Geoscience Data Journal最新文献

{"title":"Open Inventories of Rainfall-Triggered Landslides","authors":"Rachel (Soobitsky) Vershel,&nbsp;Jessica Sutton,&nbsp;Thomas Stanley,&nbsp;Pukar Amatya,&nbsp;Dalia Kirschbaum","doi":"10.1002/gdj3.70023","DOIUrl":"10.1002/gdj3.70023","url":null,"abstract":"<p>Landslide inventories support both post-event response and predictive model evaluation, but it remains challenging to create public, current, comprehensive and accurate landslide inventories. In response to this need, thousands of rainfall-triggered landslides were mapped and organised within the National Aeronautics and Space Administration's Cooperative Open-Online Landslide Repository (COOLR), which contains over 11,000 landslide reports from the Global Landslide Catalogue. Recently, 22 inventories containing thousands of rainfall-triggered landslides have been added to COOLR, which was reorganised to better accommodate large landslide inventories. All the data are available on the ‘Landslide Viewer’ web application, which also shows referenced and imported landslide inventories from other researchers. The new inventories are each connected to a landslide-triggering rainfall event, and therefore their date of occurrence was usually known. Landslide events were found by searching through credible sources or due to an external request for support during a disaster response. In either case, high-resolution imagery was utilised to digitise the landslides in the region. The resulting data can be used for various purposes, such as model training and validation. To demonstrate their potential, satellite precipitation was analysed with reference to the new inventories. The precipitation analysis highlights the potential of daily satellite precipitation estimates in areas with limited ground precipitation observations. Some of the heavy precipitation events were underestimated, but many were captured and could inform future landslide hazard assessment.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70023","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144918617","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SSP-CABra—Streamflow Scenarios Projections for Brazilian Catchments","authors":"André Almagro,&nbsp;Paulo Tarso S. Oliveira,&nbsp;André S. Ballarin","doi":"10.1002/gdj3.70029","DOIUrl":"10.1002/gdj3.70029","url":null,"abstract":"<p>Climate change has significant impacts on hydrological fluxes worldwide, with pronounced effects in Brazil, including intense and recurrent droughts and floods. Accurate streamflow prediction is therefore essential for advancing water resources engineering, improving water resources management, and informing climate adaptation strategies. Here, we introduce the Streamflow Scenarios Projections for Brazilian Catchments (SSP-CABra), which provides long-term to daily streamflow simulations for 735 Brazilian catchments. These simulations are generated using five hydrological models of varying complexity, forced by 10 bias-corrected CMIP6-based climate simulations for historical (1980–2013) and future (2015–2100; SSP2-4.5 and SSP5-8.5) periods. SSP-CABra addresses a critical gap in large-scale hydrological modelling for Brazil, offering valuable insights for researchers and policymakers. Despite its broad applicability, the dataset includes models with varying performance across regions; users should assess model skill locally to ensure appropriate use, particularly for decision-making or extreme event analyses. Still, by leveraging multiple models and climate scenarios, SSP-CABra supports not only the mitigation of climate change impacts on water security, but also advances the understanding of model performance and regional hydrological behaviour.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70029","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144914895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Geospatial Insights Into Landslide Relics in the Northern Half of the Taihang Mountains: Topography, Geology and Beyond","authors":"Chong Xu,&nbsp;Xuewei Zhang,&nbsp;Lei Li,&nbsp;Liye Feng,&nbsp;Wentao Yang","doi":"10.1002/gdj3.70028","DOIUrl":"10.1002/gdj3.70028","url":null,"abstract":"<p>Landslides are among the most widespread and recurrent natural hazards, posing significant threats due to their sudden onset and unpredictable nature. Understanding their spatial distribution patterns is crucial for improving disaster prediction and mitigation strategies. This study investigates the spatial distribution of 8349 landslide relics in the northern half of the Taihang Mountains, China, by analysing their relationship with eight influencing factors: elevation, slope, aspect, curvature, distance from rivers, distance from faults, lithology and land cover. The analysis is based on four key metrics: Landslide Number, Landslide Area, Landslide Number Density (LND) and Landslide Area Percentage (LAP). Results reveal that 66.06% of landslides (5515 events) and 40.79% of the total landslide area (61.84 km²) occur within the elevation range of 800–1300 m. The highest LND (1.04 km⁻²) and LAP (2.65%) appear in areas with curvature less than −10, indicating a preference for concave terrain. Slopes of 15°–20° host the most landslides (1713 events), whereas areas with slopes &gt; 45° show the densest distribution (LAP = 1.44%). North- and northwest-facing slopes exhibit the highest susceptibility, with LNDs of 0.51 and 0.43 