These datasets and a Jupyter notebook reproduce figures in a publication by Saito et al in Scientific Reports. The notebook also serves as a demo for kernel density estimation (smoothing) of 2D data using Python. Details are described in the notebook. If you have no idea about ipynb format, please see HTML version with your web browser instead. It contains exactly the same codes and results as ipynb version. This work is supported by the Elements Strategy Initiative Center for Magnetic Materials (ESICMM) under the outsourcing project of the Ministry of Education, Culture, Sports, Science, Technology (MEXT) and the Magnetic Materials for High- Efficient Motors (MagHEM) project commissioned by the New Energy and Industrial Technology Development Organization (NEDO). K.S. has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 701647. H.H. is partly supported by JST CREST grant number JPMJCR1761.