In abstract:

Groundbreaking text mining project highlights ‘gender gap’ in scientific research

The literature on the problems with reproducibility in science has focussed on the interpretation of statistics. A new study highlights that the methods used may not be reported rigorously enough to assess whether or not the raw data itself is useful. An interdisciplinary team at The University of Manchester looked at 15,000 papers that used a mouse model - the largest analysis of its kind ever undertaken – and found that half of the papers failed to report the sex and age of the mice involved. This is serious as these are key variables that can affect the outcome of scientific studies.

The project utilised text mining software developed at the University of Manchester to analyse large collections of documents to unearth information which would otherwise have been impossible to discover. It allowed the team to explore the research landscape on a large scale to identify key issues in reporting details of scientific methodologies, which are necessary for reproducibility, transparency and fidelity of research. Failure to consider gender in research is still very much the norm, and this must improve for science to be reproducible.

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