FAIRsharing record for: Phylogenetics Ontology
收藏中国区域地面气象要素驱动数据集 v2.0(1951-2024)
中国区域地面气象要素驱动数据集(China Meteorological Forcing Data,以下简称 CMFD)是为支撑中国区域陆面、水文、生态等领域研究而研发的一套高精度、高分辨率、长时间序列数据产品。本页面发布的 CMFD 2.0 包含了近地面气温、气压、比湿、全风速、向下短波辐射通量、向下长波辐射通量、降水率等气象要素,时间分辨率为 3 小时,水平空间分辨率为 0.1°,时间长度为 74 年(1951~2024 年),覆盖了 70°E~140°E,15°N~55°N 空间范围内的陆地区域。CMFD 2.0 融合了欧洲中期天气预报中心 ERA5 再分析数据与气象台站观测数据,并在辐射、降水数据产品中集成了采用人工智能技术制作的 ISCCP-ITP-CNN 和 TPHiPr 数据产品,其数据精度较 CMFD 的上一代产品有显著提升。 CMFD 历经十余年的发展,其间发布了多个重要版本。2019 年发布的 CMFD 1.6 是完全采用传统数据融合技术制作的最后一个 CMFD 版本,而本次发布的 CMFD 2.0 则是 CMFD 转向人工智能技术制作的首个版本。此版本与 1.6 版具有相同的时空分辨率和基础变量集,但在其它诸多方面存在大幅改进。除集成了采用人工智能技术制作的辐射和降水数据外,在制作 CMFD 2.0 的过程中,研发团队尽可能采用单一来源的再分析数据作为输入并引入气象台站迁址信息,显著缓解了 CMFD 1.6 中因多源数据拼接和气象台站迁址而产生的虚假气候突变。同时,CMFD 2.0 数据的时间长度从 CMFD 1.6 的 40 年大幅扩展到了 74 年,并将继续向后延伸。CMFD 2.0 的网格空间范围虽然与 CMFD 1.6 相同,但其有效数据扩展到了中国之外,能够更好地支持跨境区域研究。为方便用户使用,CMFD 2.0 还在基础变量集之外提供了若干衍生变量,包括近地面相对湿度、雨雪分离降水产品等。此外,CMFD 2.0 摒弃了 CMFD 1.6 中通过 scale_factor 和 add_offset 参数将实型数据化为整型数据的压缩技术,转而直接将实型数据压缩存储于 NetCDF4 格式文件中,从而消除了用户使用数据时进行解压换算的困扰。 本数据集原定版本号为 1.7,但鉴于本数据集从输入数据到研制技术都较上一代数据产品有了大幅的改变,故将其版本号重新定义为 2.0。
国家青藏高原科学数据中心 收录
MRMR
MRMR是一个专家级的多学科多模态检索基准,包含1502个经过人类专家仔细验证的查询,涵盖了23个领域。与之前的基准相比,MRMR在三个关键方面取得了进步:首先,它挑战了跨多个专业领域的检索系统,能够在不同领域之间进行细粒度的模型比较;其次,查询是推理密集型的,需要更深入地解释图像,例如诊断显微镜幻灯片;此外,还引入了矛盾检索这一新型任务,要求模型识别冲突的概念。与仅限于单个图像或单模态文档的早期基准不同,MRMR提供了一个具有多图像查询和混合模态语料库文档的现实场景。
arXiv 收录
China Health and Nutrition Survey (CHNS)
China Health and Nutrition Survey(CHNS)是一项由美国北卡罗来纳大学人口中心与中国疾病预防控制中心营养与健康所合作开展的长期开放性队列研究项目,旨在评估国家和地方政府的健康、营养与家庭计划政策对人群健康和营养状况的影响,以及社会经济转型对居民健康行为和健康结果的作用。该调查覆盖中国15个省份和直辖市的约7200户家庭、超过30000名个体,采用多阶段随机抽样方法,收集了家庭、个体以及社区层面的详细数据,包括饮食、健康、经济和社会因素等信息。自2011年起,CHNS不断扩展,新增多个城市和省份,并持续完善纵向数据链接,为研究中国社会经济变化与健康营养的动态关系提供了重要的数据支持。
www.cpc.unc.edu 收录
XS-Video
XS-Video数据集是由中国科学院自动化研究所MAIS实验室提出的一个大规模现实世界短视频传播数据集。该数据集收集了来自中国五大平台(抖音、快手、西瓜视频、今日头条、哔哩哔哩)的117720个短视频,包含381926个样本和535个话题,覆盖了从发布后的互动信息,如观看、点赞、分享、收藏、粉丝和评论等。数据集通过跨平台指标对齐方法,对视频的长期传播影响力进行评分,分为0到9级,旨在为短视频传播研究提供全面的互动信息和内容特征。
arXiv 收录
Ninapro dataset 5 (double Myo armband)
The 5th Ninapro database includes 10 intact subjects recorded with two Thalmic Myo (https://www.myo.com/) armbands. The database can be used to test the Myo armbands separately as well. The database is thoroughly described in the paper: "Pizzolato et al., Comparison of Six Electromyography Acquisition Setups on Hand Movement Classification Tasks, Plos One 2017 (accepted).". Please, cite this paper for any work related to the 5th Ninapro database. The dataset is part of the Ninapro database (http://ninapro.hevs.ch/). Please, look at the database for more information. Acquisition Protocol The subjects have to repeat several movements represented by movies that are shown on the screen of a laptop. The experiment is divided in three exercises: 1. Basic movements of the fingers 2. Isometric, isotonic hand configurations and basic wrist movements 3. Grasping and functional movements During the