Jonas Grieb

Research data management (RDM) in the Earth system sciences is complex and can be frustrating. Data come in many shapes and formats—observations, model outputs, samples, derived products—and are spread across a wide range of repositories, services, and software ecosystems. These infrastructures differ greatly in metadata quality, interoperability, and FAIR maturity. For researchers, this often means spending too much time figuring out where to publish data, how to describe it properly, or how to reuse existing datasets and software. At the same time, expectations from funders and journals on data outputs continue to rise with respect to openness, curation, and long-term stewardship. Within the German National Research Data Infrastructure (NFDI), the NFDI4Earth consortium tackles these challenges by building practical, community-driven solutions for the Earth system sciences. As developers and designers, our focus is on lowering barriers and making FAIR data and software practices easier to understand and apply in everyday research. In this contribution, we introduce two key building blocks of this effort: the Knowledge Hub and the OneStop4All. The Knowledge Hub (https://knowledgehub.nfdi4earth.de) is a knowledge graph that connects heterogeneous Earth system resources, e.g., datasets, repositories, services, software, and educational materials, using a harmonized metadata model. It harvests metadata from multiple providers—ranging from global aggregators to national and domain-specific services (see, for instance, the Helmholtz DataHub: https://earth-data.de/) - and exposes them through a well-defined SPARQL API. This allows both humans and machines to query, explore, and reuse metadata consistently, and enables developers to build custom applications on top of it. The OneStop4All (https://onestop4all.nfdi4earth.de) builds on the Knowledge Hub to offer a discovery and guidance portal for researchers. It brings together the resources in a single, coherent interface. Cross-domain search and guided navigation help users move along the research data lifecycle without having to know all standards and infrastructures upfront. A central feature is the Repository Wizard, an interactive decision-support tool that helps researchers find suitable repositories for publishing their data based on data type, discipline, and policy constraints. In addition, the NFDI4Earth Label provides a transparent, community-oriented way to communicate repository quality with respect to FAIR principles, sustainability, and relevance for Earth system sciences. Beyond discovery, the OneStop4All puts a strong emphasis on learning and cultural change. It provides integrated access to open educational resources, good-practice guides, and showcases that demonstrate the concrete benefits of FAIR and open data and software. A domain-specific chatbot complements these resources by answering practical questions on metadata, licensing, data publication, and software citation. We will showcase these services, share lessons learned from development, and highlight opportunities for community contributions. From our perspective as a distributed team of designers and software developers, combining harmonized metadata, user-centered services, and hands-on training is key to making FAIR and open research practices work at scale in the Earth system sciences.

Senckenberg’s natural history collections encompass over 45 million physical specimens distributed across 11 facilities, with 1.6 million digitized records accessible in 124 collections. Additional digital objects are stored in various infrastructures, such as Edaphobase, a digital repository for harmonized soil information (physical, chemical, and biological), and the WildlIVE Portal, a platform for FAIR (Findable, Accessible, Interoperable, and Reusable) data sharing of biodiversity monitoring with edge sensors such as camera traps. Managing this heterogeneous landscape, ranging from legacy specimen data from the pre-digital era to newly digitized objects, presents significant challenges regarding legal compliance, data sovereignty, and the implementation of FAIR principles.To address this volume, the FAIRenrich workflow automates the semantic annotation and maintenance of existing digital collection data. Handling the complexity of such enrichment requires AI models suited for partially non-deterministic tasks, incorporating an optional human-in-the-loop mechanism. By executing these workflows on a distributed network of stationary and mobile edge computing devices ('last-mile AI'), the architecture ensures strict adherence to data sovereignty and privacy requirements.Beyond data curation, FAIRenrich's distributed architecture enables systemic efficiency through resource pooling informed by industry practice. Institutional edge infrastructure is rarely fully utilized; by networking heterogeneous devices, the system dynamically reallocates idle capacity to enrichment tasks. This mirrors industry practice: major technology operators, such as Google, systematically redeploy hardware from their refresh cycles into secondary-use programs.FAIRenrich extends this model to legacy hardware: rather than treating end-of-life equipment as waste, the system enables cost-effective redeployment for delay-tolerant semantic enrichment tasks, such as inference workloads without strict latency requirements. By aligning workload scheduling to renewable peaks (e.g., photovoltaic installations), the approach implements carbon-aware scheduling principles used by major technology operators, achieving both infrastructure cost reduction and extended hardware lifecycles. This creates a circular-economy model for research institutions, transforming refresh-cycle surplus into productive scientific infrastructure.This contribution demonstrates how FAIRenrich enables sustainable semantic annotation through a distributed edge architecture that simultaneously ensures data sovereignty, optimizes infrastructure utilization, and can realize cost-effective redeployment of legacy hardware. The approach exemplifies a scalable blueprint for research institutions seeking to decouple semantic enrichment from project-resource limitations through parallelization, temporal flexibility, and circular infrastructure practices.

