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Plotting

papertrail uses bokeh for interactive, exportable visualisations.

Dashboards are rendered with horizontal tabs, so each plot is shown in its own view without squeezing the layout.

Current Plot Set

The first set is modeled on common ADS-style author views:

  • publications per year
  • citations per year
  • refereed vs non-refereed breakdown when the source provides that metadata
  • citation distribution buckets (0, 1-4, 5-9, 10-24, 25-49, 50-99, 100+)
  • publication type breakdown (e.g., journal-article, proceedings)
  • top journals/venues by publication count
  • publications vs year in grouped/stacked, total/normalized modes
  • citations vs year in grouped/stacked, total/normalized modes
  • index-vs-time overlays (total vs refereed)
  • index snapshot panel (total vs refereed) including m and riq

These views are intended to mirror the core summary analyses available in ADS Analyze for author-centric bibliometric exploration.

When --source ads is used, papertrail also queries the ADS Metrics API (POST /v1/metrics) to enrich index time-series data. ADS provides native time-series values for h, g, i10, i100, read10, and tori. ADS provides m and riq as indicators, but not as native time-series in the documented response types.

For this reason, the dashboard includes both:

  • time-series plots for indices where yearly series are available
  • a dedicated snapshot panel where m and riq are shown from ADS indicator payloads (and all indices are displayed in total vs refereed form)

Export Formats

  • html: standalone interactive dashboard that can be opened directly in a browser
  • json: embeddable Bokeh JSON suitable for integration into a web page
  • png: static image export
  • pdf: static document export

CLI Usage

papertrail plots "Marie Curie"

This writes a standalone HTML dashboard by default.

User-Data Browser App

papertrail can also open an interactive Bokeh server page to browse the local user-data database and export the current table as CSV or JSON:

papertrail user-data-app

Optional flags:

  • --db-path .papertrail/user-data.sqlite3
  • --port 5006
  • --show/--no-show
papertrail plots "Marie Curie" \
  --source ads \
  --ads-author-query "Curie, M" \
  --html michele_dashboard.html \
  --json michele_dashboard.json \
  --png michele_dashboard.png \
  --pdf michele_dashboard.pdf

Python API

from papertrail import AuthorProfile

profile = AuthorProfile("Marie Curie").fetch()
dashboard = profile.dashboard()
profile.export_dashboard("curie_dashboard.html", fmt="html")
profile.export_dashboard("curie_dashboard.json", fmt="json")
profile.export_dashboard("curie_dashboard.png", fmt="png")
profile.export_dashboard("curie_dashboard.pdf", fmt="pdf")

Why Bokeh

Bokeh gives us interactive browser-based plots while also providing exportable artifacts that are easy to embed into web pages and other digital materials.

For static CV inclusion, the HTML output is useful as a linked artifact, while the JSON output is suitable for custom embedding in a personal site.