To address the diverse expectations of the pilots and meet the grant agreement requirements, FAIR-EASE project followed an iterative development approach (aka DevCycles) structured around three main phases:
-
Needs assessment and initial architecture sketch
-
Evaluation of technical solutions
-
Implementation in project mode
This approach enabled close collaboration across work packages and led to the development of the following shared and integrated vision, including Key Exploitable Results (KERs):
{width="6.692716535433071in" height="4.0in"}
[]{#_heading=h.zc12meb1s8s4 .anchor}Figure 1 - FAIR-EASE's general architecture
This architecture is built around three key building blocks for data-driven research: data discovery, data access, and data analysis. These services are integrated within one or more e-infrastructures called Earth Analytics Lab (EAL), which can be interconnected through federating capabilities, following a system-of-systems approach. The core objective of the EAL is to provide services that support collaborative science and promote the adoption of FAIR principles across all research products.
{width="3.560465879265092in" height="3.715751312335958in"}
[]{#_heading=h.w3b5ichmu3jd .anchor}Figure 2 - EAL as an integrated e-infrastructure for collaborative Earth System science
A 3-day hackathon in March 2025 marked the culmination of this desire to collaborate on shared challenges, involving a cross-domain analysis of the Hunga Tonga volcanic eruption and the use of TerriaMap to visualize heterogeneous datasets.
For clarity and ease of understanding, this deliverable will first summarise the main elements related to the Data Discovery (lead by WP2) and Data Access (lead by WP4) building blocks. It will then present the analysis services, before providing an overview of the three EAL implementations. These implementations are used by the FAIR-EASE demonstrators (lead by WP5).
One of the main challenges of FAIR-EASE is to simplify the process of exploring and using data. This involves providing efficient and user-focused services for data discovery, data access, and data analysis, all tailored to the specific needs of the pilot use cases and Earth System science.
{width="6.296870078740158in"
height="0.8542125984251968in"}
[]{#_heading=h.umxn4bgr8yr7 .anchor}Figure 3 - From discovery to analysis: a data diving journey
The Interdisciplinary Data Discovery and Access Service (IDDAS) focuses on providing a semantically enriched metadata catalogue based on a customised DCAT application profile (DCAT-FE), enabling seamless data discovery and access across domains and platforms, both for human ([https://fair-ease-iddas.maris.nl]{.underline}) and machine ([https://fair-ease-iddas.maris.nl/sparql]{.underline}).
{width="6.692716535433071in"
height="3.763888888888889in"}
[]{#_heading=h.ofir2kfi56qq .anchor}Figure 4 - IDDAS Architecture
Full details of IDDAS are available in D2.5 - "FAIR-EASE Data Discovery and Access Service - Final Release".
The FAIR-EASE project explored both server-side mechanisms, which aim to improve performance, scalability and interoperability in heterogeneous data infrastructures, and client-side usage, which focus on simplifying data access for users, particularly those without in-depth technical expertise, but also the robustness of applications over time.
By taking these two perspectives into account, the project ensures that data can be accessed smoothly, efficiently and transparently, whether through automated workflows or user-friendly interfaces tailored to real scientific use cases.
For the server-side part we evaluated four technical solutions: S3 / ARCO combination for serverless and cloud-ready subsetting, STAC / OpenEO combination for unified data access and remote process on datacubes, Beacon for efficient data access and data harmonisation capabilities, and Apache Iceberg for managing large-scale tabular data.
S3 (Simple Storage Service) is a widely adopted cloud-based object storage system that allows data to be stored and accessed as discrete objects, each associated with both the actual data content and metadata.
{width="5.008858267716535in"
height="1.407843394575678in"}
[]{#_heading=h.6t96xzkh6pwo .anchor}Figure 5 - Storage types comparison1
Unlike traditional file systems, S3 operates over the HTTP protocol along with API's, making it highly scalable and accessible across distributed systems. It supports fine-grained access control, enabling secure, policy-driven access to individual objects. UCA has provided a 1TB S3 endpoint to help users become acculturated to this technology.
Additionally, S3 API supports HTTP range requests2, allowing users or applications to retrieve specific fragments of a file, using byte ranges, without needing to download the entire file. This is an essential feature for large-scale scientific datasets and cloud-native workflows. This feature is notably used in particular by the Volcano Space Observatory to optimize access to a specific satellite image from a TIFF file supplied by Copernicus Data Space Ecosystem.
{width="3.6027351268591428in"
height="2.2886679790026245in"}
[]{#_heading=h.ycmdenv04vce .anchor}Figure 6- HTTP range requests
Analysis-Ready, Cloud-Optimised (ARCO) formats, such as Zarr or Apache Parquet, are designed to facilitate scalable and efficient access to large data sets. These formats are said to be "analysis-ready", as they organize data into manageable chunks indexed in embedded metadata, making them ideal for intensive data processing (i.e. parallel or distributed processing), such as big data or data science.
{width="5.589492563429571in"
height="3.0063867016622923in"}
[]{#_heading=h.1i9tn1ubwcw9 .anchor}Figure 7 - Zarr structure3
They are said to be "cloud-optimised", as they are compatible with HTTP range requests, i.e. the chunks and metadata are organized in such a way that the chunks of interest can be accessed without downloading the entire file. This design provides a serverless subsetting service and greatly simplifies use. For example, a multidimensional dataset can be loaded in a single line using tools such as xarray.open_dataset("s3://.../file.zarr"). The relevant chunks will be downloaded only when a compute is performed.
Extensions to these formats exist to manage geospatial data (e.g. GeoZarr, GeoParquet). It provides simplified integration with tools such as QGIS desktop or GDAL, ensuring interoperability between desktop applications and cloud-native platforms.
{width="6.739583333333333in"
height="4.239583333333333in"}
[]{#_heading=h.tm7ue41ef0j5 .anchor}Figure 8 - Cloud-Optimised Geospatial Formats4
FAIR-EASE promotes the use of these formats to speed up the analysis of a subset of data, but also to improve the performance of standardised services (e.g. OGC standards) implemented by data providers. See Lobelia blog5 for more information.
The Spatio-Temporal Assets Catalog6 (STAC) is a standardised model and API for geospatial data indexing and discovery. It provides a lightweight yet powerful framework for organising and exposing data inventories, whether native (e.g. NetCDF, Tiff) or ARCO (e.g. COG, Zarr, Geoparquet).