The EU’s Biodiversity Strategy for 2030, a core part of the European Green Deal, addresses the complex relationship between human society and its environment by prioritizing the restoration of ecosystems and building resilience against climate change, deforestation, and biodiversity loss.These environmental stressors do more than just degrade ecosystems; they create a pressing need for policymakers, researchers, and society to actively track and mitigate ecological shifts. In order to design effective mitigation strategies, new political frameworks and massive simulation infrastructures are being developed with the aim to establish a common European Green Deal Data Space. The involved initiatives rely on the integration and standardization of diverse, large-scale datasets, ranging from long-term biodiversity records (e.g., eDNA) to real-time IoT sensor data (e.g., camera traps) and global Earth observation (EO) data combined with model-derived reanalysis datasets like ERA5.‘Biodiversity Meets Data’ (BMD) is a Horizon Europe project delivering a unified access point for AI-assisted biodiversity monitoring and cross-realm (terrestrial, marine, freshwater) analysis tools representing a key contribution to the thematic expansion of the European Green Deal Data Space ecosystem. By providing a robust technical infrastructure, BMD facilitates the quantification of diverse ecological pressures - ranging from climate change to land-use shifts - on biodiversity. The project is strategically focused on the EU Natura 2000 network, equipping stakeholders such as conservation managers and policy makers with the necessary tools to implement and evaluate EU Nature Directives such as the Birds and Habitats Directives.In this talk, we will present how BMD leverages FAIR Digital Objects (FDOs) and data space concepts around governance, licensing, and provenance tracking to synthesize computational workflows and diverse datasets into actionable knowledge units (“Workflow Run RO-Crate”, Figure 1). We will demonstrate our implementation path for such data-rich, self-contained digital containers building on web-based technologies such as RO-Crate (lightweight data packages) and FAIR Signposting (machine-interpretable layer describing resources). Those webby FDOs are designed to bridge the gap between practical needs of conservation stakeholders such as supporting data-driven decision making and technical capabilities of the Green Deal Data Space ecosystem.Integration of targeted feedback from stakeholders, notably Natura 2000 site managers, into our development process ensures that the FAIR-compliant data products and FDO service framework are not only technically robust, but also socially and politically actionable. Figure 1. Throughout its life cycle in the BMD data space, data is represented as RO-Crate. Initially (left), the data and the computational workflow are bundled as Workflow RO-Crate. Following processing, this is combined with the results and enriched with retrospective provenance and metadata to form a Workflow Run RO-Crate (right). Finally, these are presented as webby FAIR Digital Objects, incorporating a machine-interpretable layer based on FAIR Signposting (bottom).

The Distributed System for Scientific Collections (DiSSCo) is a research infrastructure to integrate European natural science collections (NSCs) digitally. The aim is to facilitate and enhance the access, management and analysis of collection assets in one unified digital collection. The Machine Annotation Services (MAS) are essential components of DiSSCo’s Digital Specimen Architecture (DSArch). These services automate the annotation of digital objects to enable labelling and categorisation of NSC's digital assets. To further advance this, a Machine Learning as a Service (MLaaS) approach was developed which provides researchers with the access to pre-trained machine-learning models for complex tasks, such as instance segmentation and morphological analysis of datasets. MLaaS enhances the DiSSCo’s scalability and flexibility and allows the integration of machine-learning tools in close alignment with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. This study employs DiSSCO's MLaaS framework for the quantitative analysis of herbarium specimens. Machine-learning models, such as Mask R-CNN and YOLO11, are comparatively applied to detect and generate the pixel-level masks of plant organs in herbarium sheets. Subsequently, these models are used to reconstruct the scale in the herbarium sheet and to calculate the surface area of identified plant organs. The determination of quantitative characteristics of plant specimens, such as measuring leaf area or the timestamp of the floral transition, opens up herbarium data for reuse in the large prognosis platforms currently developed in the framework of the Common European Data Spaces. In this way, plant trait data mobilised from natural science collections can improve the predictive capability of the vegetation model components of climate-related data spaces.

