ERA evidence synthesis & meta-analysis automation (ERAgriculture/AI-Powered-Meta-Analysis-Automation R-code GitHub)
This repository, developed under the CGIAR Sustainable Farming Program (SFP), establishes an AI-assisted workflow for evidence synthesis and meta-analysis in agriculture. The workflow automates labor-intensive stages of systematic review, including search term generation, screening, tagging, keyword...
| Autores principales: | , , , |
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| Formato: | Software |
| Lenguaje: | Inglés |
| Publicado: |
2025
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| Materias: | |
| Acceso en línea: | https://hdl.handle.net/10568/180144 |
| _version_ | 1855523221071200256 |
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| author | Muller, Lolita Joshi, Namita Steward, Peter Richard Rosenstock, Todd Stuart |
| author_browse | Joshi, Namita Muller, Lolita Rosenstock, Todd Stuart Steward, Peter Richard |
| author_facet | Muller, Lolita Joshi, Namita Steward, Peter Richard Rosenstock, Todd Stuart |
| author_sort | Muller, Lolita |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This repository, developed under the CGIAR Sustainable Farming Program (SFP), establishes an AI-assisted workflow for evidence synthesis and meta-analysis in agriculture. The workflow automates labor-intensive stages of systematic review, including search term generation, screening, tagging, keyword extraction, classification, and harmonization, while preserving methodological rigor. Outputs are systematically benchmarked against expert manual reviews to quantify accuracy and relevance, providing clear guidance on where automation excels and where human oversight remains essential.
Accompanying R scripts and analytical reports ensure transparent, reproducible methods through public vignettes and generate shareable, machine-readable artifacts for downstream use. By accelerating the mechanical components of evidence synthesis, this workflow enables researchers to concentrate on interpretation, uncertainty quantification, and domain-specific questions, offering a replicable pathway to scale literature reviews and deliver decision-ready evidence.
This work has been supported by the CGIAR Climate Action Lever and represents a collaboration between the Alliance of Bioversity International and CIAT. |
| format | Software |
| id | CGSpace180144 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| record_format | dspace |
| spelling | CGSpace1801442026-01-19T16:22:56Z ERA evidence synthesis & meta-analysis automation (ERAgriculture/AI-Powered-Meta-Analysis-Automation R-code GitHub) Muller, Lolita Joshi, Namita Steward, Peter Richard Rosenstock, Todd Stuart agriculture agricultura artificial intelligence inteligencia artificial meta-analysis large language models-llms metaanálisis This repository, developed under the CGIAR Sustainable Farming Program (SFP), establishes an AI-assisted workflow for evidence synthesis and meta-analysis in agriculture. The workflow automates labor-intensive stages of systematic review, including search term generation, screening, tagging, keyword extraction, classification, and harmonization, while preserving methodological rigor. Outputs are systematically benchmarked against expert manual reviews to quantify accuracy and relevance, providing clear guidance on where automation excels and where human oversight remains essential. Accompanying R scripts and analytical reports ensure transparent, reproducible methods through public vignettes and generate shareable, machine-readable artifacts for downstream use. By accelerating the mechanical components of evidence synthesis, this workflow enables researchers to concentrate on interpretation, uncertainty quantification, and domain-specific questions, offering a replicable pathway to scale literature reviews and deliver decision-ready evidence. This work has been supported by the CGIAR Climate Action Lever and represents a collaboration between the Alliance of Bioversity International and CIAT. 2025-10-10 2026-01-19T16:22:55Z 2026-01-19T16:22:55Z Software https://hdl.handle.net/10568/180144 en Open Access Muller, L.; Joshi, N.; Steward, P.R.; Rosenstock, T.S. (2025) ERA evidence synthesis & meta-analysis automation (ERAgriculture/AI-Powered-Meta-Analysis-Automation R-code GitHub). https://github.com/ERAgriculture/AI-Powered-Meta-Analysis-Automation |
| spellingShingle | agriculture agricultura artificial intelligence inteligencia artificial meta-analysis large language models-llms metaanálisis Muller, Lolita Joshi, Namita Steward, Peter Richard Rosenstock, Todd Stuart ERA evidence synthesis & meta-analysis automation (ERAgriculture/AI-Powered-Meta-Analysis-Automation R-code GitHub) |
| title | ERA evidence synthesis & meta-analysis automation (ERAgriculture/AI-Powered-Meta-Analysis-Automation R-code GitHub) |
| title_full | ERA evidence synthesis & meta-analysis automation (ERAgriculture/AI-Powered-Meta-Analysis-Automation R-code GitHub) |
| title_fullStr | ERA evidence synthesis & meta-analysis automation (ERAgriculture/AI-Powered-Meta-Analysis-Automation R-code GitHub) |
| title_full_unstemmed | ERA evidence synthesis & meta-analysis automation (ERAgriculture/AI-Powered-Meta-Analysis-Automation R-code GitHub) |
| title_short | ERA evidence synthesis & meta-analysis automation (ERAgriculture/AI-Powered-Meta-Analysis-Automation R-code GitHub) |
| title_sort | era evidence synthesis meta analysis automation eragriculture ai powered meta analysis automation r code github |
| topic | agriculture agricultura artificial intelligence inteligencia artificial meta-analysis large language models-llms metaanálisis |
| url | https://hdl.handle.net/10568/180144 |
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