You could generate a similar dataset in Python using scikit-learn's make_circles function.
from sklearn.datasets import make_circles
import matplotlib.pyplot as plt
n_samples = 400
samples, labels = make_circles(n_samples=n_samples, factor=.3, noise=.05)
bluecircle = samples[labels==0]
redcircle = samples[labels==1]
plt.figure()
plt.scatter(bluecircle[:, 0], bluecircle[:, 1], c='b', marker='o', s=10)
plt.scatter(redcircle[:, 0], redcircle[:, 1], c='r', marker='+', s=30)
plt.show()

In Matlab you could could generate the dataset with something like:
n = 200;
step = 1/n;
radius = 0.5;
ratio = 0.4;
ang=0:step:2*pi;
xp=radius*cos(ang);
yp=radius*sin(ang);
hold on;
% blue circle
idx = randsample(length([xp;yp]),n);
bluex = xp+(rand(length(xp),1)*radius/10)';
bluey = yp+(rand(length(yp),1)*radius/10)';
scatter(bluex(:,idx),bluey(:,idx),'b','o');
% red circle
idx = randsample(length([xp;yp]),n);
redx = ratio*xp+(rand(length(xp),1)*radius/10)';
redy = ratio*yp+(rand(length(yp),1)*radius/10)';
scatter(redx(:,idx),redy(:,idx),'r','+');
