diff --git a/.run/dqo run.run.xml b/.run/dqo run.run.xml
index c37db9ec98..899de23cb8 100644
--- a/.run/dqo run.run.xml	
+++ b/.run/dqo run.run.xml	
@@ -5,7 +5,7 @@
       <option name="region" />
       <option name="useCurrentConnection" value="false" />
     </extension>
-    <option name="JAR_PATH" value="$PROJECT_DIR$/dqops/target/dqo-dqops-1.6.0.jar" />
+    <option name="JAR_PATH" value="$PROJECT_DIR$/dqops/target/dqo-dqops-1.6.1.jar" />
     <option name="VM_PARAMETERS" value="-XX:MaxRAMPercentage=60.0 --add-opens java.base/java.nio=ALL-UNNAMED --add-opens java.base/java.util.concurrent=ALL-UNNAMED" />
     <option name="PROGRAM_PARAMETERS" value="--server.port=8888" />
     <option name="WORKING_DIRECTORY" value="C:\dev\dqoado" />
diff --git a/CHANGELOG.md b/CHANGELOG.md
index a0d3319767..19ae7a1b1e 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -1,4 +1,5 @@
 # 1.6.1
-* Small fixes in the data quality check editor - styling fixes
-* Parallel invocation of notification events
-* The calculation logic for counting incidents on the global incident screen modified to use dates correctly
+* Incident notification supports emails
+* Small bug fixes in the check editor
+* Additional check to detect empty columns
+* Copying data quality check patterns
diff --git a/VERSION b/VERSION
index ce6a70b9d8..2eda823ff5 100644
--- a/VERSION
+++ b/VERSION
@@ -1 +1 @@
-1.6.0
\ No newline at end of file
+1.6.1
\ No newline at end of file
diff --git a/distribution/pom.xml b/distribution/pom.xml
index 3649b3258a..28cdaceaee 100644
--- a/distribution/pom.xml
+++ b/distribution/pom.xml
@@ -11,7 +11,7 @@
 
 	<groupId>com.dqops</groupId>
 	<artifactId>dqo-distribution</artifactId>
-    <version>1.6.0</version>  <!-- DQOps Version, do not touch (changed automatically) -->
+    <version>1.6.1</version>  <!-- DQOps Version, do not touch (changed automatically) -->
 	<name>dqo-distribution</name>
 	<description>DQOps Data Quality Operations Center final assembly</description>
 	<packaging>pom</packaging>
diff --git a/distribution/python/dqops/version.py b/distribution/python/dqops/version.py
index 0628f32c6a..9de2b30f77 100644
--- a/distribution/python/dqops/version.py
+++ b/distribution/python/dqops/version.py
@@ -15,8 +15,8 @@
 # limit
 
 # WARNING: the next two lines with the version numbers (VERSION =, PIP_VERSION =) should not be modified manually. They are changed by a maven profile at compile time.
-VERSION = "1.6.0"
-PIP_VERSION = "1.6.0"
+VERSION = "1.6.1"
+PIP_VERSION = "1.6.1"
 GITHUB_RELEASE = "v" + VERSION + ""
 JAVA_VERSION = "17"
 
diff --git a/docs/categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md b/docs/categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md
index e8e3ff9797..89d642e7de 100644
--- a/docs/categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md
+++ b/docs/categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md
@@ -319,8 +319,8 @@ The status identifies the highest severity issue by color.
 |[*nulls_count*](../checks/column/nulls/nulls-count.md)|Maximum count of rows containing null values (incomplete column)|[Completeness](../dqo-concepts/data-quality-dimensions.md#data-completeness)|Detects incomplete columns that contain any *null* values. Counts the number of rows having a null value. Raises a data quality issue when the count of null values is above a *max_count* threshold.|:material-check-bold:|
 |[*nulls_percent*](../checks/column/nulls/nulls-percent.md)|Maximum percentage of rows containing null values (incomplete column)|[Completeness](../dqo-concepts/data-quality-dimensions.md#data-completeness)|Detects incomplete columns that contain any *null* values. Measures the percentage of rows having a null value. Raises a data quality issue when the percentage of null values is above a *max_percent* threshold. Configure this check to accept a given percentage of null values by setting the *max_percent* parameter.|:material-check-bold:|
 |[*nulls_percent_anomaly*](../checks/column/nulls/nulls-percent-anomaly.md)|Abnormal change in percentage of null values. Measured as a percentile of anomalous measures.|[Completeness](../dqo-concepts/data-quality-dimensions.md#data-completeness)|Detects day-to-day anomalies in the percentage of *null* values. Measures the percentage of rows having a *null* value. Raises a data quality issue when the rate of null values increases or decreases too much.|:material-check-bold:|
-|[*not_nulls_count*](../checks/column/nulls/not-nulls-count.md)|Minimum count of rows containing non-null values (find empty column)|[Completeness](../dqo-concepts/data-quality-dimensions.md#data-completeness)|Verifies that a column contains a minimum number of non-null values. The default value of the *min_count* parameter is 1 to detect at least one value in a monitored column.|:material-check-bold:|
-|[*not_nulls_percent*](../checks/column/nulls/not-nulls-percent.md)|Minimum percentage of rows containing non-null values (find empty column)|[Completeness](../dqo-concepts/data-quality-dimensions.md#data-completeness)|Detects incomplete columns that contain too few non-null values. Measures the percentage of rows that have non-null values. Raises a data quality issue when the percentage of non-null values is below *min_percentage*. The default value of the *min_percentage* parameter is 100.0, but DQOps supports setting a lower value to accept some nulls.|:material-check-bold:|
+|[*not_nulls_count*](../checks/column/nulls/not-nulls-count.md)|Minimum count of rows containing non-null values|[Completeness](../dqo-concepts/data-quality-dimensions.md#data-completeness)|Verifies that a column contains a minimum number of non-null values. The default value of the *min_count* parameter is 1 to detect at least one value in a monitored column.|:material-check-bold:|
+|[*not_nulls_percent*](../checks/column/nulls/not-nulls-percent.md)|Minimum percentage of rows containing non-null values|[Completeness](../dqo-concepts/data-quality-dimensions.md#data-completeness)|Detects incomplete columns that contain too few non-null values. Measures the percentage of rows that have non-null values. Raises a data quality issue when the percentage of non-null values is below *min_percentage*. The default value of the *min_percentage* parameter is 100.0, but DQOps supports setting a lower value to accept some nulls.|:material-check-bold:|
 |[*empty_column_found*](../checks/column/nulls/empty-column-found.md)|Find an empty column|[Completeness](../dqo-concepts/data-quality-dimensions.md#data-completeness)|Detects empty columns that contain only *null* values. Counts the number of rows that have non-null values. Raises a data quality issue when the count of non-null values is below *min_count*. The default value of the *min_count* parameter is 1, but DQOps supports setting a higher number to assert that a column has at least that many non-null values.|:material-check-bold:|
 |[*nulls_percent_change*](../checks/column/nulls/nulls-percent-change.md)|Maximum percentage of change in the count of null values|[Completeness](../dqo-concepts/data-quality-dimensions.md#data-completeness)|Detects relative increases or decreases in the percentage of null values since the last measured percentage. Measures the percentage of null values for each day. Raises a data quality issue when the change in the percentage of null values is above *max_percent* of the previous percentage.| |
 |[*nulls_percent_change_1_day*](../checks/column/nulls/nulls-percent-change-1-day.md)|Maximum percentage of change in the count of null values vs 1 day ago|[Completeness](../dqo-concepts/data-quality-dimensions.md#data-completeness)|Detects relative increases or decreases in the percentage of null values since the previous day. Measures the percentage of null values for each day. Raises a data quality issue when the change in the percentage of null values is above *max_percent* of the previous percentage.| |
diff --git a/docs/checks/column/nulls/index.md b/docs/checks/column/nulls/index.md
index 86ae478cf9..7a045f58e6 100644
--- a/docs/checks/column/nulls/index.md
+++ b/docs/checks/column/nulls/index.md
@@ -62,11 +62,11 @@ Verifies that a column contains a minimum number of non-null values.
 
