Data for Comparing Narrow vs. Broad Search Strategies in ML-Assisted Systematic Reviews

Supplemental data supports a 2025 article exploring machine-learning screening tools for title/abstract screening. The dataset, authored by Michelle Swab and harvested from Borealis, was last updated on November 14, 2025. Its specific contents likely relate to the performance comparison of different search strategies in knowledge synthesis projects.

Use Cases

• Benchmarking the efficiency of narrow versus broad search strategies based on the described performance comparison.
• Training or validating machine learning models for automated title/abstract screening based on the described use of ML tools.
• Informing protocol design for knowledge synthesis projects based on the described exploration of search strategy performance.

Strengths

• Directly supports a peer-reviewed research article exploring a specific methodological question.
• Author and organizational provenance (Michelle Swab, Borealis) are explicitly stated.
• Has a precise last update timestamp of 2025-11-14 03:48:11.

Limitations

• Description metadata is limited; actual data quality requires manual inspection after download.
• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file formats, and data size are unknown, which may limit suitability assessment.

Provenance

Source

Borealis Harvested Dataverse

Collection Method

Likely generated or compiled to support the findings of a specific research article.

Time Range

Publication or data generation timeframe appears to be 2025 or earlier.

Freshness

Last updated 2025-11-14 03:48:11; freshness should be verified.

Geography

null