diff --git a/README.md b/README.md
index fa6f23576..3dbf9b948 100644
--- a/README.md
+++ b/README.md
@@ -127,14 +127,14 @@ shared_preload_libraries = 'timescaledb'
### Setting up your initial database
-Now, we'll install our extension and create an initial database.
+Now, we'll install our extension and create an initial database. Below
+you'll find instructions for creating a new, empty database.
-You again have two options for setting up your initial database:
-
-1. *Empty Database* - To set up a new, empty database, please follow the instructions below.
-
-2. *Database with pre-loaded sample data* - To help you quickly get started, we have also created some sample datasets. See
-[Using our Sample Datasets](docs/UsingSamples.md) for further instructions. (Includes installing our extension.)
+To help you quickly get started, we have also created some sample
+datasets. Once you complete the initial setup below you can then
+easily import this data to play around with TimescaleDB functionality.
+See [our Sample Datasets](https://docs.timescale.com/other-sample-datasets)
+for further instructions.
#### Setting up an empty database
diff --git a/docs/UsingSamples.md b/docs/UsingSamples.md
deleted file mode 100644
index 634b28397..000000000
--- a/docs/UsingSamples.md
+++ /dev/null
@@ -1,296 +0,0 @@
-## Using our Sample Datasets
-
-### Available samples
-
-We have created several sample datasets (using `pg_dump`) to help you get
-started using TimescaleDB. These datasets vary in database size, number
-of time intervals, and number of values for the partition field.
-
-(Note that these dataset backups already include our time-series
-database, so you won't need to manually install our extension,
-nor run the setup scripts, etc.)
-
-**Device ops**: These datasets are designed to represent metrics (e.g. CPU,
-memory, network) collected from mobile devices. (Click on the name to
-download.)
-
-1. [`devices_small`](https://timescaledata.blob.core.windows.net/datasets/devices_small.bak.tar.gz) - 1,000 devices recorded over 1,000 time intervals
-1. [`devices_med`](https://timescaledata.blob.core.windows.net/datasets/devices_med.bak.tar.gz) - 5,000 devices recorded over 2,000 time intervals
-1. [`devices_big`](https://timescaledata.blob.core.windows.net/datasets/devices_big.bak.tar.gz) - 3,000 devices recorded over 10,000 time intervals
-
-For more details and example usage, see
-[In-depth: Device ops datasets](#in-depth-devices).
-
-**Weather**: These datasets are designed to represent temperature and
-humidity data from a variety of locations. (Click on the name to download.)
-
-1. [`weather_small`](https://timescaledata.blob.core.windows.net/datasets/weather_small.bak.tar.gz) - 1,000 locations over 1,000 two-minute intervals
-1. [`weather_med`](https://timescaledata.blob.core.windows.net/datasets/weather_med.bak.tar.gz) - 1,000 locations over 15,000 two-minute intervals
-1. [`weather_big`](https://timescaledata.blob.core.windows.net/datasets/weather_big.bak.tar.gz) - 2,000 locations over 20,000 two-minute intervals
-
-For more details and example usage, see
-[In-depth: Weather datasets](#in-depth-weather).
-
-
-### Importing
-Data is easily imported using the standard way of restoring `pg_dump` backups.
-
-Briefly the steps are:
-
-1. Unzip the archive,
-1. Create a database for the data (using the same name as the dataset)
-1. Import the data via `psql`
-
-Each of our archives is named `[dataset_name].bak.tar.gz`, so if you are using
-dataset `devices_small`, the commands are:
-```bash
-# (1) unzip the archive
-tar -xvzf devices_small.bak.tar.gz
-# (2) create a database with the same name
-psql -U postgres -h localhost -c 'CREATE DATABASE devices_small;'
-# (3) import data
-psql -U postgres -d devices_small -h localhost < devices_small.bak
-```
-
-The data is now ready for you to use.
