Scientific Data最新文献_第2页

A twenty-year dataset of hourly energy generation and consumption from district campus building energy systems.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-19 DOI: 10.1038/s41597-024-04244-6

Wei Liao, Xiaoyu Jin, Yi Ran, Fu Xiao, Weijun Gao, Yanxue Li

{"title":"A twenty-year dataset of hourly energy generation and consumption from district campus building energy systems.","authors":"Wei Liao, Xiaoyu Jin, Yi Ran, Fu Xiao, Weijun Gao, Yanxue Li","doi":"10.1038/s41597-024-04244-6","DOIUrl":"https://doi.org/10.1038/s41597-024-04244-6","url":null,"abstract":"Distributed energy resources (DERs) would play a crucial role in the transition towards decentralized and decarbonized energy systems. However, due to the limited availability of long-term, high-resolution datasets, there has been little research on the descriptive analysis of distributed energy systems throughout the lifespan of distributed power generators and beyond. To address this challenge, this study has collected and described a twenty-year dataset (from 2002 to 2021) consisting of hourly energy generation and consumption profiles from a campus distributed energy system, covering the entire lifespan of on-site generators. The dataset includes supply-side data such as gas consumption from combined heating and power (CHP) units (fuel cell, gas engine), absorption chiller, gas boiler, solar photovoltaic generation, CHPs output, and grid electricity import. Additionally, real-life electricity, hot water, space heating, and cooling energy load profiles from individual buildings within the campus were also collected. This long-term dataset can be utilized in various scenarios, providing researchers and policymakers with comprehensive insights into the energy efficiency of distributed energy systems.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1400"},"PeriodicalIF":5.8,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142865412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Cross-Standard Health Data Harmonization using Semantics of Data Elements.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-19 DOI: 10.1038/s41597-024-04168-1

Shuxin Zhang, Ronald Cornet, Nirupama Benis

{"title":"Cross-Standard Health Data Harmonization using Semantics of Data Elements.","authors":"Shuxin Zhang, Ronald Cornet, Nirupama Benis","doi":"10.1038/s41597-024-04168-1","DOIUrl":"https://doi.org/10.1038/s41597-024-04168-1","url":null,"abstract":"Faced with heterogeneity of healthcare data, we propose a novel approach for harmonizing data elements (i.e., attributes) across health data standards. This approach focuses on the implicit concept that is represented by a data element. The process includes the following steps: identifying concepts, clustering similar concepts and constructing mappings between the clusters using the Simple Standard for Sharing Ontological Mappings (SSSOM) and Resource Description Framework (RDF), and enabling the creation of reusable mappings. As proof-of-concept, we applied the approach to five common health data standards - HL7 FHIR, OMOP, CDISC, Phenopackets, and openEHR, across four domains, such as demographics and diagnoses, and nine topics within those domains, such as gender and vital status. These domains and topics are selected to represent the broader range of topics in the health field. For each topic, data elements were found in the health data standards after a thorough search, resulting in the analysis of 64 data elements, identification of their underlying concepts, and development of mappings. Three use cases were implemented to demonstrate the role of data element concepts in data harmonization and data querying at varying levels of granularity. The approach helps overcome the limitations of context-dependent mappings and provides valuable insight for mapping practice within the health domain.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1407"},"PeriodicalIF":5.8,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142865415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A chromosome-level genome assembly of critically endangered Ochetobius elongatus.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-19 DOI: 10.1038/s41597-024-04223-x

Lekang Li, Xuanguang Liang, Jiatong Zhang, Qun Xu, Li Wang, Xiaoping Gao, Xiangchun Song, Bao Zhang, Dan Huang, Hong Wang, Xianyong Wang, Zhen Luo, Chiping Kong, Jianguo Lu

引用次数: 0

A haplotype-resolved and chromosome-scale genome assembly of Hainan muntjac (Muntiacus nigripes).

