Angelina Kraft

Advancing Earth System Sciences (ESS) depends on our ability to integrate highly diverse data from a range of disciplines, including geographical information sciences, palaeontology, marine science, biodiversity research, atmospheric sciences and biology. A major scientific obstacle to this integration is semantic heterogeneity: different disciplines use different methods and terms to describe data, resulting in fragmented vocabularies. We address this challenge by providing the ESS terminology collection, which is a source of state-of-the-art terminologies for this scientific community. This collection already contains 50 curated terminologies. New terminologies can be suggested by scientists at any time. The ESS collection enables the consistent annotation and semantic alignment of heterogeneous ESS research data. It enhances the FAIR data principles—particularly findability, accessibility, interoperability, and reusability—thereby facilitating interdisciplinary research and data reuse. This collection enables the ESS community to assess the quality and completeness of ontology metadata and the extent to which their terms comprehensively represent the scientific content of a field.

While the goal for Earth System Sciences (ESS) is a seamless, machine-actionable and AI-ready data ecosystem, the reality is different. Current infrastructures are often isolated into siloed "islands" and large data “continents” (Figure 1), separated by inconsistent technical standards and metadata conventions - like a "data archipelago". Despite major investments from consortia like NFDI4Earth in Germany and Data Terra in France, these fragments are only loosely connected by semantic bridges, leaving the ESS community with a landscape of scattered repositories rather than a unified digital environment.To address this growing fragmentation of the ESS data landscape, the BITS 2.0 project aims to establish a Semantic Fabric for ESS. Instead of merely linking individual repositories, this approach overlays heterogeneous data holdings with an intelligent, semantic shared layer. Building on the original BITS project, which successfully established a quality-controlled hub of ESS terminologies, BITS 2.0 will develop advanced, AI-powered data annotation services combined with a sustainable, community-driven governance model.These tools will analyze and enrich diverse data assets - ranging from well-curated repositories and institutional data lakes to individual catalogues - with consistent, interoperable, and machine-actionable metadata. For researchers, this substantially lowers barriers to data discovery, integration, and reuse across sources, enabling more efficient workflows and robust cross-domain analyzes. As an initial implementation, BITS 2.0 packages will be deployed on various types of data holdings. These include 'data continents', characterised by large, highly standardized data volumes serving multiple use cases (DKRZ), and 'data islands', consisting of smaller, project-specific datasets with heterogeneous or inconsistent standardization (GEOMAR). Based on these developments, BITS 2.0 will develop AI-empowered Blueprints 2.0 that provide a broadly transferable methodology for semantic integration across these scenarios (SGN, RWTH, TIB).BITS 2.0 is envisioned as a trusted semantic enabler for the emerging hybrid ESS data space, providing the essential “semantic glue” required for meaningful interoperability. By transforming a fragmented infrastructure landscape into a coherent, searchable knowledge space, BITS 2.0 will support the combined use of larger and more diverse datasets to address complex Earth System research questions.Figure 1: The scattered landscape of ESS data infrastructures, with varying challenges for semantic integration and AI-readiness depending on architectural design, depicted here as “data islands”, “data continents” and “data archipelagos”.

Earth System Science (ESS) encompasses scientists from different scientific disciplines who use a multitude of heterogeneous terms to describe processes and data. This volume of often ambiguous, duplicate, inconsistent terms presents numerous challenges regarding interoperability and standardisation, e.g. for automated data selection or searching for data in a repository. Terminologies such as ontologies, thesauri and controlled vocabularies can enable scientists and infrastructure providers to realise a machine-processable expression of the information contained in their research data and other scholarly outputs. However, selecting the most appropriate ontology is often difficult and requires support for data producers and curators.The BITS1 project is building a Terminology Service (TS) for Earth System Sciences (ESS TS) as part of the existing Terminology Service2 (TIB TS) of the TIB – Leibniz Information Centre for Science and Technology3. It has implemented the ESS collection4 within the TIB TS, which already contains 40 terminologies that are relevant for the ESS and to which further relevant terminologies will be added. All terminologies in the TIB TS are quality controlled. New terminologies for the ESS collection can be suggested at any time via the ESS homepage. New terms for terminologies hosted on Github can also be suggested and forwarded to the developers of that terminology.One possible use case for the ESS TS is to support the annotation of data with terms from terminologies. A major challenge in this case is the breaking of annotations: this can happen if the term of a terminology used for the annotation is deleted - e.g. in a subsequent version of a terminology. Therefore, BITS is conducting a feasibility study: can we assign persistent handles to all classes and individuals of each future version of a terminology in the ESS TS collection, so that the handles redirect to a landing page of the respective terms? These handles could then be used in annotations and the TIB will ensure that they are persistent.The integration of the ESS TS into the two different data repositories of the German Climate Computing Centre (DKRZ) and the Senckenberg - Leibniz Institute for Biodiversity and Earth System Research (SGN) is another task of BITS. The experience gained will be used to develop blueprints for connecting other ESS repositories to the TS. We also work closely with NFDI4Earth and the wider ESS community, and with the BASE4NFDI basic service TS4NFDI. Feedback from the wider ESS community on their expectations and needs for such a service is welcome and necessary for the project. Our goal is a terminology service that serves as a valuable resource for researchers, students, professionals and developers in ESS, providing them with accurate and consistent terminologies to enhance their work, improve communication and data sharing, and advance knowledge in their respective fields.1: https://projects.tib.eu/bits/home 2: https://terminology.tib.eu/ts 3: https://www.tib.eu/en/4: https://terminology.nfdi4earth.de

