Data Sampling is a powerful tool that streamlines the systematic review process by creating randomized subsets of your dataset. It enables you to assign specific datasets to collaborators, reduce bias through randomization, and better train Rayyan's predictions classifier (compute ratings) feature. Data Sampling revolutionizes the way you work with your datasets, especially larger datasets.
1. Within the Review Data page, click on Samples.
2. Select the Sample Data Source either from All References or Filtered.
3. Choose between multiple sampling settings: Randomized, System ID, Title, Publication Date, Authors, or Rating.
Randomized: Ensure random data selection. For instance, divide 1000 references between 2 collaborators by creating two samples. Set the percentage (e.g., 50%) for each dataset.
System ID, Title, Publication Date, Authors, or Rating, .: Divide data based on the System ID sequence. For example, create a dataset with a 50% setting, and it will select the first 50% based on System ID sequence.
4. Add the sample information (name of the sampled data, and the percentage) Note that the percentage and the number of articles are connected.
5. Click on Create once done.
6. You will notice a new filter added in the filter's workbench named Sampled Data.
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