Complexity Inequality and Internet data|经济不平等数据集|互联网普及率数据集
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Solar Radiation Data
该数据集包含全球多个地点的太阳辐射数据,涵盖了不同时间段和气象条件下的辐射强度。数据包括直接辐射、散射辐射和总辐射等指标,适用于太阳能资源评估和气候研究。
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PCLT20K
PCLT20K数据集是由湖南大学等机构创建的一个大规模PET-CT肺癌肿瘤分割数据集,包含来自605名患者的21,930对PET-CT图像,所有图像都带有高质量的像素级肿瘤区域标注。该数据集旨在促进医学图像分割研究,特别是在PET-CT图像中肺癌肿瘤的分割任务。
arXiv 收录
OECD Statistics
OECD Statistics 数据集包含了经济合作与发展组织(OECD)发布的各种统计数据,涵盖了经济、社会、环境、教育、科技等多个领域。数据集提供了详细的指标和时间序列数据,帮助研究人员和政策制定者分析和理解全球经济和社会发展趋势。
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Data From NSCLC-Radiomics
This collection contains images from 422 non-small cell lung cancer (NSCLC) patients. For these patients pretreatment CT scans, manual delineation by a radiation oncologist of the 3D volume of the gross tumor volume and clinical outcome data are available. This dataset refers to the Lung1 dataset of the study published in Nature Communications. In short, this publication applies a radiomic approach to computed tomography data of 1,019 patients with lung or head-and-neck cancer. Radiomics refers to the comprehensive quantification of tumour phenotypes by applying a large number of quantitative image features. In present analysis 440 features quantifying tumour image intensity, shape and texture, were extracted. We found that a large number of radiomic features have prognostic power in independent data sets, many of which were not identified as significant before. Radiogenomics analysis revealed that a prognostic radiomic signature, capturing intra-tumour heterogeneity, was associated with underlying gene-expression patterns. These data suggest that radiomics identifies a general prognostic phenotype existing in both lung and head-and-neck cancer. This may have a clinical impact as imaging is routinely used in clinical practice, providing an unprecedented opportunity to improve decision-support in cancer treatment at low cost. The dataset described here (Lung1) was used to build a prognostic radiomic signature. The Lung3 dataset used to investigate the association of radiomic imaging features with gene-expression profiles consisting of 89 NSCLC CT scans with outcome data can be found here: NSCLC-Radiomics-Genomics. For scientific inquiries about this dataset, please contact Dr. Hugo Aerts of the Dana-Farber Cancer Institute / Harvard Medical School (hugo_aerts@dfci.harvard.edu). More Description
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