Managing research digital objects (RDOs) in compliance with FAIR principles is crucial for ensuring accessibility, interoperability, and reusability across scientific domains. The Leibniz Data Manager (LDM) is a state-of-the-art framework that integrates Knowledge Graphs (KGs) and Neuro-Symbolic AI, combining the reasoning power of Large Language Models (LLMs) with structured metadata. LDM supports the management and enhancement of RDOs through entity linking, connecting datasets to external KGs like Wikidata and the Open Research Knowledge Graph (ORKG). Additionally, LDM offers federated query processing across KGs, enabling users to explore related papers, datasets, and resources through natural language questions. This demo showcases LDM's capabilities to explore RDOs, compare existing datasets, and extend metadata. By blending Neuro-Symbolic AI with FAIR and federated research data management, LDM offers a powerful tool for accelerating data-driven discovery in science. LDM is publicly accessible at https://service.tib.eu/ldmservice/.

The BITS project is building a Terminology Service (TS) for Earth System Sciences (ESS TS). As a first step it will develop this service for the subfields of climate science (results from climate simulations) and geodiversity collections (representing Earth’s diversity of i.a. rocks, fossils, soils, sediments). The project will use the existing Terminology Service of the TIB – Leibniz Information Centre for Science and Technology, which currently contains 190 ontologies, more than 1.2 million terms and over 26,000 properties from a range of domains such as architecture, chemistry, computer science, mathematics and physics. It has implemented the ESS collection within the TIB TS by now, which already contains relevant terminologies for the ESS and to which further relevant terminologies will be added. The ESS TS will be integrated into the two different data repositories of the German Climate Computing Center (DKRZ) and the Senckenberg - Leibniz Institution for Biodiversity and Earth System Research (SGN):DKRZ will use the TS to develop a more user-friendly search for its World Data Center for Climate (WDCC) repository. The terminologies will be used to suggest additional and more relevant search terms to users. This will help users who are unfamiliar with the terminology used by the climate community to find the right keywords for their data search and to get accurate and high quality search results. SGN will use the TS to add standardised structured metadata to geothematic Digital Specimens in their digital collections. This will increase the FAIRness of collection data, i.e. foster self-contained discovery and processing of Digital Specimens by software agents or, in short, machines (machine actionability). The experience gained in building the ESS TS and integrating it into the repositories at DKRZ and SGN will be used to create blueprints to connect later on other Earth System Science repositories to the TS. We also aim to work closely with NFDI4Earth and the wider ESS community, and with TS4NFDI as the NFDI base service project for terminology services.As BITS evolves, ESS TS will be supplemented by additional components e.g. to support FAIR semantic mappings (leveraging on SGN’s mapping.bio service). However, feedback from the wider ESS community about expectations for such a service and their needs is welcome and required for the project. Our aim is a Terminology Service, which serves as a valuable resource for researchers, students, professionals and developers in ESS, providing them with accurate and consistent terminology to enhance their work, improve communication and data sharing and advance knowledge in their respective fields.

Zeitschriften-Portal Journal-Portal

The frameworks which have been established since the start of the NFDI allow for a coordinated development of RDM services, which include the introduction of community specific Terminology Services. To address the challenge of alignment and re-use of established ontologies and terminologies in the engineering domain, a Terminology Service (TS) was set-up for the NFDI4Ing initiative: https://terminology.nfdi4ing.de. The NFDI4Ing TS provides a single point of access to relevant research concepts and offers the building blocks for (meta-) data schemata and data annotation. As an open source platform, it features more than 50 ontologies, 147,000 terms and over 5,800 properties from a range of engineering-related terminology collections. The use of the NFDI4Ing TS fosters interoperability and reuse in a FAIR data context.

The collection of zooplankton swimmers and sinkers in time‐series sediment traps provides unique insight into year‐round and interannual trends in zooplankton population dynamics. These samples are particularly valuable in remote and difficult to access areas such as the Arctic Ocean, where samples from the ice‐covered season are rare. In the present study, we investigated zooplankton composition based on swimmers and sinkers collected by sediment traps at water depths of 180–280, 800–1320, and 2320–2550 m, over a period of 16 yr (2000–2016) at the Long‐Term Ecological Research observatory HAUSGARTEN located in the eastern Fram Strait (79°N, 4°E). The time‐series data showed seasonal and interannual trends within the dominant zooplankton groups including copepoda, foraminifera, ostracoda, amphipoda, pteropoda, and chaetognatha. Amphipoda and copepoda dominated the abundance of swimmers while pteropoda and foraminifera were the most important sinkers. Although the seasonal occurrence of these groups was relatively consistent between years, there were notable interannual variations in abundance, suggesting the influence of various environmental conditions such as sea‐ice dynamic and lateral advection of water masses, for example, meltwater and Atlantic water. Statistical analyses revealed a correlation between the Arctic dipole climatic index and sea‐ice dynamics (i.e., ice coverage and concentration), as well as the importance of the distance from the ice edge on swimmer composition patterns and carbon export.