STAC is structured hierarchically, and is based on the HATEOAS7 principle, enabling navigation between these elements. Here are the main concepts:
-
Collection: represents a logical grouping of related data (for example, a sensor, a mission or a family of datasets)
-
Item: describes a single observation or spatio-temporal scene, such as a satellite image or an in situ measurement campaign
-
Asset: data or metadata files accessible via URLs
STAC offers rich search capabilities based on spatio-temporal criteria, extended with additional filters using CQL2 (e.g. observed parameter). This enables users and applications to discover relevant items and directly retrieve the corresponding assets from distributed or cloud infrastructures.
Originally developed for earth observation satellite images, FAIR-EASE has extended its use to in situ data, taking care to provide rich metadata, with as many links as possible to controlled vocabularies.
Here is the architecture proposed by FAIR-EASE:
{width="6.824268372703412in"
height="4.022391732283465in"}
[]{#_heading=h.8lyvumjjyts7 .anchor}Figure 9 - STAC architecture
An implementation has been provided by Ifremer to demonstrate the use of STAC for in-situ data, including native ARGO datasets. This includes a public STAC API available at [stac-pg-api.ifremer.fr]{.underline} and a user-friendly STAC Browser at [stac-browser.ifremer.fr]{.underline}, which allows interactive exploration of the catalog and visualisation of spatial footprints.
Here is a screenshot of an ARGO profile item from the STAC Browser:
{width="6.615625546806649in"
height="3.8994116360454942in"}
[]{#_heading=h.u9rtypwft8hi .anchor}Figure 10 - STAC Browser example
As part of the FAIR-EASE knowledge-building and sharing, a webinar dedicated to openEO was organized by the Copernicus DataSpace Ecosystem (CDSE) to introduce researchers and infrastructure providers to the concepts, tools and practical applications of openEO.
OpenEO is a standardized web API designed to simplify access to and processing of Earth Observation (EO) data across diverse cloud-based backends. It provides an unified and language-agnostic interface that enables users to define, execute, and monitor complex remote processing workflows on datacubes. OpenEO provides a discovery interface (OpenEO Discovery) that allows users to access data and metadata exposed via a SpatioTemporal Asset Catalog (STAC) access point.
{width="4.993233814523185in"
height="1.8448895450568679in"}
[]{#_heading=h.ysmyn16ennak .anchor}Figure 11 - Unify data access with openEO8
With openEO, users can apply processing functions directly on the data where it resides, chaining operations into workflows that are executed server-side, without needing to download large datasets. This approach is particularly well-suited for cloud-native and scalable processing, and aligns with FAIR-EASE's goals of supporting interoperability between Earth System domains.
{width="6.668124453193351in"
height="2.9279910323709535in"}
[]{#_heading=h.6uximkt5x7o .anchor}Figure 12 - openEO workflow mechanism9
The API is fully compatible with ARCO formats such as Zarr and COG, as well as with STAC metadata, facilitating seamless integration with modern EO data catalogs and storage solutions.
An online Web Editor is also available for users to interactively build, test, and submit openEO workflows through a graphical interface, lowering the barrier for non-expert users and fostering reproducibility.
{width="6.266447944006999in"
height="3.1265562117235346in"}
[]{#_heading=h.uyqz0tssp9xz .anchor}Figure 13 - openEO Web Editor10
OpenEO is currently being standardized within OGC, and momentum is growing towards the creation of a federation of openEO-compliant backends, enabling interoperability between EO platforms on a European and global scale.
OpenEO has been implemented, during the FAIR-EASE project, within the Examind Community platform, an open-source geospatial framework developed and maintained by Geomatys. Examind Community is already compatible with OGC services (e.g. WMS, WCS, WFS) and OGC API (e.g. Common, Coverage, Processes). It can also manage geospatial native or ARCO formats, from local or remote storage (e.g. HTTPs, S3).
{width="4.933023840769904in"
height="2.044608486439195in"}
[]{#_heading=h.7brmtkwg4mw5 .anchor}Figure 14 - openEO on Examind Community
The OpenEO standard consists of two essential components: OpenEO Discovery and OpenEO Processing, both of which have been implemented in Examind Community.
The first one is an access point to a STAC catalog. It allows data and metadata to be exposed via a user-accessible catalogue. This is accessible in Examind via a WCS service (create a WCS service, associate data with this service, and a STAC access point will be opened with this data). For more detailed information about the STAC specification, you can refer to the official documentation11.
The second component is the core of openEO, the processing part. It enables users to access, modify, create and execute workflows by linking unit processes. In Examind, this service is accessible via WPS (turn on a wps service, and an openEO access point will be created). For more detailed information about the OpenEO (processing part), you can refer to the official documentation available openEO website12.
Here is an example with an Enhanced Vegetation Index (EVI) process via Examind. All openEO processes / workflows in Examind will follow these steps. In this example we are calculating an EVI on a forest area, but any other process to be run on a data item will follow the same procedure.
Examind offers different processes ('basic building blocks') for creating workflows. These include operations from the openEO standard, such as "load" and "save" to respectively load data into the graph and save the result in a format (e.g. GeoTIFF or NetCDF). Examind also offers a list of mathematical operations, on numbers or coverages (e.g. multiplying the values of a coverage by a value), and other specific processes.
To access the list of data and processes that can be used via openEO, you can use the user interface (via a web editor client) or the STAC / OpenEO Process API.
{width="2.125in" 
{width="2.468834208223972in"
height="0.9692979002624672in"} height="0.989595363079615in"}
[]{#_heading=h.qyt6zaxpwtv8 .anchor}Figure 15 - openEO processes on Examind
Once you have a list of the data you want to use and the processes you want to use, you can create your first graph. In this graph, you will chain processes to create the expected result. In the example of the EVI calculation, we will transfer the formula: `2.5 * (NIR - RED) / (1 + NIR + 6*RED + 7.5*BLUE)`, using mathematical operations in openEO.
Once this first graph has been created, we will execute it. To do this, we need to describe the parameters that the graph must use. For example, the source data identifier, the type of file used for output, the extent, etc..
The graph can be executed in one of two ways: synchronously or asynchronously. Synchronously, the request will be launched and you will receive the result in return. Asynchronously, a job will be created and will have to be executed. You can then query the job to find out its status and the result once it has finished.
Here is the json requests for the EVI graph, execution of the graph, and the result :
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height="2.4027777777777777in"}
[]{#_heading=h.npboh27pp42p .anchor}Figure 16 - openEO flow on Examind
More information (Jupyter notebook + documentation) on Examind Community OpenEO endpoint is available on [https://github.com/fair-ease/Examind_Tutorials]{.underline}.
There was no immediate practical use for the pilots, but the potential for further developments are significant.
