Bibliometric context
It summarizes publication metadata, journal context, citation patterns and unusual bibliographic signals.
Open-source research infrastructure
PaperQC
PaperQC is an early-stage open-source project. It is designed as a terminal toolkit with a Python package and Nextflow pipeline to help researchers screen literature before it becomes the basis for experiments, reviews or investment decisions.
# Python package
$ pip install paperqc
$ paperqc scan <publication_doi> \
--out paperqc-report/
# Nextflow pipeline
$ nextflow run paperqc/main.nf \
--input library.ris \
--outdir results/
# Output
report.html
signals.csv
citation_network.graphmlWhat it checks
Quality signals for messy scientific literature.
PaperQC starts with transparent metadata and citation checks, then it will expand toward deeper content analysis as the project matures.
Citation network risk
It flags retracted citations, clusters of suspiciously narrow citation behavior and excessive self-citation patterns.
Author and venue history
It surfaces retraction history, journal warnings and other context.
Future content checks
Later versions will explore p-hacking signals, image integrity, tortured phrases, AI-generated text and method inconsistencies.
How it works
From paper identifier to reproducible report.
Bring papers from existing tools
Users pass PDFs, RIS, BibTeX, DOI lists or metadata tables from literature searches, Zotero libraries and review workflows.
Run locally
PaperQC is a Python package and Nextflow pipeline.
Review ranked evidence, not ambiguous scores
Outputs include readable reports and machine-friendly tables that explain why each paper, citation or venue is flagged.
Science moves faster when researchers find weak links
before building on them.
Retractions, irreproducible studies and manipulated results can redirect whole fields long before corrections catch up. PaperQC helps teams inspect that risk early, at the level of the paper collection they are about to trust.
The project favors reproducibility over polish: local runs, open code, inspectable data outputs and documented assumptions. The goal is to support human judgment, not replace it.
Open-source roadmap
A practical path to the first release.
PaperQC does not exist yet as a public package. The first release focuses on dependable, explainable checks that can be extended by the community.
Command-line prototype
A Python package parses a singular paper, retrieves metadata and generates the first citation and bibliometric quality reports.
Pipeline-scale analysis
A Nextflow workflow makes large literature screens reproducible across laptops, servers and shared compute.
Community modules
The project adds modular checks for PubPeer signals, journal warnings, p-value patterns, figures and paper text.
People
Built by scientists for scientists.
The project is shaped by maintainers and researchers who understand how literature quality affects experiments, reviews and funding decisions.
