Proceedings of the Conference on Research Data Infrastructure
Research data form the basis for knowledge and innovation throughout all scientific disciplines. They play a fundamental role in the progress of our society. The key to using these data treasures is an effective infrastructure. With the first edition of the Conference on Research Data Infrastructure from 12 to 14 September 2023, the Association German National Research Data Infrastructure (NFDI) is initiating a conference that will focus on establishing interdisciplinary research data management (RDM).
TIB Open Publishing (Technische Informationsbibliothek (TIB))
Digitalization in Catalysis and Reaction Engineering: Automatizing Work Flows
Digitalization and surrounding efforts fostered by the advent of Industry 4.0 have been a development topic in the fields of chemistry and chemical engineering for several years – so timing may be right to look back and to question what the impact of efforts made, and to judge the impact of workflows and technologies developed. First and foremost, the first two key principle of Industry 4.0, namely Interconnectivity and Information Transparency play the crucial role in the context of Digitalization as only a seamless flow of data based on standardized formats remains key to enabling Decentralized Decisions and Technical Assistance. Digitization and digital tools play a key role in the acceleration data transfer and development efforts, the automation and autonomation of technical equipment employed, and the digital transformation of “classical” chemical reaction engineering processes towards an ideal originally projected by the high-tech agenda of Industry 4.0. In this presentation we will start our journey with fully integrated environments on a laboratory level where data are not only made available in data warehouses but can be used to drive feedback loops to autonomously drive experimental devices.
Copyright (c) 2023 Rinu Chacko, Hendrik Gossler, Johannes Riedel, Stephan Schunk, Olaf Deutschmann https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2023 Philipp Ost, Yusra Shakeel, Philipp Tögel https://creativecommons.org/licenses/by/4.0
As a partner in several NFDI consortia, the Bundesanstalt für Materialforschung und -prüfung (BAM, German federal institute for materials science and testing) contributes to research data standardization efforts in various domains of materials science and engineering (MSE). To implement a central research data management (RDM) infrastructure that meets the requirements of MSE groups at BAM, we initiated the Data Store pilot project in 2021. The resulting infrastructure should enable researchers to digitally document research processes and store related data in a standardized and interoperable manner. As a software solution, we chose openBIS, an open-source framework that is increasingly being used for RDM in MSE communities. The pilot project was conducted for one year with five research groups across different organizational units and MSE disciplines. The main results are presented for the use case “nanoPlattform”. The group registered experimental steps and linked associated instruments and chemicals in the Data Store to ensure full traceability of data related to the synthesis of ~400 nanomaterials. The system also supported researchers in implementing RDM practices in their workflows, e.g., by automating data import and documentation and by integrating infrastructure for data analysis. Based on the promising results of the pilot phase, we will roll out the Data Store as the central RDM infrastructure of BAM starting in 2023. We further aim to develop openBIS plugins, metadata standards, and RDM workflows to contribute to the openBIS community and to foster RDM in MSE.
Copyright (c) 2023 Rukeia El-Athman, Jörg Rädler, Oliver Löhmann, Angela Ariza, Thilo Muth https://creativecommons.org/licenses/by/4.0
Current Insights From Task Area 1 in NFDI4Energy: Building and Serving the Energy Research Community
Energy system research has become increasingly reliant on modeling and simulation approaches. These endeavors are enabled by continuously improving tools and methods for developing, maintaining and sharing models and data. Knowledge of how to better conduct, share and archive one’s research has become increasingly complex and hard to manage for individual researchers or single research groups. Identifying and including relevant scientists from energy research, social sciences, and further disciplines is sometimes difficult. Furthermore, a plethora of best practices and guidelines exist on how to prepare data, models and results in ways that make them easier to discover, verify and build upon. To present a sustainable, problem-solving, technical solution for the energy research community, NFDI4Energy develops in Task Area 1 (TA1) two services of the NFDI4Energy platform. Consequentially, the intention of this abstract within the disciplinary track “Engineering” is to provide an overview of the development process with a special focus on Competence and Best Practices. In addition, it discusses interconnections with other Task Areas as well as the chances and challenges that are associated with those connections. The academic audience, e.g., from the Information Systems and (Software) Engineering domain at CoRDI 2023, can observe and discuss our proposed procedure with other community members. Furthermore, we expect interested individuals to compare the proposed procedures with their own, which can lead to meaningful discussions and knowledge-sharing situations within the Engineering domain.
Copyright (c) 2023 Oliver Werth, Stephan Ferenz, Astrid Niesse, Reinhard German, Ludwig Huelck, Christof Weinhardt, Berthold Vogel https://creativecommons.org/licenses/by/4.0
Coscine.nrw Landesweite Basisversorgung zur Verwaltung von Forschungsdaten im Open Source Modell
An der RWTH Aachen wird seit 2018 die Forschungsdatenplattform Coscine als Open-Source-Software entwickelt und für die Verwaltung von Forschungs(meta)daten, sowie zur Kontingentierung und Provisionierung von Speicherressourcen für Forschungsdaten eingesetzt. Coscine ist dazu gemäß der FAIR-Prinzipien entwickelt und implementiert Schnittstellen für die sog. FAIR Digital Objects. Für Forschende bietet Coscine den Zugriff auf alle Forschungsdaten eines Forschungsprojekts, die Verknüpfung mit projekt- oder fachspezifischen Metadaten sowie der Verwaltung von Projektmitgliedern. Dank des niederschwelligen Zugangsmanagements kann Coscine als Kollaborationsplattform über Hochschulgrenzen hinaus verwendet werden. In die Entwicklung von Coscine fließen zudem die Erkenntnisse und Anforderungen aus nationalen (NFDI, NHR) und internationalen Vorhaben (EOSC, gaia-x, RDA) ein und ermöglichen sowohl eine fachspezifische als auch eine fachübergreifende Verwendung der Plattform. Im Rahmen des Serviceangebots Coscine.nrw wird die Software Coscine für alle Hochschulen der DH.NRW zur Verfügung gestellt.
Copyright (c) 2023 Marius Politze, Ilona Lang, Katja Jansen https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2023 Frank Meineke, Martin Golebiewski, Xiaoming Hu, Toralf Kirsten, Matthias Löbe, Sebastian Klammt, Ulrich Sax, Wolfgang Müller https://creativecommons.org/licenses/by/4.0
RDMkit: The Research Data Management Toolkit for Life Sciences
Effective data management extends over the entire life cycle of data, from the point of creation through to dissemination and archiving, and usually continues long after a research project is concluded. Tailored guidance for management of research data is increasing its importance among data-driven scientific investigations. There is now increasing demand for Research Data Management (RDM), by funders and host institutions, for researchers to develop and implement data management plans for projects. The RDMkit, by ELIXIR Europe, is an open community-led resource designed to share knowledge on how to carry out RDM in life sciences research. It fills the training and learning gap by providing knowledge on domain-focused RDM considerations, task-focused solutions and resources, and tool assemblies that showcase real-life examples addressing how combinations of tools can be used to go through the data life cycle. The technical infrastructure of the RDMkit is reproducible, making it possible for other organisations to use the structure as a guide and inspiration to set up RDM source for their users. In this talk, RDMkit will be introduced with example best practice guidelines, and integrations and knowledge sources.
Copyright (c) 2023 Nazeefa Fatima, Pinar Alper, Frederico Bianchini, Korbinian Bösl, Ulrike Wittig, Carole Goble, Frederik Coppens https://creativecommons.org/licenses/by/4.0
Open Science Best Practices in Data Science and Artificial Intelligence
In the past years, scientific research in Data Science and Artificial Intelligence has witnessed vast progress. The number of published papers and digital objects (data, code, models) is growing exponentially. However, not all of these research artifacts are findable, accessible, interoperable and reusable (FAIR), contributing to a rather low level of reproducibility of the experimental findings reported in scholarly publications (reproducibility crisis). In this paper, we focus on Data Science and Artificial Intelligence Open Science best practices, i.e., a set of recommendations that eventually contribute to the management and development of digital artefacts that are as FAIR as possible. While several guidelines exist, we add best practices for the FAIR collection, processing, storing and sharing of scholarly findings via Research Knowledge Graphs. The final list of recommendations will be openly available on the NFDI4DS webpage as an interactive web application.
Copyright (c) 2023 Ekaterina Borisova, Raia Abu Ahmad, Georg Rehm https://creativecommons.org/licenses/by/4.0
On the Design and Implementation of Easy Access to External Spatiotemporal Datasets in NFDI
Across many scientific domains, the ability to process large amounts of heterogeneous spatiotemporal data from various sources is crucial for solving challenging research questions. For example, in NFDI4Biodiversity, researchers need to combine observation data with satellite images to correlate the loss of biodiversity with climate change variables. In general, large data sets are not available on the system (called consumer) where the processing is performed, but first have to be retrieved from one or multiple external systems (called providers) that offer a corresponding service. Moreover, a consumer is often unaware of the datasets the providers offer. Ideally, a provider follows FAIR principles and thus supports mechanisms to greatly simplify the data exchange. However, in practice, there are multiple providers with valuable datasets that are not as FAIR as desired or lack spatiotemporal-specific support for data exchange. Instead of improving each potential provider at the source, we propose an intermediary spatiotemporal data exchange layer (SDExL) that helps simplify data exchange so that domain experts easily gain access to valuable data with little technical know-how.
Copyright (c) 2023 Christian Beilschmidt, Dominik Brandenstein, Johannes Drönner, Nikolaus Glombiewski, Michael Mattig, Bernhard Seeger https://creativecommons.org/licenses/by/4.0
The exponential growth of scientific data has led to an increasing demand for effective data management and storage solutions. Academic computing infrastructures are often fragmented, which can make it challenging for researchers to leverage cloud-native principles and modern data analysis tools. To address this challenge, a new distributed storage platform called Aruna Object Storage (AOS) was developed. AOS is a cloud-native, scalable, and domain-agnostic object storage system that provides an S3-compatible interface for a variety of data analysis tools like Apache Spark, TensorFlow, and Pandas. The system uses an underlying distributed NewSQL database to manage detailed information about its resources and can be deployed across multiple data centers for geo-redundancy. AOS is designed to support modern DataOps practices, including the adoption of FAIR principles. Resources in AOS are organized into Objects, Datasets, Collections and Projects, which represent relations of data objects. Additionally, these can be further annotated with key-value pairs called Labels and Hooks to provide additional information about the data. The system's event-driven architecture makes it easy to automate actions and enforce data validation checks, significantly improving accessibility and reproducibility of scientific results. AOS is open source and freely available via https://aruna-storage.org.
Copyright (c) 2023 Marius Dieckmann, Sebastian Beyvers, Jannis Hochmuth, Anna Rehm, Frank Förster, Alexander Goesmann https://creativecommons.org/licenses/by/4.0
Research infrastructures enabling scalable sharing of data are proliferating, at the institutional, project/consortium and national/international levels. Many of these are domain-specific, but there also is a growing focus on Research Commons that enable general data sharing across domains. Examples include the EUDAT Collaborative Data Infrastructure in Europe, the Gakunin RDM platform in Japan, and the Research Commons planned by Canada’s Digital Research Alliance (DRA). In some cases, such as Gakunin RDM, Research Commons are architected as an integrated set of complementary services, but in most existing and planned Research Commons a disparate set of unconnected services is offered. This paper describes the integration between RSpace, an active content management digital research platform, and iRODS, a policy-driven data management platform, and explains how it is designed to serve as a flexible connecting component that ties together other services that make up a Research Commons. The paper discusses how, by tying together otherwise unconnected services, inclusion of RSpace + iRODS in Research Commons enables streamlined flows of data and metadata between services, enhancing FAIR principles. This will be illustrated by considering inclusion of RSpace + iRODS in the two specific examples of Canada’s proposed Research Commons and the EUDAT Collaborative Data Infrastructure. In both cases the interaction between RSpace, iRODS, and individual services provided by the Commons will be discussed, and a comparison will be made between the two Commons and the benefits derived from the inclusion of RSpace + iRODS.
Copyright (c) 2023 Rory Macneil, Terrell Russell https://creativecommons.org/licenses/by/4.0
Research Data Publication at Large Scale
The MPCDF is the high performance computing center of the Max Planck Society. Beside hosting compute clusters, around 300 petabytes of research data are stored at the MPCDF. Many of these datasets have a size of several terabytes up to petabytes and are stored over a heterogeneous landscape of storage systems. The datasets are covering a wide variety of scientific disciplines, many different data formats and access restrictions. For these reasons, it is not possible to offer one centralized data publishing solution for the datasets. Instead, the MPCDF developed a flexible data publishing concept.
Copyright (c) 2023 Thomas Zastrow, Nicolas Fabas https://creativecommons.org/licenses/by/4.0
The HMC Information Portal for Enhanced Metadata Collaboration in the Helmholtz FAIR Data Space
The Helmholtz Metadata Collaboration (HMC) platform was launched in late 2019 to turn FAIR (Findable, Accessible, Interoperable, Reusable) research data into reality within the Helmholtz Association and beyond. The Information Portal was initiated to enable the structured cartography of metadata and FAIR landscape of Helmholtz, providing information for multi-level decision-making and creating a curated knowledge base for research data managers, scientists and other stakeholders. Developed through a top-down approach, 18 categories, and associated metadata schemas were defined and aligned by an HMC taskforce. Data curation followed, with resources collected from different domains based on the aligned metadata schema. The Information Portal is a web application for capturing FAIR data practices across all Helmholtz domains, offering a unified user interface for collecting and exploring results. Built using state-of-the-art technologies, including Python and Docker, the Information Portal leverages GitLab as a database. It offers a public / central read-only version for stakeholders and a personal instance for curation - synchronized to a GitLab repository. Git-based systems offer advantages, such as raw data accessibility, flexible data curation, easy synchronization, and customizable repositories. The single-page web application is user-friendly and developed in multiple iterations for an intuitive and flexible interface. The Information Portal is crucial for creating a sustainable, distributed, semantically enriched Helmholtz data space, promoting seamless data sharing and reuse.
