Scientific Data最新文献

Geothermal features in Antarctica provide favorable conditions for diverse microorganisms, yet their genomic diversity remains poorly understood. Here, we present an integrated dataset comprising PacBio HiFi and Hi-C metagenomic sequencing, along with single-cell amplified genomes (SAGs) from two high-altitude geothermal sites, Mount Melbourne and Mount Rittmann, in Antarctica. The long-read HiFi sequencing, coupled with Hi-C, enhances the understanding of microbiome diversity and functionality in this unique ecosystem by providing more complete and accurate genomic information. SAGs complement this by recovering rare microbial taxa and offering a strain-resolved perspective. This dataset aims to deepen our understanding of microbial evolution and ecology in Antarctic geothermal environments, and facilitate cross-comparison with other geothermal environments globally.

Multiaxial fatigue failure of metals, a common issue in industrial production, often leads to significant losses. Recently, many researchers have applied deep learning methods to predict the multiaxial fatigue life of metals, achieving promising results. Due to the high costs of fatigue testing, training data for deep learning is scarce and labor-intensive to collect. This study meets this need by creating a large-scale, high-quality dataset for multiaxial fatigue life prediction, consisting of 1167 samples from 40 materials collected from literature. The dataset includes key mechanical properties (elastic modulus, yield strength, tensile strength, Poisson's ratio) and 48 loading paths, along with additional relevant information (composition ratios, processing conditions). Common deep learning models validated the dataset's effectiveness. This dataset aims to support researchers applying deep learning to fatigue life prediction, addressing the long-standing issue of data scarcity, thereby advancing the intersection of artificial intelligence and metal fatigue research.

The Nilaparvata muiri (Hemiptera: Delphacidae) is a sibling species of a destructive rice insect pest, the brown planthopper (BPH), Nilaparvata lugens. Here, we generated a high-quality chromosome-level genome assembly of N. muiri using a combination of the PacBio HiFi sequencing, Illumina short-read sequencing and Hi-C scaffolding technologies. The genome assembly (524.9 Mb) is anchored to 15 pseudochromosomes, with a scaffold N50 of 43.3 Mb and 99.1% BUSCO completeness. It contains 188.1 Mb repeat sequences and 13204 protein-coding genes. As a closely related species within the same genus as the significant pest, N. lugens, the chromosome-level genome assembly of N. muiri will provide important support for the better analysis of pathogenicity mechanisms of N. lugens based on comparative genomics.

"Candidatus Liberibacter asiaticus" (CLas) is a phloem-limited alpha-proteobacteria causing Citrus Huanglongbing, the destructive disease currently threatening global citrus industry. Genomic analyses of CLas provide insights into its evolution and biology. Here, we sequenced and assembled whole genomes of 135 CLas strains originally from 20 citrus cultivars collected at ten citrus-growing provinces in China. The resulting dataset comprised 135 CLas genomes ranging from 1,221,309 bp to 1,308,521 bp, with an average coverage of 675X. Prophage typing showed that 44 strains contained Type 1 prophage, 89 strains contained Type 2 prophage, 44 strains contained Type 3 prophage, and 34 of them contained more than one type of prophage/phage. The SNP calling identified a total of 5,090 SNPs. Genome-based phylogenetic analysis revealed two major clades among CLas strains, with Clade I dominated by CLas strains containing Type 1 prophage (79/95) and Clade II dominated by CLas strains containing Type 1 or Type 3 prophage (80/95). This CLas genome dataset provides valuable resources for studying genetic diversity and evolutionary pattern of CLas strains.

Rates of nitrogen transformations support quantitative descriptions and predictive understanding of the complex nitrogen cycle, but measuring these rates is expensive and not readily available to researchers. Here, we compiled a dataset of gross nitrogen transformation rates (GNTR) of mineralization, nitrification, ammonium immobilization, nitrate immobilization, and dissimilatory nitrate reduction to ammonium in terrestrial ecosystems. Data were extracted from 331 studies published from 1984-2022, covering 581 sites. Globally, 1552 observations were appended with standardized soil, vegetation, and climate data (49 variables in total) potentially contributing to the observed variations of GNTR. We used machine learning-based data imputation to fill in partially missing GNTR, which improved statistical relationships between theoretically correlated processes. The dataset is currently the most comprehensive overview of terrestrial ecosystem GNTR and serves as a global synthesis of the extent and variability of GNTR across a wide range of environmental conditions. Future research can utilize the dataset to identify measurement gaps with respect to climate, soil, and ecosystem types, delineate GNTR for certain ecoregions, and help validate process-based models.

