Scientific Data最新文献_第10页

Weather- and climate-driven power supply and demand time series for power and energy system analyses.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-04 DOI: 10.1038/s41597-024-04129-8

Enrico G A Antonini, Alice Di Bella, Iacopo Savelli, Laurent Drouet, Massimo Tavoni

引用次数: 0

Dataset for Antenna-Based Detection of Fault Types in Covered Conductors for 22 kV Voltage Power Lines.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-04 DOI: 10.1038/s41597-024-04009-1

Ondřej Kabot, Lukáš Klein, Lukáš Prokop, Stanislav Mišák, Zdeněk Slanina

{"title":"Dataset for Antenna-Based Detection of Fault Types in Covered Conductors for 22 kV Voltage Power Lines.","authors":"Ondřej Kabot, Lukáš Klein, Lukáš Prokop, Stanislav Mišák, Zdeněk Slanina","doi":"10.1038/s41597-024-04009-1","DOIUrl":"10.1038/s41597-024-04009-1","url":null,"abstract":"This abstract presents a dataset for the detection of fault types in XLPE-covered conductors utilized in 22 kV medium voltage power distribution systems. We employed an antenna-based approach for detecting partial discharges. The dataset encompasses 12 distinct fault categories, ranging from ground phase faults to inter-phase faults, and no-fault case with steel or covered conductor as fault. We also used three different antennas. Each sample is a single measurement from antenna, consisting of 106 data points as floating numbers. The utilization of the antenna-based method offers the potential for a more cost-effective and straightforward installation for the detection of partial discharges. The objective of dataset is to enhance the identification of fault types, thereby promoting broader adoption of covered conductors in overhead power distribution lines. Such adoption proves particularly beneficial in confined areas, including natural parks, where safety is a prime concern. It is noteworthy that this dataset represents an original contribution, as no prior publication has addressed detection for this specific range of fault types and method of detection.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1319"},"PeriodicalIF":5.8,"publicationDate":"2024-12-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11618338/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-03 DOI: 10.1038/s41597-024-03997-4

Huaqing Liu, Peiyi Chen, Xiaochen Zhai, Ku-Geng Huo, Shuxian Zhou, Lanqing Han, Guoxin Fan

{"title":"PPB-Affinity: Protein-Protein Binding Affinity dataset for AI-based protein drug discovery.","authors":"Huaqing Liu, Peiyi Chen, Xiaochen Zhai, Ku-Geng Huo, Shuxian Zhou, Lanqing Han, Guoxin Fan","doi":"10.1038/s41597-024-03997-4","DOIUrl":"10.1038/s41597-024-03997-4","url":null,"abstract":"Prediction of protein-protein binding (PPB) affinity plays an important role in large-molecular drug discovery. Deep learning (DL) has been adopted to predict the changes of PPB binding affinities upon mutations, but there was a scarcity of studies predicting the PPB affinity itself. The major reason is the paucity of open-source dataset with PPB affinity data. To address this gap, the current study introduced a large comprehensive PPB affinity (PPB-Affinity) dataset. The PPB-Affinity dataset contains key information such as crystal structures of protein-protein complexes (with or without protein mutation patterns), PPB affinity, receptor protein chain, ligand protein chain, etc. To the best of our knowledge, this is the largest publicly available PPB affinity dataset, and we believe it will significantly advance drug discovery by streamlining the screening of potential large-molecule drugs. We also developed a deep-learning benchmark model with this dataset to predict the PPB affinity, providing a foundational comparison for the research community.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1316"},"PeriodicalIF":5.8,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615212/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142772085","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

The "Naturalistic Free Recall" dataset: four stories, hundreds of participants, and high-fidelity transcriptions.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-03 DOI: 10.1038/s41597-024-04082-6

