Academic Fraud is a rare phenomenon. Still, it’s effects can be very serious for the scientific community. Fraudulent research can serve as the basis for further research in the given direction, leading to a waste of time and resources. Additionally, wrong findings can serve as the arguments for policy makers and their decisions have an impact on a lot of people. Colleagues involved with fraudsters suffer from a negative impact on their career and have to regain trust after the discovery of the fraud. Finally, honest researchers get disadvantaged. Clearly, something has to be done about it. But how can we?
One way to make fraud less appealing is punishment combined with heightening the fear of being discovered. Currently, the consequences of being detected as a fraudster are already very severe, what undermines this system though, is that there is little reason to fear discovery. Whistleblowing often has very negative consequences for the whistleblower. Social pressure and fear for the own career make pointing out a fraudster a difficult act. Another point is, that there are no routine checks in place to discover fraud Research is mostly done in private, without the necessity to disclose the data and every researcher is given the benefit of doubt. Recent fraud cases however show that fraud seems to still be a gamble worth making. Years of salary, grant money and prestige seem to outweigh the chance of the negative consequences.
One way to help to prevent fraud would be a different way to think about data. Right now, many researchers consider data to be something they own. Collecting it gives them the right to do whatever they want with it. But there is a counter movement gaining momentum in science and other fields, advocating the idea of open data. Open data means that the raw data is accessible for everyone to see after the research is done. One project that is at the cutting edge of this idea, (along with other great ideas to make science better), is the open science framework.
If scientific data would have to be disclosed, it would become possible to run advanced analytics on this data. There is already a big body of knowledge about how to discover fraud in raw financial data and it could be used and extended. We humans tend to make characteristic mistakes when making up data and those can be found. Not only are we bad at making up random numbers, but we also do not know much about the distributions that are common to many statistics or the digit structure.
The next step could then be to develop tools that help to flag data as suspicious or trustworthy and trained easily on the disclosed data. This way, fraud would become much more difficult and risky.
Of course, this is only a raw sketch for the future of science, but it also paves the way for some questions: How much do we want to trust each other as scientists? Do we need a “science police”? What do you think?