On data sharing and open science – interviewing Rebecca Lawrence (F1000Research)
Rebecca Lawrence has worked in scientific publishing for over 15 years and is currently involved in several international associations and working groups on data publishing and peer review. Rebecca was responsible for the launch of F1000Research in January 2013, a novel open science publishing platform that “uses immediate publication, transparent peer review, and publishes all source data”. She came to the PRBB to talk about the future of scientific publishing.
What are the current challenges in scientific publishing?
One is the delay between the moment you’re ready to share the science and when it actually gets out there and others benefit from it – it can take from 6 months to a year, or even as long as five! In the digital era this makes no sense. Another is the bias in the peer review, which is inherent to the process because those who review your work must be experts in your field and are therefore likely to be competitors.
Lack of data sharing is a huge issue. In a paper we usually don’t see the raw data that backs up the conclusions, we just take it on trust that the analysis has been done in the best way. Really, the core of the paper should be the data. And finally, a vast amount of results, particularly negative ones, are not being published – we are building the next generation of science on facts that are wrong or at least incomplete!
What can be done to resolve these issues?
We need transparency, and that’s what Open Science advocates, trying to make everything – the article, the data, the software and the review process – as transparent as possible. We now have the tools and it is cheap enough to share all the findings and data, although obviously we need to ensure that this is done in a useful way!
What are Open Science’s potential challenges?
Many researchers are nervous about sharing their data, because they don’t want to give a potential advantage to competitors, but actually you can get priority on the data if you openly share it. And it can be time consuming to sort your data out in a way that is understandable and usable by others. But if you don’t sort it out properly, in five years’ time even you won’t be able to do anything with it!
What can be done to entice authors to share data?
We need to give credit for the data. When people are better recognised for creating the data they will be happier to share it. And this is happening: many journals have started citing datasets properly in the references and we have launched a project with several international standards organisations to help develop dataset-level metrics.
How does F1000Research fit in with all this?
We are using a completely new publishing process that is fully transparent. We offer immediate publication, following a set of basic checks, and then it goes out to invited expert referees. The names and reports of the reviewers are published alongside the article and we also make our reviewers’ reports citable, to provide our referees with additional credit for their work. Finally, we strongly encourage publication of negative and null results, replication studies, all kinds of studies.