File size: 3,254 Bytes
b83048d
 
 
 
 
 
fef0159
c37f3b9
 
 
 
1b4e2b7
 
fef0159
 
 
 
1b4e2b7
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
fef0159
1b4e2b7
fef0159
b11cdf2
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
---
license: apache-2.0
task_categories:
- summarization
language:
- en
---

>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/)

# 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>

>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