

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.

Dynamic consent: a royal road to research consent?

In recent years, the principle of informed consent has come under significant pressure with the rise of biobanks and data infrastructures for medical research. Study-specific consent is unfeasible in the...

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.

Analyzing research data repositories (RDR) from BRICS nations: a comprehensive study

PurposeThis study aims to examine the current status and different characteristic features of research data repositories established by BRICS nations in order to understand the research data infrastructure within BRICS countries.Design/methodology/approachThe data were collected from the re3data repository (http://www.re3data.org/), focusing specifically on BRICS nations. The data were analyzed to grasp the current status of research data repositories in BRICS countries. The dataset was retrieved on March 2, 2024. A total of 195 Research Data Repositories (RDRs) originating from BRICS countries were identified and selected for comprehensive analysis.FindingsAs of March 2, 2024, re3data.org indexes a total of 3,192 Research Data Repositories (RDRs) worldwide, with BRICS nations contributing 195. China leads among BRICS nations, followed by India, Russia, and Brazil. Scientific and Statistical Formats are the most shared content categories, followed closely by Standard Office Documents. There is notable diversity in the subjects covered by RDRs across BRICS nations. English is the primary interface language, followed by Chinese and Portuguese. “House, tailor-made” software is widely used for creating RDRs, followed by Dataverse and DSpace. Fourteen metadata standards are found, with Dublin Core metadata being the most prevalent, followed by the DataCite Metadata Scheme. Most repositories are disciplinary, followed by institutional ones. Most repositories specify data upload types, with “restricted” being the most common, followed by closed types. Open access is predominant in data access, followed by restricted access and embargo periods, while a small number restrict access entirely.Originality/valueThe present study will help gauge the strengths and weaknesses of the RDRs of BRICS nations and also learn how open these RDRs are for data access and upload provisions. The study contributes to a broader comprehension of the accessibility and usability of research data within the BRICS community, ultimately fostering greater transparency, collaboration, and knowledge dissemination within the scientific community, thus fostering greater innovation and advancement in research endeavors. The study provides a nuanced understanding of the research data infrastructure within BRICS nations, highlighting key trends, strengths, and areas for potential improvement.

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.

A new data infrastructure for family research and demographic analysis: the German Family Demography Panel Study (FReDA)

Abstract This data brief introduces the German Family Demography Panel Study (FReDA; https://www.freda-panel.de/), a longitudinal, multi-actor database for family research. Major substantive fields addressed in the questionnaire include fertility-related attitudes and behaviours, reproductive health, work-family conflict, couples’ division of labour, gender roles, intimate relationships, separation and divorce, parenting and intergenerational relations, and well-being. FReDA is based on two initially independent samples: the newly drawn FReDA-GGS sample (n_recruitment = 37,777 respondents, aged 18–49 years), constituting the German contribution to the Generations and Gender Surveys (GGS-II), and the FReDA-pairfam sample (n = 6,216 respondents who originally participated in the German Family Panel [pairfam]). Both samples are fully integrated, using one survey instrument consisting of the harmonized GGS-II and pairfam questionnaires. Mainly web-based interviews, complemented by paper-based interviews, are conducted biannually, with one wave being split across two subwaves. We provide a short description of FReDA’s forerunners—the GGS and pairfam—and give an overview of FReDA’s design and content, its baseline wave (collected in 2021) and data releases, as well as a brief outlook on FReDA’s road ahead.

New roles of research data infrastructure in research paradigm evolution

Abstract Research data infrastructures form the cornerstone in both cyber and physical spaces, driving the progression of the data-intensive scientific research paradigm. This opinion paper presents an overview of global research data infrastructure, drawing insights from national roadmaps and strategic documents related to research data infrastructure. It emphasizes the pivotal role of research data infrastructures by delineating four new missions aimed at positioning them at the core of the current scientific research and communication ecosystem. The four new missions of research data infrastructures are: (1) as a pioneer, to transcend the disciplinary border and address complex, cutting-edge scientific and social challenges with problem- and data-oriented insights; (2) as an architect, to establish a digital, intelligent, flexible research and knowledge services environment; (3) as a platform, to foster the high-end academic communication; (4) as a coordinator, to balance scientific openness with ethics needs.

What factors are associated with the research productivity of primary care researchers in Canada? A qualitative study

