Currently, effectively managing, retrieving, and applying environmental big data (EBD) presents a considerable challenge owing to the abundant influx of heterogeneous, fragmented, and real-time information. The existing network domain name system lacks the spatial attribute mining necessary for handling EBD, while the geographic region name system proves inadequate in achieving EBD interoperability. EBD integration faces challenges arising from diverse sources and formats. Interoperability gaps hinder seamless collaboration among systems, impacting the efficiency of data analysis.

To address the need for unified organization of EBD, precise man-machine collaborative spatial cognition, and EBD interoperability, this paper introduces the EBD grid region name model based on the GeoSOT global subdivision grid framework (EGRN-GeoSOT). EGRN-GeoSOT effectively manages location identification codes from various sources, ensuring the independence of location identification while facilitating correlation, seamless integration, and spatial interoperability of EBD. The model comprises the grid integration method of EBD (GIGE) and the grid interoperability method of EBD (GIOE), providing an approach to enhance the organization and interoperability of diverse environmental datasets. By discretizing the Earth's surface into a uniform grid, GIGE enables standardized geospatial referencing, simplifying data integration from various sources. The integration process involves the aggregation of disparate environmental data types, including satellite imagery, sensor readings, and climate model outputs. GIGE creates a unified representation of the environment, allowing for a comprehensive understanding of complex interactions and patterns. GIOE ensures interoperability by providing a common spatial language, facilitating the fusion of heterogeneous environmental datasets. The multi-scale characteristic of GeoSOT allows for scalable adaptability to emerging environmental monitoring needs.

EGRN-GeoSOT establishes a standardized framework that enhances integration, promotes interoperability, and empowers collaborative environmental analysis. To verify the feasibility and retrieval efficiency of EGRN-GeoSOT, Oracle and PostgreSQL databases were combined and the retrieval efficiency and database capacity were compared with the corresponding spatial databases, Oracle Spatial and PostgreSQL + PostGIS, respectively. The experimental results showed that EGRN-GeoSOT not only ensures a reasonable capacity consumption of the database but also has higher retrieval efficiency for EBD.

How to cite: Zhu, D., He, Y., and Moore, K.: Novel environmental big data grid integration and interoperability model, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6798, https://doi.org/10.5194/egusphere-egu24-6798, 2024.

Earth data are an archetypical case of Big Data, in all their Volume, Velocity, Variety, and Veracity challenges. Since long, therefore, standardization in ISO, OGC, and further bodies is concerned with developing and advancing specifications for structures and services suitable for Big Data. Questions to keep in mind include:
- How can data wrangling be simplified, for example through better suited concepts and elimination of unnecessary technicalities?
- How can specifications support scalable implementations?
- What is necessary to make data ready for analysis, more generally: for the various types of consumption?

Specifically, the commonly accepted concept of multi-dimensional coverages - corresponding to the notion of spatio-temporal "fields" in physics" - addresses Big Data, in practice: regular and irregular grids, point clouds, and general meshes. Recently, ISO has adopted two "abstract" standards with normative definitions of coverage concepts and terminology. 19123-1 addresses the coverage data model whereas 19123-3 is about coverage processing fundamentals, utilizing the OGC Web Coverage Processing Service (WCPS) model. OGC has adopted both as an update to its Abstract Topic 6.

On the level of "concrete" specifications directed towards implementation and conformance testing there is the joint OGC/ISO Coverage Impementation Schema. In OGC the current version is 1.1 which introduces the General Grid Coverage as a more powerful, yet simplified structure for regular and irregular grids. ISO has commenced work on updating its 19123-2, which is still based on OGC CIS 1.0), with  CIS 1.1. On the processing side, there are various activities in addition to the proven, mature WCS and WCPS, such as drafts for OAPI-Coverages and GeoDataCube.

We present the current status of ISO and OGC standardization work on coverages. The author is active as editor of adopted and in-progress standards in OGC and ISO since over 15 years, and intimately familiar with standardization work there. By sharing the status and plans of standardization the talk provides an opportunity for the community to comment on plans and share any comments and suggestions.

How to cite: Baumann, P.: Status and Planning Update on Big Data Standardization in OGC and ISO, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19379, https://doi.org/10.5194/egusphere-egu24-19379, 2024.

Studying many scientific problems, such as environmental challenges or cancer diagnosis, requires extensive data, advanced models, and distributed computing resources. Researchers often reuse assets (e.g. data, AI models, workflows, and services) from different parties to tackle these issues. This requires effective collaborative environments that enable advanced data science research: discovery access, interoperation and reuse of research assets, and integration of all resources into cohesive observational, experimental, and simulation investigations with replicable workflows. Such use cases can be effectively supported by Virtual Research Environments (VREs). Existing VRE solutions are often built with preconfigured data sources, software tools, and functional components for managing research activities. While such integrated solutions can effectively serve a specific scientific community, they often lack flexibility and require significant time investment to use external assets, build new tools, or integrate with other services. In contrast, many researchers and data scientists are familiar with notebook environments such as Jupyter. 

We propose a VRE solution for Jupyter to bridge this gap: Notebook-as-a-VRE (NaaVRE). At its core, NaaVRE allows users to build functional blocks by containerizing cells of notebooks, composing them into workflows, and managing the lifecycle of experiments and resulting data. The functional blocks, workflows, and resulting datasets can be shared to a common marketplace, enabling the creation of communities of users and customized VREs. Furthermore, NaaVRE integrates with external sources, allowing users to search, select, and reuse assets such as data, software, and algorithms. Finally, NaaVRE natively works with modern cloud technologies, making it possible to use compute resources flexibly and cost-effectively.

We demonstrate the versatility of NaaVRE by building several customized VREs that support legacy scientific workflows from different communities. This includes the derivation of ecosystem structure from Light Detection and Ranging (LiDAR) data, the tracking of bird migrations from radar observations, and the characterization of phytoplankton species. The NaaVRE is also being used to build Digital Twins of ecosystems in the Dutch NWO LTER-LIFE project.

How to cite: Pelouze, G., Koulouzis, S., and Zhao, Z.: Notebook-as-a-VRE (NaaVRE): From private notebooks to a collaborative cloud virtual research environment, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-17978, https://doi.org/10.5194/egusphere-egu24-17978, 2024.

