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Enhancing transparency of the research process to increase accuracy of findings: A guide for relationship researchers

The purpose of this paper is to extend to the field of relationship science, recent discussions and suggested changes in open research practises. We demonstrate different ways that greater transparency of the research process in our field will …

ePlatypus: an ecosystem for computational analysis of immunogenomics data

Motivation The maturation of systems immunology methodologies requires novel and transparent computational frameworks capable of integrating diverse data modalities in a reproducible manner. Results Here, we present the ePlatypus computational …

Equity, transparency, and accountability: Open science for the 21st century

Knowledge is essential to saving lives and improving wellbeing. The term open science has been applied to improving the transparency of knowledge generation, but open science also has the potential to address many of the problems of inequity, …

Equivalence Testing for Psychological Research: A Tutorial

Psychologists must be able to test both for the presence of an effect and for the absence of an effect. In addition to testing against zero, researchers can use the two one-sided tests (TOST) procedure to test for equivalence and reject the presence …

Equivalence Tests: A Practical Primer for t Tests, Correlations, and Meta-Analyses

Scientists should be able to provide support for the absence of a meaningful effect. Currently, researchers often incorrectly conclude an effect is absent based a nonsignificant result. A widely recommended approach within a frequentist framework is …

Erroneous analyses of interactions in neuroscience: a problem of significance.

In theory, a comparison of two experimental effects requires a statistical test on their difference. In practice, this comparison is often based on an incorrect procedure involving two separate tests in which researchers conclude that effects differ …

Error Tight: Exercises for Lab Groups to Prevent Research Mistakes

Scientists, being human, make mistakes. We transcribe things incorrectly, we make errors in our code, and we intend to do things and then forget. The consequences of errors in research may be as minor as wasted time and annoyance, but may be as …

Establishing trust in automated reasoning

Since its beginnings in the 1940s, automated reasoning by computers has become a tool of ever growing importance in scientific research.So far, the rules underlying automated reasoning have mainly beenformulated by humans, in the form of program …

Estimating the prevalence of transparency and reproducibility-related research practices in psychology (2014-2017)

Psychological science is navigating an unprecedented period of introspection about the credibility and utility of its research. A number of reform initiatives aimed at increasing adoption of transparency and reproducibility-related research practices …

Estimating the reproducibility of psychological science

Reproducibility is a defining feature of science, but the extent to which it characterizes current research is unknown. We conducted replications of 100 experimental and correlational studies published in three psychology journals using high-powered …
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