This dataset presents the first soil maps for the region produced using digital mapping techniques. It includes predictions for ten major physical and chemical soil properties at various depths, plus a map of total soil depth. For each property, we selected the most accurate models and key environmental drivers. In Southwestern Asia and many arid or semi-arid regions, detailed soil data are often missing. This dataset fills that gap, supporting agriculture, research, planning, and local policy.

This study explores why global greenhouse gas (GHG) emissions estimates vary. Key reasons include different coverage of gases and sectors, varying definitions of anthropogenic land use change emissions, and the Paris Agreement not covering all emission sources. The study highlights three main ways emissions data is reported, each with different objectives and resulting in varying global emission totals. It emphasizes the need for transparency in choosing datasets and setting assessment scopes.

During a recent field campaign from 15 to 30 March 2021 at Andenes, Norway, we collected a set of observations that allows to better constrain how clouds and precipitation processes work. Frequent alternations between mid-latitude and arctic weather systems were encountered during the campaign. Our dataset is unique in combining measurements in both vapour and precipitation, aerosols, ice nucleating particles, and was made simultaneously at different elevations at a high latitude location.

The accessibility of building function is essential for urban research. We reviewed the recent work and concluded three limitations: few open-source datasets, coarse building function categories, and poor model interpretability with inadequate multimodal feature fusion. Thus, we created BuildingSense with fine-grained categories and multimodal data, and proved that the large model can be used for improving the interpretability of results, with three directions for enhancing their performance.

Greenland Terminus Position Dataset (GrTPD) is a new manually delineated dataset of glacier terminus positions for the Greenland Ice Sheet, providing spatially extensive and seasonally targeted coverage across marine-, land-, and lake-terminating glaciers. The dataset includes 19 171 terminus delineations for 465 glaciers spanning 2002–2021, derived from multi-source optical and SAR satellite imagery using standardized workflows.

This 28-year study collects vital oceanographic data from the southern Baltic. We used specialized instruments on a vessel to measure water temperature and saltiness along a repeated route, covering four major sea basins. This unique, long-term record is now publicly available and crucial for improving sea models and monitoring the health and dynamics of this important European Sea.

Our research addresses the challenge of assessing forest structural diversity over large spatial scales, which is essential for understanding links between canopy structure, biodiversity, and ecosystem functioning. The advent of spaceborne Light Detection and Ranging (LiDAR) sensors such as the Global Ecosystem Dynamics Investigation (GEDI) has revolutionised the ability to measure forest structure. We provide a spatially explicit dataset of eight forest structural diversity metrics.

This study provides a high-resolution (0.25°, hourly) tropopause folding dataset for China (2014–2023) using ERA5 (ECMWF Reanalysis version 5) reanalysis data and a three-dimensional labeling method. It fills a critical data gap, enabling detailed spatiotemporal analysis of tropopause folding events. This validated resource provides a solid foundation for investigating stratosphere-troposphere transport and its impacts on near-surface air quality and extreme weather.

We developed a 24-year record revealing how river ice on the six largest Arctic rivers has changed under a warming climate. Using satellite images from the MODIS Terra and Aqua sensors, we monitored daily ice cover and seasonal freeze-up and breakup timing. On average, over 65 % of river segments show later freeze-up (~ 9 d), earlier breakup (~ 8 d), and shorter ice seasons (~ 14 d), revealing a clear signal of rapid warming across Arctic river systems.

Rivers and streams play a key role in how carbon moves through the environment, but we know little about this in Australia. To help close this gap, we compile the first national database of carbon data from rivers and streams, combining past studies, government records, and new data. The data show where and when carbon was measured and reveal major gaps in long-term monitoring. This new resource will help scientists understand carbon and water systems across Australia.

An appropriate representation of the NO_x/NO₂ ratio and NO_x lifetime is essential for satellite-based NO_x emissions estimation. We introduce a lightweight approach that applies variable NO_x/NO₂ ratios and piecewise fitting based on the directional derivative approach (DDA) to estimate regional and point-source NO_x emissions. Our method directly captures nonlinear NO_x chemistry and serves as an efficient alternative to both bottom-up inventories and computationally demanding top-down models.

