Scientific Data最新文献

Trait-based methodologies are gaining traction in the field of ecology, providing deeper insights into ecosystem structure and functions. To this end, trait databases tailored to specific taxonomic groups have become foundational. In Taiwan, the collaborative efforts of avian researchers and dedicated citizen scientists have led to the compilation of a vast array of data. This includes web-sourced images from social media, spatial distribution records from eBird, and morphological metrics from banded birds and specimens. Enriched by peer-reviewed literature, we have meticulously assembled a comprehensive trait dataset encompassing 454 bird species across 73 families. This dataset covers a wide range of traits, including foraging ecology, morphological characteristics, territorial behaviors, breeding attributes, and the roles of bird species in ecosystem regulation. As an invaluable resource, this dataset lays the foundation for in-depth exploration of functional diversity, trait-based community ecology, ecosystem function, and critical insights needed to shape conservation strategies.

Satellite megaconstellation (SMC) missions are spurring rapid growth in rocket launches and anthropogenic re-entries. These events inject pollutants and carbon dioxide (CO₂) in all atmospheric layers, affecting climate and stratospheric ozone. Quantification of these and other environmental impacts requires reliable inventories of emissions. We present a global, hourly, 3D, multi-year inventory of air pollutant emissions and CO₂ from rocket launches and object re-entries spanning the inception and growth of SMCs (2020-2022). We use multiple reliable sources to compile information needed to build the inventory and conduct rigorous and innovative cross-checks and validations against launch livestreams and past studies. Our inventory accounts for rocket plume afterburning effects, applies object-specific ablation profiles to re-entering objects, and quantifies unablated mass of objects returning to Earth. We also identify all launches and objects associated with SMC missions, accounting for 37-41% of emissions of black carbon particles, carbon monoxide, and CO₂ by 2022. The data are provided in formats for ease-of-use in atmospheric chemistry and climate models to inform regulation and space sustainability policies.

The extreme meteorological events caused by climate change have increasingly caused serious clustered landslides. A systematic and timely inventory of triggered landslides is a prerequisite for quantifying and evaluating the impact of extreme meteorological events. In addition, a landslide inventory can provide basic data for any subsequent analysis of the event or be used innovatively in landslide risk analysis. The Rainfall-induced Landslides in Beijing (RLBJ) inventory presented here contains data on 15,383 rainfall-induced shallow landslides triggered by a single extreme precipitation event in July 2023 in an area of ~3,250 km² in western mountainous areas of Beijing, China. High-resolution satellite images before and after this rainstorm event were used to visually analyze the landslides. All landslides were reported as vectorized points and polygon features and classified according to their motion forms. This inventory is now freely available for the benefit of international geohazard researchers.

The Cape hare (Lepus capensis) is among the most widely distributed hare species globally, inhabiting extensive regions across Africa, the Middle East, and Central Asia. However, evolutionary and genetic research on L. capensis was seriously impeded by the absence of a reference genome. Here, we assembled and constructed a chromosome-level genome of L. capensis (with scaffolds anchored to 25 chromosomes and a total assembled length of 2.9 Gb, achieving a contig N50 length of 124.44 Mb) using PacBio HiFi sequencing and Hi-C assembly technology. Evaluation using BUSCO indicated the genome assembly to be 98.2% complete. The de novo prediction revealed that repetitive sequences constitute 46.13% of the entire genome, and long interspersed nuclear elements (LINEs) constituted the largest portion. We annotated a total of 13, 868 protein-coding genes using transcriptomes from two tissues (muscle and skin). This high-quality reference genome serves as a valuable genomic resource for advancing genetic studies in this species.

The Hewlett Packard Enterprise-Cray EX Frontier is the world's first and fastest exascale supercomputer, hosted at the Oak Ridge Leadership Computing Facility in Tennessee, United States. Frontier is a significant electricity consumer, drawing 8-30 MW; this massive energy demand produces significant waste heat, requiring extensive cooling measures. Although harnessing this waste heat for campus heating is a sustainability goal at Oak Ridge National Laboratory (ORNL), the 30 °C-38 °C waste heat temperature poses compatibility issues with standard HVAC systems. Heat pump systems, prevalent in residential settings and some industries, can efficiently upgrade low-quality heat to usable energy for buildings. Thus, heat pump technology powered by renewable electricity offers an efficient, cost-effective solution for substantial waste heat recovery. However, a major challenge is the absence of benchmark data on high-performance computing (HPC) heat generation and waste heat profiles. This paper reports power demand and waste heat measurements from an ORNL HPC data centre, aiming to guide future research on optimizing waste heat recovery in large-scale data centres, especially those of HPC calibre.

