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Author: Koepler, Oliver ×

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    NFDI4Chem - A Research Data Network for International Chemistry
    (Berlin : De Gruyter, 2023) Steinbeck, Christoph; Koepler, Oliver; Herres-Pawlis, Sonja; Bach, Felix; Jung, Nicole; Razum, Matthias; Liermann, Johannes C.; Neumann, Steffen
    Research data provide evidence for the validation of scientific hypotheses in most areas of science. Open access to them is the basis for true peer review of scientific results and publications. Hence, research data are at the heart of the scientific method as a whole. The value of openly sharing research data has by now been recognized by scientists, funders and politicians. Today, new research results are increasingly obtained by drawing on existing data. Many organisations such as the Research Data Alliance (RDA), the goFAIR initiative, and not least IUPAC are supporting and promoting the collection and curation of research data. One of the remaining challenges is to find matching data sets, to understand them and to reuse them for your own purpose. As a consequence, we urgently need better research data management.
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    Creation of a Knowledge Space by Semantically Linking Data Repository and Knowledge Management System - a Use Case from Production Engineering
    (Laxenburg : IFAC, 2022) Sheveleva, Tatyana; Wawer, Max Leo; Oladazimi, Pooya; Koepler, Oliver; Nürnberger, Florian; Lachmayer, Roland; Auer, Sören; Mozgova, Iryna
    The seamless documentation of research data flows from generation, processing, analysis, publication, and reuse is of utmost importance when dealing with large amounts of data. Semantic linking of process documentation and gathered data creates a knowledge space enabling the discovery of relations between steps of process chains. This paper shows the design of two systems for data deposit and for process documentation using semantic annotations and linking on a use case of a process chain step of the Tailored Forming Technology.
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    NFDI4Chem - Fachkonsortium für die Chemie
    (Marburg : Philipps-Universität, 2021) Ortmeyer, Jochen; Schön, Florian; Herres-Pawlis, Sonja; Jung, Nicole; Bach, Felix; Liermann, Johannes; Neumann, Steffen; Popp, Christian; Razum, Matthias; Koepler, Oliver; Steinbeck, Christoph
    Als Fachkonsortium für die Chemie hat sich NFDI4Chem innerhalb der Nationalen Forschungsdateninfrastruktur (NFDI) gebildet. In diesem Beitrag stellt sich das Konsortium kurz vor und legt seine zentralen Ziele und wichtigsten Verbesserungen für das Forschungsdatenmanagement (FDM) in der Chemie sowie die praktischen Heraus-forderungen dar. Die Vision von NFDI4Chem ist die umfassende Digitalisierung und Vernetzung aller Prozesse im Umgang mit Forschungsdaten in der chemischen Forschung. Beginnend mit der Erzeugung der Daten, über deren Verarbeitung und Analyse bis hin zur Publikation wird eine modulare, vernetzte Infrastruktur aus Software-Tools, elektronischen Laborjournalen und Datenrepositorien entwickelt und bereitgestellt, die Forschende im Laboralltag unterstützt. Die Digitalisierung wird begleitet durch die Entwicklung von Minimalinformationen für Datenpublikationen, bestehend unter anderem aus Standards für Daten- und Metadatenformate sowie Ontologien zur semantischen Beschreibung. Seine Aufgaben verfolgt das NFDI4Chem-Konsortium wissenschaftsgeleitet und mit dem klaren Ziel, eine intuitiv und effizient nutzbare Infrastruktur zu entwickeln. Das Gestalten eines kulturellen Wandels, gemeinsam mit der wissenschaftlichen Community, zur Etablierung und Akzeptanz eines FAIRen Umgangsmit Daten ist daher ein weiteres wichtiges Element der NFDI4Chem-Aktivitäten.
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    Knowledge Graphs - Working Group Charter (NFDI section-metadata) (1.2)
    (Genève : CERN, 2023) Stocker, Markus; Rossenova, Lozana; Shigapov, Renat; Betancort, Noemi; Dietze, Stefan; Murphy, Bridget; Bölling, Christian; Schubotz, Moritz; Koepler, Oliver
    Knowledge Graphs are a key technology for implementing the FAIR principles in data infrastructures by ensuring interoperability for both humans and machines. The Working Group "Knowledge Graphs" in Section "(Meta)data, Terminologies, Provenance" of the German National Research Data Infrastructure (Nationale Forschungsdateninfrastruktur (NFDI) e.V.) aims to promote the use of knowledge graphs in all NFDI consortia, to facilitate cross-domain data interlinking and federation following the FAIR principles, and to contribute to the joint development of tools and technologies that enable transformation of structured and unstructured data into semantically reusable knowledge across different domains.
