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Having a reliable temperature monitoring and ambient monitoring system is crucial in a lab facility, but what happens if a system fails?

The Difference Between a Data acquisition system and a data logger

When a monitoring system fails, bad things happen - take for example a freezer failure at Children’s Hospital Los Angeles, leading to more than four dozen cancer patients losing their stored stem cells. Implementing a real-time laboratory monitoring system that is flexible, accurate, robust, and easy to use, prevents these type of monitoring failures from happening. 

How? Let's go over each of the details below.


First off, what are Data Loggers and Temperature Sensors?

A data logger is a device that records the temperature of particular lab equipment or lab environment. The data is stored inside the device and can be extracted, after the fact, to determine if any deviations that occurred may have adversely affected samples.

Data loggers are a reactive way to monitor for deviations in many cases. By the time you know a problem occurred it’s often too late to do anything to preserve samples and they may need to be discarded.

A temperature sensor is a device that measures the temperature in a particular piece of equipment or environment and transmits the data to a display or a data repository, such as a data logger.

This temperature is important and must be diligently watched because any deviation may impact the samples and bring research results into question.


How is a Data Acquisition System Different from a Data Logger?

  • Real-time monitoring vs timestamp sequence logging: in the event of an alarm, a DAQ system will allow you to virtually log in and check any notification while continuing to capture everything before and after the alarm. A data logger will often require you to interrupt the logging sequence to diagnose any issue and miss some valuable information in between the logs.
  • Flexible configuration of alarms: Data acquisition systems (DAQ) are usually highly configurable and will give you the ability to set various parameters for alarms. For example, if you’re monitoring the water level in a tank and you’re setting up an alarm for overflow, you would want to set up a pre-alarm and give yourself enough time to get there before it overflows.
  • High measuring accuracy: In a DAQ system, sample rates can be set to as often as 1 second. With data loggers, sample rates are normally set to every 15 minutes to preserve battery life.
  • Improved SOP's: DAQ systems help improve your SOP's by providing valuable information about how researchers interact with equipment and respond to alarms.


Here are some examples of why a high quality real-time monitoring system might be a better fit than a simple data logger:

1. Understanding incubator recovery time helps optimize the research cycles and experiments.

incubator recovery graph door opening


2. Cold Storage units with a large hysteresis can compromise the structural integrity of biological samples. The units in the example below should have their set point raised 1º to avoid freezing samples.

fridge with big histeresis


3. VOCs (Volatile Organic Compounds) in the air can affect biological substances - sensitive research or procedures shouldn’t occur when VOCs in the lab are too high.

high voc embryology lab


4. Ensuring optimal research environments by monitoring room temperature, relative humidity, airflow, and other parameters in the laboratory.

room temp relative humidity air flows


How does your lab currently monitor equipment functionality and environmental conditions?

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