Before revision:  Compared to other data visualization methods such as t-SNE or UMAP , TMAP increases the size of data sets that can be visualized due to its significantly lower memory requirements and running time and should find broad applicability in the age of big data. We exemplify TMAP in the area of cheminformatics with interactive maps for 1.16 million drug-like molecules from ChEMBL, 10.1 million small molecule fragments from FDB17,  and 131 thousand 3D- structures of biomolecules from the PDB Databank, and to visualize data from literature (GUTENBERG data set), cancer biology(PANSCAN data set) and particle physics(MiniBooNE data set).
After revision:  Compared to other data visualization methods such as t-SNE or UMAP for the exploration and interpretation of large data sets due to their tree-like nature, increased local and global neighborhood and structure preservation, and the transparency of the methods the algorithm is based on. We apply TMAP to the most used chemistry data sets including databases of molecules such as ChEMBL, FDB17, and 131 thousand 3D- structures of biomolecules from the PDB Databank, and to visualize data from literature (GUTENBERG data set), cancer biology(PANSCAN data set) and particle physics(MiniBooNE data set).