Copyright (c) 2023 Lucas Kulla, Jens Bröder, Constanze Curdt, Markus Kubin, Helen Kollai, Christine Lemster, Marco Nolden, Kai Schmieder, Annika Strupp, Karl-Uwe Stucky, Emanuel Söding, Konstantin Pascal Walter, Arndt Witold https://creativecommons.org/licenses/by/4.0
Born-fair Data Projects Using Cookiecutter Templates
Implementing research data management best practices and FAIR principles (Wilkinson et al., 2016) is vital for transparent, reproducible research, as well as efficient, sustainable science that avoids duplication of effort. Scientists can also benefit directly from incorporating data management into their data collection and analysis workflows. However, there is an initial cost to adoption that poses a burden and substantial barrier to entry even to well-intentioned researchers. In our experience in statistical and RDM-focused consulting, this cost increases as a project progresses, with a late-stage conversion being the most costly in terms of resources and energy because a working analysis needs to be adapted in one step. Therefore, we believe that it is useful to adopt best practices for data stewardship early-on in a project, and ideally from the get-go. In this contribution, we present a tool for creating and instantiating project templates that conform to good practices with regard to the data management and analysis projects more generally. In the same vein as "born-open data" (Rouder, 2016) where data is published immediately upon collection, our goal is to establish born-FAIR datasets that implement proven methods for data stewardship as early on in the research data lifecycle as is feasible. Our aim is to encourage researchers and analysts to incorporate best practices into their workflows from the onset by providing data and analysis templates that implement desirable properties. By adopting these templates, researchers immediately gain access to a number of tools that simplify their work and make it more efficient, while also providing a foundation for increased reproducibility, data documentation through codebooks, as well as metadata for long-term archival. Because a broad-strokes approach may not work in practice due to idiosyncrasies of individual research projects, one size may not fit all. For this reason, the templates contain customisation options that researchers can use to tailor the templates to their requirements. The templates build on the well-established cookiecutter library for the Python programming language (Greenfeld et al., 2022), which we additionally extend to R, a programming language somewhat more common among statisticians and social scientists, thereby creating a cross-platform infrastructure. Both libraries create a project skeleton with a pre-specified directory structure, and include configuration for commonly used tools. Upon template creation, a wizard guides users through a customisation step, allowing them to adapt templates to their needs and catering to the demands of a project at hand. Owing to the open-source nature of the project and the firmly established and well-documented standard, researchers can easily adapt templates and create their own, to accommodate their specific needs and domain requirements. We hope to foster a community of researchers who share and improve their workflows, and anticipate further uses of the templates for teaching and other purposes. At CoRDI, we hope to introduce our project to the wider NFDI community and propose it as a lightweight, interoperable and interdisciplinary standard, benefiting all researchers across domains. By streamlining advanced users' workflows, and making reproducible practices more accessible, we aim to enable and facilitate the uptake of RDM across the communities represented there, and build integrations to interoperate with the multitude of services currently under development and in use. To summarise, we introduce templates for data analysis and archival that researchers can apply themselves, to render possible and encourage better practices during analysis, and prepare data for long-term storage and later re-use. Our hope is to encourage more researchers to adopt RDM best practices more frequently and earlier in projects, demonstrating the value of a more structured workflow and facilitating a shift to FAIR principles more generally.
Copyright (c) 2023 Felix Henninger https://creativecommons.org/licenses/by/4.0
NFDI is a German initiative to set up research data infrastructures across all disciplines. Within NFDI, Base4NFDI is a unique joint effort of all NFDI consortia to develop and deploy NFDI-wide basic services. Within this talk, we will give an overview of Base4NFDI, especially its structures and emerging work program, and inform about ways to participate and contribute ideas for potential basic services.
Copyright (c) 2023 Sonja Schimmler, Reinhard Altenhöner, Lars Bernard, Juliane Fluck, Axel Klinger, Soeren Lorenz, Brigitte Mathiak, Bernhard Miller, Raphael Ritz, Thomas Schörner-Sadenius, Alexander Sczyrba, Regine Stein https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2023 Michael Diepenbroek, Ivaylo Kostadinov, Bernhard Seeger, Frank Oliver Glöckner, Marius Alfred Dieckmann, Alexander Goesmann, Barbara Ebert, Sonja Schimmler, York Sure-Vetter https://creativecommons.org/licenses/by/4.0
The increasing amount of data generated by scientific research poses the challenge of providing an adequate infrastructure and tools that facilitate FAIR (Findable, Accessible, Interoperable and Reusable) data access, manipulation, analysis and visualization. Often, the burden of managing the metadata associated with the original data and the analysis lies with the researchers. The open source Galaxy platform [1] is well-known for supplying tools and workflows for reproducible and transparent data analysis across scientific disciplines. It is a multi-user environment which facilitates sharing of e.g. tools, workflows, notebooks, visualizations, and data with others. There are three large Galaxy instances (US, Europe [2] and Australia) used by hundreds of thousands of researchers worldwide and that are using PBs of data. Galaxy handles the metadata transparently, releasing scientists from the burden and making it less prone to human errors. These features can be used without technical background by using a web browser or, for experts, through the Galaxy API.
Copyright (c) 2023 Sebastian Schaaf, Anika Erxleben-Eggenhofer, Bjoern Gruening https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2023 Felix Bach, Kerstin Soltau, Sandra Göller, Christian Bonatto Minella, Stefan Hofmann https://creativecommons.org/licenses/by/4.0
Toward the Development of NII RDC Application Profile Using Ontology Technology
In recent years, there has been international progress in developing platforms that support the reproducibility and reusability of research data. Typical platforms adopt a service architecture integrating multiple information systems to cover the entire research data lifecycle. In realizing this architecture, specifications for inheriting processes and results executed on different information systems play an essential role. This study introduces our practices for application profile development using ontology technology in the NII Research Data Cloud.
Copyright (c) 2023 Yasuyuki Minamiyama, Masaharu Hayashi, Ikki Fujiwara, Jun-ichi Onami , Shigetoshi Yokoyama, Yusuke Komiyama, Kazutsuna Yamaji https://creativecommons.org/licenses/by/4.0
LabIMotion Electronic Lab Notebook as Research Data Management Tool in Catalysis
In the field of heterogenous catalysis, Electronic Lab Notebooks (ELNs) are only rarely employed, due to complex data structure and different needs of the community with respect to the typical features provided by wide-spread ELNs. On the contrary, LabIMotion, an extension of the open-source ELN Chemotion, adapts to the characteristic complex workflows in heterogenous catalysis; these encompass catalyst synthesis, adaptation of devices and testing rigs, activity measurements, material characterization (possibly also in situ/operando, at large scale facilities) and are complemented by mathematical modelling and simulation. Direct links to metadata catalogues like SciCat (for synchrotron/neutron characterizations) and advanced research data management tools like Adacta (for improved traceability of catalytic data, experimental setups and related resources) are envisioned. The adaptability of LabIMotion in the catalysis field is presented via the topical examples of Cu-based catalysts for methanol synthesis and noble metal-based emission control catalysts.
Copyright (c) 2023 Paolo Dolcet, Mariam L. Schulte, Florian Maurer, Nicole Jung, Rinu Chacko, Olaf Deutschmann, Jan-Dierk Grunwaldt https://creativecommons.org/licenses/by/4.0
Research Data Management Curriculum of the Research Data Services at the University Library Duisburg-Essen
Reproducible and transparent science requires good research data management (RDM). However, researchers are often not familiar with how to appropriately handle research data, publication options, legal aspects, and the multitude of available software tools. To support scientists at the University of Duisburg-Essen in gaining RDM knowledge and competencies, the Research Data Services developed a structured teaching program – the RDM Curriculum – for researchers from different disciplines and career levels. The RDM Curriculum is separated into a basic and an advanced module. In the basic module, participants can select from three full-day courses that address the basics of RDM with different disciplinary focus. The advanced module consists of various 2 hour online hands-on courses on diverse RDM topics and tools. As incentive to attend multiple courses, participants can earn the RDM Badge of the University Library Duisburg-Essen. The curriculum program is complemented by specialized RDM courses and self-learning materials. With the RDM Curriculum, we created an easily accessible and individually selectable RDM course offer that enables researchers to gain discipline-specific and individually relevant RDM knowledge. The curriculum structure can gradually be expanded to further target groups (e.g. students) and allows for continuously adding new contents in the advanced module. The high request for the RDM Badge after the first iteration of the RDM Curriculum in the winter semester 2022/2023 confirmed the positive effect of incentives. Consequently, we are planning to establish a Data Champion Award as appreciation of RDM achievements and to showcase best-practice examples at the University of Duisburg-Essen.
Copyright (c) 2023 Sophia Leimer, Sonja Hendriks, Lisa Korte, Jessica Stegemann, Sarah Ann Stock, Henning Timm, Stephanie Rehwald https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2023 Manuela Richter, Dr. Johannes Putzke, Dr. Thomas M. Schimmer, Prof. Dr. Anett Mehler-Bicher https://creativecommons.org/licenses/by/4.0
The Galaxy Project [1][2] is a widely-used open-source platform for data-driven research that offers an extensive suite of tools and services for analyzing and visualizing large-scale data. Besides Galaxy as data analysis framework, the Galaxy Training Network (GTN; [3][4]) provides a huge collection of training material for researchers, developers and trainers. Among the various features offered by the Galaxy platform, the GTN and the Training Infrastructure as a Service (TiaaS; [5]) stands out as an innovative solution for delivering scalable and flexible training to researchers and educators. TIaaS is built on top of the Galaxy platforms which enable users to request dedicated compute resources for training purposes. This infrastructure, in combination with GTN, provides an excellent solution for delivering high-quality training to researchers and educators around the world, regardless of their geographical location or hardware limitations. One of the key benefits of the GTN is its ability to enable users to create and share training materials for a variety of data analysis workflows, including e.g. genomics, proteomics, and metabolomics but also for imaging and climate data. These training materials can be delivered in various formats, including interactive tutorials, videos, and webinars, and can be accessed from any location with an internet connection. Moreover, the platform offers a wide range of community-contributed training materials that cover a vast array of topics in biomedical research and beyond, making it an invaluable resource for researchers and educators. To use TIaaS, instructors simply need to register their training event in a simple form to get access to the dedicated compute resources.
Copyright (c) 2023 Anika Erxleben-Eggenhofer, Bérénice Batut, Teresa Müller https://creativecommons.org/licenses/by/4.0
Der Zertifikatskurs „Forschungsdatenmanagement“ als Blaupause für die FDM-bezogene Kompetenzentwicklung im Rahmen der NFDI
Der Zertifikatskurs Forschungsdatenmanagement hat sich als erstes strukturiertes und berufsbegleitendes Weiterbildungsangebot im Bereich des FDM-Kompetenzaufbau etabliert. Als Grundlage für eine Weiterentwicklung in der NFDI rückt vor allem dessen disziplinspezifische Adaption durch die Fachkonsortien in den Fokus. Dies geschieht u. a. im Rahmen der Sektion EduTrain. Neben der konzeptionellen Weiterentwicklung spielt das Ausrollen des Zertifikatskurses über die NFDI oder andere Akteure, wie die Landesinitiativen, aktuell eine große Rolle. Hiermit soll dem nachgewiesenen Bedarf an qualifizierten Personal im Forschungsdatenmanagement gemeinsam begegnet werden.
Copyright (c) 2023 Benjamin Slowig, Mirjam Blümm, Konrad U. Förstner, Birte Lindstädt, Rabea Müller, Marvin Lanczek https://creativecommons.org/licenses/by/4.0
Building Research Data Management (RDM) Expertise and Training Resources in ELIXIR Nodes
Research data Management (RDM) is central to the implementation of the FAIR (Findable Accessible, Interoperable, Reusable) and Open Science principles. Recognising the importance of RDM, the ELIXIR-CONVERGE project was launched in 2020 and included a work package on Training & Capacity Building in Data Stewardship, in which 21 Nodes participate. Since there is a clear need for dissemination of RDM know-how, practices and resources and a demand for RDM training material and courses, for researchers, trainers and RDM professionals, the aim of the work package was to build a comprehensive, interconnected and sustainable RDM Training Portfolio across all Nodes, and to increase the RDM training expertise and capacity in ELIXIR Nodes. RDM professionals, training experts and trainers from ELIXIR and beyond identified gaps in the Nodes’ RDM training programmes and defined priority topics (DMP, Data Stewardship, FAIR/metadata and Reproducibility) (Cardona et al, 2021) for training development. Collaborative work from all the ELIXIR Nodes during ELIXIR-CONVERGE resulted in an extensive Data Management/Data Stewardship (DM/DS) course portfolio (Cardona et al, 2021) which aligns with the Nodes’ RDM training strategies. The portfolio includes both generic DM/DS resources and materials, as well as specific materials related to the priority topics mentioned above, and it also includes training materials for specific domains (e.g. the Plant Demonstrator). The course portfolio is available in TeSS, in which training materials are tagged with RDM terms to increase their findability, and links to relevant training materials were included in RDMkit. In collaboration with the ELIXIR Training Platform, the learning paths methodology has been applied to RDM for data stewards and researchers (e.g. with the Plants Sciences and the System Biology Communities), and work is ongoing to extend the ELIXIR-GOBLET Train-the-Trainer programme to tackle the challenges that exist in teaching RDM. Moreover, during ELIXIR-CONVERGE, a strong RDM trainers network was formed, establishing solid RDM training expertise within the Nodes. This emerging network raised great interest in the Nodes, showing their willingness to work together on developing training materials, sharing their knowledge, and establishing best practices and standards for RDM training. The main RDM training related actions in ELIXIR going forward are: (i) gathering the RDM courses in the portfolio into learning paths for specific audiences, thus shaping the ELIXIR RDM curriculum, (ii) increasing findability and reusability of the developed courses and modules by optimising annotation and linking in TeSS, RDMkit and FAIR Cookbook, (iii) encouraging (re)use of the RDM materials in ELIXIR Nodes, and (iv) increasing RDM training capacity in Nodes by finalising and launching the RDM Train the Trainer programme. In 2023, the ELIXIR RDM Community was initiated, and the network of RDM trainers will be an integral part of this Community. This will enable the upskilling of RDM trainers and scaling up RDM training capacity in the Nodes, while bridging to RDM professionals outside ELIXIR as well.
Copyright (c) 2023 Celia W.G. van Gelder, Alexia Cardona, Brane Leskosek, Patricia Palagi https://creativecommons.org/licenses/by/4.0
RDM in Chemistry: How to Educate and Train Future Researchers to Manage Their Data
For in-depth research data management in chemistry, a cultural change is inevitable. To foster this change, future researchers need to be educated accordingly. The presentation will provide an overview of the first teaching approaches in student courses in chemistry at RWTH Aachen University. On the long range, the integration into curricular teaching is key to the cultural change.
Copyright (c) 2023 Jochen Ortmeyer, Fabian Fink, Alexander Hoffmann, Sonja Herres-Pawlis https://creativecommons.org/licenses/by/4.0
Experiences From FAIRifying Community Data and FAIR Infrastructure in Biomedical Research Domains
FAIR data is considered good data. However, it can be difficult to quantify data FAIRness objectively, without appropriate tooling. To address this issue, FAIR metrics were developed in the early days of the FAIR era. However, to be truly informative, these metrics must be carefully interpreted in the context of a specific domain, and sometimes even of a project. Here, we share our experience with FAIR assessments and FAIRification processes in the biomedical domain. We aim to raise the awareness that “being FAIR” is not an easy goal, neither the principles are easily implemented. FAIR goes far beyond technical implementations: it requires time, expertise, communication and a shift in mindset.
Copyright (c) 2023 Dagmar Waltemath, Esther Inau, Lea Michaelis, Venkata Satagopam, Irina Balaur https://creativecommons.org/licenses/by/4.0
RDM Compas: Building Competencies for the Professional Curation of Research Data
“RDM Compas: Research Data Management Competence Base”, consisting of an education and training centre with modular online trainings on the one hand, and a comprehensive knowledge base covering all topics of curation-specific RDM on the other hand. In addition, a certification option is envisioned for curation-specific RDM competencies. In the context of our presentation, due to the advanced state of work we want to focus on the knowledge base and present its structure and elements. The structural basis is a slightly simplified version of the Data Curation Lifecycle Model [2] offered by the UK Digital Curation Centre [3]. This lifecycle describes both the basic activities and the sequential process steps of data curation and therefore provides a helpful schema for teaching the necessary RDM competencies.