Omri Raccah, Phoebe Chen, Todd M Gureckis, David Poeppel, Vy A Vo

{"title":"The \"Naturalistic Free Recall\" dataset: four stories, hundreds of participants, and high-fidelity transcriptions.","authors":"Omri Raccah, Phoebe Chen, Todd M Gureckis, David Poeppel, Vy A Vo","doi":"10.1038/s41597-024-04082-6","DOIUrl":"10.1038/s41597-024-04082-6","url":null,"abstract":"The \"Naturalistic Free Recall\" dataset provides transcribed verbal recollections of four spoken narratives collected from 229 participants. Each participant listened to two stories, varying in duration from approximately 8 to 13 minutes, recorded by different speakers. Subsequently, participants were tasked with verbally recalling the narrative content in as much detail as possible and in the correct order. The dataset includes high-fidelity, time-stamped text transcripts of both the original narratives and participants' recollections. To validate the dataset, we apply a previously published automated method to score memory performance for narrative content. Using this approach, we extend effects traditionally observed in classic list-learning paradigms. The analysis of narrative contents and its verbal recollection presents unique challenges compared to controlled list-learning experiments. To facilitate the use of these rich data by the community, we offer an overview of recent computational methods that can be used to annotate and evaluate key properties of narratives and their recollections. Using advancements in machine learning and natural language processing, these methods can help the community understand the role of event structure, discourse properties, prediction error, high-level semantic features (e.g., idioms, humor), and more. All experimental materials, code, and data are publicly available to facilitate new advances in understanding human memory.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1317"},"PeriodicalIF":5.8,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615391/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142772091","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

GeoCrack: A High-Resolution Dataset For Segmentation of Fracture Edges in Geological Outcrops.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-03 DOI: 10.1038/s41597-024-04107-0

Mohammed Yaqoob, Mohammed Ishaq, Mohammed Yusuf Ansari, Venkata Ram Sagar Konagandla, Tamim Al Tamimi, Stefano Tavani, Amerigo Corradetti, Thomas Daniel Seers

{"title":"GeoCrack: A High-Resolution Dataset For Segmentation of Fracture Edges in Geological Outcrops.","authors":"Mohammed Yaqoob, Mohammed Ishaq, Mohammed Yusuf Ansari, Venkata Ram Sagar Konagandla, Tamim Al Tamimi, Stefano Tavani, Amerigo Corradetti, Thomas Daniel Seers","doi":"10.1038/s41597-024-04107-0","DOIUrl":"10.1038/s41597-024-04107-0","url":null,"abstract":"GeoCrack is the first large-scale open source annotated dataset of fracture traces from geological outcrops, enabling deep learning-based fracture segmentation, setting a new standard for natural fracture characterization datasets. GeoCrack contains images from photogrammetric surveys of fractured rock exposures across 11 sites in Europe and the Middle East, capturing diverse lithologies and tectonic settings. Each image was cleaned, normalized, and manually segmented, followed by a recursive annotation vetting process to ensure the quality and accuracy of the digitized fracture edges. The processed images and corresponding binary masks were divided into 224 × 224 patches, yielding 12,158 pairs. GeoCrack captures representive real-world challenges in fracture edge annotation, such as contrast variations between fracture traces and the host medium due to geological and geomorphological factors like aperture dilation, host rock composition, outcrop weathering, and groundwater staining. Physical occlusions like shadows and vegetation are also considered to minimize false positives. GeoCrack was validated using a U-Net implementation for fracture segmentation, achieving satisfactory IoU of 85%. GeoCrack holds strong potential to advance deep fracture segmentation in geological applications, effectively tackling the diverse challenges of real-world fracture identification.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1318"},"PeriodicalIF":5.8,"publicationDate":"2024-12-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11615390/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142772071","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Gridded, temporally referenced spatial information on soil organic carbon for Hungary.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-02 DOI: 10.1038/s41597-024-04158-3

Gábor Szatmári, Annamária Laborczi, János Mészáros, Katalin Takács, András Benő, Sándor Koós, Zsófia Bakacsi, László Pásztor

{"title":"Gridded, temporally referenced spatial information on soil organic carbon for Hungary.","authors":"Gábor Szatmári, Annamária Laborczi, János Mészáros, Katalin Takács, András Benő, Sándor Koós, Zsófia Bakacsi, László Pásztor","doi":"10.1038/s41597-024-04158-3","DOIUrl":"10.1038/s41597-024-04158-3","url":null,"abstract":"Soil organic carbon (SOC), known as the most important soil attribute, affects various soil functions and services, essential for nutritious food and clean drinking water. Since recognizing its key role in many environmental challenges, there has been an increasing demand for spatial information on SOC. Our objective is to present the results of a mapping activity aimed at producing spatially exhaustive information on SOC content, density, and stock for the topsoils of Hungary for 1992 and 2000. A \"time-for-space\" digital soil mapping approach was pursued to predict and map these SOC properties, with the associated uncertainty, at a resolution of 100 × 100 m. Particular attention was paid to validating the accuracy of the maps and the reliability of the uncertainty quantifications. The published maps are recommended to be used as baseline maps for Hungary. The spatial resolution makes them suitable for various practical applications (e.g., GHG inventory, sustainable agriculture, carbon sequestration). The maps are of interest to researchers, practitioners, and policymakers, helping to achieve scientifically sound results and informed decision-making.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1312"},"PeriodicalIF":5.8,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11612427/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142772080","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A DNA barcode reference of Asian ferns with expert-identified voucher specimens and DNA samples.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-02 DOI: 10.1038/s41597-024-04161-8