Data science and Machine learning techniques are increasingly important in tackling societal challenges and complex problems in environmental and earth sciences. Effectively sharing and (re)using research assets, including data sets, models, software tools, documents, and computing notebooks, are crucial for such data-centric research activities. Researchers can reproduce others’ experiments with the available research assets to verify the results and to further develop new experiments. Computational notebooks, as an important asset, comprise free-form textual descriptions and code snippets. The notebook runtime environments, e.g., Jupyter, provide scientists with an interactive environment to construct and share the experiment descriptions and code as notebooks. 

To enable effective research assets discovery, research infrastructures not only need to FAIRify the assets with rich meta information and unique identifiers but also provide search functionality and tools to facilitate the construction of scientific workflows for the data sciences experiments with research assets from multiple sources. The general-purpose search engines are helpful for initial coarse-grained search but often fail to find multiple types of research assets such as the data sets and notebooks needed by the research. The community-specific catalogues, e.g., in ICOS and LifeWatch, provide search capabilities for precisely discovering data sets, but they are often characterized by a specific type of asset. A researcher has to spend lots of time searching across multiple catalogues to discover all types of assets needed. 

In the search process, user queries tend to be short and comprised of several key phrases that demand great efforts to understand users’ information needs. Given the complexity of computational notebook contents and the mismatch between the form of user queries and the computational notebooks, it is critical to understand queries by augmentations and make explainable relevance judgments. To address these challenges, we developed a research asset search system for a Jupyter notebook-based Virtual Research Environment (called Notebook as a VRE) that supports scientific query understanding with query reformulation and explainable computational notebook relevance judgments via computational notebook summarization. 

The proposed system includes four major components: the query reformulation module, the notebook indexer and retriever, the summarization component, and the user interface.  The query reformulation module performs active query understanding via query reformulation, where we extract scientific entities from user queries and search related entities from external knowledge graphs and resources as expansions and rank the reformulated queries for users to choose from. The system has been validated via a small user group and will be further developed in the coming ENVRI-HUB next project to support conversational search and recommendation. 

How to cite: Li, N., Zhu, P., Gabriel Pelouze, G., Koulouzis, S., Zhao, Z., and Zhao, Z.: Research Notebook Retrieval with Explainable Query Reformulation, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19358, https://doi.org/10.5194/egusphere-egu24-19358, 2024.

The pilot Blue-Cloud project as part of ‘The Future of Seas and Oceans Flagship Initiative’ of EU HORIZON 2020 combined interests of developing a thematic marine EOSC cloud and serving the Blue Economy, Marine Environment and Marine Knowledge agendas. It deployed a versatile cyber platform with smart federation of multidisciplinary data repositories, analytical tools, and computing facilities in support of exploring and demonstrating the potential of cloud based open science for ocean sustainability, UN Decade of the Oceans, and G7 Future of the Oceans. The pilot Blue-Cloud delivered:

• Blue-Cloud Data Discovery & Access service (DD&AS), federating key European data management infrastructures, to facilitate users in finding and retrieving multi-disciplinary datasets from multiple repositories

• Blue-Cloud Virtual Research Environment infrastructure (VRE) providing a range of services and facilitating orchestration of computing and analytical services for constructing, hosting and operating Virtual Labs for specific applications

• Five multi-disciplinary Blue-Cloud Virtual Labs (VLabs), configured with specific analytical workflows, targeting major scientific challenges, and serving as real-life Demonstrators, which can be adopted and adapted for other inputs and analyses.    

Since early 2023, Blue-Cloud 2026 aims at a further evolution into a Federated European Ecosystem to deliver FAIR & Open data and analytical services, instrumental for deepening research of oceans, EU seas, coastal & inland waters.

The DD&AS already federates leading Blue Data Infrastructures, such as EMODnet, SeaDataNet, Argo, EuroArgo, ICOS, SOCAT, EcoTaxa, ELIXIR-ENA, and EurOBIS, and facilitates common discovery and access to more than 10 million marine datasets for physics, chemistry, geology, bathymetry, biology, biodiversity, and genomics. It is fully based on machine-to-machine brokering interactions with web services as provided and operated by the Blue Data Infrastructures. As part of Blue-Cloud 2026 it will expand by federating more leading European Aquatic Data Infrastructures, work on improving the FAIRness of the underpinning web services, incorporating semantic brokering, and adding data subsetting query services.

The Blue-Cloud VRE, powered by D4Science, facilitates collaborative research offering computing, storage, analytical, and generic services for constructing, hosting and operating analytical workflows for specific applications. Blue-Cloud 2026 will expand the VRE by federating multiple e-infrastructures as provided EGI, Copernicus WEkEO, and EUDAT. This way, it will also open the connectivity to applications as developed in other EU projects such as iMAGINE (AI applications for marine domain), and EGI-ACE (applications for ocean use cases).

During EGU we will share insight in the solutions regarding semantics supporting interoperability and harmonised data access. This will be especially illustrated via developments of new Blue-Cloud analytical Big Data “WorkBenches” that are generating harmonised and validated data collections of Essential Ocean Variables (EOVs) in physics (temperature and salinity), chemistry (nutrients, chlorophyll, oxygen) and biology (plankton taxonomy, functions and biomass). The access to harmonised subsets of the BDI’s data collections will be supported by new tools like BEACON and the I-Adopt framework. The EOV collections are highly relevant for analysing the state of the environment.  This way, Blue-Cloud 2026 will provide a core data service for the Digital Twin of the Ocean, EMODnet, Copernicus, and various research communities.

How to cite: Schaap, D. M. A., Krijger, T., Pittonet, S., and Pagano, P.: Blue-Cloud 2026, services to deliver, access and analyse FAIR & Open marine data, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7290, https://doi.org/10.5194/egusphere-egu24-7290, 2024.

The Geo-INQUIRE (Geosphere INfrastructure for QUestions into Integrated REsearch) project, supported by the Horizon Europe Programme, is aimed at enhancing the Earth Sciences Research Infrastructures and services to make data and high-level products accessible to the broad Geoscience scientific community. Geo-INQUIRE’s goal is to encourage curiosity-driven studies into understanding the Geosystem processes at the interface between the solid Earth, the oceans and the atmosphere using big data collections, high-performance computing methods and cutting-edge facilities.

