Table of Contents

Fabrication, falsification, and plagiarism

Fabrication means the invention of data or research results and reporting them as if they are fact. On the scale of severity, fabrication is amongst the worst behaviors in science (Steneck, 2006).

Falsification means the manipulation of data or research material, equipment or processes to change, withhold or remove data or research results without justification.

The difference with fabrication is that falsification can be rather subtle and that falsification has derivatives which are not as severe as the main definition of falsification, but still influence the results of research. So on a scale between misconduct and common practice, fabrication is on the clear misconduct side, we see that falsification is mainly misconduct, but that it’s derivatives are sometimes in a grey area (Fanelli, 2009).

One derivative of falsification is ‘mining’. This entails the gathering of a large data set to extract significant relations. Of course, in research one often looks for significant relations between various factors, but essential here is the opportunistic aspect, the fishing (Fanelli, 2009). Another derivative is result selection, where one only publishes the results that support your hypothesis. Although this only reflects on data, one can also make a parallel with references, that is, that one only uses sources that are consistent with your hypothesis (ALLEA, 2017).

Derivatives:

• Remove the outliers;
• Remove parts of continuous data;
• Round off 94.55% upwards to make it significant.

Plagiarism means the use of another person’s ideas, work methods, results or texts without appropriate acknowledgement.

Plagiarism Spectrum:

1. Clone: submitting another’s work, word-for-word, as one’s own.
2. Ctrl-c: contains significant portions of text from a single source without alterations.
3. Find: Replace changing key words and phrase but retaining the essential content of the source.
4. Remix: Paraphrases from multiple sources, made to fit together.
5. Recycle: borrows generously from the writer’s previous work without citation.
6. Hybrid: Combines perfectly cited sources with copied passages without citation.
7. Mashup: Mixes copied material from multiple sources.
8. 404 Error: Includes citations to non-existent or inaccurate information about sources.
9. Aggregator: Includes proper citation to sources but the paper contains almost no original work.
10. Re-tweet: includes proper citation, but relies too closely on the text’s original wording and/or structure.

How to avoid plagiarism?

You can present information coming from a source in two ways: you can do it in your own words (paraphrasing) or you can quote. Quotations should be used sparsely.

Questionable research practices

Exaggeration

Exaggeration is a grey subject of research integrity. Being visible and drawing attention to your research is, arguably, a part of being a researcher as science is a part of society.

However, in extreme cases, it might have severe consequences in the case of health care and treatments.

Predatory publishing

With the advancement of open access however, a new problem has risen. Under the new author-pays model of open access, publishers ask authors for an article processing charge (APC) when submitting papers. Some ‘publishers’ and journals have attempted to exploit this model by charging large fees to authors without providing the proper editorial and publishing services associated with more established and legitimate journals. Publishers lack transparency and use deceptive websites and emails to attract manuscript submissions and the accompanying author fees (Beall, 2013).

Publishing your work in an untrustworthy journal has the potential to damage your career. As part of your academic skill set it is therefore necessary to develop a sense of scholarly publishing literacy. Several resources exist online (VSNU, 2018) that allow you to qualitatively assess the different publisher and journals options available to you. This can assist you in making the right decision where to publish your paper in open access.

Bias

Bias in research refers to systematic errors in the collection of data, (un)intentional selection of desirable or anticipated analytic or empirical results, unfair assessments of persons, or the drawing of preferred and often unjustified conclusions based on ignoring or downplaying of contrary evidence or arguments.

So an honest scientists tries to counter biases in research as much as possible, and can do so by being open about choices, self-critical on his or her own fallibilities and the limitations of the study or the available evidence, rigorous (systematic and transparent) practices in collecting data and assessing arguments pro and con, and comprehensive in the reporting of results. Transparency further enhances accountability by allowing scientific peers to openly assess, debate, and correct any biases that might emerge during the research process or when assessing results, arguments, or persons.

Text recycling

Academic text recycling is the reuse of one’s own writing in new academic publications without reference. This can range from reusing a single sentence to reusing several pages, or even entire articles. In the latter case, scholars commonly refer to ‘duplication’.