diff --git a/GPy/util/datasets.py b/GPy/util/datasets.py
index 0e0929c7..d326f31b 100644
--- a/GPy/util/datasets.py
+++ b/GPy/util/datasets.py
@@ -217,7 +217,6 @@ def crescent_data(num_data=200, seed=default_seed):
     Y = np.vstack((np.ones((num_data_part[0] + num_data_part[1], 1)), -np.ones((num_data_part[2] + num_data_part[3], 1))))
     return {'X':X, 'Y':Y, 'info': "Two separate classes of data formed approximately in the shape of two crescents."}
 
-
 def creep_data():
     all_data = np.loadtxt(os.path.join(data_path, 'creep', 'taka'))
     y = all_data[:, 1:2].copy()
@@ -226,3 +225,49 @@ def creep_data():
     X = all_data[:, features].copy()
     return {'X': X, 'y' : y}
 
+def cmu_35_walk_jog():
+
+    skel = GPy.util.mocap.acclaim_skeleton(os.path.join(data_path, 'mocap', 'cmu', '35', '35.asf'))
+    examples = ['01', '02', '03', '04', '05', '06',
+                '07', '08', '09', '10', '11', '12',
+                '13', '14', '15', '16', '17', '19',
+                '20', '21', '22', '23', '24', '25',
+                '26', '28', '30', '31', '32', '33', '34']
+    test_examples = ['18', '29']
+    # Label differently for each sequence
+    exlbls = np.eye(31)
+    testexlbls = np.eye(2)
+    tot_length = 0
+    tot_test_length = 0
+    tY = []
+    tlbls = []
+    for i in range(len(examples)):
+        tmpchan = skel.load_channels(os.path.join(data_path, 'mocap', 'cmu', '35', '35_' + examples[i] + '.amc'))
+        tY.append(tmpchan[::4, :])
+        tlbls.append(np.tile(exlbls[i, :], (tY[i].shape[0], 1)))
+        tot_length += tY[i].shape[0]
+    Y = np.zeros((tot_length, tY[0].shape[1]))
+    lbls = np.zeros((tot_length, tlbls[0].shape[1]))
+    endInd = 0
+    for i in range(len(tY)):
+        startInd = endInd 
+        endInd += tY[i].shape[0]
+        Y[startInd:endInd, :] = tY[i]
+        lbls[startInd:endInd, :] = tlbls[i]
+    tYtest = []
+    tlblstest = [] 
+    for i in range(len(test_examples)):
+        tmpchan = skel.load_channels(os.path.join(data_path, 'mocap', 'cmu', '35', '35_' + test_examples[i] + '.amc'))
+        tYtest.append(tmpchan[::4, :])
+        tlblstest.append(np.tile(testexlbls[i, :], (tYtest[i].shape[0], 1)))
+        tot_test_length += tYtest[i].shape[0]
+
+    Ytest = np.zeros((tot_test_length, tYtest[0].shape[1]))
+    lblstest = np.zeros((tot_test_length, tlblstest[0].shape[1]))
+    endInd = 0
+    for i in range(len(tYtest)):
+        startInd = endInd 
+        endInd += tYtest[i].shape[0]
+        Ytest[startInd:endInd, :] = tYtest[i]
+        lblstest[startInd:endInd, :] = tlblstest[i]
+    return {'Y': Y, 'lbls' : lbls, 'Ytest': Ytest, 'lblstest' : lblstest, 'info': "Walk and jog data from CMU data base subject 35."}