Metadataset

Metadataset performs dynamic meta-analysis to aggregate, standardize, filter, weight, and synthesize experimental and observational data from scientific publications to support evidence synthesis for the management of crops, soil, water, and wildlife.

Key Features:

• Data aggregation: Aggregates data extracted from scientific publications focused on agricultural and natural resource management including crops, soil, water, and wildlife.
• Data extraction and standardization: Extracts, standardizes, and summarizes primary study data and metadata for synthesis.
• Dynamic filtering and weighting: Allows recalibration of global evidence through dynamic filtering and weighting of studies and observations.
• Subgroup analysis: Supports subgroup analyses to examine effects within defined subsets of studies.
• Meta-regression: Implements meta-regression to model effect moderators across studies.
• Recalibration: Provides recalibration methods to adjust global estimates for local relevance.
• Critical appraisal: Includes critical appraisal procedures for assessing study quality as part of evidence synthesis.
• Sensitivity analysis: Enables sensitivity analyses to evaluate robustness of synthesis results.
• Metadata classification: Employs standardized classification systems for metadata to enable filtering and weighting.
• Living systematic reviews: Facilitates publication and continuous updating of dynamic meta-analyses as living systematic reviews.

Scientific Applications:

• Agroecology: Synthesizes evidence to inform management and interventions in agroecological systems.
• Conservation biology: Aggregates and analyzes studies relevant to wildlife and natural resource conservation decisions.
• Local relevance assessment: Assesses local relevance of global evidence by recalibrating and weighting studies for specific contexts.
• Subject-wide evidence synthesis: Supports broad, cross-study syntheses and continuous updating across scientific disciplines as living systematic reviews.

Methodology:

Metadataset uses data extraction, standardization, and summarization of primary studies; dynamic filtering and weighting of evidence; subgroup analysis, meta-regression, and recalibration; critical appraisal and sensitivity analysis; standardized metadata classification; and publication with continuous updating of dynamic meta-analyses as living systematic reviews.