Scientific Data最新文献

Pedestrian trajectories are used to learn about human behavior in public space and the impact of spatial features on pedestrian flows. Currently, these trajectories are collected manually, with self-tracking devices, or with video cameras. Even when trajectories are obtained using computational techniques, such as using computer vision to trace them in space, these datasets are not made available for reproducibility or comparative studies between different locations. To close this gap, this paper makes available the data of pedestrian trajectories collected in 39 European squares. Firstly, we summarize the data collection process which was based on collecting footage from publicly available webcams. Secondly, we describe the process of trajectory extraction entailing object detection, tracking, and georeferencing. Lastly, we describe the data cleaning and validation steps that lead to the final dataset. The dataset ultimately includes 348,300 pedestrian trajectories extracted from 193 hours of video footage, collected at different times of the day, during working days and weekends, and during the Spring and Summer season.

Reliable electric vehicle (EV) charging depends on both sufficient infrastructure and stable power quality. In real-world distribution networks, single power quality (PQ) disturbances, such as frequency deviation, harmonics, temporary undervoltage/overvoltage, transient events, voltage deviation, interruptions, sags, and swells can significantly influence charging efficiency, equipment safety, and battery longevity. However, existing public resources rarely provide standardized, high-resolution datasets linking specific PQ disturbances to EV charging performance under controlled and replicable conditions. We present a dataset that systematically evaluates the impact of ten representative single PQ disturbances on EV charging. Test cases were designed following IEEE standards, and experiments were conducted on a proprietary full-vehicle charging test platform to capture authentic charging responses. The dataset includes grid-side voltage and current waveforms, charger telemetry, and battery charging profiles at high temporal resolution, covering the most representative AC charging scenarios. Technical validation demonstrates the reliability of data collection, consistency across repeated tests, and alignment with PQ definitions. The dataset provides foundation for: (i) benchmarking diagnostic and classification algorithms for PQ events, (ii) quantifying the impact of specific disturbances on charging current and efficiency, and (iii) supporting the design of robust EV chargers and grid-integration strategies. While the present release focuses on single disturbances, it establishes a reference framework for future studies on more complex or composite PQ scenarios.

The mud snail Bullacta exarata, a marine economic shellfish in China. It is a simultaneous androgynous molluscan species, and characteristic of euryhaline distribution and superior growth performance. To further reveal the genetic mechanisms related to environmental adaptation and reproductive traits of B. exarata, we deciphered the genomic resources of B. exarata. This study provides a chromosome - level genome assembly for B. exarata, created by PacBio and Hi - C sequencing. The genome map comprises 18 pseudochromosomes, with a total size of 867.27 Mb, a contig N50 of 4.41 Mb, and a scaffold N50 of 46.10 Mb. A total of 22,494 protein-coding genes were identified, with 21,383 genes annotated across four public databases. Overall, this study provides a foundational resource for future molecular and genetic studies on B. exarata.

We present a high-throughput behavioral dataset acquired with Ambitus, an automated reward-based corridor system that records locomotor and exploratory activities and cognitive functions after minimal handling. The collection contains 91 raw and derived variables, each measured across four consecutive trials, for 1,342 Long-Evans rats, including a triple-hit schizophrenia-like substrain (Lisket) bred through 16 generations. All data files, detailed metadata and analysis scripts are openly available on Zenodo. This resource enables longitudinal and multivariate studies of behavioral phenotypes, trans-generational effects, and strain differences, and it provides a benchmark for machine-learning-based marker discovery in rodent models.

Plant diseases pose significant threats to agriculture, making proper diagnosis and effective treatment crucial for protecting crop yields. In automatic diagnosis processing, image segmentation helps to identify and localize diseases. Developing robust image segmentation models for detecting plant diseases requires high-quality annotations. Unfortunately, existing datasets rarely include segmentation labels and are typically confined to controlled laboratory settings, which fail to capture the complexity of images taken in the wild. Motivated by these, we established a large-scale segmentation dataset for plant diseases, dubbed PlantSeg. In particular, PlantSeg is distinct from existing datasets in three key aspects: (1) Annotation types: PlantSeg includes detailed and high-quality disease area masks. (2) Image sources: PlantSeg primarily comprises in-the-wild plant disease images rather than laboratory images provided in existing datasets. (3) Scale: PlantSeg contains the largest number of in-the-wild plant disease images, including 7,774 diseased images with corresponding segmentation masks. This dataset provides an ideal yet unified benchmarking platform for developing advanced plant disease segmentation algorithms.

