Network models to evaluate reproducibility in biomedical research
_or, The Future of Science

The traditional way to publish scientific work is to write a narrative describing the performed experiments and related conclusions. Nowadays, the pressures for funding and journal impact factor generate a vicious circle promoting, at the very least, an increase in the minimum number of relevant findings required for publication and the over-stretching of claims. As a consequence, the problem of reproducibility in science has surged to the attention of the media, including The New York Times and The Wall Street Journal.

This situation has already generated several disadvantages for authors, funding bodies and research institutes:

  1. Only a small fraction of the experimental workflow is actually published, creating data loss and underestimation of the labor done by research fellows;

  2. Pressure to publish novel data decreases reproducibility and increases the amount of unconfirmed work;

  3. New technologies, usually developed at the start of a research project, are only published years later, slowing down scientific progress;

  4. Disconnect between funding and publication: research is often funded when most of the work is already done by using grant money from past proposals;

  5. Research papers become increasingly complex and specialized, forcing the peer review process to a black and white decision;

  6. Lack of metrics assessing the quality and reproducibility of research.