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-19 DOI: 10.1038/s41597-024-04167-2

Yilin Cui, Yakui Lv, Jialing Li, Mingjiang Cai, Xi Liu, Zejun Xu, Hui Liu

{"title":"A haplotype-resolved and chromosome-scale genome assembly of Hainan muntjac (Muntiacus nigripes).","authors":"Yilin Cui, Yakui Lv, Jialing Li, Mingjiang Cai, Xi Liu, Zejun Xu, Hui Liu","doi":"10.1038/s41597-024-04167-2","DOIUrl":"https://doi.org/10.1038/s41597-024-04167-2","url":null,"abstract":"The Hainan muntjac (Muntiacus nigripes) is a wild animal endemic to Hainan, China. Its species distribution and the diversity of muntjac karyotypes have attracted much attention. Although genomic resources have increased in recent years, relevant genome assembly data of Hainan muntjac are still lacking. Meanwhile, molecular evidence for the taxonomic units of this species remains lacking. In this study, we successfully assembled the Hainan muntjac haplotype genome at the chromosome level using Pacbio long read and long sequencing technologies and Hi-C data. The final assembly size was 2.66 Gb, with allele and scaffold N50 values of 29.27 and 700.27 Mb, respectively, and we scaffolded the genome sequence onto three chromosomes. The genome contains a total of 21,451 genes and 10,056 gene families. Phylogenetic analysis using single-copy gene families revealed that Hainan muntjac is most closely related to red muntjac, with a divergence time of 8-11 Ma. This new genomic resource of Hainan muntjac will be crucial for future comparative genomic analyses and genetic evolutionary studies.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1395"},"PeriodicalIF":5.8,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142865372","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

IPD-Brain: An Indian histopathology dataset for glioma subtype classification.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-19 DOI: 10.1038/s41597-024-04225-9

Ekansh Chauhan, Amit Sharma, Megha S Uppin, Manasa Kondamadugu, C V Jawahar, P K Vinod

{"title":"IPD-Brain: An Indian histopathology dataset for glioma subtype classification.","authors":"Ekansh Chauhan, Amit Sharma, Megha S Uppin, Manasa Kondamadugu, C V Jawahar, P K Vinod","doi":"10.1038/s41597-024-04225-9","DOIUrl":"https://doi.org/10.1038/s41597-024-04225-9","url":null,"abstract":"The effective management of brain tumors relies on precise typing, subtyping, and grading. We present the IPD-Brain Dataset, a crucial resource for the neuropathological community, comprising 547 high-resolution H&E stained slides from 367 patients for the study of glioma subtypes and immunohistochemical biomarkers. Scanned at 40x magnification, this dataset is one of the largest in Asia, specifically focusing on the Indian demographics. It encompasses detailed clinical annotations, including patient age, sex, radiological findings, diagnosis, CNS WHO grade, and IHC biomarker status (IDH1R132H, ATRX and TP53 along with proliferation index, Ki67), providing a rich foundation for research. The dataset is open for public access and is designed for various applications, from machine learning model training to the exploration of regional and ethnic disease variations. Preliminary validations utilizing Multiple Instance Learning for tasks such as glioma subtype classification and IHC biomarker identification underscore its potential to significantly contribute to global collaboration in brain tumor research, enhancing diagnostic precision and understanding of glioma variability across different populations.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1403"},"PeriodicalIF":5.8,"publicationDate":"2024-12-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142865390","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Chromosome-scale genome assembly and gene annotation of the Alligator Gar (Atractosteus spatula).

