Frequently Asked Questions

The ENVRI-Hub is the central gateway to Europe’s Environmental Research Infrastructures, providing unified access to FAIR data, digital services, and interdisciplinary tools across the Earth System. Evolving under the ENVRI One initiative into a community-driven open-source framework, it will serve as a permanent Thematic Node within the EOSC Federation. The Hub empowers scientists and data providers to co-create the workflows, visualisations, and cross-disciplinary interfaces needed to tackle global climate and biodiversity challenges.

The ENVRI-Hub integrates a Knowledge Base and Catalogue of Services secured by ENVRI-ID, a unified SSO AAI system that harmonises and enables seamless access to all RI assets. It features a Science Dashboard and the Environmental AI Expert, an LLM-driven recommender system designed to intelligently guide researchers toward relevant data, tools, and cross-disciplinary insights. The platform evolves in a community-led open software landscape, where scientists and data providers co-develop specialised workflows, visualisations, and analytical tools for Essential Variables. These technical pillars are anchored by a permanent governance framework, supporting the Hub’s transition into a sovereign, board-led EOSC Thematic Node.

The ENVRI-Hub is set to become a permanent, sovereign Thematic Node within the EOSC Federation. This evolution includes transitioning to an independent legal entity and implementing a mature governance and business model to ensure long-term sustainability. The Hub will shift toward a community-led open software landscape, where a growing network of RIs and scientists co-develop tools and Essential Variables. While currently in the proposal stage, this roadmap transforms the Hub into a stable, community-driven digital commons for cross-disciplinary Earth System science.

ENVRI ID is operated by GRNET, with identity services hosted at LIP (Portugal) and Virtual Research Environments (VRE) powered by CESNET (Czech Republic). This distributed architecture relies exclusively on secure, high-performance European research infrastructures. As all facilities are located within the EU, we ensure full compliance with GDPR/DSGVO standards. Your data remains under European jurisdiction, benefiting from the highest level of legal protection and sovereignty. We do not transfer personal information to third countries. For more details on your rights and data processing, please refer to our Privacy Policy.

The CoS has several filters that help you find a dataset of interest. Once found, you can click on the relative card to display data on the map, or a table/graph depending on the availability. Use the “star” to save your favourite services. Click on the “i” near the service name to display information about that source.

The official onboarding procedures are still under review. In the meantime, please write an email to info@epos-eric.eu, since the catalogue is developed by EPOS ERIC.

The COS is built on a standardised metadata framework using DCAT-AP, ensuring full interoperability with the European Open Science Cloud (EOSC). It functions as a sophisticated aggregator that automatically harvests service descriptions from distributed Research Infrastructures via REST APIs and OAI-PMH protocols. The backend utilises a high-performance search engine, such as Elasticsearch, to enable rapid discovery of technical endpoints and research tools. Access to these services is seamlessly managed through the ENVRI ID (powered by GRNET), providing a unified Authentication and Authorization Infrastructure (AAI). This architecture ensures that all service metadata is findable and accessible according to FAIR principles. By bridging distributed data sources, the COS provides a single, technically robust entry point for environmental science workflows.

You can interact with the COS through three primary channels: the web-based Discovery Portal, the REST API, and the ENVRI Python Library. For manual discovery, the portal provides a user-friendly interface to search for resources across the ENVRI ecosystem. For automated workflows, developers can query the REST API directly to retrieve service metadata in standardised formats. For data scientists, the ENVRI Python Library is the recommended method, as it abstracts the API complexities and allows you to programmatically search, filter, and integrate COS resources directly into Jupyter Notebooks or Python scripts. Access is managed via ENVRI ID, ensuring a seamless transition from discovery to service utilisation. Detailed code examples and endpoint documentation are available in our Developer Guide.

An ECV describes a physical, chemical or biological variable or an underlying group of linked variables that are critical for characterising the Earth’s climate. ENVRI-Hub NEXT takes the ECV definitions from GCOS, which currently specifies 55 ECVs. ECVs are identified based on the following criteria:

• Relevance: It is critical for characterising the climate system.
• Feasibility: Observing or deriving it is feasible on a global scale using scientifically proven methods.
• Cost-effectiveness: Generating and archiving the data is affordable, relying on coordinated observing systems.

The ENVRI Knowledge Base (ENVRI-KB) introduces the next generation of search technology by seamlessly integrating Artificial Intelligence (AI) into its core functionalities. It improves upon the monolithic design prototype developed in the previous project, ENVRI-FAIR, which tightly coupled a user interface, search functionality, and knowledge storage.

The Knowledge Finder (KB-Finder) is one of the core components that enable searchability within the KB. It is designed to help users find relevant datasets, APIs, and research outputs by leveraging advanced search techniques. The KB-Finder directly supports Web-based search with a powerful recommendation system.

In the new design, ENVRI-KB offers an innovative dialogue-based search, enabling users to interact dynamically with the system's core intelligence.

In the current KB system, we group user roles into three categories for ENVRI-KB. However, after the discussion with the RIs, this user grouping may be updated. The categories are, i) Superuser, ii) Content Management, and iii) Basic User.

The Analytical Framework proposes Python libraries, described in the Developer Guide. You can easily import them into your preferred environment as described on pypi.org. There is also a series of example notebooks, also accessible in the Developer Guide.

To access the Catalogue of Services, the associated API can be used as described in the Developer Guide. A preferred, facilitating and integrated way of programmatically accessing the Catalogue of Services is to use the ENVRI-Hub library; the link can be found in the Developer Guide.

The ENVRI Training Gateway is a search page that collects training resources produced by the ENVRI Community and other research organisations committed to FAIR principles. You can find training resources by searching by keywords or by filters, including topics and learning paths.

The training resources are for ENVRI scientists, research software engineers, and research support staff who want to learn more about research data management, ontologies and semantic technologies for research data harmonisation and sharing, and research software development for data processing, analysis, and science reproducibility.

You can access all the training resources for free. Some training resources might require a free user registration. We offer a mix of training formats: video recordings and presentations from workshops, webinars and summer schools; text-based and video tutorials; open online courses, and guides.

The training resources are produced by the partners of the ENVRI flagship projects, by the ENVRI Community, and by an interdisciplinary pool of subject matter experts. The ENVRI-Hub NEXT training task currently curates the training resources.

You can suggest training resources and topics, ask for new training resources, and volunteer as a trainer to produce a resource or facilitate a training event. You can reach out to us in our Feedback Form.

The demonstrators are taken from a static list, originally an online spreadsheet located at the icos-cp fileshare (link by request).