literature-review-tools

Citation normalization for a systematic literature review is automated by processing raw search results from formats like CSV and JSON to standardize fields such as DOI and PMID. The Skill uses Python with pandas to clean and output consistent citation datasets.

An evidence matrix is a structured table highlighting key findings and study types used in evidence synthesis. It is assembled by transforming normalized citation data into organized screening tables to automate the manual synthesis phase of systematic reviews.

Yes, you need Python and pandas to clean up raw literature search results using this Skill. Pandas is required for the underlying data manipulation, file I/O, and JSON handling needed to normalize citations and assemble evidence matrices.

The best way to process hundreds of search results for a grant proposal is to input raw hits as a CSV file for automated cleaning. The Skill normalizes citation details and generates an evidence matrix to support faster evidence-based decision-making.

Yes, you can use this Skill for data normalization in biomedical drug discovery workflows. It specifically solves the problem of manual data cleaning and evidence synthesis for systematic reviews in drug discovery and biomedical research fields.

Limitations when normalizing citations include dependency on standard libraries for file I/O and JSON handling, meaning heavily malformed data may require pre-checking. It processes standard CSV and JSON inputs but does not autonomously fetch literature from external databases.