Copyright (c) 2023 Kathrin Behrens, Katarina Blask https://creativecommons.org/licenses/by/4.0
Open Science and Language Data: Expectations vs. Reality
Language data are essential for any scientific endeavor. However, unlike numerical data, language data are often protected by copyright, as they easily meet the threshold of originality. The role of research infrastructures (such CLARIN, DARIAH, and Text+) is to bridge the gap between uses allowed by statutory exceptions and the requirements of Open Science. This is achieved on the one hand by sharing language data produced by research organisations with the widest possible circle of persons, and on the other by mutualizing efforts towards copyright clearance and appropriate licensing of datasets.
Copyright (c) 2023 Paweł Kamocki, Erhard Hinrichs, Peter Leinen, Sabine Springer, Andreas Witt, Dorothea Zechmann https://creativecommons.org/licenses/by/4.0
Object-Related Research Data Workflows Within NFDI4Objects and Beyond
NFDI4Objects (N4O) represents a broad community dealing with material remains of human history from around 3 million years and involves numerous disciplines from the humanities, cultural studies and natural sciences with an archaeological and historical focus [1]. The objects examined include potsherds of common ware, artworks such as sculptures or jewellery, serially produced objects such as coins, organic remains such as wood, bones or pollen, inscribed clay tablets, papyri and stones, architectural remains, as well as human-modified landscapes. Modern research materials such as plaster casts, analogue photographs and drawings, archival documents, books and raw digital data are equally relevant.
Copyright (c) 2023 Florian Thiery, Allard W. Mees, Bernhard Weisser, Felix F. Schäfer, Stefanie Baars, Sonja Nolte, Henriette Senst, Philipp von Rummel https://creativecommons.org/licenses/by/4.0
WissKI is a free and open source virtual research environment based on the free and open source content management system Drupal. It features everything that the content management system provides while using a triplestore for authorative data storage. Thus changes can be made in the triplestore and they are directly reflected by the system. Furthermore WissKI provides all features that are necessary for a full Linked Open Data Semantic Web platform.
Copyright (c) 2023 Mark Fichtner, Robert Nasarek, Tom Wiesing https://creativecommons.org/licenses/by/4.0
Trustworthy metadata about entities related to cultural heritage is important to correctly identify bibliographic meta-data, attribute works, identify works of public domain (based on contributors’ date of death), or to support Named Entity Linking (NEL) for digitised documents. However, in Belgium such data is currently dispersed between numerous institutions, modelled with different ontologies, represented in different formats and curated in different languages. GLAM institutions would profit from data about Belgian entities to correctly annotate their collections. Furthermore, this would facilitate research in Belgium and abroad. The current situation does not only complicate data exchange in a national and international setting, but also leads to duplicate data curation efforts and data quality issues, negatively impacting the users’ experience. This paper introduces the MetaBelgica project, coordinated by KBR - The Royal Library of Belgium with the Royal Museums of Fine Arts, the Royal institute for Cultural Heritage, and the Royal Museum of Art and History, to improve the status quo. The aforementioned Federal Scientific Institutes (FSIs), belonging to the Belgian Science Policy Office (BELSPO), joined forces to develop a shared Linked Data platform for managing shared entities. This platform, based on Wikibase (the technology behind Wikidata), aims to ensure FAIR data about persons, organisations, time/events and locations related to Belgian cultural heritage. We will integrate data about millions of Belgian entities from the four participating FSIs into a Wikibase instance and make it accessible by using Persistent Identifiers. This will not only professionalize our own data management and improve data quality, but according to received project support letters, also impact other regional, national and European institutions.
Copyright (c) 2023 Sven Lieber, Ann Van Camp, Dieter De Witte, Eva Coudyzer, Erik Buelinckx, Els Angenon, Hannes Lowagie, Julie Birkholz, Karine Lasaracina https://creativecommons.org/licenses/by/4.0
Measuring people’s sociodemographic characteristics over time and in different contexts or studies is one of the keys of quantifying social inequalities and social change within any society. While different respondent-based studies usually focus on different topics, almost all of them collect data on some key features defining respondent’s backgrounds and living situations. Summarizing the measurement of sociodemographic characteristics however, the German survey landscape could be characterized as “same but different”: studies measure the same concepts yet differ in their specific approach, despite the fact that the “Demographische Standards” (Hoffmeyer-Zlotnik et al., 2016) provide recommended questionnaire items since the early 1990s. This has several problematic implications: Firstly, it becomes quite difficult to combine knowledge about groups of respondents with the same sociodemographic characteristics across studies on a macro level, and thus to systematically accumulate knowledge on social inequalities and social change. Secondly, combining different data sets, e.g. in order to be able to analyze small groups or rare phenomena, can become a laborious effort which might still produce questionable results, since key caveats can be overlooked in survey documentation.
Copyright (c) 2023 Silke Schneider, Lennart Palm https://creativecommons.org/licenses/by/4.0
Stamp - Standardized Data Management Plan for Educational Research
While there is a strong tendency towards a harmonized, cross-disciplinary research data management (RDM) (Netscher et al., 2022), researchers require more guidelines and examples, tailored to their research discipline (Grootveld et al., 2018). Therefore, Science Europe (2018: 9) proposes developing so-called domain data protocols (DDP), “a ‘model DMP’ for a given domain or community”. Based on this concept, the project Domain Data Protocols for Educational Research[1] designed the Stamp - Standardized Data Management Plan for Educational Research (DDP-Bildung and German Network of Educational Research Data, 2023). Although the Stamp was designed to support researchers in educational research, we expect that RDM is rather a matter of the data processed, the methods employed, and the content of data, than of a particular research discipline or community, such as educational research. To discuss this expectation and the usability of the Stamp outside educational research, we organized various workshops with representatives from other research disciplines. In our talk at the CoRDI 2023, we will introduce the Stamp, recap findings of two of the workshops, introduce the next steps to examine the useability of the Stamp outside educational research, and draw some conclusions on how to adapt the Stamp to other disciplines and how it fosters a harmonized, cross-disciplinary RDM. [1] The project DDP-Bildung was funded by the German Federal Ministry of Education and Research (grant number: 16QK01).
Copyright (c) 2023 Sebastian Netscher, Alexia Meyermann, Julia Künstler-Sment, Lisa Pegelow https://creativecommons.org/licenses/by/4.0
Linked Open Research Data for Social Science
The re-use of research data is an integral part of research practice in the social and economic sciences. To find relevant data, researchers need adequate search facilities. However, a comprehensive, thematic search for research data is made more difficult by inconsistent or missing semantic indexing of data at the level of social science concepts (e.g., representing the theory language). Either the data is not documented at a granular level, or primary investigators use their ad-hoc terminology to describe their data. Consequently, researchers have to make great efforts to find relevant or comparable data. From the user's perspective, the lack of theory language in data documentation impedes effective data searches and thus significantly limits the research potential of existing data collections. Because there is currently no semantic model for indexing the data content, the specific challenge for improving data search lies in establishing concept-based indexing of research data. Research infrastructures need technology for the harmonized semantic indexing of their research data. The LORD concept registry aims at closing this gap by developing a registry of sociological and economic concepts and, following the FAIR principles, making this concept registry generally available to the scientific community. As a first step, we developed a basic data model for the Concept Registry using United Modeling Language (UML). All links between are created and managed in the form of so-called RDF triples. An annotation application allows for linking specific questions/variables to concepts. The application also includes the two SKOS-compliant thesauri, "Thesaurus Social Sciences" (TheSoz) and "Standard Thesaurus Economics" (STW) but could be extended to other resources like ELSST. We illustrate the application of the LORD concept registry with examples from three large-scale survey programmes (German Socio-Economic Panel, German General Social Survey, National Academics Panel Study). The initial focus is on variables and questions with overlapping content in the three survey programmes, as they form a sound basis for cross-linking with concepts.
Copyright (c) 2023 Pascal Siegers, Antonia May, Claudia Saalbach, Jana Nebelin, Dagmar Kern, Andreas Daniel, Ben Zapilko, Fakhri Momeni, Knut Wenzig, Jan Goebel https://creativecommons.org/licenses/by/4.0
The Open Data Infrastructure for Social Science and Economic Innovations (ODISSEI)
The Open Data Infrastructure for Social Science and Economic Innovations (ODISSEI) equips social scientists in the Netherlands with the data, tools, and skills that are necessary to answer groundbreaking questions for scientific and policy making purposes. With a variety of use cases to pick from, we aim at engaging in a discussion with other Research data infrastructures to identify synergies but also challenges ahead.
Copyright (c) 2023 Tom Emery, Kasia Karpinska, Angelica Maineri, Lucas van der Meer https://creativecommons.org/licenses/by/4.0
Harmonising, Harvesting, and Searching Metadata Across a Repository Federation
The collection of metadata for research data is an important aspect in the FAIR principles. The schema.org and Bioschemas initiatives created a vocabulary to embed markup for many different types, including BioChemEntity, ChemicalSubstance, Gene, MolecularEntity, Protein, and others relevant in the Natural and Life Sciences with immediate benefits for findability of data packages. To bridge the gap between the worlds of semantic-web-driven JSON+LD metadata on the one hand, and established but separately developed interface services in libraries, we have designed an architecture for harmonising, federating and harvesting metadata from several resources. Our approach is to serve JSON+LD embedded in an XML container through a central OAI-Provider. Several resources in NFDI4Chem provide such domain-specific metadata. The CKAN-based NFDI4Chem search service can harvest this metadata using an OAI-PMH harvester extension that can extract the XML-encapsulated JSON+LD metadata, and has search capabilities relevant in the chemistry domain. We invite the community to collaborate and reach a critical mass of providers and consumers in the NFDI.
Copyright (c) 2023 Steffen Neumann, Felix Bach, Leyla Jael Castro, Tillmann Fischer, Stefan Hofmann, Pei-Chi Huang, Nicole Jung, Bhavin Katabathuni, Fabian Mauz, René Meier, Venkata Chandra Sekhar Nainala, Noura Rayya, Christoph Steinbeck, Oliver Koepler https://creativecommons.org/licenses/by/4.0
Terminologies in RDM for Engineering – a Service Approach
The frameworks which have been established since the start of the NFDI allow for a coordinated development of RDM services, which include the introduction of community specific Terminology Services. To address the challenge of alignment and re-use of established ontologies and terminologies in the engineering domain, a Terminology Service (TS) was set-up for the NFDI4Ing initiative: https://terminology.nfdi4ing.de. The NFDI4Ing TS provides a single point of access to relevant research concepts and offers the building blocks for (meta-) data schemata and data annotation. As an open source platform, it features more than 50 ontologies, 147,000 terms and over 5,800 properties from a range of engineering-related terminology collections. The use of the NFDI4Ing TS fosters interoperability and reuse in a FAIR data context.
Copyright (c) 2023 Angelina Kraft, Felix Engel, Axel Klinger https://creativecommons.org/licenses/by/4.0
RDM Services at the Luxembourg National Data Service
The Luxembourg National Data Service (LNDS) was established on 28 July 2022 by the Luxembourg government and public research institutes. LNDS is a national organisation providing services for value creation from public sector data from different domains. LNDS’ primary mission is to offer technology and data services, know-how, capabilities, platform, and infrastructure to enable sharing and re-use of data for public and private data partners. LNDS aims to become a key component of Luxembourg's data innovation strategy by (1) supporting research and innovation through a high-quality service offering, (2) contributing to accelerating the development of Luxembourg towards a data economy, (3) enabling the participation of both public research and private actors in the complete value-creating chain of data, (4) enhancing data in the context of joint innovation among all actors, and (5) becoming a partner of choice for joint development of the next generation of data products.
Copyright (c) 2023 Wei Gu, Christophe Trefois, Pinar Alper, Danielle Welter, Yohan Jarosz, Jacek Lebioda, Linda Ebermann, Regina Becker, Venkata Satagopam, Reinhard Schneider, Bert Verdonck https://creativecommons.org/licenses/by/4.0
Swiss-AL: Platform for Language Data in Applied Sciences
Open Science is transforming the way researchers collect, process, analyze, and store empirical research data, particularly in the social sciences and humanities, where language data is crucial. This transformation process especially concerns developers and providers of large language corpora and manifests itself in at least three challenges when providing these corpora as Open Research Data (ORD). Challenges concern heterogeneous practices that researchers apply when working with language data, research data lifecycle, and legal and ethical aspect. In this paper, we present Swiss-AL, a language data platform developed in Switzerland that is being transformed into an Open Research Data Resource for Applied Sciences within the Swiss Open Science Strategy. The paper gives an overview over the data contained in Swiss-AL and the infrastructure that is used to process and analyze the data. Furthermore, it presents approaches to the three abovementioned challenges to language ORD.
Copyright (c) 2023 Julia Krasselt, Philipp Dreesen, Peter Stücheli-Herlach https://creativecommons.org/licenses/by/4.0
MaRDMO, a plugin for the Research Data Management Organiser, was developed in the Mathematical Research Data Initiative to document interdisciplinary workflows using a standardised scheme. Interdisciplinary workflows recorded this way are published directly on the MaRDI portal. In addition, central information is integrated into the MaRDI knowledge graph. Next to the documentation, MaRDMO offers the possibility to retrieve existing interdisciplinary workflows from the MaRDI Knowledge Graph to allow the reproduction of the initial work and to provide scientists with new researchimpulses. Thus, MaRDMO creates a community-driven knowledge loop that could help to overcome the replication crisis.
Copyright (c) 2023 Marco Reidelbach, Eloi Ferrer, Marcus Weber https://creativecommons.org/licenses/by/4.0
Metadata Fields and Quality Criteria - XAS Reference Database under DAPHNE4NFDI
X-ray absorption spectroscopy is important to analyse solid materials, in particular amorphous materials, disordered or multicomponent materials. Due to its vast application in diverse fields XAS has become an essential tool for studying, e.g., catalytic reactions or battery materials to mention just a few. In the field of XAS, data are often evaluated by comparing them to previously measured or calculated reference spectra [1]. This sets the high requirements concerning both spectral quality and documentation of the measurements. Under DAPHNE4NFDI, we have been working on to set up a XAS reference database including raw and processed data with an interface developed for uploading and evaluating the data. In this context, defining metadata fields about an XAS experiments and documenting this information along with data is essential to make the measured data reusable by any researcher in a similar field and beyond. Another important aspect of a curated database is that users should be able to easily judge the quality and the usability of each data set by looking at the mentioned quality criteria. In the present work, we have discussed and highlighted the importance of metadata fields and quality criteria for the data to be uploaded at the XAS database.
