bib-dedupe 0.6.0

Identify and merge duplicates in bibliographic records

bib-dedupe

Overview

Bib-Dedupe is an open-source Python library for deduplication of bibliographic records, tailored for literature reviews. Unlike traditional deduplication methods, Bib-Dedupe focuses on entity resolution, linking duplicate records instead of simply deleting them. This approach enables validation, undo operations, and a more nuanced understanding of record relationships.

Features

• Automated Duplicate Linking with Zero False Positives: Bib-Dedupe automates the duplicate linking process with a focus on eliminating false positives.
• Preprocessing Approach: Bib-Dedupe uses a preprocessing approach that reflects the unique error generation process in academic databases, such as author re-formatting, journal abbreviation or translations.
• Entity Resolution: Bib-Dedupe does not simply delete duplicates, but it links duplicates to resolve the entitity and integrates the data. This allows for validation, and undo operations.
• Programmatic Access: Bib-Dedupe is designed for seamless integration into existing research workflows, providing programmatic access for easy incorporation into scripts and applications.
• Transparent and Reproducible Rules: Bib-Dedupe's blocking and matching rules are transparent and easily reproducible to promote reproducibility in deduplication processes.
• Continuous Benchmarking: Continuous integration tests running on GitHub Actions ensure ongoing benchmarking, maintaining the library's reliability and performance across datasets.
• Efficient and Parallel Computation: Bib-Dedupe implements computations efficiently and in parallel, using appropriate data structures and functions for optimal performance.

Installation

To install Bib-Dedupe, use the following pip command:

pip install bib-dedupe

Getting Started

import pandas as pd
from bib_dedupe.bib_dedupe import merge

# Load your bibliographic dataset into a pandas DataFrame
records_df = pd.read_csv("records.csv")

# Get the merged_df
merged_df = merge(records_df)

For more detailed usage instructions and customization options, refer to the documentation.

For advanced use cases, it is also possible to complete and customize each step individually

from bib_dedupe.bib_dedupe import prep, block, match, merge, export_maybe, import_maybe

# Block records
blocked_df = block(records_df)

# Identify matches
matched_df = match(blocked_df)

# Check maybe cases
export_maybe(matched_df, records_df, matches)
matches = import_maybe(matches)

# Merge
merged_df = merge(records_df, matches=matches)

Fields used by BibDeduper

Name	Definition
ID	A unique ID
author	The author(s) of the publication
title	The title of the publication
year	The year of publication
journal	The name of the journal in which the publication appeared
volume	The volume number of the publication
number	The issue number of the publication
pages	The page numbers of the publication
doi	The Digital Object Identifier (DOI)
abstract	The abstract
search_set	Distinct sets of papers (e.g., old_search), can be empty.

Continuous evaluation

Bib-dedupe is continuously evaluated against other Python libraries (currently the asreview datatools) for duplicate removal in bibliographic datasets. Complementary data from Hair et al. (2021) is added to the overview. The notebooks are available for the evaluation, and the datasets are available in the data section. A summary of the evaluation is available in the README.md, aggregated summaries are exported to current_results.md, and detailed results are exported to a csv file.

Documentation

Explore the official documentation for comprehensive information on installation, usage, and customization of Bib-Dedupe.

Citation

If you use Bib-Dedupe in your research, please cite it as follows:

TODO

Contribution Guidelines

We welcome contributions from the community to enhance and expand Bib-Dedupe. If you would like to contribute, please follow our contribution guidelines.

License

Bib-Dedupe is released under the MIT License, allowing free and open use and modification.

Contact

For any questions, issues, or feedback, please open an issue on our GitHub repository.

Happy deduplicating with Bib-Dedupe!