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FDOs and Service Brokering, the CLARIN Switchboard Use-Case
Full text linkEven though considerable progress with making research data and also research data processing services more FAIR, there is still lacking sufficient detail in the description of workflows, data and services for their immediate application and they are mostly not machine-actionable. CLARIN has developed the Language Resource Switchboard helping users find and invoke specific services for specific types of data. We consider and explore mapping this approach onto an FDO architecture and see what additional gains can be made
Implementing FAIR Semantic Mappings Leveraging on RO-Crate
Full text linkThe mapping.bio platform for the curation of FAIR semantic mappings is presented and the implementation of the storage of the mappings as “webby” FAIR Digital Objects based on established standards is described
FDO Access Control Using FDO Operations
Full text linkThe FDO Requirement Specification Version 3.0 [1] general requirement 9 (FDO-GR-9) states that each FDO can have metadata of different types such as access permissions. FDO access permissions is a potentially complex aspect of FDOs: A simple text license could be sufficient for some FDOs, but others may need more restrictive and configurable forms of access controls. While FDOs should be ultimately findable, accessible, interoperable and reusable, some may not want to be accessible by all nor for free. Moreover, some repository managers may only make their objects accessible as FDOs on the condition that they retain full control over the access control methodology, and can restrict who can access them and under which conditions.
The FDO Requirement Specification Version 3.0 specifications do not specify any particular access control operations but the FDO Specification General Requirement 6 (FDO-GR6) suggests that application extensible FDO Operations could be used for that purpose. We call an FDO Operation that enforce an FDO’s access control an FDO Access Control Operation. Since it is an FDO operation, an FDO Access Control Operation is intrinsically FAIR and can offer a wide and extensible range of different types of access controls.
While the complete details of FDO Access Control Operations not explicitly specified in the FDO Requirements specifications, much of these concepts are described in the DOIPV2.0 [3] protocol and can be directly applied to the FDO space in an implementation neutral manner
Layered Dataspaces in GIDS
Full text linkThe Global Integrated Dataspace (GIDS) will come to finally reduce the costs for data-driven work substantially. It will be based on a minimal standard which will be transparent to rights on data, but will nevertheless transport usage information to additional federation layers which are now called dataspaces. Partners in dataspaces may agree on terms such as roles, their rights to use data, etc. and they will apply some dataspace technology that will control the usage of the data and metadata. For the basic minimal standard the FAIR Digital Objects are suitable candidates. For advanced usage control technologies such as the IDSA Eclipse Dataspace Connector might be used
Practical webby FDOs With RO-Crate and FAIR Signposting: Experiences and Lessons Learned
Full text linkResearch Object Crate (RO-Crate) is a lightweight method to package research outputs along with their metadata. Signposting provides a simple yet powerful approach to navigate scholarly objects on the Web. Combining these technologies form a "webby" implementation of the FAIR Digital Object principles which is suitable for retrofitting to existing data infrastructures or even for ad-hoc research objects using regular Web hosting platforms. Here we give an update of recent community development and adoption of RO-Crate and Signposting. It is notable that programmatic access and more detailed profiles have received high attention, as well as several FDO implementations that use RO-Crate
An FDO-Based Implementation for a Standardized Data Exchange
Full text linkIn this paper, we present an FDO[1] implementation focusing on standardized scientific research data exchange. Using DOIP[2] and Cordra[3] and based on a concrete FDO model with assumed layers and compositions, we implement some basic use-cases of exchanging/handling scientific research data based on FDOs, and introduce FDO services to handle research data based on FDOs. We introduce workflows to orchestrate the application of FDO services. These define and foster an FDO-based ecosystem research data. Our work was funded by the German Research Foundation(DFG) within the KonsortSWD, the consortium for the social, behavioral, educational and economic sciences, is part of the German National Research Data Infrastructure (NFDI) initiative
Towards Machine-Actionable Scientific Knowledge as FAIR Digital Objects
Full text linkBuilding on the Open Research Knowledge Graph as an infrastructure for the production, curation, and publication of FAIR scientific knowledge, we present a concept that models original articles and the corresponding expression in the ORKG as independent and interlinked FDOs by organizing the content describing an article into semantic units
HTTP-Based Implementation Strategies for the FAIR Digital Object Framework Identifier Resolution
Full text linkDirectly or indirectly, the FAIR principles define a number of requirements for the data and services ecosystem. Among them, there are requirements for the identifiers of digital objects, including the separation between metadata and the objects they describe, the need for identifier to be globally unique and persistent and that the metadata record includes the identifier of the object they describe. In order to pursue an increased level of automation, the FAIR Digital Object Framework defines a predictable identifier resolution behaviour that not only support the access to the target object but also allows the client application to request the reference to a minimal metadata record named Identifier Record containing basic information such as the object\u27s location, its type and reference to its metadata records. In this paper we report and comment an experiment of implementing these identifier resolution behaviours using different approaches
First Investigations on WAAM-Printed Adhesive Sockets for Reinforcement Connections
Full text linkAdditive manufacturing (AM) is attracting increasing interest in the construction sector due to its potential for automation and its ability to produce complex components. The potential of AM, particularly in the free-form design of concrete components such as beams, columns and force flow-optimised nodes, depends largely on solutions for their reinforcement. As a suitable solution for reinforcement integration, robot-assisted additive wire and arc manufacturing (WAAM) combines a high degree of automation and geometric freedom with a high deposition rate and tensile strength.
In this study, the WAAM process is investigated using the example of welded connection elements for reinforcing bars, accompanied by centric tensile tests on representative WAAM specimens and pull-out tests on reinforcing bars bonded into different sockets with two different injection mortars. In comparison to this novel approach of connecting steel components with reinforcing bars by bonding sockets produced using WAAM, comparable connection methods such as bolting and welding of the reinforcing bars are investigated.
The possible applications of the connection technology presented range from steel inserts in connecting elements and brackets to the connection of segmented rebars in AM concrete components
Phoenixes at LLMs4OL 2025 Task A: Ontology Learning With Large Language Models Reasoning
Full text linkRecent advances in large language models (LLMs) have demonstrated remarkable capabilities in various natural language understanding tasks, including Ontology Learning (OL), where they automatically or semi-automatically extract knowledge from unstructured data. This work presents our contribution to the LLMs4OL Challenge at the ISWC 2025 conference, focusing on Task A, which comprises two subtasks: term extraction (SubTask A1) and type extraction (SubTask A2). We evaluate three state-of-the-art LLMs — Qwen2.5-72B-Instruct, Mistral-Small-24B-Instruct-2501, and LLaMA-3.3-70B-Instruct — across three domain-specific datasets: Ecology, Scholarly, and Engineering. In this paper, we adopt a Chain-of-Thought (CoT) Few-Shot Prompting strategy to guide the models in identifying relevant domain terms and assigning their appropriate ontology types. CoT prompting enables LLMs to generate intermediate reasoning steps before producing final predictions, which is particularly beneficial for ontology learning tasks that require contextual reasoning beyond surface-level term matching. Model performance is evaluated using the official precision, recall, and F1-score metrics provided by the challenge organizers. The results reveal important insights into the strengths and limitations of LLMs in ontology learning tasks