 | Data quality check name | Friendly name | Check type | Description | Standard |
 |-------------------------|---------------|------------|-------------|----------|
-|[<span class="no-wrap-code">`profile_not_nulls_count`</span>](./not-nulls-count.md#profile-not-nulls-count)|Minimum count of rows containing non-null values (find empty column)|[profiling](../../../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md)|Verifies that a column contains a minimum number of non-null values. The default value of the *min_count* parameter is 1 to detect at least one value in a monitored column. Raises a data quality issue when the count of non-null values is below min_count.|:material-check-bold:|
-|[<span class="no-wrap-code">`daily_not_nulls_count`</span>](./not-nulls-count.md#daily-not-nulls-count)|Minimum count of rows containing non-null values (find empty column)|[monitoring](../../../dqo-concepts/definition-of-data-quality-checks/data-observability-monitoring-checks.md)|Verifies that a column contains a minimum number of non-null values. The default value of the *min_count* parameter is 1 to detect at least one value in a monitored column. Raises a data quality issue when the count of non-null values is below min_count. Stores the most recent captured value for each day when the data quality check was evaluated.|:material-check-bold:|
-|[<span class="no-wrap-code">`monthly_not_nulls_count`</span>](./not-nulls-count.md#monthly-not-nulls-count)|Minimum count of rows containing non-null values (find empty column)|[monitoring](../../../dqo-concepts/definition-of-data-quality-checks/data-observability-monitoring-checks.md)|Verifies that a column contains a minimum number of non-null values. The default value of the *min_count* parameter is 1 to detect at least one value in a monitored column. Raises a data quality issue when the count of non-null values is below min_count. Stores the most recent check result for each month when the data quality check was evaluated.|:material-check-bold:|
-|[<span class="no-wrap-code">`daily_partition_not_nulls_count`</span>](./not-nulls-count.md#daily-partition-not-nulls-count)|Minimum count of rows containing non-null values (find empty column)|[partitioned](../../../dqo-concepts/definition-of-data-quality-checks/partition-checks.md)|Verifies that a column contains a minimum number of non-null values. The default value of the *min_count* parameter is 1 to detect at least one value in a monitored column. Raises a data quality issue when the count of non-null values is below min_count. Stores a separate data quality check result for each daily partition.|:material-check-bold:|
-|[<span class="no-wrap-code">`monthly_partition_not_nulls_count`</span>](./not-nulls-count.md#monthly-partition-not-nulls-count)|Minimum count of rows containing non-null values (find empty column)|[partitioned](../../../dqo-concepts/definition-of-data-quality-checks/partition-checks.md)|Verifies that a column contains a minimum number of non-null values. The default value of the *min_count* parameter is 1 to detect at least one value in a monitored column. Raises a data quality issue when the count of non-null values is below min_count. Stores a separate data quality check result for each monthly partition.|:material-check-bold:|
+|[<span class="no-wrap-code">`profile_not_nulls_count`</span>](./not-nulls-count.md#profile-not-nulls-count)|Minimum count of rows containing non-null values|[profiling](../../../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md)|Verifies that a column contains a minimum number of non-null values. The default value of the *min_count* parameter is 1 to detect at least one value in a monitored column. Raises a data quality issue when the count of non-null values is below min_count.|:material-check-bold:|
+|[<span class="no-wrap-code">`daily_not_nulls_count`</span>](./not-nulls-count.md#daily-not-nulls-count)|Minimum count of rows containing non-null values|[monitoring](../../../dqo-concepts/definition-of-data-quality-checks/data-observability-monitoring-checks.md)|Verifies that a column contains a minimum number of non-null values. The default value of the *min_count* parameter is 1 to detect at least one value in a monitored column. Raises a data quality issue when the count of non-null values is below min_count. Stores the most recent captured value for each day when the data quality check was evaluated.|:material-check-bold:|
+|[<span class="no-wrap-code">`monthly_not_nulls_count`</span>](./not-nulls-count.md#monthly-not-nulls-count)|Minimum count of rows containing non-null values|[monitoring](../../../dqo-concepts/definition-of-data-quality-checks/data-observability-monitoring-checks.md)|Verifies that a column contains a minimum number of non-null values. The default value of the *min_count* parameter is 1 to detect at least one value in a monitored column. Raises a data quality issue when the count of non-null values is below min_count. Stores the most recent check result for each month when the data quality check was evaluated.|:material-check-bold:|
+|[<span class="no-wrap-code">`daily_partition_not_nulls_count`</span>](./not-nulls-count.md#daily-partition-not-nulls-count)|Minimum count of rows containing non-null values|[partitioned](../../../dqo-concepts/definition-of-data-quality-checks/partition-checks.md)|Verifies that a column contains a minimum number of non-null values. The default value of the *min_count* parameter is 1 to detect at least one value in a monitored column. Raises a data quality issue when the count of non-null values is below min_count. Stores a separate data quality check result for each daily partition.|:material-check-bold:|
+|[<span class="no-wrap-code">`monthly_partition_not_nulls_count`</span>](./not-nulls-count.md#monthly-partition-not-nulls-count)|Minimum count of rows containing non-null values|[partitioned](../../../dqo-concepts/definition-of-data-quality-checks/partition-checks.md)|Verifies that a column contains a minimum number of non-null values. The default value of the *min_count* parameter is 1 to detect at least one value in a monitored column. Raises a data quality issue when the count of non-null values is below min_count. Stores a separate data quality check result for each monthly partition.|:material-check-bold:|
 