Due to the increasing amount of data produced in science, concepts for data reusability are of immense importance. One aspect is the publication of data in a way that ensures that it is findable, reusable, traceable and comparable (FAIR1 principles). However, putting these principles into practice often causes significant difficulties for researchers. Therefore some repositories accept datasets described only with the minimum metadata required for DOI allocation. Unfortunately, this contains not  enough information to conform to the FAIR principles - many research data cannot be reused despite having a DOI. In contrast, other repositories aid the researchers by providing advice and strictly controlling the data and their metadata. To simplify the process of defining the needed amount of metadata and of controlling the data and metadata, the AtMoDat2 (Atmospheric Model Data) project developed a detailed standard for the FAIR publication of atmospheric model data.For this purpose we have developed a concept for the “ideal” description of atmospheric model data. A prerequisite for this is the data publication with a DataCite DOI. The ATMODAT standard3 was developed to implement this concept. The standard defines the data format as NetCDF, mandatory metadata (for DOI, landing page and data header), and naming conventions used in climate research - the Climate and Forecast conventions (CF-conventions4). However, many variable names used in urban climate research, for example, are not part of the CF-conventions. For this, standard names have to be defined together with the community and the inclusion in the list of CF-conventions has to be requested. Furthermore we developed and published Python routines which allow data producers as well as repositories to check model output data against the standard. The ATMODAT standard will first be applied by the project partners of the two participating universities (University of Hamburg and Leipzig). Here, climate model data are processed with a post-processor in preparation for publication. Subsequently, the files including the specified metadata for the DataCite metadata schema will be published by the World Data Center for Climate5 (WDCC). Data fulfilling the AtMoDat standard will be marked at the landing page by a special EASYDAB6 (Earth System Data Branding) logo. EASYDAB is a currently developed branding for FAIR and open data from the Earth System Sciences. This indicates to future data users that the dataset is a verified dataset that can be easily reused. The standardization of the data and the further steps are easily transferable to data from other disciplines.1 Wilkinson, M., Dumontier, M., Aalbersberg, I. et al.: The FAIR Guiding Principles for scientific data management and stewardship. Sci Data 3, 160018 (2016). https://doi.org/10.1038/sdata.2016.18 2 https://www.atmodat.de/3 https://cera-www.dkrz.de/WDCC/ui/cerasearch/entry?acronym=atmodat_standard_en_v3_04 https://cfconventions.org/5 https://cera-www.dkrz.de/WDCC/ui/cerasearch/6 https://www.easydab.de/ 

As in many scientific disciplines, there are a variety of activities in Earth system sciences that address the important aspects of good research data management. What has not been sufficiently investigated and dealt with so far is the easy discoverability and re-use of quality-checked data. This aspect is taken up by the EASYDAB label.EASYDAB1 is a currently developed branding for FAIR and open data from the Earth System Sciences. The branding can be adopted by institutions running a data repository which stores data from the Earth System Sciences. EASYDAB is always connected to a research data publication with DataCite DOIs. Data published under EASYDAB are characterized by a high maturity, extensive metadata information and compliance with a comprehensive discipline-specific standard. For these datasets, the EASYDAB logo is added to the landing page of the data repository. Thereby, repositories can indicate their efforts to publish data with high maturity.The first standard made for EASYDAB is the ATMODAT standard2, which has been developed within the AtMoDat3 project (Atmospheric Model Data). It incorporates concrete recommendations and requirements related to the maturity, publication and enhanced FAIRness of atmospheric model data. The requirements are for rich metadata with controlled vocabularies, structured landing pages, file formats (netCDF) and the structure within files. Human- and machine-readable landing pages are a core element of the ATMODAT standard and should hold and present discipline-specific metadata on simulation and variable level. The ATMODAT standard includes checklists for the data producer and the data curator so that the compliance with the standard can easily be obtained by both sides. To facilitate automatic checking of the netCDF files headers, a checker program will also be provided and published with DOI. Moreover, a checker for the compliance with the requirements for the DOI Metadata will be developed and made openly available. The integration of standards from other disciplines in the Earth System Sciences, such as oceanography, into EASYDAB is helpful and desirable to improve the re-use of reviewed, high-quality data.  1www.easydab.de2https://cera-www.dkrz.de/WDCC/ui/cerasearch/entry?acronym=atmodat_standard_en_v3_03www.atmodat.de