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---
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](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":["keyowrds"]
"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/) |