Spartan Helmet
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
PASCAL VOC 2007
这个挑战的目标是从现实场景中的许多视觉对象类别中识别对象(即不是预先分割的对象)。它基本上是一个监督学习问题,因为它提供了一组标记图像的训练集。已选择的 20 个对象类别是: 人:人 动物:鸟、猫、牛、狗、马、羊 交通工具:飞机、自行车、船、公共汽车、汽车、摩托车、火车 室内:瓶子、椅子、餐桌、盆栽、沙发、电视/显示器 将有两个主要比赛和两个较小规模的“品酒师”比赛。内容:提供的训练数据由一组图像组成;每个图像都有一个注释文件,为图像中存在的 20 个类别之一中的每个对象提供一个边界框和对象类别标签。请注意,来自多个类的多个对象可能出现在同一图像中。
OpenDataLab 收录
学生课堂行为数据集 (SCB-dataset3)
学生课堂行为数据集(SCB-dataset3)由成都东软学院创建,包含5686张图像和45578个标签,重点关注六种行为:举手、阅读、写作、使用手机、低头和趴桌。数据集覆盖从幼儿园到大学的不同场景,通过YOLOv5、YOLOv7和YOLOv8算法评估,平均精度达到80.3%。该数据集旨在为学生行为检测研究提供坚实基础,解决教育领域中学生行为数据集的缺乏问题。
arXiv 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
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