Copyright (c) 2023 Abhijeet Gaur, Sebastian Paripsa, Frank Förste, Dmitry Doronkin, Wolfgang Malzer, Christopher Schlesiger, Birgit Kanngießer, Dirk Lützenkirchen-Hecht, Edmund Welter, Jan-Dierk Grunwaldt https://creativecommons.org/licenses/by/4.0
Opportunities and Limits of a Disciplinary Repository Using the Example MO|RE data (eResearch Infrastructure for Motor Research Data)
Out of two funding periods by the German Research Foundation resulted the first disciplinary repository for sports science motor activity research data MO|RE data. MO|RE data addresses sports scientists, researchers from related disciplines and practitioners such as teachers and educators, which work with or generate human motor performance test data as well. It has five main functions: publishing, storing, searching, citing and mapping. There are still some limitations as well as not exploited opportunities. Opportunities include among others the international expansion. Limitations are e.g., that linking health and motor test data is difficult due to data protection law. The sharing of sensitive data is not possible with the current concept and needs further solutions as e.g., remote access or workstations for guest researchers. In future, those data sets must be kept in mind to cover the requirements of research data management in sports science. Overall, the need for further development and optimization of MO|RE data repository in sports science becomes apparent to maximize its potential and ensure that it meets the evolving needs of researchers in the field.
Copyright (c) 2023 Katja Klemm, Hannah Kron, Alexander Woll, Klaus Bös, Claudia Niessner https://creativecommons.org/licenses/by/4.0
Distributed Computing and Storage Infrastructure for PUNCH4NFDI
The PUNCH4NFDI consortium brings together scientists from the German particle physics, hadron and nuclear physics, astronomy, and astro-particle physics communities to improve the management and (re-)use of scientific data from these interrelated communities. The PUNCH sciences have a long tradition of building large instruments that are planned, constructed and operated by international collaborations. While the large collaborations typically employ advanced tools for data management and distribution, smaller-scale experiments often suffer from very limited resources to address these aspects. One of the aims of the consortium is to evaluate and enable or adopt existing solutions. Instances of a prototype federated and distributed computing and storage infrastructure have been set up at a handful of sites in Germany. This prototype is used to gain experience in running of scientific workflows to further guide the development of the Science Data Platform, which is an overarching goal of the consortium.
Copyright (c) 2023 Christoph Wissing, Basavaraja Bheemalingappa Sagar, Markus Blank-Burian, Alexander Drabent, Sören Fleischer, Oliver Freyermuth, Manuel Giffels, Andreas Henkel, Matthias Hoeft, Jörn Künsemöller, Nicola Malavasi, Cristina Manzano, Benoit Roland, Hubert Simma, Dominik Schwarz, Kilian Schwarz, Nandini Suvvi Neelakantaia, Luka Vomberg, Christian Voss, Vadim Vybornov, Michael Wigard, Sebastian Wozniewski https://creativecommons.org/licenses/by/4.0
Open Educational Resources (OER) allow for free access to educational materials and increase the chances of educational equity. We developed the DataPLANT OER, a teaching material resource based on the concept of annotated bricks along didactic paths. Our concept builds on a leveled approach with the brick, unit and dissemination level, is environment-agnostic and can be implemented in any desired technical framework. It balances customization and reuse and aims at one OER to teach them all.
Copyright (c) 2023 Dominik Brilhaus, Martin Kuhl, Cristina Martins Rodrigues, Andrea Schrader https://creativecommons.org/licenses/by/4.0
Transparency and Involvement of the Energy-Related Industry in a Data Sharing Platform
The integration of renewable energy sources, the decentralization of the energy system, and the increasing digitization of energy-related processes require the integration of a wide range of energy-related data. In this context, a data sharing platform can serve as a hub for exchanging energy-related data and developing innovative solutions to improve the efficiency and sustainability of the energy system. However, especially because of the involvement of the energy-related industry in such a platform poses several challenges related to data protection, intellectual property, and business interests. This paper presents a framework for ensuring transparency and involvement of the energy-related industry in a data sharing platform, based on the FAIR data principles and a co-creation approach involving industry partners.
Copyright (c) 2023 Zhiyu Pan, Gonca Gürses-Tran, Christina Speck, Patrick Jaquart, Michael Niebisch, Antonello Monti https://creativecommons.org/licenses/by/4.0
WissKI is a software which allows researchers to record data about objects of the cultural heritage in a graph database backed by a formal ontology. It acts as a database for researchers to store their results via a web interface, and nearly automatically makes data FAIR, linked and open. To install the WissKI software a nontrivial amount of effort is required and typically requires help from a system administrator. To make it easier to access WissKI and data stored within this paper introduces the WissKI Viewer, which enables researchers to directly inspect a WissKI Backup on theirown computer, without additional help.
Copyright (c) 2023 Tom Wiesing https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2023 Nanette Rißler-Pipka, Regine Stein, Laure Barbot, Sally Chambers, Toma Tasovac, Philipp Wieder https://creativecommons.org/licenses/by/4.0
Architectural Design of BERD Information Portal
The management of Business, Economic, and Related Data can be a complex task involving various types of data and diverse research needs. BERD@NFDI develops a powerful platform to collect, process, analyze, and preserve such data in one place.
Copyright (c) 2023 Ahmed Saleh, Klaus Tochtermann https://creativecommons.org/licenses/by/4.0
Because Data Shall Grow (and we With it)
Research data are a valuable asset of their own and individual researchers as well as the research community as a whole can benefit through data sharing practices and open science. These benefits include but are not limited to higher data quality or the more efficient use of time and financial resources. Despite these potential gains, data sharing is not widespread yet and processes of cultural change are needed to reap the benefits of data sharing. Against this background, the NFDI can function as a platform for discussion and provides a network that extends beyond individual research bubbles in the name of common interests and facilitate cultural change processes towards data sharing. A first collaborative workshop in April 2023 identified four central clusters of interest including 1. policies, strategies and funding; 2. communities, workshops and multipliers; 3. publications and 4. collaboration, communication and error cultures. Departing from this, we contemplate developing a vision on how to further encourage cultural change in the NFDI through a series of workshops. As a result, a vision of cultural change within and with the NFDI will be published by the end of 2023 to further guide the processes to facilitate cultural change. A first step could be to change the guiding metaphor for data: Not “Gold” or “Oil”, but “Humus” for healthy growth of a collaborative data ecosystem.
Copyright (c) 2023 Julia Rakers, Daniel Nüst, Bernhard Miller, Julia Mohrbacher, Torsten Schrade, Jörg Seegert, Holger Simon, Christian Vater, Cord Wiljes https://creativecommons.org/licenses/by/4.0
Exploring and Improving Workflows for the Donation and Curation of Research Data
Managing research data and preparing it for long term archiving at a research data center is a time-consuming, repetitive and error prone process without obvious rewards or incentives. To address these challenges, we aim at the integration of a standard data management tool into one of the research data centers of the German Network of Educational Research Data. The goal is to improve workflows for both, data donors and curators and to explore transferable solutions that allow researchers and curators to work on the same platform when entering and editing metadata.
Copyright (c) 2023 Natalie Kiesler, Daniel Schiffner https://creativecommons.org/licenses/by/4.0
For decades, the sharing of large N-dimensional datasets has posed issues across multiple domains. Interactively accessing terabyte-scale data has previously required significant server resources to properly prepare cropped or down-sampled representations on the fly. Now, a cloud-native chunked format easing this burden has been adopted in the bioimaging domain for standardization. The format — Zarr — is potentially of interest for other consortia and sections of NFDI.
Copyright (c) 2023 Josh Moore, Susanne Kunis https://creativecommons.org/licenses/by/4.0
NFDI4Earth: Improving Research Data Management in the Earth System Sciences
The increasing availability of digital research products – typically encompassing data and software – in the Earth System Sciences (ESS) calls for new approaches to facilitate the management and reuse of these digital resources. NFDI4Earth addresses this gap and targets the harmonization of services related to digital research products in the ESS. In our initial phase, we aim to support researchers in 1) discovering and exploring relevant data and software sources, 2) exploratory spatial data analysis, 3) solving research data management problems and 4) creating and publishing information products. This abstract briefly presents concepts and the first results of the harmonization efforts within NFDI4Earth. It touches upon four points: the harmonization of resource descriptions, the harmonization of ESS-specific support for research data management (RDM), joint training activities for researchers in the ESS, and the harmonization of discovery and publishing workflows.
Copyright (c) 2023 Lars Bernard, Christin Henzen, Auriol Degbelo, Daniel Nüst, Jörg Seegert https://creativecommons.org/licenses/by/4.0
Digital Twin-Based Concept for Reliable Research Data Management
In data-intensive research, reliable management of research data is a major challenge. In the field of Mass Spectrometry Imaging, vast amounts of data are being acquired from mostly proprietary data sources. Consequently, hindering seamless data integration into Research Data Management systems. Without a data repository, the continuous generation of scientific knowledge and innovative research based on existing information is limited. Moreover, to maintain the value of data to researchers throughout and beyond its lifecycle, FAIR principles for reliable data management approaches must be applied. To enable the required data transmission, the Digital Twin paradigm can be considered a reliable solution. The conceptual implementation of a heterogeneous mass spectrometer generating hyperspectral images leverages the Digital Twin to overcome common data management problems in data-intensive research.
Copyright (c) 2023 Alessa Rache, Tim Häußermann, Joel Lehmann, Julian Reichwald https://creativecommons.org/licenses/by/4.0
Ten Simple Rules for Designing and Building a FAIR Research Infrastructure
One of the key priorities of The European Strategy Forum on Research Infrastructures (ESFRI) is to build sustainable and FAIR (Findable, Accessible, Interoperable, Reusable) infrastructures. However, designing and building such infrastructures requires careful consideration of various factors, such as data interoperability, operational sustainability, and governance. This poster proposes ten simple rules, inspired by Ten Simple Rules for scientific research, for designing and building a research infrastructure drawing from existing initiatives particularly from experiences in preparation of DiSSCo (Distributed System of Scientific Collections) – a new research infrastructure that was in the ESFRI 2018 roadmap. While these rules are not comprehensive, they highlight a few essential traits that can be applied across different disciplines. For each rule, we highlight how within DiSSCo we accomplished the specific aspect.
Copyright (c) 2023 Sharif Islam https://creativecommons.org/licenses/by/4.0
Ontology-Based Laboratory Data Acquisition With EnzymeML for Process Simulation of Biocatalytic Reactors
The presented work explores the use of ontologies and standardized enzymatic data to set up enzymatic reactions in process simulators, such as DWSIM. Setting up an automated workflow to start a process simulation based on enzymatic data obtained from the laboratory can help save costs and time during the development phase. Standardized conditions are crucial for accurate comparison and analysis of enzymatic data, where ontologies provide a standardized vocabulary and semantic relations between relevant concepts. To ensure standardized data, an electronic lab notebook (ELN) is used based on EnzymeML, an open standard XML-based format for enzyme kinetics data. Furthermore, two ontologies are merged and the result is extended for the use in the Python-based workflow. The resulting data is stored in a knowledge graph for research data in a machine-accessible and human-readable format. Thus, the study demonstrates a workflow that allows for the direct translation of ELN data into a process simulation via ontologies.
Copyright (c) 2023 Alexander S. Behr, Elnaz Abbaspour, Katrin Rosenthal, Jürgen Pleiss, Norbert Kockmann https://creativecommons.org/licenses/by/4.0
Research institutions, especially in the human sciences, have been confronted with strict guidelines for the processing of personal data since the European General Data Protection Regulation came into force in 2018. This presents them with new challenges in recruiting and managing study participants and processing the associated data. To meet these challenges, Castellum has been developed at the Max Planck Institute for Human Development since 2016 and has been used successfully since May 2020. Castellum is a turnkey open-source web application for the data protection-compliant management of volunteer data. Among other things, Castellum simplifies study recruitment, appointment management and study implementation. Various institutions have expressed interest in Castellum in the recent past. On the one hand, this may be due to the fact that no comparable open source project exists. On the other hand, Castellum was explicitly designed to be so flexible and expandable that it can be adapted to the workflows and processes of other research institutions with relatively little effort. The use of Castellum has so far been particularly successful for institutions that conduct several studies in parallel, that want to proactively recruit participants from an internal pool of people interested in the study, and that generate data when dealing with these subjects. Since Castellum is subject to the AGPL licence, the software may be used free of charge without restrictions.
Copyright (c) 2023 Karolina Mader, Maike Kleemeyer https://creativecommons.org/licenses/by/4.0
A key challenge for the NFDI community is to share and connect datasets across spatial and temporal scales. In particular for Earth System Sciences (ESS) coupling of different RDIs and access to long-term time series data are crucial. In this paper we propose a workflow pipeline for making analogue bound archival geodata FAIR and hence accessible to the different scientific disciplines and reusable in a long-term perspective. We describe an interface for enabling data exchange between an archive (GDA) and a geospatial RDI (IOER Monitor) as well as other RDIs by applying the FAIR data principles.
Copyright (c) 2023 Hendrik Herold, André Hartmann, Anna Lisa Schwartz, Michael Hellstern, Markus Schmalzl https://creativecommons.org/licenses/by/4.0
Several Partners – Joint Effort: RDM Synergies in Large Scale Research
We report about the synergies of NFDI, ErUM-Data, and related partners in RDM for large scale research in universe and matter.
Copyright (c) 2023 Astrid Schneidewind, Kilian Schwarz, Thomas Schörner, Bridget M. Murphy, Martin Erdmann https://creativecommons.org/licenses/by/4.0
Data Management Plan Tools: Overview and Evaluation
Data Management Plans (DMPs) are crucial for a structured research data management and often a mandatory part of research proposals. DMP tools support the development of DMPs. Among the variety of tools available, it can be difficult for researchers, data stewards and institutions to choose the one that is most appropriate for their specific needs and context. We evaluated 18 DMP tools according to 31 requirement parameters covering aspects relating to basic functions, DMP contents, technical aspects and user friendliness. The highest total evaluation scores were reached by Data Stewardship Wizard (703.5), DMPTool (615.5) and RDMO NFDI4Ing (549.5). The tools evaluated satisfied between 10 % and 87 % of the requirement parameters. 11 tools cover at least half of the parameters. In terms of correlation among the tools, which indicates to which degree their scores in the different requirement parameters are alike, we found the highest correlation for ezDMP and GFBio DMPT. Regarding the relatedness between the tools, 85 % of the DMP tools were positively and 16 % negatively correlated. Accounting for the recent developments in the area of DMP tools, this study provides an up-to-date evaluation that can support tool developers in identifying potential improvements, and hosting institutions to select a tool suited to their specific needs.
Copyright (c) 2023 Carina Becker, Carolin Hundt, Claudia Engelhardt, Johannes Sperling, Moritz Kurzweil, Ralph Müller-Pfefferkorn https://creativecommons.org/licenses/by/4.0
When Data Crosses Borders – Join Forces!
A multidisciplinary use-case for integrating research data involves data, software and methods across research disciplines to address research questions that cannot be effectively addressed by a single discipline or method alone. They thus contribute to NFDI’s goal to make a significant contribution to answering novel interdisciplinary research questions. This paper presents some examples for multidisciplinary use-cases that illustrate that, in order to reap the added benefits of these use-cases, some effort is required. The paper concludes by proposing some cornerstones for handling multidisciplinary use-cases.