The European Green Deal addresses a wide array of critical topics regarding the interaction and exchange between society and ecosystems involving human-induced climate change, biodiversity loss, and deforestation. To support the development of appropriate and sustainable mitigation strategies, both political frameworks as well as large-scale scenario forecast and simulation infrastructures such as Destination Earth Data Lake (DEDL, Puechmaille et al. 2025) and the Green Deal Data Space (Penninga et al. 2021) are currently implemented. Setting up these data spaces requires the consolidation and harmonization of vast amounts of data, among them data from long-term biodiversity monitoring (e.g. eDNA, Abarenkov et al. 2023), near real-time data captured by IoT devices (e.g. camera traps, Grieb et al. (2025)) and large-scale Earth observation data (e.g. ERA5 global reanalysis, Hersbach et al. (2020)). Key tools and research methodologies for integration of such data have been developed in the Destination Earth-linked Biodiversity Digital Twin project (BioDT). Biodiversity digital twins provide digital replicas of biodiversity patterns and processes (Khan et al. 2025), for FAIR-compliant reuse and cross-platform portability, they are packaged in BioDT using the RO-Crate specification. Resulting datasets as well as computational workflows are bundled as digital objects and annotated with rich machine-interpretable metadata using schema.org and its extensions Bioschemas and Croissant (high-level metadata specification supporting AI-Readiness, Bolleman et al. 2025). The workflow.earth pilot was developed to migrate and functionally integrate BioDT's digital twin for crop wild relatives (BioDT CWR) into DEDL. BioDT CWR is designed to identify genetic resources in crop wild relatives (CWR) and aid in creating strategies to mitigate food-related crises linked to climate change (Chala et al. 2024). CWR are crucial sources of untapped genetic diversity, offering greater adaptability to extreme weather events such as droughts, cold waves, and heavy precipitation. They are therefore of particular importance to ensure food security with regard to Sustainable Development Goals (notably Goal 2 “End hunger, achieve food security and improved nutrition and promote sustainable agriculture”, United Nations 2023). Major objectives for workflow.earth are to enhance visibility and long-term availability of the BioDT CWR, to improve its prediction capabilities using DEDL’s near data processing services, and to facilitate (in perspective) the orchestrated interplay with other digital twins such as the Climate Change Adaptation DT (Weiland et al. 2024). to enhance visibility and long-term availability of the BioDT CWR, to improve its prediction capabilities using DEDL’s near data processing services, and to facilitate (in perspective) the orchestrated interplay with other digital twins such as the Climate Change Adaptation DT (Weiland et al. 2024). On top of RO-Crate, workflow.earth employs FAIR Signposting to add machine-interpretable links to human-readable digital objects using link relation types (RFC8288, Fig. 1) to present the web topology of a resource (Soiland-Reyes et al. 2024). In this presentation, we will demonstrate the deployment of agrobiodiversity workflows in DEDL using workflow.earth and the publication of model and results as webby FDOs.

