Registered Reports & F1000 by Emma Henderson & Demitra Ellina

About the speakers

Emma Henderson

Emma is studying for her PhD in Psychology at Kingston University. She is interested in the use of heuristics in every day judgements and decisions. She is also interested in psychological methods, meta-science and philosophy of science. Emma is passionate about improving the reliability of scientific research through using and promoting open science practices including pre-registration and open data. She is an Ambassador for the Center for Open Science, a community committee member for Open Thesis.

Demitra Ellina

Demitra Ellina is the Editorial Community Manager at F1000 which she joined in 2017 after working in journal development at Springer Nature. She is a strong advocate of Open Research and engages with the research community to raise awareness of the F1000 publishing platforms.

About the talk

Emma will give a presentation entitled Ten reasons to write a Registered Report (now). Registered Reports (RR), a type of empirical article that is published based on the scientific merit of a preregistered protocol. Specifically, before results are known, and in principle acceptance is given to a prospective study and/or analysis plan following successful peer-review. Providing that the authors closely follow the protocol, the final submitted manuscript is published: regardless of the results. Emma will expand on this description, provide examples, and give an overview of the impact RRs have had since their introduction in 2013 by the journal Cortex.

The talk will be a ~30-minutes pre-recording followed by a live Q & A.

Demitra will give a live 10-minute talk on the wonderful work she does at F1000Research followed by a Q & A. She helps researchers understand Open Science practices and reproducibility at F1000Research, which is the first publisher to integrate the Registered Report format using an open post-publication peer review model (the first RR we published is linked here). Alongside their open data policy, this format helps enhance credibility while reducing researcher bias and supports reproducibility.