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

> ## ⚠️ Heads up: Updated Dataset Available
> This dataset has been updated with a newer version published on **27 Feb 2025**. The latest version includes more updated and refined set of documents.
> We recommend using the latest version, available at [https://huggingface.co/datasets/CGIAR/gardian-cigi-ai-documents](https://huggingface.co/datasets/CGIAR/gardian-cigi-ai-documents). This version remains accessible for reference and reproducibility purposes.


# A Curated Research Corpus for Agricultural Advisory AI Applications
This dataset represents a comprehensive collection of 45,232 agricultural research publications from [CGIAR](https://cgiar.org/),
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](https://grobid.readthedocs.io/en/latest/Introduction/) 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](https://gardian.bigdata.cgiar.org/), 
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](https://grobid.readthedocs.io/en/latest/Introduction/), 
a specialized tool for extracting the structure of scientific publications. A complete description of the system architecture can be found [here](https://scio.atlassian.net/wiki/spaces/CiGi/pages/45711361/Pipeline+Architecture)

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

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

### 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](https://www.cgiar.org/)
and [SCiO](https://scio.systems/)