 
 
@@ -78,11 +78,11 @@ Detects incomplete columns that contain too few non-null values. Measures the pe
 
 | Data quality check name | Friendly name | Check type | Description | Standard |
 |-------------------------|---------------|------------|-------------|----------|
-|[<span class="no-wrap-code">`profile_not_nulls_percent`</span>](./not-nulls-percent.md#profile-not-nulls-percent)|Minimum percentage of rows containing non-null values (find empty column)|[profiling](../../../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md)|Detects incomplete columns that contain too few non-null values. Measures the percentage of rows that have non-null values. Raises a data quality issue when the percentage of non-null values is below min_percentage.|:material-check-bold:|
-|[<span class="no-wrap-code">`daily_not_nulls_percent`</span>](./not-nulls-percent.md#daily-not-nulls-percent)|Minimum percentage of rows containing non-null values (find empty column)|[monitoring](../../../dqo-concepts/definition-of-data-quality-checks/data-observability-monitoring-checks.md)|Detects incomplete columns that contain too few non-null values. Measures the percentage of rows that have non-null values. Raises a data quality issue when the percentage of non-null values is below min_percentage. Stores the most recent captured value for each day when the data quality check was evaluated.|:material-check-bold:|
-|[<span class="no-wrap-code">`monthly_not_nulls_percent`</span>](./not-nulls-percent.md#monthly-not-nulls-percent)|Minimum percentage of rows containing non-null values (find empty column)|[monitoring](../../../dqo-concepts/definition-of-data-quality-checks/data-observability-monitoring-checks.md)|Detects incomplete columns that contain too few non-null values. Measures the percentage of rows that have non-null values. Raises a data quality issue when the percentage of non-null values is below min_percentage. Stores the most recent check result for each month when the data quality check was evaluated.|:material-check-bold:|
-|[<span class="no-wrap-code">`daily_partition_not_nulls_percent`</span>](./not-nulls-percent.md#daily-partition-not-nulls-percent)|Minimum percentage of rows containing non-null values (find empty column)|[partitioned](../../../dqo-concepts/definition-of-data-quality-checks/partition-checks.md)|Detects incomplete columns that contain too few non-null values. Measures the percentage of rows that have non-null values. Raises a data quality issue when the percentage of non-null values is below min_percentage. Stores a separate data quality check result for each daily partition.|:material-check-bold:|
-|[<span class="no-wrap-code">`monthly_partition_not_nulls_percent`</span>](./not-nulls-percent.md#monthly-partition-not-nulls-percent)|Minimum percentage of rows containing non-null values (find empty column)|[partitioned](../../../dqo-concepts/definition-of-data-quality-checks/partition-checks.md)|Detects incomplete columns that contain too few non-null values. Measures the percentage of rows that have non-null values. Raises a data quality issue when the percentage of non-null values is below min_percentage. Stores a separate data quality check result for each monthly partition.|:material-check-bold:|
+|[<span class="no-wrap-code">`profile_not_nulls_percent`</span>](./not-nulls-percent.md#profile-not-nulls-percent)|Minimum percentage of rows containing non-null values|[profiling](../../../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md)|Detects incomplete columns that contain too few non-null values. Measures the percentage of rows that have non-null values. Raises a data quality issue when the percentage of non-null values is below min_percentage.|:material-check-bold:|
+|[<span class="no-wrap-code">`daily_not_nulls_percent`</span>](./not-nulls-percent.md#daily-not-nulls-percent)|Minimum percentage of rows containing non-null values|[monitoring](../../../dqo-concepts/definition-of-data-quality-checks/data-observability-monitoring-checks.md)|Detects incomplete columns that contain too few non-null values. Measures the percentage of rows that have non-null values. Raises a data quality issue when the percentage of non-null values is below min_percentage. Stores the most recent captured value for each day when the data quality check was evaluated.|:material-check-bold:|
+|[<span class="no-wrap-code">`monthly_not_nulls_percent`</span>](./not-nulls-percent.md#monthly-not-nulls-percent)|Minimum percentage of rows containing non-null values|[monitoring](../../../dqo-concepts/definition-of-data-quality-checks/data-observability-monitoring-checks.md)|Detects incomplete columns that contain too few non-null values. Measures the percentage of rows that have non-null values. Raises a data quality issue when the percentage of non-null values is below min_percentage. Stores the most recent check result for each month when the data quality check was evaluated.|:material-check-bold:|
+|[<span class="no-wrap-code">`daily_partition_not_nulls_percent`</span>](./not-nulls-percent.md#daily-partition-not-nulls-percent)|Minimum percentage of rows containing non-null values|[partitioned](../../../dqo-concepts/definition-of-data-quality-checks/partition-checks.md)|Detects incomplete columns that contain too few non-null values. Measures the percentage of rows that have non-null values. Raises a data quality issue when the percentage of non-null values is below min_percentage. Stores a separate data quality check result for each daily partition.|:material-check-bold:|
+|[<span class="no-wrap-code">`monthly_partition_not_nulls_percent`</span>](./not-nulls-percent.md#monthly-partition-not-nulls-percent)|Minimum percentage of rows containing non-null values|[partitioned](../../../dqo-concepts/definition-of-data-quality-checks/partition-checks.md)|Detects incomplete columns that contain too few non-null values. Measures the percentage of rows that have non-null values. Raises a data quality issue when the percentage of non-null values is below min_percentage. Stores a separate data quality check result for each monthly partition.|:material-check-bold:|
 
