diff --git a/advanced/databases/livedet-iris-2020/1.json b/advanced/databases/livedet-iris-2020/1.json
new file mode 100644
index 0000000000000000000000000000000000000000..2ffbec62b458b8d849b7ed010e9f6190a3cc047b
--- /dev/null
+++ b/advanced/databases/livedet-iris-2020/1.json
@@ -0,0 +1,26 @@
+{
+    "description": "Livedet Iris 2020 test database",
+    "environment": {
+        "name": "db.examples",
+        "version": "I don't know"
+    },
+    "protocols": [
+        {
+            "name": "Main",
+            "sets": [
+                {
+                    "name": "test",
+                    "outputs": {
+                        "category": "system/integer/1",
+                        "image": "system/array_2d_uint8/1",
+                        "label": "system/boolean/1"
+                    },
+                    "template": "iris_pad",
+                    "view": "Test"
+                }
+            ],
+            "template": "iris_pad"
+        }
+    ],
+    "root_folder": "/somewhere"
+}
diff --git a/advanced/databases/livedet-iris-2020/1.py b/advanced/databases/livedet-iris-2020/1.py
new file mode 100644
index 0000000000000000000000000000000000000000..43ef6307392ccca62b089b6fa268eb0dd114a600
--- /dev/null
+++ b/advanced/databases/livedet-iris-2020/1.py
@@ -0,0 +1,40 @@
+import os
+
+import numpy as np
+import pandas as pd
+from beat.backend.python.database import View
+from PIL import Image
+
+
+class Test(View):
+    def index(self, root_folder, parameters):
+        """Creates the data for the database indexation"""
+
+        csv_path = os.path.join(root_folder, "test.csv")
+        df = pd.read_csv(csv_path)
+
+        df["filename"] = df["filename"].apply(lambda x: os.path.join(root_folder, x))
+        df = df.rename(columns={"filename": "image"})
+
+        # ------------- v1 random labels -----
+        # remove this part for v2 release
+        num_files = len(df)
+        np.random.seed(0)
+        df["label"] = np.random.randint(0, 2, size=num_files, dtype=bool)
+        df["category"] = np.random.randint(1, 7, size=num_files, dtype=int)
+        # ------------------------------------
+
+        return list(df.itertuples(index=False))
+
+    def get(self, output, index):
+        """Returns the data for the output based on the index content"""
+
+        obj = self.objs[index]
+
+        if output == "image":
+            img = np.asarray(Image.open(obj.image))
+            return {"value": img}
+        elif output == "label":
+            return {"value": bool(obj.label)}
+        elif output == "category":
+            return {"value": int(obj.category)}