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ResearchRabbit

by ResearchRabbit

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Visual citation mapping for discovering related papers

Pricing
Free
What it costs
Free to use, with no paid tier required for core features
Available on
Web
Category
Research

What ResearchRabbit does

ResearchRabbit approaches literature discovery through relationships rather than keywords. You add a few papers you already know are relevant, and it builds a visual network of works that cite them, that they cite, and that share citation patterns, so you can move outward through a field and see clusters and central works. Collections can be shared with collaborators, updates notify you when new related work appears, and the service exports to reference managers. It has been free to use since launch, which makes it unusually accessible for students. It suits anyone starting in an unfamiliar area, or checking that a review has not missed an important strand. The limitations are purpose and interface: it finds and relates papers but does not read or extract from them, the network view becomes crowded on broad topics, and coverage depends on the underlying citation databases, so very recent or obscure work may be missing.

How to use it

Sign up free at researchrabbit.ai and create a collection, then seed it with three to five papers you know are central to your question; the quality of everything that follows depends on those seeds, so choose deliberately rather than adding whatever you find first. Use the similar work and earlier or later work views to expand outward, adding relevant results to your collection as you go, which refines subsequent suggestions. Switch to the network graph to see clusters and identify the papers that connect them, which is usually where the important reviews sit. Set up alerts on a collection so new work arrives as it is published. Export to a reference manager for writing. Two practical notes: broad topics produce crowded graphs, so narrow the collection rather than trying to read the whole field at once, and use it alongside a tool that extracts findings, since it deliberately does not do that itself.

Best for

  • exploring a new field
  • finding related work
  • checking review coverage
  • citation networks
  • collaborative reading lists

Strengths and limitations

What it does well

  • Free with no subscription for core features.
  • Visual networks reveal clusters and central papers.
  • Alerts notify you of new related work.
  • Exports to common reference managers.

Where it falls short

  • Does not read or extract findings from papers.
  • Graphs become crowded on broad topics.
  • Coverage depends on underlying citation databases.
citation mappingliterature discoveryresearch visualisationreference managementcollaboration

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