Copyright (c) 2023 Barbara Ebert, Sami Domisch, Christin Henzen, Jimena Linares, Kati Mozygemba, Bernhard Miller, Bernhard Seeger, Jörg Seegert https://creativecommons.org/licenses/by/4.0
Science Data Platform and Digital Research Product
The participating scientific communities in PUNCH4NFDI have long experiences with data collections which are often huge, compared to other NFDI communities, and also – from necessity – organized collective access to these data collections. The focus of the work, next to the questions of determining the common ground for identifying and finding the data across the existing community silos, is to make use of the vast experiences for operating collaborative execution environments for large distributed communities. Leaving the always implied questions of AAI and Identity Management aside, building and operating a distributed and heterogeneous execution environment is only feasible using an approach which borrows heavily from the notion of micro-services, with a few building blocks which serve as connecting elements.
Copyright (c) 2023 Harry Enke, Thomas Schörner-Sadenius https://creativecommons.org/licenses/by/4.0
CorWiz a Platform for Exploring Corrosion Data and Accessing Corrosion Models
Corrosion is a major cause of material degradation and failure in various industries and applications. Damage caused by corrosion causes billions in damage each year and is a major cause of infrastructure degradation. Besides that, corrosion mechanisms, countermeasures and effects are generally not good understood. The platform CorWiz will provide an easily accessible way to corrosion research and data. Research data on corrosion mechanisms, rates, and prevention methods are essential for developing effective solutions and improving the performance and reliability of materials. However, corrosion research data are often scattered, inconsistent, or inaccessible, limiting their reuse and impact. Providing a more straightforward way to access available data and models has thus a significant impact on the research field and application areas. In the following we present the components of the minimum viable platform focusing on stainless steel corrosion.
Copyright (c) 2023 Sven Berger, Aravinth Ravikumar, Mikhail Zheludkevich, Daniel Hoeche https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2023 Janete Saldanha Bach , Fidan Limani, Yudong Zhang, Atif Latif, Brigitte Mathiak, Peter Mutschke https://creativecommons.org/licenses/by/4.0
Where does one go to find information on Open Science? At the moment, the literature is scattered across the various fora that comprise scientific discourse. An inquiry about any topic in Open Science, for example, how to manage a particular type of data in chemistry or what the current stance of the DFG or the NSF might be on data on photovoltaics or the relationship between Kuhn and Open Science, invariably require a mixture of asking colleagues or conducting random searches in academic indexing services, with varying degrees of success. Part of the problem is that so much of Open Science discourse has occurred in fora made possible by the advent of the internet. Between blog posts, twitter threads, talks and podcasts, there is a wealth of information that needs to be organized and archived to be most useful to the scientific community. There are efforts to organize some of this information in various online databases, and they are laudable, but to serve as long-term tools, they require 1. orchestration to gather and curate materials as a community effort, 2. long-term infrastructure to store copies of the materials, and 3. the expertise of librarians to manage such a collection.
Copyright (c) 2023 Monica Gonzalez-Marquez, Ines Schmahl https://creativecommons.org/licenses/by/4.0
We report on ongoing discussions and plans of NFDI consortia in physics and reltated natural sciences with respect to (persistent) identifiers.
Copyright (c) 2023 Thomas Schörner, Anton Barty, Markus Demleitner, Harry Enke, Oliver Koepler, Martin Köhler, Bridget Murphy, Sonja Schimmler, Lisa-Marie Stein https://creativecommons.org/licenses/by/4.0
Exchanging research data between systems is a vital task in research data management, especially in the context of collaborations. Therefore mapping data into standardized data descriptions and offering interfaces for efficient data exchange are required. The metadata database and electronic lab notebook SampleDB supports various ways of exporting data to other services, such as Dataverse, SciCat, and JupyterHub, as well as file exports in open formats. The concept of SampleDB federations allows data exchange in loosely coupled federations of SampleDB instances.
Copyright (c) 2023 Malte Deckers, Florian Rhiem https://creativecommons.org/licenses/by/4.0
The Data Steward Service Center (DSSC): FAIRagro RDM-Expertise Hub
The Data Steward Service Center (DSSC) is the central institution within FAIRagro to develop data management tools based on the needs of the scientific community. The DSSC organizes the continuous exchange of RDM knowledge and experience with other institutions, channels user requests from the community, and transfers knowledge from the FAIRagro task areas to the FAIRagro data stewards. FAIRagro data stewards are experts in the field of RDM for agrosystems research supervising and will train data curators in our community. Data stewards have core competencies in research data management (e.g., cross-scale from genes, phenomics, management to region; sensitive data, remote sensing, time series, plant, soil and related FAIRagro data). Knowledge and expertise is pooled to provide the full range of expertise to the community in one place to foster the coalescence of the community. The DSSC is headed by a coordinator and will house five data stewards, who are active in the community e.g. train data curators, give legal support. In the course of the project, further institutional or project data stewards will be integrated and the pool of experts will be further expanded. The network to the other NFDI consortia is continuously growing.
Copyright (c) 2023 Nikolai Svoboda, Lucia Vedder, Franziska Böhm, Markus Möller , Elena Rey-Mazón, Marcus Schmidt , Birte Lindstädt , Ulrike Stahl https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2023 Jens Dierkes, Birte Lindstädt, Ulrich Sax, Canan Hastik, Julia Fürst, Tanja Hörner, Sebastian Klammt, Ines Perrar, Iris Pigeot, Katja Restel, Carsten Oliver Schmidt, Aliaksandra Shutsko, Dagmar Waltemath, Atinkut Zeleke https://creativecommons.org/licenses/by/4.0
Spreading the Love for Mathematical Research Data
The Mathematical Research Data Initiative (MaRDI) is the NFDI consortium for the mathematics community. We outline some of the challenges we face in spreading a culture of mathematical research data to a large community and starting a cultural change. We highlight our approach to tackling these challenges and present some successful activities: a colorful newsletter, personal interviews, an entertaining rabbit, FAIR chocolate, and interactive formats.
Copyright (c) 2023 Tabea Bacher, Christiane Görgen, Tabea Krause, Andreas Matt, Daniel Ramos, Bianca Violet https://creativecommons.org/licenses/by/4.0
As a funding measure of the German Federal Ministry of Education and Research and from the EU's Development and Resilience Facility, the project “DALIA - Knowledge-Base for FAIR data usage and supply" not only creates technical bridges between NFDI consortia but also an evolving approach that contributes to transdisciplinary community building in the long term. By harmonising, networking and making visible teaching and learning materials for the development and promotion of data literacy, the project makes an important contribution to education, training and cultural change.
Copyright (c) 2023 Canan Hastik, Gabor Kismihok, Frank Lange, Petra Steiner https://creativecommons.org/licenses/by/4.0
FDO to Structure the Domain of Knowledge
The Globally Integrated Dataspace will evolve and result in a domain full of digital artefacts and relations which in its size and complexity is unprecedented. Traditional methods of structuring the domain such as file structures are not sufficient any longer. On top of a layer of data centres and federations which need to implement mechanisms establishing trust, there will be layer of Virtual Collections serving different purposes. These VCs to a large extent need to be FAIR and persistent as well, since they include important information. We claim that the concept of FAIR Digital Objects is an excellent framework to build and manage these VC. Currently, there are a few implementations of the FDO concept.
Copyright (c) 2023 Peter Wittenburg, Dimitris Koureas https://creativecommons.org/licenses/by/4.0
FAIRmat Guide to Writing Data Management Plans
Research data management is becoming an increasingly important topic due to the growing amounts of types, formats, and sizes of data produced by scientific research. In addition, a growing demand to make data accessible and comprehensible requires standardizing, managing, and planning the data life-cycle. For this reason, many funding agencies now require a data management plan (DMP) as part of submitted research proposals. While some of them and other scientific bodies offer DMP templates, there is no one-size-fits-all solution, due to the heterogeneity of data generated by different scientific disciplines. Here, we present as an example FAIRmat’s effort in enhancing data literacy on the topic of DMP aiming to guide physicists and materials scientists to writing DMPs that comply with the requirements of the German Research Foundation (Deutsche Forschungsgemeinschaft, DFG).
Copyright (c) 2023 Ahmed E. Mansour, Lucia Rotheray, Kerstin Helbig, Silvana Botti, Heiko B. Weber, Martin Aeschlimann, Claudia Draxl https://creativecommons.org/licenses/by/4.0
Embedding the de.NBI Cloud in the National Research Data Infrastructure Activities
In recent years, modern life sciences research underwent a rapid development driven mainly by the technical improvements in analytical areas leading to miniaturization, parallelization, and high throughput processing of biological samples. This has led to the generation of huge amounts of experimental data. To meet these rising demands, the German Network for Bioinformatics Infrastructure (de.NBI) was established in 2015 as a national bioinformatics consortium aiming to provide high quality bioinformatics services, comprehensive training, powerful computing capacities (de.NBI Cloud) as well as connections to the European Life Science Infrastructure ELIXIR, with the goal to assist researchers in exploring and exploiting data more effectively. Since its foundation, de.NBI Cloud has formed the scientific and collaborative backbone for new major German initiatives like NFDI or EOSC-Life in the European sector of computational biosciences. Above all, the cooperation with various NFDI consortia such as NFDI4Biodiversity, DataPLANT, GHGA, FAIRagro or NFDI4Microbiota showcases the power, range and flexibility of the de.NBI Cloud, especially for the national life science community. In conclusion, the de.NBI Cloud provides the ability to unlock the full potential of research data and enables easier collaboration across different ecosystems and research areas, which in turn enables scientists to innovate and scale-up their data-driven research, not only in the life and computational biosciences, but across the different science domains addressed by the NFDI.
Copyright (c) 2023 Nils Hoffmann, Irena Maus, Sebastian Beier, Peter Belmann, Jan Krüger, Andreas Tauch, Alexander Goesmann, Roland Eils, Peer Bork, Oliver Kohlbacher, Ursula Kummer, Rolf Backofen, Ivo Buchhalter, Alexander Sczyrba https://creativecommons.org/licenses/by/4.0
Monitoring the State of Open and FAIR Data in Helmholtz
In this contribution we present an integrated approach to monitoring and assessing the state of open and FAIR data in the Helmholtz Association. The project is part of a multi-method approach by Hub Matter in the Helmholtz Metadata Collaboration (HMC). In a harvesting-approach, data published by Helmholtz researchers is found starting from literature metadata, harvested from the research centers. Data publications linked to that literature are identified using the SCHOLIX API. In a first approach to automated FAIR assessment, we adopted the F-UJI framework, as developed by the FAIRsFAIR consortium. The information collected is presented in an interactive dashboard. It allows to explore in which repositories Helmholtz researchers make their data publicly available, to engage Helmholtz communities, and to identify gaps towards improving the FAIRness of Helmholtz data. The dashboard is publicly available on https://fairdashboard.helmholtz-metadaten.de. The general approach as well as all program code are reusable by all research communities.
Copyright (c) 2023 Gabriel Preuß, Alexander Schmidt, Mojeeb Sedeqi, Vivien Serve, Oonagh Mannix, Markus Kubin https://creativecommons.org/licenses/by/4.0
©NFDI4DataScience (NFDI4DS) is a consortium to support researchers in all stages of the research data lifecycle to conduct their research in line with the FAIR principles. The developed infrastructure targets researchers from a wide range of disciplines in data science and AI. We present the ideas of the NFDI4DS gateway and the NFDI4DS portal. Two approaches to navigate digital objects (articles, data, machine learning models, workflows, scripts/code, etc.) from various NFDI4DS resources such as the ORKG, the DBLP database, and other research knowledge graphs (KGs). Transparency, reproducibility, and fairness will be fostered by a step-wise integration of existing and newly developed services into the overall system. With this paper, we want to engage with the community and understand the needs and challenges of researchers in various disciplines regarding data science and AI. Therefore, we will discuss the currently developed prototypes and outline our plans for future development steps.
Copyright (c) 2023 Ricardo Usbeck, Tilahun Abedissa Taffa, Rudy Alexandro Garrido Veliz, Rana Abdullah, Najeebullah Shams, Bianca Wentzel, Zongxiong Chen, Sonja Schimmler https://creativecommons.org/licenses/by/4.0
Establishing the Research Data Management Container in NFDIxCS
NFDIxCS is a consortium within the family of NFDI, which defines and establishes a research data management (RDM) infrastructure for Computer Science (CS). Based on a broad community process, the various types of research data, their metadata and quality criteria are agreed upon in the community. The resulting research data, along with all associated supplementary information and context such as software, metadata, and the corresponding execution environment are provided as an integral part of the overall infrastructure to meet the FAIR principles. This extended abstract addresses the interaction of the presented task area Architectures & Interfaces within the NFDIxCS consortium and the planned activities, challenges, and objectives of the recently launched project.
Copyright (c) 2023 Firas Al Laban, Jan Bernoth, Michael Goedicke, Ulrike Lucke, Michael Striewe, Philipp Wieder, Ramin Yahyapour https://creativecommons.org/licenses/by/4.0
RO-Crates makes it easier to package research digital objects together with their metadata so both dependencies and context can be captured. Combined with FAIR good practices such as the use of persistent identifiers, inclusion of license, clear object provenance, and adherence to community standards, RO-crates provides a way to increase FAIRness in science. In this abstract we present the use of RO-Crates, combined with Linked Data best practices, as an implementation for lightweight FAIR Digital Objects, and its possible use in NFDI consortia.
Copyright (c) 2023 Leyla Jael Castro, Stian Soiland-Reyes, Dietrich Rebholz-Schuhmann https://creativecommons.org/licenses/by/4.0
ICT Infrastructure Supporting the Italian Research Infrastructure on Microbial Resources MIRRI-IT
In this extended abstract we present the main objectives of SUS-MIRRI.it and the ICT infrastructure that was designed and implemented to support such a project. This ICT infrastructure is adequate for the needs of similar research infrastructures
Copyright (c) 2023 Marco Beccuti, Antonio d´ Acierno, Simone Donetti, Sandro Gepiro Contaldo, Paolo Romano, Giovanna Cristina Varese https://creativecommons.org/licenses/by/4.0
Enhancing Reproducibility in Research Through FAIR Digital Objects
The FAIR principles were introduced to enhance data reuse by providing guidelines for effective data management practices. In the broader context of research, assets encompass not only data but also artifacts such as code, software, and publications. FAIRifying these artifacts is as essential as FAIRifying data, given the increasing complexity of current AI approaches that make reproducibility extremely challenging. Therefore, the reuse of these artifacts is growing in importance. The concept of FAIR Digital Objects (FDOs) presents a solution to FAIRify these artifacts, treating them as FDOs. NFDI4DataScience is embracing FDOs and proposing an architecture to efficiently manage them.
