清远市连山壮族瑶族自治县教育局实施清单明细信息|教育管理数据集|行政管理数据集
收藏ERIC (Education Resources Information Center)
ERIC (Education Resources Information Center) 是一个广泛的教育文献数据库,包含超过130万条记录,涵盖从1966年至今的教育研究、政策和实践。数据集内容包括教育相关的期刊文章、书籍、研究报告、会议论文、技术报告、政策文件等。
eric.ed.gov 收录
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
EdNet-Behavior Dataset
EdNet-Behavior Dataset 是一个包含学生学习行为数据的大型数据集,主要用于教育数据挖掘和个性化学习系统的研究。数据集包括学生在不同学习平台上的互动记录,如答题、观看视频、参与讨论等。
github.com 收录
Food101
Food101是一个包含101种食物类别的数据集,共有101,000张图片。每个类别提供250张手动审查的测试图像和750张训练图像。训练图像未经清理,因此仍包含一定量的噪声。所有图像都被缩放到最大边长为512像素。图像包括光照、视角和背景的变化,使其成为一个具有挑战性的数据集。
github 收录
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
DataCite Commons 收录
