README.md

---
language:
- en
license: mit
multilinguality:
- monolingual
task_categories:
- question-answering
task_ids:
- closed-domain-qa
- extractive-qa
size_categories:
- 1K<n<10K
source_datasets:
- original
tags:
- agriculture
- Extension
- agriculture Extension
- irrigation
pretty_name: AgXQA1.1
dataset_info:
  config_name: agxqa_v1
  features:
  - name: id
    dtype: string
  - name: category
    dtype: string
  - name: context
    dtype: string
  - name: question
    dtype: string
  - name: answers
    sequence:
    - name: text
      dtype: string
    - name: answer_start
      dtype: int32
  - name: references
    dtype: string
  splits:
  - name: train
    num_examples: 1503
  - name: validation
    num_examples: 353
  - name: test
    num_examples: 330
configs:
- config_name: agxqa_v1
  default: true
  data_files:
  - split: train
    path: agxqa-train-2024-06-11.jsonl
  - split: validation
    path: agxqa-validation-2024-06-11.jsonl
  - split: test
    path: agxqa-test-2024-06-11.jsonl
---

# DISCLAIMER: DUE TO AN ONGOING PUBLICATION REVIEW FOR ITS ASSOCIATED JOURNAL PAPER, THIS REPO IS CURRENTLY EMPTY. THE REST OF THE DATA WILL BE UPLOADED UPON THE PAPER ACCEPTANCE. 

# Dataset Card for AgXQA 1.1

## Table of Contents
- [Dataset Card for "agxqa_v1"](#dataset-card-for-agxqa_v1)
  - [Table of Contents](#table-of-contents)
  - [Dataset Description](#dataset-description)
    - [Dataset Summary](#dataset-summary)
    - [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
    - [Languages](#languages)
  - [Dataset Structure](#dataset-structure)
    - [Data Instances](#data-instances)
      - [agxqa_v1](#agxqa_v1)
    - [Data Fields](#data-fields)
      - [agxqa_v1](#agxqa_v1-1)
    - [Data Splits](#data-splits)
  - [Dataset Creation](#dataset-creation)
    - [Curation Rationale](#curation-rationale)
    - [Source Data](#source-data)
      - [Initial Data Collection and Normalization](#initial-data-collection-and-normalization)
      - [Who are the source language producers?](#who-are-the-source-language-producers)
    - [Annotations](#annotations)
      - [Annotation process](#annotation-process)
      - [Who are the annotators?](#who-are-the-annotators)
    - [Personal and Sensitive Information](#personal-and-sensitive-information)
  - [Considerations for Using the Data](#considerations-for-using-the-data)
    - [Social Impact of Dataset](#social-impact-of-dataset)
    - [Discussion of Biases](#discussion-of-biases)
    - [Other Known Limitations](#other-known-limitations)
  - [Additional Information](#additional-information)
    - [Licensing Information](#licensing-information)
    - [Citation Information](#citation-information)
    - [Contributions](#contributions)

## Dataset Description

- **Homepage:** https://huggingface.co/datasets/msu-ceco/agxqa_v1
- **Paper:** [TO-DO]()
- **Point of Contact:** Dr. A.Pouyan Nejadhashemi (pouyan@msu.edu)

### Dataset Summary

The Agricultural eXtension Question Answering Dataset (AgXQA 1.1) is a small-scale, SQuAD-like QA dataset targeting the Agriculture Extension domain. 
Version 1.1 currently contains 2.1K+ questions related to irrigation topics across the US, with a focus on the Midwest since our crops of interest were mainly soybean and corn.

### Supported Tasks and Leaderboards

Question Answering.

### Languages

English (`en`).

## Dataset Structure

### Data Instances

#### agxqa_v1

An example from the 'test' split looks as follows.
```
Please note that the "context" of this example was too long and was cropped:

{
    "answers": {
        "answer_start": [78, 21],
        "text": [' the rate water can enter the soils surface', 'the quantity of water that can enter the soil in a specified time interval']
    },
    "context": "Irrigation Fact Sheet # 2: Instantaneous Rates. The soils infiltration rate is the rate water can enter the soils surface. Michigan soils...",
    "id": "1170477",
    "question": "what is infiltration rate?",
    "category": "Irrigation",
    "references": "Kelley, L. (2007a). Irrigation Fact Sheet # 2 - Irrigation Application Instantaneous Rates. https://www.canr.msu.edu/uploads/235/67987/FactSheets/2_IrrigationApplicationRates1.30.pdf",
}
```

### Data agxqa_v1

The data fields are the same among all splits.

