Data Extraction & Coding

Similar to searching and screening, the process of recording information about each study requires careful planning to ensure consistency and reliability. When planning extraction and coding teams should pilot-test a subset of included studies to ensure agreement. To gain efficiencies, data extraction and coding may be integrated into the full-text screening phase and assist in selecting eligible studies. 

Systematic Reviews

Data extraction focuses on recording key details such as study year, location, design, sample size, and results (e.g. effect size, statistical outcomes). This information is essential for synthesizing the evidence.

Data coding involves classifying extracted data according to predefined categories (e.g., intervention types, population characteristics) to standardize and structure the data for further analysis.

Systematic Maps

Data extraction focuses on broad study characteristics such as study location, ecosystem type, population, intervention/exposure, and study design.

Data coding involves organizing extracted data into predefined categories to support the creation of visualizations (e.g., heatmaps, interactive databases), helping to identify research trends and knowledge/evidence gaps.