Research Data Infrastructure Research Articles

MaRDI


 
 
 MaRDI is building a research data infrastructure for mathematics and be- yond based on semantic technologies (metadata, ontologies, knowledge graphs) and data repositories. Focusing on the algorithms, models and workflows, the MaRDI in- frastructure will connect with other disciplines and NFDI consortia on data processing methods, solving real world problems and support mathematicians on research data management.
 
 

We are Still Here, too! Research Data Management at Universities of Applied Sciences

The objective of the German non-profit association NFDI (German short form for ”National Research Data Infrastructure”) is to make the data stock of the entire German science system accessible to the public. To do so, it should involve all stakeholders. However, currently the Universities of Applied Sciences (UAS) are underrepresented in the NFDI, and there is a danger of neglecting their needs. Therefore, we present the project ”Research Data Management at Universities of Applied Sciences in the State of Rhineland-Palatinate” (FDM@HAW.rlp), which is funded by the German Federal Ministry of Education and Research (BMBF) and financed within the Recovery and Resilience Facility of the European Union. In the project, seven public UAS in Rhineland-Palatinate and the Catholic University of Applied Sciences (CUAS) Mainz follow a common goal: They intend to establish an institutional RDM within a period of three years by building up competencies at the UAS, setting up services for researchers and finding solutions for a common technical infrastructure.

Data Collections Explorer

There is a wide variety of archives, databases, and repositories currently available that provide access to research data. However, basic information about these systems is often difficult to gather, such as whether there are limits to the size of data sets that can be published or whether there is any publication fee that applies. In addition to that, there are plenty of research groups publishing their research data sets independently of these infrastructures, making it difficult for scientists to find them since they are not centrally registered. Research data must be easily discoverable and accessible for scientists to use it effectively. The Data Collections Explorer [1, 2], developed within the national research data infrastructure for the engineering sciences NFDI4Ing, is an easy-to-use information system addressing these needs. It is a low threshold information system that provides an overview of research data repositories, archives, databases as well as individually published data sets. Similar systems exist in other subject areas, for example the Data Repository Finder [3] focusing on the medical, life and social sciences. Contrary to the Data Collections Explorer, the Data Repository Finder only lists repositories.

Connecting National and International Data Infrastructures in Biodiversity Research

NFDI4Biodiversity is a consortium within the German National Research Data Infrastructure (NFDI) dedicated to data and services for biodiversity research and ecology. In the domain, well-developed international networks exist, with quite mature tools and data standards. These are used and disseminated in the NFDI4Biodiversity project in order to mobilise and publish data collected by national stakeholders according to the FAIR Guiding Principles for scientific data management and stewardship.The consortium partners provide methods and tools for archiving, publishing, searching and analysing data that are suitable for everyday use and have been tried and tested in practice. The consortium also functions as a forum for technical and legal matters of data and data-related workflows. In this way, the consortium is providing added value to the community regarding access to modern technologies and a comprehensive stock of biodiversity and environmental data.

RADAR: Building a FAIR and Community Tailored Research Data Repository

The research data repository RADAR is designed to support the secure management, archiving, publication and dissemination of digital research data from completed scientific studies and projects. Developed as a collaborative project funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) (2013-2016), the system is operated by FIZ Karlsruhe - Leibniz Institute for Information Infrastructure - and currently serves as a generic cloud service for about 20 universities and non-university research institutions. Since its launch, RADAR has witnessed significant changes in the landscape of research data repositories and the evolving needs of researchers, research communities and institutions. In our presentation within the “Enabling RDM” Track, we will show how RADAR is responding to these dynamic changes. In order to create a sufficiently large user base for the sustainable operation of the system, we have moved RADAR away from its previous single focus on a discipline-agnostic cloud service and towards a demand-driven functional optimisation. In 2021, we introduced an additional operating model for institutions (RADAR Local), where we operate a separate RADAR instance locally at the institution site exclusively using the institutional IT-infrastructure. In 2022 we opened up RADAR to new target groups with community-specific service offerings, in particular in the context of the National Research Data Infrastructure (NFDI). Beside the expansion of the functional scope, our ongoing development work focuses also on strengthening the system's support for the FAIR principles [1] and the concepts of FAIR Digital Objects (FDO) [2] and Schema.org. Our presentation will outline recent RADAR developments and achievements as well as future plans thus providing solutions and synergy potential for the scientific community and for other service providers.

