Open Source and Validated Computational Tools for Physiological Time Series Analysis

We are announcing a call for papers for a focus collection on the topic of Open Source and Validated Computational Tools for Physiological Time Series Analysis. This Focus collection aims to provide a platform for the dissemination of such tools and to give the community opportunity to shape the way in which such tools are disseminated through the exemplar publications in this collection.

The motivation:

There is a lack of robust algorithms for physiological time series analysis. This deficiency arises because of the limited availability of scientifically validated resources and often inadequate documentation. This often leads researchers to have to redevelop classical algorithmics (e.g., peak detectors) resulting in significant human years spent "reinventing the wheel" and limiting the reproducibility of findings. As a community we do not yet have a well-used approach to disseminating open source and scientifically validated tools.

Objective:

Physiological time series analysis plays a crucial role in understanding the complex dynamics of biological systems and their response to various stimuli and interventions. The availability of reliable, open-source computational tools is essential for advancing research in this field, facilitating reproducibility, promoting collaboration, and accelerating scientific discoveries. This focus collection aims to showcase the latest advancements in open-source tools and methodologies that have been rigorously validated for the analysis of physiological time series data. To ensure the maximum impact and facilitate reproducibility, authors should aim to make their software open access, well-documented, and with the necessary scripts enabling the replication of their experiments to validate the software. By doing so, we aim to foster a culture of openness and collaboration within the research community, allowing fellow researchers to benefit from the new algorithm/software and independently validate the findings. The new resource may be made available on a personal GitHub repository or on research community platforms such as PhysioZoo (physiozoo.com).

Topics of interest include, but are not limited to:

• Development and validation of open-source software for physiological time series analysis.
• Signal processing and feature extraction methods for physiological data.
• Machine learning and deep learning techniques for physiological time series classification and prediction.
• Interpretability and explainability of computational models in physiological data analysis.
• Integration of multiple physiological data modalities for comprehensive analysis.
• Application of open-source tools in specific physiological domains (e.g., cardiovascular, respiratory, neurological, etc.).
• Benchmarking and comparison of open-source tools.
• Benchmarking and comparison of open-source tools against proprietary software.
• Visualization and interactive tools for exploring physiological time series data.

Articles should be submitted using our online submission form. In Step 1, at the bottom of the page, please select " Open Source and Validated Computational Tools for Physiological Time Series Analysis " in the 'Select Focus Issue' drop down box.

Submissions will be accepted until 31^st August 2024, however submissions earlier than this date are encouraged.