metadata

license: cc-by-4.0
task_categories:
  - summarization
language:
  - en
tags:
  - science
  - agriculture
  - academic
size_categories:
  - 10M<n<100M

A Curated Research Corpus for Agricultural Advisory AI Applications

This dataset represents a comprehensive collection of 45,232 agricultural research publications from CGIAR, specifically processed and structured for Large Language Model (LLM) applications in agricultural advisory services. This dataset bridges the gap between advanced agricultural research and field-level advisory needs, drawing from CGIAR's extensive scientific knowledge base that has been used by both public and private extension services. Each document has been systematically processed using GROBID to extract structured content while preserving critical scientific context, metadata, and domain-specific agricultural knowledge. The corpus covers diverse agricultural topics including crop management, pest control, climate adaptation, and farming systems, with particular emphasis on small-scale producer contexts in low and middle-income countries. This machine-readable dataset is specifically curated to enhance the accuracy and contextual relevance of AI-generated agricultural advisories through Retrieval-Augmented Generation (RAG) frameworks, ensuring that advanced agricultural science can effectively benefit those at the heart of agriculture.

Data Sources and RAG Pipeline

The dataset is sourced from GARDIAN, a comprehensive hub for agri-food data and publications. Utilizing its robust API, the GAIA-CIGI pipeline has systematically discovered and gathered all open-access reports and publications from the various CGIAR centers. Each document has been converted into a structured, machine-readable format using GROBID, a specialized tool for extracting the structure of scientific publications. A complete description of the system architecture can be found here

Document Structure

{
  "metadata": {
    "id": "",
    "source": "",
    "url": ""
  },
  "pagecount": 1,
  "title": "",
  "abstract": "",
  "keywords":["keyowrds"]
  "chapters": [
    {
      "index": 1,
      "head": "",
      "paragraphs": [
        {
          "text": "",
          "size": 1,
          "index": 1
        },
        {
          "text": "",
          "size": 2,
          "index": 2
        }
      ]
    }
  ],
  "figures": [
    {
      "text": ""
    }
  ],
"sieverID":""
}

Property Description

"metadata" (object, required): Contains information related to the document's metadata.
1. "id" (string): the identifier for the document.
2. "source" (string): the source or origin of the document.
3. "url" (string): the url of the downloaded document.
"pageCount" (integer, required): the number of pages of the document.
"title" (string, required): the title of the document.
"abstract" (string, required): the abstract of the document.
"chapters" (array of objects, required): represents chapters or sections within the document.
1. "index" (integer, required): the numerical order of the chapter.
2. "head" (string, required): the heading of the chapter.
3. "paragraphs" (array of objects, required): contains paragraphs within the chapter.
  1. "text" (string, required): the content of the paragraph.
  2. "size" (integer, required): represents the size of the paragraph (words separated by one space).
  3. "index" (integer, required): the numerical order of paragraph within the chapter.
"figures" (array of objects, required): represents tables within the document.
1. "text" (string, required): the content of the table.
"sieverID" (string, required): Internal identifier of the document.

Acknowledgement

This dataset was developed for the Generative AI for Agriculture (GAIA) project, supported by the Bill and Melinda Gates Foundation, in collaboration between CGIAR and SCiO