Transparency and open science principles in reporting guidelines in sleep research and chronobiology journals.
Manuel SpitschanMarlene H SchmidtChristine BlumePublished in: Wellcome open research (2020)
Background: "Open science" is an umbrella term describing various aspects of transparent and open science practices. The adoption of practices at different levels of the scientific process (e.g., individual researchers, laboratories, institutions) has been rapidly changing the scientific research landscape in the past years, but their uptake differs from discipline to discipline. Here, we asked to what extent journals in the field of sleep research and chronobiology encourage or even require following transparent and open science principles in their author guidelines. Methods: We scored the author guidelines of a comprehensive set of 28 sleep and chronobiology journals, including the major outlets in the field, using the standardised Transparency and Openness (TOP) Factor. This instrument rates the extent to which journals encourage or require following various aspects of open science, including data citation, data transparency, analysis code transparency, materials transparency, design and analysis guidelines, study pre-registration, analysis plan pre-registration, replication, registered reports, and the use of open science badges. Results: Across the 28 journals, we find low values on the TOP Factor (median [25 th, 75 th percentile] 2.5 [1, 3], min. 0, max. 9, out of a total possible score of 28) in sleep research and chronobiology journals. Conclusions: Our findings suggest an opportunity for sleep research and chronobiology journals to further support the recent developments in transparent and open science by implementing transparency and openness principles in their guidelines and making adherence to them mandatory.
Keyphrases
- minimally invasive
- public health
- meta analyses
- clinical practice
- physical activity
- sleep quality
- primary care
- healthcare
- randomized controlled trial
- emergency department
- type diabetes
- systematic review
- skeletal muscle
- machine learning
- quality improvement
- adipose tissue
- weight loss
- deep learning
- patient reported outcomes
- preterm birth