Copyright (c) 2023 Zeyd Boukhers, Leyla Jael Castro https://creativecommons.org/licenses/by/4.0
Automated Documentation of Research Processes Using RDM
Published research results usually represent only a fraction of the data generated at a research institute. The unpublished data created in the process of producing the final result, however, often contain valuable information that can be reused. Through research data management, all these data should be stored centrally according to the FAIR principles (Findable, Accessible, Interoperable, Reusable). However, a significant part of knowledge is often not found in the data, but in the processes that led to their generation. It is therefore important to map these processes to archive and document this knowledge in a structured way. Procedures for documenting scientific processes already exist and are actively used at research institutes. However, these are often analogue or paper-based and hence do not meet the requirements for FAIR data management. At the Institute for Microstructure Technology of the KIT, such a paper-based procedure is used to document the production of microstructure components. During their manufacturing, it is essential to adhere to the correct process parameters in order to enable error-free production. Therefore, a so-called job ticket always accompanies the production of components. On this job ticket, the correct process sequence is listed and a detailed description of the respective process step is given. Depending on the component to be produced, a distinction is made between different types of job tickets according to internal conventions. On the one hand, there are so-called green job tickets, which describe a standardised process sequence, and on the other hand, blue job tickets, which are intended to document experimental manufacturing processes. The process sequence on the blue job tickets is initially empty and is filled in during the manufacturing process. Common to both types of job tickets is that they are stored in the institute's archive after completion of the component production. However, since the job tickets are paper-based, the corresponding archive of job tickets cannot be searched quickly and, given the sheer volume of archived job tickets, represents an unmanageable collection of data. The existing system for process documentation is therefore to be implemented with the help of the research data infrastructure Kadi4Mat [1] in accordance with FAIR principles, thereby making the available process knowledge more accessible.
Copyright (c) 2023 Lars Christoph Griem, Richard Thelen, Michael Selzer https://creativecommons.org/licenses/by/4.0
Improving the Research Desktop Experience for OpenStack VDI
Using virtual machines with dedicated rendering and remote access capabilities, virtual workplaces for various usecases can be created, and then accessed from anywhere at any time. If this is to happen on a large scale in the cloud, so-called VDI for the dynamic provision of virtual desktops play an increasingly important role. A sustainable VDI should be freely available to everyone for modification and redistribution at no cost, be scalable, and should support various desktop use cases with different resource requirements. Some use cases involve hundreds of similar VM running in parallel, which requires proper resource planning.
Copyright (c) 2023 Dirk von Suchodoletz, Yi Sun, Jean-Karim Hériché, Manuel Messner https://creativecommons.org/licenses/by/4.0
DataPLANT Cloud Oriented Service Infrastructure
A core objective of the DataPLANT consortium is to provide a science gateway as a comprised of flexible cloud based (micro) services. The setup keeps both on-premise installation and future integration into a joint NFDI common infrastructure. We are presenting the DataPLANT DataHUB which provides various RDM workflows to support research data scientists in different phases of the data life cycle – from the development to the publication of the results obtained.
Copyright (c) 2023 Dirk von Suchodoletz, Jonathan Bauer, Marcel Tschöpe https://creativecommons.org/licenses/by/4.0
The emergence of FAIR data management (FDM) is being witnessed in more and more disciplines, including the engineering sciences. However, until recently, little academic credit has been given for the work that sound FDM practices in research publications require. Moreover, there has been a lack of space where the engineering sciences community could discuss and share experiences, ideas and advice about this topic. In academia, a suitable platform for such information exchange are journals. In this publication, the concept behind ing.grid, the newly established open access journal for FDM in engineering sciences, is presented, illuminating how these challenges can be addressed by providing a platform for the publication of manuscripts, research data, and software as well as by incorporating open peer review.
Copyright (c) 2023 Izadora Silva Pimenta, Kevin T. Logan, Michaela Leštáková, Peter F. Pelz https://creativecommons.org/licenses/by/4.0
Abstract. The lifecycle of scientific tools comprises the creation of code releases, packages and containers which can be deployed into cloud platforms, such as the Galaxy Project, where they are run and integrated into workflows. The tools and workflows are further used to create training material that benefits a broad community. The need to organize and streamline this tool development lifecycle has led to a sophisticated development and deployment architecture.
Copyright (c) 2023 Paul Zierep, Sanjay Kumar Srikakulam , Sebastian Schaaf, Bjoern Gruening https://creativecommons.org/licenses/by/4.0
Galaxy [1] is a GUI-based scientific data management and analysis platform that aims to expand the target audience of scientific computing to scientists without advanced computer literacy or access to computing infrastructure. During the operation of a typical asynchronous workflow, the interaction of the user triggers a job submission to a hosted computing system, the results of which are then reported back to the user upon completion of the task. However, such an asynchronous interaction scheme is not compatible with scientific software that require user interaction during the execution itself (synchronous), e.g. many data visualisation software. Interactive Tools on Galaxy (IT; [2]) leverage tool and job components of Galaxy by taking advantage of the latest containerization solutions to provide access to interactive executable software. With ITs, Galaxy is enabling the more and more common use-case of asynchronous workflows while keeping the possibility to bridge at any time to synchronous workflows and reusing all the other Galaxy data management features.
Copyright (c) 2023 Tunc Kayikcioglu, Sanjay Kumar Srikakulam, Wolfgang Maier, Björn Grüning https://creativecommons.org/licenses/by/4.0
Quality Assessment for Research Data Management in Research Projects
In the context of research data management (RDM), researchers are confronted with a multitude of new tasks and responsibilities. The totality of all tasks to ensure the re-use of data, long-term archiving, and access to data through data management planning, further data documentation, and provinces of data collection and analysis are described as research data management [1]. Often, the process of RDM is represented with data life cycle models, which include the basic phases of planning, data collection, analysis, archiving, access, and reuse [2].
Copyright (c) 2023 Max Leo Wawer, Johanna Wurst, Roland Lachmayer https://creativecommons.org/licenses/by/4.0
Building Ontologies and Knowledge Graphs for Mathematics and its Applications
Ontologies and knowledge graphs for mathematical algorithms and models are presented, that have been developed by the Mathematical Research Data Initiative. This enables FAIR data handling in mathematics and the applied disciplines. Moreover, challenges of harmonization during the ontology development are discussed.
Copyright (c) 2023 Björn Schembera, Frank Wübbeling, Thomas Koprucki, Christine Biedinger, Marco Reidelbach, Burkhard Schmidt, Dominik Göddeke, Jochen Fiedler https://creativecommons.org/licenses/by/4.0
Modelling Scientific Processes With the m4i Ontology
We present an approach to document research data in a human and machine readable way by creating JSON-LD metadata files based on the m4i ontology. m4i is based on top level ontologies and reuses concepts of widely accepted ontologies to embed information modelled in m4i in larger contexts like a knowledge graph connecting research data with projects, actors, methods, tools and publications. We use a real-life research example from the engineering domain to show how to describe a research process with its object of research, the different steps with input and output data, the actors, and the used methods and tools. The resulting metadata files can serve as low-threshold documentation in a file system, as an exchange format between tools, as an input for data repositories and as a source of information to be used by scripts and tools.
Copyright (c) 2023 Dorothea Iglezakis, Džulia Terzijska , Susanne Arndt, Sophia Leimer, Johanna Hickmann, Marc Fuhrmans, Giacomo Lanza https://creativecommons.org/licenses/by/4.0
Schema.org as a Lightweight Harmonization Approach for NFDI
Schema.org is a controlled vocabulary that makes it easier for web pages to describe their actual content in a semantic, structured and machine-processable way. It is recognized by major search engines and data aggregators, making it easier for researchers to expose metadata describing their research outcomes. Here we present how Schema.org is used (or planned to be) by some NFDI consortia, becoming a lightweight approach to harmonize digital objects coming from different sources so they can be connected to each other in a meaningful way
Copyright (c) 2023 Leyla Jael Castro, Juliane Fluck, Daniel Arend, Matthias Lange, Daniel Martini, Steffen Neumann, Sonja Schimmler, Dietrich Rebholz-Schuhmann https://creativecommons.org/licenses/by/4.0
Investigating the Landscape of Ontologies for Catalysis Research Data Management
This work provides a survey of ontologies for catalysis research to improve the findability, accessibility, interoperability, and reusability (FAIRness) of research data. Applying tools that are commonly used by lab scientists, ontologies relevant to catalysis research are classified in a simple, well formatted spreadsheet template (Excel). This enables a scientist and domain expert without programming skills to evaluate a certain ontology. The entries of this template are then processed and visualized through automated creation of markdown files on GitHub using Python scripts. Furthermore, ontology mapping by searching for similar pairs of classes across different ontologies is performed, using the outcome of the ontology classification. This work contributes to the development of ontologies for catalysis research, facilitating better data integration and knowledge sharing while reusing existing semantic artefacts.
Copyright (c) 2023 Alexander S. Behr, Hendrik Borgelt, Taras Petrenko, Mark Dörr, Norbert Kockmann https://creativecommons.org/licenses/by/4.0
The exchange, dissemination, and reuse of biological specimens and data have become essentialfor life sciences research. This requires standards that enable cross-organizational documentation, traceability, and tracking of data and its corresponding metadata. Thus, data provenance, or the lineage of data, is an important aspect of data management in any information system integrating data from different sources [1]. It provides crucial information about the origin, transformation, and accountability of data, which is essential for ensuring trustworthiness, transparency, and quality of healthcare data [2]. For biological material and derived data, a novel ISO standard was recently introduced that specifies a general concept for a provenance information model for biological material and data and requirements for provenance data interoperability and serialization [3,4]. However, a specific standard for health data provenance is currently missing. In recent years, there has been a growing need for developing a minimal core data set for representing provenance information in health information systems. This paper presents a Provenance Core Data Set (PCDS), a generalized data model that aims to provide a set of attributes for describing data provenance in health information systems and beyond.
Copyright (c) 2023 Ulrich Sax, Christian Henke, Christian Dräger, Theresa Bender, Alessandra Kuntz, Martin Golebiewski, Hannes Ulrich, Mattias Löbe https://creativecommons.org/licenses/by/4.0
The NFDI4Energy consortium aims to establish new services filling a variety of needs for the energy system research community, from making FAIR research data easily accessible to promoting collaboration among community entities. Seven Task Areas (TAs) have been defined to achieve the consortium’s objectives, each with a specific focus. Task Area 4 (TA4): FAIR Data for Energy System Research shall develop ontologies, metadata standards, and services to promote semantic consistency and improve interoperability of energy research projects, thereby supporting the harmonization of data management among various institutions and research fields.
Copyright (c) 2023 Amanda Wein, Jan Reinkensmeier, Anke Weidlich, Johan Lilliestam, Veit Hagenmeyer, Mascha Richter; Sören Auer; Astrid Nieße, Sebastian Lehnhoff https://creativecommons.org/licenses/by/4.0
Good data management is necessary to maintain the trustworthiness and reliability of data. This is particularly important in metrology, the science of measurement, which ensures stable, comparable, coherent, and traceable measurement results. The digitalization of metrology has increased the demand for structured and harmonised research data management (RDM). To meet this demand, the project TC-IM 1449 "Research data management in European metrology" was established in 2018. The project aims to promote good RDM practices underpinned by the FAIR principles, supporting traceability and reproducibility of measurement results. For that purpose, the project is providing researchers with the knowledge, competency, awareness, and tools to implement good RDM practices. The project has formulated a vision for RDM in metrology for the support of scientists by developing and disseminating recommendations and in the organisation of training. As part of this vision, the project has produced several deliverables, including a template research data management policy, guidelines for data documentation, creation of metadata, and quality assurance for data publication. The project is also creating a comprehensive guide to RDM, a checklist for project coordinators, and providing training modules. The project's activities reflect the needs of metrologists that are collated and communicated by the technical experts from the relevant Technical Committees and European Metrology Networks. Furthermore, the project's deliverables will be an invaluable resource for researchers seeking to effectively manage and share their research data.
Copyright (c) 2023 Giacomo Lanza, Martin Koval, Jean-Laurent Hippolyte, Maitane Iturrate-García, Olivier Pellegrino, Anne-Sophie Piette, Federico Grasso Toro https://creativecommons.org/licenses/by/4.0
Scientific research is becoming increasingly data centric, which requires more effort to manage, share, and publish data. NOMAD is a web-based platform that provides research data management (RDM) for materials-science data. In addition to core RDM functions like uploading and sharing files, NOMAD automatically extracts structured data from supported file formats, normalizes, and converts data from these formats. NOMAD provides an extendable framework for managing not just files, but structured machine-actionable harmonized and inter-operable data. This is the basis for a faceted search with domain-specific filters, a comprehensive API, structured data entry via customizable ELNs, integrated data-analysis and machine-learning tools. NOMAD is run as a free public service and can additionally be operated by research institutes. Connecting NOMAD installations through the public services will allow a federated data infrastructure to share data between research institutes and further harmonize RDM within a large research domain such as materials science.
Copyright (c) 2023 Markus Scheidgen, Sebastian Brückner, Sandor Brockhauser, Luca M. Ghiringhelli, Felix Dietrich, Ahmed E. Mansour, Martin Albrecht, Heiko B. Weber, Silvana Botti, Martin Aeschlimann, Claudia Draxl https://creativecommons.org/licenses/by/4.0
Persistent identifiers (PIDs) are an integral part of research data management and can be found throughout the entire lifecycle of research data. However, their ability to function – to ensure persistence – depends on numerous factors: technical infrastructure, international standards and best practices and their dissemination, agreements on long-term governance of infrastructures, etc. Their applicability is diverse and requires adaptation to the resources and entities referenced by them. The paper describes two projects – PID4NFDI and PID Network Germany – that aim to address these challenges.
Copyright (c) 2023 Stephanie Hagemann-Wilholt, Antonia C. Schrader, Andreas Czerniak https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2023 Roman Baum, Oliver Koepler https://creativecommons.org/licenses/by/4.0
Finding a Common Ground for NFDI Terminologies
We present the goals and first results of the NFDI Section Metadata Working Group on Ontology Harmonisation and Mapping. The ongoing analysis of the used terminologies within the NFDI consortia suggests that, agreeing on a single NFDI wide ontological framework for very general and interdisciplinary concepts needed in the semantic annotation of research date is not feasible in the short run due to domain specific requirements. We thus present how the Research Data Alliance (RDA) framework I-ADOPT, which focuses only on the formal description of observation variables in scientific studies, could be utilised to provide an NFDI wide and global common ground for data interoperability.
Copyright (c) 2023 Robert Huber, Naouel Karam, Oliver Koepler, Philip Strömert https://creativecommons.org/licenses/by/4.0
A Survey on the Current Status of Research Data Management in Mecklenburg-Vorpommern
High quality research data management (RDM) is essential to support state of the art comprehensible and reproducible research processes and, thus, foster the sustainable production of novel and trustworthy research findings. While there are lots of national and international initiatives supporting researchers in all respects of RDM, the local infrastructures provide the foundation for these concepts. In this contribution, we present preliminary results of a study that collects the requirements of researchers on these local infrastructures in all seven higher education research institutions in Mecklenburg-Vorpommern (MV).
Copyright (c) 2023 Max Schröder, Sascha Genehr, Rüdiger Köhling, Stefan Schmidt, Ralf Schneider, Sascha Spors, Gero Szepannek, Dagmar Waltemath, Frank Krüger https://creativecommons.org/licenses/by/4.0
Machine-Actionable Metadata for Software and Software Management Plans for NFDI
Research data is on its way to be recognized as a first-class citizen in research; however, and despite its importance for science, software still has a long way to go. Recent initiatives are paving the way, including FAIR for Research Software and Software Management Plans. A step further towards machine-actionability is adding a structured metadata layer. Here we discuss some metadata elements useful to represent software and integrate it into management plans, and how it could be of benefit for NFDI.
