five

512-Channel Kitt Peak Vacuum Telescope Corrected Magnetograms|太阳磁场数据集|天文数据数据集

收藏
DataONE2023-07-31 更新2024-06-08 收录
太阳磁场
天文数据
下载链接:
https://search.dataone.org/view/sha256:f71911cf3280e363e53a72a2afaee8be61da0b16f1950f715844e9975a301e05
下载链接
链接失效反馈
资源简介:
Citation and Acknowledgements Please cite this database (see above), as well as adding the following acknowledgement: \"512-Channel Kitt Peak Vacuum Telescope Corrected Magnetograms were downloaded from the solar dynamo dataverse (https://dataverse.harvard.edu/dataverse/solardynamo), maintained by Andrés Muñoz-Jaramillo.\" Main Limitations The main limitations of these data are: Cadence is one image per day, but it is common to have missing days Some magnetograms may display reading errors (they look like repeated vertical rows/patterns). Some magnetograms may have slight variations due to cloud cover. Overall, the data is in great shape, especially compared to the original state. We have used them to create a catalog of active regions https://doi.org/10.7910/DVN/QEMSZ2t However, it is worth visually inspecting the magnetograms before using them. File Structure The database is comprised of one tar.gz file per month, each containing all available magnetograms for that given month. A month without data will not have an associated tar.gz file. Each magnetogram is stored using fits format with header information that includes a new set of parameters related to the correction. They are relatively large files because we have kept the original data untouched. The fits table has 5 layers (across the 3rd dimension): Layer 1: Original untouched data (Mx/cm^2) Layer 2: 868.8nm wing intensity (intensity units; gives a sense of cloud coverage) Layer 3: Fully corrected magnetogram (Mx/cm^2) Layer 4: New X pixel position to transform original into corrected geometry Layer 5: New Y pixel position to transform original into corrected geometry Description The KPVT/512 instrument operated by doing four scanning passes, each of a quarter if the solar disk, a process that would take a total of ~48 minutes (~12 minutes per scan). These would happen as the Sun moved in the sky. In order to assemble the 4 scans into a single full disk image, it is necessary to fit the angle inclination of the slit with respect to the scan, atmospheric refraction, as well as taking limb information into consideration to deal with offsets in each of the scans. Jack Harvey and I (Andrés Muñoz-Jaramillo) worked in 2016 on improve on this process as well as making the identification of the zero point of the magnetograph much more stable. This has resulted in a significantly improvement on the quality of the majority of the KPVT/512 magnetograms. While we never got around to make a publication documenting these changes, we feel that it is important for us to ensure that these magnetograms are open and available to the public so we are releasing them as they are. If possible, we will document better the process of improvement and will update this description with more information. Current corrections to the data include: 1. Remove the slit curvature. 2. Correct for atmospheric refraction. 3. Find the boundaries of the first two isophotes (circles of constant 868.8nm wing intensity) and find the center of each swath and its offset with respect of a perfectly centered Sun. During this step several things are done to try to remove pathologic deviations from an almost circular Sun. 4. Find the optimum values that minimize the difference between both isophotes and a circular sun with the size of the ephemeris radius that is perfectly centered in the image array. 5. Create the corrected image by remaping it using natural neighbor interpolation. 6. Remove pixels outside the ephemeris radius and assign them a NaN value. 7. Intensity adjustment based on previous and next observation (to fix drifting amplitudes on days of strong cloud coverage).
创建时间:
2023-11-21
用户留言
有没有相关的论文或文献参考?
这个数据集是基于什么背景创建的?
数据集的作者是谁?
能帮我联系到这个数据集的作者吗?
这个数据集如何下载?
点击留言
数据主题
具身智能
数据集  4098个
机构  8个
大模型
数据集  439个
机构  10个
无人机
数据集  37个
机构  6个
指令微调
数据集  36个
机构  6个
蛋白质结构
数据集  50个
机构  8个
空间智能
数据集  21个
机构  5个
5,000+
优质数据集
54 个
任务类型
进入经典数据集
热门数据集

MOOCs Dataset

该数据集包含了大规模开放在线课程(MOOCs)的相关数据,包括课程信息、用户行为、学习进度等。数据主要用于研究在线教育的行为模式和学习效果。

www.kaggle.com 收录

中国交通事故深度调查(CIDAS)数据集

交通事故深度调查数据通过采用科学系统方法现场调查中国道路上实际发生交通事故相关的道路环境、道路交通行为、车辆损坏、人员损伤信息,以探究碰撞事故中车损和人伤机理。目前已积累深度调查事故10000余例,单个案例信息包含人、车 、路和环境多维信息组成的3000多个字段。该数据集可作为深入分析中国道路交通事故工况特征,探索事故预防和损伤防护措施的关键数据源,为制定汽车安全法规和标准、完善汽车测评试验规程、

北方大数据交易中心 收录

URPC系列数据集, S-URPC2019, UDD

URPC系列数据集包括URPC2017至URPC2020DL,主要用于水下目标的检测和分类。S-URPC2019专注于水下环境的特定检测任务。UDD数据集信息未在README中详细描述。

github 收录

Beijing Traffic

The Beijing Traffic Dataset collects traffic speeds at 5-minute granularity for 3126 roadway segments in Beijing between 2022/05/12 and 2022/07/25.

Papers with Code 收录

Houston2013, Berlin, Augsburg

本研究发布了三个多模态遥感基准数据集:Houston2013(高光谱和多光谱数据)、Berlin(高光谱和合成孔径雷达数据)和Augsburg(高光谱、合成孔径雷达和数字表面模型数据)。这些数据集用于土地覆盖分类,旨在通过共享和特定特征学习模型(S2FL)评估多模态基线。数据集包含不同模态和分辨率的图像,适用于评估和开发新的遥感图像处理技术。

arXiv 收录