The project has a strong focus on supporting dynamic development of the actual use of research infrastructures. Training, networking, and community-building activities will be key to foster it. The methodology ensures empowering participation of both young and experienced researchers, also from often underrepresented communities, but also incorporates new and intersectional perspectives, while addressing current major environmental and economic challenges and fertilising curiosity-driven, cross-disciplinary research.

The project dissemination activities include a series of open online training and more specialised on-site workshops focused on data, data products and software solutions. Researchers, early-stage scientists, students are communities which will be able to explore the various fields of geosphere-related science, also not directly related to their field, with the possible connection through Research Infrastructures. Through lectures and use cases, we expect to show and teach them how to use data and information coming from cross-disciplinary RIs. We would like to increase the awareness of the capacity and capabilities of “other” RIs, as well as data integration and importance of FAIR principles. The training offer is constantly updated on the project web page www.geo-inquire.eu.

In addition, two summer schools will be organised, dedicated to cross-disciplinary interactions of solid Earth with marine science and with atmospheric physics. The first school will be organised in autumn 2024 in Gulf of Corinth (Greece), and the second one in autumn 2025 in Catania, Sicily (Italy).

The applications for training activities will be evaluated by a panel that reviews the technical and scientific feasibility of the proposed application project, ensuring equal opportunities and diversity in terms of gender, geographical distribution and career stage. The data and products generated during the Transnational Accesses to research facilities will be made available to the scientific community via the project strict adherence to FAIR principles.

Geo-INQUIRE is funded by the European Commission under project number 101058518 within the HORIZON-INFRA-2021-SERV-01 call.

How to cite: Majdanski, M., Christadler, I., Puglisi, G., Michalek, J., Weege, S., Cotton, F., Strollo, A., Prestes, M., Pedersen, H., Danciu, L., Urvois, M., Lorito, S., Bailo, D., Lange, O., and Festa, G.: Fostering cross-disciplinary research - Training, workshops and summer schools of Geo-INQUIRE EU-project, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-7391, https://doi.org/10.5194/egusphere-egu24-7391, 2024.

Earthquakes of engineering significance (magnitude 5 and above) are generated by pre-existing, relatively mature geological faults. These faults generally span a length from a few to several tens or hundreds of kilometers and can break the entire Earth’s crust.   

Defining the three-dimensional configuration of such seismogenic faults is crucial for developing applications for earthquake hazard analyses at different spatial scales and, in turn, contributing robust information to promoting earthquake risk mitigation strategies.

The reconstruction of geological fault surfaces is a typical multidisciplinary study involving a large variety of data types and processing methods that, inevitably, imply various degrees of geometric simplifications depending on the available data. Among them, the most powerful, although expensive, approaches are the techniques developed for hydrocarbon exploration, namely seismic reflection (2D-3D) data combined with logs of drilled wells, which can illuminate the Earth’s subsurface at several kilometers depth. The mining and oil and gas industries have historically collected a large amount of this data, which remained classified depending on the regulations of the country from which they obtained the license for exploration. As time passes, and with the waning of fossil fuel exploitation, the exploration licenses expire or are not renovated, and more of such data becomes available to amalgamate with data collected by research institutions or public/private ventures using public funding. 

Despite the vast literature on and applications of hydrocarbon exploration data, no standard procedure exists for documenting the use of such data in characterizing seismogenic faults. In this respect, scientists face challenges posed by the intersection of industry data with public research outputs, with important societal implications and barriers to ensuring FAIRness. To this end, we devised a workflow detailing the best practices to follow in the various steps geologists undertake in using hydrocarbon exploration data, starting from the source of the raw/processed data (public vs confidential) and ending with the final geological fault model. The workflow output is then ready to be integrated with the information and data from other scientific disciplines (e.g., seismology, paleoseismology, tectonic geomorphology, geodesy, geomechanical modeling, earthquake statistics) to obtain the most reliable seismogenic fault model. As proof of concept, we will present a simplified version of a software tool that guides the user in incorporating the workflow's various elements into a structured database of seismogenic faults.

How to cite: Vico, G., Taccone, R. C., Maesano, F. E., Tiberti, M. M., and Basili, R.: Best practices for using and reporting subsurface geological/geophysical data in defining and documenting seismogenic faults., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-9486, https://doi.org/10.5194/egusphere-egu24-9486, 2024.

The Geo-INQUIRE (Geosphere INfrastructure for QUestions into Integrated REsearch) project, supported by the Horizon Europe Programme, is aimed at enhancing services to make data and high-level products accessible to the broad Geoscience scientific community. Geo-INQUIRE’s goal is to encourage curiosity-driven studies into understanding the geosphere dynamics at the interface between the solid Earth, the oceans and the atmosphere using long data streams, high-performance computing and cutting-edge facilities. 

The Geo-INQUIRE Transnational Access (TA) covers both virtual and on-site access to a variety of state of the art laboratories, facilities, experimental sites (testbeds) and computational resources with the aim of enabling the development of excellent ground-breaking science. Six research infrastructures located across Europe, referred to as “testbeds”, will provide locations for users to perform experiments in a variety of environments from the Earth’s surface (both on land and at sea) to the subsurface; over different spatial scales: from small-scale experiments in laboratories to kilometric submarine fibre cables. These sites are: the Bedretto Laboratory (Switzerland); the Ella-Link Geolab (Portugal); the Liguria-Nice-Monaco submarine infrastructure (Italy/France); the Irpinia Near-Fault Observatory (Italy); the Eastern Sicily facility (Italy); and the Corinth Rift Laboratory (Greece). In addition, ECCSEL-ERIC is providing access to 5 of its research facilities focussing on CO2 Capture, Utilisation, Transport and Storage. The facilities providing access are: Svelvik CO2 Field Lab (Norway), PITOP Borehole Geophysical Test Site (Italy), Sotacarbo Fault Laboratory (Italy), Catenoy experimental site and gas-water-rock interaction Laboratory in Oise (France) and the Mobile Seismic Array (the Netherlands) which is fully mobile and can be deployed anywhere in the world. 

TA will be also offered for software and workflows belonging to the EPOS-ERIC and the ChEESE Centre of Excellence for Exascale in Solid Earth. These are grounded on simulation of seismic waves and rupture dynamics in complex media, tsunamis, subaerial and submarine landslides. HPC-based Probabilistic Tsunami, Seismic and Volcanic Hazard workflows are offered to assess hazard at high-resolution with extensive uncertainty exploration. Support and collaboration will be offered to the awardees to facilitate the access and usage of HPC resources for tackling geoscience problems. 

