We introduce a daily-resolution dataset capturing global maritime liquefied natural gas (LNG) trade flows from 2020 to 2024, derived from Automatic Identification System (AIS) vessel tracking and port call records. The dataset, LNG-T3, integrates LNG tanker fleet specifications, LNG terminal metadata, and AIS-based vessel position and draught reports to map global maritime LNG trade flows. It provides individual LNG tanker export and return voyage events, as well as daily LNG volumes for tanker transport, terminal throughput, and country-to-country trade. The LNG-T3 dataset reveals the major shift in global LNG trade following the 2022 Russian invasion of Ukraine, including a redirection of exports from China to the EU and increased Russian LNG exports to Europe despite the conflict. The United States became the world's largest LNG exporter, boosting exports to Europe to replace Russian pipeline gas. By offering comprehensive tanker, infrastructure, and trade information, the LNG-T3 dataset enables future analyses of market dynamics, infrastructure bottlenecks, broader geopolitical factors shaping international LNG trade, and climate impacts of the LNG industry.

Obtaining high-resolution spatial data to monitor post-fire impacts in environmentally and historically sensitive areas is essential for both heritage conservation and environmental assessment. The "Las Médulas Post-Fire lidar dataset" was acquired in September and November 2025, immediately after the wildfire that affected this UNESCO World Heritage Cultural Landscape in northwestern Spain. Las Médulas, the largest Roman gold-mining complex in the ancient world, is characterized by extensive hydraulic infrastructures and a distinctive geomorphology shaped by the ruina montium technique. The dataset includes UAV-based lidar point clouds and derived topographic products such as Digital Elevation Models (DEMs), Digital Surface Models (DSMs) and RGB orthomosaics, suitable for 3D reconstruction of the archaeological and natural landscape. These products document post-fire terrain conditions, vegetation loss and erosional patterns, while preserving fine-scale mining landforms. All files are openly accessible in a structured repository, providing a reproducible resource for research on cultural heritage management, ecological recovery, geomorphology and risk assessment in Mediterranean mountain environments.

Maritime weather data from historical ship logbooks are used to assess 19th century surface wind conditions. Housed across several New England archives, logbooks of U.S. whaling voyages contain systematic weather observations (e.g., wind strength/direction, sea state, precipitation) at (sub-)daily temporal resolution. Here, qualitative wind descriptions by the whalers from ~200 ship logbooks are quantified to generate a dataset with ~81,000 daily records of wind strength and direction en route and covering key whaling grounds in the Atlantic, Pacific, Indian, and Southern Ocean during the period 1820-1900 CE. Following extensive quality control, we find good agreement in wind strength and direction for the whaling records when compared with 20th Century Reanalysis winds for mean and seasonal conditions. For the North Atlantic with the densest coverage of whaling records, interannual variations in the basin-wide wind field associated with different phases of the North Atlantic Oscillation are also captured in the whaling records. Our results demonstrate that the historical records provide an important long-term context for maritime wind patterns in ocean regions lacking direct observational data during the 19th century.

We present a comprehensive, decade-long (2014-2024) aerial survey dataset of northern Eurasian waterbirds, covering a broad northern Eurasian study area, with survey effort concentrated in the Russian Arctic and Subarctic. Surveys in a purpose-built light hydroplane covered 1,140,337 km of flight tracks across 67 administrative districts and a net, non-overlapping area of 1,918,845 km² (45.20-76.52°N, 35.49°E-180.00°E / 180.00°W-172.05°W). The dataset contains 97,638 georeferenced occurrence records, comprising 2 distinct survey components: marine (14%) and inland basin (86%) surveys. The 10 most abundant species (4 geese and 6 ducks) account for 76% of all individuals. Following spatial deduplication with survey-type grouping, the dataset encompasses 2,438,521 deduplicated individuals from 42 species in 19 genera, distributed as: marine surveys: 397,373 individuals (16%) from 34 species; basin surveys: 2,041,148 individuals (84%) from 42 species. These records provide a spatially explicit baseline for analyses of broad distribution patterns and conservation planning in surveyed regions of northern Eurasia.

Administrative boundaries in the United States frequently shift due to population growth and redistricting, complicating longitudinal analyses of census-based socio-economic data. In California, the number and configuration of census tracts and block groups have changed substantially between 1990 and 2020, often resulting in misaligned or missing historical data-especially for variables such as housing value. This study presents a geospatial methodology for reconstructing historical housing data using Kriging interpolation and zonal statistics. Kriging, a geostatistical method that accounts for spatial autocorrelation, was applied to estimate missing median house values across decades. The interpolated surfaces were then aggregated to consistent 2020 census block group geographic units via zonal statistics, enabling cross-temporal comparison on a uniform spatial basis. Using California as a case study, this work offers a reproducible framework for reconstructing historical datasets across evolving administrative boundaries, supporting more accurate spatial and socio-economic research.

