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Methodology for Establishing a Community-Wide Life Laboratory for Capturing Unobtrusive and Continuous Remote Activity and Health Data

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How Reproducibility Will Accelerate Discovery Through Collaboration in Physio-Logging.

Max F Czapanskiy¹, Roxanne S Beltran²

¹Hopkins Marine Station, Stanford University, Pacific Grove, CA, United States.

Frontiers in Physiology

|July 25, 2022

Summary

This summary is machine-generated.

Physio-logging research faces computational hurdles due to insufficient cyberinfrastructure, hindering scientific progress. Embracing computational reproducibility can accelerate discovery by improving data sharing and interdisciplinary collaboration in ecophysiology.

Keywords:

bio-logging cyberinfrastructure ecoinformatics ecophysiology technical debt

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Area of Science:

Ecophysiology
Computational Biology
Bio-logging

Background:

Physio-loggers generate complex data, exceeding current ecophysiology informatics capacity.
Insufficient cyberinfrastructure creates technical debt, limiting data sharing and comparative analyses.
Current data practices hinder interdisciplinary research and scientific collaboration.

Purpose of the Study:

To identify computational challenges in physio-logging research.
To connect these challenges to technical debt and cyberinfrastructure limitations.
To propose a framework for enhancing computational reproducibility in bio-logging.

Main Methods:

Reviewing computational challenges in physio-logging.
Analyzing the concepts of technical debt and cyberinfrastructure.
Examining how other scientific fields adopted reproducibility.
Presenting a framework for computational reproducibility.

Main Results:

Physio-logging is siloed due to technical debt and inadequate cyberinfrastructure.
Other scientific fields have successfully overcome similar data challenges through reproducibility.
A shift from 'data as a noun' to 'data as a sentence' is proposed.

Conclusions:

Computational reproducibility is crucial for advancing ecophysiology and bio-logging.
Reproducibility fosters collaboration, accelerates discovery, and promotes diverse scientific perspectives.
Implementing a framework for reproducibility can overcome current data-related obstacles.