PRISMA

A Python toolkit for systematic literature reviews — by Proportione.

Covers the full pipeline of a transparent, reproducible SLR: corpus ingestion (OpenAlex), cross-source deduplication, two-tier title-abstract screening with traceable rule sets, full-text extraction with PyMuPDF, MMAT 2018 quality assessment, PRISMA 2020 flow diagrams, and bibliometric clustering with VOSviewer integration.

Built and battle-tested while preparing the doctoral thesis of Javier Cuervo (Universidade de Aveiro, DEGEIT) and the journal articles co-authored with Rui Pedro Figueiredo Marques (ISCA-UA, GOVCOPP). Released as open-source so reviewers can audit the methodology and other researchers can reuse the pipeline.

Why this exists

Most SLR tooling solves one step (e.g. screening, or visualisation, or deduplication). Stitching them together usually means brittle glue scripts that nobody else can re-run a year later. PRISMA bundles the steps that we found we needed every single time, in a single installable Python package, with rule sets in YAML so the what is decoupled from the how.

Install

pip install proportione-prisma            # CLI + library
pip install "proportione-prisma[streamlit]"  # + Streamlit demo

Python 3.10+. PyMuPDF, pandas, networkx, rapidfuzz, click, pyyaml, plus matplotlib/seaborn for plots.

Quickstart (5 commands)

# 1. Build a small corpus from OpenAlex
prisma ingest openalex \
  --query "google trends forecasting" \
  --max 50 \
  --mailto you@example.com \
  --out data/demo.ris

# 2. (Optional) merge multiple sources, deduplicate
prisma ingest dedup \
  -s openalex-A=data/demo.ris \
  -s scopus-A=data/scopus.ris \
  --out data/

# 3. Screen titles + abstracts with a YAML rule set
prisma screen \
  --in data/corpus-deduplicated.ris \
  --rules examples/screening_rules_signal_kpi.yaml \
  --out data/screening/

# 4. Extract structured data from the PDFs you retrieved
prisma extract \
  --pdfs data/pdfs/ \
  --taxonomy examples/extraction_taxonomy_signal_kpi.yaml \
  --out data/extracted.csv

# 5. Score quality (MMAT 2018) and render the PRISMA flow
prisma quality --pdfs data/pdfs/ --out data/mmat.csv
prisma report --counts examples/prisma_counts_demo.json --out reports/prisma-flow.png

The Streamlit demo walks through the same pipeline visually:

streamlit run streamlit_app/Home.py

What's in the box

Module	What it does	Reused from
prisma.ingest	OpenAlex search · Unpaywall PDF discovery · RIS I/O · cross-source dedup (DOI + rapidfuzz)	Pub1-Fusion, Pub3
prisma.screening	Two-tier rule engine (hard exclusion + multi-group inclusion), YAML-defined, full audit log	Pub3
prisma.extraction	PyMuPDF text extraction · section detection · taxonomy-driven field extraction	Pub3, Pub3-keywords
prisma.quality	MMAT 2018 quantitative-descriptive heuristic scoring (Q1–Q5, High/Medium/Low)	Pub3
prisma.reporting	PRISMA 2020 flow diagram from PRISMACounts dataclass	Pub3
prisma.bibliometrics	VOSviewer .net loader · Louvain communities (modularity, density, centrality) · co-occurrence matrix	Pub1-Fusion
prisma.viz	Matplotlib config with the Proportione brand palette	shared

Methodology references

• PRISMA 2020 — Page, M.J. et al. (2021). BMJ 372:n71. https://doi.org/10.1136/bmj.n71
• MMAT 2018 — Hong, Q.N. et al. (2018). Education for Information 34:285-291. https://doi.org/10.3233/EFI-180221
• Case Survey Method — Larsson, R. (1993). Academy of Management Journal 36(6):1515-1546.
• Bibliometric methods — Donthu, N. et al. (2021). Journal of Business Research 133:285-296.

How to cite

If this toolkit informs your research, please cite the software (CITATION.cff) and, where applicable, the articles that introduced the rule sets and taxonomies bundled in examples/:

Cuervo, J. & Marques, R.P.F. (2026). Where search data meets business intelligence: a bibliometric mapping of the Ibero-American research landscape. (Manuscript under review.)

Trademark / endorsement notice

This toolkit helps authors comply with the PRISMA 2020 reporting standard. It is not affiliated with, endorsed by, or sponsored by the PRISMA Statement Group or the EQUATOR Network. "PRISMA" here refers to the toolkit name; users remain responsible for following the official PRISMA 2020 checklist when reporting.

Contributing

Issues and PRs welcome. Please run ruff check src/ and pytest -q before submitting.

Licence

MIT — see LICENSE. Copyright © 2026 Proportione, LDA.

About Proportione

Proportione is an independent research and engineering firm working on data-driven decision systems and BI. Our research output lives at proportione.com/investigacion.