However, fully implementing FAIR principles, particularly machine-actionability—the core idea behind FAIR—has proven challenging. Despite progress in awareness, standard-compliant data sharing, and the automation of data provenance, the ESS community continues to struggle to reach a community-wide consensus on the design, adoption, interpretation and implementation of the FAIR principles.
In this session, we invite contributions from all fields in Earth System Sciences that provide insights, case studies, and innovative approaches to advancing the adoption of the FAIR data principles. We aim to foster a collaborative dialogue on the progress our community has made, the challenges that lie ahead, and the strategies needed to achieve widespread acceptance and implementation of these principles, ultimately enhancing the future of data management and reuse.
We invite contributions focusing on, but not necessarily limited to,
- Challenges and solutions in interpreting and implementing the FAIR principles in different sub-domains of the ESS
- FAIR onboarding strategies for research communities
- Case studies of successful FAIR data implementation (or partial implementation) in ESS at infrastructure and research project level
- Methods and approaches to gauge the impact of FAIR data implementation in ESS
- Considerations on how AI might help to implement FAIR
- Future direction for FAIR data in ESS
Orals: Wed, 30 Apr | Room -2.92
Since their development, the FAIR principles have been met with broad acceptance in the scientific community. Tools based on various approaches are available to assess the FAIRness of individual data sets. These range from qualitative assessments based on questionnaires to automated quantitative measurements of fairness. As the FAIR principles are rather vaguely formulated, these approaches are based on individual, often differing, interpretations of the FAIR principles. In addition, the authors of the FAIR principles explicitly recognize the different implementations of FAIR within the various specialist communities. This makes it necessary to develop community-specific metrics and tests and to adapt FAIR assessment tools accordingly.
This diversity of methods for assessing FAIR is encouraging, as it sheds light on a variety of aspects of FAIR. However, this also sometimes leads to different, divergent results from these tools, which is difficult for users to work with. In addition, the measurement of FAIRness of individual datasets is heavily dependent on various technical implementations on the part of the data providers and their service providers. Numerous, possibly unintentional restrictions on the accessibility of datasets can influence or falsify FAIR measurements.
In this presentation, we would like to report on our experiences with the applied FAIR assessment within this context. We will report on the further development of F-UJI, in particular our experiences with discipline-specific FAIR metrics and their implementation. Furthermore, we will discuss the limitations of FAIR measurements and try to delineate FAIR from aspects of data quality and accessibility and how to derive informative holistic assessments of datasets that include all these aspects in the future.
How to cite: Huber, R.: Opportunities and limitations of applied FAIR evaluation of data sets, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20064, https://doi.org/10.5194/egusphere-egu25-20064, 2025.
The NSF NCAR Earth Observing Laboratory (EOL) has supported over 600 national and international field campaigns which represent half a century of field-based observational science. Our mission is to provide responsive, high quality data services to researchers in field campaigns including pre-field phase planning, real-time decision-making tools, and long-term data curation to support the complete project life cycle. Such support includes (1) serving as the online hub for field campaign operations with access to real-time mission coordination displays and communication tools, (2) ensuring a secure, easily accessible archive of campaign observations, and (3) providing long-term stewardship and curation of observational datasets. All datasets in the EOL’s Field Data Archive are publicly accessible and findable at https://data.eol.ucar.edu/.
EOL data management services are continuously evolving as we pursue FAIR and TRUSTed principles based on industry standards, user feedback and the desire to increase data discovery and accessibility to the broader scientific community. The management of our field campaign data is an iterative, human-driven and agile process. Thus, to address challenges arising from data preparation, preservation, and provenance metadata as the volume and variety of our data grows, EOL has developed tools and workflows that track and maintain the collection of data. In this presentation we will introduce highlights and functionalities of the Field Catalog and the Field Data Archive that together provide end-to-end customized data management services for field campaigns.
How to cite: Witte, J. C. and the Data Management and Services Team: Data Lifecycle Management for Field Campaigns: Welcome to the Earth Observing Laboratory Field Catalog and Archive, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2295, https://doi.org/10.5194/egusphere-egu25-2295, 2025.
Vocabularies or thesauri, lists of terms with their definitions and unique ID, like a dictionary of a language, play a critical role in the domain of soil science, providing a standardized framework for accurately documenting and communicating soil characteristics. In soil science, the use of precise and consistent terminology ensures the effective exchange of data, promoting interoperability among researchers, practitioners, and decision-makers. A well-structured vocabulary, part of soil classification or soil description standards, facilitates the classification of soil properties, such as texture, structure, fertility, and organic content, allowing combining data from different sources but described in a similar way. And thereby enabling reliable comparison and interpretation across different regions and time periods. Furthermore, these vocabularies enable and support the development of standardized databases, soil datasets and soil monitoring systems, which are essential for environmental management, land use planning, and agricultural practices. Inaccurate or ambiguous soil descriptions can lead to misinformed decisions, making the establishment of clear, universally accepted vocabularies crucial for advancing soil science, conservation efforts, and sustainable land management practices. Such practices would greatly enhance the FAIRness of the data being managed, ensuring data conservation over time.
Soil vocabularies come from many sources, some national or regional, some from international organizations such as the Food and Agriculture Organization of the United Nations (FAO) or the International Union of Soil Sciences (IUSS), e.g. World Reference Base for Soil Resources (WRB) or FAO Guidelines on Soil Description. Several initiatives worked on the identification and provision of agreed vocabularies in order to ensure the interoperability of their results at different scales (national, EU, international). This includes work by standard setting organizations (eg. ISO TC190), legislation (eg. EU INSPIRE Directive) and of course numerous collaborative projects, such as SIEUSOIL, EJP SOIL, ISLANDR, SoilWise, SPADES, Soil Mission Support and MARVIC. At present, many existing vocabularies have not been exposed in a referenceable and machine-readable manner, and instead remain “trapped” within PDF documents. Extracting the relevant concepts and exposing them in both human and machine readable forms on persistent URIs would be a valuable step towards soil data harmonization.
The European Mission: A Soil Deal for Europe, with currently about 50 research projects and a network of 100 living-labs and lighthouses, offers an interesting environment and opportunity for the co-creation of a harmonised framework for soil vocabulary description. Due to the diversity of Soil Mission Projects, gaps in existing vocabularies can be identified and experience can be gained in how to best present vocabularies for both data annotation as well as data discovery.
In this presentation we will share the current status on this topic, offering a non-exhaustive yet hopefully informative overview on existing materials (vocabularies and associated technologies to share them), on-going work and key challenges for achieving better soil data interoperability.
This study was made possible through funding from the EU's Horizon Europe program, specifically the ISLANDR and SoilWise projects.
How to cite: Beaufils, M., van Genuchten, P., van Egmond, F., and Schleidt, K.: FAIR EU soil vocabularies: an overview of joint efforts from some EU Soil Mission projects, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-10627, https://doi.org/10.5194/egusphere-egu25-10627, 2025.