Geo-INQUIRE will grant TA to researchers to develop their own lab-based or numerical experiments with the aim of advancing scientific knowledge of Earth processes while fostering cross-disciplinary research across Europe. The data and products generated during the TAs will be made available to the scientific community via the project’s strict adherence to FAIR principles. 

To be granted, researchers submit a proposal to the TA calls that will be issued three times during the project life. The first call was launched on the 9th January. Calls will be advertised on the Geo-INQUIRE website https://www.geo-inquire.eu/ and through the existing community channels.

How to cite: Murphy, S., Festa, G., Lorito, S., Röhling, V., Cotton, F., Strollo, A., Urvois, M., Babeyko, A., Bailo, D., Michalek, J., Lange, O., Quinteros, J., Majdanski, M., Christadler, I., Prestes, M., and Weege, S.: EU-financed transnational access in Geo-INQUIRE: an opportunity for researchers to develop leading-edge science at selected test-beds and research facilities across Europe., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10742, https://doi.org/10.5194/egusphere-egu24-10742, 2024.

In the last decade, the scientific community has witnessed growing emphasis on data integration. The primary objective is to harness multidisciplinary data and resources to drive novel methodological approaches and scientific breakthroughs. Among the projects that have emerged in response to this trend is the Geosphere INfrastructures for QUestions into IntegratedREsearch (Geo-INQUIRE, https://www.geo-inquire.eu/).

Geo-INQUIRE was launched in October 2022 and comprises a unique consortium of 51 partners, including prominent national research institutes, universities, national geological surveys, and European consortia. Geo-INQUIRE is dedicated to surmounting cross-domain challenges, particularly those pertaining to land-sea-atmosphere environments. To accomplish this mission, Geo-INQUIRE is committed to consolidating the resources and capabilities of key research infrastructures (RIs) specializing in geosphere observations. These RIs include EPOS, EMSO, ARISE, ECCSEL, and ChEESE.

By providing access to its expanded collection of data, products, and services, Geo-INQUIRE empowers the upcoming generation of scientists to conduct cutting-edge research that addresses complex societal challenges from a multidisciplinary viewpoint. This encourages the utilization of these resources to foster curiosity-driven research endeavors.

To harmonize and prepare the data produced by these different RIs for integration, substantial efforts have been undertaken, which required cataloging all installations provided by the data providers, their analysis, and assessment concerning the maturity level required for FAIR (Findable, Accessible, Interoperabile, and Reusable) data integration. In addition, dedicated seminars focused on data integration were carried out to boost the FAIR data provision process. Technical activities have been carried out to achieve cross-RI integration. In this contribution, we demonstrate and exemplify one such integration: between EMSO (https://emso.eu/) and EPOS (https://www.epos-eu.org/). This has been achieved on multiple fronts, including metadata and services.

The successful integration of metadata and services was made possible by adopting the EPOS-DCAT Application Profile (https://epos-eu.github.io/EPOS-DCAT-AP/v3/), allowing an intelligent system like the EPOS platform (https://www.ics-c.epos-eu.org/) to access the EMSO services seamlessly. Work is currently underway to develop software that will enable the visualization of heterogeneous time series data from EMSO within the integrated framework, a crucial step to achieve full data integration.

How to cite: Giuliacci, K., Bailo, D., Michalek, J., Paciello, R., Vinciarelli, V., Goffi, C., Strollo, A., Cotton, F., Nedrebø, H., Näsholm, S. P., Brissaud, Q., Kaschwich, T., Martinez, E., Maslo, A., Röhling, V., Frezot, O., Quinteros, J., Atakan, K., and zu Castell, W.: Multidisciplinary integration of FAIR Research Infrastructures in the Geo-INQUIRE initiative: the EPOS – EMSO case, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11000, https://doi.org/10.5194/egusphere-egu24-11000, 2024.

The use of airborne cloud imaging probes has resulted in decades of in situ particle-by-particle data taken across the gamut of pristine and anthropogenically-modified cloud types around the globe. Image data from such probes is recorded in proprietary and instrument- or system-specific formats. Binary formats have evolved to minimise the stress on, now possibly outdated, hardware and communication systems that must operate in the difficult aircraft environment. This means that there is a significant knowledge and technical barrier to new users, particularly for those that are not from fields that have traditionally used such cloud data. Processed image data is generally available, however this precludes the application of more advanced or specialised processing of the raw data. For example, historical cloud campaigns of the 1970s and 80s used imaging probes for cloud microphysical measurements at a time when satellite measurements of those regions were sparse or nonexistent. Fields such as atmospheric processes modelling, climate modelling, and remote sensing may well benefit by being able to ingest raw cloud particle data into their processing streams to use in new analyses and to address issues from a perspective not normally used by those in the cloud measurement community.

The Single Particle Image Format (SPIF) data standard has been designed to store decoded raw binary data in netCDF4 with a standardised vocabulary in accordance with FAIR Guiding Principles. This improves access to this data for users from a wide range of fields and facilitates the sharing, refinement, and standardisation of data processing routines. An example is the National Research Council of Canada (NRC) Single Particle Image Format (SPIF) conversion utility which converts binary data into SPIF files. In a similar fashion to  the Climate and Forecast (CF) Conventions, SPIF defines a minimum vocabulary (groups, variables, and attributes) that must be included for compliance while also allowing extra, non-conflicting data to be included. 

The ability to easily check files for compliance to a data standard or convention is an important component of building a sustainable and community supported data standard. We have developed a Python package called vocal as a tool for managing netCDF data product standard vocabularies and associated data product specifications. Vocal projects define standards for netCDF data, and consist of model definitions and associated validators. Vocal then provides a mapping from netCDF data to these models with the Python package pydantic being used for compliance checking of files against the standard definition. 

We will present the vocal package and the SPIF data standard to illustrate its use in building standard compliant files and compliance-checking of SPIF netCDF files.

How to cite: Nott, G. and Sproson, D.: An Open Data Standard for Cloud Particle Images and Reference Software to Produce and Validate Compliant Files, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11901, https://doi.org/10.5194/egusphere-egu24-11901, 2024.

Climate researchers have access to astronomical amounts of data; but finding that data and downloading it so that it can be useful for research can be burdensome and expensive.

