Reproducibility crisis

From Bioblast

high-resolution terminology - matching measurements at high-resolution

Reproducibility crisis


The reproducibility crisis is alarming.1 An experiment or study is reproducible or replicable when subsequent experiments confirm the results. This is re-search. However, we can define different types of reproducibility depending on the conditions that we use to replicate the previous work or in the information available. Our aim is to focus mostly on two different kinds2: 1. Direct: is when we obtaining the same results using the same experimental conditions, materials, and methods as described in the original experiment. This would be the ideal reproducibility of an experiment. However, it requires a very accurate description of how the original experiment was performed. Some journals are trying to resolve the reproducibility crisis improving the rigor and the excellence on the reported methods and results (e.g. STAR Methods in Cell Press). 2. Systematical: refers to obtaining the same results, but under different conditions; for example, using another cell line or mouse strain or humman study, or inhibiting a gene pharmacologically instead of genetically. This opens the door to subsequent studies to find the conditions under which an initial finding holds.


1. Ioannidis JPA (2005) Why most published research findings are false. PLoS Med 2:e124.
2. Stanley EL (2016) Experimental design for laboratory biologists. Maximizing information and improving reproducibility. Cambridge University Press.
3. Baker M (2016) 1,500 scientists lift the lid on reproducibility. Survey sheds light on the β€˜crisis’ rocking research. Nature 533:452–4.

Is there a reproducibility crisis?

According to a survey conducted by Nature2 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

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 transparency in science. In this way, Open Access - as manifested in the Bioblast website (see Gentle Science - contributes to solving the reproducibility crisis.
  • Methods: Identify the methods, apparatus (manufacturer's name and address in parentheses), and procedures in sufficient detail to allow other workers to reproduce the results. Give references to established methods. - Quoted from International Committee of Medical Journal Editors.

Compare: the ambiguity crisis

Further links

  • National Academies of Sciences, Engineering, and Medicine (2019) Reproducibility and replicability in science. Washington, DC: The National Academies Press.
Β» Validating key experimental results via independent replication
Β» Reproducibility Initiative


Bioblast linkReferenceYear
Begley CG, Ioannidis JPA (2015) Reproducibility in science: improving the standard for basic and preclinical research. Circ Res 116:116-26.
Chiolero A, Tancredi S, Ioannidis JPA (2023) Slow data public health. Eur J Epidemiol 38:1219-25.
Chiu K, Grundy Q, Bero L (2017) `Spin' in published biomedical literature: A methodological systematic review. PLoS Biology 15(9): e2002173.2017
Gall T, Ioannidis JPA, Maniadis Z (2017) The credibility crisis in research: Can economics tools help? PLoS Biol 15:e2001846.
Gnaiger E (2019) Editorial: A vision on preprints for mitochondrial physiology and bioenergetics.
Gnaiger E et al ― MitoEAGLE Task Group (2020) Mitochondrial physiology. Bioenerg Commun 2020.1.
Ioannidis JPA (2005) Why most published research findings are false. PLoS Med 2:e124.
Ioannidis JPA, Greenland S, Hlatky MA, Khoury MJ, Macleod MR, Moher D, Schulz KF, Tibshirani R (2014) Increasing value and reducing waste in research design, conduct, and analysis.
Kahneman D (2011) Thinking, fast and slow. Penguin Books:499 pp.2011
National Academies of Sciences, Engineering, and Medicine (2019) Reproducibility and replicability in science. National Academies Press, Washington, DC.
Stodden Victoria, Seiler Jennifer, Ma Zhaokun (2020) An empirical analysis of journal policy effectiveness for computational reproducibility. Proc Natl Acad Sci U S A 115:2584-9.2020
Triggle Chris R, Triggle David J (2017) From Gutenberg to Open Science: an unfulfilled odyssey. Drug Dev Res 78:3-23.2017

MitoPedia O2k and high-resolution respirometry: Oroboros QM 

MitoPedia topics: Gentle Science 

Cookies help us deliver our services. By using our services, you agree to our use of cookies.