The fulfillment of the FAIR principles is a central requirement in modern research. Data findability and reusability are highly dependent on the quality and interoperability of their metadata. Among other attributes in earth and environmental sciences, FAIR metadata should ensure consistent and uniquely referenceable naming of geoscientific variables that support machine-interpretable semantic annotations. But in practice, most terminologies used to describe datasets and observed variables vary wildly in their granularity, quality, governance and interconnectivity which, in turn, limits their interoperability. The RDA endorsed I-ADOPT Framework addresses this issue by breaking down descriptions of observed variables into five well-defined atomic components ObjectofInterest, Property, Matrix, Constraint and Context anticipating their annotation with generic terms from FAIR semantic artefacts. As of today, the I-ADOPT decomposition is still a highly manual process that requires semantic and domain skills. Here, we propose the application of Large Language Models (LLM) to transform scientific terms into I-ADOPT-aligned descriptions. This model will enable the transformation into machine-interpretable representations by simply using natural language descriptions of observational research provided by domain experts. We will leverage the existing set of high-quality, human-made formalizations of I-ADOPT variables to adjust the LLM for this task. We will consider LLM in zero-shot scenarios where the LLM is used in its pretrained version and in-context learning where the LLM sees some examples of the task. We will also consider training specialist LLM where the LLM is further fine-tuned for this task, although the success of this approach depends on the amount of training data available. For developing this model and a first demonstrator, we will build on our previous experience in developing the I-ADOPT Framework, in transfer learning and fine-tuning neural networks, FAIR data stewardship, research data infrastructures and research software engineering. Our project will be further linked to several other ongoing activities and initiatives both on a national and also European level, which allows us to directly evaluate the performance of our LLM by potential end-users and communities. Such a service will be integrated into platforms like RoHub to help scientists make research datasets FAIR.
How to cite: Gómez Pérez, J. M. and García, A.: Automatic annotation following the I-ADOPT framework, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20123, https://doi.org/10.5194/egusphere-egu25-20123, 2025.
The availability of reusable data and their associated metadata is increasingly demanded to address global societal challenges. Research data repositories and databases are the primary access points for geosciences data, and especially domain repositories are known to publish well documented and reusable data. This is due to a thorough data and metadata curation provided by the repository staff that usually includes domain scientists. Overall, the documented publication of a complex data set via a domain repository often takes time and additional preparation by the scientists, but the results clearly show a significant increase of the metadata and data quality, including the provision of cross-references to other publications, datasets, code and originating physical samples.
The largest challenge for domain repositories is to provide incentives to the researchers that reduce their workload and in the same time ensure a high quality of metadata and data documentation already at an early stage of a planned data publication. This challenge is especially high in repositories with a focus on the highly variable and usually small data from so-called “long-tail communities”. GFZ Data Services is a domain repository for DOI-referenced geosciences data and scientific software, hosted at the GFZ Helmholtz Centre for Geosciences. The repository has both a focus on the curation of long-tail data, and offers data publication services for international projects and services in the geosciences. To support researchers with the provision of descriptive metadata and receive structured data documentation, GFZ Data Services has developed an online metadata editor and data description templates. This presentation will focus on these support tools and demonstrate how both help the researchers and in the same time reduce the data curation workload.
A major focus will lay on our new metadata editor that is currently jointly developed between the University of Applied Sciences Potsdam and GFZ Data Services. The new metadata editor will enhance the support of users in data entry, so that the manual curation effort by the GFZ Data Services is reduced, and the metadata quality is improved at the same time. Technically, it has a responsive design and offers a dark mode. New facets include the ability to retrieve specific information, e.g., affiliations from the ROR API via a dropdown menu. Keywords are made uniquely identifiable through the automatic storage of schema names and uniform resource identifiers of the specific terms. All integrated thesauri can be updated via API calls. Real time validation of the input fields prevents the submission of incomplete or incorrect entries, so that significantly less work is required in data curation. The integrated help guide supports users to fill in the input fields.
The data description templates collect additional technical description in a structured form and are essential for data reuse. They are available in “commented” and “usable” versions and ensure that the descriptions meet our requirements (for many researchers the data documentation is new), offer clear instructions and even reduce the workload of the curators, because the descriptions are already provided at a very high level of content.
How to cite: Meistring, M., Ehrmann, H., Franz, J., Frenzel, S., Mohammed, A., and Elger, K.: How domain repositories support reusable data: metadata tools from GFZ Data Services, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20132, https://doi.org/10.5194/egusphere-egu25-20132, 2025.
Text-based literature remains the primary expression of scientific knowledge. Since the first scientific article published in 1665, we have managed the switch from physically printed articles to PDFs, but nothing more. While PDF publications can be easily shared electronically, they remain unstructured text-based documents that machines cannot easily interpret (i.e., they are not machine-reusable). This limits our ability to use digital support tools to efficiently extract and organize knowledge from scientific articles. Rather, to reuse most scientific results (e.g., for synthesis research), we must first extract them from articles and organize them into databases, which is time consuming and prone to error.
Here, we present reborn articles, which offer a novel approach to producing scientific knowledge. By integrating with programming languages commonly used for data analysis, like R and Python, reborn articles allow researchers to produce scientific results in a machine-reusable format from the outset. This means subsequent data users can download the results of a reborn article as a CSV file with just a click of a button and bypass post-publication data extraction. To support the production, publication, and reuse of reborn article data, we developed ORKG reborn, a FAIR knowledge online infrastructure.
Using an ecological dataset, we showcase the production of a reborn article, and its impact on knowledge integration and synthesis. Building on the author’s original data analyses conducted in R, we developed an accompanying R script to produce machine-reusable descriptions of the original statistical models that were automatically harvested by ORKG reborn, eliminating manual data entry. We envision that the use of programming languages, like R, to facilitate the production of machine-reusable scientific knowledge could feasibly be streamlined into existing FAIR data management requirements that are already implemented by many academic publishers. Broad adoption of the approach across research communities could transform the way we share and synthesize scientific knowledge.
How to cite: Snyder, L., Ghaemi, H., Perez-Alvarez, R., and Stocker, M.: Escaping from the 1600s: Advancing FAIR scientific knowledge with reborn articles, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-4203, https://doi.org/10.5194/egusphere-egu25-4203, 2025.
The international Earth, space, environmental sciences informatics community has recently formed a new Research Data Alliance Community of Practice. Here we are focused on improving data and software management and sharing practices that result in our researchers having access to community informatics resources that support their research. This community of practice will provide a place for teams and organizations in the Earth, space, and environmental research ecosystem to coordinate on common challenges, share information, review and consider RDA recommendations, seek leading practices, and work towards finding approaches to discipline-specific challenges and issues around data and software management and sharing. The international Earth, space, and environmental community is broad and includes researchers, data managers, data curators, institutions, instrument creators and manufacturers, software developers, tools, repositories, journal editors and more.
An RDA community of practice is where those with common interests can collaborate on complex challenges that need multiple stakeholders to work through the layers of a solution. It is a place where projects can be highlighted and shared for the benefit of building collaboration and connection.
Join us for this session and learn more about how we envision supporting the many global data and software management efforts.
How to cite: Stall, S., Kinkade, D., Raia, N., Wyborn, L., and Corrêa, P.: International Earth, space, and environmental coordination of data and software management efforts, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20583, https://doi.org/10.5194/egusphere-egu25-20583, 2025.
The geochemical community increasingly generates and requires large volumes of analytical data from a wide array of acquisition methods, analytical scales, and sample types in order to address broad research applications. Resulting datasets are commonly collected and reported through non-standardised protocols and reporting formats, if indeed standards are applied at all, which inhibits easy sharing of data during collaborative research projects or repurposing of legacy data. Existing repository services do not presently satisfy requirements for Findable, Accessible, Interoperable and Reusable (FAIR) data, and especially contain significant flaws as to the reuse and interoperability of geochemical data. Generalist repositories such as Zenodo or Figshare do not provide consistent data structures or curation, hence data held within these services is highly variable with regard to format, parameters reported and potentially quality. While domain repositories commonly do implement internally consistent data formats and a level of curation, data within repositories is gathered from published sources which may be incomplete or unstructured, and hence often lack the complete information (metadata) required to appropriately describe the data and allow it to be confidently reused.
