Comprehensive Bitcoin holdings, market data, and treasury information for Lightning Network Public Channels ()

I collected this data about the structure of the Lightning network from December 2019 to March 2020 for use in one research. Now I am happy to share this data so that you can do something interesting too.

Dataset structure

The dataset contains two folders: channels (which are edges in terms of the graph) and nodes (list of additional node's features like geo coordinates, alias of the node in the network and etc.)

The filename is the timestamp of the snapshot in the format %Y_%m_%d_%h_%m_%s. So you can match files from nodes and channels folders by the filename.

The NASA Marshall Space Flight Center Lightning Nitrogen Oxides Model (LNOM) combines detailed, flash-specific measurements of lightning with both theoretical and empirical laboratory results to obtain estimates of lightning NOx production. Each LNOM dataset is based on measurements from a specific regional VHF Lightning Mapping Array (LMA), and on ground flash location, peak current, and stroke multiplicity data from the National Lightning Detection Network (NLDN). Both the LMA and NLDN data are used to determine the flash type (ground or cloud) of each flash occurring within an analysis cylinder. The LNOM analyzes the LMA sources to estimate the total channel length of each flash. It also produces the Segment Altitude Distribution (SAD) product by dicing up the lightning channel into 10-m segments, and then tallies those segments as a function of altitude. From all of the 10-m segments, the LNOM computes the vertical lightning NOx profile inside the analysis cylinder and the total NOx produced by each flash. A summation of the NOx profiles contributed to the analysis cylinder by each flash gives the final lightning NOx profile product for the analysis period studied (typically a 1 month profile). The LNOM NOx profiles include NOx from several non-return stroke lightning NOx production mechanisms. Users of LNOM data typically include regional air quality and global chemistry/climate modelers who need to better-parameterize lightning NOx sources. Rather than assigning an unrealistic fixed amount of NOx to ground and cloud flashes, the modeler can employ LNOM data to assign realistic (and statistical) NOx profiles to each flash.

The attached is the dataset assoicated with the paper titled "Guangdong Lightning Mapping Array: Errors Evaluation and Preliminary Results" which was submitted to Journal of Earth and Space Science.

The Guangdong Lightning Mapping Array (GDLMA), as the first LMA in China, was deployed in Guangzhou, Guangdong province, China, in November 2018 by the Chinese Academy of Meteorological Sciences (CAMS) and New Mexico Institute of Mining and Technology (NMT). An evaluation was conducted using Monte Carlo and an aircraft track. The average timing uncertainty of GDLMA is 35 ns based on the distributions of reduced chi-square values. The detection efficiency of radiation sources within a 100 km range of the center of GDLMA exceeds 90%. Based on the aircraft track, at an altitude of 4-5 km within the network, the average horizontal error was 13 m and the average horizontal error was 41 m, consistent with the Monte Carlo results. Location errors outside the network exhibit noticeable directionality. The ability to characterize lightning channels varies with different location errors. In locations that are far from the network center, only the basic structure of lightning flash can be presented, while closer to the network, the flash channel structure can be mapped well. Compared with Low-to-Mid Frequency E-field Detection Array (MLFEDA), they were generally similar in overall structure, and some lightning flash characteristics such as flash duration and convex hull area exhibited consistency. However, GDLMA demonstrated better channel characterization capability, while MFLEDA performed better in processes such as leader/return strokes.