If you don't know where to start, here are 10 easy steps, that you can take towards a more transparent and reproducible research practice. Every step will add a little more scientific value to your research.
- Create an account on OSF (http://osf.io/)
- Upload the material for an existing study (questionnaires, maybe reproducible analysis scripts) to an OSF project.
- Add an open license to all of your figures (so that you can reuse them in later publications, blog posts, or presentations: „Figure available under a CC-BY4.0 license at osf.io/XXXX.“. For details, see this blog post by Malte Elson.
- For the next project: Change the consent forms in a way that open data would be possible for that project (see https://osf.io/mgwk8/wiki/Consent%20form%20templates%20for%20open%20data/).
- Sign the PRO initiative and expect openness (or a justification why not) if you review another paper (https://opennessinitiative.org/)
- For the next data analysis: Practice to create scripts for reproducible data analysis (e.g., SPSS syntax, R scripts). All analytic steps that lead from raw data to the final results should be reproducible.
- Let a master student preregister his/her thesis. Can be either a „local preregisteration“, or a proper preregistration at OSF or at https://aspredicted.org/. See this workshop material for how to do a preregistration: https://osf.io/yd487/https://osf.io/mx7yp/
- Do you own first preregistration: https://cos.io/prereg/
- Publish your first open data set: Ensure anonymity, provide a codebook. See here for details: http://econtent.hogrefe.com/doi/pdf/10.1026/0033-3042/a000341
- Team up with colleagues and establish a local open science initiative (enter your name and affiliation in this list and see other colleagues that want to engage in a local initiative)