To truly unlock the potential of the ever expanding wealth of geochemical data and meet FAIR requirements, improvements to the data infrastructure landscape are clearly required. The AuScope Geochemistry Network (AGN) is an Australian-based collaboration of geoscientists producing bespoke data resources and infrastructure for the international community to capture, normalise, and share geochemical data resources. These resources include best practice data reporting schema and vocabularies for a variety of data types, produced through collaborations with expert advisory groups and, where available, following or expanding on existing international community recommendations. These data resources have been implemented to the EarthBank platform (formerly AusGeochem), an open web service designed by the AGN to capture, share, store and evaluate geochemical data and metadata. Unlike many other services, researchers are able to upload data prior to publication which can assist both in allowing researchers to compare their data with other existing resources prior to submission, but importantly also improves the likelihood of capturing the full data and metadata associated with analyses required for reuse. Once data is uploaded to this service it may be associated with a dataset DOI to support data access requirements for publication, in order to streamline the publication process and provide a domain specific repository for supplemental data. Data models for U/Pb, fission track, (U-Th-Sm)/He, 40Ar/39Ar and inorganic major and trace geochemistry data types are presently implemented within EarthBank, allowing users to freely upload generated research data for these systems, or explore and integrate existing datasets. Best practice templates for upload are openly available through the EarthBank platform, and vocabularies are openly discoverable through the Research Vocabularies Australia (RVA) service. These resources may be used not only to upload data, but also to develop cross-walks for machine-to-machine interoperability with other repository services to build a global FAIR compliant infrastructure required to maximise data access and improve research outcomes.
How to cite: Nixon, A., Ware, B., McInnes, B., Kohlmann, F., Theile, M., Noble, W., Gréau, Y., Dalton, H., Ananuer, H., McMillan, M., and Savelkouls, A.: EarthBank by AuScope: Building FAIR research data infrastructure for the global geochemical community, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14320, https://doi.org/10.5194/egusphere-egu25-14320, 2025.
Excellence in exposome research and chemical risk assessment (CRA) relies on robust capacities, innovative technologies, and skilled human resources. Research infrastructures are vital in providing access to these resources and driving innovation. Over recent decades, Europe has developed numerous research infrastructures, including EIRENE RI (Research Infrastructure for Environmental Exposure Assessment in Europe), the first EU research infrastructure dedicated to the human exposome. EIRENE RI aims to integrate interdisciplinary data, offering harmonized workflows and services to users across various sectors. Other initiatives, such as the Partnership for the Assessment of Risks from Chemicals (PARC), work on advancing harmonization and innovation in CRA.
A robust data infrastructure aligned with FAIR data and Open Science principles is essential for these research infrastructures. Mapping and evaluating the current data landscape is a critical step toward enhancing FAIR implementation and machine actionability. This contribution highlights existing strategies for harmonizing and managing global data on chemical occurrences developed through two decades, using the use case of the GENASIS information system.
GENASIS information is a platform originally developed for storing, harmonizing, and visualizing global environmental monitoring data. Over time, it has expanded to include data on chemical occurrences in indoor environments, consumer products, and human matrices. Today, it hosts over 3 million harmonized records on more than 800 chemicals, described with rich metadata, and it is continuously expanding. This enables the identification of gaps, locality comparisons, and evaluation of global trends in chemical concentrations in the environemnt and humans. GENASIS also serves as a model and sister database for the Global Monitoring Plan Data Warehouse of the Stockholm Convention and supports the United Nations Environment Programme in managing environmental and human monitoring data to evaluate the effectiveness of global treaties on chemical pollutants. GENASIS’ ongoing development and associated services contribute to the European Open Science Cloud (EOSC) in the Czech Republic, EIRENE RI and PARC initiatives.
This contribution evaluates GENASIS in terms of FAIR principles, detailing its current status, roadmap for further FAIR implementation, efforts to enhance machine actionability, and challenges encountered. The discussion is framed within the broader context of initiatives such as PARC, EIRENE RI, and EOSC CZ, emphasizing their role in advancing exposome research and CRA in Europe.
Acknowledgement: This project was supported from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 857560 (CETOCOEN Excellence), and from the Horizon Europe programme under grant agreements No 101057014 (PARC) and 101079789 (EIRENE PPP). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union, European Health and Digital Executive Agency (HADEA) or European Research Executive Agency (REA). Neither the European Union nor the granting authorities can be held responsible for any use that may be made of the information it contains. Authors thank the RECETOX Research Infrastructure (No LM2023069) financed by the Ministry of Education, Youth and Sports.
How to cite: Řiháčková, K., Borůvková, J., Bednářová, Z., Hůlek, R., and Klánová, J.: Advancing Data Infrastructure for Chemical Risk Assessment and Exposome Research: The GENASIS Platform in the Context of FAIR Principles, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21516, https://doi.org/10.5194/egusphere-egu25-21516, 2025.
The Geospatial Open Science Yielding Applications (GOYAS) project, under the umbrella of the Horizon Europe project “OSCARS”, proposes a new approach for open science and open data in remote-sensing, integrating FAIR principles (Findable, Accessible, Interoperable, and Reusable) from the initial design phase. GOYAS provides innovative and/or experimental Earth Observation (EO) data and open science practices to address diverse environmental challenges, delivering advanced geospatial products that are tailored to meet the needs of multiple stakeholders, including researchers, decision-makers, and environmental managers.
GOYAS focuses on generating innovative and accessible remote sensing products for a variety of applications: monitoring water quality parameters, such as turbidity or chlorophyll-a; deriving high-resolution bathymetric maps over coastal regions based on optical instruments; assessing oceanographic variables like sea surface temperature and salinity; improving ocean and atmosphere forecasting capabilities with enhanced sea-surface wind & stress products; and supporting ecosystem monitoring and management in protected areas such as Doñana National Park. These products are generated through the integration of multi-source EO data, including Copernicus Sentinel satellites and complementary datasets, with advanced processing pipelines built on machine learning algorithms and geospatial standards.
A core strength of the GOYAS project lies in its FAIR-by-design system architecture, which prioritizes:
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Findability: Metadata-rich datasets indexed through open repositories and geospatial catalogues to enhance discoverability.
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Accessibility: FAIR-compliant platforms with user-friendly interfaces that provide seamless access to data products, ensuring usability across diverse technical expertise levels. GOYAS aims at facilitating the access providing data in common formats and contextualizing them with proper metadata.
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Interoperability: Adoption of open geospatial standards (e.g., OGC, INSPIRE) to ensure compatibility with existing systems and facilitate data exchange, specially under the context of Research Infrastructure hubs like ENVRI.
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Reusability: Comprehensive documentation and adherence to open licenses that allow users to adapt and build upon project outputs.
Key innovations include the automated processing of remote-sensing data to extract actionable insights and the application of machine learning to improve the accuracy and reliability of derived parameters. For example, GOYAS employs advanced spectral analysis techniques to calculate shallow bathymetry with sub-meter precision in coastal environments, as well as algorithms for near-real-time detection of water quality anomalies in inland waters.
The system also provides support for the monitoring and management of sensitive ecosystems. In Doñana National Park, GOYAS enables the identification of changes in hydrological regimes or vegetation health through the integration of long-term EO datasets with local ecological studies. Similar applications extend to marine protected areas, where GOYAS aids in monitoring oceanographic dynamics and ecosystem responses to climate change.