FAIR Assessment Practices

The poster presents FAIR assessment experiences in the context of the two NFDI consortia KonsortSWD and BERD@NFDI, employing the established Research Data Alliance's FAIR Data Maturity Model (RDA-FDMM) and the F-UJI Tool, an automated solution. RDA-FDMM, a manual technique, is more comprehensive, while the automated F-UJI tool effectively detects areas of improvement in metadata presentation that automated means can address. Our experiences highlight the need to examine both machine-readable as well as non-machine-readable elements and acknowledge automated tools' limitations, while valuing their insights. As the research ecosystem advances, metadata representation should be made increasingly machine-readable. We recommend a "FAIR by design" approach from the beginning to ensure alignment with FAIR principles in project outcomes. Continuous assessments during a project’s lifetime promote ongoing research data infrastructure improvements within the NFDI consortia context, contributing to NFDI infrastructure innovation and optimization.

German Human Genome-Phenome Archive in an International Context: Toward a Federated Infrastructure for Managing and Analyzing Genomics and Health Data

With increasing numbers of human omics data, there is an urgent need for adequate resources for data sharing while also standardizing and harmonizing data processing. As part of the National Research Data Infrastructure (NFDI), the German Human Genome-Phenome Archive (GHGA) strives to connect the data from German researchers and their institutions to the international landscape of genome research. To achieve this, GHGA partners up with international activities such as the federated European Genome-Phenome Archive (EGA) [1] and the recently funded European Genomic Data Infrastructure (GDI) project to enable participation in international studies while ensuring at the same time the proper protection of the sensitive patient data included in GHGA.

The road to success: drawing parallels between 'road' and 'research data' infrastructures to foster understanding between service providers, funders and policymakers.

Background: The work of data research infrastructure operators is poorly understood, yet the services they provide are used by millions of scientists across the planet. Policy and implications: As the data services and the underlying infrastructure are typically funded through the public purse, it is essential that policymakers, research funders, experts reviewing funding proposals, and possibly even end-users are equipped with a good understanding of the daily tasks of service providers. Recommendations: We suggest drawing parallels between research data infrastructure and road infrastructure. To trigger the imagination and foster understanding, this policy brief contains a table of corresponding aspects of the two classes of infrastructure, and a table of policy implications. Conclusions: Just as economists and specialist evaluators are typically brought in to inform policies and funding decisions for road infrastructure, we encourage this to also be done for research infrastructures.

Die Nationale Forschungsdateninfrastruktur – eine Lösung infrastruktureller Bedarfe für die Inhaltsanalyse?

ZusammenfassungObwohl die Inhaltsanalyse eine zentrale Stellung in der Kommunikations- und Medienforschung besitzt, existieren kaum Forschungsinfrastrukturen für diese Methode. Gleichzeitig werden in Deutschland seit 2018 große Dateninfrastrukturen in den 27 Konsortien der Nationalen Forschungsdateninfrastruktur (NFDI) aufgebaut. In diesem Beitrag gehen wir aus Perspektive der Forschenden der Frage nach, inwiefern die NFDI-Konsortien Lösungen für die infrastrukturellen Anforderungen in Bezug auf Inhaltsanalysen bieten. Zunächst beleuchten wir diese Anforderungen entlang des Forschungsdaten-Lebenszyklus und identifizieren Leerstellen. Dann explorieren wir, welche Bedarfe die NFDI-Konsortien decken können. Der Schwerpunkt liegt auf Konsortien, die sich auf die Sammlung und Aufbereitung von Text oder multimodalen Daten konzentrieren: KonsortSWD, BERD@NFDI, Text+, NFDI4Memory, NFDI4Culture und NFDI4DataScience. Unsere Untersuchung zeigt, dass die Konsortien bereits viele der Bedarfe abdecken. Allerdings gibt es weder ein Konsortium, in dem Kommunikationswissenschaftler:innen treibende Kräfte sind, noch wird die Inhaltsanalyse explizit berücksichtigt. Wir diskutieren, wie sich Forschungsinfrastrukturen für die Inhaltsanalyse durch die NFDI-Strukturen weiterentwickeln ließen.

