Outdated Format of Research Outputs
The predominant format for communicating research outputs is the scientific paper. This structure was developed during the scientific revolution and is responsible for bringing the necessary order required for scientists to validate and build upon one another’s findings. The typical of a scientific paper employs the following structure:
Abstract: A short summary of the entire paper
Introduction: The relevant background required to interpret the paper’s findings
Methods: A description of the methodology employed to obtain the results
Results: The experimental data discovered during the study represented in figures
Discussion: The author’s interpretation of the significance of the results
Scientific research has grown in complexity to the point that this format no longer serves its intended purpose. There are estimates that over 50% of studies do not include the methodological detail required for independent replication. With the ever-increasing complexity of research methods and statistical analysis techniques, this is not surprising. Traditional text and pictures will never do the information captured by an f-MRI or genomic sequencing justice.
Furthermore, the narrative nature of the scientific paper is severely flawed because it inherently introduces an element of bias. Even the most ethical researcher is unable to avoid the bias inherent to human cognition when presented with the challenge of finding a pattern within large amounts of information. By definition the results section contains hand-picked experiments meant to derive signal from noise to fit a compelling story worthy of publication in a high-impact factor journal.
Fortunately, researchers have recognized the need to improve upon the traditional structure of the scientific paper:
In 2015, Dr. Lawrence Rajendran founded ScienceMatters.io, an online, open-access journal for stand-alone observations.
A 2018 pre-print from Hartgerink and van Zelst calls for an “as-you-go” approach to scholarly publishing, where researchers share modular collections of research outputs in a chronological fashion that’s more representative of the research cycle.
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