The unpublished results taboo

Late last year, I retweeted a university press release about some topical research on bees that hadn’t yet been published or, apparently, peer reviewed (I can’t find the paper online anywhere, so it looks like it is still yet to be published).

To be fair, the release stated upfront that the research was ‘preliminary findings’ and the source mentioned at the end was an upcoming conference presentation, not a journal article. But should it have been the subject of a press release in the first place? Continue reading

The benefits of pre-submission peer review

I’ve noticed that acknowledgements sections in papers published before the 2000s usually thank people who read and commented on the paper before it was published. Yet recently-published papers are more likely to thank funding bodies or data collectors than peer reviewers. Why is this?

park 1931
Park (1931) Ecology 12:188-207
Solomon 1949
Solomon (1949) Journal of Animal Ecology 18:1-35
Lloyd 1987
Lloyd (1987) Functional Ecology 1: 83-89

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Co-authorship in science communication

Authorship is a really important issue when publishing academic work, particularly when multiple authors are involved. It’s about giving appropriate credit for intellectual property, but also about authors taking responsibility for their work. Leaving off an author’s name who did contribute to the research is just as bad as including an author who didn’t contribute anything at all. Most institutions and academic journals have standards and guidelines to help authors understand these issues.

But how does a scientist navigate the co-authorship issue when translating their work beyond their discipline?

Say you have co-authored an academic paper that’s just been published in a journal, and now you want to translate those findings into a popular science article for a public audience. Who should initiate the scicomm piece? Do all authors on the paper have to be involved? Continue reading

Why I don’t want to be paid for peer review

If you believe the hype, peer review is flawed and corrupt, a broken system threatening to undermine the very foundations of academia…particularly science. From fake reviews to biased ones, one of the main arguments for ditching the system is the myth that reviewers can no longer be trusted to give a fair assessment of another scientist’s paper.

But the problem is not with peer review per se, it’s with our expectations of the system.

Right now, many people think peer review means, “This paper is great and trustworthy!” In reality, it should mean something like, “A few scientists have looked at this paper and didn’t find anything wrong with it, but that doesn’t mean you should take it as gospel. Only time will tell.”

The academic review system as we know it today began around the 1960s. But the process of peer review has been around for centuries, formally and informally, from the Greek Agora to the first Royal Society meetings.

We need peer review because science (and scholarship generally) is a community endeavour.

Sure, there are some cheaters, but most scientists behave ethically. In my short career, most of the reviews I’ve received have been genuinely helpful and I’ve published better papers because of them. Continue reading

Can too much Innovation be a bad thing?

Innovation and disruption are ideas that originated in the arena of business but which have since been applied to arenas whose values and goals are remote from the values and goals of business. People aren’t disk drives. – Jill Lepore

Innovation is one of the most overused buzzwords in the business world. Now it’s started turning up in discussions about research and policy, in government science goals and research funding priorities.

But there are two main caveats to the pro-Innovation argument that are often glossed over. The first is the word’s corporate connotations.

Once upon a time, innovation didn’t have a great reputation. It was a punishable crime to the worst degree. It was (ironically) reinvented in the 1930s, grounded in economic theory – it became seen as a source of positive economic change at a time when things weren’t going so well for some national economies. New products would get money flowing again, so the theory went. And a buzzword was born. Benoît Godin has done some excellent research on the evolution of the term and its place in society.

If you’re an ‘innovator’, you’re now a star. You can start trends and make lots of money. And because economic growth is the goal of most countries, innovation is now promoted, not punished. Continue reading

Local food for global thought

I recently visited the town of Bendigo for the first time, a beautiful heritage town in central Victoria, Australia. It was one of the first places gold was discovered in the state, sometime in the 1850s, and much of the town’s original business and residential districts still stand, in all their gold rush glory.

Bendigo CBD from Rosalind Park
Bendigo CBD from the poppet tower lookout in Rosalind Park

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Data, not just for scientists

Early last year I wrote a post on ecology and mathematics that was inspired by an online discussion happening at the time. Although comprehensive advanced maths skills are not essential to being an influential or inspiring ecologist, a good level of mathematical knowledge and understanding of statistical analysis is definitely necessary to create honest science and communicate the importance of your work to others.

But it’s not just ecologists who need mathematical common sense. Anyone who deals with, or is interested in science needs to understand the ambiguity of an average, or the difference between a regression and a correlation. In fact, anyone who cares about the society they live in should be aware how deeply statistics and data now influence the way we live – policies and decisions on anything from what product choices you find in retail stores to how much tax you pay are all based on data.

Why does this matter to us? Well, if those data are a bit dodgy, or haven’t been analysed and presented appropriately, problems arise. And when these kinds of data misrepresentations are used to fuel public opinion or inform government policy, there can be serious impacts on communities, individuals and ecosystems. Continue reading