Tana is a modern PKM app that combines an outliner interface with a powerful supertag system — every bullet node can be given a type with structured fields, turning your notes into queryable structured data. For researchers, this means your reading notes on academic papers can become a semantic database: filterable by author, year, methodology, or finding type.
The reading phase is better on an e-reader than in a browser. Converting your academic PDFs to EPUB lets you read comfortably on a Kindle or Kobo, build up highlights, and then bring those highlights into Tana via Readwise — where they become typed, connected knowledge nodes.
| App | Primary model | Structured data | Queries | E-reader sync | Best for |
|---|---|---|---|---|---|
| Tana | Outliner + supertags | Excellent (typed nodes) | Inline queries | Via Readwise | Semantic knowledge graph |
| Notion | Blocks + databases | Good (database views) | Filtered views | Via Readwise | Team wikis, databases |
| Obsidian | Linked markdown | Plugin (Dataview) | Dataview queries | Via Readwise | Long-term knowledge graph |
| Logseq | Outliner + graph | Properties (YAML) | Limited queries | Via Readwise | Daily notes + PKM |
| Roam Research | Outliner + links | Attributes (limited) | Limited | Via Readwise | Dense networked thinking |
| Heptabase | Whiteboard + cards | No (visual only) | No | Via Readwise | Visual synthesis |
Before starting your reading workflow, define a #paper supertag in Tana with these fields:
#author nodes)#journal nodes)Every paper you add to Tana becomes a structured record with these fields populated, enabling powerful queries across your literature.
#concept nodes or create new ones from the highlights.Tana is a modern outliner-based PKM app where every bullet point can be tagged with a 'supertag' — a user-defined type that adds structured fields to the node. For example, tagging a node as #paper adds fields for Author, Year, DOI, and Summary automatically. This turns your notes into a structured database: you can query 'all papers tagged #neuroscience published after 2020' directly in the outliner. For academic reading, this means your highlights and notes become typed, queryable data — not just raw text.
Tana does not have a native PDF viewer or EPUB importer. The recommended workflow is: convert PDF to EPUB → read on Kindle or Kobo → export highlights (via Readwise, which has a Tana integration) → highlights arrive in Tana as structured nodes tagged with the paper's supertag, ready for you to annotate and connect. Readwise's Tana integration is the smoothest bridge between e-reader highlights and the Tana knowledge graph.
Tana's primary strength over Obsidian is structured data: supertags create a typed schema for your notes, so you can query and filter papers, authors, and concepts as database records. Obsidian's strength is its link graph and plugin ecosystem. For researchers who want database-style queries over their literature (e.g., 'show all papers by Author X that I've rated 5 stars'), Tana is more powerful. For long-form writing and graph visualisation, Obsidian has the edge.
Readwise (readwise.io) syncs highlights from Kindle, Kobo, and other reading apps and can push them to Tana via an official integration. In Readwise's Export settings, connect your Tana account and enable the Tana sync. Highlights appear in Tana as supertag-typed nodes under a designated inbox node, ready for you to tag with additional supertags (#paper, #concept, #author) and connect to your knowledge graph.
Yes, especially if you define a consistent #paper supertag schema upfront (fields: Title, Author, Year, Journal, DOI, Key finding, Methodology, Quality rating). Each paper becomes a structured record. You can then create Tana views that filter and sort papers by methodology, quality, or finding type — similar to a systematic review evidence table, but built interactively as you read.
Related: Obsidian + EPUB · Heptabase + EPUB · RemNote + EPUB · PDF to EPUB for researchers