The East China Sea (ECS) is a climate-sensitive region experiencing rapid oceanic and ecological changes, with warming rates approximately twice those of the global average. Sustained long-term observations are essential to detect and understand these changes. The Ieodo Ocean Research Station (I-ORS), established in June 2003 on the northern ECS continental shelf, serves as the first continental shelf platform in the global ocean observation network OceanSITES. Over two decades (2004-2023), I-ORS has continuously monitored oceanographic and meteorological variables in real time. Here, we present quality-controlled hourly datasets, including water temperatures at 5, 21, and 38 m, air temperature and pressure, winds, relative humidity, and precipitation, derived through systematic processing. Comprehensive validation demonstrates the dataset's quality, its capability to resolve variability from diurnal to decadal timescales, and its regional representativeness across the northern ECS. This openly available dataset supports studies of air-sea interactions and climate change impacts, with applications in forecasting, early warning systems, and disaster management for the region.

Achieving intelligent and automated detection of defects in wind turbine blades has become a critical task for contemporary wind farm inspection operations. However, existing datasets for blade defect detection exhibit notable shortcomings, including insufficient defect attributes and limited scale, which hinder the advancement of related detection algorithms. This paper presents a standardized multiclass dataset of visible images of wind turbine blade defects for visual inspection, comprising six categories and 1,065 real blade images captured by unmanned aerial vehicles (UAVs). To provide a comprehensive characterization of this dataset, we conducted a feature space analysis using t-SNE to identify unique attributes of the defective targets. The dataset addresses the lack of diverse defect types and high-resolution samples in existing resources, providing a benchmark for the development of visual inspection algorithms.

Ophiorrhiza japonica, a medicinal plant of Rubiaceae, has been selected as a model plant for the study of MIA biosynthesis and regulation, as well as a sustainable source of camptothecin. Here, we performed an assembly and annotation of O. japonica genome. To achieve this, we employed a range of advanced techniques, including flow cytometry, PacBio HiFi sequencing, ONT RNA-sequencing and Hi-C technology. This approach enabled us to construct a high quality, chromosome-level genome of O. japonica. The assembled O. japonica genome spanned 549.81 Mb with a contig N50 size of 43 Mb and a scaffold N50 size of 46.45 Mb. The 24 contigs, representing 99.42% of the total assembled genome, were anchored to 11 chromosomes using Hi-C scaffolding. A total of 313.49 Mb of repeat sequences were identified and 28,182 protein-coding genes were predicted. The findings of this study provide invaluable genomic resources that will facilitate a deeper understanding of species evolution and enable the investigation of a range of crucial traits.

Characterizing ecosystem services demand (ESD) is key to understanding the diverse preferences for various benefits from nature. However, direct evidence of the variations in ESDs among different groups of people and places remains limited. Here, a national-scale dataset of ESDs derived from a non-probabilistic survey of 20,075 urban residents across 31 provinces in China is presented. The dataset captures preferences for nine typical urban ecosystem services using a point-allotment experiment, where participants allocated a total of 100 importance points to prioritize ESDs. Key findings reveal significant variations in ESDs, with air purification receiving the highest average importance point (22.17), followed by recreation (15.60) and local climate regulation (13.62). This pattern of variation in ESDs is evident in 28 of 31 provinces. The dataset also includes detailed socioeconomic and environmental metadata, enabling further analyses of regional disparities and their drivers among ESDs. This resource offers exploratory insights into tailoring urban design and ecosystem management strategies to diverse societal needs, thereby advancing sustainable land use planning and ESD research.