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Difference between revisions of "Reproducibility crisis"

From Bioblast
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== Further links ==
== Further links ==


::::* [https://www.nature.com/articles/s41467-018-06012-8 www.nature.com/articles/s41467-018-06012-8]  
::::* [https://www.nature.com/articles/s41467-018-06012-8 www.nature.com/articles/s41467-018-06012-8 Reproducibility: let’s get it right from the start, Nature Communicationsvolume 9, Article number: 3716 (2018)]  


::::* [https://de.slideshare.net/OpenAIRE_eu/20170530open-research-data-in-horizon-2020  20170530_Open Research Data in Horizon 2020]  
::::* [https://de.slideshare.net/OpenAIRE_eu/20170530open-research-data-in-horizon-2020  20170530_Open Research Data in Horizon 2020]  

Revision as of 16:40, 24 September 2018


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



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.
  • Avoid black-box performance of software.
  • 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.
  • An Open Access policy fosters the establishment of an error culture and a culture of transparence in science. In this way, Open Access - as manifested in e.g. this website - contributes in solving the reproducibility crisis.

Further links


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