Keith Jeffery

The increasing complexity and volume of data in Solid Earth Science necessitate robust solutions for workflow representation, sharing, and reproducibility. Within the DT-GEO (https://dtgeo.eu/) project, we addressed the challenge of creating interoperable and discoverable representations of computational workflows to facilitate data reuse and collaboration. Leveraging the EPOS Platform (https://www.epos-eu.org/), a multidisciplinary research infrastructure focused on Solid Earth Science, we aimed to expose workflows, datasets, and software to the community while adhering to the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. While the EPOS-DCAT-AP (https://github.com/epos-eu/EPOS-DCAT-AP) model, already used in EPOS, can effectively represent datasets and software, it lacks direct support for computational workflows, necessitating the adoption of alternative standards.To overcome this limitation, we employed the Common Workflow Language (CWL, https://www.commonwl.org/) to describe workflows, capturing their structure, software, datasets, and dependencies. The developed CWL representations are "abstract" focusing on general workflow structures while omitting execution-specific details to prioritize interoperability. To package these workflows along with metadata, we utilized Workflow Run Crate, an extension of the RO-Crate (https://www.researchobject.org/ro-crate/) standard. Together, these technologies enable workflows to become self-contained entities, simplifying sharing and reuse. This approach not only aligns with community standards but also benefits from a mature ecosystem of tools and libraries, ensuring seamless integration and widespread applicability. Initial implementations within the DT-GEO project serve as a model for adoption in related initiatives such as Geo-INQUIRE (https://www.geo-inquire.eu/), where similar methodologies are being used to share workflows derived from the Simulation Data Lake (SDL) infrastructure. These implementations pave the way for broader integration within the EPOS Platform, enhancing access to advanced workflows across disciplines.Our contribution highlights the value of adopting standardized tools and methodologies for workflow management in Solid Earth Science, showcasing how CWL and RO-Crate streamline interoperability and foster collaboration. These advances address challenges in data and computational management, contributing to the scalable FAIR workflows essential for tackling the complexities of Solid Earth Science. Moving forward, the integration of these standards across projects like DT-GEO and Geo-INQUIRE will further enhance the EPOS Platform's capabilities, offering a unified gateway to reproducible, secure, and trustworthy workflows that meet the evolving needs of the scientific community.

Research infrastructures play an increasingly essential role in scientific research. They provide rich data sources for scientists, such as services and software packages, via catalog and virtual research environments. However, such research infrastructures are typically domain-specific and often not connected. Accordingly, researchers and practitioners face fundamental challenges introduced by fragmented knowledge from heterogeneous, autonomous sources with complicated and uncertain relations in particular research domains. Additionally, the exponential growth rate of knowledge in a specific domain surpasses human experts’ ability to formalize and capture tacit and explicit knowledge efficiently. Thus, a knowledge management system is required to discover knowledge effectively, automate the knowledge acquisition based on artificial intelligence approaches, integrate the captured knowledge, and deliver consistent knowledge to agents, research communities, and end-users. In this study, we present the development process of a knowledge management system for ENVironmental Research Infrastructures, which are crucial pillars for environmental scientists in their quest for understanding and interpreting the complex Earth System. Furthermore, we report the challenges we have faced and discuss the lessons learned during the development process.

The European Plate Observing System (EPOS) addresses the problem of homogeneous access to heterogeneous digital assets in geoscience of the European tectonic plate. Such access opens new research opportunities. Previous attempts have been limited in scope and required much human intervention. EPOS adopts an advanced Information and Communication Technologies (ICT) architecture driven by a catalogue of rich metadata. The architecture of the EPOS system together with challenges and solutions adopted are presented. The EPOS Data Portal is introducing a new way for cross-disciplinary research. The multidisciplinary research is raising new requirements both to students and teachers. The EPOS portal can be used either to explore the available datasets or to facilitate the research itself. It can be very instructive in teaching as well by demonstrating scientific use cases. EPOS ERIC had been established in 2018 as European Research Infrastructure Consortium for building a pan-European infrastructure and accessing solid Earth science data. The sustainability phase of the EPOS (EPOS-SP – EU Horison2020 – InfraDev Programme – Project no. 871121; 2020-2022) is focusing on finding solutions for the long-term sustainability of EPOS developments. The ambitious plan of geoscientific data integration started already in 2002 with a Conception Phase and continued by an EPOS-PP (Preparatory Phase, 2010-2014) where about 20 partners joined the project. The finished EPOS-IP project (EPOS-IP – EU Horison2020 – InfraDev Programme – Project no. 676564; 2015-2019) included 47 partners plus 6 associate partners from 25 countries from all over Europe and several international organizations.The EPOS Data Portal provides access to data and data products from ten different geoscientific areas: Seismology, Near Fault Observatories, GNSS Data and Products, Volcano Observations, Satellite Data, Geomagnetic Observations, Anthropogenic Hazards, Geological Information and Modelling, Multi-scale laboratories and Tsunami Research. The Data portal Graphic User Interface (GUI) provides search functionalities to enable users to filter data by using several criteria (e.g. spatio-temporal extents, keywords, data/service providers, free-text); also, it enables users to pre-visualize data in Map, Tabular or Graph formats; the GUI finally provides details about the selected data (e.g., name, description, license, DOI), as well as to further refine the search in order to dig into a smaller level of granularity of data.The presentation is showing achievements of the EPOS community with focus on the EPOS Data Portal which is providing information about available datasets from TCS and access to them. We are demonstrating not only features of the graphical user interface but also the underlying architecture of the whole system.

Research infrastructures play an increasingly essential role in scientific research. They provide rich data sources for scientists, such as services and software packages, via catalog and virtual research environments. However, such research infrastructures are typically domain-specific and often not connected. Accordingly, researchers and practitioners face fundamental challenges introduced by fragmented knowledge from heterogeneous, autonomous sources with complicated and uncertain relations in particular research domains. Additionally, the exponential growth rate of knowledge in a specific domain surpasses human experts’ ability to formalize and capture tacit and explicit knowledge efficiently. Thus, a knowledge management system is required to discover knowledge effectively, automate the knowledge acquisition based on artificial intelligence approaches, integrate the captured knowledge, and deliver consistent knowledge to agents, research communities, and end-users. In this study, we present the development process of a knowledge management system for ENVironmental Research Infrastructures, which are crucial pillars for environmental scientists in their quest for understanding and interpreting the complex Earth System. Furthermore, we report the challenges we have faced and discuss the lessons learned during the development process.

Research infrastructures play an increasingly essential role in scientific research. They provide rich data sources for scientists, such as services and software packages, via catalog and virtual research environments. However, such research infrastructures are typically domain-specific and often not connected. Accordingly, researchers and practitioners face fundamental challenges introduced by fragmented knowledge from heterogeneous, autonomous sources with complicated and uncertain relations in particular research domains. Additionally, the exponential growth rate of knowledge in a specific domain surpasses human experts’ ability to formalize and capture tacit and explicit knowledge efficiently. Thus, a knowledge management system is required to discover knowledge effectively, automate the knowledge acquisition based on artificial intelligence approaches, integrate the captured knowledge, and deliver consistent knowledge to agents, research communities, and end-users. In this study, we present the development process of a knowledge management system for ENVironmental Research Infrastructures, which are crucial pillars for environmental scientists in their quest for understanding and interpreting the complex Earth System. Furthermore, we report the challenges we have faced and discuss the lessons learned during the development process.