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Related Experiment Video

Updated: Oct 29, 2025

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PyMoDAQ: An open-source Python-based software for modular data acquisition.

S J Weber1

  • 1CEMES-CNRS, Université de Toulouse, 29 rue Jeanne Marvig, 31055 Toulouse, France.

The Review of Scientific Instruments
|July 10, 2021
PubMed
Summary
This summary is machine-generated.

PyMoDAQ is an open-source Data Acquisition framework (DAQ-F) offering a flexible, modular, and user-friendly interface for automating diverse experimental setups. It supports live monitoring, data logging, and FAIR data principles, making it a versatile alternative to commercial solutions.

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Area of Science:

  • Scientific instrumentation
  • Open-source software development
  • Data acquisition systems

Background:

  • Increasing availability of scientific Python packages enables open-source Data Acquisition frameworks (DAQ-Fs).
  • These DAQ-Fs offer versatile alternatives to custom-made or commercial solutions.
  • PyMoDAQ is developed to address the need for user-friendly experimental control and automation.

Purpose of the Study:

  • Introduce PyMoDAQ as a versatile Data Acquisition framework (DAQ-F).
  • Highlight its modular design, user interface flexibility, and additional functionalities.
  • Compare PyMoDAQ with existing frameworks based on key criteria to showcase its novelty.

Main Methods:

  • Development of a modular Python-based Data Acquisition framework (PyMoDAQ).
  • Implementation of a graphical user interface for experiment control and automation.
  • Integration of features like live plotting, data saving with metadata, and logging.
  • Evaluation of PyMoDAQ using seven criteria and comparison with other DAQ-Fs.

Main Results:

  • PyMoDAQ provides an easy-to-use interface for controlling and automating various experimental setups.
  • Its modular structure facilitates data acquisition as a function of multiple parameters.
  • The framework includes functionalities for instrument configuration, live visualization, and data logging.
  • PyMoDAQ's data saving mechanism is compatible with FAIR data principles.
  • Comparison highlights PyMoDAQ's novelty and pertinence as a versatile DAQ-F.

Conclusions:

  • PyMoDAQ offers a flexible and user-friendly solution for experimental data acquisition and automation.
  • Its modularity and comprehensive features make it adaptable for diverse scientific applications.
  • PyMoDAQ's compatibility with FAIR data principles enhances data reusability and accessibility.
  • It represents a significant advancement in open-source DAQ frameworks.