Provenance plays an important role in natural history collections, but capturing this information accurately, proved challenging for legacy digital collection management systems. DINA*1 is being developed to address these limitations through a process-oriented data model that more effectively captures the complexity and context of provenance information (Bölling et al. 2022). DINA is an open-source, robust sample- and specimen-based collection management system in production and developed by an unincorporated international consortium of technologists and practitioners of the natural sciences (Glöckler et al. 2020). Its governance and data models help to foster the adoption of FAIR principles (Findable, Accessible, Interopable, Reusable) and to integrate objects across science domains. DINA's innovative "samplistic" data model records metadata on stepwise, hierarchical processes that generate physical and digital derivatives from parent material samples, accommodating complex real-life sample trajectories, for example: A fossil specimen is acquired by a museum, which contains the remains of several organisms in hardened resin. Later, it is sawed into smaller pieces; some pieces are stored under new catalog numbers, a tiny piece is sent for a destructive C14 (radioactive carbon) analysis (Fig. 1 a). A naturally deceased individual of a mammal species is collected under a material transfer agreement (MTA). Later, subsamples like teeth, bones, and tissues undergo different preparation and preservation processes and are finally stored in specialized collections, each with its own identifier (scheme); a subsample might even be retrieved for sequencing. Strong provenance tracking is required to ensure that regulatory constraints like the MTA are consistently passed down to any derivatives. A soil core is collected from a sampling site and registered together with observational metadata according to the MIxS (Minimum Information about Any Sequence) soil extension standard. Several subsamples are extracted before the remaining core is preserved. The subsamples are subjected to DNA extraction and sequencing to analyze environmental DNA (eDNA). The resulting sequences are then processed computationally to identify the species present (Fig. 1 b). A fossil specimen is acquired by a museum, which contains the remains of several organisms in hardened resin. Later, it is sawed into smaller pieces; some pieces are stored under new catalog numbers, a tiny piece is sent for a destructive C14 (radioactive carbon) analysis (Fig. 1 a). A naturally deceased individual of a mammal species is collected under a material transfer agreement (MTA). Later, subsamples like teeth, bones, and tissues undergo different preparation and preservation processes and are finally stored in specialized collections, each with its own identifier (scheme); a subsample might even be retrieved for sequencing. Strong provenance tracking is required to ensure that regulatory constraints like the MTA are consistently passed down to any derivatives. A soil core is collected from a sampling site and registered together with observational metadata according to the MIxS (Minimum Information about Any Sequence) soil extension standard. Several subsamples are extracted before the remaining core is preserved. The subsamples are subjected to DNA extraction and sequencing to analyze environmental DNA (eDNA). The resulting sequences are then processed computationally to identify the species present (Fig. 1 b). In all examples, the processing history of each subsample can be modeled in DINA so provenance can always be traced back to the original sample. Knowledge from any derivative analysis can be linked back to the parent sample and other derivatives. This ensures that regulatory restrictions applying to a parent material sample are passed down consistently. Besides parent-child relationships, the model can represent other associations, e.g., host, parasite, and vector relationships linking samples in different collections. Each sample in the provenance chain, as well as first-class related objects like projects, collections, events, people, protocols, and storage, has a system-defined globally unique identifier. DINA integrates with external systems where possible via persistent identifiers, including reuse of scientific names from community-curated biodiversity sources through the Global Names Architecture. DINA’s application programming interface simplifies data import and migration and can be used by scientific programming languages like Python or R to access data objects or their relationships. Flexible user-defined “managed attributes” enhance object and derivative metadata when standards do not yet exist. When standards like MIxS or MIDS (Minimum Information about a Digital Specimen) exist, these are incorporated as field extensions. Looking ahead, we aim to further strengthen linkages and provenance tracking in DINA, extending support to model complex bio-geo relationships and enhancing linkage of material samples to external resources such as publications.

