Scientific Data最新文献_第10页

A spatially constrained remote sensing-based inventory of glacial lakes worldwide.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2025-03-20 DOI: 10.1038/s41597-025-04809-z

Chunqiao Song, Chenyu Fan, Jinsong Ma, Pengfei Zhan, Xinyuan Deng

{"title":"A spatially constrained remote sensing-based inventory of glacial lakes worldwide.","authors":"Chunqiao Song, Chenyu Fan, Jinsong Ma, Pengfei Zhan, Xinyuan Deng","doi":"10.1038/s41597-025-04809-z","DOIUrl":"10.1038/s41597-025-04809-z","url":null,"abstract":"Climate change accelerates the extensive retreat of glaciers, leading to the widespread development of glacial lakes. A holistic picture of the spatial distribution of glacial lakes worldwide is a critical base for tracking the outburst hazards. By employing a semi-automated mapping approach and rigorous quality control, this study inventories 117,352 glacial lakes (≥0.01 km2) worldwide (the ice cap/sheet of Antarctic and Greenland excluded), with a net area of 24,755.84 ± 2,971.33 km2. The evaluation result shows this global inventory of glacial lakes (GIGLak) has an overall accuracy of 89.37% and 91.42% in number and area, respectively. These glacial lakes are widely distributed in different altitudes, ranging from the Earth's third pole to the coasts. Most glacial lakes are distributed in the Greenland periphery, High-Mountain Asia, Alaska, Canada, and the Cordilleras. The number of glacial lakes between 0.01-0.1 km2 accounts for 77.24% of the total count but only 11.82% in area. The classification of glacial lakes as four types indicates that the ice-uncontacted proglacial lakes dominate the number (67.07%) and area (53.04%) worldwide.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"464"},"PeriodicalIF":5.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926188/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670927","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 fMRI dataset for appetite neural correlates in people living with Motor Neuron Disease.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2025-03-20 DOI: 10.1038/s41597-025-04828-w

Jeryn Chang, JingLei Lv, Christine C Guo, Diana Lucia, Saskia Bollmann, Kelly Garner, Pamela A McCombe, Robert D Henderson, Thomas B Shaw, Frederik J Steyn, Shyuan T Ngo

{"title":"An fMRI dataset for appetite neural correlates in people living with Motor Neuron Disease.","authors":"Jeryn Chang, JingLei Lv, Christine C Guo, Diana Lucia, Saskia Bollmann, Kelly Garner, Pamela A McCombe, Robert D Henderson, Thomas B Shaw, Frederik J Steyn, Shyuan T Ngo","doi":"10.1038/s41597-025-04828-w","DOIUrl":"10.1038/s41597-025-04828-w","url":null,"abstract":"The dataset investigates the neural correlates of appetite in people living with motor neuron disease (plwMND) compared to non-neurodegenerative disease controls. Thirty-six plwMND and twenty-three controls underwent two fMRI sessions: one in a fasted state and one postprandial. Participants viewed visual stimuli of non-food items, low-calorie foods, and high-calorie foods in a randomised block design. Imaging data included T1w, T2w, and task-based and resting-state fMRI scans, and measures are complemented by subjective appetite questionnaires and anthropometric measures. This dataset is unique for its inclusion of functional imaging across prandial states, offering insights into the neural mechanisms of appetite regulation in patients with MND. Researchers can explore various aspects of the data, including the functional responses to food stimuli and their associations with clinical and appetite measures. The data, deposited in OpenNeuro, follows the Brain Imaging Data Structure (BIDS) standard, ensuring compatibility and reproducibility for future research. This comprehensive dataset provides a resource for studying the central mechanisms of appetite regulation in MND.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"466"},"PeriodicalIF":5.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926184/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670928","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

Telomere-to-telomere reference genome of Rhinogobio nasutus, an endangered endemic fish from the Yellow River.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2025-03-20 DOI: 10.1038/s41597-025-04793-4