Beacon is able to provide users with fast and easy access to multidisciplinary data originating from large collections, on the fly with high performance, and extract specific data based on the user's request.
This software has been customised and deployed in the Blue-Cloud2026 project, FAIR-EASE, among other projects including national ones and is designed to return one single harmonised file as output, regardless of whether the input contains different data types. It allows everyone to set-up their own Beacon 'instance' to enhance the access to their data or use existing Beacon instances from well-known data infrastructures such as Euro-Argo or the World Ocean Database for fast and easy access to harmonized data subsets.
More technical details, example applications and general information on Beacon can be found on the website [https://beacon.maris.nl/]{.underline} and the documentation on Github13.
{width="1.454682852143482in"
height="2.1914709098862644in"}
[]{#_heading=h.rd96lgn31848 .anchor}Figure 17 - Beacon architecture
In order to use Beacon for providing access to harmonised subsets, a set of in total 8 monolithic Beacon instances were set-up for relevant data collections such as the WOD, Copernicus Marine Cora, Euro-Argo, SeaDataNet, and more. The term 'monolithic' is used to indicate that the Beacon instance concerns one data infrastructure. After initial configuration at a MARIS server, all 8 instances have been deployed operationally, with Argo also being available on the UCA Testbed. All have been integrated with the D4Science federated AAI service, by which access is arranged. All Beacon instances also have been provided with its own dedicated Jupyter-notebook which sits on the Beacon API and which makes it easier for the users to interact. The notebooks already contain several queries and users can adapt their own notebooks.
The following figure shows access to the Argo data collection that is hosted on the UCA Testbed. The user is able to retrieve on-the-fly access to Argo data following their query criteria in less than 10 seconds. The data can be stored in different output formats and easily converted to a dataframe as depicted here for further analysis.
{width="6.182276902887139in"
height="2.448047900262467in"}
[]{#_heading=h.bhf7h9s6i8co .anchor}Figure 18 - Access to the Argo data collection that is hosted on the UCA Testbed via a Jupyter Notebook for on-the-fly access to Temperature and Salinity data
The following figure shows an example application of Beacon for access to the WOD. By sending a POST request to the Beacon endpoint, with a set of columns and filters, the user retrieves on-the-fly in a few seconds all data in one single file, fitting the filter criteria. This is then available for further processing. Here a simple plot is shown for temperature data from the WOD.
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height="6.902777777777778in"}
[]{#_heading=h.hkbdblde7xrt .anchor}Figure 19 - Example application of Beacon in a Jupyter Notebook for on-the-fly access to Temperature data from the World Ocean Database.
Apache Iceberg is an open table format designed for managing large-scale tabular data on cloud object storage, such as S3. It enables efficient querying and data management by supporting features like schema evolution, snapshot versioning, and time travel. Originally developed to handle big data workloads, Iceberg is optimized for performance and reliability in distributed environments. It separates the table's metadata from the physical data files (often stored in columnar formats like Parquet), allowing scalable SQL queries through query engines like Trino or Spark. Its support for atomic operations and concurrent reads/writes makes it a powerful foundation for building modern data lakes and federated data platforms.
An Apache Iceberg instance was deployed on the UCA Infrastructure14, using Ceph S3 storage, PySpark to initialise the Iceberg Lake house files and then using Trino and the Trino JDBC driver to expose the Data Lakehouse tables to a public ERDDAP instance. The dataset GLODAP v2.202315 was loaded into the Iceberg Lakehouse and published through the ERDDAP instance.16
An architecture diagram on how Apache Iceberg was deployed on the UCA Infrastructure is displayed below. An example local docker setup has also been created for users to experiment with on their own systems, it is available on Github17 and uses Minio as a local S3 object store. This can also be reconfigured to use cloud hosted S3 object stores.
{width="6.410340113735783in"
height="1.9703258967629047in"}
[]{#_heading=h.1gzs412new65 .anchor}Figure 20 - Apache Iceberg Trino and ERDDAP
Geomatys is actively working to integrate Apache SIS, an open-source geospatial framework they maintain and which is at the core of the Examind Community platform, in order to handle geospatial [meta]data associated with Iceberg tables. The goal is to eventually offer standardized OGC-compliant services powered by an Apache Iceberg backend, bridging big data infrastructures with geospatial interoperability. Work to standardise geospatial components in iceberg is also in progress, as is the development and design of a standard schema (like geojson) for iceberg to manage geospatial data, temporal data, 4D data, chunked data, etc. This work is still on-going.
The diversity of protocols and standards, sometimes even for the same dataset, can be a barrier for less technical users. There is a need to access relevant data fragments (i.e. "what") without needing to know "where" the data is stored or "how" it is accessed.
The original FAIR-EASE architectural document D4.118 introduced a client-side component responsible for the data-access. This concept got elaborated in the deliverable D4.319 through the Uniform Data Access Layer (UDAL). Those documents describe the vision and practical approach behind this technique to formally streamline all data access.
{width="6.307280183727034in"
height="2.1563648293963253in"}
[]{#_heading=h.2r7xtfjfs03q .anchor}Figure 21 - UDAL principle
By introducing formal data contracts, UDAL allows different components of the data flow to interact without being tightly coupled, making it easier to evolve or replace individual parts without disrupting the whole system. This decoupling of interdependencies extends the lifespan of each component, as they can be updated or maintained independently. At the same time, the interface makes it possible to adopt new technical solutions or innovations at a lower cost, by reducing the need for extensive reengineering or custom integration.
A UDAL python interface20 and a python implementation example21 have been published on Github. Two pilots have implemented the UDAL principle, demonstrating its applicability in real-world contexts :
-
Ocean BGC : [https://github.com/fair-ease/py-udal-pokapok]{.underline}
-
Marine Omics Observation: [https://github.com/fair-ease/py-udal-mgo]{.underline}
Most notably in this final phase of the project we would like to highlight the introduction of the Dataset Demand Registry 22 and its associated "Query Editor":
{width="6.0in"
height="6.791666666666667in"}
[]{#_heading=h.4149c2to9o12 .anchor}Figure 22 - screenshot of the UDAL Query Editor
FAIR-EASE promotes many data analysis services and platforms that help users explore, process, and visualise data more easily. Whether for interactive use, running predefined workflows, or viewing data on maps and charts, these tools are designed to be accessible and adaptable to different user needs.
Technical support was also provided to help pilots improve the TRL and FAIRness of their tools, for example by publishing them in code repositories, creating containers or integrating them into Galaxy toolshed.