This presentation will detail the design, architecture, implementation, and outcomes of the GOYAS project, emphasizing its alignment with FAIR principles and its transformative potential for environmental monitoring. By fostering interoperability and collaboration across disciplines, GOYAS serves as a model for how open science and advanced remote sensing can drive innovation, sustainability, and informed decision-making in geospatial research.
How to cite: Aguilar Gómez, F., González-Gambau, V., González-Haro, C., García-Espriu, A., Flo, E., Olmedo, E., Caballero, I., Makarova, E., Portabella, M., García-Díaz, D., and Afán, I.: GOYAS: A FAIR-by-Design System for Innovative remote-sensing data products, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-2803, https://doi.org/10.5194/egusphere-egu25-2803, 2025.
Driven by the scientific need for global exchange of data to study earthquakes and related phenomena, community standards and best practices have evolved in seismology for decades. These developments are largely driven by operational and scientific requirements coming directly from the community of academic research and seismological monitoring, and have resulted in standardised data formats, data models and services for data access and exchange.
Initial developments, promotion and further evolution of these standards are coordinated mainly within the International Federation of Digital Seismic Networks (FDSN, https://fdsn.org), a commission of IASPEI (International Association of Seismology and Physics of the Earth's Interior, httwww.iaspei.org) that is one of eight associations of the IUGG (International Union of Geodesy and Geophysics, https://iugg.org).
With the introduction of the FAIR (Findable, Accessible, Interoperable, Reusable) principles in 2016 and the subsequent appearance of FAIR assessment methods and tools it became clear that these seismological community standards only cover parts of the FAIR principles. Interoperability remains challenging, for example, due to the lack of community standardised FAIR vocabularies, and the lack of a harmonised and consistently applied data license policy impacts Reproducibility.
The emergence of new data types and the drastic increase in data volumes due to new measurement techniques require updates and evolution of the existing community standards, highlighting another general challenge: Who are the recognised and appropriate governance bodies for curation and further development of 'relevant community standards' (as required by the FAIR principles)?
In this presentation we describe the current status of FAIRness for seismological waveform data and beyond, also looking towards seismology in general, geodesy and some other fields of geophysics. Based on our assessment of current challenges we discuss open questions and possible ways forward. We look at FAIR-relevant development and governance of standards, the potential role of existing international organisations like FDSN, IASPEI and IUGG, and the possibility and need to coordinate across domains for harmonisation as well as demarcation.
How to cite: Haslinger, F., Wyborn, L., Casey, R., Pederson, H., D’Anastasio, E., Quinteros, J., Hanson, J., and Carter, J.: Status, issues and challenges with FAIRness of seismological waveform data and beyond, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21605, https://doi.org/10.5194/egusphere-egu25-21605, 2025.
Assessing the status and trends of water quality in inland water bodies requires access to reliable water quality monitoring data and associated metadata such as the monitoring locations, sampling methods, monitoring equipment and analytical methods. Many environmental agencies and research organizations collect water quality monitoring data, but unlike in other environmental domains and due to a lack of common best practices and standards, most organizations use their own data models, formats and controlled vocabularies to store and share these data. As a result, large-scale water quality analyses with a transboundary, continental or global scope require significant efforts to collect the necessary monitoring data from different sources and to harmonize the different data structures. Several international initiatives such as the UNEP Global Environment Monitoring System for Freshwater (GEMS/Water)1 or research activities such as the Global River Water Quality Archive (GRQA)2 have compiled global water quality datasets to facilitate large-scale hydrological studies, all facing the same challenges and often duplicating data processing efforts.
Over the last 20 years, the observing community has developed data models and semantic ontologies such as the OGC Observations, Measurements, and Samples (OMS)3 standard or the OGC/W3C Semantic Sensor Network (SSN)4 ontology to describe observations and associated metadata. These form the basis of several standards for the exchange of hydrological observation data such as the WaterML 2.0 family of standards. However, water quality specific aspects such as the description of sampling activities and associated metadata have not yet been included in these water specific standards.
To address this issue, several government agencies and research organizations have started a Water Quality Interoperability Experiment (WQIE) within the Open Geospatial Consortium (OGC) in 2022. Several use cases for the exchange of water quality monitoring data of physical and chemical parameters monitored in surface and groundwater bodies using in-situ (sensor) or ex-situ (laboratory) monitoring were developed and described as object diagrams in UML based on the OMS conceptual model. Based on this exercise, a physical data model was developed by extending the OGC SensorThingsAPI (STA)5 with a plugin for the open source FROST server6. Several WQIE participants deployed pilot instances of water quality enabled FROST servers, making their water quality data publicly available. A web client was developed to facilitate access to the various STA endpoints and to enable data visualisation7.
This presentation will give an overview of the developments of the OGC Water Quality Interoperability Experiment, highlighting achievements, outstanding challenges and future development plans.
References:
1 https://www.unep.org/explore-topics/water/monitoring-water-quality
2 Virro, H., Amatulli, G., Kmoch, A., Shen, L., and Uuemaa, E.: GRQA: Global River Water Quality Archive, Earth Syst. Sci. Data, 13, 5483–5507, https://doi.org/10.5194/essd-13-5483-2021, 2021.
3 https://docs.ogc.org/as/20-082r4/20-082r4.html
4 https://www.w3.org/TR/vocab-ssn/
5 https://www.ogc.org/publications/standard/sensorthings/
6 https://github.com/hylkevds/FROST-Server.Plugin.WaterQualityIE/tree/main
7 https://api4inspire.k8s.ilt-dmz.iosb.fraunhofer.de/servlet/is/226/
How to cite: Heinle, M. and Saile, P.: A step towards FAIR water quality data – lessons learned from the OGC Water Quality Interoperability Experiment, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19719, https://doi.org/10.5194/egusphere-egu25-19719, 2025.
ECMWF's move towards an extensive free and open data policy is approaching its final phase, extending its user base far beyond operational forecasters in Member and Co-operating States and other licensed customers. Beginning in 2020, the first phase saw the opening of hundreds of web forecast charts (www.charts.ecmwf.int) and made archived data available under a Creative Commons (CC BY 4.0) open licence. This transition continued in January 2022 with the introduction of a free and open subset of real-time forecast data, with ongoing updates incorporating new parameters and datasets. Notably, the latest updates in 2024 included increasing the resolution from 0.4° to 0.25° and including the new Artificial Intelligence Forecasting System (AIFS) forecast data.
This phased move towards free and open data supports the UN EW4All initiative and also aims to support creativity, innovation and reproducibility in scientific research and weather applications. However, this can not be achieved by only opening the real time and archived data. The users need to be able to find and easily use the data and integrate it into their own research work or application workflows.
To address this, additional efforts are underway to improve the data's FAIR (Findable, Accessible, Interoperable and Reusable) attributes. Key developments include the creation of open source Python libraries for data downloading, processing and visualisation under the EarthKit umbrella, alongside the introduction of a set of Jupyter notebooks, each of which is reproducing one open weather forecast chart - from the downloading the data to processing and visualisation.
However, the tools and data constantly change, and keeping up with these changes in the example Jupyter notebooks presents a significant challenge if not designed with the maintenance in mind.
This talk will provide an overview of the open forecast web charts and the use of Jupyter notebooks for their reproduction, followed by an exploration of the maintenance challenges and future plans.
How to cite: Vuckovic, M., Pidduck, E., Sahin, C., and Russell, I.: Improving the accessibility of ECMWF open weather forecast data and charts: maintenance challenges, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16485, https://doi.org/10.5194/egusphere-egu25-16485, 2025.