km⁻², respectively. Landslides are concentrated within 0–4 km of rivers (81.35% of total number), and proximity to faults strongly influences landslide size and density. The Quaternary loess and Neoproterozoic clay strata are particularly prone to landslides, with the Neoproterozoic displaying the highest LND (5.59 km⁻²). In terms of land cover, grasslands account for 48.4% of landslide occurrences, whereas forests contain over half of the total landslide area. Barren lands, though limited in extent, show the highest LND (0.95 km⁻²) and LAP (1.6%). These findings contribute to a deeper understanding of landslide susceptibility in the region and provide a scientific basis for future landslide risk assessments. Additionally, the results offer valuable insights for local governments in formulating targeted landslide prevention and mitigation strategies.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70028","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144869445","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modelling the Importance of Ground and Strong-Motion Variables on the Damage Status in the 2023 Kahramanmaraş Earthquakes Using Supervised Machine Learning","authors":"Mustafa Senkaya,&nbsp;Serhat E. Akhanlı,&nbsp;Ali Silahtar,&nbsp;Hasan Karaaslan","doi":"10.1002/gdj3.70020","DOIUrl":"10.1002/gdj3.70020","url":null,"abstract":"<p>The damage status of 44 locations was investigated, incorporating ground condition parameters such as Vs30, engineering bedrock depth (EBd), and predominant frequency (f0), as well as strong-motion parameters including PGA, Repi, and Rrup (epicentre and rupture distance, respectively). Various machine learning methods—logistic regression (LR), classification and regression trees (CART), random forest (RF), support vector machine (SVM), k-nearest neighbours (KNN), and artificial neural networks (ANN)—were employed to evaluate the dataset through three approaches: the complete parameter set, solely ground-based parameters, and strong-motion parameters alone. Model performance, measured by Area Under the Curve of the Receiver Operating Characteristic (AUC-ROC), ranged from 0.466 to 0.989, with KNN achieving the highest performance (0.989) when using the complete dataset and 0.988 with ground-based parameters alone. The analysis highlighted EBd and f0 as the most significant contributors to damage outcomes (normalised variable importance of 100% and 85%, respectively), demonstrating strong correlations with structural vulnerability. Among earthquake-related parameters, PGA was identified as the most influential factor in models established through strong-motion parameters, whereas Repi and Rrup demonstrated a considerably lower influence. On the other hand, specificity values (determining no-damage status) consistently exceeded sensitivity (determining damage status) in models based solely on earthquake parameters. Overall, the outputs demonstrate that traditional seismic hazard approaches based on earthquake parameters could provide a broad framework for risk mitigation; local site conditions, particularly EBd and f0, are the primary drivers of seismic risk. Integrating these detailed ground parameters into seismic assessments is critical for improving predictive accuracy and advancing earthquake engineering practices.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70020","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144815001","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ecohydrological Land Reanalysis: Vegetation Water Content and Soil Moisture Data by Land Data Assimilation","authors":"Yohei Sawada,&nbsp;Hideyuki Fujii,&nbsp;Hiroyuki Tsutsui,&nbsp;Kentaro Aida,&nbsp;Rigen Shimada,&nbsp;Misako Kachi,&nbsp;Toshio Koike","doi":"10.1002/gdj3.70025","DOIUrl":"10.1002/gdj3.70025","url":null,"abstract":"<p>The accurate estimation of terrestrial water and vegetation is a grand challenge in hydrometeorology. Many previous studies developed land data assimilation systems (LDASs) and provided global-scale land surface data sets by integrating numerical simulation and satellite data. However, vegetation dynamics have not been explicitly solved in these land reanalysis data sets. Here we present the newly developed land reanalysis data set, ECoHydrological Land reAnalysis (ECHLA). ECHLA is generated by sequentially assimilating C- and X-band microwave brightness temperature satellite observations into a land surface model which can explicitly simulate the dynamic evolution of vegetation biomass. The ECHLA data set provides semiglobal soil moisture from surface to 1.95 m depth, Leaf Area Index (LAI), and vegetation water content. The ECHLA data set is publicly available in the Japan Aerospace eXploration Agency's repository and is expected to contribute to understanding terrestrial ecohydrological cycles and water-related natural disasters such as drought.