珠海市斗门区城市管理和综合执法局行政许可事项办理材料清单信息|城市管理数据集|政务服务数据集
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
The MaizeGDB
The MaizeGDB(Maize Genetics and Genomics Database)是一个专门为玉米(Zea mays)基因组学研究提供数据和工具的在线资源。该数据库包含了玉米的基因组序列、基因注释、遗传图谱、突变体信息、表达数据、以及与玉米相关的文献和研究工具。MaizeGDB旨在支持玉米遗传学和基因组学的研究,为科学家提供了一个集成的平台来访问和分析玉米的遗传和基因组数据。
www.maizegdb.org 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
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
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
