About this Research Topic
Network physiology is a growing field that leverages open-source algorithms and datasets for the analysis of multimodal biosignals such as ECG and EEG. This area of research is crucial for understanding physiological states and the interactions within networks of biological units. However, the rapid pace of software development often leads to outdated tools and environments, posing significant challenges for reproducibility and standardization. Many scientific publications lack comprehensive documentation of relevant parameters and algorithmic details, increasing the risk of misuse and compromising scientific integrity. Addressing these issues requires a concerted effort to evaluate and document existing and new algorithms systematically, ensuring they are robust, reproducible, and well-documented.
This Research Topic aims to promote open science in network physiology by evaluating and consolidating methods, ensuring the quality and reproducibility of open-source algorithms. The primary objectives include the systematic testing and benchmarking of these algorithms using freely available datasets, as well as maintaining detailed documentation. By fostering a collaborative environment, this initiative seeks to uncover the strengths and weaknesses of various biosignal analysis methods, providing a valuable resource for future research and application.
To gather further insights in the evaluation and consolidation of biosignal analysis methods, we welcome articles addressing, but not limited to, the following themes:
- Signal classification
- Detection of information flow and coupling directions
- Dimension reduction and visualization
- (Automatic) clustering
- Detection and prediction of extreme events
- Cross prediction and forecasting of physiological variables
- Data-driven modeling
- Preprocessing (e.g., handling missing data points, noise removal)
- Multimodal approaches combining multiple biosignals
Each submitted manuscript should include a link to a freely available version control system (e.g., GitHub, GitLab) offering the source code and a README file to reproduce the reported results.
Topic Editor Dr. Alexander Schlemmer is Co-Founder and External Scientific Advisor for IndiScale GmbH, Germany. All other Topic Editors declare no competing interests with regards to the Research Topic subject
This Research Topic aims to promote open science in network physiology by evaluating and consolidating methods, ensuring the quality and reproducibility of open-source algorithms. The primary objectives include the systematic testing and benchmarking of these algorithms using freely available datasets, as well as maintaining detailed documentation. By fostering a collaborative environment, this initiative seeks to uncover the strengths and weaknesses of various biosignal analysis methods, providing a valuable resource for future research and application.
To gather further insights in the evaluation and consolidation of biosignal analysis methods, we welcome articles addressing, but not limited to, the following themes:
- Signal classification
- Detection of information flow and coupling directions
- Dimension reduction and visualization
- (Automatic) clustering
- Detection and prediction of extreme events
- Cross prediction and forecasting of physiological variables
- Data-driven modeling
- Preprocessing (e.g., handling missing data points, noise removal)
- Multimodal approaches combining multiple biosignals
Each submitted manuscript should include a link to a freely available version control system (e.g., GitHub, GitLab) offering the source code and a README file to reproduce the reported results.
Topic Editor Dr. Alexander Schlemmer is Co-Founder and External Scientific Advisor for IndiScale GmbH, Germany. All other Topic Editors declare no competing interests with regards to the Research Topic subject
Keywords: Reproducible research, benchmarking, evaluation of methods, good scientific practice, time series analysis, open source, network physiology
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
