We embrace the values of openness and transparency in science. We believe that such research practices increase the informational value and impact of our research, as the data can be reanalyzed and synthesized in future studies. Furthermore, they increase the credibility of the results, as independent verification of the findings is possible.

Here, we express a voluntary commitment about how we will conduct our research. Please note that to every guideline there can be justified exceptions.

But whenever we deviate from one of the guidelines, we give an explicit justification for why we do so (e.g., in the manuscript, or in the README file of the project repository). As signatories, we warrant to follow these guidelines from the day of signature on:

Own Research

1. Open Data: Whenever possible, we publish, for every first-authored empirical publication, all raw data which are necessary to reproduce the reported results on a reliable repository with high data persistence standards (for example zenodo.org, preferably it should be on a university server)
2. Whenever possible, for every first authored empirical publication, simulations or computational modelling work we will publish the data analysis scripts that were used to obtain the results.
3. For every first authored empirical publication, "we report how we determined our sample size, all data exclusions (if any), all manipulations, and all measures in the study." [1]
4. As co-authors we try to convince the respective first authors to act according to I-III.

Reviewers of journal articles

5. As reviewers, we add the “standard reviewer disclosure request”, if necessary (https://osf.io/hadz3/). It asks the authors to add a statement to the paper confirming whether, for all experiments, they have reported all measures, conditions, data exclusions, and how they determined their sample sizes.
6. As reviewers, we ask for Open Data or a justification why it is not possible.

Supervision of Dissertations

7. As PhD supervisors we put particular emphasis on the propagation of methods that enhance the informational value and the replicability of studies. From the very beginning of a supervisor-PhD student relationship we discuss these requirements explicitly.
8. From PhD students, we expect that they provide data, materials and scripts to the first supervisor (they do not have to be public yet).
9. If PhD projects result in journal publications, we encourage that they follow points I to III.
10. In the case of a series of experiments with a confirmatory orientation, it is expected that at least one pre-registered study is conducted with a justifiable a priori power analysis (in the frequentist case), or a strong evidence threshold (e.g., if a sequential Bayes factor design is implemented). A pre-registration consists of the hypotheses, design, data collection stopping rule, and planned analyses.
11. The grading of the final PhD thesis is independent of the studies’ statistical significance. Journal publications are aspired; however, a successful journal publication is not a requirement for passing.

Service to the field

12. As members of committees (e.g., tenure track, appointment committees, teaching, professional societies) or editorial boards, we support the values of open science.