Should open science focus more on open methods?
We’ve heard of open data, but should we also consider open methods as standard?
Research integrity in the COVID-19 era: insights from Retraction Watch co-founder Ivan Oransky
Read Ivan Oransky’s take on the retraction landscape and his perspectives on how to tackle research integrity issues in the future.
Research integrity across the Atlantic: our summary of the first Biomedical Transparency Summit series webinar
Find out more about what was covered during the first 2021 webinar from the Center for Biomedical Research Transparency.
Overcoming barriers to reporting negative data: perspectives from the Center for Biomedical Research Transparency’s founder
Sandra Petty discusses the Null Hypothesis Initiative and looks to the future of reporting negative data.
Improving reproducibility in scientific research: insights from the UK Reproducibility Network
Professor Marcus Munafò discusses the importance of reproducibility in scientific research and how we can overcome barriers to achieving it.
Meeting report: summary of session 2 of the 8th EMWA Symposium
Missed the meeting? Read our report on the symposium entitled “Research integrity & the medical communicator: what we do when no one is watching.”
Meeting report: summary of session 1 of the 8th EMWA Symposium
Missed the meeting? Read our report on the symposium entitled “Research integrity & the medical communicator: what we do when no one is watching.”
Will standardised research data policies make publishing more FAIR?
The Research Data Alliance has published standardised research data policies, but wider collaboration is needed to ensure they are supported and implemented.
The TOP Factor: a new journal metric to assess transparency and reproducibility policies
Find out more about the TOP Factor, an alternative metric to the impact factor, evaluating academic journals based on open science policies.
Negative results can have a positive impact, but only if they are available!
The reproducibility crisis is linked to publication bias and a paucity of negative data. Find out how and why to get such data published.