AbstractThe idea that the same experimental findings can be reproduced by a variety of independent approaches is one of the cornerstones of science's claim to objective truth. However, in recent years, it has become clear that science is plagued by findings that cannot be reproduced and, consequently, invalidating research studies and undermining public trust in the research enterprise. The observed lack of reproducibility may be a result, among other things, of the lack of transparency or completeness in reporting. In particular, omissions in reporting the technical nature of the experimental method make it difficult to verify the findings of experimental research in biomedicine. In this context, the assessment of scientific reports could help to overcome - at least in part - the ongoing reproducibility crisis.In addressing this issue, this Thesis undertakes the challenge of developing strategies for the evaluation of reporting biomedical experimental methods in scientific manuscripts. Considering the complexity of experimental design - often involving different technologies and models, we characterise the problem in methods reporting through domain-specific checklists. Then, by using checklists as a decision making tool, supported by miniRECH - a spreadsheet-based approach that can be used by authors, editors and peer-reviewers - a reasonable level of consensus on reporting assessments was achieved regardless of the domain-specific expertise of referees. In addition, by using a text-mining system as a screening tool, a framework to guide an automated assessment of the reporting of bio-experiments was created. The usefulness of these strategies was demonstrated in some domain-specific scientific areas as well as in mouse models across biomedical research.In conclusion, we suggested that the strategies developed in this work could be implemented through the publication process as barriers to prevent incomplete reporting from entering the scientific literature, as well as promoters of completeness in reporting to improve the general value of the scientific evidence.
|Date of Award||31 Dec 2016|
|Supervisor||Andrew Brass (Supervisor) & Robert Stevens (Supervisor)|
- Text Mining