Update README.md

README.md

@@ -12,22 +12,27 @@ size_categories:
 - 10M<n<100M
 ---
 
->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/)
+# 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
+### 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
+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": {
@@ -65,8 +70,8 @@ A complete description of the system architecture can be found [here](https://sc
 "sieverID":""
 }
 ```
-# Property Description
 
+### Property Description
 <ol>
   <li>"metadata" (object, required): Contains information related to the document's metadata. 
     <ol>
@@ -101,6 +106,6 @@ A complete description of the system architecture can be found [here](https://sc
   <li>"sieverID" (string, required): Internal identifier of the document.</li>
 </ol>
 
->Each document carries a persistent GARDIAN id that allows the retrieval of the full metadata record of the associated publication
-
-Number of documents: 45,235
+### 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/)