BackgroundResearch evidence to inform primary care policy and practice is essential for building high-performing primary care systems. Nevertheless, research output relating to primary care remains low worldwide. This study describes the factors associated with the research productivity of primary care researchers.MethodsA qualitative, descriptive key informant study approach was used to conduct semi-structured interviews with twenty-three primary care researchers across Canada. Qualitative data were analyzed using reflexive thematic analysis.ResultsTwenty-three primary care researchers participated in the study. An interplay of personal (psychological characteristics, gender, race, parenthood, education, spousal occupation, and support), professional (mentorship before appointment, national collaborations, type of research, career length), institutional (leadership, culture, resources, protected time, mentorship, type), and system (funding, systematic bias, environment, international collaborations, research data infrastructure) factors were perceived to be associated with research productivity. Research institutes and mentors facilitated collaborations, and mentors and type of research enabled funding success. Jurisdictions with fewer primary care researchers had more national collaborations but fewer funding opportunities. The combination of institutional, professional, and system factors were barriers to the research productivity of female and/or racialized researchers.ConclusionsThis study illuminates the intersecting and multifaceted influences on the research productivity of primary care researchers. By exploring individual, professional, institutional, and systemic factors, we underscore the pivotal role of diverse elements in shaping RP. Understanding these intricate influencers is imperative for tailored, evidence-based interventions and policies at the level of academic institutions and funding agencies to optimize resources, promote fair evaluation metrics, and cultivate inclusive environments conducive to diverse research pursuits within the PC discipline in Canada.

An Overview of Real-World Data Infrastructure for Cancer Research

AimsThere is increasing interest in the opportunities offered by Real World Data (RWD) to provide evidence where clinical trial data does not exist, but access to appropriate data sources is frequently cited as a barrier to RWD research. This paper discusses current RWD resources and how they can be accessed for cancer research. Materials and methodsThere has been significant progress on facilitating RWD access in the last few years across a range of scales, from local hospital research databases, through regional care records and national repositories, to the impact of federated learning approaches on internationally collaborative studies. We use a series of case studies, principally from the UK, to illustrate how RWD can be accessed for research and healthcare improvement at each of these scales. ResultsFor each example we discuss infrastructure and governance requirements with the aim of encouraging further work in this space that will help to fill evidence gaps in oncology. ConclusionThere are challenges, but real-world data research across a range of scales is already a reality. Taking advantage of the current generation of data sources requires researchers to carefully define their research question and the scale at which it would be best addressed.

User perspectives through cross-connections. The role of archives as part of the German digital research data infrastructure

PurposeStarting from the status quo, the paper outlines perspectives and challenges for the connection and interlinking of digitised and digital archival data. The following topics are addressed: Where are fields of action and what are the means of archives? Which functional and technical requirements are to be considered, and what is the role of portal infrastructures linking together various different institutions?Design/methodology/approachConsidering needs of users and general framework conditions, the paper examines new approaches emerging in Germany. It outlines recent projects and considerations aiming to improve services and visibility of archives within the national data infrastructure in Germany.FindingsCross-connections are no new phenomenon, but change their appearance significantly in a digital context. In this respect, both smaller and bigger archives profit from participation in larger digital networks. Furthermore, archives need to keep in mind to reflect the quality of their digital (meta)data regularly and to offer or join systems that functionally and technically support cross-connection and interlinking of data.Originality/valueThe paper endeavours to show the importance of digital cross-connections and the role of portal infrastructures for visibility, online-distribution and use of digital archival metadata and data.

Research data infrastructures in environmental sciences—Challenges and implementation approaches focusing on the integration of software components

AbstractResearch data infrastructures are quickly evolving and show a wide variety, for instance in the way they address user requirements and use cases as well as how they provide user‐required information through their software architecture. In this article, we discuss challenges and provide approaches to developing software architectures and software components for research data infrastructures in environmental sciences. Taking the GeoKur research project on harmonizing land use time series as the use case for curation and quality assurance of environmental research data, we designed, implemented, and tested approaches and software components with particular regard to data management planning as well as provenance and quality information management. We aim to illustrate how to better meet researchers’ needs and provide tightly interlinked software components.

Assessing the use of HL7 FHIR for implementing the FAIR guiding principles: a case study of the MIMIC-IV Emergency Department module.

To provide a real-world example on how and to what extent Health Level Seven Fast Healthcare Interoperability Resources (FHIR) implements the Findable, Accessible, Interoperable, and Reusable (FAIR) guiding principles for scientific data. Additionally, presents a list of FAIR implementation choices for supporting future FAIR implementations that use FHIR. A case study was conducted on the Medical Information Mart for Intensive Care-IV Emergency Department (MIMIC-ED) dataset, a deidentified clinical dataset converted into FHIR. The FAIRness of this dataset was assessed using a set of common FAIR assessment indicators. The FHIR distribution of MIMIC-ED, comprising an implementation guide and demo data, was more FAIR compared to the non-FHIR distribution. The FAIRness score increased from 60 to 82 out of 95 points, a relative improvement of 37%. The most notable improvements were observed in interoperability, with a score increase from 5 to 19 out of 19 points, and reusability, with a score increase from 8 to 14 out of 24 points. A total of 14 FAIR implementation choices were identified. Our work examined how and to what extent the FHIR standard contributes to FAIR data. Challenges arose from interpreting the FAIR assessment indicators. This study stands out for providing a real-world example of a dataset that was made more FAIR using FHIR. To the best of our knowledge, this is the first study that formally assessed the conformance of a FHIR dataset to the FAIR principles. FHIR improved the accessibility, interoperability, and reusability of MIMIC-ED. Future research should focus on implementing FHIR in research data infrastructures.

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.