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bib678 B

BibTeX Bibliography (.bib)

A BibTeX bibliography with three real citations (Lovelace, Knuth, Shannon) across article, book, and inproceedings types — for testing BibTeX parsers, reference managers, and citation converters.

Preview — first 27 linesbib
@article{lovelace1843notes,
  author  = {Lovelace, Ada},
  title   = {Notes on the Analytical Engine},
  journal = {Taylor's Scientific Memoirs},
  year    = {1843},
  volume  = {3},
  pages   = {666--731}
}

@book{knuth1997taocp,
  author    = {Knuth, Donald E.},
  title     = {The Art of Computer Programming},
  publisher = {Addison-Wesley},
  year      = {1997},
  edition   = {Third},
  isbn      = {978-0201896831}
}

@inproceedings{shannon1948communication,
  author    = {Shannon, Claude E.},
  title     = {A Mathematical Theory of Communication},
  booktitle = {The Bell System Technical Journal},
  year      = {1948},
  volume    = {27},
  pages     = {379--423}
}

Specifications

Format
BibTeX
Entries
3
Types
article, book, inproceedings

What is a .bib file?

BibTeX (.bib) is a plain-text bibliography database used with LaTeX and reference managers. Each entry has a type (@article, @book, @inproceedings), a citation key, and tagged fields like author, title, year, and journal. It is the standard citation format in academic writing.

How to use this file

Use an example .bib file to test BibTeX and BibLaTeX parsers, reference managers (Zotero, Mendeley, JabRef), and citation-format converters (for example BibTeX to RIS or CSL-JSON).

How to use this file for testing

“BibTeX Bibliography (.bib)” is a deterministic Novus Examples fixture for Scientific data, Editor testing. Citation catalogs (BibTeX, RIS), chemistry structures (MDL Molfile, PDB), and gridded binary data (NetCDF, FITS) — for testing reference managers, molecule viewers, and scientific-data loaders.

Documented properties for this file: 3 entries · BibTeX. Compare results against paired or grouped companions on this page when present (clean↔damaged, searchable↔scanned, or format twins) so scores stay reproducible across runs.

Download the file once, keep the path stable in CI or local scripts, and treat the spec table as the contract: dimensions, seeds, field lists, and roles are intentional. Corrupt or invalid samples are labelled as such — expect parsers to fail loudly rather than silently accept them.

Data fixtures document their exact quirks — delimiters, encodings, null handling, schema, and row counts — in the spec table. Point your parser or importer at the file and assert it handles the documented edge cases; clean and deliberately-messy siblings make before/after diffs straightforward.

Code examples

import bibtexparser  # pip install bibtexparser

lib = bibtexparser.parse_file("references.bib")
for e in lib.entries[:5]:
    print(e.key, "-", e.fields_dict.get("title"))

Generated by generation/sci_data.py. Free for any use, no attribution required — license.