This contribution presents a joint collaboration between PID4NFDI, TS4NFDI, and the electronic lab notebook provider ResearchSpace to support interoperable research workflows within the National Research Data Infrastructure (NFDI). It demonstrates how early, structured capture of high-quality metadata and persistent identifiers (PIDs) in ELNs, combined with shared reference schemas and...
Laboratory data reuse and reproducibility depend on rapid, accurate, and complete capture of experimental (meta)data. In practice, metadata creation in electronic lab notebooks (ELNs) remains a bottleneck because form-based entry interrupts workflows, free-text input is time-consuming and error-prone, and heterogeneous terminology complicates harmonisation across projects and infrastructures....
Traditional mechanical testing often relies on manual observation and fragmented data storage, creating bottlenecks in scientific progress. To reduce development times and make mechanical testing more sustainable, we must transition from manual logging to high-throughput, standardized data acquisition. This presentation demonstrates a paradigm shift in human-machine collaboration within...
Hydrogen and electrochemical energy research produces rapidly evolving, heterogeneous outputs - protocols, instrument settings, conditioned performance metrics, and multi-scale materials descriptors - that are difficult to curate into FAIR, machine-actionable metadata [1]. Many “LLM-first” knowledge-graph pipelines rely on monolithic prompts and ad-hoc post-processing, which can lead to...
In energy research facilities, such as the Energy Lab at the Karlsruhe Institute of Technology, the systematic acquisition of high-quality metadata remains a significant challenge because manual documentation can be error-prone and time-consuming. To ensure data findability and reproducibility according to the FAIR principles, we present an innovative approach that utilizes Business Process...
Cultural heritage metadata systems face fundamental challenges
representing experimental artworks that resist conventional classification. This research proposes a domain-specific ontology framework to address these challenges, using Theresa Hak Kyung Cha's Dictee (1982)—a seminal postcolonial feminist artists' books—as a critical case study.
Dictee is a hybrid work combining...
Trustworthy and impactful decision support systems (DSS) critically depend on data that are fit for their intended use. Here, data quality assessments should be understood not merely as a validation step, but as a systematic metadata enrichment process that produces structured, machine-actionable descriptors of data plausibility, and correctness.
Building on established data quality concepts...
Within the Kadi4Mat ecosystem, Kadi4Mat Workflows support the systematic deployment of laboratory workflows by transitioning execution from previously exclusively local desktop systems (KadiStudio) to remote, containerized infrastructures, such as Docker and Kubernetes. This approach ensures controlled, scalable, and reproducible execution conditions across experimental runs. Interactions via...
Persistent identifiers (PID) are critical elements of digital research data infrastructures, enabling the unambiguous identification, location and citation of digital representations of a growing range of entities, such as publications and data. Physical samples form the basis for many research results and data. The International Generic Sample Number (IGSN) provides a globally unique,...
This talk presents the [FDO-Ops][1] model as a prototype framework that makes FAIR Digital Objects (FDOs) machine-actionable by discovering, assessing, and executing operations across heterogeneous data resources in an interoperable way.
The prototype builds on a [DOIP/HTTP][2] client interface where every management function and every Operation FDO is invoked with a uniform request...
While the theoretical benefits of FAIR data are well-established, the operational reality of integrating these principles into active R&D environments reveals a distinct set of challenges and opportunities. This presentation moves beyond the "why" of digitalization to the "how," based on observations from scaling semantic architectures in tribology and materials engineering.
We identify...
Computational Science and Engineering relies on complex, multi-step workflows that combine simulations, data processing, and parameter-driven analyses across heterogeneous environments. Ensuring reproducibility in such settings requires not only abstract workflow descriptions but also semantically rich metadata that is interoperable across domains.
In this work, we present MaRDIFlow, a...
To structure metadata of health-specific research studies a tailored metadata schema (MDS) has been developed in the context of the German National Research Data Infrastructure for Personal Health Data (NFDI4Health) [1,2]. The MDS supports metadata publication from clinical, epidemiological, and public health studies, while maintaining interoperability with other resources. Designed in a...
[LabID][1] is an open-source platform designed to streamline research data management for scientists, research groups, and core facilities of life-science institutes. By integrating sample and dataset management, inventory tracking, and an electronic lab notebook, LabID enables users to organize, annotate, and share experimental data in compliance with FAIR principles. At its core, LabID uses...
FAIRification is huge global innovation proces. A major challenge is the steep learning curve for every new user especially, if it comes to actionalble semantics: Which globally vocablary or ontology do I use? How can I use it for my data? Essentially every new adopter, who has never collected a single semantic dataset before, is asked to do everything right from the first moment and select...
Molecular dynamics (MD) simulations generate vast amounts of data foundational to structural biology, yet their value is often limited by inconsistent metadata and software-specific formats that create isolated data silos. To address this "semantic gap," we introduce MOLSIM, an interoperable ontology designed to formalize the description of atomistic biomolecular simulations and enhance the...
Comprehensive and standardized sample management is crucial for advancing field research under challenging conditions. We present the “O2A SAMPLES” prototype: a generic Sample Management System designed as a hosted service that unifies diverse sample metadata in one interoperable framework (samples.o2a-data.de). Developed collaboratively by AWI and GEOMAR, O2A SAMPLES not only facilitates...
Environmental research relies on a wide variety of sensors deployed across oceanic, terrestrial, and atmospheric platforms, yet maintenance metadata remains poorly standardized and difficult to integrate into FAIR-compliant workflows. Metadata is crucial for interpreting sensor data, particularly on research vessels equipped with diverse instruments operating globally. Contextual details...
As open access (OA) becomes the dominant model for scholarly book publishing, the integration of open, standard-compliant metadata into publishing workflows, library systems, and preservation infrastructures has become increasingly urgent. This presentation reports on key findings from a recent metadata study ([Steiner et. al. 2026][1]) that reviewed international standards and requirements...
The CoreTrustSeal certified institutional data repository RDR, built on Dataverse, is central to KU Leuven’s efforts to support FAIR data publication. Since its launch in 2022, the growing number of dataset submissions has highlighted the need for an efficient, transparent, and consistent curation workflow. To address this, the RDR team developed an open-source review dashboard that integrates...
The mTeSS-X project (“Multi-space Training e-Support System with eXchange”) aims to address one of the central challenges in modern research infrastructures: how to provide coordinated, yet domain-specific training resources across diverse scientific communities. Within the framework of ELIXIR and PaNOSC, the project develops a federated training catalog infrastructure called TeSSHub that...
Agentic AI systems—autonomous software driven by large language models (LLMs)—promise significant efficiency gains by performing tasks that traditionally required human judgment. However, their deployment fundamentally involves control inversion: humans must step back and allow the system to take command. The ease of building impressive prototypes with current LLMs creates a dangerous...
High-quality environmental time series data require transparent, reproducible, and well-documented quality control (QC) workflows that integrate automated procedures and expert judgment. While many QC frameworks offer algorithmic methods, the processing information explaining how data quality decisions are made — including parameterization, flag semantics, and manual interventions — is often...
Most research workflows involve use of multiple research tools, services, and IT infrastructure, each addressing one phase of the research life cycle. The lack of interoperability between resources hinders research productivity and prevents streamlined passage of data between tools and sustainable data FAIRification. This presentation discusses implementation of PIDs in RSpace to enhance...
