24 Software

We develop software packages, primarily for literature reviews. Our software is available in the CoLRev Environment and the digital-work lab organization.

Collaborative Literature Reviews (CoLRev)

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CoLRev is an open-source environment for collaborative literature reviews. It integrates with different synthesis tools, takes care of the data, and facilitates Git-based collaboration. To accomplish these goals, CoLRev advances the design of review technology at the intersection of methods, design, cognition, and community building. The following features stand out:

  • Supports all literature review steps: problem formulation, search, dedupe, (pre)screen, pdf retrieval and preparation, and synthesis
  • An open and extensible environment based on shared data and process standards
  • Builds on git and its transparent collaboration model for the entire literature review process
  • Offers a self-explanatory, fault-tolerant, and configurable user workflow
  • Operates a model for data quality, content curation, and reuse
  • Enables typological and methodological pluralism throughout the process

search-query

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search-query is a Python package for parsing, validating, simplifying, and serializing search queries for academic databases. It currently supports PubMed, EBSCOHost, and Web of Science, using a standardized JSON schema (Haddaway et al., 2022).

Highlights:

  • Programmatic use, CLI interface, and optional integration via pre-commit hooks
  • Zero dependencies: easily embeddable across environments
  • Extensible parser/validator architecture
  • Tested on real-world queries from searchRxiv

BibDedupe

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BibDedupe is an open-source Python library for deduplication of bibliographic records, tailored for literature reviews. Unlike traditional deduplication methods, BibDedupe focuses on entity resolution, linking duplicate records instead of simply deleting them.

  • Automated Duplicate Linking with Zero False Positives: BibDedupe automates the duplicate linking process with a focus on eliminating false positives.
  • Preprocessing Approach: BibDedupe 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: BibDedupe does not simply delete duplicates, but it links duplicates to resolve the entity and integrates the data. This allows for validation, and undo operations.
  • Programmatic Access: BibDedupe is designed for seamless integration into existing research workflows, providing programmatic access for easy incorporation into scripts and applications.
  • Transparent and Reproducible Rules: BibDedupe’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: BibDedupe implements computations efficiently and in parallel, using appropriate data structures and functions for optimal performance.