diff --git a/Makefile b/Makefile
index 0d7f5e5f..feb71b7b 100644
--- a/Makefile
+++ b/Makefile
@@ -1,7 +1,7 @@
 
 
 JAR := matsim-berlin-*.jar
-V := v6.2
+V := v6.3
 CRS := EPSG:25832
 
 p := input/$V
diff --git a/pom.xml b/pom.xml
index 33cc4cc4..ad41c4c8 100644
--- a/pom.xml
+++ b/pom.xml
@@ -15,7 +15,7 @@
 	<modelVersion>4.0.0</modelVersion>
 	<groupId>com.github.matsim-scenarios</groupId>
 	<artifactId>matsim-berlin</artifactId>
-	<version>6.2-SNAPSHOT</version>
+	<version>6.3-SNAPSHOT</version>
 
 	<name>MATSim Open Berlin scenario</name>
 	<description>MATSim Open Berlin scenario</description>
diff --git a/src/main/java/org/matsim/run/OpenBerlinScenario.java b/src/main/java/org/matsim/run/OpenBerlinScenario.java
index abaeb224..161472c5 100644
--- a/src/main/java/org/matsim/run/OpenBerlinScenario.java
+++ b/src/main/java/org/matsim/run/OpenBerlinScenario.java
@@ -30,7 +30,7 @@ public class OpenBerlinScenario extends MATSimApplication {
 
 	private static final Logger log = LogManager.getLogger(RunOpenBerlinCalibration.class);
 
-	public static final String VERSION = "6.2";
+	public static final String VERSION = "6.3";
 	public static final String CRS = "EPSG:25832";
 	@CommandLine.Mixin
 	private final SampleOptions sample = new SampleOptions(10, 25, 3, 1);
diff --git a/src/main/python/extract_income.py b/src/main/python/extract_income.py
deleted file mode 100644
index 2a20ecac..00000000
--- a/src/main/python/extract_income.py
+++ /dev/null
@@ -1,37 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-import argparse
-import numpy as np
-import os
-from matsim.scenariogen.data import read_all
-
-if __name__ == "__main__":
-    parser = argparse.ArgumentParser(description="Converter for survey data")
-
-    parser.add_argument("-d", "--directory", default=os.path.expanduser(
-        "~/Development/matsim-scenarios/shared-svn/projects/matsim-berlin/data/SrV/"))
-
-    args = parser.parse_args()
-
-    hh, persons, trips = read_all([args.directory + "Berlin+Umland", args.directory + "Brandenburg"])
-
-    hh = hh[hh.income >= 0]
-
-    # Large households are underrepresented and capped (same operation as in input)
-    hh.n_persons = np.minimum(hh.n_persons, 5)
-
-    groups = list(sorted(set(hh.income)))
-
-    def calc(x):
-        counts = x.groupby("income").size()
-        prob = counts / sum(counts)
-        return prob.to_frame().transpose()
-
-
-    dist = hh.groupby(["economic_status"]).apply(calc).fillna(0).reset_index().drop(columns=["level_1"])
-
-    print("Income groups:", groups)
-
-    for t in dist[["economic_status"] + groups].itertuples():
-        print('"%s", new double[]{%s},' % (t.economic_status, ", ".join("%.3f" % x for x in t[2:])))
diff --git a/src/main/python/extract_trips.py b/src/main/python/extract_trips.py
deleted file mode 100644
index 5ec0d123..00000000
--- a/src/main/python/extract_trips.py
+++ /dev/null
@@ -1,49 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-
-import numpy as np
-from matsim.scenariogen.data import read_all
-
-from extract_ref_data import trip_filter
-
-if __name__ == "__main__":
-    hh, persons, trips = read_all("../../../../shared-svn/projects/matsim-berlin/data/SrV/Berlin+Umland")
-
-    trips = trip_filter(trips)
-    trips = trips[trips.valid]
-    trips = trips[(~trips.from_zone.isna()) & (~trips.to_zone.isna())]
-    trips = trips[(~trips.from_location.isna()) & (~trips.to_location.isna())]
-
-    trips_hh = trips.merge(hh, left_on="hh_id", right_index=True)
-
-    trips = trips[trips_hh.location == "Berlin"]
-
-    # Duplication factor
-    factor = 3
-
-    repeats = np.maximum(1, np.rint(trips.t_weight * factor)).to_numpy(int)
-    index = trips.index.repeat(repeats)
-    df = trips.loc[index]
-
-    # Each sample has a unique sequence number
-    seq = np.zeros(len(df), dtype=int)
-
-    i = 0
-    for r in repeats:
-        for s in range(r):
-            seq[i] = s
-            i += 1
-
-    df["seq"] = seq
-
-    df = df.drop(columns=["valid"])
-    # Norm weight to 3 as well
-    df.t_weight = df.t_weight / 3
-
-    df_hh = df.merge(hh, left_on="hh_id", right_index=True)
-    df["hh_cars"] = df_hh["n_cars"]
-
-    df = df.sort_values(["p_id", "seq", "n"])
-    df.to_csv("trips-scaled.csv", index=False)
-
-    persons.to_csv("persons-unscaled.csv", index=False)