literature-review

To conduct a systematic literature review across PubMed, arXiv, bioRxiv, and Semantic Scholar, this skill orchestrates multi-database searches, deduplication, screening, data extraction, and PRISMA-aligned reporting to produce a reproducible synthesis.

The best way to ensure reproducibility and PRISMA compliance in evidence synthesis is to enforce structured workflow steps, including search strategy documentation, study selection tracking, quality assessment, and automated citation verification.

Yes, you can automate data extraction and citation verification for scoping reviews by applying rigorous extraction templates that capture study characteristics and automatically validate DOIs for accurately styled references.

Yes, this systematic review workflow supports thematic synthesis and knowledge mapping by identifying research trends, gaps, and questions, subsequently generating automated reports to visualize the evidence landscape.

To handle deduplication when aggregating literature search results from Semantic Scholar and bioRxiv, the workflow automatically identifies and removes duplicate records during the multi-database search orchestration phase.

Yes, you need the Python requests library installed in your environment, as it is the required dependency for executing multi-database search orchestration and retrieving records from external scientific sources.