Li-Yaung Kuo, Sheng-Kai Tang, Yu-Hsuan Huang, Pei-Jun Xie, Cheng-Wei Chen, Zhi-Xiang Chang, Tian-Chuan Hsu, Yi-Han Chang, Yi-Shan Chao, Chien-Wen Chen, Susan Fawcett, Joel H Nitta, Michael Sundue, Tzu-Tong Kao, Hong Truong Luu, Andi Maryani A Mustapeng, Fulgent P Coritico, Victor B Amoroso, Yong Kien Thai

{"title":"A DNA barcode reference of Asian ferns with expert-identified voucher specimens and DNA samples.","authors":"Li-Yaung Kuo, Sheng-Kai Tang, Yu-Hsuan Huang, Pei-Jun Xie, Cheng-Wei Chen, Zhi-Xiang Chang, Tian-Chuan Hsu, Yi-Han Chang, Yi-Shan Chao, Chien-Wen Chen, Susan Fawcett, Joel H Nitta, Michael Sundue, Tzu-Tong Kao, Hong Truong Luu, Andi Maryani A Mustapeng, Fulgent P Coritico, Victor B Amoroso, Yong Kien Thai","doi":"10.1038/s41597-024-04161-8","DOIUrl":"10.1038/s41597-024-04161-8","url":null,"abstract":"Ferns belong to species-rich group of land plants, encompassing more than 11,000 extant species, and are crucial for reflecting terrestrial ecosystem changes. However, our understanding of their biodiversity hotspots, particularly in Southeast Asia, remains limited due to scarce genetic data. Despite harboring around one-third of the world's fern species, less than 6% of Southeast Asian ferns have been DNA-sequenced. In this study, we addressed this gap by sequencing 1,496 voucher-referenced and expert-identified fern samples from (sub)tropical Asia, spanning Malaysia, the Philippines, Taiwan, and Vietnam, to retrieve their rbcL and trnL-F sequences. This DNA barcode collection of Asian ferns encompasses 956 species across 152 genera and 34 families, filling major gaps in fern biodiversity understanding and advancing research in systematics, phylogenetics, ecology and conservation. This dataset significantly expands the Fern Tree of Life to over 6,000 species, serving as a pivotal and global reference for worldwide barcoding identification of ferns.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1314"},"PeriodicalIF":5.8,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11612234/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142772019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

African bat database: curated data of occurrences, distributions and conservation metrics for sub-Saharan bats.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-02 DOI: 10.1038/s41597-024-04170-7

Ara Monadjem, Cecilia Montauban, Paul W Webala, Theresa M Laverty, Eric M Bakwo-Fils, Laura Torrent, Iroro Tanshi, Adam Kane, Abigail L Rutrough, David L Waldien, Peter J Taylor

{"title":"African bat database: curated data of occurrences, distributions and conservation metrics for sub-Saharan bats.","authors":"Ara Monadjem, Cecilia Montauban, Paul W Webala, Theresa M Laverty, Eric M Bakwo-Fils, Laura Torrent, Iroro Tanshi, Adam Kane, Abigail L Rutrough, David L Waldien, Peter J Taylor","doi":"10.1038/s41597-024-04170-7","DOIUrl":"10.1038/s41597-024-04170-7","url":null,"abstract":"Accurate knowledge of species distributions is foundational for effective conservation efforts. Bats are a diverse group of mammals, with important roles in ecosystem functioning. However, our understanding of bats and their ecological importance is hindered by poorly defined ranges, mostly as a result of under-recording. This issue is exacerbated in Africa by the ongoing rapid discovery of new species, both de novo and splits of existing species, and by inaccessibility to museum specimens that are mostly hosted outside of the continent. Here we present the African bat database - a curated set of 17,285 unique locality records of all 266 species of bats from sub-Saharan Africa, vouched for by specimens and/or genetic sequencing, and aligned with current taxonomy. Based on these records, we also present Maxent-based distribution models and calculate the IUCN Red List metrics for Extent of Occurrence and Area of Occupancy. This database and online visualization tool provide an important open-source resource and is expected to significantly advance studies in ecology, and aid in bat conservation.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1309"},"PeriodicalIF":5.8,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11611906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142772045","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Borrelia PeptideAtlas: A proteome resource of common Borrelia burgdorferi isolates for Lyme research.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-02 DOI: 10.1038/s41597-024-04047-9