Australia’s Climate Simulator (ACCESS-NRI) is a national research infrastructure established to support the Australian Community Climate and Earth System Simulator (ACCESS) modelling system. Since its launch in 2022, ACCESS-NRI has focused on modernising climate modelling software and data practices for ACCESS. Guided by the needs of our community, our goal is to make the modelling framework and data outputs more FAIR (Findable, Accessible, Interoperable, and Reusable) and easier to use.
One of the key challenges in achieving FAIR for ACCESS data is the reliance on often optional post-processing steps to meet most of the FAIR guidelines. While ACCESS model outputs generally follow community standards (e.g., CF-Conventions), their implementation can be inconsistent across modelling components (e.g., atmosphere, ocean, and land models) as well as among individual data generators. As a result, using direct model output data frequently requires users to have previous knowledge and understanding of the specific climate models and leads to significant overheads for compatibility with data discovery and evaluation tools (e.g., Intake, ESMValTool).
As a new infrastructure dedicated to Australian climate software and data, ACCESS-NRI has a unique opportunity to uplift and directly embed FAIR practices into the climate modelling software components we maintain and support. Building on successes and lessons learned from participation in global intercomparison activities such as CMIP6, ACCESS-NRI is working to apply similar data standardisation practices for the lower-level model outputs in a way that enhances consistency and usability. The effort involves close collaboration with the research community, identifying gaps and commonalities to establish a data specification that can be versioned and linked to future ACCESS model releases. This includes minimum and recommended requirements for file and dataset metadata such as: controlled vocabularies, file and variable naming conventions, provenance statements, and other critical elements to ensure data consistency and usability across all ACCESS components.
By embedding FAIR principles directly into the ACCESS modelling system, ACCESS-NRI is not only addressing current challenges but is also future-proofing Australia’s climate modelling capabilities to meet the evolving needs of the research community. This approach will make data and tools more accessible, reduce research overheads, and enhance the adaptability of the infrastructure to future changes and new technologies.
How to cite: Druken, K., Richards, C., Beucher, R., Basevi, J., Bull, C., Carouge, C., Dix, M., Heerdegen, A., Leopardi, P., Marchegiani, D., Nettelbeck, H., Steketee, A., Turner, C., White, M., and Wong, S.: Uplifting and streamlining FAIR data implementation for Australia’s climate modelling outputs, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-14828, https://doi.org/10.5194/egusphere-egu25-14828, 2025.
The Intergovernmental Panel on Climate Change (IPCC) Data Distribution Centre (DDC) serves as a critical registry for climate change data, providing a shared infrastructure to ensure data quality and accessibility for the scientific community. Managing data to support IPCC reports presents challenges due to its multidisciplinary nature and diverse sources.
Key to this effort is the curation of metadata, particularly developing a metadata schema that enables data to be FAIR (Findable, Accessible, Interoperable, and Reusable). This presentation examines the IPCC's experience over the past four years in curating and preserving digital objects, focusing on the implementation of FAIR and open data principles. We will explore the successes and setbacks of the AR6 experience, with particular attention to the development and application of a metadata schema. Finally, we will offer recommendations for consolidating and expanding this approach for AR7 to enhance transparency, reproducibility, and reusability of assessment outcomes.
This initiative aims to increase the transparency of IPCC's work, improve the reproducibility and reusability of assessment outcomes, optimize the utilization of the IPCC DDC's services, and promote compliance with open science best practices.
How to cite: Milward, D., Milward, A., and Xing, X.: Managing a FAIR Climate Change Data Catalogue: Lessons Learned from IPCC AR6 and Recommendations for AR7, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-20454, https://doi.org/10.5194/egusphere-egu25-20454, 2025.
Intergovernmental Panel on Climate Change (IPCC) authors of assessment reports (ARs) and special reports (SRs) use a huge volume of input data, generate a great deal of intermediate data in the process, and produce a large amount of final data for figures and annexes in the published reports. In previous assessment cycles before the Sixth Assessment Report (AR6), only a limited amount of IPCC data were archived and made publicly available. There was great progress in the AR6, but many critical data sets were still not properly curated. This resulted in a data rescue effort during the transition from AR6 to AR7, supported by the IPCC and government fundings. The challenges encountered during the data rescue effort included missing or lost data after the report publication, missing data licensing agreements, version control issues, and missing data quality assurance/quality control (QA/QC) so that some data did not match the published figures. Addressing these issues demanded significantly more resources than the regular process to track, retrieve, archive, and resolve the legal and technical issues.
In the Seventh Assessment Report (AR7), IPCC progressively promotes the FAIR data principles (Findable, Accessible, Interoperable, and Reusable) through the IPCC Task Group on Data Support for Climate Change Assessments (TG-Data) and the Data Distribution Centre (DDC) (1, 2). The Working Group Technical Support Units (TSUs) have also designated data specialists in the TG-Data (3). This provides opportunities to support authors in implementing open and FAIR data in IPCC AR7. For example, in Chapter 2 of the Special Report on Climate Change and Cities (SRCities), there is an area of focus on “Data, information, tools accessibility/availability/usability/transparency" (4). By collaborating the TSUs and DDC can provide a coordinated approach that supports authors with training and tools on data workflow, metadata schema, data provenance, licensing and citation, persistent identifiers, etc., to improve the data curation process and to avoid the issues encountered in previous cycles.
References:
- 1. Intergovernmental Panel on Climate Change. (2023). TG-Data Recommendations for AR7 (1.0). Zenodo. https://doi.org/10.5281/zenodo.10059282
- 2. Stockhause M, Huard D, Al Khourdajie A, Gutiérrez JM, Kawamiya M, Klutse NAB, Krey V, Milward D, Okem AE, Pirani A, Sitz LA, Solman SA, Spinuso A, Xing X. (2024). Implementing FAIR data principles in the IPCC seventh assessment cycle: Lessons learned and future prospects. PLOS Climate 3(12): e0000533. https://doi.org/10.1371/journal.pclm.0000533
- 3. https://www.ipcc.ch/data/ (2025)
- 4. IPCC Special Report on Climate Change and Cities (SRCities) report outline. (2024). https://www.ipcc.ch/site/assets/uploads/2024/08/IPCC-61_decisions-adopted-by-the-Panel.pdf
How to cite: Xing, X., Delgado Ramos, G. C., Alikadic, A., Lamb, A., Stockhause, M., Sitz, L. E., and Milward, A.: Challenges and opportunities in implementing open and FAIR data in Intergovernmental Panel on Climate Change (IPCC) Seventh Assessment Report (AR7) , EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19487, https://doi.org/10.5194/egusphere-egu25-19487, 2025.
Global climate change requires urgent and actionable adaptation planning.
Current Climate Change Adaptation (CCA) strategies often lack the necessary data and other relevant information to be scientifically competent. These limitations can complicate effective action and evaluation locally, and in combination with other regions. The recently awarded FAIR2Adapt Project aims to establish a comprehensive FAIR and open data framework for CCA and to demonstrate the impact of FAIR data on CCA strategies. By making CCA data FAIR, FAIR2Adapt will accelerate adaptation actions that are tailored to local needs.
Next to the technical development of FAIR data and services, a key issue in the effective uptake of FAIR is the transfer of knowledge regarding FAIR practices, and in many cases hands-on skills related to the design, creation and governance of domain-relevant FAIR Enabling Resources. Beginning in February 2025, the FAIR2Adapt, stakeholders (including members of it’s six use cases) will participate in FAIR awareness and training based on the GO FAIR Foundation’s FAIR Capacity Building Programme [https://zenodo.org/records/14187859]. This will include general FAIR Awareness workshops, training on the creation of FAIR Implementation Profiles and community-specific metadata and vocabulary in Metadata for Machine workshops. In addition to this, special attention will be given to the identification and prioritization of user requirements (both the technical approach in FAIR2Adapt as well as the case studies). Having both the technical expertise and building up the salient knowledge and skills, the FAIR2Adapt community will be well positioned to co-design, implement and share CCA related data and services that can accelerate meaningful and customized CCA. In this presentation, we will report the first draft user requirements for FAIR2Adapt and the emerging list of CCA community-specific FAIR Enabling Resources.
