Scientific Data最新文献

Freezing rain (FR) event is a highly catastrophic event, significantly impact human habitats. However, there is still a substantial lack of gridded FR data. Here, we present a comprehensive gridded FR dataset across China from January 1, 2000, to December 31, 2019, utilizing station data from the China Meteorological Administration combined with ERA5-land and pressure level data. Employing Deep Kernel Learning (DKL), we effectively classified and predicted FR occurrences, demonstrating significant advancements in capturing complex atmospheric conditions conducive to FR. The DKL model, validated against ERA5 data for the winter of 2024 and the Ramer Scheme in 2008, 2011, and 2018, showcases superior classified power over traditional methods, achieving remarkable accuracy of 0.991, Area Under the Curve (AUC) of 0.999, recall of 0.973, and precision of 0.989. The implications of this research are profound, offering a robust database for academic and practical applications in weather forecasting, climate modelling, and disaster management, thereby enhancing our understanding and mitigation strategies for FR impacts.

Mercury exposure poses significant threats to human health, particularly in its organic form, methylmercury (MeHg). Diet is the main pathway for human MeHg exposure, especially through seafood consumption. In this context, numerous studies have established seafood MeHg concentration datasets to assess MeHg-related health risks from seafood consumption. However, existing datasets are limited to specific regions and short-term observations, making it difficult to support continuous and dynamic assessments of global MeHg-related health risks. This study takes a bottom-up approach to construct a global seafood MeHg concentration dataset during 1995-2022. Firstly, it compiles a long-term time series marine-scale dataset of seafood MeHg concentrations, based on the reported seafood mercury concentrations from existing literature and machine learning methods. Subsequently, this study used the seafood catch volumes of each nation in different marine areas as weights to estimate the national-scale seafood MeHg concentrations. This dataset can provide essential data support for environmental impact assessment of mercury and its compounds as mentioned in Articles 12 and 19 of the Minamata Convention on Mercury.

Online sex advertisements (sex ads) have been linked to many U.S. sex trafficking cases. However, since the closure of the dominant website, Backpage.com (Backpage), many competing sites have emerged that are hosted in countries where U.S. law enforcement organizations have no jurisdiction. Although the online ecosystem has changed significantly, very little research uses data from sites other than Backpage, and even less uses data from multiple sites. This paper presents an anonymized dataset derived from the text and image artifacts of more than 10 million sex ads. By making this dataset publicly available, we aim to reduce barriers to entry for researchers interested in conducting data-driven counter-trafficking research. The dataset can be used to test hypotheses related to sex ads and intersite connectivity, understand the posting processes employed by prominent sites in the current online sex ad ecosystem, and develop multidisciplinary approaches for estimating ad legitimacy. Progress in any of these areas can result in potentially lifesaving interventions for ST victims.

Millions of river barriers have been constructed worldwide for flood control, hydropower generation, and agricultural irrigation. The lack of comprehensive records on river barriers' locations and types, particularly small barriers including weirs, limits our ability to assess their societal and environmental impacts. Integrating satellite imagery with object detection algorithms holds promise for the automatic identification of river barriers on a global scale. However, achieving this objective requires high-quality image datasets for algorithm training and testing. Hence, this study presents a large-scale dataset named the River Barrier Object Detection (RBOD). It comprises 4,872 high-resolution satellite images and 11,741 meticulously annotated oriented bounding boxes (OBBs), encompassing five classes of river barriers. The effectiveness of the RBOD dataset was validated using five typical object detection algorithms, which provide performance benchmarks for future applications. To the best of our knowledge, RBOD is the first publicly available dataset for river barrier object detection, providing a valuable resource for the understanding and management of river barriers.

The data sets provide long-term information (2013-2022) of the presence-only of eight wild ungulates and red fox derived from harvest data in a grid of 5 × 5 km of Spain (21,836 cells). The collected data has been processed and reported yearly, as well as in two monitoring periods in accordance with Habitats Directive from the European Union to facilitate data reporting about the State of nature, and the sum of the whole period. Data sets are structured following the Darwin Core biological standard. The data set was published in the Spanish node of the Global Biodiversity Information Facility (GBIF), which are the most updated publicly available information for these species' presence in Spain.

