diff --git a/src/smefit/analyze/report.py b/src/smefit/analyze/report.py
index d9a87368..f9f3236a 100644
--- a/src/smefit/analyze/report.py
+++ b/src/smefit/analyze/report.py
@@ -70,17 +70,47 @@ def __init__(self, report_path, result_path, report_config):
             self.fits.append(fit)
         self.fits = np.array(self.fits)
 
+        # Get names of datasets for each fit
+        self.dataset_fits = []
+        for fit in self.fits:
+            self.dataset_fits.append([data["name"] for data in fit.config["datasets"]])
+
         # Loads useful information about data
-        self.data_info = self._load_grouped_info(report_config["data_info"], "datasets")
+        self.data_info = self._load_grouped_data_info(report_config["data_info"])
         # Loads coefficients grouped with latex name
-        self.coeff_info = self._load_grouped_info(
-            report_config["coeff_info"], "coefficients"
-        )
+        self.coeff_info = self._load_grouped_coeff_info(report_config["coeff_info"])
         self.html_index = ""
         self.html_content = ""
 
-    def _load_grouped_info(self, raw_dict, key):
-        """Load grouped info of coefficients and datasets.
+    def _load_grouped_data_info(self, raw_dict):
+        """Load grouped info of datasets.
+
+        Only elements appearing at least once in the fit configs are kept.
+
+        Parameters
+        ----------
+        raw_dict: dict
+            raw dictionary with relevant information
+
+        Returns
+        _______
+        grouped_config: pandas.DataFrame
+            table with information by group
+
+        """
+        out_dict = {}
+        for group, entries in raw_dict.items():
+            out_dict[group] = {}
+            for val in entries:
+                if np.any([val[0] in datasets for datasets in self.dataset_fits]):
+                    out_dict[group][val[0]] = val[1]
+
+            if len(out_dict[group]) == 0:
+                out_dict.pop(group)
+        return pd.DataFrame(out_dict).stack().swaplevel()
+
+    def _load_grouped_coeff_info(self, raw_dict):
+        """Load grouped info of coefficients.
 
         Only elements appearing at least once in the fit configs are kept.
 
@@ -88,8 +118,6 @@ def _load_grouped_info(self, raw_dict, key):
         ----------
         raw_dict: dict
             raw dictionary with relevant information
-        key: "datasets" or "coefficients"
-            key to check
 
         Returns
         _______
@@ -101,7 +129,7 @@ def _load_grouped_info(self, raw_dict, key):
         for group, entries in raw_dict.items():
             out_dict[group] = {}
             for val in entries:
-                if np.any([val[0] in fit.config[key] for fit in self.fits]):
+                if np.any([val[0] in fit.config["coefficients"] for fit in self.fits]):
                     out_dict[group][val[0]] = val[1]
 
             if len(out_dict[group]) == 0:
diff --git a/src/smefit/analyze/summary.py b/src/smefit/analyze/summary.py
index e10ec053..0630de6c 100644
--- a/src/smefit/analyze/summary.py
+++ b/src/smefit/analyze/summary.py
@@ -54,6 +54,10 @@ def __init__(self, fits, data_groups, coeff_config):
         self.data_info = data_groups
         self.coeff_info = coeff_config
         self.nfits = len(self.fits)
+        # Get names of datasets for each fit
+        self.dataset_fits = []
+        for fit in self.fits:
+            self.dataset_fits.append([data["name"] for data in fit.config["datasets"]])
 
     def fit_settings(self):
         """Fit settings table.
@@ -103,9 +107,9 @@ def write_dataset_table(self):
             L.append(f"\\multirow{{{datasets.shape[0]}}}{{*}}{{{group}}}")
             for isub, (dataset, link) in enumerate(datasets.items()):
                 temp = r" & \href{" + link + "}{" + dataset + "} "
-                for fit in self.fits:
+                for data in self.dataset_fits:
                     temp += " & "
-                    if dataset in fit.config["datasets"]:
+                    if dataset in data:
                         temp += r"$\checkmark$"
                 if isub != datasets.shape[0] - 1:
                     temp += f"\\\\ \\cline{{2-{(2 + self.nfits)}}}"