Scientific Data最新文献

While housing price prediction is well-studied, the prediction of large-scale housing conditions remains underexplored due to data limitations. This paper addresses this gap by developing a machine-learning model to predict housing conditions across the United States. We integrated property-level data from the Warren Group with neighborhood characteristics from the U.S. Census Bureau's American Community Survey and trained three gradient-boosting algorithms: CatBoost, LightGBM, and XGBoost. Despite XGBoost's slightly higher balanced accuracy, CatBoost was selected as the best model due to its superior resistance to overfitting. The final model's predictions were aggregated to census tracts, ZIP code tabulation areas, and a 36.13 km² resolution hexagonal grid for national-scale spatial analysis. The resulting comprehensive dataset can serve as a valuable resource for researchers and practitioners to analyze the geography of housing quality with applications in urban planning, disaster management, community resilience, public health, and more.

Rapid and continuous urbanization has increased water demand in cities worldwide. Global assessments of urban water scarcity should be conducted using gridded hydrological information, but are hampered by the lack of water-resource-based city boundary information. This study introduces HydroUrbanMap (HUM), a global gridded dataset of city boundaries for 1,604 cities at 5 arcmin resolution, incorporating hydrological attributes. HUM consists of two key components: delineation of city boundaries that include the population served by urban water services (supply and drainage), and estimation of accessible surface water sources within and outside these boundaries. The estimated city populations closely match census-based populations, with a correlation coefficient of 0.997. HUM incorporates hydrological inlets and outlets of main rivers in each city by overlaying the city boundaries with the river network. Combining our HUM dataset with outputs from global hydrological models supports city-specific water resource assessments worldwide, filling gaps in cities where data on urban water services are unavailable or incomplete.

Accurate farmland market data plays a crucial role in decision-making for policymakers, investors and operators. We present a nationwide, parcel-level dataset of farmland rent in China, derived from eight survey waves conducted between 2021 and 2025 across 27 provinces. The dataset comprises 7,237 rigorously validated samples, covering 191 cities and 422 counties in major agricultural production regions. Each record contains georeferenced parcel attributes and detailed transaction information, including parcel size, rental price, contracting parties, contractual terms. Compared with existing sources, this dataset offers broader coverage, finer spatial resolution, and improved temporal continuity, thereby addressing long-standing limitations of farmland transfer data in China. No other publicly available dataset provides comparable scope, reliability, and detail. Its release will enhance transparency in farmland markets and enable robust analyses of spatial and temporal dynamics, cross-regional comparisons, and the effects of institutional arrangements on land transactions. The dataset provides a valuable empirical foundation for advancing research in agricultural economics, rural development, and land use policy.

This paper describes an acoustic dataset collected on a frozen shallow freshwater lake between February and March of 2024. This collection took place over one full week on Portage Lake in the Upper Peninsula of Michigan, USA. The first sub-dataset consists of ambient ice and environmental noises collected by an array of hydrophones, microphones and geophones placed below, above and on the ice respectively. The second sub-dataset consists of instrumented force hammer impacts at a series of locations on the the ice with the corresponding response at each acoustic sensor. All acoustic data were recorded at a sample rate f_s = 51, 200 Hz. Corresponding local weather data is also provided. This dataset offer a rich look into the physics of ice cover, response to weather phenomena, and shallow water and ice cover waveguide acoustic propagation. The datasets consist of time series data from all sensors, array dimensions, array placement and hardware descriptions.

Developing musculoskeletal hand models requires a variety of experimental biomechanics data. However, collecting robust biomechanics hand data is a time intensive process leading to a lack of widely available datasets. To address this issue the biomechanics hand modeling database (BHaM) was made as a collection of experimental data to aid the development, testing, and validation of musculoskeletal models and simulations. BHaM includes two datasets: (1) a population dataset (n = 726 adults) describing hand strength (pinch and grip), self-reported hand function (Michigan Hand Questionnaire), and anthropometric measurements (from photographs), and (2) a biomechanics dataset (n = 30 adults) describing kinematics (marker-based motion capture), kinetics (isometric and isokinetic data), and electromyography (surface and fine wire) during 19 tasks across the elbow, wrist, and hand. A subset of the biomechanics dataset (n = 15 adults) also includes magnetic resonance imaging of the shoulder through wrist. Participants for both datasets were recruited to represent a diverse population of healthy adults, ranging from 18 to 91 years.