 
 
diff --git a/docs/checks/column/nulls/not-nulls-count.md b/docs/checks/column/nulls/not-nulls-count.md
index 42ecdff436..8fa402d60a 100644
--- a/docs/checks/column/nulls/not-nulls-count.md
+++ b/docs/checks/column/nulls/not-nulls-count.md
@@ -21,7 +21,7 @@ Verifies that a column contains a minimum number of non-null values. The default
 
 |Data quality check name|Friendly name|Category|Check type|Time scale|Quality dimension|Sensor definition|Quality rule|Standard|
 |-----------------------|-------------|--------|----------|----------|-----------------|-----------------|------------|--------|
-|<span class="no-wrap-code">`profile_not_nulls_count`</span>|Minimum count of rows containing non-null values (find empty column)|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[profiling](../../../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md)| |[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_count*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-count)|[*min_count*](../../../reference/rules/Comparison.md#min-count)|:material-check-bold:|
+|<span class="no-wrap-code">`profile_not_nulls_count`</span>|Minimum count of rows containing non-null values|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[profiling](../../../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md)| |[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_count*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-count)|[*min_count*](../../../reference/rules/Comparison.md#min-count)|:material-check-bold:|
 
 **Command-line examples**
 
@@ -846,7 +846,7 @@ Verifies that a column contains a minimum number of non-null values. The default
 
 |Data quality check name|Friendly name|Category|Check type|Time scale|Quality dimension|Sensor definition|Quality rule|Standard|
 |-----------------------|-------------|--------|----------|----------|-----------------|-----------------|------------|--------|
-|<span class="no-wrap-code">`daily_not_nulls_count`</span>|Minimum count of rows containing non-null values (find empty column)|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[monitoring](../../../dqo-concepts/definition-of-data-quality-checks/data-observability-monitoring-checks.md)|daily|[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_count*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-count)|[*min_count*](../../../reference/rules/Comparison.md#min-count)|:material-check-bold:|
+|<span class="no-wrap-code">`daily_not_nulls_count`</span>|Minimum count of rows containing non-null values|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[monitoring](../../../dqo-concepts/definition-of-data-quality-checks/data-observability-monitoring-checks.md)|daily|[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_count*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-count)|[*min_count*](../../../reference/rules/Comparison.md#min-count)|:material-check-bold:|
 
 **Command-line examples**
 
@@ -1673,7 +1673,7 @@ Verifies that a column contains a minimum number of non-null values. The default
 
 |Data quality check name|Friendly name|Category|Check type|Time scale|Quality dimension|Sensor definition|Quality rule|Standard|
 |-----------------------|-------------|--------|----------|----------|-----------------|-----------------|------------|--------|
-|<span class="no-wrap-code">`monthly_not_nulls_count`</span>|Minimum count of rows containing non-null values (find empty column)|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[monitoring](../../../dqo-concepts/definition-of-data-quality-checks/data-observability-monitoring-checks.md)|monthly|[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_count*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-count)|[*min_count*](../../../reference/rules/Comparison.md#min-count)|:material-check-bold:|
+|<span class="no-wrap-code">`monthly_not_nulls_count`</span>|Minimum count of rows containing non-null values|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[monitoring](../../../dqo-concepts/definition-of-data-quality-checks/data-observability-monitoring-checks.md)|monthly|[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_count*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-count)|[*min_count*](../../../reference/rules/Comparison.md#min-count)|:material-check-bold:|
 
 **Command-line examples**
 
@@ -2500,7 +2500,7 @@ Verifies that a column contains a minimum number of non-null values. The default
 
 |Data quality check name|Friendly name|Category|Check type|Time scale|Quality dimension|Sensor definition|Quality rule|Standard|
 |-----------------------|-------------|--------|----------|----------|-----------------|-----------------|------------|--------|
-|<span class="no-wrap-code">`daily_partition_not_nulls_count`</span>|Minimum count of rows containing non-null values (find empty column)|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[partitioned](../../../dqo-concepts/definition-of-data-quality-checks/partition-checks.md)|daily|[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_count*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-count)|[*min_count*](../../../reference/rules/Comparison.md#min-count)|:material-check-bold:|
+|<span class="no-wrap-code">`daily_partition_not_nulls_count`</span>|Minimum count of rows containing non-null values|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[partitioned](../../../dqo-concepts/definition-of-data-quality-checks/partition-checks.md)|daily|[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_count*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-count)|[*min_count*](../../../reference/rules/Comparison.md#min-count)|:material-check-bold:|
 
 **Command-line examples**
 
@@ -3431,7 +3431,7 @@ Verifies that a column contains a minimum number of non-null values. The default
 
 |Data quality check name|Friendly name|Category|Check type|Time scale|Quality dimension|Sensor definition|Quality rule|Standard|
 |-----------------------|-------------|--------|----------|----------|-----------------|-----------------|------------|--------|
-|<span class="no-wrap-code">`monthly_partition_not_nulls_count`</span>|Minimum count of rows containing non-null values (find empty column)|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[partitioned](../../../dqo-concepts/definition-of-data-quality-checks/partition-checks.md)|monthly|[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_count*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-count)|[*min_count*](../../../reference/rules/Comparison.md#min-count)|:material-check-bold:|
+|<span class="no-wrap-code">`monthly_partition_not_nulls_count`</span>|Minimum count of rows containing non-null values|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[partitioned](../../../dqo-concepts/definition-of-data-quality-checks/partition-checks.md)|monthly|[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_count*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-count)|[*min_count*](../../../reference/rules/Comparison.md#min-count)|:material-check-bold:|
 
