核发6年免检标志地点|车辆管理数据集|公共服务数据集
收藏flames-and-smoke-datasets
该仓库总结了多个公开的火焰和烟雾数据集,包括DFS、D-Fire dataset、FASDD、FLAME、BoWFire、VisiFire、fire-smoke-detect-yolov4、Forest Fire等数据集。每个数据集都有详细的描述,包括数据来源、图像数量、标注信息等。
github 收录
MeSH
MeSH(医学主题词表)是一个用于索引和检索生物医学文献的标准化词汇表。它包含了大量的医学术语和概念,用于描述医学文献中的主题和内容。MeSH数据集包括主题词、副主题词、树状结构、历史记录等信息,广泛应用于医学文献的分类和检索。
www.nlm.nih.gov 收录
Breast Cancer Dataset
该项目专注于清理和转换一个乳腺癌数据集,该数据集最初由卢布尔雅那大学医学中心肿瘤研究所获得。目标是通过应用各种数据转换技术(如分类、编码和二值化)来创建一个可以由数据科学团队用于未来分析的精炼数据集。
github 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。
stats.oecd.org 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