Data repositories play a central role in enabling sustainable research data management and long-term accessibility of scientific information. Within the Earth System Science (ESS) domain, a large number of repositories exist, often operated by different institutions and communities. However, their heterogeneous levels of standardization and varying technical maturity present a challenge for cross-repository data integration. Moreover, researchers seeking reliable and sustainable repositories for data deposition require transparent indicators of trustworthiness and interoperability to make informed decisions. Within NFDI4Earth - the German National Research Data Infrastructure for the Earth System Sciences (Bernard et al. 2021) - the NFDI4Earth Label is being developed to address these challenges. The NFDI4Earth Label provides a framework for assessing the FAIRness of research data infrastructures, referring to the principles of Findability, Accessibility, Interoperability, and Reusability (Wilkinson et al. 2016). The assessment focuses particularly on technical interoperability and organizational trustworthiness (Grieb et al. 2024). Its overarching goal is to support the harmonization of data infrastructures by: Defining concrete metrics that enable a systematic assessment of interoperability and trustworthiness within an evolving ecosystem of digital ESS services, and Providing recommendations to close identified gaps and improve the FAIR alignment of participating repositories. Defining concrete metrics that enable a systematic assessment of interoperability and trustworthiness within an evolving ecosystem of digital ESS services, and Providing recommendations to close identified gaps and improve the FAIR alignment of participating repositories. The framework integrates three complementary assessment components: An automated assessment, which evaluates metadata about repositories already registered in the international re3data registry of data repositories. This fosters collaboration with an established community resource and ensures up-to-date information on repository standards and interfaces. The extracted information is evaluated against a set of predefined criteria, with particular focus on metadata standards, availability of application programming interfaces (APIs), and the use of persistent identifiers. A self-assessment, in which repository representatives provide information on governance, sustainability, and institutional policies that cannot be automatically retrieved. Repositories already certified with the CoreTrustSeal (CTS) are recognized as fulfilling this component, reflecting the shared objectives and overlapping standards between CTS and the NFDI4Earth Label. An optional F-UJI-based evaluation which analyses a subset of datasets at the metadata level, providing additional insights into the FAIRness of data holdings (Huber and Devaraju 2021). An automated assessment, which evaluates metadata about repositories already registered in the international re3data registry of data repositories. This fosters collaboration with an established community resource and ensures up-to-date information on repository standards and interfaces. The extracted information is evaluated against a set of predefined criteria, with particular focus on metadata standards, availability of application programming interfaces (APIs), and the use of persistent identifiers. A self-assessment, in which repository representatives provide information on governance, sustainability, and institutional policies that cannot be automatically retrieved. Repositories already certified with the CoreTrustSeal (CTS) are recognized as fulfilling this component, reflecting the shared objectives and overlapping standards between CTS and the NFDI4Earth Label. An optional F-UJI-based evaluation which analyses a subset of datasets at the metadata level, providing additional insights into the FAIRness of data holdings (Huber and Devaraju 2021). Each component contributes to a defined set of evaluation metrics that address the FAIR principles. To obtain the Label, repositories must meet a minimum threshold; partial fulfillment results in actionable recommendations for improvement. The entire assessment workflow is implemented within the established OneStop4All service of NFDI4Earth (see Fig. 1), which provides the technical infrastructure for performing and managing evaluations. Repositories that meet the requirements are awarded the NFDI4Earth Label, which is visualized as a digital badge confirming the successful assessment. In summary, the NFDI4Earth Label provides a practical and community-driven approach to harmonize research data infrastructures, fostering transparency, interoperability, and trust across repositories. While it is currently being developed within the Earth System Sciences domain, the underlying concept and assessment framework are designed to be transferable to other disciplines, supporting broader alignment and integration into international data ecosystems such as the European Open Science Cloud (EOSC).

Growing numbers of biodiversity monitoring data recorded by camera trap devices are becoming increasingly difficult to manage and analyze. The WildLIVE platform is being developed to address these challenges by providing a comprehensive virtual research environment (VRE) that integrates citizen science, machine learning-based annotation, and geo-analysis functionality. Additionally, it serves as a centralized platform for FAIR (Findable, Accessible, Interoperable, and Reusable) data sharing. This paper presents the core concepts of the WildLIVE platform and its underlying data model, highlighting its potential to streamline biodiversity research and enhance data accessibility.

The Distributed System for Scientific Collections (DiSSCo) is a research infrastructure to integrate European natural science collections (NSCs) digitally. The aim is to facilitate and enhance the access, management and analysis of collection assets in one unified digital collection. The Machine Annotation Services (MAS) are essential components of DiSSCo’s Digital Specimen Architecture (DSArch). These services automate the annotation of digital objects to enable labeling and categorization of NSC's digital assets. To further advance this, a Machine Learning as a Service (MLaaS) approach was developed which provides researchers with the access to pre-trained machine learning models for complex tasks such as instance segmentation and morphological analysis of datasets. MLaaS enhances the DiSSCo’s scalability and flexibility and allows the integration of machine learning tools in close alignment with the FAIR (Findable, Accessible, Interoperable, Reusable) principles. This study employs DiSSCO's MLaaS framework for the quantitative analysis of herbarium specimens. Machine learning models such as Mask R-CNN and YOLO11 are comparatively applied to detect and generate the pixel-level masks of plant organs in herbarium sheets. Subsequently, these models are used to reconstruct the scale in the herbarium sheet and to calculate the surface area of identified plant organs. Based on our finding that YOLO11 performs better than the Mask R-CNN for our use case, we deployed a YOLO11-based service as MAS in DSArch to open up natural science collections on scale for research fields such as plant phenology and climate change science.

The 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.