How to cite: Schultes, E. and Magagna, B.: Community Support and Engagement for FAIR Science in Climate Change Adaptation, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21472, https://doi.org/10.5194/egusphere-egu25-21472, 2025.
During the last years, Open Science has been gaining increasing attention from research communities and policy makers because of the benefits it can provide not only to scientists, but also to society in general, as it can accelerate the production of science and the quality of results. Open science is a policy priority for the European Commission (EC) and the standard method of working under its research and innovation funding programmes. Thus, the EC initiated the European Open Science Cloud (EOSC) initiative, which aims to create a virtual environment for sharing and accessing research data across borders and scientific disciplines, aligning with Open Science and FAIR principles. EOSC specified a layered approach with a set of core services at its center, a federated data layer, a rich set of exchange services to expand the capabilities offered to researchers across disciplines, plus a set of thematic/discipline-specific services. To fully realise EOSC’s vision, it is envisioned as a federation of distributed systems, combined into a system of systems, consisting of multiple Nodes’. At the end of last year, the first of such nodes (EOSC EU node) was launched featuring the core services enabling scientific research infrastructures to federate and a set of common exchange “horizontal services” for end-users to benefit from.
Based on the integration of thematic, horizontal, and core resources, the goal is that EOSC enables the creation of thematic execution environments/VREs. A VRE is an online support system for researchers, encompassing online tools, network resources and technologies interoperating with each other to ease/enhance the research process within and across institutional boundaries, facilitating collaboration, data management, analysis, and other research-related activities in one online space.
To build an EOSC-based VRE, we have leveraged and integrated different core and exchange services. At the center of the proposed VRE are RO-Crate based research objects (providing an implementation of the FAIR digital object), as well as the associated technological support (provided by ROHub platform), to manage the research lifecycle and the associated scientific resources used and produced. The VRE leverages data cubes services for efficient and scalable structured data access and discovery, AI-based text mining services that extract machine-readable metadata from scientific resources supporting recommendations and comprehension analysis, and FAIR assessment tools supporting researchers in the FAIRification of their outcomes. Additionally, the VRE relies on EOSC services for authentication and authorization to enable seamless access to different services, the computing platforms to execute computational methods, and data repositories to store and/or share their data in their personal/community workspaces or general repositories. The VRE also connects DMP platforms to enable the creation of machine-actionable plans, and with the scientific knowledge graph to enable the discovery of resources by different communities. In the FAIR2Adapt project, such environment is being enhanced with a set of added-value services (e.g., search and discovery using NL questions, multilingual semantic enrichment, sentence detection, FAIRness-aware search and recommendations, and multilingual generative question answering) and adapted to boost FAIR adoption in Climate Change Adaptation communities and research.
How to cite: Palma, R., Wolniewicz, M., Rynkiewicz, A., Manuel Gómez, J., Garcia Silva, A., Garijo, D., Gonzalez Guardia, E., and Fouilloux, A.: Building an EOSC based virtual research environment to support the adoption of FAIR and Open Science practices in Climate Change Adaptation communities, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19699, https://doi.org/10.5194/egusphere-egu25-19699, 2025.
The USAGE objective is to identify, implement, and demonstrate an architecture and solutions for a data space supporting the European Green Deal priorities. It implements the methodology based on the data USAGE data space framework built around specific use cases in the context of local and European policies and guidelines as well as digitalization agendas. Use cases, considered the primary value proposition for the data uptake, are developed and maintained in the USAGE framework. They cover urgent municipalities scenarios like heat islands, clean energy, qir quality and mobility. Target requirements are translated into data and service requirements expressed in the ISO catalog-based model tailored to the specific data quality measures for the Decision Ready Information. Implementation of the value chain goes across various data inputs including satelite and airborne images, local sensors and citizen science data, surface and urban models producing intermediary and end user products and services. Disciplined and tool-supported collection of the data and application assets consistent with the INSPIRE-compliant schemas and data requirements model which allows them to leverage the solutions' potential and implement the value proposition for their providers. Profiled models create the frames of the data value chain, documenting processing steps from the data requirements through BPMN data flow models linking to the used and produced assets. In addition, licensing schema, including the constraints model, allows for data sovereignty and trust among the data space actors.
The outcome blueprint for the urban data space goes beyond the USAGE pilots to test scalable solutions based on adopting the proposed set of standards coming mainly from ISO, OGC, W3C, OASC and their extensions. It is built in the European initiatives and legal references (i.e., the European strategy for data, the European interoperability framework, the European interoperability reference architecture), and reviewed several projects and initiatives results contributing to shaping data spaces: Open DEI design principles, the International Data Spaces Association (IDSA) reference architecture, Gaia-X architecture, Data Spaces Business Alliance (DSBA) documents, the Data Spaces Support Centre (DSSC) results, Data Space for Smart and Sustainable Cities and Communities (DS4SSCC) outcomes, and the GREAT project Technical Blueprint. Presentation goes across the best practices and guidances extracted from the implementation of the FAIR dataspace and considerations given defined frameworks.
How to cite: Zaborowski, P., Noardo, F., Martiano, G., and Vandenbroucke, D.: Scalable Solutions for Urban Data Spaces: Insight from the USAGE blueprint, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9401, https://doi.org/10.5194/egusphere-egu25-9401, 2025.
The GEOROC synthesis database, a pioneering open-access resource for geochemical and isotopic data, marks 25 years of service to the geoscience community. Over its history, GEOROC has compiled data from more than 22,750 publications in the field of geochemistry, and provides free access to over 39 million individual data values, primarily on igneous and metamorphic rocks, minerals and their inclusions. As a cornerstone for interdisciplinary research, GEOROC is complementary to other geochemical synthesis databases like PetDB, AstroMat and GeoReM, in facilitating reuse of data for innovative studies that leverage data analytics and machine-learning approaches across geoscientific disciplines and beyond.
The Digital Geochemical Data Infrastructure (DIGIS) project for GEOROC 2.0 is providing an up-to-date IT infrastructure that aligns GEOROC with the FAIR principles. Data findability and accessibility are ensured through the newly developed API and the improved GEOROC web interface that allows users to retrieve a variety of distinct data products and services, including a fully customizable search functionality. Interoperability is achieved via implementation of a feature-based data model compatible with the OGC Observations and Measurements standard and controlled, machine-readable vocabularies that harmonize geospatial, analytical and sample-related metadata, and enabling seamless integration in multiple databases and portals (e.g., EarthChem). Reusability is further supported by archiving time-stamped GEOROC data products in the DIGIS Data Repository, hosted by GFZ Data Services, where datasets with digital object identifiers (DOIs) are archived for the long-term. Additionally, researchers are encouraged to directly submit new or already “published” datasets to this domain repository—through standardized (meta-)data templates, ensuring high-quality data submissions that facilitate data quality assessment and reuse.
