Logistics
20 columns
200 rows
8 formats

Cold Chain Logistics Temperature Data

This dataset provides granular temperature and humidity logs for perishable goods in cold chain logistics, including location, container, and product details. It supports compliance monitoring, quality assurance, and real-time alerting for temperature excursions, making it invaluable for food, pharmaceutical, and supply chain safety management.

opened 30 times
last updated 20 Aug 2025
by GoMask

The brief that made it

Regulatory compliance and audit reporting for temperature-sensitive shipments

Sample rows

preview · 8 of 200 rows · all 20 columns

log_idstring	humidity_percentfloat	is_within_safe_rangeboolean	address_statestring	shipment_idstring	container_idstring	product_typestring	sensor_idstring	temperature_celsiusfloat	log_timestampdatetime	location_latitudefloat	location_longitudefloat	address_streetstring	address_citystring	address_postal_codestring	address_countrystring	safe_temperature_minfloat	safe_temperature_maxfloat	alert_flagboolean	operator_idstring
LOG0001	87.2	true	Berlin	SHIPA1	CONT01	dairy	SEN1001	4.1	2024-05-01T09:15:00	52.52	13.405	Alexanderplatz 1	Berlin	10178	DE	1	7	false	OP-DE001
LOG0002	88.9	false	Berlin	SHIPA1	CONT01	dairy	SEN1002	8.4	2024-05-01T13:15:00	52.52	13.405	Alexanderplatz 1	Berlin	10178	DE	1	7	true	OP-DE001
LOG0003	95	true	NY	SHIPB2	CONT02	meat	SEN1003	1.9	2024-05-02T11:10:00	40.7128	-74.006	Broadway 100	New York	10007	US	-1	4	false	OP-US002
LOG0004	93.2	false	NY	SHIPB2	CONT02	meat	SEN1004	4.7	2024-05-02T15:10:00	40.7128	-74.006	Broadway 100	New York	10007	US	-1	4	true	OP-US002
LOG0005	28	true	Tokyo	SHIPC3	CONT03	pharmaceuticals	SEN1005	20.2	2024-05-03T10:20:00	35.6895	139.6917	Shinjuku 2-8-1	Tokyo	160-0022	JP	15	25	false	OP-JP001
LOG0006	30.1	false	Tokyo	SHIPC3	CONT03	pharmaceuticals	SEN1006	26.1	2024-05-03T14:20:00	35.6895	139.6917	Shinjuku 2-8-1	Tokyo	160-0022	JP	15	25	true	OP-JP001
LOG0007	82.5	true	Moscow	SHIPD4	CONT04	seafood	SEN1007	-3	2024-05-04T08:30:00	55.7558	37.6173	Tverskaya St 7	Moscow	125009	RU	-5	0	false	OP-RU001
LOG0008	85.1	false	Moscow	SHIPD4	CONT04	seafood	SEN1008	2.1	2024-05-04T12:30:00	55.7558	37.6173	Tverskaya St 7	Moscow	125009	RU	-5	0	true	OP-RU001
LOG0009	77.2	true	NSW	SHIPE5	CONT05	fruit	SEN1009	8.1	2024-05-05T09:45:00	-33.8688	151.2093	George St 200	Sydney	2000	AU	4	10	false	OP-AU001
LOG0010	79.3	false	NSW	SHIPE5	CONT05	fruit	SEN1010	12.3	2024-05-05T13:45:00	-33.8688	151.2093	George St 200	Sydney	2000	AU	4	10	true	OP-AU001
LOG0011	80.6	true	Île-de-France	SHIPF6	CONT06	flowers	SEN1011	5.2	2024-05-06T10:05:00	48.8566	2.3522	Rue de Rivoli 99	Paris	75001	FR	4	7	false	OP-FR001
LOG0012	78.4	false	Île-de-France	SHIPF6	CONT06	flowers	SEN1012	9.4	2024-05-06T14:05:00	48.8566	2.3522	Rue de Rivoli 99	Paris	75001	FR	4	7	true	OP-FR001
LOG0013	32.1	true	England	SHIPG7	CONT07	vaccines	SEN1013	4	2024-05-07T08:50:00	51.5074	-0.1278	Baker St 221B	London	NW1 6XE	GB	2	8	false	OP-GB001
LOG0014	29.5	false	England	SHIPG7	CONT07	vaccines	SEN1014	1.4	2024-05-07T12:50:00	51.5074	-0.1278	Baker St 221B	London	NW1 6XE	GB	2	8	true	OP-GB001
LOG0015	33.2	false	England	SHIPG7	CONT07	vaccines	SEN1015	8.7	2024-05-07T16:50:00	51.5074	-0.1278	Baker St 221B	London	NW1 6XE	GB	2	8	true	OP-GB001
LOG0016	47.4	true	CDMX	SHIPH8	CONT08	eggs	SEN1016	17.8	2024-05-08T09:50:00	19.4326	-99.1335	Av Insurgentes 1500	Mexico City	03100	MX	10	20	false	OP-MX001
LOG0017	49	false	CDMX	SHIPH8	CONT08	eggs	SEN1017	21.2	2024-05-08T13:50:00	19.4326	-99.1335	Av Insurgentes 1500	Mexico City	03100	MX	10	20	true	OP-MX001
LOG0018	blank	false	blank	SHIPI9	CONT09	ice cream	SEN1018	-50	2024-05-09T07:10:00	-75.25	0.0715	blank	McMurdo	blank	AQ	-35	-20	true	OP-AQ001
LOG0019	12.1	true	Dubai	SHIPJ10	CONT10	chocolate	SEN1019	20.5	2024-05-10T11:10:00	25.2048	55.2708	Sheikh Zayed Rd 1	Dubai	00000	AE	15	25	false	OP-AE001
LOG0020	11.2	false	Dubai	SHIPJ10	CONT10	chocolate	SEN1020	26.3	2024-05-10T15:10:00	25.2048	55.2708	Sheikh Zayed Rd 1	Dubai	00000	AE	15	25	true	OP-AE001