Copyright (c) 2023 Olga Giraldo, Danilo Dessi, Stefan Dietze, Dietrich Rebholz-Schuhmann, Leyla Jael Castro https://creativecommons.org/licenses/by/4.0
Determining the Similarity of Research Data by Using an Interoperable Metadata Extraction Method
Determining the similarity of research data is not a simple task, as the formats can differ widely depending on the domain. Especially, since many formats are represented as binary files, the raw comparison of these will not yield good results. This makes it hard to accurately tell how similar certain research work is by comparing the data. With the emergence of extracted interoperable metadata, a form to describe data has been provided which is independent of the data format. Therefore, this work tries to use this extracted interoperable metadata and create a method to determine the similarity of research data based on their metadata. The produced method utilizes domain knowledge about the extracted metadata and the way they are formulated. A baseline is created, and further methods are created to compare to. The results show that our method outperforms all other methods, especially the ones which are focused on comparing the research data itself, not the metadata. Since the results are promising, we propose further investigations against other datasets and possible use cases.
Copyright (c) 2023 Benedikt Heinrichs, M. Amin Yazdi https://creativecommons.org/licenses/by/4.0
In data management the high-level FAIR principles are interpreted and implemented in various FAIR metrics. While this specific interpretation is intended, it leads to the situation of several metrics with different evaluation results for the same digital object. This work conducts an organizational-formal comparison, showing up elements like categories of importance in the considered metrics, as well as a content-wise comparison of selected metrics how their differ in their interpretation. The results give orientation especially to everyone in science aiming to find the right metric to make their data FAIR.
Copyright (c) 2023 Mario Moser, Jonas Werheid, Tobias Hamann, Anas Abdelrazeq, Robert H. Schmitt https://creativecommons.org/licenses/by/4.0
LARAsuite (https://gitlab.com/larasuite) is a free and open source research data management system that addresses the problematic of manual data insertion and metadata assignment into the corresponding databases by a radically automated processes. Data and Metadata is mainly not inserted by humans, but by the machines producing the data and automatically transferring this generated data and metadata to the LARA RDM system. This data transfer is achieved through the intensive utilisation of the free and open lab-automation standard SiLA (Standardisa- tion in Labautomation) (https://sila-standard.org), combined with a simple to note process description language (pythonLab) and orchestration, scheduling, data aggre- gation and evaluation. Many LARA instances can be selectively synchronised to form a decentralised network of data infrastructures across labs and institutions. Research data and meta-data can be queried by a SPARQL endpoint. As the LARAsuite comes close to an ideal FAIR-principles based RDM System1, is generic to the most common natural science applications, is open source (python based), modular and easy deploy- able, it can be used to showcase NFDI goals for a wide range of scientists
Copyright (c) 2023 Mark Doerr, Stefan T. Maak, Marian J. Menke, Uwe T. Bornscheuer https://creativecommons.org/licenses/by/4.0
Harmonized Research Information for Classifying and Linking Research Data
A well-described, harmonized in standard formats and linked metadata accelerates and refines the search process. Hence, researchers can ensure that their data is properly organized, easily discoverable, and accessible to others. Machine-readable metadata is especially essential for automatic discovery of research datasets and outputs. It facilitates the search and retrieval of data with increased accuracy, which saves the user time in finding crucial information within their field of research. This requires rich and categorized metadata that allows computers to automatically retrieve and sort relevant search results. Well documented metadata which is harmonized, standardized and serialized in linked data formats increases not only the accuracy of the search results within a field of research, but can also provide relevant and helpful suggestions within disciplinary and interdisciplinary fields of research.
Copyright (c) 2023 Fadwa Alshawaf, Rolf Guescini, Florian Kotschka, Malte Dreyer, Maik Bierwirth https://creativecommons.org/licenses/by/4.0
The Case for a Common, Reusable Knowledge Graph Infrastructure for NFDI
The Strategic Research and Innovation Agenda (SRIA) of the European Commission identifies Knowledge Graphs (KGs) as one of the most important technologies for building an interoperability framework and enabling data exchange among users across countries, sectors, and disciplines [1]. KG is a graph-structured knowledge base containing a terminology (vocabulary or ontology) and data entities interrelated via the terminology [2]. KGs are based on semantic web technologies (RDF, SPARQL, etc.) and often used for agile data integration. KGs also play an essential role within Germany as a vehicle to connect research data and research-related entities and make those accessible – examples include the GESIS Knowledge Graph Infrastructure, TIB Open Research Knowledge Graph, and GND.network. Furthermore, the Wikidata knowledge graph, maintained by Wikimedia Germany, contains a large number of research-related entities and is widely used in scientific knowledge management in addition to being an important advocacy tool for open data [3]. Extending domain-specific ontology-supported KGs with the multidisciplinary, crowdsourced knowledge in Wikidata KG would enable significant applications. The linking between expert knowledge systems and world knowledge empowers lay persons to benefit from high-quality research data and ultimately contributes to increasing confidence in scientific research in society.
Copyright (c) 2023 Lozana Rossenova, Moritz Schubotz, Renat Shigapov https://creativecommons.org/licenses/by/4.0
Organizing Scholarly Knowledge in the Open Research Knowledge Graph
The Open Research Knowledge Graph (ORKG) is an Open Science digital infrastructure for the production, curation, publication, and reuse of machine-actionable scholarly knowledge. Built on top of the RDF data model and extensible ontologies, the ORKG provides a common vocabulary for researchers to describe their research contributions and data, improving the discoverability and reusability of scholarly knowledge and research data. The ORKG includes tools for visualizing the relationships between different entities, making it easier to understand the connections between different pieces of research and their findings. It facilitates collaboration between researchers by providing a collaborative platform for organizing and sharing scholarly knowledge and data, reducing duplication and enabling more efficient use of resources. As research becomes increasingly data-driven, tools like the ORKG will become essential for enabling efficient, transparent, and collaborative research.
Copyright (c) 2023 Sören Auer, Markus Stocker, Oliver Karras, Allard Oelen, Jennifer D'Souza, Anna-Lena Lorenz https://creativecommons.org/licenses/by/4.0
Based on our experience within the NFDI4Culture and NFDI-MatWerk projects we propose generalized knowledge graph based research data management solutions, which are applicable to other consortia. Our solution covers the construction of a common NFDI core ontology adapted to specific domains via domain extensions as a basis for a knowledge graph (KG) providing information about a consortium and its related research data and software resources. This KG serves as a backend for the web portal that enables interactive access and management of this data. Already implemented for NFDI4Culture and to be adapted by NFDI-MatWerk, this solution might serve as an example solution also for other consortia. We are synchronizing our efforts with ongoing work to implement knowledge graph based research data management in NFDI4DataScience.
Copyright (c) 2023 Harald Sack, Torsten Schrade, Oleksandra Bruns, Etienne Posthumus, Tabea Tietz, Ebrahim Norouzi, Jörg Waitelonis, Heike Fliegl, Linnaea Söhn, Julia Tolksdorf, Jonatan Jalle Steller, Abril Az´ocar Guzm´an, Said Fathalla, Ahmad Zainul Ihsan, Volker Hofmann, Stefan Sandfeld, Felix Fritzen, Amir Laadhar, Sonja Schimmler, Peter Mutschke https://creativecommons.org/licenses/by/4.0
Transparency and Involvement of Society and Policy in a Data Sharing Platform
Today, energy system models are becoming increasingly powerful and detailed regarding techno-economic parameters. However, current models rarely include social and political factors, although these factors constitute important determinants for the design of energy systems. In Task Area 2 of the nfdi4energy research project, we therefore explore social and political drivers and constraints of the energy transition, generate and link the relevant data, and prepare it for incorporation on a data sharing platform. The aim of this task area is to co-design a scientific energy data and research sharing platform that can feed into new or existing energy models to help inform the public and political decision makers to determine socially acceptable energy pathways of the future. In addition, we will involve citizens during the project lifetime in the development of a platform that enables and incentivizes the active participation of public stakeholders in energy system research. Consequentially, the intention of this abstract within the “Linking RDM Track” is to provide an overview of the platform engagement design process for society and policy.
Copyright (c) 2023 Christina Speck, Patrick Jaquart, Christof Weinhardt, Johan Lilliestam, Mirko Schäfer, Anke Weidlich, Julia Zilles, Nina Kerker https://creativecommons.org/licenses/by/4.0
Data, Tools and Services for Spatial Sustainability Science
The emerging research data centre (RDC) at the Leibniz Institute of Ecological Urban and Regional Development (IOER) constitutes an essential milestone towards promoting sustainable land transition and transformative urban and regional development. The IOER RDC leverages spatial data science and artificial intelligence to process and analyse diverse and complex data sources. It provides high-resolution indicator maps of land use, ecosystems, and settlement structures, as well as cross-scale and cross-disciplinary spatial analyses, modelling, and simulations. Moreover, the IOER RDC offers digital tools to support decision-making, policy planning, and sustainable transformations. Hence, the IOER RDC has the potential to foster a sustainable future by facilitating the transition towards sustainable land use and development in urban and regional areas. The IOER RDC's endeavours offer a path towards addressing pressing societal challenges, such as rapid urbanisation, environmental degradation, climate change, and social inequality.
Copyright (c) 2023 Ramona Voshage, Sujit Sikder, Stefano Della Chiesa, Tobias Krüger, Martin Schorcht, Gotthard Meinel https://creativecommons.org/licenses/by/4.0
NFDI4Energy Case-Study: Comparative Analysis and Visualisation of Long-Term Energy System Scenarios
Analysis and comparison of energy system scenarios provide valuable insights into potential transformation pathways. These studies on long-term developments can serve as new inputs for scientific research and decision-making processes, providing policymakers and other stakeholders with the necessary guidance to achieve sustainable energy systems. Generally, such scenarios are derived from energy system models which often seek a cost-optimal system design under a variety of boundary conditions, ranging from technical constraints to limits of land availability or a cap on overall greenhouse gas emissions [1]. For Germany, several larger energy system scenario studies have been published, addressing the goal of carbon neutrality in 2045 as prescribed in the German climate protection act [2]. These studies show differences in their specific methodology, sector representation, parameter settings or, more generally, overall scenario narratives. This diversity represents a challenge regarding the comparability of these studies, and consequently the ability to identify consensus and controversies in their findings. Often only limited access to data for parameter settings and scenario results is provided. Almost always the data is presented in different detail and formats, thus imposing further barriers for comparison and usability for the scientific community [3]. As one of the three use cases applied in Task Area 6 of the NFDI4Energy research project, we aim to address this challenge by providing transparent and open comparative information and data on long-term energy system scenarios. Selected scenarios for the transition towards a climate-neutral Germany will be annotated with terms form the Open Energy Ontology (OEO) [4]. The comparison is building on an already existing database infrastructure from the Open Energy Platform (OEP) [5]. Existing concepts for qualitative and quantitative comparisons will be used and improved to cover a wide range of existing energy system studies.
Copyright (c) 2023 Mirko Schäfer, Ramiz Qussous, Ludwig Hülk, Johan Lilliestam, Anke Weidlich https://creativecommons.org/licenses/by/4.0
A large amount of different data is currently collected in the mobility sector. However, due to technical, legal, and economic hurdles, it cannot be made usable. The "TreuMoDa" project, in which a Mobility Data Trustee (MDT) is being designed and tested as a prototype in the autonomous driving test area in Baden-Württemberg, provides a solution to this problem. Data trustees are a pioneering option to enable cross-sectoral data exchange between different actors from industry, science, and society. We provide an insight into the concept of the MDT. Specifically, the requirements of various stakeholders from the automotive industry, software developers, infrastructure, cities, and transport companies are analyzed with regard to the expected organizational, legal, and technical functions of such an MDT. The concept of a data trustee is particularly relevant to research data infrastructures, as it can enable the flow of data from research to industry and vice versa. Our work therefore also will look at the MDT as part of a larger research data management scheme.
Copyright (c) 2023 Andreas Czech, Vivien Geenen, Constantin Breß, Marija Turkovic Popovski, Peter Krauß, Till Riedel, Frank Gauterin https://creativecommons.org/licenses/by/4.0
The Chemistry consortium NFDI4Chem aims to digitalise key steps in chemical research, supporting scientists in managing research data throughout its life cycle. The SmartLab, embedded in a federation of services, integrates various tools such as electronic lab notebooks, data repositories, and search services, to create a smart lab environment for structured data gathering. Utilizing terminology services and adhering to data format standards, NFDI4Chem promotes secure and FAIR data sharing, fostering collaboration and expediting scientific discoveries. This development is supported by community building measures, workshops, and training initiatives, along with collaboration on international minimum information standards.
Copyright (c) 2023 Oliver Koepler, Christoph Steinbeck, Felix Bach, Sonja Herres-Pawlis, Nicole Jung, Johannes Liermann, Steffen Neumann, Matthias Razum https://creativecommons.org/licenses/by/4.0
The DAPHNE4NFDI and PUNCH4NFDI Consortia in the NFDI
The DAPHNE4NFDI and PUNCH4NFDI consortia represent the large scale facilities in the German physical sciences community. Work in DAPHNE4NFDI and PUNCH4NFDI is characterised by the use of large-scale research infrastructures – reactors, light sources, accelerators, telescopes, observatories, satellites – that serve international research communities of up to several thousand users and produce data in the terrabyte, often petabyte and in future exabyte range. The communities will have to master massive challenges in data management, building on and extending their leadership in “big data” management, distributed computing, multi-user management, and data loss / data irreversibility issues.
Copyright (c) 2023 Lisa Amelung, Anton Barty, Bridget Murphy, Christiane Schneide, Astrid Schneidewind, Thomas Schoerner https://creativecommons.org/licenses/by/4.0
Research Data Management for Experiments in Solid-State Physics: Concepts
FAIRmat develops concepts for paving the way to enable FAIR research data in solid-state physics. For selected theoretical data in this field, the NOMAD portal has developed mature concepts and technological solutions for storing data according to the FAIR principles. Extending this approach to experimental data is challenging due to their diversity and missing standards. In this paper we present our comprehensive approach to establish FAIR data in the field of experimental solid-state physics despite its heterogeneity. The concept includes elaboration of standards, community building and methods that facilitate the community’s transition to FAIR standards.
Copyright (c) 2023 Heiko B. Weber, Sandor Brockhauser, Christoph Koch, Laurenz Rettig, Martin Aeschlimann, Walid Hetaba, Marius Grundmann, Markus Kühbach, Michael Krieger https://creativecommons.org/licenses/by/4.0
Integrating Data Literacy Into University Curricula
The implementation of competencies regarding Research Data Management (RDM) and Data Literacy (DL) into the curricula is a major challenge for the NFDI community. In physics as well as in other disciples, the beginners’ lab courses are the first point in time where students have to deal with data, and are therefore an intuitive point for the integration of first RDM learning objectives. However, the modification of teaching materials is a time-consuming and potentially expensive task. In this contribution the authors show how this can be achieved in the physics beginners’ lab on the basis of concrete examples, thereby affecting the education of students in a wide range of subjects, including the sciences, engineering, but potentially also medicine or geology.