-
-```bash
-# To access your database (e.g., devices_small)
-psql -U postgres -h localhost -d devices_small
-```
-
-### In-depth: Device ops datasets
-After importing one of these datasets (`devices_small`, `devices_med`,
-`devices_big`), you will find a plain Postgres table called `device_info`
-and a hypertable called `readings`. The `device_info` table has (static)
-metadata about each device, such as the OS name and manufacturer. The
-`readings` hypertable tracks data sent from each device, e.g. CPU activity,
-memory levels, etc. Because hypertables are exposed as a single table, you
-can query them and join them with the metadata as you would normal SQL
-tables (see Example Queries below).
-
-#### Schemas
-```sql
-Table "public.device_info"
- Column | Type | Modifiers
--------------+------+-----------
-device_id | text |
-api_version | text |
-manufacturer | text |
-model | text |
-os_name | text |
-```
-
-```sql
-Table "public.readings"
-Column | Type | Modifiers
---------------------+------------------+-----------
-time | bigint |
-device_id | text |
-battery_level | double precision |
-battery_status | text |
-battery_temperature | double precision |
-bssid | text |
-cpu_avg_1min | double precision |
-cpu_avg_5min | double precision |
-cpu_avg_15min | double precision |
-mem_free | double precision |
-mem_used | double precision |
-rssi | double precision |
-ssid | text |
-Indexes:
-"readings_device_id_time_idx" btree (device_id, "time" DESC)
-"readings_time_idx" btree ("time" DESC)
-```
-
-#### Example Queries
-_Note: Uses dataset_ `devices_med`
-
-**10 most recent battery temperature readings for charging devices**
-```sql
-SELECT time, device_id, battery_temperature
-FROM readings
-WHERE battery_status = 'charging'
-ORDER BY time DESC LIMIT 10;
-
-time | device_id | battery_temperature
------------------------+------------+---------------------
-2016-11-15 23:39:30-05 | demo004866 | 101.1
-2016-11-15 23:39:30-05 | demo004848 | 96
-2016-11-15 23:39:30-05 | demo004836 | 97.2
-2016-11-15 23:39:30-05 | demo004827 | 99.5
-2016-11-15 23:39:30-05 | demo004792 | 101.7
-2016-11-15 23:39:30-05 | demo004761 | 95.7
-2016-11-15 23:39:30-05 | demo004740 | 100.2
-2016-11-15 23:39:30-05 | demo004729 | 96.1
-2016-11-15 23:39:30-05 | demo004723 | 95.4
-2016-11-15 23:39:30-05 | demo004711 | 98.8
-(10 rows)
-```
-
-**Busiest devices (1 min avg) whose battery level is below 33% and
-is not charging**
-```sql
-SELECT time, readings.device_id, cpu_avg_1min,
-battery_level, battery_status, device_info.model
-FROM readings
-JOIN device_info ON readings.device_id = device_info.device_id
-WHERE battery_level < 33 AND battery_status = 'discharging'
-ORDER BY cpu_avg_1min DESC, time DESC LIMIT 5;
-
-time | device_id | cpu_avg_1min | battery_level | battery_status | model
------------------------+------------+--------------+---------------+----------------+---------
-2016-11-15 21:26:00-05 | demo000559 | 98.99 | 32 | discharging | mustang
-2016-11-15 20:31:00-05 | demo000312 | 98.99 | 29 | discharging | pinto
-2016-11-15 18:00:00-05 | demo002979 | 98.99 | 26 | discharging | pinto
