Reproducibility crisis

high-resolution terminology - matching measurements at high-resolution

Reproducibility crisis

Description

Is there a reproducibility crisis? According to a survey conducted by Nature of 1,576 researchers, "52% agree that there is a significant 'crisis' of reproducibility, less than 31% think that failure to reproduce published results means that the result is probably wrong, and most say that they still trust the published literature" (Baker 2016). Chemistry and biology are the subjects with the highest share of failed attempts of reproduction of results.

Asking the researchers for the causes of this inability to reproduce published results, the top three answers are:

• Selective reporting
• Publication pressure
• Low statistical power and poor analysis

The top three mentioned countermeasures are:

• Better understanding of statistics
• Better mentoring and supervision
• More robust design

Reference: Baker M (2016) 1,500 scientists lift the lid on reproducibility. Survey sheds light on the ‘crisis’ rocking research. Nature 533:452–4.

Further links

• 20170530_Open Research Data in Horizon 2020

• Webinar by John PA Ioannidis and Gregor Witte: Reproducibility in crisis: Sample quality and the importance of early and ongoing analysis

• França TFA, Monserrat JM (2018) Reproducibility crisis in science or unrealistic expectations? EMBO reports:e46008.

Solve the reproducibility crisis

While it is probably impossible to fully prevent human self-deception and inadequate command of statistical methods, what we can do is minimize sources of error connected to the instrumental equipment and its handling:

• Select instrumental equipment for which appropriate specifications are available.
• Have yourself trained on your equipment and make sure you know what you (both, you and the device you operate) are doing in each step of your experiment.
• Same for data analysis: get trained on analysis software. In the best case, use software that comes with your instrument in order to minimize errors during data transfer and translation.

• Open-research data in Horizon 2020
• How can we increase reproducibility in neuroscience studies? BioTechniques 2018.

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