The team at Data Analytics for Canadian Climate Services (DACCS) is solving that problem by creating a new system for conducting climate research and providing the software to support it. The system works by providing researchers the tools to analyze the data where it’s hosted, eliminating the need to download the data at all.

In order to accomplish this, the DACCS team has developed a software stack that includes the following services:

- data hosting
- data serving (using OPeNDAP protocols)
- data search and cataloging
- interactive computational environments preloaded with climate analysis tools
- remote analysis tools (WPS and OGCAPI features)

Partner organizations can deploy this software stack and choose to host any data that they wish. This data then becomes available to every other participating organization, allowing them seamless access each others data without having to move it for analysis.

This system will allow researchers to more easily:

- discover available data hosted all over the world
- develop analysis workflows that can be run anywhere
- share their work with collaborators without having to directly share data

The DACCS team is currently participating in the Open Science Persistent Demonstrator (OSPD) initiative and we hope that this software will contribute to the ecosystem of earth science software platforms available today.

How to cite: Schwartz, M., Chandan, D., and Easterbrook, S.: Federated Climate Research Software: improving data and workflow management for climate researchers, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-13654, https://doi.org/10.5194/egusphere-egu24-13654, 2024.

Advances in remote sensing and computing infrastructure, and demands of modern climate research are driving the production of new climate datasets at a breathtaking pace. It is increasingly felt by researchers, that the growing volume of climate datasets is challenging to store, analyze or generally "shepherd" through their analysis pipelines. Quite often, the ability to do this is limited to those with access to government or institutional facilities in wealthier nations, raising important questions around equitable access to climate data.

The Data Analytics for Canadian Climate Services (DACCS) project has built a cloud based network of federated nodes, called Marble, that allows anyone seeking to extract insights from the large volumes of climate data to undertake their study without concerning themselves with the logistics of acquiring, cleaning and storing data. The aspiration for building this network is to provide a low-barrier entry not only to those working in core climate change research, but also to those involved in climate mitigation, resilience and adaptation work and to policy makers, non-profits, educators and students. Marble is one of the platforms selected to contribute to the 'Open Science Platform' component of the OGC’s OSPD initiative.

The user-facing aspect of the platform is comprised of two components: (i) the Jupyter compute environment and (ii) the data server and catalogue. Here, we focus on the latter and present details of the infrastructure, developed on top of proven open-source software and standards (e.g. STAC), that allows for discovery and access of climate datasets stored anywhere on the network by anyone on the network. We will also discuss the publication capability of the platform that allows a user to host their own data on the network and make it quickly available to others.

How to cite: Chandan, D., Schwartz, M., and Easterbrook, S.: The Marble climate informatics platform: data discovery and data access, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12962, https://doi.org/10.5194/egusphere-egu24-12962, 2024.

How to cite: Abu-Alam, T., Smart, K. A., Aspaas, P. P., Longva, L., Haugstvedt, N., and Nilsen, K. M.: Open Polar: A Comprehensive Database for Advancing Arctic and Antarctic Research, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-15898, https://doi.org/10.5194/egusphere-egu24-15898, 2024.

The Data Center for Deep Geotermal Energy (CDGP-Centre de Données de Géothermie Profonde, https://cdgp.u-strasbg.fr/) was launched in 2016 and managed by Interdisciplinary Thematic Institute for Geosciences for the energy system Transition (ITI GeoT, https://geot.unistra.fr/), with the purpose of archiving, preserving and distributing deep geothermal data in the Alsace region (France) for the scientific community and R&D activities. The CDGP is furthermore an internal node of EPOS TCS Anthropogenic Hazards (https://www.epos-eu.org/tcs/anthropogenic-hazards), the data provided concerning geothermal sites in Alsace are therefore also available on the EPISODES platform (https://episodesplatform.eu/), which enables users to process and analyze the data they download. The CDGP collects high-quality data from different phases of deep geothermal projects, especially from exploration and development phases. The aim of this service is to provide downloadable multi-disciplinary data, ranging from industrial hydraulic information to seismic records and catalogs, through geological logs and fault maps for example. The data are thoroughly filtered, controlled and validated by analysts, and are grouped into “episodes”, referring to a set of relevant geophysical data correlated over time, and establishing links between anthropogenic seismicity and an industrial activity.

As part of the European Geo-INQUIRE project (GA n. 101058518, https://www.geo-inquire.eu/), we are now expanding the types of data that we distribute. The raw data (RINEX) from GNSS stations that are monitoring the surface deformation around geothermal site are now available on the website. In a next step, we will add complementary information and metadata to our provided database (e.g. precise position/velocity/strain) thanks to our collaboration with EPOS TCS GNSS. We are currently in the process of developing strategies with EPOS TCS GIM (Geological Information and Modeling)  to provide geological maps and borehole data for the “episodes” sites. The aim is to use the TCS GIM services currently under development and benefit of the synergy from the various leading projects.

Specific procedures have also been implemented since the beginning of the project to respect international requirements for data management. FAIR recommendations, for example, are followed to distribute data that are Findable, Accessible, Interoperable, and Reusable. 

How to cite: Benlalam, S., Derode, B., Engels, F., Grunberg, M., and Schmittbuhl, J.: CDGP data center, new data for interdisciplinarity research , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-10536, https://doi.org/10.5194/egusphere-egu24-10536, 2024.

The Italian Integrated Environmental Research Infrastructures System (ITINERIS) Project coordinates a network of national nodes from 22 RIs.

ITINERIS has been designed looking at synergy with the European RI framework, and it will support the participation of Italian scientists in pan-European initiatives (like ENVRI-Hub NEXT and EOSC). ITINERIS will have significant impact on national environmental research, providing scientific support to the design of actionable environmental strategies.

To this end, ITINERIS will build the Italian HUB of Research Infrastructures in the environmental scientific domain providing a single and unified access point to facilities, FAIR data and related services available within the Italian RIs network through an easy-to-navigate interface.

Through the ITINERIS HUB all users will have access to data and services, with a complete catalogue of data and services and a proper access management system. ITINERIS will not duplicate existing data provided by European RIs, but will make them discoverable in a unique place together with other National RI resources. Additionally, ITINERIS HUB will make accessible data resulting from specific ITINERIS project activities like campaign data and novel data products.

ITINERIS will also offer a system of Virtual Research Environments (VRE), that will provide new services to address scientifically and societally relevant issues starting from an ensemble of cross-disciplinary actions on the data, information and knowledge generated by the different RIs in the different environmental subdomains.

