Dataset is a word I often run into when reading things about AI, machine learning, data engineering, etc. I don't fully grasp what a dataset actually is. Maybe I'm overthinking it, or perhaps it's just semantics, but let's try to clarify it.

This is what ChatGPT says,

A dataset is a structured collection of data that is organized and stored in a way that makes it accessible and manageable for analysis, research, or other purposes. Datasets can come in various forms, including:

1. Tabular Data: This is one of the most common types of datasets. It consists of rows and columns, similar to a spreadsheet, where each row represents a data point or observation, and each column represents a variable or attribute. Examples include Excel spreadsheets, CSV files, and database tables.

2. Text Data: Text datasets contain textual information, such as documents, articles, tweets, or chat logs. Natural Language Processing (NLP) often involves working with text datasets.

3. Image Data: Image datasets contain images or photographs. Each image can be considered a data point. Image datasets are commonly used in computer vision tasks, such as image classification or object detection.

4. Time Series Data: Time series datasets record data points at specific time intervals. Examples include stock price data, weather measurements, and sensor readings.

5. Geospatial Data: Geospatial datasets contain information related to geographic locations. This can include maps, GPS coordinates, and geographic features.

6. Graph Data: Graph datasets represent data as a network of interconnected nodes and edges. These are used in network analysis, social network analysis, and recommendation systems.

7. Audio Data: Audio datasets contain audio recordings, such as speech, music, or sound effects. They are used in tasks like speech recognition and audio classification.

8. Biological Data: Biological datasets include genetic sequences, protein structures, and other biological information used in bioinformatics and genomics.

Datasets are fundamental in various fields, including machine learning, data analysis, scientific research, and business intelligence. Researchers and analysts use datasets to train models, test hypotheses, gain insights, and make data-driven decisions. Open datasets are often made available to the public for research purposes, while proprietary datasets are kept private for commercial or proprietary reasons.

OK, that is helpful. Let's take a look at an example of one of these types of datasets: audio data.

I found this blog post that shared some free audio datasets. From there, I found one called the CHiME Home Dataset. The CHiME-Home dataset is a collection of annotated domestic environment audio recordings that can be downloaded as a 3.9Gb tar file.

Here is what was included in that tarball:

├── CHANGES
├── LICENSE
├── README
├── VERSION
├── chunks
├── development_chunks_raw.csv
├── development_chunks_refined.csv
├── development_chunks_refined_crossval_dcase2016.csv
├── evaluation_chunks_raw.csv
└── evaluation_chunks_refined.csv

The chunks directory contains about 19,000 files, .wav files of the audio recordings, and .csv files that have information about each recording.

What would someone do with this data? Well, that's a different area to explore. But for now, this quick research has helped me understand what a dataset is: a collection of organized data and metadata.