Datasets refer to collections of structured or unstructured data that are organized and made available for analysis, research, or other purposes. Datasets can vary widely in size, format, and content, and they are used across various disciplines and industries. Datasets are valuable resources for a wide range of applications, including machine learning, data analysis, scientific research, business intelligence, and more. They provide the raw material for extracting insights, making informed decisions, and developing models or algorithms.
Datasets can be sourced from various places. They may be collected through surveys, experiments, observations, sensors, web scraping, public repositories, government agencies, research institutions, or generated through simulations.
Datasets can have different structures and formats. Structured datasets have a predefined organization, often stored in tabular form with rows and columns (e.g., CSV, Excel). Unstructured datasets lack a fixed organization and may include text documents, images, audio, video, or other forms of data.
Datasets can be classified into different types based on their characteristics. Common types include numerical datasets (containing numeric values), categorical datasets (containing categories or labels), textual datasets (containing text), image datasets (containing images), and time series datasets (containing data ordered over time).
Datasets can range from small to large scales. Small datasets may consist of a few hundred or thousand records, while large datasets can contain millions or billions of records. The size of a dataset can impact the computational requirements and analysis techniques used.
SPIDER advises iDART members to create datasets and save their datasets as Open and public datasets that are freely available to the public on free and open public data repositories, online platforms. These datasets can be valuable resources for research, analysis, and the development of applications. Researchers and data users should also comply with legal and ethical guidelines and obtain necessary permissions when working with sensitive data.
SPIDER advises iDART members dataset pre-processing before analysis. This can involve cleaning the data (removing errors, outliers, or duplicates), transforming the data into a suitable format, handling missing values, normalizing or scaling variables, and other necessary steps to ensure data quality.