State-of-the-art applications and custom-made tools will be integrated in the HUB to respond to the needs of: collecting and preparing information on products, resources and services to be advertised in a way that users can easily discover and access them and the RIs; facilitating the management of user access requests through the automated workflows and specific features that are peculiar to the online submission and management systems.

The main concept includes a GeoServer with the possibility of discovery, visualization and plotting RI data available. To be more precise, a scalable infrastructure for the provision of mapping services compliant with the standards of the Open Geospatial Consortium (OGC) WMS (Web Map Service), WFS (Web Feature Service), WCS (Web Coverage Service) will be implemented. A Metadata Service will be responsible for providing OGC CSW services. It will therefore offer a data discovery service through metadata consultation, corresponding to the most common metadata profiles. A stack of support services (REST/SOAP interfaces) for queries on geographic databases will be provided.

The ITINERIS HUB is candidate to become the integrated system of the Italian Environmental RIs to be connected to European initiatives like ENVRI-Hub NEXT and EOSC, fostering the ability to address current and expected environmental challenges at National level and beyond.

Acknowledgement

IR0000032 – ITINERIS, Italian Integrated Environmental Research Infrastructures System (D.D. n. 130/2022 - CUP B53C22002150006) Funded by EU - Next Generation EU PNRR- Mission 4 “Education and Research” - Component 2: “From research to business” - Investment 3.1: “Fund for the realisation of an integrated system of research and innovation infrastructures”

How to cite: Dema, C., Izzi, F., Mona, L., Salvia, V., Cornacchia, C., Ripepi, E., Volini, M., and Pappalardo, G.: ITINERIS HUB: the unified access point to Italian environmental facilities, FAIR data and related services, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-19603, https://doi.org/10.5194/egusphere-egu24-19603, 2024.

Clouds strongly regulate the Earth’s water cycle and the planetary radiation balance. Clouds are one of the largest contributors to the overall uncertainty in climate feedbacks, propagating into global temperature projections (Arias et al., 2021). Cloudnet data repository provides long-term datasets of cloud property profiles with a high temporal and vertical resolution, derived from synergetic ground-based measurements and numerical weather prediction model data.

These datasets can be used, for example, to validate satellite-based products and to improve the accuracy of climate and weather forecast models. Cloudnet is part of the Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) which is now in the implementation phase and plans to be fully operational in 2025 (Häme et al., 2018).

Cloudnet receives data regularly from around 20 stationary observational facilities. Each facility is equipped with instruments that meet the requirements of the ACTRIS Centre for Cloud Remote Sensing (CCRES). These instruments include Doppler cloud radars, Doppler lidars, ceilometers, microwave radiometers, disdrometers, and weather stations. We also host and process data from mobile and campaign platforms.

Cloudnet processes raw instrument data into cloud property products such as target classification of the scatterers, liquid and ice water content, and drizzle drop size distribution (Illingworth et al., 2007) using the open-source Python package CloudnetPy (Tukiainen et al., 2020). Processed data products are provided in near-real time, typically within one hour from the measurement. In the future, Cloudnet will also provide wind and boundary layer height products derived from Doppler lidar data.

All the raw and processed data are freely available according to the FAIR principles (Wilkinson et al., 2016) via cloudnet.fmi.fi. Furthermore, our software are freely and openly available from https://github.com/actris-cloudnet/.

ACKNOWLEDGEMENTS

We thank the Academy of Finland Flagship (grant no. 337552), the European Union’s Horizon 2020 research and innovation programme (grant no. 654109), and ACTRIS (project no. 739530, grant no. 871115) for funding this project.

REFERENCES

Arias et.al. (2021) Technical Summary. Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. (Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA)

Häme et.al. (2018). ACTRIS stakeholder handbook 2018. (Painotalo Trinket Oy).

Illingworth et.al. (2007). Cloudnet: Continuous Evaluation of Cloud Profiles in Seven Operational Models Using Ground-Based Observations. Bulletin of the American Meteorological Society, 6, 88.

Tukiainen, S., O’Connor, E., and Korpinen, A. (2020). CloudnetPy: A Python package for processing cloud remote sensing data. Journal of Open Source Software, 5(53), 2123.

Wilkinson et.al. (2016). The FAIR Guiding Principles for scientific data management and stewardship. Scientific Data, 3(1), 160018.

How to cite: Tukiainen, S., Siipola, T., Leskinen, N., and O'Connor, E.: Cloudnet – an ACTRIS data repository for cloud remote sensing observations, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20005, https://doi.org/10.5194/egusphere-egu24-20005, 2024.

OpenAQ is the largest open-source, open-access repository of air quality data in the world, integrating and hosting over 50 billion measurements from air monitors and sensors at more than 59,000 ground-level locations across 153 countries. The OpenAQ platform supports data on a variety of pollutants in different temporal frequencies. The platform is a one-stop solution for accessing air quality data in a consistent and harmonized format, thereby facilitating findability, accessibility, interoperability and reusability. OpenAQ utilizes modern cloud computing architectures and open-source data tools to maintain a highly scalable data pipeline, which can be resource- and computationally intensive, thus requiring thoughtful and efficient data management and engineering practices. Being an open-source platform that is grounded in community, OpenAQ strives to be transparent, responsible, user-focused, sustainable and technologically-driven.

OpenAQ supports innovation and collaboration in the air quality space by: 

• Ingesting and sharing data on an open, low-bandwidth platform to ensure data is broadly accessible
• Providing tools  to help  interpret the data and create visualizations for users with varied technical skills
• Providing a user guide and trainings on how to use the OpenAQ platform for community-level pilot purposes and beyond
• Catalyzing specific analyses through intentional outreach to a broad community of data stakeholders

OpenAQ has been widely used for research, informing nearly 300 scientific and data-oriented publications/proceedings. OpenAQ trainings and workshops around the world have resulted in community statements demanding increased coverage and frequency of air quality monitoring, the donation of air quality monitoring equipment to local communities, and adoption of APIs to make open-source city data available. As one example, our work with the Clean Air Catalyst supports pilots to clean the air in Jakarta (Indonesia), Indore (India) and Nairobi (Kenya). As another example, our Community Ambassador program trains emerging air quality leaders in low- and middle-income countries to utilize open data to spur community action to fight air pollution. 