-2016-11-15 17:40:00-05 | demo003978 | 98.99 | 25 | discharging | pinto
-2016-11-15 13:55:30-05 | demo004548 | 98.99 | 12 | discharging | pinto
-(5 rows)
-```
-
-**Minimum and maximum battery levels per hour for the first 12 hours**
-```sql
-SELECT date_trunc('hour', time) "hour",
-min(battery_level) min_battery_level,
-max(battery_level) max_battery_level
-FROM readings r
-WHERE r.device_id IN (
- SELECT DISTINCT device_id FROM device_info
- WHERE model = 'pinto' OR model = 'focus'
-) GROUP BY "hour" ORDER BY "hour" ASC LIMIT 12;
-
-hour | min_battery_level | max_battery_level
------------------------+-------------------+-------------------
-2016-11-15 07:00:00-05 | 20 | 99
-2016-11-15 08:00:00-05 | 12 | 98
-2016-11-15 09:00:00-05 | 8 | 97
-2016-11-15 10:00:00-05 | 6 | 100
-2016-11-15 11:00:00-05 | 6 | 100
-2016-11-15 12:00:00-05 | 6 | 100
-2016-11-15 13:00:00-05 | 6 | 100
-2016-11-15 14:00:00-05 | 6 | 100
-2016-11-15 15:00:00-05 | 6 | 100
-2016-11-15 16:00:00-05 | 6 | 100
-2016-11-15 17:00:00-05 | 6 | 100
-2016-11-15 18:00:00-05 | 6 | 100
-(12 rows)
-```
-
-**
-
-### In-depth: Weather datasets
-After importing one of these datasets (`weather_small`, `weather_med`,
-`weather_big`), you will find a plain Postgres table called `locations` and
-a hypertable called `conditions`. The `locations` table has metadata about
-each of the locations, such as its name and environmental type. The
-`conditions` hypertable tracks readings of temperature and humidity from
-those locations. Because hypertables are exposed as a single table, you can
-query them and join them with the metadata as you would normal SQL tables (see Example Queries below).
-
-#### Schemas
-```sql
-Table "public.locations"
-Column | Type | Modifiers
-------------+------+-----------
-device_id | text |
-location | text |
-environment | text |
-```
-```sql
-Table "public.conditions"
-Column | Type | Modifiers
-------------+--------------------------+-----------
-time | timestamp with time zone | not null
-device_id | text |
-temperature | double precision |
-humidity | double precision |
-Indexes:
-"conditions_device_id_time_idx" btree (device_id, "time" DESC)
-"conditions_time_idx" btree ("time" DESC)
-```
-
-#### Example Queries
-_Note: Uses dataset_ `weather_med`
-
-**Last 10 readings**
-```sql
-SELECT * FROM conditions c ORDER BY time DESC LIMIT 10;
-
-time | device_id | temperature | humidity
------------------------+--------------------+-------------------+-------------------
-2016-12-06 02:58:00-05 | weather-pro-000999 | 35.49999999999991 | 50.60000000000004
-2016-12-06 02:58:00-05 | weather-pro-000998 | 34.7999999999999 | 48.70000000000001
-2016-12-06 02:58:00-05 | weather-pro-000997 | 84.90000000000035 | 86.30000000000001
-2016-12-06 02:58:00-05 | weather-pro-000996 | 83.40000000000038 | 83.40000000000055
-2016-12-06 02:58:00-05 | weather-pro-000995 | 83.30000000000038 | 82.4000000000006
-2016-12-06 02:58:00-05 | weather-pro-000994 | 83.9000000000004 | 70.70000000000024
-2016-12-06 02:58:00-05 | weather-pro-000993 | 35.79999999999991 | 51.30000000000005
