diff --git a/HowBadIsMyBatch.ipynb b/HowBadIsMyBatch.ipynb
index a5d5b9bc911..c33846386ab 100644
--- a/HowBadIsMyBatch.ipynb
+++ b/HowBadIsMyBatch.ipynb
@@ -146,8 +146,7 @@
     "                                 [np.NaN, np.NaN,     'Y']],\n",
     "                        index = [\n",
     "                           \"1996873\",\n",
-    "                           \"1996874\"],\n",
-    "),\n",
+    "                           \"1996874\"]),\n",
     "                    'VAERSVAX': self.createDataFrame(\n",
     "                        columns = ['VAX_TYPE', 'VAX_MANU',         'VAX_LOT', 'VAX_DOSE_SERIES'],\n",
     "                        data = [  ['HPV9',     'MERCK & CO. INC.', 'R017624', 'UNK'],\n",
diff --git a/help.txt b/help.txt
index 201f932f4ba..cb7c69f0de0 100644
--- a/help.txt
+++ b/help.txt
@@ -1,6 +1,7 @@
 jupyter notebook
 
 FK-TODO:
+- Tabelle von https://www.howbadismybatch.com/combined.html nachprogrammieren
 - "I would suggest that you filter the vax table first for just C19 vaccines, and for just first dose.
   Then carry out the analysis as before.
   Repeat for second dose and third dose separately. The cumulative effect will then appear.