Interactive notebooks offer a flexible environment for combining code, text, and visualisations, making them a widely used tool for exploratory data analysis and reproducible research. Within FAIR-EASE, two platforms are supported:
-
JupyterLab, mainly used by data scientists, which is compatible with numerous languages (e.g. Python, R, Julia) and provide a lot of extensions ;
-
Pluto.jl, dedicated for Julia programmers, which provides a reactive environment.
A special focus is placed on JupyterGIS, a new approach for collaborative geospatial analysis within notebooks. It combines the flexibility of Jupyter with tools tailored for GIS operations and visualisation, enabling users to explore spatial data, apply processing functions, and share results, all within a unified interface designed for collaborative use.
{width="6.0400415573053365in"
height="3.0461001749781276in"}
[]{#_heading=h.ufywcjkgd1ju .anchor}Figure 23 - JupyterGIS example23
The Galaxy Project is an open, web-based platform designed to make data-intensive research more accessible, reproducible, and collaborative. Originally developed for bioinformatics, Galaxy has been successfully extended by FAIR-EASE to support Earth System sciences and geospatial data processing.
Galaxy is first and foremost a processing platform. Galaxy is built to scale, capable of executing processes on heterogeneous infrastructures, including HPC clusters and cloud systems. With Galaxy Pulsar, jobs can be delegated to remote compute nodes, improving performance and distribution.
At its core, Galaxy provides seamless access to a vast and diverse ToolShed (i.e. Appstore) comprising nearly 10,000 tools, ranging from format converters and statistical analysis tools to data visualization applications and interactive environments like JupyterLab, as well as desktop applications that run directly in the browser.
Galaxy supports a wide range of data access protocols (e.g. WebDAV, S3, FTP, and iRODS), enabling users to easily work with curated reference datasets or bring in their own data.
A key feature of Galaxy is its powerful workflow engine, which allows users to create, share, and reuse complex data analysis pipelines. Thanks to a comprehensive provenance tracking, Galaxy supports RO-Crate metadata export, ensuring full traceability of both data and processes.
Security plays a central role in the platform, with SSO interfacing (e.g. via OpenID Connect), and secret management to avoid having passwords in plain text.
Galaxy is also designed to integrate with key research infrastructures, offering direct connections to repositories such as Zenodo and WorkflowHub. Its dynamic Galaxy training network promotes continuous learning and skills development through collaborative tutorials, workshops and training materials.
{width="6.064058398950131in"
height="2.3129910323709537in"}
[]{#_heading=h.r3bzvedfuy5o .anchor}Figure 24 - Galaxy web UI
Within FAIR-EASE, Galaxy was enhanced in several ways to better support FAIR principles:
-
RO-Crate adding tool information : Id, name, version, description, toolshed's url from tools used in an analysis are now added in RO-Crate archive ;
-
RO-Crate Validation Tool: a validator was developed to check RO-Crate compliance with declared profiles. It supports validation from local/remote sources, has CLI and Python API access, and accepts custom SHACL profiles ;
-
[OGC API:Processes Integration]{.underline}: Initial work began to expose Galaxy workflows as OGC-compliant computational services. It consists of a [FastAPI]{.underline} bridge server between [Galaxy's]{.underline} API and [Open Geospatial Consortium APIs]{.underline} ;
-
ODV Format Support: Galaxy can now recognize and handle Ocean Data View (ODV) files, including an interactive tool for visualization ;
-
ZIP Explorer: lets users browse and selectively extract files from ZIP archives (local or remote), including RO-Crates, improving data ingestion and metadata recognition ;
-
Improved Interactive Tools Panel: provides better access and status tracking for user sessions.
These developments significantly improved Galaxy's usability, interoperability, and alignment with FAIR data management principles, making it a core component of FAIR-EASE.
{width="5.552083333333333in"
height="3.1852088801399825in"}
[]{#_heading=h.yvlxl18amt7g .anchor}Figure 25 - FAIR-EASE usage of Galaxy
webODV is the online version of the popular Ocean Data View (ODV) desktop software for analysis and visualization of marine and other environmental data. ODV has over 130,000 registered users, and, based on recent software download counts, is actively used by more than 13,000 researchers worldwide. Like ODV, webODV provides an interactive graphical user interface and offers rich feature sets via context specific menus. While desktop ODV requires all datasets to reside on the end user machine, webODV works differently. All datasets as well as a special version of the ODV software reside and run on dedicated webODV servers. Users do not have to install any software or download the sometimes-bulky datasets. Instead, users simply connect to datasets using their web-browser. New browser tabs open for every opened dataset, each tab providing an "ODV-like" interactive user interface. Previous ODV users will find it very easy to work with webODV. Concise getting-started documents help guide new users.
Large volumes of important environmental datasets for all parts of the
Earth System are accessible from webODV servers summarized at
[https://webodv.awi.de/]{.underline}.
This includes global TS- and BGC-Argo profile data, GLODAP carbon system
data, SOCAT surface fCO2 data, MEOP marine mammals data, and the World
Ocean Atlas (versions 2023 and 2018). In addition, we also provide
global collections of historical meteorological as well as river-runoff
data. The webODV deployments at D4Science
([https://fair-ease.d4science.org/group/coastalwaterdynamics/webodv]{.underline})
and UCA
([https://webodv.eoscfe.mesocentre.uca.fr/]{.underline})
focus on satellite data and model output made available via netCDF
files. Support for netCDF files in ODV and webODV was implemented during
the FAIR-EASE project.
The figure below shows a typical situation on the user's computer, where several multi-disciplinary datasets residing on different webODV servers are open in different browser panes. In addition, the user has several local data collections open in separate instances of the ODV desktop software running locally. Publication-ready figures can easily be created from any of the open datasets.
{width="5.667011154855643in"
height="2.8439227909011375in"}
[]{#_heading=h.4dzmrt2gd4j8 .anchor}Figure 26 - Screenshot of a data analysis session with several multi-disciplinary datasets open in webODV and ODV desktop
As a new feature developed during the FAIR-EASE project, users can now link the open multi-disciplinary datasets by exchanging graphics elements as well as actual data between any two data windows in webODV or ODV desktop.
{width="4.338990594925634in"
height="3.4725820209973755in"}
[]{#_heading=h.uqirkkz822l8 .anchor}Figure 27 - Contours of surface water Chorophyll-a concentrations determined by satellite sensors overlain on color-shaded salinity at 10 m depth
This is an example of exchanging graphics elements between datasets. Here surface water Chorophyll-a concentrations determined by satellite sensors are overlain on color-shaded salinity at 10 m depth. It is clearly visible that high chlorophyll values in the northwestern Adriatic and along the Italian coast coincide with low salinity caused by runoff and transport of Po river water. This shows the importance of river discharge for the biological productivity and eventually fish stock size.