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://rmets.onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70025","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144810980","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"GloSAT LATsdb: A Global Compilation of Land Air Temperature Station Records With Updated Climatological Normals From Local Expectation Kriging","authors":"Michael Taylor,&nbsp;Timothy J. Osborn,&nbsp;Kathryn Cowtan,&nbsp;Colin P. Morice,&nbsp;Philip D. Jones,&nbsp;Emily J. Wallis,&nbsp;David H. Lister","doi":"10.1002/gdj3.70024","DOIUrl":"10.1002/gdj3.70024","url":null,"abstract":"<p>To accurately determine multi-centennial trends in climate data records of the Earth's surface temperature, measurements are commonly analysed in the form of anomalies relative to a climatological reference period such as the World Meteorological Organization (WMO) 1961–1990 baseline. One of many climate-monitoring challenges is that weather records of land surface temperature can be short, typically of the order of several years or decades, and often do not sufficiently overlap the reference period to allow calculation of the climatological normals needed to convert the observations to anomalies. Moreover, the volume of records of this type is increasing due to the rescue of early (pre-baseline) instrumental paper-based records and the growing prevalence of newer (post-baseline) weather stations. To address this, we apply a method to estimate the climatological normal for each calendar month of temperature time series that do not have sufficient data during the baseline period, using an approximation to local expectation kriging with station holdout (LEK). This exploits the information in neighbouring time series to estimate the expected mean level of short series of observations. We apply the method to a global database of monthly land air temperature at 11865 stations based on CRUTEM5 but with the acquisition of an additional 1233 station series including some that extend back to 1781, and with mid-latitude stations adjusted for exposure bias arising from the transition to Stevenson screens. We evaluate the LEK-based normals using climatological normals calculated directly from the station observations. Using this method, we obtain estimated normals for 2699 stations that did not previously have normals and we improve the estimated normals for a further 2611 which had previously been estimated from incomplete data. Finally, we demonstrate how incorporating these thousands of previously unused station observation fragments affects hemispheric temperature averages. Pre-1850 data—primarily from Europe—show a modest warming trend but pronounced multidecadal variability that is greater than after 1850. The additional stations improve spatial coverage by a few percent in recent decades and raise pre-1860 Northern Hemisphere temperature estimates by approximately 0.1°C.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70024","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773438","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Monthly Snow and Sleet Series for the Greater Dublin Area 1867–2024","authors":"Csaba Horvath,&nbsp;Ciara Ryan,&nbsp;Conor Murphy","doi":"10.1002/gdj3.70022","DOIUrl":"10.1002/gdj3.70022","url":null,"abstract":"<p>This paper details the compilation of data and application of quality assurance procedures for constructing a 157-year snow and sleet series for the Greater Dublin Area, Ireland. Snowfall is particularly sensitive to climate variability in temperate regions, and long-term records are essential for understanding changes in winter weather extremes over time. The dataset integrates observations from six sites and provides a regional snow and sleet frequency dataset at monthly, seasonal (October–May) and annual resolutions. Data sources include archived meteorological records, digitised station logs and synoptic weather reports. A brief analysis offers insights into long-term snowfall climatology in the Greater Dublin region from 1867 to 2024, revealing substantial interannual and decadal variability, as well as notable reductions in snow frequency in recent decades. This dataset provides a valuable baseline for assessing historical trends in snowfall and contributes to broader efforts in climate reconstruction and climate change impact studies in Ireland and beyond.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 4","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-08-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70022","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144773507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Near-Surface Air Temperature Profile in Maritime Antarctica (2006–2023)","authors":"Miguel Angel de Pablo","doi":"10.1002/gdj3.70021","DOIUrl":"10.1002/gdj3.70021","url":null,"abstract":"<p>This dataset comprises vertical arrays of air temperature measurements collected on Livingston and Deception Islands, Antarctica, between 2006 and early 2024. The arrays, part of the PERMATHERMAL network integrated into the Global Terrestrial Network for Permafrost (GTN-P) database, were designed to support studies on permafrost thermal regimes and snow cover dynamics. Standard configurations included temperature sensors placed at heights of 2.5, 5, 10, 20, 40, 80, and 160 cm above the ground, mounted on wooden masts to minimise thermal interference. Higher-resolution configuration with up to 15 vertical measurements (between 2.5 and 160 cm above the ground surface) and miniature configuration with 8 sensors (between 2.5 and 40 cm above the ground surface) were also occasionally deployed for spatial-specific studies. Data were mainly recorded using iButton DS1921G (Miniature configuration) and DS1922L (standard and high-resolution configurations) temperature loggers (Maxim Integrated). Despite occasional gaps due to energy depletion or device damage, the dataset provides reliable long-term monitoring in a region where