Fostering Excellent Research by the Austrian Micro Data Center (AMDC)

Abstract Access to high quality microdata is a precondition for the empirical investigation of many interrelationships in the economic and social sciences. Therefore, well-functioning research data infrastructure is a cornerstone of a successful science location. While other countries in Europe, such as Denmark and the Netherlands, have had microdata centres at their respective National Statistical Offices for quite some time, microdata access for research purposes in Austria was very limited for a long time. Established in 2022, the Austrian Micro Data Center (AMDC) at Statistics Austria enables researchers of accredited research institutions to work with pseudonymized microdata on individuals and firms. The available microdata includes not just microdata of Statistics Austria but also registry data of the Austrian federal government. The main novelty is that microdata can be linked deterministically to each other via unique pseudonymized identifiers among data sets of Statistics Austria, administrative registers, and also to microdata brought in by the researchers themselves. The AMDC is operated by Statistics Austria and its services are open to research institutions worldwide.

Method and Subject

Research activities in the social sciences and humanities have traditionally conceived digital outputs in terms of databases and websites. Practically, most of these are still implemented using SQL, table-based, data structures and synchronic web technologies. However, the constant evolution of software means that browser functionality and security arrangements are constantly changing. As a result, few digital research outputs, except published literature, remain accessible for more than a few years. Recent progress with research data infrastructures is described, which has the potential to improve the sustainability of research investments. This article presents new standards-based annotation techniques, developed in the biodiversity community, which have been applied to global history research questions. Open repository software platforms supporting this ‘scientific treatment’ approach can now generate technology-independent data resources – supporting long-term reuse by the global community. Promoting institutional change to adopt these developments is discussed, so that costs of data stewardship can be made forecast-able.

AI in Process Industries – Incubator Labs and Use Cases

AbstractNo abstract.

President Biden Chooses Monica M. Bertagnolli, MD, to Lead the NIH

President Biden Chooses Monica M. Bertagnolli, MD, to Lead the NIH

Research Data Management and Data Sharing for Reproducible Research-Results of a Community Survey of the German National Research Data Infrastructure Initiative Neuroscience.

Science is changing: the volume and complexity of data are increasing, the number of studies is growing and the goal of achieving reproducible results requires new solutions for scientific data management. In the field of neuroscience, the German National Research Data Infrastructure (NFDI-Neuro) initiative aims to develop sustainable solutions for research data management (RDM). To obtain an understanding of the present RDM situation in the neuroscience community, NFDI-Neuro conducted a comprehensive survey among the neuroscience community. Here, we report and analyze the results of the survey. We focused the survey and our analysis on current needs, challenges, and opinions about RDM. The German neuroscience community perceives barriers with respect to RDM and data sharing mainly linked to (1) lack of data and metadata standards, (2) lack of community adopted provenance tracking methods, (3) lack of secure and privacy preserving research infrastructure for sensitive data, (4) lack of RDM literacy, and (5) lack of resources (time, personnel, money) for proper RDM. However, an overwhelming majority of community members (91%) indicated that they would be willing to share their data with other researchers and are interested to increase their RDM skills. Taking advantage of this willingness and overcoming the existing barriers requires the systematic development of standards, tools, and infrastructure, the provision of training, education, and support, as well as additional resources for RDM to the research community and a constant dialogue with relevant stakeholders including policy makers to leverage of a culture change through adapted incentivization and regulation.

The road to success: drawing parallels between 'road' and 'research data' infrastructures to foster understanding between service providers, funders and policymakers.

