A normally distributed dataset is one where the data follows a bell-shaped curve when plotted on a graph. Normal distribution is characterized by a mean, median, and mode that are all equal, and by a symmetrical distribution of data around the mean.
The data type needed for normally distributed data depends on the type of data being collected and the analysis you plan to perform. In general, numerical data (such as continuous variables or integers or ratio scale or interval scale) is more likely to be normally distributed than categorical data (such as nominal or ordinal variables).
If you are working with normally distributed data, you can use a variety of statistical techniques to analyze the data, including parametric tests (which assume that the data is normally distributed) and nonparametric tests (which do not assume a particular distribution).
It's important to note that not all data is normally distributed, and that it is often necessary to transform data in order to make it more normally distributed before performing certain types of statistical analysis. It's also important to visually inspect the data to determine whether it appears to be normally distributed, rather than relying on summary statistics or assumptions about the data.