Exchange of actual data between windows allows the computation of new quantities, such as difference or ratio. This can be applied to detect spatial and temporal changes, for instance between reoccupations of oceanographic sections. This will benefit research in our changing environment.
TerriaMap is an Australian Open Source web application for building web-based map viewers, Atlases and Digital Twin Interfaces. The platform was originally developed by CSIRO (Australia's national Science Agency). It is similar in concept to the European [EDITO]{.underline} digital twin interface. TerriaMap is now supported by a commercial Support company Terria Pty ltd. ([terria.io]{.underline})
Terriamap was used in the "Terriamap Challenge" at the FAIREASE Hackathon in Brest in March 2025. The hackathon challenge explored how diverse environmental datasets (e.g. Argo, Omics data) and OGC and other data services (from Examind, Thredds, ERDDAP, Beacon) could be visualized and interacted with in the TerriaMap web application, allowing for cross disciplinary integration of data services from the FAIR-EASE use cases and infrastructure.
{width="6.30239501312336in"
height="2.8959765966754154in"}
[]{#_heading=h.ht5nsi3yqbxy .anchor}Figure 28 - Terriamap 2D and 3D display modes
Terriamap is based on the Terriajs javascript library, the source code and user configurable files are published on Github in the [TerriaMap github repository]{.underline}. Documentation on the installation and configuration of Terriamap and the various supported data services and data sources is available at [https://docs.terria.io/guide/]{.underline}
{width="5.279413823272091in"
height="2.4946686351706036in"}
[]{#_heading=h.j02u2532w8id .anchor}Figure 29 - Terriamap displaying OMICs metadata and ARGO profile data from ERDDAP
An instance of Terriamap configured with example datasets from the Brest Hackathon is published on the the University Clermont Auvergne(UCA) at [https://terriamap.eoscfe.mesocentre.uca.fr/]{.underline}
{width="5.534900481189851in"
height="2.6188615485564304in"}
[]{#_heading=h.atfaaqf917ua .anchor}Figure 30 - Terriamap displaying CMEMS Chlorophyll data from Examind and SO2 data from Thredds
The Terriamap example configurations compiled during the Hackathon are available on [https://github.com/fair-ease/terria-config]{.underline}
The Earth Analytics Lab is an integrated e-infrastructure that provides value-added services to support research and foster collaborative science. See D3.124 for the full specifications.
{width="6.151020341207349in"
height="3.2710061242344706in"}
[]{#_heading=h.crzk83hi7436 .anchor}Figure 31 - EAL functional view
Based on a "system of systems" approach, FAIR-EASE allows for interconnecting three EAL implementations through federated capabilities, enabling users to seamlessly move from one EAL to another and to access and use the available computing services and resources, which may differ between EALs.
{width="6.501041119860018in"
height="1.3042530621172352in"}
[]{#_heading=h.y6j5d29pljj0 .anchor}Figure 32 - EAL and system of systems approach
We will present these three EALs, including FAIR-EASE contributions, followed by an analysis of their strengths as well as areas for improvement.
As part of FAIR-EASE, the Galaxy Europe Earth System subdomain ([earth-system.usegalaxy.eu]{.underline}) was created to serve the needs of environmental scientists working with marine, atmospheric, land, and biodiversity data.
{width="5.932202537182852in"
height="4.199691601049869in"}
[]{#_heading=h.nlljhx8v870k .anchor}Figure 33 - Galaxy Europe - Earth System subdomain welcome page
It supports a wide range of scientific and operational needs, and strengthens links with European data infrastructures such as Copernicus, CMEMS and OBIS.
These tools help make environmental data more accessible and usable by:
-
Connecting users to major data sources through simple, reproducible workflows ;
-
Offering both batch processing and interactive exploration, all inside the Galaxy platform ;
-
Supporting training, education and open science thanks to public code, Docker images and documentation ;
-
Enabling long-term reusability and compatibility with EOSC and other European initiatives.
These tools show how FAIR-EASE helps bridge the gap between data providers and users, making complex data more usable for science, policy, and operational services.
{width="5.611360454943132in"
height="3.323475503062117in"}
[]{#_heading=h.wkxqdce9m0m3 .anchor}Figure 34 - ODV interactive tool in Galaxy
Tools for Oceanographic Data
+-------------------------------------------------------------------------------------------------------------------------------+----------------------------+--------------------------------------------------------------------------------------------------------------+ | Tool | Description | Links | +-------------------------------------------------------------------------------------------------------------------------------+----------------------------+--------------------------------------------------------------------------------------------------------------+ | Argo_getdata | Allows retrieval of Argo | [GitHub content]{.underline} | | | glider data (physical and | | | | biogeochemical). | [GitHub Galaxy tool]{.underline} | | | | | | | | [Galaxy PR]{.underline} | +-------------------------------------------------------------------------------------------------------------------------------+----------------------------+--------------------------------------------------------------------------------------------------------------+ | DIVA_full_analysis | Implemented as both batch | [GitHub content]{.underline} | | | and interactive tools, | | | | this module enables | [GitHub Galaxy tool]{.underline} | | | advanced spatial | | | | interpolation of marine | [Batch PR]{.underline} | | | data. | | | | | [GxIT PR]{.underline} | +-------------------------------------------------------------------------------------------------------------------------------+----------------------------+--------------------------------------------------------------------------------------------------------------+ | Copernicus Marine Data Store (copernicusmarine) | Batch tool to query and | [GitHub content]{.underline} | | | download datasets from | | | | CMEMS. | [GitHub Galaxy tool]{.underline} | | | | | | | | [Galaxy_PR]{.underline} | +-------------------------------------------------------------------------------------------------------------------------------+----------------------------+--------------------------------------------------------------------------------------------------------------+ | Ocean Data View | Used to plot | [GitHub Galaxy tool]{.underline} | | | geo-referenced ocean data | | | | from NetCDF and other | [GxIT_PR]{.underline} | | | formats. | | +-------------------------------------------------------------------------------------------------------------------------------+----------------------------+--------------------------------------------------------------------------------------------------------------+ | ODV collection manager (tool_odv) | Merges various datasets | [GitHub content]{.underline} | | | with a common vocabulary | | | | and creates a single | [GitHub Galaxy | | | generic ODV spreadsheet in | tool]{.underline} | | | an automatic way | | +-------------------------------------------------------------------------------------------------------------------------------+----------------------------+--------------------------------------------------------------------------------------------------------------+ | ODV history manager (tool_odv_history) | Report in the input file | [GitHub content]{.underline} | | | the ODV history including | | | | the change of QC flag | [GitHub Galaxy | | | | tool]{.underline} | +-------------------------------------------------------------------------------------------------------------------------------+----------------------------+--------------------------------------------------------------------------------------------------------------+ | Canyon B | Robust Estimation of Open | [GitHub content]{.underline} | | | Ocean CO2 Variables and | | | (bgc_canyonb) | Nutrient Concentrations | [GitHub Galaxy | | | From T, S, and O2 Data | tool]{.underline} | | | Using Bayesian Neural | | | | Network | [Conda recipe]{.underline} | | | | | | | | [Galaxy_PR]{.underline} | +-------------------------------------------------------------------------------------------------------------------------------+----------------------------+--------------------------------------------------------------------------------------------------------------+ | Sanntis | The Sanntis tool identify | [GitHub content]{.underline} | | | biosynthetic gene clusters | | | (sanntis_marine) | (BGCs) in genomic & | [GitHub Galaxy | | | metagenomic data | tool]{.underline} | | | | | | | | [Galaxy_PR]{.underline} | | | | [Galaxy_PR_2]{.underline} | +-------------------------------------------------------------------------------------------------------------------------------+----------------------------+--------------------------------------------------------------------------------------------------------------+ | QCV Harmonizer | Harmonizes oceanographic | [GitHub content]{.underline} | | ([harmonize_insitu_to_netcdf]{.underline}) | biogeochemical data. | | | | | [GitHub Galaxy | | | | tool]{.underline} | | | | | | | | [Galaxy_PR]{.underline} | +===============================================================================================================================+============================+==============================================================================================================+
Tools for Interactive Visualisation and Data Handling
+-----------------------+----------------------------+--------------------------------------------------------------------------------------------------------------------+ | Tool | Description | Links | +-----------------------+----------------------------+--------------------------------------------------------------------------------------------------------------------+ | QGIS | Full integration of QGIS | [GitHub Galaxy tool]{.underline} | | | as an interactive tool in | | | | Galaxy. | [GxIT_PR]{.underline} | +-----------------------+----------------------------+--------------------------------------------------------------------------------------------------------------------+ | HoloViz Ecosystem | A set of interactive | [GitHub Galaxy tool]{.underline} | | | notebooks for data | | | | visualisation using Python | [GxIT PR]{.underline} | | | libraries as Panel, Bokeh, | | | | Datashader, etc. | | +-----------------------+----------------------------+--------------------------------------------------------------------------------------------------------------------+ | STAC Browser | Access and navigation | [GitHub content]{.underline} | | | interface for STAC | | | | (SpatioTemporal Asset | [GxIT PR]{.underline} | | | Catalogs). | | +-----------------------+----------------------------+--------------------------------------------------------------------------------------------------------------------+ | TerriaMap | Geospatial visualisation | [GitHub | | | | content]{.underline} | | | | | | | | [GxIt]{.underline} | +=======================+============================+====================================================================================================================+
Access to Global and Biodiversity Data
+-----------------------+----------------------------+-----------------------------------------------------------------------------------------------------+ | Tool | Description | Links | +-----------------------+----------------------------+-----------------------------------------------------------------------------------------------------+ | OBIS occurences | A tool to search and | [GitHub content]{.underline} | | | retrieve species | | | (obis_data) | occurrences from the OBIS | [GitHub Galaxy | | | database. | tool]{.underline} | | | | | | | | [Galaxy_PR]{.underline} | +-----------------------+----------------------------+-----------------------------------------------------------------------------------------------------+ | Copernicus Data | Jupyter notebooks to | [GitHub content]{.underline} | | Space Ecosystem | explore Copernicus data | | | | using SentinelHub and | [GxIT PR]{.underline} | | | OpenEO. | | +-----------------------+----------------------------+-----------------------------------------------------------------------------------------------------+ | Trends.Earth | Batch tool for computing | [GitHub content]{.underline} | | | land cover and degradation | | | | indicators, supporting | [GitHub Galaxy | | | monitoring of SDG 15.3.1. | tool]{.underline} | | | | | | | | [Galaxy_PR]{.underline} | +=======================+============================+=====================================================================================================+
Several Galaxy workflows have been developed and shared as part of FAIR-EASE to support Earth system science. These include workflows to process and analyse Argo float data, extract biogeochemical variables like phosphate from large NetCDF datasets, and combine oceanographic data with tools like ODV and the Pangeo ecosystem. All workflows are openly available on WorkflowHub - [FAIR-EASE Galaxy Project on WorkflowHub]{.underline} - and can be imported into Galaxy by clicking the "Run on Galaxy" button on the WorkflowHub pages. They are designed for environmental scientists working with oceanographic and Earth system data, and are compatible with the [Galaxy Europe For Earth System instance.]{.underline}
In addition, these workflows were demonstrated during major scientific events such as EGU 2024, and training materials have been created to help new users. Tutorials are available through the Galaxy Training Network, showing how to run the workflows, understand the data, and use FAIR practices like RO-Crate to describe and share results.
{width="5.802407042869642in"
height="3.843964348206474in"}
[]{#_heading=h.5670cqsigjgs .anchor}Figure 35 - Water Coastal Dynamics workflow
Workflow Description Access
Marine Omics: Detects biosynthetic gene clusters [WorkflowHub]{.underline}
Biosynthetic Gene in marine omics data using tools
Clusters like Prodigal and SanntiS.
Marine Omics Converts OBIS biodiversity records [WorkflowHub]{.underline}
Visualisation into indicators such as Shannon
(OBIS and Simpson indices.
Indicators)
Process Argo Processes Argo float data and [WorkflowHub]{.underline}
Data with Pangeo & visualizes oceanographic variables
ODV using Pangeo and Ocean Data View
(ODV).
Subset Subsets NetCDF data for the [WorkflowHub]{.underline}
Mediterranean Sea Mediterranean Sea and extracts
& Extract phosphate levels for analysis.
Phosphate
Full Analysis of End-to-end workflow to analyse and [WorkflowHub]{.underline} Argo Data visualise Argo profile datasets.