NASA's Goddard Earth Observing System (GEOS) infrastructure was used to couple a cloud-admitting (7-km grid, 72 levels) configuration of the GEOS atmospheric model with a mesoscale-resolving (2-4-km grid, 90 levels) Estimating the Circulation and Climate of the Ocean (ECCO) configuration of the Massachusetts Institute of Technology general circulation model (MITgcm), and to conduct a 14-month "nature" simulation initialized with January 20, 2020, 21Z conditions. The output of this simulation is contained in the dataset described here. The NASA GEOS/ECCO Coupled Nature Run includes astronomical tidal forcing in the ocean component of the simulation, an interactive aerosol component and aerosol-cloud interactions in the atmosphere, and the storage of copious amounts of model output. The inclusion of tidal forcing permits a more realistic representation of vertical mixing in the ocean and of high-frequency variability that is aliased in satellite observations. All of the above make this simulation well suited as a nature run in Observing System Simulation Experiments (OSSEs) and for the study of high frequency/wavenumber coupled processes in weather and climate.

Microcirculatory deterioration in diabetes mellitus causes severe organ-specific complications, yet a systemic understanding of its cross-organ pathophysiology remains elusive due to a lack of comprehensive data. To address this gap, we present a high-dimensional dataset mapping microhemodynamic and oxygenation profiles across six organs in murine models of health, pre-diabetes, and type 1 and 2 diabetes. Structured as a third-order tensor, the dataset comprises 10-parameter physio-signatures for each condition, documenting responses to insulin and the GLP-1 receptor agonist liraglutide at one- and two-week endpoints. Our resource enables direct deconvolution of disease- and organ-specific signatures and provides a quantitative platform for comparing therapeutic pharmacodynamics. We propose a vectorial and tensorial analytical framework to dissect systemic patterns, quantify disease perturbation, and identify significant drug-organ interactions. Our foundational dataset is intended to catalyze the development of system-level computational models for managing diabetic microvascular disease.

Stream classification plays an important role in the study and management of freshwater ecosystems. Many classification schemes exist that focus predominantly on physical habitat, hydrology, and thermal regimes, but few frameworks explicitly include river network connectivity. Because river network connectivity directly affects the movement of water, nutrients, sediments, and aquatic species throughout a watershed, including connectivity metrics in river classification provides opportunities for advancing riverine research at large spatial extents. We developed a robust framework and dataset that incorporates a network connectivity-based stream classification system for the conterminous United States (NetConUS), utilizing the National Hydrography Dataset Plus version 2 (NHD). Connectivity classes are differentiated by degree centrality, eigen-vector centrality, clustering co-efficient, closeness centrality and betweenness centrality and comprise five types: central streams, peripheral streams, mainstem streams, cluster stream and convergent streams. The streams classification was validated using Bayesian Neural Networks (BNNs) to account for uncertainty in the assignment of network connectivity classes. The dataset captures the structural roles of stream segments within river networks and provides opportunities to include network connectivity metrics alongside geophysical descriptors in analyses of freshwater fauna at the extent of the conterminous US.

Accurate regional climate information is vital for hydrological applications, impact studies, and adaptation planning. However, Global Climate Model (GCM) outputs are constrained by coarse resolution and systematic biases, limiting their utility over the climatically diverse and topographically complex Indian subcontinent. To address this, we present INDRA-CMIP6, a high-resolution (0.1° × 0.1°) daily dataset of precipitation, maximum temperature (Tmax), and minimum temperature (Tmin) for the historical period (1951-2014) and future projections (2015-2100) under four emission scenarios (SSP126, SSP245, SSP370, SSP585). The dataset, derived from 14 CMIP6 GCMs and their multi-model ensemble (MME) mean, was developed using the Double Bias-Corrected Constructed Analogue (DBCCA) approach with MSWEP and MSWX as reference datasets. Evaluation against reference data shows reduced warm and cold biases and improved representation of regional climate. INDRA-CMIP6 enables assessments of climate extremes and supports water resource planning.

Genomic resources for Tor species are currently limited, and their molecular systematics remain poorly resolved. In this study, we report the first chromosome-level genome assembly of Tor sinensis, generated using PacBio long-read sequencing and Hi-C scaffolding. The final genome assembly spans approximately 1.80 Gb and consists of 228 contigs, with a contig N50 of 31.67 Mb. A total of 97 contigs were successfully anchored and ordered into 50 chromosomes, covering 96.57% of the genome. Chromosome lengths range from 23.67 Mb to 62.88 Mb. Repeat analysis identified 807.40 Mb of repetitive sequences, representing 44.84% of the genome. In addition, 3,794 rRNA genes and 11,264 tRNA genes were annotated, along with 379 snRNAs and 35 snoRNAs. The average length of tRNA genes was 72 bp. A total of 62,000 protein-coding genes were predicted, yielding 263,453 protein sequences. This high-quality reference genome provides a valuable foundation for future studies and facilitate comparative genomic analyses across the Tor genus and support biodiversity conservation efforts in the Lancang-Mekong River basin.