In collaboration with national and global initiatives, such as OneGeochemistry and NFDI4Earth, the DIGIS project further promotes practical approaches to the FAIR principles for geochemistry by developing unified controlled vocabularies for geochemical data and their metadata (e.g., analytical methods, sample description, location). These vocabularies also integrate external standards, such as the International Mineralogical Association’s "List of Minerals" and MinDat’s "Subdivisions of Rocks," alongside newly developed (and published) frameworks for categories such as geological setting and analytical methods (collaboration with EarthChem). By harmonizing metadata across geospatial, analytical and sample-related categories, these efforts ensure consistency, improve data quality assessment and control and enhance interoperability across data systems, including but not limited to GEOROC, PetDB, and AusGeochem. Such advancements expand the potential applications of geochemical data, fostering innovation in fields such as environmental science, remote sensing, archaeology and geohealth.
With 25 years of experience and ongoing innovation through the DIGIS project, the GEOROC database exemplifies how operationalizing the FAIR principles enhances its value as a critical resource for the geoscience community. By providing both FAIR and open data, GEOROC empowers researchers to conduct reproducible, impactful studies and fosters interdisciplinary collaboration, driving innovation and advancing progress across the geosciences.
How to cite: Kallas, L., Klöcking, M., Elger, K., Sarbas, B., Sturm, A., Möller-McNett, S., Willbold, M., and Wörner, G.: Advancing FAIR geochemical data: 25 Years of GEOROC database service, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-11467, https://doi.org/10.5194/egusphere-egu25-11467, 2025.
Ontologies offer significant potential for advancing Earth System Science (ESS) by improving the discoverability and usability of complex datasets and tools. This poster builds on last year’s comic, which illustrated the foundational benefits of ontologies, and presents the first steps in implementing generic tools from already existing terminology services designed to enhance data findability and data comprehension. These tools enable scientists to easily search for appropriate data and retrieve information about data from specific repositories, thus supporting the FAIR (Findable, Accessible, Interoperable, and Reusable) principles in ESS.
Key aspects of terminologies include the clear and consistent description of scientific terms, their relationships, and the unambiguous identification of terms to prevent inconsistencies. By using terminologies we can ensure that terms are defined in a way that is both standardized and interoperable across different datasets and research communities. Concrete examples will be drawn from the World Data Center for Climate (WDCC), where first steps have been taken to implement generic tools and extend the application of terminologies, and to thus enhance data discoverability and facilitate better searchability of climate-related information.
How to cite: Loehden, A., Martens, C., and Lammert, A.: Reducing the Pain of Data Discovery in Earth System Science, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-9739, https://doi.org/10.5194/egusphere-egu25-9739, 2025.
The FAIR data principles are a common theme in many discussions and focus of work within research data management. Such work often focuses on particular parts of the data management lifecycle, for example: FAIR through data management planning, FAIR through data discovery and, more recently, areas of FAIR as applied to software and machine learning.
However, whilst there are many successful attempts at enhancing metadata and data FAIRness for specific parts of the data lifecycle, there may be issues that only arise when considering the overall interconnections between the various stages and the associated actors. For example, a domain may follow common file and metadata conventions for data interoperability, such as CF conventions, enabling research to take place utilising multiple data sources, but pertinent metadata to long-term curation or wider end-usability may not be presented or indeed captured at source. This can have ongoing issues around the level that wider (true?) FAIRness that can be reached and present additional overheads for other actors wishing to handle such data resources, such as manual effort needed for full long-term curation or missed opportunities for data re-use in other spheres.
Recognising these issues and, crucially, the interplay between all actors along the data lifecycle, the UK’s National Centre for Atmospheric Science (NCAS) have developed the frameworks to ensure all actors’ needs are considered. These are succinctly captured in the ‘NCAS Data Pyramid’, where each corner represents a given actor (data provider, long-term archive, those creating tools aiding data flows and utilisation, end-user community), whilst the sides explore the interconnections between these actors. All parts of the pyramid (corners and sides) provide a range of use-cases and requirements that need to be supported. This approach has enabled NCAS to then develop a range of data standards to enhance data FAIRness for surface and remote sensing data (including from ships and aircraft), imagery data and, in due course, laboratory data.
Furthermore, to aid establishing new data standards NCAS has developed data standards development framework, utilising the ‘Scope -> Define -> Develop -> Sustain’ data standard lifecycle:
- Scope: Identify community groups. Assess their needs. Determine the scope for the standard.
- Define the standard by: ensuring all stakeholder needs are covered; defining user-focused data products that it will deliver; and the underpinning standards to be drawn on for wider interoperability.
- Develop: provider tools (including checkers for compliance); data delivery pipelines (including those workflows to capture internal/external metadata required for data use/contextualisation of data (e.g. project info); develop end-user data exploitation(visualisation) tools
- Sustain: having developed standards and workflows have a governance structure to maintain and manage future iterations of the standards development cycle. This must ensure that it refers back to the community groups (as in step 1).
The approach also keeps wider inter-standards interoperability a key focus throughout. The success of this approach is demonstrated through the establishment of data pipelines aiding data to flow with associated metadata from provider to end-user and has seen wider adoption of NCAS data standards within the wider atmospheric community.
How to cite: Parton, G., Brooks, B., Garland, W., Hampton, J., Hooper, D., Marsden, N., Price, H., Ricketts, H., Spronson, D., Stephens, A., and Walden, C.: Establishing FAIRness through all-actor approaches to data pipelines: Frameworks for successful development of data standards and pipelines at the UK’s National Centre for Atmospheric Sciences, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-19266, https://doi.org/10.5194/egusphere-egu25-19266, 2025.
We attempt to make EERIE data FAIR (Findable, Accessible, Interoperable, and Reusable) to enhance its scientific impact and utility. These principles of FAIRness ensure global access, integration, and reuse by researchers and decision-makers, thereby promoting collaboration and innovation.
Findability is enhanced through persistent identifiers such as DOIs and PIDs, ensuring data remains reliably locatable. Metadata standards, including CF conventions and CMIP standard names, ensure precise and efficient searchability. We enhance findability through data catalogs produced in the EERIE and nextGEMS projects, as well as platforms like World Data Center for Climate (WDCC) and DOKU. The WDCC ensures long-term storage with a focus on FAIRness, quality control, and DOI assignment following CF standards. Our EERIE data is also archived on DOKU with PIDs to ensure discoverability.
Accessibility is ensured by providing data through open protocols with clear terms of use. While accessibility does not always mean free access, it guarantees transparency and ease of use. Open-access repositories such as EERIE Cloud, Earth System Grid Federation (ESGF), and, WDCC combination with standardized formats such as NetCDF and Zarr, ensure broad accessibility. Additionally, tools like Zarr provide API access via HTTP, facilitating seamless and efficient data retrieval.
Interoperability is fundamental for integrating datasets across disciplines and platforms. The EERIE project supports this by linking datasets through initiatives such as EERIE Cloud, FREVA and by using standards such as CF conventions to ensure compatibility, facilitating multidisciplinary research.
Reusability is supported through detailed metadata, clear licensing models like CC-BY and CC0, and strong version control practices (e.g, v20240304). Documentation platforms such as easy.gems.dkrz.de assist users to understand and reproduce results. The maintenance of high data quality and the emphasis on archival and replication further enhance the long-term scientific use of these datasets.
Despite these efforts, the implementation of the FAIR data principles in a comprehensive manner poses significant challenges. In the EERIE project, for instance, we work with vast amounts of data, and standardizing it (e.g., CMORizing) can be complex. Obtaining CF-compliant names for all variables is particularly difficult, as there is often no one-to-one documentation from modeling groups. In some cases, this requires manually analyzing code to determine the correct definitions for certain variables.
