Published December 21, 2018 | Version v1
Dataset Open

HamSCI 2017 Solar Eclipse QSO Party (SEQP) Data

  • 1. New Jersey Institute of Technology


The 2017 Solar Eclipse QSO Party (SEQP) was a ham radio contest-like experiment designed to study the effects of the 21 August 2017 Total Solar Eclipse on the ionosphere and radio propagation. The SEQP was coordinated by the Ham Radio Science Citizen Investigation ( This archive contains the locations, logs, and station descriptions submitted by operators to following the SEQP, as well as an aggregated, geolocated archive in CSV format of all Reverse Beacon Network (RBN), WSPRNet, PSKReporter, DXCluster, and SEQP log QSOs. The final rules of the SEQP have also been archived here. More information about the SEQP can be found at, and a published analysis of RBN observations over the United States by Frissell et al. (2018) may be found at

Please see README.pdf for detailed information regarding this dataset.

Data Rules of the Road

Please acknowledge the HamSCI project and Nathaniel Frissell, W2NAF, when using data from this archive in presentations and publications. Also, please be sure to acknowledge the upstream data providers (Reverse Beacon Network, PSKReporter, WSPRNet, and DX Cluster) as appropriate.


Please contact Nathaniel Frissell, W2NAF, at or


Support for the Solar Eclipse QSO Party is in part thanks to NSF Grant AGS‐1552188/479505‐19C75 and the New Jersey Institute of Technology Center for Solar Terrestrial Research. Special thanks to the operators and participants of numerous ham radio observation networks contributing data to this project. RBN data are provided by, with thanks to Felipe Ceglia (PY1N/CT7ANO), Pete Smith (N4ZR), and Dick Williams (W3OA). Thanks to Philip Gladstone (N1DQ) for PSKReporter ( observations. WSPRNet observations were downloaded from Thanks to Bill Engelke (AB4EJ) for providing DXCluster data. We thank the global amateur radio community, Ward Silver (N0AX), and the ARRL for making the SEQP possible. Select geolocation data is provided by We acknowledge the use of the Free Open Source Software projects used in this analysis: Ubuntu Linux, python, matplotlib, NumPy, SciPy, pandas, and others.



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