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-18 DOI: 10.1038/s41597-024-04256-2

Qing Wang, Qianqian Yu, Xiangqian Dong, Hengjin Chen, Xue Tian, Peng Qi, Haitao Wu, Yuxiang Yuan

{"title":"Chromosome-scale genome assembly and gene annotation of the Alligator Gar (Atractosteus spatula).","authors":"Qing Wang, Qianqian Yu, Xiangqian Dong, Hengjin Chen, Xue Tian, Peng Qi, Haitao Wu, Yuxiang Yuan","doi":"10.1038/s41597-024-04256-2","DOIUrl":"https://doi.org/10.1038/s41597-024-04256-2","url":null,"abstract":"Given the aggressive nature and robust survival capabilities of the alligator gar (Atractosteus spatula), if it was to exist in a new environment as an invasive species, it could cause significant disruption to the invaded ecosystem. Building on the continuity and completeness of the existing draft genome were not optimal, this study has updated a high-quality genome of the alligator gar at the chromosome level, which was assembled using Oxford Nanopore Technology and chromatin interaction mapping (Hi-C) sequencing techniques. In summary, the alligator gar genome in this study was 1.05 Gb in size with a contig N50 of 15.7 Mb and scaffold N50 of 56.8 Mb. We captured 98.26% of assembled bases in 28 pseudochromosomes. The completeness of the final chromosome-level genome reached 96.7%. Meanwhile, a total of 19,103 protein-coding genes were predicted, of which 99.83% could be predicted with functions. Taken together, the present high-quality alligator gar chromosome-level genome provides a valuable resource for exploring the underlying genomic basis to comprehend the functional genomics, chromosome evolution, and population management of this species.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1337"},"PeriodicalIF":5.8,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142855301","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Synthetic dataset of ID and Travel Documents.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-18 DOI: 10.1038/s41597-024-04160-9

Carlos Boned, Maxime Talarmain, Nabil Ghanmi, Guillaume Chiron, Sanket Biswas, Ahmad Montaser Awal, Oriol Ramos Terrades

引用次数: 0

Plasma metabolomics profiling of EGFR-mutant NSCLC patients treated with third-generation EGFR-TKI.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-18 DOI: 10.1038/s41597-024-04169-0

Ning Lou, Ruyun Gao, Yuankai Shi, Xiaohong Han

{"title":"Plasma metabolomics profiling of EGFR-mutant NSCLC patients treated with third-generation EGFR-TKI.","authors":"Ning Lou, Ruyun Gao, Yuankai Shi, Xiaohong Han","doi":"10.1038/s41597-024-04169-0","DOIUrl":"https://doi.org/10.1038/s41597-024-04169-0","url":null,"abstract":"Third-generation epidermal growth factor receptor-tyrosine kinase inhibitors (EGFR-TKIs) are the latest and a vital treatment option for non-small cell lung cancer (NSCLC) patients. Although EGFR-sensitive mutations are an indication for third-generation EGFR-TKI therapy, 30% of NSCLC patients lack response and all patients inevitably progress. There is a lack of biomarkers to predict the efficacy of EGFR-TKI therapy. In this report, we performed comprehensive plasma metabolomic profiling on 186 baseline and 20 post-treatment samples, analyzing 1,019 metabolites using four ultrahigh performance liquid chromatography-tandem mass spectroscopy (UPLC-MS/MS) methods. The dataset contains detailed clinical and metabolic information for 186 patients. Rigorous quality control measures were implemented. No significant differences in body mass index and biochemical metabolic parameters were observed between responders and non-responders. The datasets were utilized to characterize the responsive metabolic traits of third-generation EGFR-TKI therapy. All datasets are available for download on the OMIX website. We anticipate that these datasets will serve as valuable resources for future studies investigating NSCLC metabolism and for the development of personalized therapeutic strategies.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1369"},"PeriodicalIF":5.8,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142855412","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Primate Phenotypes: A Multi-Institution Collection of 3D Morphological Data Housed in MorphoSource.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-18 DOI: 10.1038/s41597-024-04261-5

Sergio Almécija, Kelsey D Pugh, Alisha Anaya, Christopher M Smith, Nancy B Simmons, Robert S Voss, Neil Duncan, Darrin P Lunde, Megan K Viera, Teresa Hsu, Emmanuel Gilissen, Stephanie A Maiolino, Julie M Winchester, Biren A Patel, Caley M Orr, Matthew W Tocheri, Eric Delson, Ashley S Hammond, Doug M Boyer, Santiago A Catalano

