
The data here are for an article submission to Journal of Geophysical Research: Machine Learning and Computation. It includes: A dataset of THEMIS and MMS magnetic field and ion observations used to train the model (mms_and_themis.csv) The bow shock (bs.csv) and magnetopause (mp.csv) crossings found from the resulting predictions A labeled dataset containing MMS1 observations that is joined with the labels of Olshevsky et al (2021) (labeled_mms1_NovDec2017.csv). Our model's predictions are also included in this dataset. These datasets can be easily loaded using the Python Data Analysis Library, Pandas. The models we created for our article are included in a separate Zenodo repository.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 1 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