 **Command-line examples**
 
diff --git a/docs/checks/column/nulls/not-nulls-percent.md b/docs/checks/column/nulls/not-nulls-percent.md
index a3f2c284e9..ea7c8a5b6a 100644
--- a/docs/checks/column/nulls/not-nulls-percent.md
+++ b/docs/checks/column/nulls/not-nulls-percent.md
@@ -22,7 +22,7 @@ Detects incomplete columns that contain too few non-null values. Measures the pe
 
 |Data quality check name|Friendly name|Category|Check type|Time scale|Quality dimension|Sensor definition|Quality rule|Standard|
 |-----------------------|-------------|--------|----------|----------|-----------------|-----------------|------------|--------|
-|<span class="no-wrap-code">`profile_not_nulls_percent`</span>|Minimum percentage of rows containing non-null values (find empty column)|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[profiling](../../../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md)| |[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_percent*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-percent)|[*min_percent*](../../../reference/rules/Comparison.md#min-percent)|:material-check-bold:|
+|<span class="no-wrap-code">`profile_not_nulls_percent`</span>|Minimum percentage of rows containing non-null values|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[profiling](../../../dqo-concepts/definition-of-data-quality-checks/data-profiling-checks.md)| |[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_percent*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-percent)|[*min_percent*](../../../reference/rules/Comparison.md#min-percent)|:material-check-bold:|
 
 **Command-line examples**
 
@@ -971,7 +971,7 @@ Detects incomplete columns that contain too few non-null values. Measures the pe
 
 |Data quality check name|Friendly name|Category|Check type|Time scale|Quality dimension|Sensor definition|Quality rule|Standard|
 |-----------------------|-------------|--------|----------|----------|-----------------|-----------------|------------|--------|
-|<span class="no-wrap-code">`daily_not_nulls_percent`</span>|Minimum percentage of rows containing non-null values (find empty column)|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[monitoring](../../../dqo-concepts/definition-of-data-quality-checks/data-observability-monitoring-checks.md)|daily|[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_percent*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-percent)|[*min_percent*](../../../reference/rules/Comparison.md#min-percent)|:material-check-bold:|
+|<span class="no-wrap-code">`daily_not_nulls_percent`</span>|Minimum percentage of rows containing non-null values|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[monitoring](../../../dqo-concepts/definition-of-data-quality-checks/data-observability-monitoring-checks.md)|daily|[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_percent*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-percent)|[*min_percent*](../../../reference/rules/Comparison.md#min-percent)|:material-check-bold:|
 
 **Command-line examples**
 
@@ -1922,7 +1922,7 @@ Detects incomplete columns that contain too few non-null values. Measures the pe
 
 |Data quality check name|Friendly name|Category|Check type|Time scale|Quality dimension|Sensor definition|Quality rule|Standard|
 |-----------------------|-------------|--------|----------|----------|-----------------|-----------------|------------|--------|
-|<span class="no-wrap-code">`monthly_not_nulls_percent`</span>|Minimum percentage of rows containing non-null values (find empty column)|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[monitoring](../../../dqo-concepts/definition-of-data-quality-checks/data-observability-monitoring-checks.md)|monthly|[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_percent*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-percent)|[*min_percent*](../../../reference/rules/Comparison.md#min-percent)|:material-check-bold:|
+|<span class="no-wrap-code">`monthly_not_nulls_percent`</span>|Minimum percentage of rows containing non-null values|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[monitoring](../../../dqo-concepts/definition-of-data-quality-checks/data-observability-monitoring-checks.md)|monthly|[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_percent*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-percent)|[*min_percent*](../../../reference/rules/Comparison.md#min-percent)|:material-check-bold:|
 
 **Command-line examples**
 
@@ -2873,7 +2873,7 @@ Detects incomplete columns that contain too few non-null values. Measures the pe
 
 |Data quality check name|Friendly name|Category|Check type|Time scale|Quality dimension|Sensor definition|Quality rule|Standard|
 |-----------------------|-------------|--------|----------|----------|-----------------|-----------------|------------|--------|
-|<span class="no-wrap-code">`daily_partition_not_nulls_percent`</span>|Minimum percentage of rows containing non-null values (find empty column)|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[partitioned](../../../dqo-concepts/definition-of-data-quality-checks/partition-checks.md)|daily|[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_percent*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-percent)|[*min_percent*](../../../reference/rules/Comparison.md#min-percent)|:material-check-bold:|
+|<span class="no-wrap-code">`daily_partition_not_nulls_percent`</span>|Minimum percentage of rows containing non-null values|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[partitioned](../../../dqo-concepts/definition-of-data-quality-checks/partition-checks.md)|daily|[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_percent*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-percent)|[*min_percent*](../../../reference/rules/Comparison.md#min-percent)|:material-check-bold:|
 
 **Command-line examples**
 
@@ -3928,7 +3928,7 @@ Detects incomplete columns that contain too few non-null values. Measures the pe
 
 |Data quality check name|Friendly name|Category|Check type|Time scale|Quality dimension|Sensor definition|Quality rule|Standard|
 |-----------------------|-------------|--------|----------|----------|-----------------|-----------------|------------|--------|
-|<span class="no-wrap-code">`monthly_partition_not_nulls_percent`</span>|Minimum percentage of rows containing non-null values (find empty column)|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[partitioned](../../../dqo-concepts/definition-of-data-quality-checks/partition-checks.md)|monthly|[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_percent*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-percent)|[*min_percent*](../../../reference/rules/Comparison.md#min-percent)|:material-check-bold:|
+|<span class="no-wrap-code">`monthly_partition_not_nulls_percent`</span>|Minimum percentage of rows containing non-null values|[nulls](../../../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|[partitioned](../../../dqo-concepts/definition-of-data-quality-checks/partition-checks.md)|monthly|[Completeness](../../../dqo-concepts/data-quality-dimensions.md#data-completeness)|[*not_null_percent*](../../../reference/sensors/column/nulls-column-sensors.md#not-null-percent)|[*min_percent*](../../../reference/rules/Comparison.md#min-percent)|:material-check-bold:|
 
