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Earth Observations (EO) have become crucial for advancing climate and Earth System research, enabling significant scientific discoveries. Advanced EO missions, such as ESA’s Earth Explorers and the EU's Copernicus Programme, provide vast data volumes and continuous and global observations with cutting-edge technology, a vital resource for understanding processes and interactions within Earth's sub-systems, offering critical evidence of climate change impacts on society and ecosystems. Open access to such data products has been instrumental in facilitating global scientific collaboration and innovation. Reproducibility in Earth Science is essential for validating discoveries, enabling the scientific community to trust and build upon each other’s work. Open data, coupled with open-source software, is key to achieving this reproducibility, ensuring transparency, and facilitating peer review. Addressing scientific challenges requires collaborative efforts supported by fit-for-purpose technology to enable new insights and ensuring they are FAIR (findable, accessible, interoperable, and reusable), trustable and up to date, supporting long-term climate studies and action-driven science. Several initiatives within ESA’s Earth Observation Programme are fostering FAIR and Open Science. EarthCODE is one such initiative, focusing on implementing ESA’s vision of EO Open Science and Innovation by adopting FAIR and Open principles in Earth Science activities funded by ESA’s FutureEO Programme. EarthCODE leverages existing European platform solutions for effective cloud-based analyses of EO data and higher-level geospatial products. It aims to ensure the persistence of end-to-end scientific workflows and provide means for managing open research data, including data, code, and documentation. EarthCODE aims to make Earth Science research more reproducible, transparent, and collaborative, driving progress in understanding and addressing global environmental challenges.

The Deep Earth System Data Lab (DeepESDL) provides an AI-ready, collaborative environment for researchers aiming to study the Earth's complex dynamics using various datasets and empirical approaches. Recently opened to Early Adopters, it builds on projects like CAB-LAB and ESDL, utilizing well-established Python and Julia technology stacks. DeepESDL offers programmatic access to extensive analysis-ready data cubes and computational resources, enabling researchers to focus on analysis without extensive preparations. Scientists can use persistently available data cubes or generate user-tailored cubes from own data or publicly available datasets. The goal is to streamline data processing through empirical or AI methods within high-dimensional Earth Observation workflows. DeepESDL addresses the complete research cycle, from discovery of earth data to powerful analyses, collaborative scientific research, advanced data visualisation and publication of results, promoting FAIR and Open Science. Apart from serving as a research environment, DeepESDL showcases scientific use cases and supports educational purposes through capacity building, academic programs, and Open Science initiatives. This paper presents an overview of DeepESDL.

The "Cubes and Clouds" Massive Open Online Course (MOOC) addresses the growing challenges and opportunities in Earth Observation (EO) data analysis posed by the exponential growth of satellite missions and data volumes. This course introduces cloud-native EO concepts and open science principles, facilitating collaboration and data sharing within the EO community.Key topics covered in the MOOC include data discovery, processing workflows, and data sharing using real-world examples like snow cover mapping in the Alps. Participants engage with interactive lessons, videos, and hands-on coding exercises, leveraging freely available geospatial data and emphasizing open data principles and interoperability.The course infrastructure seamlessly integrates with cloud platforms like the Copernicus Data Space Ecosystem, enabling learners to apply concepts in a practical, cloud-based environment. Initial user statistics indicate strong interest, particularly among early-career professionals and researchers, with participant surveys suggesting increased confidence in using EO cloud platforms and embracing open science practices upon course completion.Overall, "Cubes and Clouds" serves as a valuable resource for the EO community, fostering transparency, collaboration, and innovation in geospatial science. Future developments aim to expand course content and attract diverse user groups, ensuring broader accessibility and long-term impact in advancing cloud-native EO and open science initiatives.

Open Science is a catalyst for innovation. Across the Earth Observation value chain, from R&D to prototyping new products and development of commercial applications, openness can play an important role by promoting long-term sustainable, community-contributed science and technology. The FAIR principles provide essential support to implementing Open Science, by offering guidelines for how researchers can adapt their EO and Earth Science practice to enable that their work (taking place increasingly in the cloud) and results are discovered, accessed, used, and reproduced by others. The Open Science Data Catalogue (OSC) (https://opensciencedata.esa.int) is an ESA Open Science activity aiming to enhance the discoverability and use of the various scientific and value-added results (i.e. data, code, documentation) achieved in Earth System Science research activities funded by ESA EO. The OSC provides open access for the scientific community to geoscience products (based on EO data from ESA and non-ESA missions and other geospatial information and models) across the whole spectrum of Earth Science domains. The OSC adheres to FAIR principles and promotes reproducibility of scientific studies. The OSC makes use of various Open-Source geospatial technologies such as pycsw, PySTAC, and OpenLayers and tries to contribute back to these projects in terms of software and standardisation. This paper reviews the EO OSC architecture, technology stack, and illustrates how this tool can be used to discover and publish Earth System Science products from ESA activities. It also looks at future evolutions of the product and how it contributes to ESA’s EO Open Science and Innovation goals.

Motivated by tunnel accidents in the recent past, several investigations into the sustained load behavior of adhesive anchors have been initiated. Nevertheless, the reliable lifetime prediction of bonded anchor systems based on a relatively short testing period still represents an unsolved challenge due to the complex nonlinear viscoelastic behavior of concrete and adhesives alike. This contribution summarizes the results of a comprehensive experimental investigation and systematically carried out time-to-failure analysis performed on bonded anchors under sustained tensile load. Two different adhesive materials that find widespread application in the building industry were used, one epoxy and one vinylester based. Performed experiments include full material characterizations of concrete and the adhesives, bonded anchor pull-out tests at different loading rates, and time-to-failure sustained load tests. All anchor tests are performed in a confined configuration with close support. After a thorough review of available experimental data and analysis methods in the literature, the experimental data are presented with the main goal to (i) provide guidance for the analysis of load versus time-to-failure test data, and (ii) to derive a set of recommendations for efficient time-to-failure tests having in mind the needs associated with different analysis techniques. Finally, a new approach based on a sigmoid function, previously used only for concrete, is for the first time applied to bonded anchors systems and compared to the established regression models.