The dataset presents a 43 year-long reanalysis of pollen seasons for three major allergenic genera of trees in Europe: alder (Alnus), birch (Betula), and olive (Olea). Driven by the meteorological reanalysis ERA5, the atmospheric composition model SILAM predicted the flowering period and calculated the Europe-wide dispersion pattern of pollen for the years 1980-2022. The model applied an extended 4-dimensional variational data assimilation of in-situ observations of aerobiological networks in 34 European countries to reproduce the inter-annual variability and trends of pollen production and distribution. The control variable of the assimilation procedure was the total pollen release during each flowering season, implemented as an annual correction factor to the mean pollen production. The dataset was designed as an input to studies on climate-induced and anthropogenically driven changes in the European vegetation, biodiversity monitoring, bioaerosol modelling and assessment, as well as, in combination with intra-seasonal observations, for health-related applications.

An inertial navigation system (INS) utilizes three orthogonal accelerometers and gyroscopes to determine platform position, velocity, and orientation. There are countless applications for INS, including robotics, autonomous platforms, and the internet of things. Recent research explores the integration of data-driven methods with INS, highlighting significant innovations, improving accuracy and efficiency. Despite the growing interest in this field and the availability of INS datasets, no datasets are available for gyro-free INS (GFINS) and multiple inertial measurement unit (MIMU) architectures. To fill this gap and to stimulate further research in this field, we designed and recorded GFINS and MIMU datasets using 54 inertial sensors grouped in nine inertial measurement units. These sensors can be used to define and evaluate different types of MIMU and GFINS architectures. The inertial sensors were arranged in three different sensor configurations and mounted on a mobile robot, a passenger car and a turntable. In total, the dataset contains 45 hours of inertial data and corresponding ground truth trajectories. The data is freely accessible through our figshare repository.

The deciduous American sweetgum (Liquidambar styraciflua, Altingiaceae) is a popular ornamental and economically valuable tree renowned for its sweet-smelling bark resin, abundant volatile substances, and spectacular fall leaf color. However, the absence of a reference genome hinders thorough investigations into the mechanisms underlying phenotypic variation, secondary metabolite synthesis and adaptation, both in this species and other Liquidambar members. In this study, we sequenced and constructed a chromosome-level assembly of the L. styraciflua genome, covering 662.48 Mb with a scaffold N50 of 39.54 Mb, by integrating PacBio, Illumina and chromosome conformation capture data. We identified 58.83% of the genome sequences as repetitive elements and 25,713 protein-coding genes, 97.28% of which were functionally annotated. The genome sequencing reads, assembly and annotation data have been deposited in publicly available repositories. This high-quality genome assembly provides valuable resources for further evolutionary and functional genomic studies in American sweetgum and other Liquidambar species.

Electroencephalography (EEG)-based open-access datasets are available for emotion recognition studies, where external auditory/visual stimuli are used to artificially evoke pre-defined emotions. In this study, we provide a novel EEG dataset containing the emotional information induced during a realistic human-computer interaction (HCI) using a voice user interface system that mimics natural human-to-human communication. To validate our dataset via neurophysiological investigation and binary emotion classification, we applied a series of signal processing and machine learning methods to the EEG data. The maximum classification accuracy ranged from 43.3% to 90.8% over 38 subjects and classification features could be interpreted neurophysiologically. Our EEG data could be used to develop a reliable HCI system because they were acquired in a natural HCI environment. In addition, auxiliary physiological data measured simultaneously with the EEG data also showed plausible results, i.e., electrocardiogram, photoplethysmogram, galvanic skin response, and facial images, which could be utilized for automatic emotion discrimination independently from, as well as together with the EEG data via the fusion of multi-modal physiological datasets.

Remote detection and mapping of surface materials using optical sensors relies predominantly on analyzing multispectral and hyperspectral imagery employing classification algorithms. The classification process involves comparing the spectra of individual pixels within the image to spectra from reference databases, commonly referred to as spectral libraries. Here, we introduce a comprehensive compilation of spectral libraries specifically tailored for petroleum hydrocarbons (PHC), meticulously crafted under controlled laboratory conditions. This compilation includes reference spectral libraries for various PHC forms, including crude oils, mineral substrate-PHC mixtures (comprising crude oils and fuels), oil-film on water, and oil-water emulsions. Data collection was conducted within the visible, near, and shortwave IR (VNIR-SWIR - 0.35-2.5 µm) spectra and thermal IR (TIR - 3-15 µm) range. The openly accessible spectral libraries presented herein support the scientific community and industry in characterizing field samples or spectral data from onshore and offshore sites. Furthermore, these libraries are instrumental in developing and applying classification algorithms designed for processing spectral images captured by cameras coupled to multiple platforms (e.g., tripods, drones, airborne, orbital satellites).