Copyright (c) 2023 Janice Bode, Philipp Jaeger, Sonja Schneidewind https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2023 Renita Danabalan, Michael Hintermüller, Thomas Koprucki, Karsten Tabelow https://creativecommons.org/licenses/by/4.0
MaRDIFlow: A Workflow Framework for Documentation and Integration of FAIR Computational Experiments
Numerical algorithms and computational tools are essential for managing and analyzing complex data processing tasks. With ever increasing availability of meta-data and parameter-driven simulations, the demand and the need for reliable and automated workflow frameworks to reproduce computational experiments has grown. In this work, we aim to develop a novel computational workflow framework, namely MaRDIFlow, that describes the abstraction of multi-layered workflow components. Herein, we plan to enable and implement scientific computing data FAIRness into actionable guidelines for FAIR computational experiments.
Copyright (c) 2023 Pavan L. Veluvali, Jan Heiland, Peter Benner https://creativecommons.org/licenses/by/4.0
Overarching Data Management Ecosystem at HZDR
When dealing with research data management, researchers at Helmholtz- Zentrum Dresden – Rossendorf (HZDR) face a variety of systems and tools. These range from the project planning phase (proposal management, data management plans and policies), over documentation during the experiment or simulation campaign, to the publication (collaborative authoring tools, metadata catalogs, publication systems, data repositories). In addition, modern research projects usually are required to interact with a variety of software stacks and workflow management systems to allow comprehensi- ble and FAIR science on the underlying IT infrastructure (HPC, data storage, network file systems, archival). This article first demonstrates the data management systems and services provided at HZDR, followed by an overview of a self-developed guidance system. It is concluded by a real-world example.
Copyright (c) 2023 Oliver Knodel, Thomas Gruber, Jeffrey Kelling, Mani Lokamani, Stefan Müller, David Pape, Martin Voigt, Guido Juckeland https://creativecommons.org/licenses/by/4.0
Two-Step Approach in Metadata Management for Data Publications at Research Centers
Data repositories like Zenodo have a limited list of metadata to search for. Metadata catalogue are designed to provide a community-specific parameters search, but their deployment has just started. These catalogues require metadata standards for interoperability, which in turn are often in development for many communities. To support publications with a metadata standard in the future, a two-step concept is presented in this article. It discusses how the electronic documentation should be constructed, in order to convert this later into a standardized schema for publication. We will present examples from the laser-plasma community for both steps, firstly how we deal with the complex challenges of metadata management and secondly for methods for developing metadata schemas.
Copyright (c) 2023 Thomas Gruber, Hans-Peter Schlenvoigt, Oliver Knodel, Kristin Tippey, Guido Juckeland https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2023 Eric Apondo, Andrea Züger, Andreas Bruns, Katja Mehlis, Christoph Schickhardt, Eva Winkler https://creativecommons.org/licenses/by/4.0
Developing Consent Tools for the Research Community at the German Human Genome-Phenome Archive (GHGA)
The German Human Genome-Phenome Archive (GHGA) aims to enable the responsible sharing of human omics data for secondary research use across Germany and Europe. Informed consent is the most commonly used legal and ethical basis for processing omics data for secondary use. However, obtaining informed consent from Data Subjects can be challenging when data is to be widely shared and reused beyond the initial purpose of collection. To address these challenges, the ELSI (Ethical, Legal, and Social Implications) Group of GHGA has developed consent tools for the research community. First, we have developed a toolkit for prospective data collection, which consists of consent modules and complementary advice on how to update or create new consent forms. Second, we have created a legacy consent toolkit that can be used by researchers to assess whether the consent under which data was originally collected covers further data processing for secondary research purposes.
Copyright (c) 2023 Andreas Bruns, Simon Parker, Fruzsina Molnár-Gábor, Eva C. Winkler https://creativecommons.org/licenses/by/4.0
Distributed Privacy-Preserving Data Analysis in NFDI4Health With the Personal Health Train
Data sharing is often met with resistance in medicine and healthcare, due to the sensitive nature and heterogeneous characteristics of health data. The lack of standardization and semantics further exacerbate the problems of data fragments and data silos, which makes data analytics challenging. NFDI4Health aims to develop a data infrastructure for personalized medicine and health research and to make data generated in clinical trials, epidemiological, and public health studies FAIR (Findable, Accessible, Interoperable, and Reusable). Since this research data infrastructure is distributed over various partners contributing to their data, the Personal Health Train (PHT) complements this infrastructure by providing a required analytics infrastructure considering the distribution of data collections. Our research have demonstrated the capability of conducting data analysis on sensitive data in various formats distributed across multiple institutions and shown great potential to facilitate medical and health research.
Copyright (c) 2023 Yongli Mou, Feifei Li, Sven Weber, Sabith Haneef, Hans Meine, Liliana Caldeira; Mehrshad Jaberansary; Sascha Welten, Yeliz Yediel Ucer, Guido Prause, Stefan Decker, Oya Beyan, Toralf Kirsten https://creativecommons.org/licenses/by/4.0
The proposed contribution will shed light on current and future challenges on legal and ethical questions in research data infrastructures. The authors of the proposal will present the work of NFDI’s section on Ethical, Legal and Social Aspects (hereinafter: ELSA), whose aim is to facilitate cross-disciplinary cooperation between the NFDI consortia in the relevant areas of management and re-use of research data.
Copyright (c) 2023 Franziska Boehm, Ulrich Sax, Oliver Vettermann, Paweł Kamocki, Vasilka Stoilova https://creativecommons.org/licenses/by/4.0
The 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.
Copyright (c) 2023 Luiz Gadelha, Jan Eufinger https://creativecommons.org/licenses/by/4.0
International Data Access Network (IDAN) for Sensitive Microdata in Humanities & Social Sciences
In a globalized world it becomes increasingly important to provide international research data. This is particularly true in Humanities & Social Sciences. With legal frameworks changing, administrative data can increasingly be utilised both for official statistics and to facilitate new research, enabling the development of evidence-based policy for the public benefit. Secure access conditions generally apply to using these rich, highly detailed level data. However, using data from various sources is difficult when they are fragmented in ‘silos’ between several Research Data Centres (RDCs). While this might be the case at a national level, it is very likely to be the case at an international level. The latter is a major obstacle for international comparative research. The International Data Access Network (IDAN, https://idan.network/) aims at creating a concrete operational international framework enabling access to controlled data for research. The Network was founded in 2018, at the same time when the European General Data Protection Regulation became active. It currently involves six RDCs from France, Germany, the Netherlands and the United Kingdom. Within IDAN, step by step cooperative solutions are developed taking into account the particular legal and security requirements that are at the core of both, national and transnational access to confidential data. Initially, the partners’ access systems are being implemented in each partners’ premise based on bilateral agreements. This process involves combining requirements of security and surveillance for Safe Rooms. It sets up a new concrete environment for researchers to work remotely with data from the other partners from their local RDC. IDAN is a perfect match for the topic “Connecting RDM” regarding confidential data which are a highly demanded by the international research community. The presentation will show the opportunities and obstacles in developing the IDAN infrastructure. Based on discussions with research experts with heterogeneous backgrounds in Europe, it will also discuss researcher’s expectations and needs to further improve access to international research data. IDAN could be a role model for other research disciplines and research infrastructure for pushing the boundaries of international data access.
Copyright (c) 2023 Dana Müller, Matthias Umkehrer https://creativecommons.org/licenses/by/4.0
Connecting Infrastructures: The Physical Sciences Data Infrastructure (PSDI) in the UK
In this presentation we discuss the activities undertaken in the UK through the Physical Sciences Data Infrastructure (PSDI) initiative, part of the wider Digital Research Infrastructure (DRI) Programme. We will present the aims of the PSDI initiative, our initial scoping work and trials, and suggest how this project can and should interact with other related initiatives on a national and global scale. Physical Scientists are crying out for a socio-technical data infrastructure that connects existing experimental and computational facilities. We believe that a cross-discipline and cross-technique digital infrastructure that builds on and bridges across existing initiatives, while these continue to serve their particular fields, is crucial to, and the best way to achieve global collaborations in the 21st century.
Copyright (c) 2023 Juan Bicarregui, Simon Coles, Brian Matthews, Jeremy Frey, Barbara Montanari, Vasily Bunakov, Nicola Knight https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2023 Nina Leonie Weisweiler, Roland Bertelmann, Steffi Genderjahn, Heinz Pampel https://creativecommons.org/licenses/by/4.0
In this paper we describe with the help of two examples how FAIR Digital Objects can be used to bridge between different dataspaces, repositories using different technologies, and model worlds. The first example is the digital specimen as defined by DiSSCO which offers access to a variety of information from different repositories in a persistent way. The second example results from a collaboration with the I4.0 Asset Administration Shell experts where we integrate the two domains using the Digital Product Pass as an example. In this example we also show how FDOs can be used to implement secure access on shared data as it occurs in almost all supply chain processes.
Copyright (c) 2023 Peter Wittenburg, Ulrich Schwardmann, Christophe Blanchi, Claus Weiland https://creativecommons.org/licenses/by/4.0
Long Term Interoperability of Distributed Research Data Infrastructures
Research institutions have established a variety of research data infrastructures that orient towards discipline or methodology specific needs of their respective research community. Technically, these infrastructures ultimately are based on “off-the-shelf” hardware and software building blocks – both commercial and open-source. While such “enterprise ready” infrastructures can scale well, they apt to data silos and typically do not adhere to scientific standards like the FAIR (Findability, Accessibility, Interoperability, Reusability) principles out of the box. Using common architecture concepts such as the FAIR Digital Object (FAIR DO) allows interconnection of these silos by adding a long term interoperability layer on top of the existing infrastructure components. The presented approach provides a practical solution for interconnecting distributed research data infrastructures to national (like NFDI) and international (like EOSC and Gaia-X) infrastructures and preventing the creation of data silos. By allowing existing data infrastructures to make data FAIR, we enable researchers to access and reuse data from different domains, facilitating cross-disciplinary research and advancing new methods for scientific discoveries.
Copyright (c) 2023 Marius Politze, Yusra Shakeel, Siream Hunke, Philipp Ost, Rossella Aversa, Benedikt Heinrichs, Ilona Lang https://creativecommons.org/licenses/by/4.0
The Federal State Initiatives for RDM as Intermediaries in a Dynamic Landscape of RDM Infrastructures and Services
A number of German federal states have established initiatives to support the institutionalization of RDM infrastructures and services. They can serve as intermediaries between disciplinary approaches to RDM like NFDI and RDM services in individual research institutions. This presentation gives an overview of the current state of these initiatives and provides an outlook on their role in creating synergies in RDM with a focus on their integrating potential for NFDI.
Copyright (c) 2023 Ortrun Brand, Karen Bruhn, Magdalene Cyra, Matthias Fingerhuth, Roman Gerlach, Boris Jacob, Cora Krömer, Ralph Müller-Pfefferkorn, Heike Neuroth, Thilo Paul-Stüve, Stephanie Rehwald, Janine Straka, Barbara Weiner https://creativecommons.org/licenses/by/4.0
Data Trustees – They Do Work! The Example of Research Data Centers
This contribution presents the long established system of accredited Research Data Centers (RDCs). Created in the data-landscape of the social, behavioral, educational, and economic sciences, they enable access to restricted data and bridge interests of data providers and researchers. A distinctive feature of most research data in the above disciplines is the coverage of real persons. Such sensitive data require specific safeguards. The focus of this contribution is on the institutionalized processes of connecting and securing appropriate research data management strategies for this sort of data. It includes quality assurance measures through the accreditation of RDCs, a monitoring system and the regular cooperation of accredited RDCs.
Copyright (c) 2023 Daniel Fuß, Marie-Christine Laible https://creativecommons.org/licenses/by/4.0
Data Publication for Personalised Health Data
Health data collected in clinical trials and epidemiological as well as public health studies cannot be freely published, but are valuable datasets whose subsequent use is of high importance for health research. The National Research Data Infrastructure for Personal Health Data (NFDI4Health) aims to promote the publication of such health data without compromising privacy. Based on existing international standards, NFDI4Health has established a generic information model for the description and preservation of high-level metadata describing health-related studies, covering both clinical and epidemiological studies. As an infrastructure for publishing such preservation metadata as well as more detailed representation information of study data (e.g. questionaries and data dictionaries), NFDI4Health has developed the German Central Health Study Hub. Content is either harvested from existing distributed sources or entered directly via a user interface. This metadata makes health studies more discoverable, and researchers can use the published metadata to evaluate the content of data collections, learn about access conditions and how and where to request data access. The goal of NFDI4Health is to establish interoperable and internationally accepted standards and processes for the publication of health data sets to make health data FAIR.
Copyright (c) 2023 Juliane Fluck, Martin Golebiewski, Johannes Darms https://creativecommons.org/licenses/by/4.0
COVID-19 posed one of the greatest challenges to individuals and societies worldwide in recent decades. Public health research, epidemiological and clinical studies were essential to track the spread of SARS-CoV-2 responsible for the pandemic and its variants, to better understand the consequences for health and social life, and to identify effective treatment and vaccination methods. Such studies provided policy makers, industry, health care providers, and society with an empirical basis for containing and managing the pandemic and for making decisions that were based on the most recent data. Therefore, the COVID-19 pandemic excellently illustrates the relevance of data sharing and the importance of providing an effective infrastructure. From the researchers’ perspective, there were significant challenges associated with this request. In a very short time, numerous projects, studies, and networks had emerged to investigate the pandemic, making it increasingly difficult to maintain an overview. Such an overview would have been essential to coordinate research activities, avoid unplanned duplication of research, and to implement studies in a harmonized manner.
Copyright (c) 2023 Iris Pigeot, Juliane Fluck, Johannes Darms, Carsten Oliver Schmidt https://creativecommons.org/licenses/by/4.0
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.
Copyright (c) 2023 Barbara Ebert, Judith Sophie Engel, Ivaylo Kostadinov, Anton Güntsch, Frank Oliver Glöckner https://creativecommons.org/licenses/by/4.0
The EOSC-Life Workflow Collaboratory for the Life Sciences
Workflows have become a major tool for the processing of Research Data, for example, data collection and data cleaning pipelines, data analytics, and data update feeds populating public archives. The EOSC-Life Research Infrastructure Cluster project brought together Europe’s Life Science Research Infrastructures to create an Open, Digital and Collaborative space for biological and medical research to develop a cloud-based Workflow Collaboratory. As adopting FAIR practices extends beyond data, the Workflow Collaboratory drives the implementation of FAIR computational workflows and tools. It fosters tool-focused collaborations and reuse via the sharing of data analysis workflows and offers an ecosystem of services for researchers and workflow specialists to find, use and reuse workflows. It’s web-friendly Digital Object Metadata Framework, based on RO-Crate and Bioschemas, supports the description and exchange of workflows across the services.
Copyright (c) 2023 Carole Goble, Finn Bacall, Stian Soiland-Reyes, Stuart Owen, Ignacio Eguinoa, Bert Droesbeke, Hervé Ménager, Laura Rodriguez-Navas, José M. Fernández, Björn Grüning, Simone Leo, Luca Pireddu, Michael Crusoe, Johan Gustafsson, Salvador Capella-Gutierrez, Frederik Coppens https://creativecommons.org/licenses/by/4.0