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metadata
task_categories:
  - fill-mask
language:
  - en
tags:
  - agriculture Extension
pretty_name: AEC1.1

DISCLAIMER: DUE TO AN ONGOING PUBLICATION REVIEW FOR ITS ASSOCIATED JOURNAL PAPER, THIS REPO ONLY CONTAINS PARTIAL DATA SPLITS. THE REST OF THE DATA WILL BE UPLOADED UPON ACCEPTANCE.

Dataset Card for AEC

Dataset Description

The Agricultural Extension Corpus 1.1 is a compilation of 1655 official agricultural Extension documents (e.g., fact sheets, digital books) concerning water-related and sustainable agricultural practices research.

Uses

Direct Use

It is intended to train ML models in general and for NLP tasks in particular (e.g., Masked Language Modeling).

Out-of-Scope Use

This dataset should not be used as a reference/answer to address or respond to time-sensitive and location-sensitive questions.

Dataset Structure

This dataset does not contain any fields. It is a corpus of paragraphs and sentences that has been split into training, validation, and test sets using an 80-18-2 ratio

[More Information Needed]

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Two categories of sources:

Data Collection and Processing

The dataset was compiled from publicly available documents: (1) 212 scholarly full-text articles from the Journal Of Extension and (2) 1443 materials extracted from 40 land-grant institutions that provide Extension services throughout the USA. These materials include factsheets, e-books, and articles related to agricultural research, and they are in HTML, text, and PDF formats.

The PDFs were processed and converted into text using Amazon's Textract.

Furthermore, we removed non-UTF8 characters, bibliographic references, and URLs (when possible).

Who are the source data producers?

The original documents were authored by Extension staff (educators, faculty, specialists, etc.) from diverse land-grant institutions. The references to these authors can be found here (TTO-DO).

Personal and Sensitive Information

To the best of our ability, we tried removing any personal information (e.g., emails) during the automatic processing stage. However, considering that this dataset was programmatically compiled from other existing texts, there could be residues of personal information, (especially from bibliographical sections) that could have carried over into our dataset as well. If you are the original author of a document compiled inside AEC and found your personal details in the dataset, please refer to our list of references here TO_DO. If you have not been cited, please contact us to request removal.

[More Information Needed]

Bias, Risks, and Limitations

[More Information Needed]

Recommendations

Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

Citation [optional]

BibTeX:

[More Information Needed]

APA:

[More Information Needed]