**Argo-Glider Qualification, Calibration and [Galaxy Earth
Nitrate QCV** validation of Argo floats and System]{.underline}
Gliders ocean Biogeochemical Data
Using Galaxy
Several high-quality, FAIR-aligned tutorials and learning pathways have been developed and published on the Galaxy Training Network (GTN). These resources are tagged with the "earth-system" label and cover a wide range of topics including marine biodiversity, oceanographic data processing, land monitoring, and FAIR metadata practices. Designed for interdisciplinary Earth and environmental scientists, they support hands-on learning with real datasets and workflows, and encourage reuse, openness, and reproducibility. All materials are licensed under CC-BY 4.0.
{width="6.206229221347332in"
height="3.4838867016622923in"}
[]{#_heading=h.kdn5uj5nztzy .anchor}Figure 36 - GTN example
Title Type Description Access
Getting your Learning Introduction to accessing and [Run Tutorial]{.underline}
hands-on earth Pathway analyzing ocean, land,
data atmosphere, biodiversity data in
Galaxy.
**OBIS Marine Tutorial Calculate biodiversity indices [Run Indicators** (Shannon, Simpson, ES50) from Tutorial]{.underline} OBIS.
From NDVI with Tutorial Process NDVI satellite data for [Run Tutorial]{.underline}
OpenEO to time land degradation analysis and
series with time-series visualization.
Holoviews
Marine Omics: Tutorial Detect biosynthetic gene clusters [Run Tutorial]{.underline}
Identifying using Prodigal, InterProScan,
BGCs SanntiS.
Ocean's Tutorial Subset Mediterranean ocean data [Run Tutorial]{.underline}
Variables Study and explore variables (e.g.,
phosphate).
Ocean Data View Tutorial Visualize NetCDF-based [Run Tutorial]{.underline}
(ODV) oceanographic variables using
ODV.
Sentinel 5P Tutorial Explore and analyze Sentinel-5P [Run Tutorial]{.underline}
Data atmosphere data interactively.
Visualisation
Analyse Argo Tutorial Process Argo float datasets with [Run Tutorial]{.underline}
Data Pangeo tools and visualize using
ODV
Make your tools Tutorial Guide to managing tools in a [Run Tutorial]{.underline}
available on your Galaxy subdomain.
subdomain
Create a Tutorial Steps to create and administer a [Run Tutorial]{.underline}
subdomain for Galaxy subdomain for your
your community community.
As part of FAIR-EASE, several actions were undertaken to integrate and leverage PULSAR as a distributed execution backend within Galaxy workflows. PULSAR endpoints were deployed on multiple infrastructures, including at the University of Clermont Auvergne (UCA) and the Hellenic Centre for Marine Research (HCMR) in Greece, enabling remote processing from Galaxy. A proof-of-concept with the EGI Federated Cloud was also carried out, demonstrating the ability to dynamically deploy PULSAR nodes using the EGI Infrastructure Manager. These deployments supported the execution of real-world workflows, validating the portability, scalability, and interoperability of distributed processing in an EAL platform.
{width="6.253650481189851in"
height="2.590508530183727in"}
[]{#_heading=h.tf6xqvihd4uk .anchor}Figure 37 - French Galaxy Pulsar endpoint at the UCA
[D4Science]{.underline} is a hybrid data infrastructure, hosted by CNR Italy, designed to support scientific collaboration and resource sharing across various domains, including biology, ecology, environmental studies, social sciences. Established in 2014, it connects over 24,000 scientists from 50+ countries, integrates data from 50+ providers, and provides access to over a billion records in global repositories. Its key feature is the provision of Virtual Research Environments (VREs) and Virtual Laboratory (VLab), web-based platforms tailored to specific community needs. D4Science is notably used by Blue Cloud 2026.
A Memorandum Of Understanding (MoU) was signed between Blue Cloud 2026 and FAIR-EASE in November 2024, to strengthen collaboration between the two projects. This led to the creation of a FAIR-EASE VRE, including the launch of a dedicated gateway ([https://fair-ease.d4science.org/]{.underline}) and the development of two VLABs for Coastal Water Dynamics and Marine Omics Observation.
{width="5.139067147856518in"
height="2.458254593175853in"}
[]{#_heading=h.qk3hsvm0itgm .anchor}Figure 38 - FAIR-EASE VRE home page on D4Science
{width="5.396681977252843in"
height="4.267834645669291in"}
[]{#_heading=h.bop6vx8z2cqp .anchor}Figure 39 - Marine Omics Observation VLAB on D4Science
For the Marine Omics Observation VLAB, we collaborated with the D4Science team to provide a Galaxy service that enables the chaining of predefined tools. This instance is available at [https://galaxy-gcp.d4science.org/MarineOmicsObservations/]{.underline}. Currently, only non-interactive tools can be executed due to a technical issue that still needs to be resolved.
{width="6.692716535433071in"
height="2.388888888888889in"}
[]{#_heading=h.ajmpeaatej25 .anchor}Figure 40 - Galaxy project service on D4Science
For Coastal Water Dynamics, we collaborated with D4Science team to provide a webODV server providing access to satellite data as well as reanalysis model output in the form of netCDF files: [https://fair-ease.d4science.org/group/coastalwaterdynamics/webodv]{.underline}. These data complement the wide range of environmental data available on the other webODV servers.
The IDDAS is also available as a VRE widget inside the FAIR-EASE VRE on D4Science as illustrated in Figure below. The asset selector, like the current web version of the IDDAS, allows for discovery and access to the assets harvested from the data infrastructures. It is possible to push the assets into the user's local workspace in their virtual lab to conduct their analysis, or use for input to their models and services. The asset selector includes a human readable as well as a machine accessible interface with similar functionalities as the IDDAS.
{width="6.405485564304462in"
height="2.8999956255468065in"}
[]{#_heading=h.71paqiezlw87 .anchor}Figure 41 - Access point of IDDAS from the FAIR-EASE EAL on D4Science
Since mid-2024, we have been working on the implementation of an EAL at Université de Clermont-Auvergne (UCA), building upon its existing hardware infrastructure. This infrastructure also hosts the FAIR-EASE data lake, as described in Deliverable D4.5 - Deployment of the data lake: operational version.
The EAL FAIR-EASE@UCA remains in an incubation phase but is progressively taking shape through key developments. Notably, it has been interfaced with the EGI Check-in SSO service to support federated authentication and has integrated an S3 object storage ([s3://s3.mesocentre.uca.fr]{.underline}) with Ceph deployed by UCA. Applications are being deployed via OpenStack, providing a flexible and scalable environment for future services. These foundational components will support the upcoming deployment of analytical tools and workflows aligned with FAIR-EASE objectives.
