Use a set of existing data aggregation models to combine crowd labels and learn information about the workers.
The ActiveCrowdToolkit .NET v0.1 includes the methods: Majority voting, Vote distribution, Dawid&Skene , Bayesian Classifier Combination (BCC) , Community-Based BCC 
By combining existing data aggregation models, task selection methods and worker selection methods.
Import data in comma-separated format using integrated data parsing methods.
Use the graphical interface to understand the relative performance of different strategies, and visualise internal models such as the belief over a task’s true label and confusion matrices of individual workers.
Want to run repeated experiments? Use the toolkit's command-line API to run experiments in batches. This is ideal for cluster deployment.