What the 200 rows show

from the 200-row sample

51%is_within_safe_range = true
71.2median humidity_percent
10product types
10address countries
23address cities
44containers

humidity_percent191 rows, in bands of 10

Median 71.2, from 8.2 to 95.2.

address_state192 rows with a value · 8 left blank

Tokyo22
Berlin20
CDMX16
Dubai16
England15
NSW14
Moscow12
NY10
CA10
Saint Petersburg10

20 columns by typefrom the column list below

string 11
float 6
datetime 1
boolean 2

Columns

20 columns in four groups

blueprint · 20 columns
column	type	description	example
Text 11 columns
`log_id`	string	Unique identifier for each temperature log entryunique	LOG0001
`shipment_id`	string	Unique identifier for the shipment associated with the temperature log	SHIPA1
`container_id`	string	Unique identifier for the refrigerated container or storage unit	CONT01
`product_type`	string	Type of perishable goods being transported (e.g., dairy, meat, pharmaceuticals)10 types	dairy
`sensor_id`	string	Unique identifier for the temperature sensor	SEN1001
`address_street`	string	Street address where the temperature was logged (if available)optional	Alexanderplatz 1
`address_city`	string	City where the temperature was loggedoptional	Berlin
`address_state`	string	State or province where the temperature was loggedoptional	Berlin
`address_postal_code`	string	Postal code of the logging locationoptional	10178
`address_country`	string	Country of the logging location10 countries · optional	DE
`operator_id`	string	Identifier for the operator or responsible personnel at the time of loggingoptional	OP-DE001
Numbers 6 columns
`temperature_celsius`	float	Recorded temperature in degrees Celsius-50 to 50	4.1
`humidity_percent`	float	Recorded relative humidity percentage0 to 100 · optional	87.2
`location_latitude`	float	Latitude coordinate where the temperature was logged-90 to 90 · optional	52.52
`location_longitude`	float	Longitude coordinate where the temperature was logged-180 to 180 · optional	13.405
`safe_temperature_min`	float	Minimum safe temperature for the product type at the time of loggingoptional	1
`safe_temperature_max`	float	Maximum safe temperature for the product type at the time of loggingoptional	7
Dates and times 1 column
`log_timestamp`	datetime	Date and time when the temperature reading was recorded	2024-05-01T09:15:00
True or false 2 columns
`is_within_safe_range`	boolean	Indicates if the recorded temperature is within the safe range for the product type	true
`alert_flag`	boolean	Indicates if an alert was triggered due to temperature out of safe rangeoptional	false