This dataset encompasses a comprehensive investigation of combined calendar and cycle aging in commercially available lithium-ion battery cells (Samsung INR21700-50E). A total of 279 cells were subjected to 71 distinct aging conditions across two stages. Stage 1 is based on a non-model-based design of experiments (DoE), including full-factorial and Latin hypercube experimental designs, to determine the degradation behavior. Stage 2 employed model-based parameter individual optimal experimental design (pi-OED) to refine specific dependencies, along with a second non-model-based approach for fair comparison of DoE methodologies. While the primary aim was to validate the benefits of optimal experimental design in lithium-ion battery aging studies, this dataset offers extensive utility for various applications. They include training of machine learning models for battery life prediction, calibrating of physics-based or (semi-)empirical models for battery performance and degradation, and numerous other investigations in battery research. Additionally, the dataset has the potential to uncover hidden dependencies and correlations in battery aging mechanisms that were not evident in previous studies, which often relied on pre-existing assumptions and limited experimental designs.

Coptis teeta Wall. (Ranunculaceae), an endangered plant species of significant medicinal value, predominantly undergoes clonal propagation, potentially compromising the species' evolutionary potential and ultimately increase its risk of extinction. In this study, we successfully assembled two sets of haploid genomes (Hap1 and Hap2) for C. teeta, comprising nine homologous chromosome pairs, by employing Illumina and PacBio sequencing technologies. The genome annotation identified a total of 43,979 and 46,311 protein-coding genes in Hap1 and in Hap2, and most of them were functionally annotated. The high-quality reference genome will serve as an indispensable genomic resource for conservation and comprehensive exploitation of this endangered species. Between the two haploid genomes, numerous structural alterations were detected within the nine homologous chromosome pairs, potentially resulting in aberrant synapsis and irregular chromosomal segregation and thus contributing to the sustained preservation of clonal propagation in C. teeta. The findings offer new perspective for elucidating the genetic mechanism underlying the compromised sexual reproductive capacity of C. teeta, thereby facilitating its enhancement though molecular breeding and genetic improvement.

Heteroptera (the true bugs), one of the most diverse lineages of insects, diversified in feeding strategies and living habitats, and thus become an ideal lineage for studies on adaptive evolution. Chinese water scorpion Ranatra chinensis (Heteroptera: Nepidae) is a predaceous bug living in lentic water systems, representing an ideal model for studying habitat transition and adaptation to water environment. However, genetic studies on this water bug remain limited. Here, we obtained a chromosome-level genome of R. chinensis using PacBio HiFi long reads and Hi-C sequencing reads. The total assembly size of genome is 867.89 Mb, with a scaffold N50 length of 26.48 Mb and the GC content of 39.50%. All contigs were assembled into 23 pseudo-chromosomes (N = 19 A + X1X2X3X4), and we predicted 18,424 protein-coding genes in this genome. This study will provide valuable genomic resources for future studies on the biology, water adaptation, and genome evolution of water bugs.

The upas tree (Antiaris toxicaria Lesch.) is a medically important plant that contains various specialized metabolites with significant bioactivity. The lack of a reference genome hinders the in-depth study as well as rational exploitation and conservation of this plant. Here, we present the first holotype-resolved chromosome-scale genome of the upas tree. The assembled genome consisted of 26 chromosomes that contain 1.34 Gb of sequencing data with a contig N50 length of 60 Mb. Genome annotation identified 43,500 protein-coding genes in the upas tree genome, of which 98.75% were functionally annotated. This high-quality reference genome will lay the foundation for further studies on the evolution and functional genomics of the upas tree.

The Pattern Electroretinogram (PERG) is an essential tool in ophthalmic electrophysiology, providing an objective assessment of the central retinal function. It quantifies the activity of cells in the macula and the ganglion cells of the retina, assisting in the differentiation of macular and optic nerve conditions. In this study, we present the IOBA-PERG dataset, an extensive collection of 1354 transient PERG responses accessible on the PhysioNet repository. These recordings were conducted at the Institute of Applied Ophthalmobiology (IOBA) at University of Valladolid, over an extended period spanning nearly two decades, from 2003 to 2022. The dataset includes 336 records, ensuring at least one PERG signal per eye. The dataset thoughtfully includes demographic and clinical data, comprising information such as age, gender, visual acuity measurements, and expert diagnoses. This comprehensive dataset fills a gap in ocular electrophysiological repositories, enhancing ophthalmology research. Researchers can explore a broad range of eye-related conditions and diseases, leading to enhanced diagnostic accuracy, innovative treatment strategies, methodological advancements, and a deeper understanding of ocular electrophysiology.