Create README.md

README.md

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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 53,981 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. Morever, chunking
+methods that preserver the semantic coherence have been applied. More specifically, documents are split 
+into chunks based on a fixed number of tokens and a portion of tokens at the end of each chunk 
+overlaps with the beginning of the next chunk. This implementation Preserves contextual continuity between chunks, 
+which improves the model's understanding of the document's flow and can lead to better predictions and is useful 
+for tasks that rely on context spread over multiple chunks, such as question answering or summarization
+([Chunking Methods](https://scio.atlassian.net/wiki/spaces/CiGi/pages/221675526/Chunking+methods)).
+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": {
+    "gardian_id": "",
+    "source": "",
+    "url": "",
+    "id": ""
+  },
+  "keywords":["keywords"],
+  "sieverID": "",
+  "content": ""
+}
+```
+
+### Property Description
+<ol>
+  <li>"metadata" (object, required): Contains information related to the document's metadata. 
+    <ol>
+      <li>"gardian_id" (string): an identifier for the document within the GARDIAN ecosystem.</li>
+      <li>"source" (string): the source or origin of the document.</li>
+      <li>"url" (string): the url of the downloaded document.</li>
+      <li>"id" (string): internal identifier of the document generated by hashing the URL string.</li>      
+    </ol>
+  </li>
+  <li>"keywords" (list of strings): the keyword list as obtained from origin index metadata.</li>
+  <li>"sieverID" (string, required): internal identifier of the document.</li>
+  <li>"content" (string): The useful textual content of the publication as retrieved using GROBID and PDFbox.</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/)