The reproducible, equity-aware, question-driven, AI-assisted bibliometric toolkit for working biomedical researchers.
scimapR is a comprehensive R package for bibliometric and scientometric analysis. It provides unified ingestion from 12+ bibliographic sources, classical and modern science-mapping analytics, embedding-based research cluster discovery, and a publication-ready Shiny application – all in one CRAN-compatible package with tibble-based outputs and viridis-themed visualisations.
sm_refresh(),
sm_staleness(), sm_lock()).sm_question(),
sm_screen_against_question()).sm_certificate(), sm_verify_certificate(),
sm_rebuild_from_cert()).sm_author_trajectory()).sm_audit_geographic(), sm_audit_gender()).sm_chat()).scimapR is inspired by and designed as a complement to the excellent bibliometrix package by Massimo Aria and Corrado Cuccurullo (2017, Journal of Informetrics, doi:10.1016/j.joi.2017.08.007).
bibliometrix is the foundational R package for science mapping. It pioneered many of the analyses that scimapR also provides. scimapR is not a fork, not a derivative, and contains no code copied or adapted from bibliometrix. For shared bibliographic formats, scimapR ships clean-room parsers written from public format specifications.
First-class round-trip interop is provided:
M <- sm_to_bibliometrix(corpus) # use with bibliometrix
corpus <- as_sm_corpus(M) # come back to scimapR
See vignette("relationship-to-bibliometrix") for details.
# Install from GitHub (development version)
# install.packages("pak")
# pak::pak("CTTIR/scimapR")
install.packages("scimapR", repos = c('https://cttir.r-universe.dev', 'https://cloud.r-project.org'))
library(scimapR)
# Generate a synthetic corpus
corpus <- sm_example_corpus(n_works = 100, seed = 42)
print(corpus)
#>
#> ── <sm_corpus> ─────────────────────────────────────────────────────────────────
#> Works: 100 | Authors: 80 | Institutions: 0
#> Years: 2015 - 2024
#> Sources (journals): 10
#> Embeddings: 100 x 64
#> Provenance: synthetic (100)
#> Status: Unlocked (last refreshed: 2026-10-23 19:47 UTC)
# Visualise production
sm_plot_production(corpus)
| Module | Functions |
|---|---|
| File ingestion |
sm_read_bib(), sm_read_ris(), sm_read_wos(), sm_read_scopus(), sm_read_pubmed_xml(), … |
| API ingestion |
sm_fetch_openalex(), sm_fetch_crossref(), sm_fetch_pubmed(), sm_fetch_semantic_scholar(), … |
| Enrichment |
sm_enrich_unpaywall(), sm_enrich_altmetric(), sm_enrich_concepts(), … |
| Networks |
sm_network_citation(), sm_network_cocitation(), sm_network_coupling(), sm_network_collab(), sm_network_coword()
|
| Embeddings |
sm_embed_works(), sm_cluster_hdbscan(), sm_cluster_leiden(), sm_cluster_label()
|
| Indicators |
sm_metric_h_index(), sm_metric_disruption(), sm_metric_rcr(), sm_metric_fnci(), sm_metric_novelty()
|
| Visualisation |
sm_plot_landscape(), sm_plot_thematic_map(), sm_plot_production(), sm_plot_equity_dashboard(), … |
| Export |
sm_export_figure(), sm_export_table(), sm_export_zip(), sm_export_gephi()
|
| Shiny app | sm_run_app() |
vignette("scimapR") – Getting startedvignette("ingestion") – Building a corpusvignette("embeddings-and-clusters") – Semantic landscapevignette("modern-indicators") – CD/RCR/FNCI/noveltyvignette("question-driven-reviews") – Research questions + screeningvignette("reproducibility-and-certificates") – Corpus certificatesvignette("equity-trajectory-and-chat") – Equity audit + trajectoriesvignette("relationship-to-bibliometrix") – Interop and creditscimapR stands on the shoulders of the bibliometrix project. We are deeply grateful to Massimo Aria and Corrado Cuccurullo for creating the foundational R package for science mapping, and for their landmark 2017 paper which defined the field of R-based bibliometrics.
We also acknowledge the many data sources that make scimapR possible: OpenAlex, Crossref, PubMed, Semantic Scholar, Unpaywall, and others.
If you use scimapR in your research, please cite both scimapR and the foundational bibliometrix package:
citation("scimapR")
BibTeX entries:
@Manual{scimapR,
title = {scimapR: Reproducible, Question-Driven, Embedding-Aware Science Mapping},
author = {R. Heller},
year = {2026},
note = {R package version 0.4.0.9000},
url = {https://github.com/CTTIR/scimapR},
}
@Article{bibliometrix,
title = {bibliometrix: An R-tool for comprehensive science mapping analysis},
author = {Massimo Aria and Corrado Cuccurullo},
journal = {Journal of Informetrics},
year = {2017},
volume = {11},
number = {4},
pages = {959--975},
doi = {10.1016/j.joi.2017.08.007},
}
For a complete citation block including each data source used in your
corpus, run sm_cite_corpus(your_corpus).
MIT
Issues and pull requests are welcome at github.com/CTTIR/scimapR.
Where applicable, LLM tools were used for narrowly defined, non-authorial tasks: copyediting, prose refinement, Markdown/LaTeX formatting, boilerplate scaffolding (such as CI configurations and build scripts), and code refactoring. Tools included Chat AI, the KISSKI (GWDG) service, and self-hosted Mistral Small (24B, Apache-2.0) served through a local vLLM architecture on an Ubuntu Linux workstation with Intel processors and NVIDIA GPUs. The self-hosted model ran locally, with no data sent to third parties.
All scientific claims, methodological choices, analyses, interpretations and conclusions remain the authors' own. Where applicable, LLM-generated material must be reviewed and revised before inclusion, and references must be verified against their DOI, arXiv ID or ISBN.
Local software setup: vLLM with Python/PyTorch, plus R and RStudio.
Editable R artwork and regeneration instructions are in tools/branding.
The unfinished A11 documentation integration is preserved here for continuation on another machine. This checkpoint is not a qualified release or a completed M5 milestone. Documentation regeneration, end-to-end validation, application smoke checks and the remaining A12 release checks are still pending. No full qualification run was completed for this checkpoint.