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    Ontologies4Chem: The landscape of ontologies in chemistry
    (Berlin : de Gruyter, 2022) Strömert, Philip; Hunold, Johannes; Castro, André; Neumann, Steffen; Koepler, Oliver
    For a long time, databases such as CAS, Reaxys, PubChem or ChemSpider mostly rely on unique numerical identifiers or chemical structure identifiers like InChI, SMILES or others to link data across heterogeneous data sources. The retrospective processing of information and fragmented data from text publications to maintain these databases is a cumbersome process. Ontologies are a holistic approach to semantically describe data, information and knowledge of a domain. They provide terms, relations and logic to semantically annotate and link data building knowledge graphs. The application of standard taxonomies and vocabularies from the very beginning of data generation and along research workflows in electronic lab notebooks (ELNs), software tools, and their final publication in data repositories create FAIR data straightforwardly. Thus a proper semantic description of an investigation and the why, how, where, when, and by whom data was produced in conjunction with the description and representation of research data is a natural outcome in contrast to the retrospective processing of research publications as we know it. In this work we provide an overview of ontologies in chemistry suitable to represent concepts of research and research data. These ontologies are evaluated against several criteria derived from the FAIR data principles and their possible application in the digitisation of research data management workflows.
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    Development of a Domain-Specific Ontology to Support Research Data Management for the Tailored Forming Technology
    (Amsterdam [u.a.] : Elsevier, 2020) Sheveleva, Tatyana; Koepler, Oliver; Mozgova, Iryna; Lachmayer, Roland; Auer, Sören
    The global trend towards the comprehensive digitisation of technologies in product manufacturing is leading to radical changes in engineering processes and requires a new extended understanding of data handling. The amounts of data to be considered are becoming larger and more complex. Data can originate from process simulations, machines used or subsequent analyses, which together with the resulting components serve as a complete and reproducible description of the process. Within the Collaborative Research Centre "Process Chain for Manufacturing of Hybrid High Performance Components by Tailored Forming", interdisciplinary work is being carried out on the development of process chains for the production of hybrid components. The management of the generated data and descriptive metadata, the support of the process steps and preliminary and subsequent data analysis are fundamental challenges. The objective is a continuous, standardised data management according to the FAIR Data Principles so that process-specific data and parameters can be transferred together with the components or samples to subsequent processes, individual process designs can take place and processes of machine learning can be accelerated. A central element is the collaborative development of a domain-specific ontology for a semantic description of data and processes of the entire process chain.
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    Harmonising, Harvesting, and Searching Metadata Across a Repository Federation
    (Hannover : TIB Open Publishing, 2023) Neumann, Steffen; Bach, Felix; Castro, Leyla Jael; Fischer, Tillmann; Hofmann, Stefan; Huang, Pei-Chi; Jung, Nicole; Katabathuni, Bhavin; Mauz, Fabian; Meier, René; Nainala, Venkata Chandra Sekhar; Rayya, Noura; Steinbeck, Christoph; Koepler, Oliver
    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.
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    Digitalizing the Chemical Landscape: A Comprehensive Overview and Progress Report of NFDI4Chem
    (Hannover : TIB Open Publishing, 2023) Koepler, Oliver; Steinbeck, Christoph; Bach, Felix; Herres-Pawlis, Sonja; Jung, Nicole; Liermann, Johannes; Neumann, Steffen; Razum, Matthias
    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.
