Theme » Integrity and quality assurance

The ability to validate findings and working within standards of your research area is essential to good research practice. A simple question to begin with is; can I prove what I claim? And the next question may be; am I in line with good practice within my research field, e.g. applying methods correctly on data? You are not the only one asking these questions – others do it too.

Defining integrity

The Danish Code of Conduct for Research Integrity defines three basic principles that should be taken into account in all research activities; honesty, transparency and accountability. It contains an entire chapter on data management, but other chapters also include sections on how to manage data, in order to protect and maintain the highest standard of research integrity.

Failing on integrity can jeopardize your entire research project. Not being able to reproduce your research can force you to withdraw your research results, and eventually lead to accusations of research misconduct.

Quality assurance

Almost all research areas have to follow certain standards to ensure the right and acceptable level of quality. This can be based on protocols, standards, or simple procedures that ensure that the quality of data created is as expected, and that this is maintained throughout the research process.

It’s not only about you!

As a producer of research data, you must consider documenting your data, procedures etc. Not only for your own sake, but also to make sure that you can share data with others. If others rely on your data, you must ensure the integrity and quality of the data, so that nobody else will get incorrect or inaccurate results. Although you may find it hard to imagine that your data has any reuse potential, others might think differently. Others may consider your findings to be crucial.

As a re-user of data, you must be able to trust other peoples findings. So in the evaluation of use, you must make sure that you have the right amount of knowledge in order to validate the integrity and quality of the data according to your research scenario.