Scientific Data最新文献

Daily meteorological observation data of the early period (pre-1950) were critically important for investigating the long-term trends and multi-decadal scale variability of extreme climate events. The high-resolution surface air temperature (SAT) data for time period before 1950 are lacking in China. We extended the SAT observations of China back to 1840 through developing a pre-1950 daily SAT dataset. The early-period daily SAT were manually corrected for the input and clerical errors, and then according to the length or coverage of time, the main series for each of the cities was determined. The observation time system of unknown sites was determined by the minimum difference method. After these operations, the data of all sites were unified into the same format. By using the ridge regressions established based on data from modern reference stations, the missing maximum temperature (Tmax) and minimum temperature (Tmin) were interpolated. The early-period data were combined with modern data to form the long-term daily SAT dataset of 1840-2020 in China. RHtest software was used to detect and adjust the inhomogeneities in the station data series. Finally, the century-long homogenized daily SAT dataset including 45 key city stations in China was obtained. Among the stations, there are 20 stations with observation record more than one hundred years. The length of temperature observation series of 17 stations is between 80 and 100 years. The series length of the remaining 7 stations is between 68 and 80 years. Finally, the angular distance weighting (ADW) method was used to interpolate the data into grid products, and the grid size is 2.5 ° × 2.5 °. The dataset was named CUG-CMA CHDT, which is applicable in monitoring, studies and assessments of regional extreme temperature change and variability in China.

Wasabi (Eutrema japonicum) is a plant belonging to the Brassicaceae family that produces its distinctive pungent taste through allyl isothiocyanate. This study achieved a high-quality chromosome-level genome assembly of the E. japonicum 'Magic' bred in Korea for its rapid growth cycle. The assembly was accomplished using a combination of Illumina, PacBio HIFI, Nanopore MinION, and Pore-C scaffolding technologies. The final assembled genome size is 794.6 Mb, anchored to 14 chromosomes. The genome comprises 67.56% repetitive elements and has a BUSCO score of 99.3%, indicating a high level of completeness. Compared to previously published assemblies with a different cultivar, the total length increased by approximately 48.08 Mb, while the number of Ns decreased from 89,000 to 49,000, and the assembly gaps (500 N padding) reduced from 178 to 98, resulting in a higher quality assembly. This genome will be a valuable resource for genetic and biological research on E. japonicum, aiding in its breeding and genetic improvement.

Wildfires result in forest loss or degradation and release substantial emissions into the atmosphere. Forest regrowth following these fires allows for ecosystem repair and carbon replenishment. However, there is a lack of datasets explicitly characterizing the forest regrowth after fires. Here we employed multiple remotely sensed datasets to generate the first global maps of forest structure regrowth including forest height, aboveground biomass (AGB), leaf area index (LAI), and fraction of photosynthetically active radiation (FPAR) following wildfires at a 30 m spatial resolution. The regrowth index for each structural parameter includes regrowth ratio and rate at 5-year intervals, primarily from 2000 to 2020. The dataset developed in this study provides detailed insights into the characteristics of global forest regrowth following forest fires in both spatial and temporal dimensions, contributing to the assessment of forest ecology equilibrium and the quantification of forest carbon dynamics.

Dataset acquisitions devised specifically for robotic planetary exploration are key for the advancement, evaluation, and validation of novel perception, localization, and navigation methods in representative environments. Originating in the Bardenas semi-desert in July 2023, the data presented in this Data Descriptor is primarily aimed at Martian exploration and contains relevant rover sensor data from approximately 1.7km of traverses, a high-resolution 3D map of the test area, laser-induced breakdown spectroscopy recordings of rock samples along the rover path, as well as local weather data. In addition to optical cameras and inertial sensors, the rover features a thermal camera and six force-torque sensors. This setup enables, for example, the study of future localization, mapping, and navigation techniques in unstructured terrains for improved Guidance, Navigation, and Control (GNC). The main features of this dataset are the combination of scientific and engineering instrument data, as well as the inclusion of the thermal camera and force-torque sensors in particular.

