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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.

Aims: Acute heart failure (AHF) is typified by inflammatory and oxidative stress responses, which are associated with unfavorable patient outcomes. Given the anti-inflammatory and antioxidant properties of high-density lipoprotein (HDL), this study sought to examine the relationship between impaired HDL function and mortality in AHF patients. The complex interplay between various HDL-related biomarkers and clinical outcomes remains poorly understood. Methods: HDL subclass distribution was quantified by nuclear magnetic resonance spectroscopy. Lecithin–cholesterol acyltransferase (LCAT) activity, cholesterol ester transfer protein (CETP) activity, and paraoxonase (PON-1) activity were assessed using fluorometric assays. HDL-cholesterol efflux capacity (CEC) was assessed in a validated assay using [3H]-cholesterol-labeled J774 macrophages. Results: Among the study participants, 74 (23.5 %) out of 315 died within three months after hospitalization due to AHF. These patients exhibited lower activities of the anti-oxidant enzymes PON1 and LCAT, impaired CEC, and lower concentration of small HDL subclasses, which remained significant after accounting for potential confounding factors. Smaller HDL particles, particularly HDL3 and HDL4, exhibited a strong association with CEC, PON1 activity, and LCAT activity. Conclusions: In patients with AHF, impaired HDL CEC, HDL antioxidant and anti-inflammatory function, and impaired HDL metabolism are associated with increased mortality. Assessment of HDL function and subclass distribution could provide valuable clinical information and help identify patients at high risk.

Although research into multimodal stance-taking has gained momentum over the past years, the multimodal construction of so-called stacked stances has not yet received systematic attention in the literature. Mocking enactments are a prime example of such complex social actions as they are layered both interactionally and stance-related, and they rely significantly on the use of bodily visual resources, depicting rather than describing events and stances. Using Du Bois’ Stance Triangle as a framework, this study investigates mocking enactments as a case study to unravel the multimodal aspects of layered stance expressions. Drawing on three data sets—music instruction in Dutch, German, and English, spontaneous face-to-face interactions among friends in Dutch, and narrations on past events in Flemish Sign Language (VGT)—this study provides a qualitative exploration of mocking enactments across different communicative settings, languages, and modalities. The study achieves three main objectives: (1) illuminating how enactments are used for mocking, (2) identifying the layers of stance-taking at play, and (3) examining the multimodal construction of mocking enactments. Our analysis reveals various different uses of enactments for mocking. Aside from enacting the target of the mockery, participants can include other characters and viewpoints, highlighting the breadth of the phenomenon under scrutiny. Second, we uncover the layered construction of stance on all axes of the Stance Triangle (evaluation, positioning, and alignment). Third, we find that mocking enactments are embedded in highly evaluative contexts, indexed by the use of bodily visual resources. Interestingly, not all mocking enactments include a multimodally exaggerated depiction, but instead, some merely allude to an absurd hypothetical scenario. Our findings contribute to the growing body of literature on multimodal stance-taking, by showing how a nuanced interpretation of the Stance Triangle can offer a useful framework for analyzing layered stance acts.