A framework for streamlining research workflow in neuroscience and psychology

Area of Science:

• Neuroscience
• Psychology
• Computational Science

Background:

• Scientific reproducibility is crucial for knowledge accumulation but hindered by ad-hoc workflows and poorly maintained code.
• Current practices in neuroscience and psychology often lack standardized, robust computational pipelines.

Purpose of the Study:

• To introduce psychopy_ext, a unifying framework designed to address workflow challenges in neuroscience and psychology research.
• To facilitate automation and improve the robustness of scientific code development and collaboration.

Main Methods:

• Development of psychopy_ext, a framework integrating experiment building, analysis, and manuscript preparation tools.
• Implementation of rigid workflow patterns and reasonable defaults for enhanced standardization.
• Leveraging the framework for automated task execution, including UI generation, testing, and data analysis.

Main Results:

• psychopy_ext enables automation of tasks like generating user interfaces, unit testing, stimulus control analyses, descriptive statistics, and publication-quality plotting.
• The framework promotes a more structured and efficient approach to research workflow management.
• Demonstrates potential for faster and more reliable code development within the target communities.

Conclusions:

• psychopy_ext offers a significant advancement for neuroscience and psychology researchers by providing a standardized and automated workflow.
• The framework has the potential to accelerate scientific discovery through improved code robustness and enhanced collaboration.
• Adoption of psychopy_ext can lead to more verifiable and reproducible scientific conduct.