Changping Jiang, Yanyan Du, Zhongyu Lou, Yanping Zhang, Tai Wang

引用次数: 0

Publisher Correction: A chromosome-level genome assembly of the male darkbarbel catfish (Pelteobagrus vachelli) using PacBio HiFi and Hi-C data.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2025-03-20 DOI: 10.1038/s41597-025-04795-2

Haiyang Liu, Jin Zhang, Tongxin Cui, Xincheng Zhang, Kaibin Li, Fang Wang, Qing Luo, Shuzhan Fei, Baixiang Chen, Chunkun Zhu, Kunci Chen, Xinping Zhu, Bingjie Li, Jian Zhao, Lingzhao Fang, Mi Ou

引用次数: 0

Publisher Correction: Six years of high-resolution monitoring data of 40 borehole heat exchangers.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2025-03-20 DOI: 10.1038/s41597-025-04830-2

Elisa Heim, Phillip Stoffel, Dirk Müller, Norbert Klitzsch

引用次数: 0

Oral microbiome and mycobiome dynamics in cancer therapy-induced oral mucositis.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2025-03-20 DOI: 10.1038/s41597-025-04671-z

Laurentia Nodit, Joseph R Kelley, Timothy J Panella, Antje Bruckbauer, Paul G Nodit, Grace A Shope, Kellie Peyton, Dawn M Klingeman, Russell Zaretzki, Alyssa Carrell, Mircea Podar

{"title":"Oral microbiome and mycobiome dynamics in cancer therapy-induced oral mucositis.","authors":"Laurentia Nodit, Joseph R Kelley, Timothy J Panella, Antje Bruckbauer, Paul G Nodit, Grace A Shope, Kellie Peyton, Dawn M Klingeman, Russell Zaretzki, Alyssa Carrell, Mircea Podar","doi":"10.1038/s41597-025-04671-z","DOIUrl":"10.1038/s41597-025-04671-z","url":null,"abstract":"Cancer therapy-induced oral mucositis is a frequent major oncological problem, secondary to cytotoxicity of chemo-radiation treatment. Oral mucositis commonly occurs 7-10 days after initiation of therapy; it is a dose-limiting side effect causing significant pain, eating difficulty, need for parenteral nutrition and a rise of infections. The pathobiology derives from complex interactions between the epithelial component, inflammation, and the oral microbiome. Our longitudinal study analysed the dynamics of the oral microbiome (bacteria and fungi) in nineteen patients undergoing chemo-radiation therapy for oral and oropharyngeal squamous cell carcinoma as compared to healthy volunteers. The microbiome was characterized in multiple oral sample types using rRNA and ITS sequence amplicons and followed the treatment regimens. Microbial taxonomic diversity and relative abundance may be correlated with disease state, type of treatment and responses. Identification of microbial-host interactions could lead to further therapeutic interventions of mucositis to re-establish normal flora and promote patients' health. Data presented here could enhance, complement and diversify other studies that link microbiomes to oral disease, prophylactics, treatments, and outcome.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"463"},"PeriodicalIF":5.8,"publicationDate":"2025-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11926371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143670931","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 telomere-to-telomere genome assembly of Chinese grain sorghum 654.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2025-03-19 DOI: 10.1038/s41597-025-04791-6

Fulin Wang, Jiandong Bao, Heng Zhang, Guowei Zhai, Tao Song, Zhijian Liu, Yu Han, Fan Yu, Guihua Zou, Ying Zhu