such measurements are logistically challenging. Originally acquired to estimate seasonal snow thickness through the analysis of vertical thermal gradients, the dataset has broader applications. These include investigating snowpack thermophysical properties, ground-atmosphere energy exchanges, snow hydrology, ecological processes, and remote sensing calibration. Raw data in American Standard Code for Information Interchange (ASCII) format, without filtering or preprocessing, are made available to ensure flexibility for diverse research needs, allowing users to apply tailored cleaning and analysis protocols. The dataset is particularly valuable for addressing the scarcity of observational air temperature data in Antarctica. It provides a ground-based complement to satellite measurements and supports studies on snow-atmosphere interactions, soil thermal regimes, and the micrometeorology of polar environments. This unique resource facilitates multidisciplinary research across cryospheric science, hydrology, ecology, and remote sensing, offering insights into processes in extreme environments. The contribution of these long-term measurements highlights the importance of accessible, high-resolution datasets to advance understanding of Antarctica's complex environmental systems.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70021","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716470","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Permafrost Table Temperature (2008–2021) in Deception Island, Antarctica","authors":"M. A. de Pablo,&nbsp;M. Ramos","doi":"10.1002/gdj3.70019","DOIUrl":"10.1002/gdj3.70019","url":null,"abstract":"<p>This manuscript presents a comprehensive presentation of ground temperature data collected at 16 nodes of the 121 of the Crater Lake Circumpolar Active Layer Monitoring (CALM) site on Deception Island, Antarctica, from 2008 to early 2022. Each one of the 16 shallow boreholes has been equipped with miniature temperature loggers, providing valuable insights into the thermal regime of the ground at a depth of 50 cm, which corresponds to the mean depth of the top of the permafrost table as observed by annual mechanical probing in the CALM site. Despite a 9-month long gap in data collection during 2017 due to persistent snow cover, the time series remains largely intact, with annual measurements taken every 3 h. The manuscript details the methodologies employed for data collection, including the use of iButton loggers, and outlines the challenges faced in retrieving and processing the data in the harsh Antarctic environment. The cleaned dataset, which consolidates data from various nodes while removing erroneous records, is made freely accessible to the scientific community without any additional processing of the data such as offset corrections or gaps interpolation. This resource is expected to facilitate further research into the thermal dynamics of the active layer and permafrost and its implications for climate change since both are influenced by external factors such as snow cover, air temperature and others. Overall, the presented dataset contributes to the limited body of knowledge regarding Antarctic permafrost and provides a foundation for future investigations into the effects of climate change on frozen ground dynamics. The dataset serves as a vital tool for researchers aiming to model ground thermal behaviour and assess the impacts of environmental changes in polar regions.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-07-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70019","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144672849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of the Total Nitrogen Distribution Characteristics and Land-Based Correlation in the Sea Areas Under the Jurisdiction of Weifang City in 2022","authors":"Wenbo Qiu,&nbsp;Yilin Zhai,&nbsp;Ruiqi Zhu,&nbsp;Liqin Sun,&nbsp;Feng Li","doi":"10.1002/gdj3.70015","DOIUrl":"10.1002/gdj3.70015","url":null,"abstract":"<p>The nitrogen distribution in Weifang was examined using measured data from August 2022 to investigate the distribution features of nitrogen in the Weifang Sea region and its relationship with land-based rivers. The findings indicate that nitrogen levels in Weifang are higher during the August flood season, and water quality varies by location. Land-based pollution usually affects the water quality, with lower amounts of nitrogen seen in the open sea and rather steady levels in other places. The distribution of nitrogen across different coastal regions is influenced by nearby rivers, with higher concentrations observed in the Mi River and Xiaoqing River. This leads to the most severe nitrogen exceedances in the sea area near Weifang Port. Based on these findings, strategies for targeted actions, improved land and sea management, and heightened environmental awareness are recommended. The research results enhance the understanding of water quality distribution in Weifang waters and provide valuable data for controlling nitrogen pollution and improving environmental management in the region.</p>","PeriodicalId":54351,"journal":{"name":"Geoscience Data Journal","volume":"12 3","pages":""},"PeriodicalIF":2.4,"publicationDate":"2025-06-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/gdj3.70015","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144511185","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}