{width="6.535603674540682in"
height="2.7886679790026245in"}
[]{#_heading=h.uwtsmvpukv5p .anchor}Figure 42 - Accessing JupyterLab on UCA via EGI CheckIn
Here are the services deployed:
+------------+----------------+----------------------------------------------------------------------------------------------------------------------+ | Building | Service | URL | | block | | | +------------+----------------+----------------------------------------------------------------------------------------------------------------------+ | Data | Examind | [https://examind.eoscfe.mesocentre.uca.fr/examind/]{.underline} | | Access | Community | | | +----------------+----------------------------------------------------------------------------------------------------------------------+ | | Thredds | [https://thredds.eoscfe.mesocentre.uca.fr/]{.underline} | | +----------------+----------------------------------------------------------------------------------------------------------------------+ | | ERDDAP | [https://erddap.eoscfe.mesocentre.uca.fr]{.underline} | | +----------------+----------------------------------------------------------------------------------------------------------------------+ | | Beacon | [https://beacon-argo.eoscfe.mesocentre.uca.fr/]{.underline} | +------------+----------------+----------------------------------------------------------------------------------------------------------------------+ | Data | WebODV | [https://webodv.eoscfe.mesocentre.uca.fr/]{.underline} | | Analysis | | | | +----------------+----------------------------------------------------------------------------------------------------------------------+ | | TerriaMap | [https://terriamap.eoscfe.mesocentre.uca.fr/]{.underline} | | +----------------+----------------------------------------------------------------------------------------------------------------------+ | | JupyterHub/Lab | [https://jupyterhub.eoscfe.mesocentre.uca.fr/]{.underline} | +============+================+======================================================================================================================+
+-------------------------+-------------------------+-------------------------+ | EAL Implementation | Strengths | Areas for | | | | improvement | +-------------------------+-------------------------+-------------------------+ | Galaxy for Earth | - Galaxy as PaaS | - Implement user group | | System | | management features | | | - Galaxy Pulsar | | | (TRL = 9) | integration | - Enhance integration | | | | for discovering and | | | - Galaxy Training | accessing geospatial | | | Materials | reference datasets | | | | | | | - CI/CD with GitHub | | | | | | | | - Community-based | | | | contributions | | +-------------------------+-------------------------+-------------------------+ | FAIR-EASE@D4Science | - Integration of | - S3 API direct access | | | various services | for better tool/service | | (TRL = 9) | (discovery, access, | integration and support | | | analysis) | for HTTP Range | | | | | | | - Catalogue and | - Streamline | | | publication mechanisms | deployment of new | | | | services by external | | | - Group management | teams | | | (VLAB, VRE, landing | | | | pages) | | | | | | | | - Collaboration tools | | +-------------------------+-------------------------+-------------------------+ | FAIR-EASE@UCA | - S3 storage | - Design a landing | | | (reference data, | page for better user | | (TRL = 6) | userspace) | entry points | | | | | | | - Deployment of new | - Implement group and | | | services | collaboration | | | | management features | | | - Integration of | | | | access and analysis | - Develop a dedicated | | | workflows | service catalogue in | | | | connection with the | | | - Links to other EALs | EOSC node federation | | | (e.g., Galaxy Europe | | | | via Pulsar) | - Deploy a vault | +=========================+=========================+=========================+
Over the course of the project, FAIR-EASE has achieved a series of notable results that have contributed to both technological progress and community engagement across multiple scientific domains.
FAIR-EASE has exploited existing solutions and successfully adapted them to new domains and contexts, demonstrating the versatility and robustness of tools such as Galaxy and webODV for data analysis services. These technologies, initially designed for specific communities, have been enhanced to meet broader scientific needs, promoting interoperability between fields. At the same time, FAIR-EASE has fostered innovation by introducing new concepts and tackling technical challenges. The development and implementation of principles such as UDAL and EAL reflect this commitment to innovation. Collaboration has been the cornerstone of this success. FAIR-EASE fostered close interaction between internal partners while building bridges with external initiatives.
FAIR-EASE has also played a key role in disseminating good practice, particularly in improving the fairness of data, software and processes. In response to the growing demand for sustainable computing, FAIR-EASE has also advocated for remote data processing to minimize unnecessary data transfers. By supporting standards and technologies such as OGC API:Processes, S3/ARCO formats, OpenEO, and Galaxy Pulsar, the project has paved the way for more efficient and scalable processing approaches. By integrating these principles into tools and workflows, the project has helped to improve the overall quality and sustainability of research products.
The project has provided integrated e-infrastructures (EAL) that support large-scale collaborative science, enabling users to move from one to another EAL through a system of systems approach, using common federated capabilities. Platforms such as Galaxy for Earth System Science, FAIR-EASE@D4Science and the FAIR-EASE@UCA have provided pilots with robust environments for experimentation, sharing and validation.
Finally, FAIR-EASE has produced a significant number of KERs25, each contributing tangible value to the scientific and technical communities. These results are not only deliverables but also stepping stones for further innovation, adoption, and impact beyond the lifetime of the project.
Footnotes
-
[https://developer.mozilla.org/en-US/docs/Web/HTTP/Guides/Range_requests]{.underline} ↩
-
[https://blog.lobelia.earth/arco-the-smartest-way-to-access-big-geospatial-data-eaf689eff3c9]{.underline} ↩
-
Source: [https://openeo.org]{.underline} ↩
-
[https://openeo.org/documentation/1.0/developers/api/reference.html#section/Processes]{.underline} ↩
-
[https://glodap.info/index.php/merged-and-adjusted-data-product-v2-2023/]{.underline} ↩
-
[https://erddap.eoscfe.mesocentre.uca.fr/erddap/tabledap/glodap_v2_2023_iceberg2.graph]{.underline} ↩
-
[https://github.com/fair-ease/erddap-trino-iceberg]{.underline} ↩
-
Marc Portier (2023) FAIR-EASE_D4.1_Landscaping exercise_The -meta-data, software and cloud needs for the data lake. Zenodo. doi: 10.5281/zenodo.7965398. ↩
-
PORTIER, M. (2024) FAIR-EASE - D4.3 Status and expectations of the FAIR-EASE data lake. Zenodo. doi: 10.5281/zenodo.13933551. ↩
-
[https://github.com/fair-ease/py-udal-interface]{.underline} ↩
-
[https://github.com/fair-ease/py-udal-fe-impl]{.underline} ↩
-
[https://lab.fairease.eu/dataset-demand-register/registry/]{.underline} ↩
-
Source: [https://jupytergis.readthedocs.io/en/latest/]{.underline} ↩