{"title":"A telomere-to-telomere genome assembly of Chinese grain sorghum 654.","authors":"Fulin Wang, Jiandong Bao, Heng Zhang, Guowei Zhai, Tao Song, Zhijian Liu, Yu Han, Fan Yu, Guihua Zou, Ying Zhu","doi":"10.1038/s41597-025-04791-6","DOIUrl":"10.1038/s41597-025-04791-6","url":null,"abstract":"The grain sorghum inbred line 654 serves as a parent for numerous Chinese commercial hybrids and recombinant inbred lines (RILs), which have played a pivotal role in the cloning of several agronomically important traits. In this study, we present a telomere-to-telomere (T2T) genome assembly of the inbred line 654 (728.81 Mb) using PacBio HiFi, ultra-long Oxford Nanopore Technology, and Hi-C sequencing data. The T2T genome assembly has high integrity (contains all of 10 centromeres and 20 telomeres without any gaps), high contiguity (contig N90: 52.02 Mb), high completeness (98.33% BUSCO completeness, 98.88% k-mer completeness, and LAI 24.38), and extremely low base error (3.37 × 10-7, QV: 64.72). A total of 62.34% sequences were identified as repetitive, and rest region contained 44,399 protein-coding genes, of which 30,245 were functionally annotated. The gap-free T2T genome assembly enables the full picture of the potential translational genomics, and provides the highest resolution genetic map for future studies on genome evolution, structure variation, and the genetic control of agronomic traits in sorghum breeding.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"460"},"PeriodicalIF":5.8,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11923156/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664021","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 chromosomal-level genome assembly of Trichogramma chilonis Ishii, 1941 (Hymenoptera: Trichogrammatidae).

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2025-03-19 DOI: 10.1038/s41597-025-04792-5

Chengxing Wang, Zhenjuan Yin, Yan Liu, Xiaoyan Dai, Shan Zhao, Ruijuan Wang, Yu Wang, Long Su, Hao Chen, Li Zheng, Yifan Zhai

{"title":"A chromosomal-level genome assembly of Trichogramma chilonis Ishii, 1941 (Hymenoptera: Trichogrammatidae).","authors":"Chengxing Wang, Zhenjuan Yin, Yan Liu, Xiaoyan Dai, Shan Zhao, Ruijuan Wang, Yu Wang, Long Su, Hao Chen, Li Zheng, Yifan Zhai","doi":"10.1038/s41597-025-04792-5","DOIUrl":"10.1038/s41597-025-04792-5","url":null,"abstract":"Trichogramma spp. is a genus of minute egg parasitoids frequently used in agricultural pest management that can feed on the eggs of various lepidopteran pests. Currently, there is a scarcity of high-quality genomic resources for this category of tiny parasitoids, which impedes our comprehension of the population evolution and parasitic ecology of this collective. In this case, a chromosome-level genome of Trichogramma chilonis was produced by integrating PacBio HiFi, Illumina, and Hi-C data. The genome size totals 202.48 Mb, with a scaffold N50 length of 40.00 Mb. A total of 98.59% (199.63 Mb) of contigs were effectively mapped onto five chromosomes. The BUSCO assessment revealed that the genome assembly achieved 98.1% (n = 1,367) completeness, with 95% representing single-copy BUSCOs and 3.1% duplicated BUSCOs. Also, the genome comprises 24.16% (48.91 Mb) repeat elements and 12,163 predicted protein-coding genes. The high-quality genome of T. chilonis presented in this study offers an invaluable asset for elucidating its evolutionary path and ecological interactions.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"457"},"PeriodicalIF":5.8,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11923239/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664519","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 dataset of multi-level street-block divisions of 985 cities worldwide.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2025-03-19 DOI: 10.1038/s41597-025-04704-7

Jintong Tang, Liyan Xu, Hongbin Yu, Hezhishi Jiang, Dejie He, Tianshu Li, Wanchen Xiao, Xinying Zheng, Keyi Liu, Yiqin Li, Shijie Li, Qian Huang, Jun Zhang, Yinsheng Zhou, Lun Wu, Yu Liu

