1) The document discusses using full-text data rather than just metadata to create improved term maps for visualizing topics in scientific literature. 2) It compares different approaches for creating term maps using full-text data from publications in the Journal of Informetrics, including using titles/abstracts vs full text, binary vs full counting of term co-occurrences, and mapping at the publication level vs paragraph level. 3) The results show that full-text data yields richer maps than just titles and abstracts, and that full counting is preferable to binary counting when using full text. Paragraph-level maps provide more fine-grained structure but areas may not always represent literature topics.