The aquatic flora of Minnesota's freshwater lakes have been extensively surveyed for purposes of resource assessment, research, and ecosystem management. Despite widespread use of a common method for vegetation sampling ("point-intercept surveys"), these records have existed to-date in disparate locations without unification. Here we present a first-of-its-kind dataset of point-level occurrences, relative abundances, and associated environmental data for macrophytes (freshwater plants) across Minnesota. The data encompass 3,194 surveys of 1,520 lakes and ponds performed over a 19-year timespan. A total of 367,382 points were sampled, across which 231 taxa were recorded. Macrophyte occurrence data and depth, as well as point-level relative-plant-abundance measures for a subset of surveys, were collated, cleaned, and joined to geospatial data and Secchi-depth measurements of water clarity, enabling light availability, a primary control on aquatic plant growth, to be estimated. The data are well-suited for ecological analyses across multiple spatial scales and can be used to address both fundamental and applied ecological questions.

Ichthyurus bourgeoisi Gestro is a representative species of the tribe Ichthyurini within the beetle family Cantharidae. This tribe is particularly noteworthy because of its brachelytrous characteristics. However, the lack of high-quality genomic resources hinders our understanding of the evolution and ecological adaptions associated with this beetle group. In this study, we present a chromosome-level genome assembly for I. bourgeoisi constructed using a combination of PacBio HiFi and Hi-C sequencing data. The genome spans 664.72 Mb, with a scaffold N50 of 98.12 Mb, and is organized into seven pseudo-chromosomes, including a chromosome X validated through analyses of genome collinearity and sequencing depth. Repeat sequences account for 65.35% of the genome, and 13,386 protein-coding genes are identified. The high-quality genome assembly and annotation has been corroborated by multiple metrics, including genome size, reads mapping rate, and BUSCO completeness (98.6%). This comprehensive genomic resource provides a foundation for elucidating the ecological adaption of I. bourgeoisi and advancing our understanding of morphological evolution in Ichthyurini within Cantharidae.

Wildfires are becoming more frequent and severe under the influence of climate change, posing increasing risks to ecosystems, human health, and infrastructure. Accurate spatiotemporal data on wildfire propagation is essential for advancing fire behavior modeling, improving management strategies, and mitigating future impacts. However, existing datasets with both high spatial and temporal resolution are rare, costly, and time-consuming to produce. To address this gap, we present FireSpread_MedEU, a dataset comprising 320 consecutive burned area maps from 103 wildfire events across the Mediterranean and Europe between 2017 and 2023. Burned areas were derived from high-resolution Planet optical satellite imagery (~3 m spatial, mostly daily temporal resolution) using a semi-automated workflow, followed by manual refinement to ensure highest accuracy. Each dataset entry is enriched with detailed metadata and a subjective quality assessment. With its high level of spatiotemporal precision, FireSpread_MedEU provides essential data for the development and validation of machine learning models or wildfire simulation models. It opens new research opportunities in wildfire behavior analysis, risk assessment, and predictive modeling.

We present a first global high-resolution map (30 m x 30 m) of high-altitudinal wetlands in the world's major mountain regions, i.e. the Andes, Rocky Mountains, Alps and High Mountain Asia. To map these wetlands, we employed a supervised classification approach using a random forest machine learning model and a selected set of predictors including vegetation, topographic, and surface moisture features. The predictors were derived from freely available radar and optical satellite imagery (Sentinel-1 and Sentinel-2), SRTM elevation data, and the global ecoregion map RESOLVE. We identify a total area of >30,500 km² of high-mountain wetlands. With this map we aim to enhance the understanding of wetland distribution in remote and often inaccessible mountain regions and enable a more reliable understanding of their role in the ecosystem functioning and water cycles of high mountain areas.

As electric vehicle (EV) adoption increases, the resulting EV battery charging will increase demand on the electric power grid. Through EV managed charging (EVMC) programs, charging can be shifted in time to support electric grid reliability and reduce electricity costs. EVMC can offer an alternative to additional supply-side generation, but the costs of EVMC implementation must be understood to evaluate the cost-benefits of EVMC. This paper presents bottom-up, forward-looking (from 2025 through 2050) estimates of the incremental costs associated with different EVMC dispatch mechanisms available to electric utilities. The costs of enabling EVMC for a range of customer participation levels are presented in the form of supply curves, which provide per-EV costs for a targeted level of participation. The largest drivers of cost variation are assumptions about future charging flexibility paradigms described in four scenarios. These supply curves can be used to quantify the expected costs of EVMC programs and enable comparison with supply-side or other demand flexibility alternatives.