In recent years, the progress of artificial intelligence has greatly advanced computer-assisted intervention, surgical learning, and postoperative surgical video analysis techniques, greatly improving the skill levels of surgeons and overall outcomes. Deep learning based endoscopic surgery phase recognition has a very high dependency on large-scale datasets and annotations. This study introduces the Renji endoscopic submucosal dissection (ESD) video dataset for early gastric cancer (EGC), comprising 20 ESD endoscopic videos and 66,656 phase recognition annotations jointly annotated by three endoscopists. To the best of our knowledge, this is the first publicly available ESD dataset for the treatment of EGC, and we believe this work will contribute to the standardization of ESD dataset construction. The dataset and annotations are publicly available in Figshare.

Leptopilina wasps are crucial for biological pest control, particularly against the globally emerging pest Drosophila suzukii. Despite their ecological significance, the genomic basis of host selection and parasitism in this genus remains underexplored. In this study, we assembled a high-quality, chromosome-level genome of Leptopilina myrica, a species collected in Taizhou, Zhejiang Province, China. We employed a combination of PacBio long-read sequencing, Illumina short-read sequencing, and Hi-C technology to produce a genome assembly of approximately 462.30 Mb, with a scaffold N50 of 47.32 Mb and a contig N50 of 4.07 Mb. By comparing the protein-coding genes of L. myrica with those of other Hymenoptera species, we gained insights into the evolutionary history of parasitoid wasps. This high-quality genome will provide a foundation for future research on the genetic and functional traits of parasitoid wasps, shedding light on the evolutionary dynamics of host-parasite interactions. The genome of L. myrica provides a valuable resource for future studies on host-parasite interactions and the genetic basis of parasitoid wasp biology.

Although rare diseases (RDs) affect over 260 million individuals worldwide, low data quality and scarcity challenge effective care and research. This work aims to harmonise the Common Data Set by European Rare Disease Registry Infrastructure, Health Level 7 Fast Healthcare Interoperability Base Resources, and the Global Alliance for Genomics and Health Phenopacket Schema into a novel rare disease common data model (RD-CDM), laying the foundation for developing international RD-CDMs aligned with these data standards. We developed a modular-based GitHub repository and documentation to account for flexibility, extensions and further development. Recommendations on the model's cardinalities are given, inviting further refinement and international collaboration. An ontology-based approach was selected to find a common denominator between the semantic and syntactic data standards. Our RD-CDM version 2.0.0 comprises 78 data elements, extending the ERDRI-CDS by 62 elements with previous versions implemented in four German university hospitals capturing real world data for development and evaluation. We identified three categories for evaluation: Medical Data Granularity, Clinical Reasoning and Medical Relevance, and Interoperability and Harmonisation.

Upper-limb movement characterization is crucial for many applications, from research on motor control, to the extraction of relevant features for driving active prostheses. While this is usually performed using electrophysiological and/or kinematic measurements only, the collection of tactile data during grasping movements could enrich the overall information about interaction with external environment. We provide a dataset collected from 10 healthy volunteers performing 16 tasks, including simple movements (i.e., hand opening/closing, wrist pronation/supination and flexion/extension, tridigital grasping, thumb abduction, cylindrical and spherical grasping) and more complex ones (i.e., reaching and grasping). The novelty consists in the inclusion of several types of recordings, namely electromyographic -both with bipolar and high-density configuration, kinematic-both with motion capture system and a sensorized glove, and tactile. The data is organized following the Brain Imaging Data Structure standard format and have been validated to ensure its reliability. It can be used to investigate upper-limb movements in physiological conditions, and to test sensor fusion approaches and control algorithms for prosthetics and robotic applications.

Alternative promoter (AP) events, as a major pre-transcriptional mechanism, can initiate different transcription start sites to generate distinct mRNA isoforms and regulate their expression. At present, hundreds of thousands of APs have been identified across human tissues, and a considerable number of APs have been demonstrated to be associated with complex traits and diseases. Recent researches have also proven important effects of APs on animals. However, the landscape of APs in animals has not been fully recognized. In this study, 102,349 AP profiles from 23,077 samples across 12 species were systematically characterized. We further identified tissue-specific APs and investigated trait-related promoters among various species. In addition, we analyzed the associations between APs and enhancer RNAs (eRNA)/transcription factors (TF) as a means of identifying potential regulatory factors. Integrating these findings, we finally developed Animal-APdb, a database for the searching, browsing, and downloading of information related to Animal APs. Animal-APdb is expected to serve as a valuable resource for exploring the functions and mechanisms of APs in animals.