acquisition, the subjects were asked to repeat the movements with the right hand. Each movement repetition lasted 5 seconds and was followed by 3 seconds of rest. The protocol includes 6 repetitions of 52 different movements (plus rest) performed by 10 intact subjects. The movements were selected from the hand taxonomy as well as from hand robotics literature. Acquisition Setup The muscular activity is gathered using 2 Thalmic Myo armbands. The database can be used to test the Myo armbands separately as well. The subjects in this database wore two Myo armbands one next to the other, including 16 active single–differential wireless electrodes. The top Myo armband is placed closed to the elbow with the first sensor placed on the radio humeral joint, as in the standard Ninapro configuration for the equally spaced electrodes; the second Myo armband is placed just after the first, nearer to the hand, tilted of 22.5 degrees. This configuration provides an extended uniform muscle mapping at an extremely affordable cost. The Myo sensors do not require the arm to be shaved and after few minutes the armband tighten very firmly to the arm of the subject. The sEMG signals are sampled at a rate of 200 Hz. The kinematic information is recorded with a dataglove (22 sensors Cyberglove 2). The cyberglove signal corresponds to raw data from the cyberglove sensors located as shown in the following pictures. The raw data are declared to be proportional to the angles at the joints in the CyberGlove manual. Data Sets For each exercise, for each subject, the database contains one matlab file with synchronized variables. The variables included in the matlab files are: • subject: the subject number; • sensor: the name of the sEMG sensor; • frequency: the frequency in Hertz of the recorded data • exercise: exercise number; • emg: sEMG signal. Columns 1-8 are the electrodes equally spaced around the forearm at the height of the radio humeral joint. Columns 9-16 represent the second Myo, tilted by 22.5 degrees clockwise. • acc (3 columns): raw signals from the three axis accelerometer of the first Myo, found in the Myo DB • glove (22 columns): uncalibrated signal from the 22 sensors of the CyberGlove. The raw data are declared to be proportional to the angles of the joints in the CyberGlove manual. • stimulus: the original label of the movement repeated by the subject; • restimulus: the corrected stimulus, processed with movement detection algorithms; • repetition: stimulus repetition index; • rerepetition: restimulus repetition index; • age: subject’s age; • gender: subject’s gender, ”m” for male ”f” for female; • weight: subject’s weight in kilograms; • height: subject’s height in centimeters; • laterality: subject’s laterality, ”r” for right-handed, ”l” for left-handed; • circumference: circumference of the subject’s forearm at the radio-humeral joint height, measured in centimeters;
Mendeley Data 收录