Our poster describes how OpenAQ ingests and harmonizes heterogeneous air quality data at scale and how we conduct outreach to increase impactful usage of the hosted data.

How to cite: Hagerbaumer, C., Rosales, C. M. F., Biggs, R., and Fosse, G.: OpenAQ: Harmonizing Billions of Air Quality Measurements into an Open and FAIR Database, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-6810, https://doi.org/10.5194/egusphere-egu24-6810, 2024.

Earthquakes, intricate natural events spanning multiple spatio-temporal scales, necessitate a comprehensive understanding of the physical and chemical processes driving a broad spectrum of fault slip modes. To achieve this, the acquisition of multidisciplinary and dense datasets is imperative. Near Fault Observatories (NFOs) play a pivotal role by offering spatially and temporally dense, high-precision near-fault data, fostering the generation of novel observations and innovative scientific insights. However, the integration and interpretation of diverse datasets from various disciplines (geophysics, geochemistry, hydrology, etc.) present challenges. These datasets often consist of time-series depicting the temporal evolution of different parameters, sampling diverse temporal and spatial scales, depths, and the distinct or cumulative effects of various multiscale processes. In this presentation, we share outcomes from the INGV multidisciplinary project MUSE: M​ultiparametric and m​U​ltiscale ​S​tudy of ​Earthquake preparatory phase in the central and northern Apennines. Our emphasis lies in showcasing the approaches developed to analyze, integrate, and extract new knowledge from the EPOS Near Fault Observatory TABOO. This state-of-the-art observatory, managed by the Istituto Nazionale di Geofisica e Vulcanologia (INGV), boasts a dense network with an average inter-distance of approximately 5 km between multidisciplinary sensors. These sensors, deployed at the surface and within shallow boreholes, include seismometrical, geodetic, geochemical, hydrological, and strain stations. The project's core objective is to unravel the interconnections between different observables and explore the causal relationships among them. We will present the datasets, the methods employed, and discuss the significance of considering the interaction between fluid and solid geophysical processes in comprehending earthquake phenomena. Additionally, we will articulate the potential innovative scientific products that can arise from this research, contributing to a deeper understanding of earthquake processes.

How to cite: Serpelloni, E., Zaccarelli, L., Faenza, L., Caracausi, A., Almagro Vidal, C., Pintori, F., Mandler, E., and Chiaraluce, L.: Multidisciplinary analysis of near fault observatory data: example from the Alto Tiberina fault (Northern Apennines, Italy), EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12891, https://doi.org/10.5194/egusphere-egu24-12891, 2024.

The Strait of Messina, separating Sicily from continental Italy, is an area prone to different, high-grade, geological hazards. Here, many of the most devastating earthquakes of Italy have occurred, including the M 7.1 Messina-Reggio Calabria earthquake of 28 December 1908, the most intense event recorded in southern Europe in the instrumental epoch. The strait, on both sides, is surmounted by a mountain chain, directly degrading on a narrow, densely urbanized, coastal belt. Its steep slopes, composed of geological terrains with poor geotechnical characteristics, are affected by diffuse mass movements, as the 1 October 2009 landslide, triggered by an intense rainfall, which destroyed several little villages immediately southward of Messina, causing 37 causalities. The Peloro Cape area, the north-eastern termination of Sicily, hosts a lacunar environmental system, protected by the Ramsar Convention but also of economic interest, because exploited for shellfish livestock; these lagoons are extremely sensible to changes in sea level and temperature, which can pose serious threats to its ecological stability. This complex scenario exhibits a further criticality: the planned bridge for linking Sicily and continental Italy that, if realized, will be the longest single span bridge of the world.

This complex natural-built environment needs a multidisciplinary monitoring network for mitigating the multiple risks that affect both its natural and anthropic components. Its implementation is the aim of the Work Package 5 “NEMESI” of the Italian PNRR project MEET, the post-Covid 19 pandemic national plan for recovery and resilience, financed in the framework of the European Next Generation EU initiative.

Part of this multidisciplinary monitoring system will consist of a hydrogeochemical network, composed of 11 stations measuring, acquiring in a local logger and transmitting to the INGV data centre, data of temperature, level, electric conductivity, turbidity and dissolved O₂ and CO₂.

The main challenge in the implementation of the Strait of Messina hydrogeochemical network is the correct selection of the monitoring sites, which will be based in underground and surface water bodies, whose physic-chemical characteristics should contemporary work as indicators of very different processes: changes in electrical conductivity due to sea level rise, variations of temperature and piezometric levels induced by permeability changes driven by seismic and aseismic deformations, changes in oxygenation, turbidity and dissolved CO₂, which can be controlled by both eutrophication and mixing with deep volatiles, whose flux is driven by neotectonic activity.

For accomplishing this mission, and producing open access data of interest for the different stakeholders, spanning from the scientific community to the shellfish food industry, it will be mandatory a real multidisciplinary approach, embracing geological, geophysical, geodetic, geochemical, eco-hydrological and socio-economic data.

How to cite: Cangemi, M., Doglioni, C., Madonia, P., Mattia, M., and Selvaggi, G.: Implementation of a hydrogeochemical monitoring network following a multi-risk vision: the Strait of Messina (Italy) case., EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-16403, https://doi.org/10.5194/egusphere-egu24-16403, 2024.

This study employs spatial and semantic modeling to formally specify the intersection of environmental concerns and social justice, focusing on the unequal impact of environmental hazards on the economically disadvantaged people living in the Proctor Creek watershed within the Atlanta Metropolitan area. Our 'Public Health-Urban-Socio-economic-Environmental Justice' (PUSH-EJ) ontology formally integrates environmental justice indices and all concepts from the EPA’s EJScreen, such as Environmental Indicator, Demographic Indicator, EJ Index, Particulate Matter, Risk, and Proximity to hazardous sites. PUSH-EJ also formalizes the National Air Toxics Assessment (NATA)’s Air Toxics Cancer Risk, Air Toxics Respiratory Hazard Index, and Diesel Particulate Matter. The modeled environmental indicators include levels of ozone, particulate matter 2.5 (micrometer or smaller-sized particles), and lead paint (for houses built before 1960) in the air, count of underground leaking storage tanks, and count and proximity to wastewater discharge areas. The ontology also models proximity of housing units to waste and hazardous chemical facilities or sites related to National Priorities List (NPL) Superfund Program, Risk Management Plan (RMP) sites, Treatment, Storage, and Disposal Facilities (TSDFs), and Traffic volume. Environmental, demographic, and socioeconomic indicators are mapped to the objectives of UN SDGs 1, 3, 4, 5, 8, and 10, bridging the gap between environmental justice, public health, urban dynamics, and socio-economic aspects. Our analysis of Proctor Creek's socioeconomic indicators reveals a combined Demographic Index of 73%, driven by Low Income (61%) and People of Color (90%). These findings indicate that Proctor Creek exhibits the lowest scores across all categories when compared to other regions in Georgia, EPA Region 4, and the nation. Our results call for minimizing contamination in the Proctor Creek area and uplifting socioeconomic conditions by the authorities responsible for the watershed. Our spatial analysis highlights urgent priorities in the Proctor Creek area, for the management of air toxic sources, emissions, and addressing proximity issues linked to environmental pollutants from hazardous waste sites, lead paint, and traffic.

