onecite

OneCite

Citation & Academic Reference Toolkit

Features • Quick Start • 📖 Advanced Usage • 🗺️ Roadmap • 🤝 Contributing

OneCite is a command-line tool and Python library for citation management. It accepts DOIs, paper titles, arXiv IDs, and mixed inputs, and outputs formatted bibliographic entries.

Statement of Need

Researchers frequently accumulate reference lists in ad-hoc formats — DOIs copied from browser tabs, arXiv IDs from paper PDFs, titles typed by hand, and BibTeX fragments from various sources. Cleaning these into a consistent, complete .bib file is tedious and error-prone.

OneCite solves this by accepting any mix of identifiers and text queries and automatically resolving them to structured BibTeX through a pipeline of academic APIs (CrossRef, arXiv, PubMed, Semantic Scholar, and others). It is designed for researchers who work primarily in the terminal, use LaTeX, and want a lightweight, scriptable tool — not a full reference manager.

When to use OneCite vs. alternatives:

Tool	Best for
OneCite	One-shot conversion of messy reference lists to BibTeX in a terminal/script
Zotero	Long-term reference management, GUI-based, browser integration
CrossRef API directly	When you have clean DOIs and want canonical metadata
doi2bib	Single DOI → BibTeX conversion, no fuzzy matching

Features

Feature	Description
Fuzzy Matching	Match references against multiple academic databases even from incomplete or inaccurate info.
Multiple Formats	Input .txt/.bib → Output BibTeX.
4-stage Pipeline	A 4-stage process (clean → query → validate → format) to produce consistent output.
Field Completion	Enrich entries by filling in missing fields like journal, volume, pages, authors, and abstract.
🎓 7+ Citation Types	Handles journal articles, conference papers, books, software, datasets, theses, and preprints.
Multi-Source Lookup	Queries CrossRef, arXiv, PubMed, Semantic Scholar, Google Books, and others for every entry.
Many Identifier Types	Accepts DOI, PMID, arXiv ID, ISBN, GitHub URL, Zenodo DOI, or plain text queries.
🎛️ Interactive Mode	Manually select the correct entry when multiple potential matches are found.
Custom Templates	YAML-based presets that provide a fallback BibTeX entry type when auto-detection is inconclusive.

🌐 Data Sources

Quick Start

Install and try OneCite in a few steps.

1. Installation

# Recommended: Install from PyPI
pip install onecite

2. Create an Input File

Create a file named references.txt with your mixed-format references:

# references.txt
# Add blank lines between entries to avoid misidentification

10.1038/nature14539

Attention is all you need, Vaswani et al., NIPS 2017

Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.

https://github.com/tensorflow/tensorflow

10.5281/zenodo.3233118

arXiv:2103.00020

Smith, J. (2020). Neural Architecture Search. PhD Thesis. Stanford University.

3. Run OneCite

Execute the command to process your file and generate a clean .bib output.

onecite process references.txt -o results.bib --quiet

4. View Output

Your results.bib file now contains entries of different types.

View Complete Output (results.bib)

@article{LeCun2015Deep,
  doi = "10.1038/nature14539",
  title = "Deep learning",
  author = "LeCun, Yann and Bengio, Yoshua and Hinton, Geoffrey",
  journal = "Nature",
  year = 2015,
  volume = 521,
  number = 7553,
  pages = "436-444",
  publisher = "Springer Science and Business Media LLC",
  url = "https://doi.org/10.1038/nature14539",
  type = "journal-article",
  abstract = "Deep learning allows computational models that are composed of multiple processing layers to learn representations of data with multiple levels of abstraction...",
}
@inproceedings{Vaswani2017Attention,
  arxiv = "1706.03762",
  title = "Attention Is All You Need",
  author = "Vaswani, Ashish and Shazeer, Noam and Parmar, Niki and Uszkoreit, Jakob and Jones, Llion and Gomez, Aidan N. and Kaiser, Lukasz and Polosukhin, Illia",
  year = 2017,
  booktitle = "Advances in Neural Information Processing Systems (NeurIPS)",
  url = "https://arxiv.org/abs/1706.03762",
}
# ... and 5 more entries ...

📖 Advanced Usage

Direct String and Stdin Input

onecite process "10.1038/nature14539"
onecite process "Attention is all you need, Vaswani et al., NIPS 2017"
echo "10.1038/nature14539" | onecite process -

Interactive Disambiguation

For ambiguous entries, use the --interactive flag to manually select the correct match and ensure accuracy.

Command:

onecite process ambiguous.txt --interactive

Example Interaction:

Found multiple possible matches for "Deep learning Hinton":
1. Deep learning
   Authors: LeCun, Yann; Bengio, Yoshua; Hinton, Geoffrey
   Journal: Nature, 2015
   DOI: 10.1038/nature14539

2. Deep belief networks
   Authors: Hinton, Geoffrey E.
   Journal: Scholarpedia, 2009
   DOI: 10.4249/scholarpedia.5947

Please select (1-2, 0=skip): 1
Selected: Deep learning

🐍 Use as a Python Library

Use OneCite directly in your Python scripts.

from onecite import process_references

# A callback can be used for non-interactive selection (e.g., always choose the best match)
def auto_select_callback(candidates):
    return 0 # Index of the best candidate

result = process_references(
    input_content="Deep learning review\nLeCun, Bengio, Hinton\nNature 2015",
    input_type="txt",
    template_name="journal_article_full",
    output_format="bibtex",
    interactive_callback=auto_select_callback
)

print('\n\n'.join(result['results']))

🗺️ Roadmap

• OneCite Skill — Skill package for AI coding agents (Claude Code, Windsurf, etc.).

🤝 Contributing

Contributions are always welcome! Please see CONTRIBUTING.md for development guidelines and instructions on how to submit a pull request.

📄 License

This project is licensed under the MIT License. See the LICENSE file for details.

Disclosure

Development was assisted by standard productivity tools including Generative AI for streamlining implementation details. All output was verified and integrated by the maintainer, and no LLMs are used by the package at runtime.

OneCite

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