15 SQL practice questions for the Cold Chain Logistics Temperature Data

Written for this data and checked against it: 12 SQL and 3 Power BI exercises from beginner to advanced. Try the first three free; the practice kit has all 15, a starter .sql file and an answer key with every expected result.

01BeginnerFreeSELECT · WHERE · ORDER BY
Alerted vaccine logs
Return log_id, shipment_id, product_type and temperature_celsius for every log where alert_flag is 1 and product_type is 'vaccines'. Order the results by log_id ascending.
Tables: cold_chain_logistics_temperature_data · your result: 4 columns, 10 rows
02BeginnerFreeDISTINCT · ORDER BY
Distinct cities by country
Return the distinct combinations of address_country and address_city. Order the results by address_country, then by address_city.
Tables: cold_chain_logistics_temperature_data · your result: 2 columns, 23 rows
03BeginnerFreeGROUP BY · COUNT
Logs per product
Count the logs for each product_type. Return product_type and log_count, ordered by log_count descending and then by product_type ascending.
Tables: cold_chain_logistics_temperature_data · your result: 2 columns, 10 rows

04Average temperature by productBeginner
05Alert totals by countryPower BI
06Products with many excursionsIntermediate
07Humidity bucketsIntermediate
08Monthly alert rateIntermediate
09Cities warmer than averageIntermediate
10Excursion rate by productPower BI
11Rank products by max temperatureAdvanced
12Large temperature changesAdvanced
13Running alert totalAdvanced
14Share of alerts by countryAdvanced
15Temperature anomaly visualPower BI

Get the practice kit: the full dataset, all 15 exercises, a starter .sql and the answer key. One-time $3.99.

Get the practice kit · $3.99

Use it for

A logistics dashboard
The is_within_safe_range rate, humidity_percent and a breakdown of address_state. Excel, Power BI or Tableau.
Why do 101 of 200 rows have is_within_safe_range = true?
A class exercise
Hand out the rows and one question. Everyone works from the same 200 rows.
Logs200
LOG000187.2true
LOG000288.9false
LOG000395true
A software demo
Believable logs with shipment_id, container_id and product_type to fill a screen in front of a buyer.

This dataset200 rows20 columns

Yours1,000 rows20 columnslocation_latitude: UK only

blueprint · cold-chain-logistics-temperature-data

Behind this dataset

Same schema. As many rows as you need.

These 200 rows came out of a blueprint — 20 columns with generation rules behind each one. Open it in Data Factory to retune a column, add your own, wire in foreign keys, and run it at the size you actually need.

Rules it was built with

Log temperature readings per hour
Include shipment ID and product type
Flag temperature excursions
Track route and transport method

Rows1,000 rows10,000 rows100,000 rows1,000,000 rows

Open the blueprint in Data Factory

1 credit per row. Preview 20 rows free. Buy rows for the full run.

Exports: CSV, JSON, JSONL, Parquet, SQL, Excel, TSV, XML
Licence: yours to use, including commercially
API slug: cold-chain-logistics-temperature-data

What should your data show?

No signup. No card.

Sample rows

What the 200 rows show

Columns

Alerted vaccine logs

Distinct cities by country

Logs per product

Use it for

A logistics dashboard

A class exercise

A software demo

Make it yours.

Same schema. As many rows as you need.

More in Logistics

What should your data show?