Scientific Data最新文献

The ivory shell Babylonia areolata is an economically important marine benthic gastropod known for its rapid growth and high nutritional value. B. areolata is distributed in Southeast Asia and the southeast coastal areas of China. In this study, we constructed a high-quality genome for B. areolata using PacBio, Illumina, and Hi-C sequencing technologies. The genome assembly comprised 35 chromosomal sequences with a total length of 1.65 Gb. The scaffold and contig N50 lengths were 53.17 Mb and 2.64 Mb, respectively, with repeat sequences constituting 64.46% of the genome. Furthermore, 26,130 protein-coding genes and 96.75% of the genome's BUSCOs were identified. This inaugural report of a B. areolata genome provides crucial foundational information for further investigations into the biology, genomics, and genetic improvement of economic traits of this species.

This paper describes a synthetic aperture sonar (SAS) dataset collected in-air consisting of four types of targets in four environments of different complexity. The in-air laboratory based experiments produced data with a level of fidelity and ground truth accuracy that is not easily attainable in data collected underwater. The range of complexity, high level of data fidelity, and accurate ground truth provides a rich dataset with acoustic features on multiple scales. It can be used to develop new signal-processing and image reconstruction algorithms, as well as machine learning models for object detection and classification. It may also find application in model verification and validation for acoustic simulators. The dataset consists of raw acoustic time series returns, associated environmental conditions, hardware configuration, array motion, as well as the reconstructed imagery.

VegDischarge v1, which covers over 64,000 river segments in Africa, is a natural river discharge dataset produced by coupled modeling; the agro-hydrologic VegET model and the mizuRoute routing model for the period 2001-2021. Using remote sensing data and hydrological modeling system, the 1-km runoff field simulated by VegET, was routed with mizuRoute. Performance metrics show strong model reliability, with R² of 0.5-0.9, NSE of 0.6-0.9, and KGE of 0.5-0.8 at the continental scale. The total average annual discharge for Africa is quantified at 3271.4 km³·year^-1, with contributions to oceanic basins: 1000.0 km³·year^-1 to the North Atlantic, primarily from the Senegal, Gambia, Volta, and Niger Rivers; 1327.2 km³·year^-1 to the South Atlantic, largely from the Congo River; 214.7 km³·year^-1 to the Mediterranean Sea, predominantly from the Nile River; and 729.4 km³·year^-1 to the Indian Ocean, with inputs from rivers such as the Zambezi. The dataset is valuable for stakeholders and researchers to understand water availability, its temporal and spatial variations that affect water-related infrastructure planning, sustainable resource allocation, and the development of climate resilience strategies.

Grazing is a significant anthropogenic disturbance to grasslands, impacting their function and composition, and affecting carbon budgets and greenhouse gas emissions. However, accurate evaluations of grazing impacts are limited by the absence of long-term high-resolution grazing intensity data (i.e., the number of livestock per unit area). This study utilized census livestock data and a satellite-based vegetation index to develop the first Long-term High-resolution Grazing Intensity (LHGI) dataset of grassland in seven pastoral provinces in western China from 1980 to 2022. The LHGI dataset effectively captured spatial variations in grazing intensity, with validation at 73 sites showing a correlation coefficient (R²) of 0.78. The county-level validation showed an averaged R² values of 0.73 ± 0.03 from 1980 to 2022. This dataset serves as a vital resource for estimating grassland carbon cycling and livestock system CH₄ emissions, as well as contributing to grassland management.

Ocean ecosystems have been subjected to anthropogenic influences for centuries, but the scale of past ecosystem changes is often unknown. For centuries, the European flat oyster (Ostrea edulis), an ecosystem engineer providing biogenic reef habitats, was a culturally and economically significant source of food and trade. These reef habitats are now functionally extinct, and almost no memory of where or at what scales this ecosystem once existed, or its past form, remains. The described datasets present qualitative and quantitative extracts from written records published between 1524 and 2022. These show: (1) locations of past flat oyster fisheries and/or oyster reef habitat described across its biogeographical range, with associated levels of confidence; (2) reported extent of past oyster reef habitats, and; (3) species associated with these habitats. These datasets will be of use to inform accelerating flat oyster restoration activities, to establish reference models for anchoring adaptive management of restoration action, and in contributing to global efforts to recover records on the hidden history of anthropogenic-driven ocean ecosystem degradation.

The olive flounder, Paralichthys olivaceus, also known as the Korean halibut, is an economically important flatfish in East Asian countries. Here, we provided four fully phased genome assemblies of two different olive flounder individuals using high-fidelity long-read sequencing and their parental short-read sequencing data. We obtained 42-44 Gb of ~15-kb and ~Q30 high-fidelity long reads, and their assembly quality values were ~53. We annotated ~30 K genes, ~170-Mb repetitive sequences, and ~3 M 5-methylcytosine positions for each genome assembly, and established a graph-based draft pan-genome of the olive flounder. We identified 5 M single-nucleotide variants and 100 K structural variants with their genotype information, where ~13% of the variants were possibly fixed in the two Korean individuals. Based on our chromosome-level genome assembly, we also explored chromosome evolution in the Pleuronectiformes family, as reported earlier. Our high-quality genomic resources will contribute to future genomic selection for accelerating the breeding process of the olive flounder.

Human activity recognition (HAR) using smartphone inertial sensors, like accelerometers and gyroscopes, enhances smartphones' adaptability and user experience. Data distribution from these sensors is affected by several factors including sensor hardware, software, device placement, user demographics, terrain, and more. Most datasets focus on providing variability in user and (sometimes) device placement, limiting domain adaptation and generalization studies. Consequently, models trained on one dataset often perform poorly on others. Despite many publicly available HAR datasets, cross-dataset generalization remains challenging due to data format incompatibilities, such as differences in measurement units, sampling rates, and label encoding. Hence, we introduce the DAGHAR benchmark, a curated collection of datasets for domain adaptation and generalization studies in smartphone-based HAR. We standardized six datasets in terms of accelerometer units, sampling rate, gravity component, activity labels, user partitioning, and time window size, removing trivial biases while preserving intrinsic differences. This enables controlled evaluation of model generalization capabilities. Additionally, we provide baseline performance metrics from state-of-the-art machine learning models, crucial for comprehensive evaluations of generalization in HAR tasks.

Nitrogen (N) is essential for agricultural productivity, yet its surplus poses significant environmental risks. Currently, over half of applied nitrogen is lost, resulting in resource wastage, contributing to increased greenhouse gas emissions and biodiversity loss. Excess nitrogen persists in the environment, contaminating soil and water bodies for decades. Quantifying detailed historical N-surplus estimation in India remains limited, despite national and global-scaled assessments. Our study develops a district-level dataset of annual agricultural N-surplus from 1966-2017, integrating 12 different estimates to address uncertainties arising from multiple data sources and methodological choices across major elements of the N surplus. This dataset supports flexible spatial aggregation, aiding policymakers in implementing effective nitrogen management strategies in India. In addition, we verified our estimates by comparing them with previous studies. This work underscores the importance of setting realistic nitrogen management targets that account for inherent uncertainties, paving the way for sustainable agricultural practices in India, reducing environmental impacts, and boosting productivity.