For climate science, the application of FAIR principles is transformative. These efforts promote global collaboration, enhance the transparency of climate models, and equip policymakers with reliable data to address critical challenges such as climate adaptation and mitigation. Initiatives like EERIE cloud, ESGF and advancements in data processing, such as kerchunking massive datasets, further enhance the FAIRness of climate data, driving innovation and impact.
How to cite: Widmann, H., Wickramage, C., and Wachsmann, F.: Implementing FAIR Principles for Earth System Data: Insights from the European Eddy-Rich Earth-System Models (EERIE) project, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-21162, https://doi.org/10.5194/egusphere-egu25-21162, 2025.
Posters virtual: Tue, 29 Apr, 14:00–15:45 | vPoster spot 4
EGU25-13330 | ECS | Posters virtual | VPS19
Advancing and Supporting FAIR Principle Adoption through Innovative Social Infrastructure ToolsTue, 29 Apr, 14:00–15:45 (CEST) | vP4.19
The adoption of the FAIR data principles has revolutionised data management in Earth System Sciences (ESS), yet challenges persist in achieving true machine-actionability and comprehensive implementation.
DeSci Labs introduces two innovative tools—Decentralised Persistent Identifiers (DPIDs) and the CODEX protocol—to address the barriers to FAIR data practice implementation in general; whilst fostering widespread uptake of FAIR principles in the ESS community in particular through involvement in the FAIR2Adapt consortium.
DPIDs are globally unique persistent identifiers based on, and linked directly to, the content of the files it refers to. Each version of every file, regardless of type, is automatically assigned a cryptographic fingerprint, ensuring deterministic resolution and transparent versioning. The DPID has been specifically designed to support FAIR digital research objects. The flexibility to alias DPIDs with existing systems (e.g., DOIs) and programmatic publishing capabilities via NodesLib enhances interoperability while preserving data sovereignty.
The CODEX protocol, an open scholarly infrastructure, further complements this by enabling the storage and retrieval of FAIR digital research objects via a decentralised peer-to-peer network (IPFS). This architecture allows multiple copies of the same content to be stored by different network participants using the same PID. By empowering researchers to collaborate within an open-state repository, the protocol minimises reliance on centralised actors, ensuring long-term accessibility, data integrity, and transparency. Its modular design facilitates diverse gateway applications, maximising participation and reducing barriers to entry.
These tools address core challenges in FAIR adoption by providing robust, scalable, and interoperable solutions tailored to the ESS community. By integrating DPIDs and CODEX into data workflows, researchers can enhance data reusability, improve the provenance of research outputs, and safeguard the collective scientific record. This presentation explores how these technologies can catalyse the next decade of FAIR data practices in ESS, fostering trust, reproducibility, and innovation.
How to cite: Raijmakers, L., Hübinette, E., DeSota, E., Iman, S., and Koellinger, P.: Advancing and Supporting FAIR Principle Adoption through Innovative Social Infrastructure Tools, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-13330, https://doi.org/10.5194/egusphere-egu25-13330, 2025.
EGU25-16092 | Posters virtual | VPS19
Transforming GNSS Data into FAIR Digital ObjectsTue, 29 Apr, 14:00–15:45 (CEST) | vP4.20
GNSS (Global Navigation Satellite System) data play a crucial role in both scientific research and practical applications. GNSS datasets are used to monitor atmospheric conditions, tectonic plate movements, and Earth deformation, providing valuable insights for geodetic and geophysical studies. Although widely accessible, GNSS data often lacks the necessary structure and metadata for effective reuse, particularly for data-driven research based on machine learning. To address these challenges, we applied the FAIR (Findable, Accessible, Interoperable, Reusable) data principles to GNSS RINEX observation files hosted by the EUREF Historical Data Centre (EUREF-HDC).
The EU action plan “Turning FAIR into Reality” introduced the concept of FAIR Digital Objects (FDOs), emphasizing the need for Persistent Identifiers (PIDs) and rich, standardized metadata to ensure data can be reliably found, accessed, utilized, and cited. Building on this foundation, we developed a multi-layered FDO structure centered on GNSS RINEX data. Given the established nature of the EUREF-HDC repository, we adapted the FDO concept by prioritizing structured metadata, followed by persistent identifiers and robust (meta)data access procedures.
To implement this approach, we designed the GNSS-DCAT-AP metadata schema, assigned PIDs to both data and metadata, and developed web services enabling humans and machines alike to seamless search, retrieve, and download (meta)data. The effectiveness of our solution was evaluated using the FAIRsFAIR Data Object Assessment Metrics, demonstrating a significant improvement in FAIR compliance.
This work showcases the feasibility of transforming GNSS RINEX data into FAIR Digital Objects and could provide a practical roadmap for other geospatial data repositories seeking alignment with FAIR principles.
How to cite: Bruyninx, C., Miglio, A., Fabian, A., Legrand, J., Pottiaux, E., and Bamahry, F.: Transforming GNSS Data into FAIR Digital Objects, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-16092, https://doi.org/10.5194/egusphere-egu25-16092, 2025.
EGU25-18822 | Posters virtual | VPS19
Visualizing a climate and disaster resilience taxonomy from research evidence: scaling and accelerating knowledge interoperabilityTue, 29 Apr, 14:00–15:45 (CEST) | vP4.21
The urgency of the climate crisis and the need to accelerate learning and climate action requires that we build on previous knowledge, rather than replicating it. There is an abundance of knowledge on climate change adaptation and mitigation dispersed across websites, projects, platforms, and documents. There is either too much information that is not easily discoverable (sitting in silos) or it is too technical or complex, and not ‘usable’ or fit for purpose in terms (e.g. language or format). Both issues cause redundancy and sometimes replication of work, wasting resources. In the worst case, they can also cause unintended consequences such as maladaptation, or increased vulnerability. However, the issue is not a lack of information, but rather how to organise and connect such knowledge to allow people to discover what already exists and put it to effective use. As such, our goal is to make climate action knowledge findable, accessible, interoperable and reusable (FAIR) and reduce climate change knowledge silos. The recently awarded FAIR2Adapt Project aims to establish a comprehensive FAIR and open data framework for CCA and to demonstrate the impact of FAIR data on CCA strategies. By making CCA data FAIR, FAIR2Adapt will accelerate adaptation actions so that they are visible, understandable, and actionable for various purposes and different types of stakeholders. FAIR taxonomies are one approach to help tackle this issue by making climate change knowledge FAIR and by ensuring, that going forward, platforms have a way to make their knowledge FAIR and thus more reusable by the climate change community.
The Climate Connectivity Hub and Taxonomy seek to visualize and connect online platform data (e.g. Cordis, Climate-ADAPT, weADAPT, PreventionWeb) to increase discoverability, interoperability and a shared understanding of the research results and their potential application in future policy, research and practice. It builds on past knowledge to scale up and accelerate climate action whilst also identifying key knowledge gaps. This presentation will show that: 1) taxonomies are useful supporting the interoperability of online climate knowledge and can usefully emerge from combined expert and machine learning of project results (e.g. Cordis); 2) shared vocabularies and different interpretations of language and terminology add value to project planning and implementation ; and, 3) the visualization of these elements for decision-makers, planners, researchers, policy makers, etc. can help to enable and scale accelerated climate action.
How to cite: Bharwani, S., Witton, R., Williamson, K., and Butterfield, R.: Visualizing a climate and disaster resilience taxonomy from research evidence: scaling and accelerating knowledge interoperability, EGU General Assembly 2025, Vienna, Austria, 27 Apr–2 May 2025, EGU25-18822, https://doi.org/10.5194/egusphere-egu25-18822, 2025.
