Some suggestions for visualizing periodical data from MJP. What are your ideas?
This page features a growing list of ideas for visualizing MJP data and metadata. The following list of color-coded categories indicates the variety of information categories that inform each idea.
Build tables that list all authors who published in a magazine, first sorted alphabetically and then ranked by the number of each author’s contributions. Also, build tables that represent all texts of a certain genre (like poetry) that appeared in a magazine. [▘authors, genres; ▘entire journal; ▘MODS files; ▘table; ▘Google Docs charts; ▘new info]
Use a bar chart to compare the same author’s contributions (by number and frequency over time) in multiple journals, or the contributions that multiple authors made in a single journal. [▘selected authors; ▘one or more journals; ▘MODS files; ▘bar chart; ▘Google Docs charts ▘new view]
Same as idea 2, but with genre: compare the number of items from the same genre (e.g., letters) published in multiple journals, or the number of items from multiple genres (e.g., poetry, fiction, and articles) that appear in a single journal. [▘selected genres; ▘one or more journals; ▘MODS files; ▘bar chart; ▘ ? ▘new info]
Provide new and exciting ways to see, in a single instance, an entire run of a journal, thus amalgamating the information that’s currently segregated in the contents pages for the various issues of an MJP journal. [▘authors, titles, page lengths; ▘entire journal; ▘MODS files; ▘nodetree, sunburst graphics; ▘Protovis ▘new view]
Compare multiple journals in terms of their total pagination (page length of all issues added together) or their total word counts, and then display the results as relatively-sized circles—and perhaps represent each journal further as a set of smaller circles, each representing the length of a single issue. [▘word or page lengths; ▘one or more journals; ▘MODS files (for page counts), TEI files (for word counts); ▘?; ▘?; ▘new info]
Use a dataset drawn from several journals to sort out which authors published in all of them, which authors published in any two, and which published in only one—and then represent that visually (maybe using some sort of Venn diagram, with overlapping circles). Also, calculate/represent the number of each author’s contributions to each set of journals. [▘authors, titles; ▘multiple journals; ▘MODS files; ▘network graph; ▘Gephi, ORA; ▘new info]
Identify the gender of contributors against a list of names to group them by gender. Then, count the contributions (item by item, or page by page) that authors of either gender made to each magazine and represent those percentages with a chart. Also, complicate the binary encoding of gender by adding fluidity and other states to the dataset. [▘gender of authors; ▘one journal; ▘MODS files; ▘pie chart; ▘ Google Docs charts; ▘new info]
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