 **Command-line examples**
 
diff --git a/docs/dqo-concepts/data-quality-dimensions.md b/docs/dqo-concepts/data-quality-dimensions.md
index e01ec898ce..9d4c896d66 100644
--- a/docs/dqo-concepts/data-quality-dimensions.md
+++ b/docs/dqo-concepts/data-quality-dimensions.md
@@ -208,8 +208,8 @@ The following table lists data quality checks that detect completeness issues on
 |[*nulls_count*](../checks/column/nulls/nulls-count.md)|Maximum count of rows containing null values (incomplete column)|[nulls](../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|Detects incomplete columns that contain any *null* values. Counts the number of rows having a null value. Raises a data quality issue when the count of null values is above a *max_count* threshold.|:material-check-bold:|
 |[*nulls_percent*](../checks/column/nulls/nulls-percent.md)|Maximum percentage of rows containing null values (incomplete column)|[nulls](../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|Detects incomplete columns that contain any *null* values. Measures the percentage of rows having a null value. Raises a data quality issue when the percentage of null values is above a *max_percent* threshold. Configure this check to accept a given percentage of null values by setting the *max_percent* parameter.|:material-check-bold:|
 |[*nulls_percent_anomaly*](../checks/column/nulls/nulls-percent-anomaly.md)|Abnormal change in percentage of null values. Measured as a percentile of anomalous measures.|[nulls](../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|Detects day-to-day anomalies in the percentage of *null* values. Measures the percentage of rows having a *null* value. Raises a data quality issue when the rate of null values increases or decreases too much.|:material-check-bold:|
-|[*not_nulls_count*](../checks/column/nulls/not-nulls-count.md)|Minimum count of rows containing non-null values (find empty column)|[nulls](../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|Verifies that a column contains a minimum number of non-null values. The default value of the *min_count* parameter is 1 to detect at least one value in a monitored column.|:material-check-bold:|
-|[*not_nulls_percent*](../checks/column/nulls/not-nulls-percent.md)|Minimum percentage of rows containing non-null values (find empty column)|[nulls](../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|Detects incomplete columns that contain too few non-null values. Measures the percentage of rows that have non-null values. Raises a data quality issue when the percentage of non-null values is below *min_percentage*. The default value of the *min_percentage* parameter is 100.0, but DQOps supports setting a lower value to accept some nulls.|:material-check-bold:|
+|[*not_nulls_count*](../checks/column/nulls/not-nulls-count.md)|Minimum count of rows containing non-null values|[nulls](../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|Verifies that a column contains a minimum number of non-null values. The default value of the *min_count* parameter is 1 to detect at least one value in a monitored column.|:material-check-bold:|
+|[*not_nulls_percent*](../checks/column/nulls/not-nulls-percent.md)|Minimum percentage of rows containing non-null values|[nulls](../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|Detects incomplete columns that contain too few non-null values. Measures the percentage of rows that have non-null values. Raises a data quality issue when the percentage of non-null values is below *min_percentage*. The default value of the *min_percentage* parameter is 100.0, but DQOps supports setting a lower value to accept some nulls.|:material-check-bold:|
 |[*empty_column_found*](../checks/column/nulls/empty-column-found.md)|Find an empty column|[nulls](../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|Detects empty columns that contain only *null* values. Counts the number of rows that have non-null values. Raises a data quality issue when the count of non-null values is below *min_count*. The default value of the *min_count* parameter is 1, but DQOps supports setting a higher number to assert that a column has at least that many non-null values.|:material-check-bold:|
 |[*nulls_percent_change*](../checks/column/nulls/nulls-percent-change.md)|Maximum percentage of change in the count of null values|[nulls](../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|Detects relative increases or decreases in the percentage of null values since the last measured percentage. Measures the percentage of null values for each day. Raises a data quality issue when the change in the percentage of null values is above *max_percent* of the previous percentage.| |
 |[*nulls_percent_change_1_day*](../checks/column/nulls/nulls-percent-change-1-day.md)|Maximum percentage of change in the count of null values vs 1 day ago|[nulls](../categories-of-data-quality-checks/how-to-detect-empty-or-incomplete-columns-with-nulls.md)|Detects relative increases or decreases in the percentage of null values since the previous day. Measures the percentage of null values for each day. Raises a data quality issue when the change in the percentage of null values is above *max_percent* of the previous percentage.| |
diff --git a/dqo b/dqo
index 84bd519caa..b475196028 100755
--- a/dqo
+++ b/dqo
@@ -15,7 +15,7 @@
 # limitations under the License.
 #
 
-export DQO_VERSION=1.6.0
+export DQO_VERSION=1.6.1
 
 # Configure local development environment overrides
 if [ -f $(dirname $0)/set-dqo-envs.sh ]; then
diff --git a/dqo.cmd b/dqo.cmd
index d5f67072bf..94951abc76 100644
--- a/dqo.cmd
+++ b/dqo.cmd
@@ -15,7 +15,7 @@
 @REM limitations under the License.
 @REM
 
-set DQO_VERSION=1.6.0
+set DQO_VERSION=1.6.1
 
 rem Configure local development environment overrides
 if exist "%~dp0set-dqo-envs.cmd" (
diff --git a/dqops/pom.xml b/dqops/pom.xml
index 3fd777621b..fe6cc285b3 100644
--- a/dqops/pom.xml
+++ b/dqops/pom.xml
@@ -27,7 +27,7 @@
 	</parent>
 	<groupId>com.dqops</groupId>
 	<artifactId>dqo-dqops</artifactId>
-    <version>1.6.0</version>  <!-- DQOps Version, do not touch (changed automatically) -->
+    <version>1.6.1</version>  <!-- DQOps Version, do not touch (changed automatically) -->
 	<packaging>jar</packaging>
 	<name>dqo-dqops</name>
 	<description>DQOps Data Quality Operations Center</description>
diff --git a/dqops/src/main/frontend/package.json b/dqops/src/main/frontend/package.json
index 7dbfab3911..40018a90fd 100644
--- a/dqops/src/main/frontend/package.json
+++ b/dqops/src/main/frontend/package.json
@@ -1,6 +1,6 @@
 {
   "name": "DQOps",
-  "version": "1.6.0",
+  "version": "1.6.1",
   "private": true,
   "dependencies": {
     "@codemirror/lang-python": "6.1.3",
diff --git a/dqops/src/main/java/com/dqops/checks/column/checkspecs/nulls/ColumnNotNullsCountCheckSpec.java b/dqops/src/main/java/com/dqops/checks/column/checkspecs/nulls/ColumnNotNullsCountCheckSpec.java
index eb20f4ccd5..3a187a9ffa 100644
--- a/dqops/src/main/java/com/dqops/checks/column/checkspecs/nulls/ColumnNotNullsCountCheckSpec.java
+++ b/dqops/src/main/java/com/dqops/checks/column/checkspecs/nulls/ColumnNotNullsCountCheckSpec.java
@@ -163,7 +163,7 @@ protected ChildHierarchyNodeFieldMap getChildMap() {
     @Override
     @JsonIgnore
     public String getFriendlyName() {
-        return "Minimum count of rows containing non-null values (find empty column)";
+        return "Minimum count of rows containing non-null values";
     }
 