How to cite: Davarpanah, A., Shafiei, F., and Jelks, N.: Semantic and spatial Proximity Modeling of Equitable Sustainability in Proctor Creek, Atlanta: Merging Environmental Justice and Sustainable Development, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-20704, https://doi.org/10.5194/egusphere-egu24-20704, 2024.

The fields of information technology and geoinformatics have experienced rapid growth and widespread public adoption, with technologies like crowdsourcing facilitating advances in how the public can communicate with scientific communities and even contribute valuable data.

However, there is still hesitation in actively engaging the public in environmental or landscape related studies. The start contract of availability of crowdsourcing technologies and lack of use thereof is particularly noticeable in university education, where the technological potential of smartphones, widely owned and used by students, remains largely untapped for educational and research purposes. This study is part of a larger exploration of the potential of engaging students in participatory georeferenced landscape assessment, aiming to advance relevant environmental research and also make education in landscape and architecture more interactive and synergistic.

Starting from an initial theoretical investigation our work proceeded to the examination of the developed ideas in practice. A dedicated crowdsourcing mobile application was developed and tested as a pilot study with a small number of students, before proceeding to the inclusion of large numbers of students which is the end goal of the ARCHIMAP crowdsourcing project. This initial “test” targeted both potential practical challenges as well as software and generated-data related challenges. To this aim the Lycabettus hill and surrounding neighborhoods were investigated as a case study. Students were given the application and their interactions with it were recorded in detail, tracking their movement and location, recording their landscape and architecture assessments and evaluating the technical performance of the application.

Other than the observation of technical and functional challenges the study also initiated a brief investigation of the potential utility of the results. This was carried out by implementing a conventional method of analysis of landscapes, the so called ULQI (Urban Landscape Quality Index) and investigating its correlation and potential synergy with the results submitted by the students through the novel crowdsourcing app for georeferenced landscape assessment.

The results demonstrated that the developed app was both functional and useful and could therefore be shared to more students of NTUA, with expected benefits both for the educational processes but also for the scientific research of the institution on landscape quality.

How to cite: Mamassis, N., Ioannidis, R., Daskalakis, C., Loukidis-Andreou, F., Zakynthinou-Xanthi, M., Gicquel, L., Samah--Ribeiro, L., Iliopoulou, F., Sargentis, G.-F., and Moraitis, K.: A preliminary analysis of a crowdsourcing platform for participatory assessment of urban landscapes by university students using GIS, EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-12102, https://doi.org/10.5194/egusphere-egu24-12102, 2024.

Geospatial data are data with location information. Geospatial data are very diverse and widely used in various socioeconomic applications and decision makings. Typically, geospatial data obtained from data providers needs to go through a long chain of pre-processes and quality measures before the data can be analyzed for a specific application. For a specific type of geospatial data, many of the pre-processes and quality measures are common to different data users regardless the data applications. It is possible to pre-apply those common pre-processes and quality measures to the geospatial data so that the repetitive preprocesses can be avoided, the pre-process chain at user side can be significantly shorten, and the data is more ready for analysis. The geospatial data, which has been pre-applied with a set of pre-processes to meet certain quality specifications and be ready for analysis in applications, are called geospatial analysis ready data (ARD). In the satellite remote sensing domain, the Committee on Earth Observation Satellites (CEOS) has defined the CEOS Analysis Ready Data (CEOS-ARD) as satellite remote sensing data that have been processed to a minimum set of requirements and organized into a form that allows immediate analysis with a minimum of additional user effort and interoperability both through time and with other datasets. CEOS has set a number of ARD product family specifications (PFS) and encouraged its member space agencies to produce CEOS ARD PFS compliant products. However, CEOS ARD PFS are limited to satellite remote sensing data and are not the recognized international standards, which prevents them from being widely accepted and adopted by the broad geospatial community. Other geospatial communities, such as ARD.Zone, are also developing their ARD concepts.  Formal ARD standardization through authoritative international standard bodies is necessary to achieve broad uptake, particularly by the commercial sector, promote widely acceptance of the standardized concept, and help avoid the divergence that can be caused by various groups working towards different interpretations of the concept. Therefore, a joint effort between ISO TC 211 and the Open Geospatial Committee (OGC) was officially formed in May 2023 to set international ARD standards through forming the broadest consensus within the geospatial community. ISO has designated the geospatial ARD standards as ISO 19176, and the first one to be developed is ISO 19176-1: Geographic information —Analysis Ready Data — Part 1: Framework and Fundamentals. In addition, OGC, through its testbed and pilot initiatives, has been evaluating the applicability, advantage, and gaps of using existing geospatial ARD products from various sources in different applications. The findings and lessons learned from the evaluation are reinforcing the development of ISO 19176.  This presentation will report the progress so far on the development of ISO 19176-1 and recapture the findings from ARD activities in OGC Testbed 19. It will discuss the joint ISO/OGC ARD standard development process, the ISO 19176-1 development timeline, the ARD framework and UML models defined in ISO 19176-1, the findings from OGC Testbed 19 on ARDs, and the future workplan.

How to cite: Di, L., Yu, E., Guo, L., Quinn, P., and Lieberman, J.: Standardization of geospatial analysis ready data via OGC and ISO , EGU General Assembly 2024, Vienna, Austria, 14–19 Apr 2024, EGU24-11659, https://doi.org/10.5194/egusphere-egu24-11659, 2024.