How Reproducibility Will Accelerate Discovery Through Collaboration in Physio-Logging

Abstract

What new questions could ecophysiologists answer if physio-logging research was fully reproducible? We argue that technical debt (computational hurdles resulting from prioritizing short-term goals over long-term sustainability) stemming from insufficient cyberinfrastructure (field-wide tools, standards, and norms for analyzing and sharing data) trapped physio-logging in a scientific silo. This debt stifles comparative biological analyses and impedes interdisciplinary research. Although physio-loggers (e.g., heart rate monitors and accelerometers) opened new avenues of research, the explosion of complex datasets exceeded ecophysiology’s informatics capacity. Like many other scientific fields facing a deluge of complex data, ecophysiologists now struggle to share their data and tools. Adapting to this new era requires a change in mindset, from “data as a noun” (e.g., traits, counts) to “data as a sentence”, where measurements (nouns) are associate with transformations (verbs), parameters (adverbs), and metadata (adjectives). Computational reproducibility provides a framework for capturing the entire sentence. Though usually framed in terms of scientific integrity, reproducibility offers immediate benefits by promoting collaboration between individuals, groups, and entire fields. Rather than a tax on our productivity that benefits some nebulous greater good, reproducibility can accelerate the pace of discovery by removing obstacles and inviting a greater diversity of perspectives to advance science and society. In this article, we 1) describe the computational challenges facing physio-logging scientists and connect them to the concepts of technical debt and cyberinfrastructure, 2) demonstrate how other scientific fields overcame similar challenges by embracing computational reproducibility, and 3) present a framework to promote computational reproducibility in physio-logging, and bio-logging more generally.

Biologging for the future: how biologgers can help solve fundamental questions, from individuals to ecosystems

“…Standardized and reproducible data can fast-track comparative biological analyses and interdisciplinary research using biologgers 92 by ensuring that data are both available and usable. This is especially needed for non-spatial data such as physiologging, accelerometry, and video data that do not fit the standards developed for spatial data 93 . Open-access publication of those datasets could credit those who collect and share data 68 .…”

Minimum reporting standards can promote animal welfare and data quality in biologging research

“…Furthermore, concerted community efforts to bring vast amounts of unpublished knowledge into the published literature (e.g., [41,42]) would greatly benefit the biologging field. Embracing open data and reproducibility will accelerate the pace of discovery in biologging data [43], ultimately allowing researchers to save time and money, reduce the impacts to animals, and continue to publish cutting-edge science. Although many biologging studies are published in journals with clear data accessibility requirements, many ecological studies archive incomplete datasets that are insufficient to reproduce the analysis or reuse data.…”