#scidata16: Work reproducibly for the sake of your career

Making sure others can do your experiments doesn’t just help them — it’s good for you, too.

Publishing Better Science through Beter Data writing competition Jonathan Page

A core tenet of science is reproducibility: the results of one scientist must be able to be reproduced by another, lest the findings be dismissed as a fluke or even fraudulent. In today’s data-driven realms of research, ‘reproducibility’ doesn’t simply mean publishing methods, many journals now require that datasets, and the code used to analyse them, be published too. This requirement ensures that both data, and methods, can be scrutinised. If other researchers can’t reach the same results, the study will need to be treated with caution. In doing this, scientists avoid damaging their reputation by publishing flawed studies, and journals avoid publishing bad science. It’s a win-win situation.

So why don’t scientists always work reproducibly?

JPage_Profile-smaller

Jonathan Page

Continue reading

Opening doors to open data at #scidata16

Want to embrace open data but don’t know where to start? The tools are out there, says Matthew Edmonds.

The Publishing Better Science through Better Data conference, or #scidata16 for short, took place at the Wellcome Collection in London at the end of October. This one-day event organised by the journal Scientific Data, Springer Nature and the Wellcome Trust explored the challenges facing early-career researchers as we enter the era of open data.

analytics-282739_1280

As a data novice, I arrived without really knowing what to expect. The types of experiments I perform generate only small datasets needing a simple statistical test, easily summarised in a graph in the manuscript. The original data can be safely left to gather dust in a shared drive. Continue reading