• NSRI

An interview with Daniël Lakens

Updated: Oct 23, 2020

On: The relevance of open science practices to research integrity

Daniël is associate Professor in the Human-Technology interaction group at Eindhoven University of Technology. He will speak on the topic of 'The relevance of open science practices to research integrity' at the national Symposium on Research Integrity on October 15th.

We asked him to share some of his thoughts on this topic in advance:

1. What made you interested in joining this symposium as a speaker? Why is the topic of research integrity important to you?

Scientists are often strongly driven by norms in their fields. But norms do not always correspond to best practices. As a psychologist, I am interested in how we can improve the way that scientists work, by making them reflect on current norms, and how well these are aligned with the code of conduct for research integrity.

2. Regarding the topic of your talk at the symposium:

a. Why is this topic particularly relevant now, to the Dutch research community?

We see a move towards transparency. This makes several aspects of the way researchers work more visible, and open for criticism. Meta-scientific research has shown certain problematic research practices (e.g., selective reporting, publication bias, lack of data sharing) are more widespread than we perhaps feared. It is time to address these issues, to make sure the public can trust scientific research.

b. Can you tell us one of your main take away messages from your talk that we can already highlight?

Current scientific norms are not always aligned with the scientific code for research integrity, and as scientists we should take it upon ourselves to improve scientific practice.

3. The symposium is being organised for researchers across the Netherlands. What do you hope as an outcome for an event where research integrity is being discussed?

A coordinated approach to examining and preventing practices that have been known for almost 2 centuries to reduce the reliability of scientific findings, such as selective reporting and publication bias.

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