25% of authors can’t find one/any of the images (e.g. gels) that ther “included” in the paper. That’s a strong case for the sort of work we are doing in data capture with CLARION.

Objective To understand belief in a specific scientific claim by studying the pattern of citations among papers stating it. Design A complete citation network was constructed from all PubMed indexed English literature papers addressing the belief that β amyloid, a protein accumulated in the brain in Alzheimer’s disease, is produced by and injures skeletal muscle of patients with inclusion body myositis. Social network theory and graph theory were used to analyse this network. Main outcome measures Citation bias, amplification, and invention, and their effects on determining authority. Results The network contained 242 papers and 675 citations addressing the belief, with 220 553 citation paths supporting it. Unfounded authority was established by citation bias against papers that refuted or weakened the belief; amplification, the marked expansion of the belief system by papers presenting no data addressing it; and forms of invention such as the conversion of hypothesis into fact through citation alone. Extension of this network into text within grants funded by the National Institutes of Health and obtained through the Freedom of Information Act showed the same phenomena present and sometimes used to justify requests for funding. Conclusion Citation is both an impartial scholarly method and a powerful form of social communication. Through distortions in its social use that include bias, amplification, and invention, citation can be used to generate information cascades resulting in unfounded authority of claims. Construction and analysis of a claim specific citation network may clarify the nature of a published belief system and expose distorted methods of social citation.

Investigators of scientific misconduct have had their hands full in recent months. So far this year, a regional hospital in western Massachusetts has announced the discovery of what may be the biggest case of research fraud in history, scientists in Texas have used a new computer algorithm to uncover thousands of apparently plagiarized papers in peer-reviewed journals and a congressional subcommittee questioned the consistency of institutional and independent review boards (IRBs) that oversee clinical trials