{"title":"Primate Phenotypes: A Multi-Institution Collection of 3D Morphological Data Housed in MorphoSource.","authors":"Sergio Almécija, Kelsey D Pugh, Alisha Anaya, Christopher M Smith, Nancy B Simmons, Robert S Voss, Neil Duncan, Darrin P Lunde, Megan K Viera, Teresa Hsu, Emmanuel Gilissen, Stephanie A Maiolino, Julie M Winchester, Biren A Patel, Caley M Orr, Matthew W Tocheri, Eric Delson, Ashley S Hammond, Doug M Boyer, Santiago A Catalano","doi":"10.1038/s41597-024-04261-5","DOIUrl":"https://doi.org/10.1038/s41597-024-04261-5","url":null,"abstract":"The field of phenomics is experiencing unprecedented advances thanks to the rapid growth of morphological quantification based on three-dimensional (3D) imaging, online data repositories, team-oriented collaborations, and open data-sharing policies. In line with these progressions, we present an extensive primate phenotypic dataset comprising >6,000 3D scans (media) representing skeletal morphologies of 386 individual specimens covering all hominoid genera (except humans) and other selected primates. The digitized specimens are housed in physical collections at the American Museum of Natural History, the National Museum of Natural History, the Royal Museum for Central Africa (Belgium), the Cleveland Museum of Natural History, and Stony Brook University. Our technical validation indicates that despite the diverse digitizing devices used to produce the scans, the final 3D models (meshes) can be safely combined to collect comparable morphometric data. The entire dataset (and detailed associated metadata) is freely available through MorphoSource. Hence, these data contribute to empowering the future of primate phenomics and providing a roadmap for future digitization and archiving of digital data from other collections.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1391"},"PeriodicalIF":5.8,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142855415","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

QM40, Realistic Quantum Mechanical Dataset for Machine Learning in Molecular Science.

IF 5.8 2区综合性期刊

Scientific Data Pub Date : 2024-12-18 DOI: 10.1038/s41597-024-04206-y

Ayesh Madushanka, Renaldo T Moura, Elfi Kraka

{"title":"QM40, Realistic Quantum Mechanical Dataset for Machine Learning in Molecular Science.","authors":"Ayesh Madushanka, Renaldo T Moura, Elfi Kraka","doi":"10.1038/s41597-024-04206-y","DOIUrl":"https://doi.org/10.1038/s41597-024-04206-y","url":null,"abstract":"The growing popularity of machine learning (ML) and deep learning (DL) in scientific fields is hindered by the scarcity of high-quality datasets. While quantum mechanical (QM) predictions using DL techniques such as graph neural networks (GNNs) and generative models are gaining traction, insufficient training data remains a bottleneck. The QM40 dataset addresses this challenge by representing 88% of the FDA-approved drug chemical space. It includes molecules containing 10 to 40 atoms and composed of elements commonly found in drug molecular structures (C, O, N, S, F, Cl). QM40 offers valuable resources for researchers which include the core QM40 main dataset, containing 16 key quantum mechanical parameters for 162,954 molecules calculated using the B3LYP/6-31G(2df,p) level of theory in Gaussian16, ensuring consistency with established datasets like QM9 and Alchemy. This compatibility allows for future concatenation of QM40 with these datasets. In addition to other valuable information, the QM40 dataset offers the initial and optimized Cartesian coordinates, Mulliken charges, and detailed bond information, including local vibrational mode force constants, which serve as indicators of bond strength. QM40 can be used to benchmark both existing and new methods for predicting QM calculations using ML and DL techniques.","PeriodicalId":21597,"journal":{"name":"Scientific Data","volume":"11 1","pages":"1376"},"PeriodicalIF":5.8,"publicationDate":"2024-12-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142855420","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"综合性期刊","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0