-2016-12-06 02:58:00-05 | weather-pro-000992 | 81.90000000000046 | 82.4000000000006
-2016-12-06 02:58:00-05 | weather-pro-000991 | 62.1 | 48.2
-2016-12-06 02:58:00-05 | weather-pro-000990 | 83.60000000000042 | 76.70000000000014
-(10 rows)
-```
-**Last 10 readings from 'outside' locations**
-```sql
-SELECT time, c.device_id, location,
-trunc(temperature, 2) temperature, trunc(humidity, 2) humidity
-FROM conditions c
-INNER JOIN locations l ON c.device_id = l.device_id
-WHERE l.environment = 'outside'
-ORDER BY time DESC LIMIT 10;
-
-time | device_id | location | temperature | humidity
------------------------+--------------------+---------------+-------------+----------
-2016-12-06 02:58:00-05 | weather-pro-000999 | arctic-000204 | 35.49 | 50.60
-2016-12-06 02:58:00-05 | weather-pro-000998 | arctic-000203 | 34.79 | 48.70
-2016-12-06 02:58:00-05 | weather-pro-000997 | swamp-000213 | 84.90 | 86.30
-2016-12-06 02:58:00-05 | weather-pro-000996 | field-000199 | 83.40 | 83.40
-2016-12-06 02:58:00-05 | weather-pro-000995 | field-000198 | 83.30 | 82.40
-2016-12-06 02:58:00-05 | weather-pro-000994 | swamp-000212 | 83.90 | 70.70
-2016-12-06 02:58:00-05 | weather-pro-000993 | arctic-000202 | 35.79 | 51.30
-2016-12-06 02:58:00-05 | weather-pro-000992 | field-000197 | 81.90 | 82.40
-2016-12-06 02:58:00-05 | weather-pro-000990 | swamp-000211 | 83.60 | 76.70
-2016-12-06 02:58:00-05 | weather-pro-000989 | field-000196 | 83.30 | 81.70
-(10 rows)
-```
-
-**Hourly average, min, and max temperatures for "field" locations**
-```sql
-SELECT date_trunc('hour', time) "hour",
-trunc(avg(temperature), 2) avg_temp,
-trunc(min(temperature), 2) min_temp,
-trunc(max(temperature), 2) max_temp
-FROM conditions c
-WHERE c.device_id IN (
- SELECT device_id FROM locations
- WHERE location LIKE 'field-%'
-) GROUP BY "hour" ORDER BY "hour" ASC LIMIT 24;
-
-hour | avg_temp | min_temp | max_temp
------------------------+----------+----------+----------
-2016-11-15 07:00:00-05 | 73.59 | 68.10 | 79.09
-2016-11-15 08:00:00-05 | 74.59 | 68.89 | 80.19
-2016-11-15 09:00:00-05 | 75.60 | 69.79 | 81.69
-2016-11-15 10:00:00-05 | 76.59 | 70.49 | 82.79
-2016-11-15 11:00:00-05 | 77.59 | 71.69 | 84.19
-2016-11-15 12:00:00-05 | 78.59 | 72.79 | 85.49
-2016-11-15 13:00:00-05 | 79.62 | 73.69 | 86.49
-2016-11-15 14:00:00-05 | 80.62 | 74.29 | 87.49
-2016-11-15 15:00:00-05 | 81.62 | 75.09 | 88.59
-2016-11-15 16:00:00-05 | 82.63 | 76.09 | 89.89
-2016-11-15 17:00:00-05 | 83.64 | 77.19 | 90.00
-2016-11-15 18:00:00-05 | 84.63 | 77.99 | 90.00
-2016-11-15 19:00:00-05 | 85.58 | 79.09 | 90.00
-2016-11-15 20:00:00-05 | 85.48 | 79.19 | 90.00
-2016-11-15 21:00:00-05 | 84.47 | 78.29 | 89.50
-2016-11-15 22:00:00-05 | 83.48 | 77.09 | 88.49
-2016-11-15 23:00:00-05 | 82.46 | 75.79 | 87.70
-2016-11-16 00:00:00-05 | 81.45 | 75.09 | 86.80
-2016-11-16 01:00:00-05 | 80.45 | 73.89 | 85.70
-2016-11-16 02:00:00-05 | 79.45 | 72.79 | 84.90
-2016-11-16 03:00:00-05 | 78.46 | 71.99 | 84.00
-2016-11-16 04:00:00-05 | 77.48 | 71.09 | 83.00
-2016-11-16 05:00:00-05 | 77.50 | 71.09 | 82.90
-2016-11-16 06:00:00-05 | 78.49 | 71.69 | 84.20
-(24 rows)
-```