Data Harmonization and Why Automate it
In this article about data harmonization, we will define what it is and where it takes place in the process of data logging.
Here is a diagram that summarizes the concepts that will be discussed in this article for you to refer to during your reading. It is tailored with an emphasis on data harmonization, in a context of using measurement instruments.
Data logging is defined as any of various chronological records made concerning the use of a computer system, the changes made to data, etc. (1)
The chronology does not have to be established with a timestamp, it can be provided by any numerical order. Data logging is the core action of a data logger and data acquisition system (DAQ). After collecting the data, it is necessary to extract it from the sensors. With a data logger, one file per sensor is produced. With a DAQ system, there will be a single file containing all the data from all the sensors.
Data harmonization: a definition
In a very broad sense, especially in the biomedical sector, data harmonization means combining studies, experiments, different file formats or databases. In order to combine them, common parameters must be established. (2)
There are companies or software to do such data harmonization. Otherwise, it is an arduous, tedious and error-prone manual task.
Data harmonization applied to metrology
Data harmonization “seeks to bring together various types, levels and sources of data, which represent measurement of the same latent construct(s), in such a way that they can be made compatible and comparable”. (3)
In short, it is a matter of combining measures according to common parameters. To harmonize the data, the collection method and the pre-existing data must be compatible. In the case of metrology, this compatibility can be established by timestamp and unit of measurement.
Why automate data harmonization?
Here is a situation where we have three different types of measurements, with three measuring instruments being part of a system supporting data harmonization.
In our CO2 incubator, we want to make sure that the temperature, humidity and CO2 concentration are optimal for the growth of our cells.
If we have data loggers, each instrument will have to be set up individually. Then, the collected data will be transferred to a computer and they will have to be aligned manually. To do the alignment, the time stamp is an important parameter. Make sure that the logging starts at the same time and that the sampling rate is the same for all three instruments. This will make it easier to align the data. Also, perhaps the files produced by each of the sensors will not be of the same type. Some programming skills will be needed here to align the data.
With a DAQ system, the logging of the sensor data starts at the same time, with a defined sampling rate for the duration of the experiment. Only one action for all three instruments. This makes the measurement task much faster than with several different instruments.
Small caveat. Not all DAQ systems allow the harmonization of different types of data (e.g. temperature, relative humidity and carbon dioxide level). This is something to check when purchasing the instruments.
With a DAQ system that allows for data harmonization, a single file will be created containing all the aligned data. With the software, logging and harmonization are done simultaneously. No programming skills are required and the hours invested in producing an aligned data file are reduced to zero.
Dracal and data harmonization
With DracalView data acquisition software, each Dracal measurement instrument is part of a perfectly harmonized Mix & Match ecosystem. Each of the USB sensors logs data with a synchronized timestamp, allowing for automated data harmonization. In addition, the units of measurement are standardized for a given quantity. Thus, taking the example of our CO2 incubator and the three required measurements, a single log file will be generated with DracalView, containing a single line per timestamp for all measurements taken simultaneously.
Harmonization of metrological data is their combination and alignment according to common parameters in a single file. These parameters are often time and measurement units. Harmonization can be difficult or simple, depending on the automation tools used. We have seen that a complete data acquisition system like the one offered by Dracal Technologies, including measurement instruments and a software solution, automates data harmonization.
If you have any questions or comments, we look forward to hearing from you!
- Collins Dictionary (2022). Definition of ‘log’. [online] collinsdictionary.com. Available at: https://www.collinsdictionary.com/dictionary/english/log [Accessed 17 Oct. 2022].
- Tetrascience.com. (2022). Scientific Data Harmonization | TetraScience. [online] Available at: https://www.tetrascience.com/platform/data-harmonization [Accessed 28 Sep. 2022].
- NIHR | Cambridge Biomedical Research Centre (2012). Harmonisation. [online] DAPA Measurement Toolkit. Available at: https://dapa-toolkit.mrc.ac.uk/concepts/harmonisation [Accessed 28 Sep. 2022].