Background: The work of data research infrastructure operators is poorly understood, yet the services they provide are used by millions of scientists across the planet. Policy and implications: As the data services and the underlying infrastructure are typically funded through the public purse, it is essential that policymakers, research funders, experts reviewing funding proposals, and possibly even end-users are equipped with a good understanding of the daily tasks of service providers. Recommendations: We suggest drawing parallels between research data infrastructure and road infrastructure. To trigger the imagination and foster understanding, this policy brief contains a table of corresponding aspects of the two classes of infrastructure. Conclusions: Just as economists and specialist evaluators are typically brought in to inform policies and funding decisions for road infrastructure, we encourage this to also be done for research infrastructures.

Difficulties of FAIR Principles Implementation in Cross-Domain Research Infrastructures

Continuously increasing complexity of scientific research lead to the growing need for close integration of heterogeneous scientific communities. Such integration can be provided by the development of cross-domain research data infrastructures. In this paper, problems of the effective organization of scientific data in research infrastructures, based on FAIR principles, are considered. Analysis of the representation of various subjects in the existing infrastructures used for highlighting the main difficulties facing researchers, the ways of its overcoming are considered.

Die deutsche Dateninfrastruktur aus Sicht der empirischen Wirtschaftsforschung

Abstract The paper discusses the state of the research data infrastructure in Germany and presents ongoing activities for its improvement. The text emphasizes the need for continued advocacy and engagement for the advancement of the German data infrastructure to catch up with neighboring countries. It is for the common good when we enable researchers to access and blend data from multiple sources: it improves the quality, timeliness, granularity, and usefulness of statistics, facilitates more rigorous social and economic research, and supports evidence based policymaking and program evaluation.

Old Wood in a New Light: An Online Dendrochronological Database

Abstract The Old Wood in a New Light database project focuses on the digitization and accessibility of the results of dendrochronological samples analyzed and archived at four Swedish university-based tree-ring laboratories at Lund University, Stockholm University, University of Gothenburg, and the Swedish University of Agricultural Sciences. Collaboration with the Environmental Archaeology Laboratory and Humlab at Umeå University enables long-term open access to data, raw data, and metadata. In this project, we (1) systematically undertake large-scale entry and open access publication of results from wood samples scientifically analyzed and archived by Swedish laboratories and the associated metadata, into the Strategic Environmental Archaeology Database (SEAD; www.sead.se) research data infrastructure, and (2) actively promote the database as a resource for new and ongoing interdisciplinary research initiatives. Including dendrochronological data in SEAD infrastructure allows interdisciplinary studies that combine major scientific and societal questions. Building on a pilot study of construction timber from southern Sweden and adaptation of SEAD digitization workflows, more than 70 000 samples archived at the four dendrochronological laboratories are now being handled in the project. The broad coverage of research networks, stakeholder interaction, and strategic support from the cultural heritage community is guaranteed owing to the ongoing collaboration between laboratories and an established international and multidisciplinary reference group.

The Leipzig Health Atlas-An Open Platform to Present, Archive, and Share Biomedical Data, Analyses, and Models Online.

Clinical trials, epidemiological studies, clinical registries, and other prospective research projects, together with patient care services, are main sources of data in the medical research domain. They serve often as a basis for secondary research in evidence-based medicine, prediction models for disease, and its progression. This data are often neither sufficiently described nor accessible. Related models are often not accessible as a functional program tool for interested users from the health care and biomedical domains. The interdisciplinary project Leipzig Health Atlas (LHA) was developed to close this gap. LHA is an online platform that serves as a sustainable archive providing medical data, metadata, models, and novel phenotypes from clinical trials, epidemiological studies, and other medical research projects. Data, models, and phenotypes are described by semantically rich metadata. The platform prefers to share data and models presented in original publications but is also open for nonpublished data. LHA provides and associates unique permanent identifiers for each dataset and model. Hence, the platform can be used to share prepared, quality-assured datasets and models while they are referenced in publications. All managed data, models, and phenotypes in LHA follow the FAIR principles, with public availability or restricted access for specific user groups. The LHA platform is in productive mode (https://www.health-atlas.de/). It is already used by a variety of clinical trial and research groups and is becoming increasingly popular also in the biomedical community. LHA is an integral part of the forthcoming initiative building a national research data infrastructure for health in Germany.

Current Developments in the Research Data Repository RADAR

Current Developments in the Research Data Repository RADAR