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Palestine Health Annual Report 2018|公共卫生数据集|健康统计数据集
收藏Global Health Data Exchange ()2024-06-26 收录
下载链接：
https://ghdx.healthdata.org/record/palestine-health-annual-report-2018
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链接失效反馈资源简介：
The Palestine Health Annual Report 2018 presents general demographic and population information, and health indicators, as well as data on health sector topics, including government spending and programs, health facilities use and health infrastructure. The report includes detailed information on infectious diseases, cancers, and mortality. Causes of death tables include ICD-10 codes, and are broken down by age, sex, and location.
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Paper III (Walker et al. 2024)
Data products used in 3-D CMZ Paper III, Walker et al. (2024). The full cloud catalogue is provided in tabular format, along with a full CMZ map showing the clouds and their assigned IDs. For each cloud ID in the published catalogue there are: - Individual cube cutouts from the MOPRA 3mm CMZ survey (HC3N, HCN, and HNCO). - Individual cube cutouts from the APEX 1mm CMZ survey (13CO, C18O, and H2CO). - Cloud-averaged spectra of the ATCA H2CO 4.83 GHz line. - PV slices of the ATCA H2CO 4.83 GHz line, taken across the major axis of the source. - Where applicable, there are mask files which correspond to the different velocity components of the cloud. In these cases, there are two mask files per velocity component, corresponding to the different masking approaches described in the paper.
DataCite Commons 收录
UAVDT Dataset
The authors constructed a new UAVDT Dataset focused on complex scenarios with new level challenges. Selected from 10 hours raw videos, about 80, 000 representative frames are fully annotated with bounding boxes as well as up to 14 kinds of attributes (e.g., weather condition, flying altitude, camera view, vehicle category, and occlusion) for three fundamental computer vision tasks: object detection, single object tracking, and multiple object tracking.
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gold-prices
自1950年以来的每月黄金价格数据，以美元计价（伦敦市场）。数据来源于德国联邦银行。
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Google Scholar
Google Scholar是一个学术搜索引擎，旨在检索学术文献、论文、书籍、摘要和文章等。它涵盖了广泛的学科领域，包括自然科学、社会科学、艺术和人文学科。用户可以通过关键词搜索、作者姓名、出版物名称等方式查找相关学术资源。
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MIMII数据集
MIMII数据集是由日立有限公司研究与开发集团创建的，专注于工业机器异常声音检测的数据集。该数据集包含26,092个正常操作条件下的声音文件，涵盖阀门、泵、风扇和滑轨四种机器类型。数据集的创建过程中，使用了TAMAGO-03麦克风阵列进行声音采集，并在多个真实工厂环境中混合背景噪声以模拟实际环境。MIMII数据集主要用于机器学习和信号处理社区开发自动化设施维护系统，特别是在无监督学习场景下检测机器异常声音。
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