{"title":"A dataset of multi-level street-block divisions of 985 cities worldwide.","authors":"Jintong Tang, Liyan Xu, Hongbin Yu, Hezhishi Jiang, Dejie He, Tianshu Li, Wanchen Xiao, Xinying Zheng, Keyi Liu, Yiqin Li, Shijie Li, Qian Huang, Jun Zhang, Yinsheng Zhou, Lun Wu, Yu Liu","doi":"10.1038/s41597-025-04704-7","DOIUrl":"10.1038/s41597-025-04704-7","url":null,"abstract":"Street-blocks, as basic geographical units for dividing urban space, are widely used in urban planning and statistics. However, the availability and quality of street-block data vary significantly across different countries or regions worldwide. While developed countries tend to have mature urban street-block division systems and corresponding public data, such data in most developing countries are often incomplete or non-existent. Even in countries with available data, the lack of consistent standards for street-block division causes difficulty in international comparative research. To address this gap, we are releasing a new open dataset: Multi-level Street-block Divisions of 985 Cities Worldwide (MSDCW), offering a logical, standardized, and user-friendly street-block division system for cities with the estimated population over 500,000 by Demographia from 142 countries or regions, with results at five spatial levels. Validation shows that compared with official datasets, MSDCW offers a reasonable division of urban street-blocks, and is therefore suitable as foundational data for related research. Additionally, researchers can use our method to generate their own street-block division datasets.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"456"},"PeriodicalIF":5.8,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11923264/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664522","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 large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2025-03-19 DOI: 10.1038/s41597-025-04707-4

Lidia Garrucho, Kaisar Kushibar, Claire-Anne Reidel, Smriti Joshi, Richard Osuala, Apostolia Tsirikoglou, Maciej Bobowicz, Javier Del Riego, Alessandro Catanese, Katarzyna Gwoździewicz, Maria-Laura Cosaka, Pasant M Abo-Elhoda, Sara W Tantawy, Shorouq S Sakrana, Norhan O Shawky-Abdelfatah, Amr Muhammad Abdo Salem, Androniki Kozana, Eugen Divjak, Gordana Ivanac, Katerina Nikiforaki, Michail E Klontzas, Rosa García-Dosdá, Meltem Gulsun-Akpinar, Oğuz Lafcı, Ritse Mann, Carlos Martín-Isla, Fred Prior, Kostas Marias, Martijn P A Starmans, Fredrik Strand, Oliver Díaz, Laura Igual, Karim Lekadir

{"title":"A large-scale multicenter breast cancer DCE-MRI benchmark dataset with expert segmentations.","authors":"Lidia Garrucho, Kaisar Kushibar, Claire-Anne Reidel, Smriti Joshi, Richard Osuala, Apostolia Tsirikoglou, Maciej Bobowicz, Javier Del Riego, Alessandro Catanese, Katarzyna Gwoździewicz, Maria-Laura Cosaka, Pasant M Abo-Elhoda, Sara W Tantawy, Shorouq S Sakrana, Norhan O Shawky-Abdelfatah, Amr Muhammad Abdo Salem, Androniki Kozana, Eugen Divjak, Gordana Ivanac, Katerina Nikiforaki, Michail E Klontzas, Rosa García-Dosdá, Meltem Gulsun-Akpinar, Oğuz Lafcı, Ritse Mann, Carlos Martín-Isla, Fred Prior, Kostas Marias, Martijn P A Starmans, Fredrik Strand, Oliver Díaz, Laura Igual, Karim Lekadir","doi":"10.1038/s41597-025-04707-4","DOIUrl":"10.1038/s41597-025-04707-4","url":null,"abstract":"Artificial Intelligence (AI) research in breast cancer Magnetic Resonance Imaging (MRI) faces challenges due to limited expert-labeled segmentations. To address this, we present a multicenter dataset of 1506 pre-treatment T1-weighted dynamic contrast-enhanced MRI cases, including expert annotations of primary tumors and non-mass-enhanced regions. The dataset integrates imaging data from four collections in The Cancer Imaging Archive (TCIA), where only 163 cases with expert segmentations were initially available. To facilitate the annotation process, a deep learning model was trained to produce preliminary segmentations for the remaining cases. These were subsequently corrected and verified by 16 breast cancer experts (averaging 9 years of experience), creating a fully annotated dataset. Additionally, the dataset includes 49 harmonized clinical and demographic variables, as well as pre-trained weights for a baseline nnU-Net model trained on the annotated data. This resource addresses a critical gap in publicly available breast cancer datasets, enabling the development, validation, and benchmarking of advanced deep learning models, thus driving progress in breast cancer diagnostics, treatment response prediction, and personalized care.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"12 1","pages":"453"},"PeriodicalIF":5.8,"publicationDate":"2025-03-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11923173/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143664525","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