diff --git a/nnpdf_data/nnpdf_data/commondata/CMS_WCHARM_7TEV/filter_utils.py b/nnpdf_data/nnpdf_data/commondata/CMS_WCHARM_7TEV/filter_utils.py
index 78096bafba..e6e33eb232 100644
--- a/nnpdf_data/nnpdf_data/commondata/CMS_WCHARM_7TEV/filter_utils.py
+++ b/nnpdf_data/nnpdf_data/commondata/CMS_WCHARM_7TEV/filter_utils.py
@@ -196,7 +196,7 @@ def __build_unc_definitions(self, variant='default'):
             for idx in range(self.ndata):
                 unc_definitions[f'{ART_LABEL}_{idx+1}'] = {
                     'description': f'Correlated systematic uncertainty {idx+1}',
-                    'treatment': 'MULT',
+                    'treatment': 'ADD',
                     'type': 'CORR',
                 }
 
@@ -205,7 +205,7 @@ def __build_unc_definitions(self, variant='default'):
 
         return unc_definitions
 
-    def generate_covmat(self, diag_stat_uncs=None):
+    def generate_covmat(self, diag_uncs=None):
         table = self.metadata["tables"][1]
         tab_dict = self.__retrieve_table(table)
         matlist = tab_dict['dependent_variables'][0]['values']
@@ -213,7 +213,7 @@ def generate_covmat(self, diag_stat_uncs=None):
         covmat = np.zeros((self.ndata, self.ndata))
         for i in range(self.ndata):
             for j in range(self.ndata):
-                covmat[i, j] = matlist[i + self.ndata * j] * diag_stat_uncs[i] * diag_stat_uncs[j]
+                covmat[i, j] = matlist[i + self.ndata * j] * diag_uncs[i] * diag_uncs[j]
         return covmat
 
     def generate_data(self, variant='default', save_to_yaml=False, path='./'):
@@ -226,7 +226,7 @@ def generate_data(self, variant='default', save_to_yaml=False, path='./'):
         sys_artificial = []  # Initialize vector of artificial uncertainties
 
         if self.observable == 'WPWM-TOT':
-            covmat = self.generate_covmat(stat_unc)
+            covmat = self.generate_covmat(sys_unc)
             eigvals, eigvecs = np.linalg.eig(covmat)
             art_unc = np.sqrt(eigvals) * eigvecs
 
@@ -242,6 +242,8 @@ def generate_data(self, variant='default', save_to_yaml=False, path='./'):
             for data_idx, data in enumerate(central_data):
                 # Statistical uncertainty
                 unc_dict = {STAT_LABEL: stat_unc[data_idx]}
+
+                # Systematic uncertainty
                 unc_dict[f'{ART_LABEL}'] = sys_unc[data_idx]
                 sys_artificial.append(unc_dict)
 
diff --git a/nnpdf_data/nnpdf_data/commondata/CMS_WCHARM_7TEV/uncertainties_WPWM-TOT.yaml b/nnpdf_data/nnpdf_data/commondata/CMS_WCHARM_7TEV/uncertainties_WPWM-TOT.yaml
index d53545763f..19e422c314 100644
--- a/nnpdf_data/nnpdf_data/commondata/CMS_WCHARM_7TEV/uncertainties_WPWM-TOT.yaml
+++ b/nnpdf_data/nnpdf_data/commondata/CMS_WCHARM_7TEV/uncertainties_WPWM-TOT.yaml
@@ -5,52 +5,52 @@ definitions:
     type: UNCORR
   art_corr_1:
     description: Correlated systematic uncertainty 1
-    treatment: MULT
+    treatment: ADD
     type: CORR
   art_corr_2:
     description: Correlated systematic uncertainty 2
-    treatment: MULT
+    treatment: ADD
     type: CORR
   art_corr_3:
     description: Correlated systematic uncertainty 3
-    treatment: MULT
+    treatment: ADD
     type: CORR
   art_corr_4:
     description: Correlated systematic uncertainty 4
-    treatment: MULT
+    treatment: ADD
     type: CORR
   art_corr_5:
     description: Correlated systematic uncertainty 5
-    treatment: MULT
+    treatment: ADD
     type: CORR
 bins:
 - stat_uncorr: 2700.0
-  art_corr_1: -2.50638824e+03
-  art_corr_2: -7.95697332e+02
-  art_corr_3: 5.18818026e+02
-  art_corr_4: 3.24524090e+02
-  art_corr_5: 1.98921572e+01
+  art_corr_1: -4.30085960e+03
+  art_corr_2: 1.48958525e+02
+  art_corr_3: -1.49014534e+03
+  art_corr_4: -3.33760715e+02
+  art_corr_5: 5.55417584e+02
 - stat_uncorr: 2500.0
-  art_corr_1: -2.26430174e+03
-  art_corr_2: 9.54417285e+02
-  art_corr_3: 3.80750387e+02
-  art_corr_4: 2.58001370e+02
-  art_corr_5: 2.21295434e+01
+  art_corr_1: -3.61128670e+03
+  art_corr_2: 1.46245103e+02
+  art_corr_3: 1.37172210e+03
+  art_corr_4: -3.95171209e+02
+  art_corr_5: 9.48387525e+02
 - stat_uncorr: 2400.0
-  art_corr_1: -2.16891682e+03
-  art_corr_2: -4.98172990e+01
-  art_corr_3: -9.87453634e+02
-  art_corr_4: 2.78677305e+02
-  art_corr_5: 2.43385786e+01
+  art_corr_1: -3.42032648e+03
+  art_corr_2: 1.58201894e+02
+  art_corr_3: 2.94011007e+02
+  art_corr_4: -5.54441014e+02
+  art_corr_5: -1.52397231e+03
 - stat_uncorr: 1900.0
-  art_corr_1: -1.64250437e+03
-  art_corr_2: -1.68762567e+01
-  art_corr_3: -1.03617739e+01
-  art_corr_4: -6.53762602e+02
-  art_corr_5: -6.95975341e+02
+  art_corr_1: -2.77487954e+03
+  art_corr_2: 3.53874801e+02
+  art_corr_3: 1.26728625e+02
+  art_corr_4: 1.53882437e+03
+  art_corr_5: -1.75429767e+02
 - stat_uncorr: 1700.0
-  art_corr_1: -1.39269626e+03
-  art_corr_2: -2.22542263e+01
-  art_corr_3: -2.70634325e+00
-  art_corr_4: -6.66474311e+02
-  art_corr_5: 7.11130479e+02
+  art_corr_1: -1.94086828e+03
+  art_corr_2: -1.38692811e+03
+  art_corr_3: 5.04690564e+01
+  art_corr_4: 2.51872601e+02
+  art_corr_5: -5.89390783e+01