#### agxqa_v1
- `id`: a `string` feature.
- `category`: a `string` feature.
- `context`: a `string` feature.
- `question`: a `string` feature.
- `answers`: a dictionary feature containing:
  - `text`: a `string` feature.
  - `answer_start`: a `int32` feature.
- `references`: a `string` feature.
  
### Data Splits

| name     |  train | validation | test  |
| -------- | -----: | ---------: | ----: |
| agxqa_v1 | 1503   |    353     |  330  |

## Dataset Creation

### Curation Rationale

The creation of this dataset aims to enhance the performance of NLP models (e.g., LLMs) in understanding and extracting relevant information about agro-hydrological practices for crops such as corn and soybeans.


### Scope and Domain

The dataset specifically focuses on irrigation practices, techniques, and related agricultural knowledge concerning corn and soybeans. 
This includes, but is not limited to:
 - irrigation laws and policies
 - irrigation methods (e.g., drip, sprinkler, furrow),
 - irrigation scheduling,
 - soil moisture monitoring,
 - crop growth stage,
 - crop water requirements,
 - general crop (soybean and corn) characteristics


### Source Data

#### Initial Data Collection and Normalization

About ~600 paragraphs (e.g., context) were extracted from the Agriculture Extension Corpus [(AEC1.1)](https://huggingface.co/datasets/msu-ceco/aec_v1). For more details about AEC1.1's data sources, please refer to its dataset card [here](https://huggingface.co/datasets/msu-ceco/aec_v1#source-data).


#### Who are the source language producers?

 - [CECO](https://huggingface.co/msu-ceco) curated and supervised the creation and annotations of the QA pairs.
 - Regarding the original paragraphs/contexts, please see [here](https://huggingface.co/datasets/msu-ceco/aec_v1#who-are-the-source-data-producers).

### Annotations

#### Annotation process

We followed the general guidelines described in [Rajpurkar et al. (2016)](https://arxiv.org/abs/1606.05250), which also inspired us to create a SQUAD-like dataset.
We leveraged [Deepset's annotation tool](https://docs.haystack.deepset.ai/v1.20/docs/annotation) to annotate the paragraphs and create the QA pairs. 

Our main guidelines can be summarized as follows: 

- Question formulation: Based on the rationale in the paragraph, the extracted questions represented common queries by farmers and agricultural practitioners regarding irrigation.
- Answer collection: Already present in the paragraph, so the annotations cover both short and long:
  - clauses
  - subjects
  - predicates
  - phrases (nouns, verbs, adjectives and adverbials)
- Quality control: Domain experts reviewed and validated the QA pairs to ensure accuracy and relevance. This review was conducted weekly on 50% of the annotated batch (randomly selected) for that week.
- Diversity and Coverage: Since the crops of interest (soybean and corn) are mostly grown in the Midwest states of the USA, most of the QA pairs cover those states. However, the dataset also includes general irrigation QA pairs, that are applicable in most states.
- Ethical considerations: To maintain transparency and credibility, we cited the original authors of the annotated paragraphs for each QA pair. Please see the annotated example provided above.

For more information on the annotation process, please refer to [here (TO_DO)]().

#### Who are the annotators?

There were three annotators in total, two with a background in agricultural topics. Two experts in water and irrigation research hired them and supervised their annotations.

### Personal and Sensitive Information

* Some of the original paragraphs contained extension educators' names and email addresses, but these have been analyzed accordingly. In other words, they have been replaced with `x` 's in our dataset.
* For each paragraph, we referenced the main article, where the context was extracted. 

## Considerations for Using the Data

### Social Impact of Dataset

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

### Discussion of Biases

* Version 1.1 is small and only contains irrigation-related topics, so we suggested not using it in production since, in the real world, agriculture-based questions require temporal and geospatial information, which is not covered yet. 
* We found three paragraphs that contained URLs (links to an Extension YouTube video and a decision support tool). These are outliers and do not necessarily provide implicit answers. They will be removed in version 2.  

### Other Known Limitations

[More Information Needed](https://github.com/huggingface/datasets/blob/master/CONTRIBUTING.md#how-to-contribute-to-the-dataset-cards)

## Additional Information

### Licensing Information

The dataset is distributed under the [TO-DO] license.

### Citation Information

[TO-DO]


### Contributions

[More Information Needed]