diff --git a/tests/test_pipeline.py b/tests/test_pipeline.py
index 8ab1d2e..d93acdd 100644
--- a/tests/test_pipeline.py
+++ b/tests/test_pipeline.py
@@ -1,11 +1,64 @@
 from pCRscore import pipeline
 import pandas
 import math
+import os
+import numpy
+
+
+def mock_data(shap=False):
+    columns = [
+        'Unnamed: 0', 'Trial', 'Mixture', 'B.cells.Memory',
+        'B.cells.Naive', 'CAFs.MSC.iCAF.like', 'CAFs.myCAF.like', 'DCs',
+        'Endothelial.ACKR1', 'Endothelial.CXCL12', 'Endothelial.LYVE1',
+        'Endothelial.RGS5', 'GenMod1', 'GenMod2', 'GenMod3', 'GenMod4',
+        'GenMod5', 'GenMod6', 'GenMod7', 'Luminal.Progenitors',
+        'Macrophage', 'Mature.Luminal', 'Monocyte', 'Myoepithelial',
+        'NK.cells', 'NKT.cells', 'Plasmablasts', 'PVL.Differentiated',
+        'PVL.Immature', 'T.cells.CD4.', 'T.cells.CD8.', 'Cancer.Cells',
+        'Normal.Epi', 'TCells', 'Myeloids', 'BCells', 'CAFs', 'PVLs',
+        'Endothelials', 'ER', 'Response', 'Cohort', 'PAM50_Basal',
+        'PAM50_Her2', 'PAM50_LumA', 'PAM50_LumB', 'PAM50_Normal'
+    ]
+    num_rows = 1009
+    if shap:
+        n_floats = 44
+    else:
+        n_floats = 36
+    df = pandas.DataFrame(columns=columns, index=numpy.arange(num_rows))
+    df['Unnamed: 0'] = 0
+    df['Trial'] = 'GSE22093'
+    df['Mixture'] = 'GSM549230'
+    for i in range(n_floats):
+        df[columns[i + 3]] = numpy.random.uniform(0, 1, num_rows)
+    if not shap:
+        df['ER'] = numpy.random.choice([0, 1], num_rows)
+    df['Response'] = numpy.random.choice([0, 1], num_rows)
+    df['Cohort'] = 'Discovery'
+    if not shap:
+        df['PAM50_Basal'] = numpy.random.choice([True, False], num_rows)
+        df['PAM50_Her2'] = numpy.random.choice([True, False], num_rows)
+        df['PAM50_LumA'] = numpy.random.choice([True, False], num_rows)
+        df['PAM50_LumB'] = numpy.random.choice([True, False], num_rows)
+        df['PAM50_Normal'] = numpy.random.choice([True, False], num_rows)
+    # Dropping columns not present in SHAP data
+    if shap:
+        df = df.drop(columns=['Trial', 'Mixture', 'Response', 'Cohort'])
+    return df
+
 
 # Import DiscoveryData.csv and drop invalid columns
-# FIXME: replace local data with mock data (after finishing the pipeline)
-data_raw = pandas.read_csv(".meta/DiscoveryData.csv")
-shap_raw = pandas.read_csv(".meta/DiscoverySHAP.csv")
+if os.path.exists(".meta"):
+    # Running locally
+    local = True
+    data_raw = pandas.read_csv(".meta/DiscoveryData.csv")
+    shap_raw = pandas.read_csv(".meta/DiscoverySHAP.csv")
+else:
+    # On GitHub Actions
+    local = False
+    data_raw = mock_data()
+    shap_raw = mock_data(shap=True)
+
+
 data = pipeline.drop_non_float(data_raw)
 shap = pipeline.drop_non_float(shap_raw, extra_cols=range(36, 42))
 
@@ -22,12 +75,17 @@ def test_drop_non_float_and_unnamed():
 # Test
 def test_normalize_data():
     assert data_norm.shape == data.shape
-    tol = 1e-3
-    assert math.isclose(data['B.cells.Memory'].iloc[0], 0.01789, rel_tol=tol)
-    assert math.isclose(
-        data_norm['B.cells.Memory'].iloc[0], 0.34186, rel_tol=tol
-    )
-    assert math.isclose(shap['Endothelials'].iloc[0], 0.037792, rel_tol=tol)
+    if local:
+        tol = 1e-3
+        assert math.isclose(
+            data['B.cells.Memory'].iloc[0], 0.01789, rel_tol=tol
+        )
+        assert math.isclose(
+            data_norm['B.cells.Memory'].iloc[0], 0.34186, rel_tol=tol
+        )
+        assert math.isclose(
+            shap['Endothelials'].iloc[0], 0.037792, rel_tol=tol
+        )
 
 
 # Combine data and SHAP values