Towards Semantic Integration of Federated Research Data

Digitization of the research (data) lifecycle has created a galaxy of data nodes that are often characterized by sparse interoperability. With the start of the European Open Science Cloud in November 2018 and facing the upcoming call for the creation of the National Research Data Infrastructure (NFDI), researchers and infrastructure providers will need to harmonize their data efforts. In this article, we propose a recently initiated proof-of-concept towards a network of semantically harmonized Research Data Management (RDM) systems. This includes a network of research data management and publication systems with semantic integration at three levels, namely, data, metadata, and schema. As such, an ecosystem for agile, evolutionary ontology development, and the community-driven definition of quality criteria and classification schemes for scientific domains will be created. In contrast to the classical data repository approach, this process will allow for cross-repository as well as cross-domain data discovery, integration, and collaboration and will lead to open and interoperable data portals throughout the scientific domains. At the joint lab of L3S research center and TIB Leibniz Information Center for Science and Technology in Hanover, we are developing a solution based on a customized distribution of CKAN called the Leibniz Data Manager (LDM). LDM utilizes the CKAN’s harvesting functionality to exchange metadata using the DCAT vocabulary. By adding the concept of semantic schema to LDM, it will contribute to realizing the FAIR paradigm. Variables, their attributes and relationships of a dataset will improve findability and accessibility and can be processed by humans or machines across scientific domains. We argue that it is crucial for the RDM development in Germany that domain-specific data silos should be the exception, and that a semantically-linked network of generic and domain-specific research data systems and services at national, regional, and organization levels should be promoted within the NFDI initiative

Ontologies4Chem: The landscape of ontologies in chemistry

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

Development of a Domain-Specific Ontology to Support Research Data Management for the Tailored Forming Technology

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

Digitalizing the Chemical Landscape: A Comprehensive Overview and Progress Report of NFDI4Chem

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

Creation of a Knowledge Space by Semantically Linking Data Repository and Knowledge Management System - a Use Case from Production Engineering

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

NFDI4Chem - A Research Data Network for International Chemistry

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

Enhanced findability and reusability of Engineering Data by Contextual Metadata

Complex research problems are increasingly addressed by interdisciplinary, collaborate research projects generating large amounts of heterogeneous amounts of data. The overarching processing, analysis and availability of data are critical success factors for these research efforts. Data repositories enable long term availability of such data for the scientific community. The findability and therefore reusability strongly builds on comprehensive annotations of datasets stored in repositories. Often generic metadata schema are used to annotate data. In this publication we describe the implementation of discipline specific metadata into a data repository to provide more contextual information about data. To avoid extra workload for researchers to provide such metadata a workflow with standardised data templates for automated metadata extraction during the ingest process has been developed. The enriched metadata are in the following used in the development of two repository plugins for data comparison and data visualisation. The added values of discipline-specific annotations and derived search features to support matching and reusable data is then demonstrated by use cases of two Collaborative Research Centres (CRC 1368 and CRC 1153)

Parametrization of a Hybrid Component Production Process Chain Based on Semantically Annotated Data

The development of a novel manufacturing process chain is a complex scientific challenge and requires interdisciplinary collaboration, as well as technological solutions that extend the boundaries of automation and customize the information flows between different organizational units. Due to these challenges an approach to parametrize each step in the production chain and its careful documentation must be considered. This publication describes an approach to provide seamless digital access to sample and processrelated data through semantic annotation. In this way, it can be traced how and with which process layouts of the individual process steps a sample was manufactured along the process chain. The implementation of the approach is shown in the example of a novel process chain for the production of multi-material bearing washers within the Collaborative Research Center (CRC) 1153. A workflow based on a semantic annotation using a domainspecific vocabulary is presented, which allows to obtain the necessary process parameter values according to the individual requirements of the multi-material component