Winter wheat constitutes approximately 20% of China's total cereal production. However, calculations of total production based on multiplying the planted area by the yield have tended to produce overestimates. In this study, we generated sample points from existing winter wheat maps and obtained samples for different years using a temporal migration method. Random forest classifiers were then constructed using optimized features extracted from spectral and phenological characteristics and elevation information. Maps of the harvested and planted areas of winter wheat in Chinese eight provinces from 2018 to 2022 were then produced. The resulting maps of the harvested areas achieved an overall accuracy of 95.06% verified by the sample points, and the correlation coefficient between the CROPGRIDS dataset is about 0.77. The harvested area was found to be about 13% smaller than the planted area, which can primarily be attributed to meteorological hazards. This study represents the first attempt to map the winter wheat harvested area at 10-m resolution in China, and it should improve the accuracy of yield estimation.

Access to large amounts of data is essential for successful machine learning research. However, there is insufficient data for many applications, as data collection is often challenging and time-consuming. The same applies to automated pain recognition, where algorithms aim to learn associations between a level of pain and behavioural or physiological responses. Although machine learning models have shown promise in improving the current gold standard of pain monitoring (self-reports) only a handful of datasets are freely accessible to researchers. This paper presents the PainMonit Dataset for automated pain detection using physiological data. The dataset consists of two parts, as pain can be perceived differently depending on its underlying cause. (1) Pain was triggered by heat stimuli in an experimental study during which nine physiological sensor modalities (BVP, 2×EDA, skin temperature, ECG, EMG, IBI, HR, respiration) were recorded from 55 healthy subjects. (2) Eight modalities (2×BVP, 2×EDA, EMG, skin temperature, respiration, grip) were recorded from 49 participants to assess their pain during a physiotherapy session.

Site-specific crop management in heterogeneous fields has emerged as a promising avenue towards increasing agricultural productivity whilst safeguarding the environment. However, successful implementation is hampered by insufficient availability of accurate spatial information on crop growth, vigor, and health status at large scales. Challenges persist particularly in interpreting remote sensing signals within commercial crop production due to the variability in canopy appearance resulting from diverse factors. Recently, high-resolution imagery captured from unmanned aerial vehicles has shown significant potential for calibrating and validating methods for remote sensing signal interpretation. We present a comprehensive multi-scale image dataset encompassing 35,000 high-resolution aerial RGB images, ground-based imagery, and Sentinel-2 satellite data from nine on-farm wheat fields in Switzerland. We provide geo-referenced orthomosaics, digital elevation models, and shapefiles, enabling detailed analysis of field characteristics across the growing season. In combination with rich meta data such as detailed records of crop husbandry, crop phenology, and yield maps, this data set enables key challenges in remote sensing-based trait estimation and precision agriculture to be addressed.

G-protein coupled receptors (GPCRs) are encoded by nonabundant mRNAs, and it is difficult to detect them reliably with the highly parallel methods that are in general use. Because of this, we developed and validated a sensitive, specific, semi-quantitative method for detecting these transcripts. We have used the method to profile GPCR transcripts in white blood cells (WBCs)-B, CD4, CD8, NK, and dendritic cells; monocytes, and macrophage-like monocytes treated with granulocyte-macrophage colony-stimulating factor-as well as platelets. On average, the white cells studied expressed 160 receptor mRNAs (range, 123-206). Platelets made 69. Some, but far from all, of the receptors we found have been detected earlier. We believe our data should stimulate studies of receptor function and contribute to drug development.

A simple surface modification, called NanoSuit, by electron beam or by plasma irradiation can form a nanoscale layer, allowing to keep small animals alive and hydrous under the high vacuum required for field-emission scanning electron microscopy (FE-SEM). We previously applied NanoSuit to aquatic insects, Dixa longistyla larvae (Diptera: Dixidae), which always lie on their ventral surface just under the water surface. We found that the crown-like structures on the ventral side of the hind segments enable the larvae to reside in such ecological niche. Moreover, fine structures in the crown protected with NanoSuit appeared intact, unlike those subjected to conventional sample fixation. However, a fundamental understanding of these structures (living and/or not treated with conventional fixation) interacting directly with water should be established using FE-SEM. This data descriptor introduces a rich dataset of images acquired using NanoSuit for various aquatic insects. The image data can be accessed and viewed through Figshare.