     /**
diff --git a/dqops/src/main/java/com/dqops/checks/column/checkspecs/nulls/ColumnNotNullsPercentCheckSpec.java b/dqops/src/main/java/com/dqops/checks/column/checkspecs/nulls/ColumnNotNullsPercentCheckSpec.java
index 0235025254..23c2fa183e 100644
--- a/dqops/src/main/java/com/dqops/checks/column/checkspecs/nulls/ColumnNotNullsPercentCheckSpec.java
+++ b/dqops/src/main/java/com/dqops/checks/column/checkspecs/nulls/ColumnNotNullsPercentCheckSpec.java
@@ -166,7 +166,7 @@ protected ChildHierarchyNodeFieldMap getChildMap() {
     @Override
     @JsonIgnore
     public String getFriendlyName() {
-        return "Minimum percentage of rows containing non-null values (find empty column)";
+        return "Minimum percentage of rows containing non-null values";
     }
 
     /**
diff --git a/dqops/src/main/resources/banner.txt b/dqops/src/main/resources/banner.txt
index a75e0baf4a..bf79928984 100644
--- a/dqops/src/main/resources/banner.txt
+++ b/dqops/src/main/resources/banner.txt
@@ -3,4 +3,4 @@
  | |) | | (_) | | (_) | | '_ \ (_-<
  |___/   \__\_\  \___/  | .__/ /__/
                         |_|
- :: DQOps Data Quality Operations Center ::    (v1.6.0)
+ :: DQOps Data Quality Operations Center ::    (v1.6.1)
diff --git a/home/checks/column/monitoring/daily/nulls/daily_not_nulls_count.dqocheck.yaml b/home/checks/column/monitoring/daily/nulls/daily_not_nulls_count.dqocheck.yaml
index 90e0b16212..3a085ff2e8 100644
--- a/home/checks/column/monitoring/daily/nulls/daily_not_nulls_count.dqocheck.yaml
+++ b/home/checks/column/monitoring/daily/nulls/daily_not_nulls_count.dqocheck.yaml
@@ -9,6 +9,6 @@ spec:
     in a monitored column. Raises a data quality issue when the count of non-null
     values is below min_count. Stores the most recent captured value for each day
     when the data quality check was evaluated.
-  friendly_name: Minimum count of rows containing non-null values (find empty column)
+  friendly_name: Minimum count of rows containing non-null values
   standard: true
   default_severity: error
diff --git a/home/checks/column/monitoring/daily/nulls/daily_not_nulls_percent.dqocheck.yaml b/home/checks/column/monitoring/daily/nulls/daily_not_nulls_percent.dqocheck.yaml
index 1a38b72cbe..9fc3ac4764 100644
--- a/home/checks/column/monitoring/daily/nulls/daily_not_nulls_percent.dqocheck.yaml
+++ b/home/checks/column/monitoring/daily/nulls/daily_not_nulls_percent.dqocheck.yaml
@@ -8,7 +8,6 @@ spec:
     the percentage of rows that have non-null values. Raises a data quality issue
     when the percentage of non-null values is below min_percentage. Stores the most
     recent captured value for each day when the data quality check was evaluated.
-  friendly_name: Minimum percentage of rows containing non-null values (find empty
-    column)
+  friendly_name: Minimum percentage of rows containing non-null values
   standard: true
   default_severity: error
diff --git a/home/checks/column/monitoring/monthly/nulls/monthly_not_nulls_count.dqocheck.yaml b/home/checks/column/monitoring/monthly/nulls/monthly_not_nulls_count.dqocheck.yaml
index 011e7b632c..134146a28d 100644
--- a/home/checks/column/monitoring/monthly/nulls/monthly_not_nulls_count.dqocheck.yaml
+++ b/home/checks/column/monitoring/monthly/nulls/monthly_not_nulls_count.dqocheck.yaml
@@ -9,6 +9,6 @@ spec:
     in a monitored column. Raises a data quality issue when the count of non-null
     values is below min_count. Stores the most recent check result for each month
     when the data quality check was evaluated.
-  friendly_name: Minimum count of rows containing non-null values (find empty column)
+  friendly_name: Minimum count of rows containing non-null values
   standard: true
   default_severity: error
diff --git a/home/checks/column/monitoring/monthly/nulls/monthly_not_nulls_percent.dqocheck.yaml b/home/checks/column/monitoring/monthly/nulls/monthly_not_nulls_percent.dqocheck.yaml
index 7c6730d799..c6c9302787 100644
--- a/home/checks/column/monitoring/monthly/nulls/monthly_not_nulls_percent.dqocheck.yaml
+++ b/home/checks/column/monitoring/monthly/nulls/monthly_not_nulls_percent.dqocheck.yaml
@@ -8,7 +8,6 @@ spec:
     the percentage of rows that have non-null values. Raises a data quality issue
     when the percentage of non-null values is below min_percentage. Stores the most
     recent check result for each month when the data quality check was evaluated.
-  friendly_name: Minimum percentage of rows containing non-null values (find empty
-    column)
+  friendly_name: Minimum percentage of rows containing non-null values
   standard: true
   default_severity: error
diff --git a/home/checks/column/partitioned/daily/nulls/daily_partition_not_nulls_count.dqocheck.yaml b/home/checks/column/partitioned/daily/nulls/daily_partition_not_nulls_count.dqocheck.yaml
index 6bbb395031..23873d04f9 100644
--- a/home/checks/column/partitioned/daily/nulls/daily_partition_not_nulls_count.dqocheck.yaml
+++ b/home/checks/column/partitioned/daily/nulls/daily_partition_not_nulls_count.dqocheck.yaml
@@ -9,6 +9,6 @@ spec:
     in a monitored column. Raises a data quality issue when the count of non-null
     values is below min_count. Stores a separate data quality check result for each
     daily partition.
-  friendly_name: Minimum count of rows containing non-null values (find empty column)
+  friendly_name: Minimum count of rows containing non-null values
