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Updated: Jun 25, 2026

AQRNA-seq for Quantifying Small RNAs
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Published on: February 2, 2024

AQQD: Annotated Quranic Qira'at Dataset.

Linda Smail1, Mohammed Lataifeh2, Md Sohazur Islam Sozib1

  • 1Mathematics Department, Zayed University, Dubai, 19282, United Arab Emirates.

Data in Brief
|June 24, 2026
PubMed
Summary
This summary is machine-generated.

The Annotated Quranic Qira'at Dataset (AQQD) offers 24,183 audio files for studying Quranic recitations. This dataset supports research in machine learning, linguistics, and Islamic studies.

Keywords:
AudioClassificationMachine learningPhonetic variationQuranRecitationSpeech processing

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Published on: August 13, 2012

Area of Science:

  • Computational Linguistics
  • Speech and Audio Processing
  • Islamic Studies

Background:

  • Quranic recitations exhibit variations across canonical Qira'at styles.
  • Developing computational models for these variations requires specialized datasets.

Purpose of the Study:

  • To introduce the Annotated Quranic Qira'at Dataset (AQQD), an open-access audio resource.
  • To facilitate research in machine learning, speech processing, computational linguistics, and Quranic studies.

Main Methods:

  • Compiled 24,183 WAV audio files from 309 reciters covering 70 Surahs.
  • Included publicly available recordings and a controlled subset for comparative analysis.
  • Standardized audio to 44.1 kHz, 16-bit, mono WAV with metadata in filenames.

Main Results:

  • The dataset contains phonetic variations across Qira'at styles.
  • Metadata enables efficient data filtering, indexing, and preprocessing.
  • The dataset is released under a CC0 1.0 Public Domain license.

Conclusions:

  • AQQD supports diverse research applications, including Qira'at classification and phonetic analysis.
  • The dataset advances the development of educational technologies for Quranic studies.
  • AQQD promotes open research in Quranic recitation analysis.