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    Thesenpapier Nationale Forschungsdateninfrastruktur für die Chemie (NFDI4Chem)
    (Zenodo, 2018) Koepler, Oliver; Jung, Nicole; Kraft, Angelina; Neumann, Janna; Auer, Sören; Bach, Felix; Bähr, Thomas; Engel, Thomas; Kettner, Carsten; Kowol-Santen, Johanna; Liermann, Johannes; Lipp, Anne; Porzel, Andrea; Razum, Matthias; Schlörer, Niels; Solle, Dörte; Winkler, Torsten
    “Der stufenweise Aufbau einer Nationalen Forschungsdateninfrastruktur in Netzwerkform hat das Ziel, ein verlässliches und nachhaltiges Dienste-Portfolio zu schaffen, welches generische und fachspezifische Bedarfe des Forschungsdatenmanagements in Deutschland abdeckt.” Für das Fachgebiet Chemie ermöglicht eine solche nationale Forschungsdateninfrastruktur, öffentlich-finanzierte Forschungsdaten effizient zu erheben, standardisiert zu beschreiben, dauerhaft zu speichern und durch Persistent Identifier (PID) eindeutig referenzierbar und auffindbar zu machen. Sie unterstützt gemäß den Vorgaben des RfII die Reproduzierbarkeit und Nachnutzbarkeit der Daten zum Zwecke einer perpetuierten Wissensgenerierung. Mit der Reproduzierbarkeit von Forschungsergebnissen unterstützt eine solche Forschungsdateninfrastruktur den Peer Reviewing Prozess zur Förderung der wissenschaftlichen Selbstkontrolle und erhöht die Datenqualität, insbesondere in wissenschaftlichen Publikationen. Die NFDI4Chem ist ein gemeinschaftlicher Ansatz von Wissenschaftlern aus der Chemie, der Fachgesellschaft Gesellschaft Deutscher Chemiker und deren Fachgruppen, Einrichtungen aus der Forschungsförderung und Infrastruktureinrichtungen (Technische Informationsbibliothek). Eine Gruppe von Vertretern dieser Stakeholder hat sich Ende April 2018 zu einem Auftakttreffen “Fachgespräch NFDI4Chem” in Hannover getroffen. Dieses Papier fasst die Erkenntnisse des Fachgespräches zusammen. Weitere Stakeholder wie Verlage oder Datenbankanbieter sind im folgenden Diskurs willkommen.
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    Towards an Open Research Knowledge Graph
    (Zenodo, 2018) Auer, Sören; Blümel, Ina; Ewerth, Ralph; Garatzogianni, Alexandra; Heller,, Lambert; Hoppe, Anett; Kasprzik, Anna; Koepler, Oliver; Nejdl, Wolfgang; Plank, Margret; Sens, Irina; Stocker, Markus; Tullney, Marco; Vidal, Maria-Esther; van Wezenbeek, Wilma
    The document-oriented workflows in science have reached (or already exceeded) the limits of adequacy as highlighted for example by recent discussions on the increasing proliferation of scientific literature and the reproducibility crisis. Despite an improved and digital access to scientific publications in the last decades, the exchange of scholarly knowledge continues to be primarily document-based: Researchers produce essays and articles that are made available in online and offline publication media as roughly granular text documents. With current developments in areas such as knowledge representation, semantic search, human-machine interaction, natural language processing, and artificial intelligence, it is possible to completely rethink this dominant paradigm of document-centered knowledge exchange and transform it into knowledge-based information flows by representing and expressing knowledge through semantically rich, interlinked knowledge graphs. The core of the establishment of knowledge-based information flows is the distributed, decentralized, collaborative creation and evolution of information models, vocabularies, ontologies, and knowledge graphs for the establishment of a common understanding of data and information between the various stakeholders as well as the integration of these technologies into the infrastructure and processes of search and knowledge exchange in the research library of the future. By integrating these information models into existing and new research infrastructure services, the information structures that are currently still implicit and deeply hidden in documents can be made explicit and directly usable. This revolutionizes scientific work because information and research results can be seamlessly interlinked with each other and better mapped to complex information needs. As a result, scientific work becomes more effective and efficient, since results become directly comparable and easier to reuse. In order to realize the vision of knowledge-based information flows in scholarly communication, comprehensive long-term technological infrastructure development and accompanying research are required. To secure information sovereignty, it is also of paramount importance to science – and urgency to science policymakers – that scientific infrastructures establish an open counterweight to emerging commercial developments in this area. The aim of this position paper is to facilitate the discussion on requirements, design decisions and a minimum viable product for an Open Research Knowledge Graph infrastructure. TIB aims to start developing this infrastructure in an open collaboration with interested partner organizations and individuals.