We introduce a large and diverse dataset that supports the development of machine learning methods for studying Earth structures through surface wave dispersion curves. Existing research has been limited by the absence of such benchmark data. Our dataset includes both computer-generated and real-world examples, allowing models to be tested and compared in a consistent way. By making these resources openly available, we aim to advance research on the shallow and deep Earth.

We present tethered-balloon measurements from BELUGA (Balloon-bornE moduLar Utility for profilinG the lower Atmosphere) at Villum Research Station (spring 2024), detailing instrumentation, data processing, and environmental conditions. The data stored in the PANGAEA database include instrument-separated subsets with frequent in situ profiling of thermodynamic, turbulence conditions, and thermal-infrared irradiances across the Arctic atmospheric boundary layer (ABL), supplemented by radiosondes. The data help examine transition events between Arctic ABL states.

In-situ data of cloud microphysics is essential for targeted studies of cloud processes, validating remote sensing, and both assessing and improving the accuracy of weather and climate models. In this work we adopt a novel technique using a small uncrewed aircraft (SUA) and a bespoke sensor to produce vertical profiles of cloud microphysical parameters. The data are publicly available from https://zenodo.org/records/14756233.

In summary, the PROMICE (Programme for Monitoring of the Greenland Ice Sheet ) | GC-NET AWS (Greenland Climate Network automatic weather station) data product update represents a significant advancement in Arctic climate monitoring. Through enhanced station designs, state-of-the-art instrumentation, and a transparent, automated data processing workflow, the dataset offers an essential resource for studying the Greenland Ice Sheet and its periphery, validating climate models, and supporting global assessments of cryospheric change.

Snow microstructure undergoes constant shape transformations known as snow metamorphism. Observing first-hand snow metamorphism is key to improving the modelling of these transformations. In this work, we monitor snow microstructure evolution during metamorphism by X-ray tomography. We provide a data set at high spatial and temporal resolution of 3D images of snow microstructure evolving through a wide range of experimental conditions, along with videos showing these transformations.

The Electrically Scanning Microwave Radiometer, the first spaceborne microwave instrument to map global sea ice, was launched aboard the NIMBUS 5 satellite in December 1972 and remained operational until May 1977. As part of the European Space Agency's Climate Change Initiative, this dataset has been reprocessed and validated to provide a nearly complete global record of sea ice concentration, with only a few data gaps in 1973 and 1975.

We introduce CCAV-10m, an annual, species-level 10m resolution coastal wetland vegetation dataset for eastern China. It was produced using a novel phenology-guided vegetation classification network that integrates Sentinel-1/2 satellite imagery with field observations. Validation demonstrates strong classification accuracy (overall accuracy: 0.916, Kappa: 0.898). Regular updates will support long-term coastal wetland research, invasive species monitoring, and precision wetland management.

DeepFlux provides a global, gap-free, daily record of air temperature, humidity, and turbulent heat flux from 1992 to 2020. Using satellite data and deep learning, it fills missing observations and delivers continuous estimates. Tests against in situ measurements show it is closer to reality and more reliable than existing products. This open resource supports improved climate studies and model evaluation.

Nutrient levels govern oceanic primary production, but measuring them is labor-intensive and costly. To address this, we used machine learning models to learn the hidden relationships between easy-to-measure ocean properties (like temperature and salinity) and nutrient levels. Applying this model, we created ~ 470 million nutrient data points across the North Pacific from 1895 to 2024. This data will help to understand nutrient dynamics and marine ecosystem variability under climate change.

The DFM (Database Fotografico Macrosismico), managed by the INGV's macroseismic field survey team – preserves essential photographic records of earthquake effects that might otherwise be lost. Each image is carefully validated by experts using the European Macroseismic Scale 1998 to classify building damage. This expanding archive is a key resource for training surveyors, improving vulnerability assessments, and strengthening national preparedness for future seismic emergencies.

Tenerife faces multiple natural hazards, including volcanic eruptions, floods, and landslides. Our study compiles over 500 years of historical data to identify patterns and vulnerabilities, providing key insights for improving natural risk prevention and management. This dataset helps decision-makers understand hazard risks and develop better mitigation strategies. Our approach serves as a model for other regions to enhance long-term natural risk resilience.