Panga J Reddy, Zhi Sun, Helisa H Wippel, David H Baxter, Kristian Swearingen, David D Shteynberg, Mukul K Midha, Melissa J Caimano, Klemen Strle, Yongwook Choi, Agnes P Chan, Nicholas J Schork, Andrea S Varela-Stokes, Robert L Moritz

{"title":"Borrelia PeptideAtlas: A proteome resource of common Borrelia burgdorferi isolates for Lyme research.","authors":"Panga J Reddy, Zhi Sun, Helisa H Wippel, David H Baxter, Kristian Swearingen, David D Shteynberg, Mukul K Midha, Melissa J Caimano, Klemen Strle, Yongwook Choi, Agnes P Chan, Nicholas J Schork, Andrea S Varela-Stokes, Robert L Moritz","doi":"10.1038/s41597-024-04047-9","DOIUrl":"10.1038/s41597-024-04047-9","url":null,"abstract":"Lyme disease is caused by an infection with the spirochete Borrelia burgdorferi, and is the most common vector-borne disease in North America. B. burgdorferi isolates harbor extensive genomic and proteomic variability and further comparison of isolates is key to understanding the infectivity of the spirochetes and biological impacts of identified sequence variants. Here, we applied both transcriptome analysis and mass spectrometry-based proteomics to assemble peptide datasets of B. burgdorferi laboratory isolates B31, MM1, and the infective isolate B31-5A4, to provide a publicly available Borrelia PeptideAtlas. Included are total proteome, secretome, and membrane proteome identifications of the individual isolates. Proteomic data collected from 35 different experiment datasets, totaling 386 mass spectrometry runs, have identified 81,967 distinct peptides, which map to 1,113 proteins. The Borrelia PeptideAtlas covers 86% of the total B31 proteome of 1,291 protein sequences. The Borrelia PeptideAtlas is an extensible comprehensive peptide repository with proteomic information from B. burgdorferi isolates useful for Lyme disease research.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1313"},"PeriodicalIF":5.8,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11612207/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142772061","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

An unmanned aerial vehicle captured dataset for railroad segmentation and obstacle detection.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-02 DOI: 10.1038/s41597-024-03952-3

Rampriya R S, Taher Al-Shehari, Sabari Nathan, Jenefa A, Suganya R, Shunmuga Perumal P, Taha Alfakih, Hussain Alsalman

{"title":"An unmanned aerial vehicle captured dataset for railroad segmentation and obstacle detection.","authors":"Rampriya R S, Taher Al-Shehari, Sabari Nathan, Jenefa A, Suganya R, Shunmuga Perumal P, Taha Alfakih, Hussain Alsalman","doi":"10.1038/s41597-024-03952-3","DOIUrl":"10.1038/s41597-024-03952-3","url":null,"abstract":"Safety is crucial in the railway industry because railways transport millions of passengers and employees daily, making it paramount to prevent injuries and fatalities. In order to guarantee passenger safety, computer vision, unmanned aerial vehicles (UAV), and artificial intelligence will be essential tools in the near future for routinely evaluating the railway environment. An unmanned aerial vehicle captured dataset for railroad segmentation and obstacle detection (UAV-RSOD) comprises high-resolution images captured by UAVs over various obstacles within railroad scenes, enabling automatic railroad extraction and obstacle detection. The dataset includes 315 raw images, along with 630 labeled and 630 masked images for railroad semantic segmentation. The dataset consists of 315 original images captured by the UAV for object detection and obstacle detection. To increase dataset diversity for training purposes, we applied data augmentation techniques, which expanded the dataset to 2002 augmented and annotated images for obstacle detection cover six different classes of obstacles on railroad lines. Additionally, we provide the original 315 images along with a script for augmentation, allowing users to generate their own augmented data as needed, offering a more sustainable and customizable option. Each image in the dataset is accurately annotated with bounding boxes and labeled under six categories, including person, boulder, barrel, branch, jerry can, and iron rod. This comprehensive classification and detailed annotation make the dataset an essential tool for researchers and developers working on computer vision applications in the railroad domain.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1315"},"PeriodicalIF":5.8,"publicationDate":"2024-12-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11612275/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142772052","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0