   standard: true
   default_severity: error
diff --git a/home/checks/column/partitioned/daily/nulls/daily_partition_not_nulls_percent.dqocheck.yaml b/home/checks/column/partitioned/daily/nulls/daily_partition_not_nulls_percent.dqocheck.yaml
index c0b54ee9f3..914d3c84a4 100644
--- a/home/checks/column/partitioned/daily/nulls/daily_partition_not_nulls_percent.dqocheck.yaml
+++ b/home/checks/column/partitioned/daily/nulls/daily_partition_not_nulls_percent.dqocheck.yaml
@@ -8,7 +8,6 @@ spec:
     the percentage of rows that have non-null values. Raises a data quality issue
     when the percentage of non-null values is below min_percentage. Stores a separate
     data quality check result for each daily partition.
-  friendly_name: Minimum percentage of rows containing non-null values (find empty
-    column)
+  friendly_name: Minimum percentage of rows containing non-null values
   standard: true
   default_severity: error
diff --git a/home/checks/column/partitioned/monthly/nulls/monthly_partition_not_nulls_count.dqocheck.yaml b/home/checks/column/partitioned/monthly/nulls/monthly_partition_not_nulls_count.dqocheck.yaml
index cbac0a9801..a669973812 100644
--- a/home/checks/column/partitioned/monthly/nulls/monthly_partition_not_nulls_count.dqocheck.yaml
+++ b/home/checks/column/partitioned/monthly/nulls/monthly_partition_not_nulls_count.dqocheck.yaml
@@ -9,6 +9,6 @@ spec:
     in a monitored column. Raises a data quality issue when the count of non-null
     values is below min_count. Stores a separate data quality check result for each
     monthly partition.
-  friendly_name: Minimum count of rows containing non-null values (find empty column)
+  friendly_name: Minimum count of rows containing non-null values
   standard: true
   default_severity: error
diff --git a/home/checks/column/partitioned/monthly/nulls/monthly_partition_not_nulls_percent.dqocheck.yaml b/home/checks/column/partitioned/monthly/nulls/monthly_partition_not_nulls_percent.dqocheck.yaml
index 5f131f4993..f515944b29 100644
--- a/home/checks/column/partitioned/monthly/nulls/monthly_partition_not_nulls_percent.dqocheck.yaml
+++ b/home/checks/column/partitioned/monthly/nulls/monthly_partition_not_nulls_percent.dqocheck.yaml
@@ -8,7 +8,6 @@ spec:
     the percentage of rows that have non-null values. Raises a data quality issue
     when the percentage of non-null values is below min_percentage. Stores a separate
     data quality check result for each monthly partition.
-  friendly_name: Minimum percentage of rows containing non-null values (find empty
-    column)
+  friendly_name: Minimum percentage of rows containing non-null values
   standard: true
   default_severity: error
diff --git a/home/checks/column/profiling/nulls/profile_not_nulls_count.dqocheck.yaml b/home/checks/column/profiling/nulls/profile_not_nulls_count.dqocheck.yaml
index 7f3db07ba9..05c9e8561d 100644
--- a/home/checks/column/profiling/nulls/profile_not_nulls_count.dqocheck.yaml
+++ b/home/checks/column/profiling/nulls/profile_not_nulls_count.dqocheck.yaml
@@ -8,6 +8,6 @@ spec:
     The default value of the *min_count* parameter is 1 to detect at least one value
     in a monitored column. Raises a data quality issue when the count of non-null
     values is below min_count.
-  friendly_name: Minimum count of rows containing non-null values (find empty column)
+  friendly_name: Minimum count of rows containing non-null values
   standard: true
   default_severity: error
diff --git a/home/checks/column/profiling/nulls/profile_not_nulls_percent.dqocheck.yaml b/home/checks/column/profiling/nulls/profile_not_nulls_percent.dqocheck.yaml
index bbc90c5b3c..64b05022f8 100644
--- a/home/checks/column/profiling/nulls/profile_not_nulls_percent.dqocheck.yaml
+++ b/home/checks/column/profiling/nulls/profile_not_nulls_percent.dqocheck.yaml
@@ -7,7 +7,6 @@ spec:
   help_text: Detects incomplete columns that contain too few non-null values. Measures
     the percentage of rows that have non-null values. Raises a data quality issue
     when the percentage of non-null values is below min_percentage.
-  friendly_name: Minimum percentage of rows containing non-null values (find empty
-    column)
+  friendly_name: Minimum percentage of rows containing non-null values
   standard: true
   default_severity: error
diff --git a/lib/pom.xml b/lib/pom.xml
index a858fc26fc..95212f8375 100644
--- a/lib/pom.xml
+++ b/lib/pom.xml
@@ -11,7 +11,7 @@
 
 	<groupId>com.dqops</groupId>
 	<artifactId>dqo-lib</artifactId>
-    <version>1.6.0</version>  <!-- DQOps Version, do not touch (changed automatically) -->
+    <version>1.6.1</version>  <!-- DQOps Version, do not touch (changed automatically) -->
 	<name>lib</name>
 	<description>POM for a list of dependencies to libraries that should be distributed in the "lib" folder, especially all JDBC drivers.</description>
 	<packaging>jar</packaging>
diff --git a/pom.xml b/pom.xml
index fe16dc853a..c4a3991661 100644
--- a/pom.xml
+++ b/pom.xml
@@ -5,7 +5,7 @@
 
     <groupId>com.dqops</groupId>
     <artifactId>dqo-data-quality-observer</artifactId>
-    <version>1.6.0</version>  <!-- DQOps Version, do not touch (changed automatically) -->
+    <version>1.6.1</version>  <!-- DQOps Version, do not touch (changed automatically) -->
     <packaging>pom</packaging>
     <description>DQOps Data Quality Operations Center</description>