Early career researchers have an essential role to play in the move towards open research, says #SciData17 writing competition winner Sarah Lemprière.
It is becoming increasingly clear that the current method of publishing scientific findings does not make the most of the vast amounts of data generated during scientific investigation. The message from the #SciData17 conference, held in London on the 25th of October, was that making data, code and detailed methods openly available will increase reproducibility, decrease redundancy and allow scientific discovery to advance at a faster pace. Funding bodies and journals are taking steps to encourage this kind of open research policy, and the decision to release datasets usually sits with lab heads or PIs, but there is a place for bottom-up change from students and postdocs too.
So while funding bodies, journals and professors discuss patents and IP, here are five steps you can take today in your lab to make your research more open
1. Start the conversation
A key message from the talks at SciData was that a cultural change in the way scientists view data will be essential if open research practice is to become widespread. For this to take place, as a community we need to start debating the pros and cons and discussing the practicalities of making our data open. As early career researchers, this is the place we can have the most impact. After first informing ourselves, we should start conversations with PIs, students and each other. These kind of conversations lay the groundwork for action, get people thinking about potential problems, and can generate creative ideas and solutions.
2. Build foundations of reproducibility
The move towards public data sharing could start with openness within labs and research institutes. If you can, share your full datasets within your lab and within collaborations openly, and in a way which can easily be scaled up. Various platforms were mentioned at #SciData17 as good places to start doing this; figshare, for example, enables you to upload private datasets and share them with select people. Then, if you wanted to make that information freely available to all, it’s ready to go. As Kirstie Whitaker made clear in her talk, it’s important that when you’re sharing your data with others you aren’t afraid to show your working. An electronic lab book can provide a brilliant platform for collating detailed methods and raw results in a format that is easily shared with other lab members or with the public. Being open about how you collected and analysed your data makes it easier for others to replicate your experiments, which is essential to confirm and build upon your findings.
3. Share the small things
Open research isn’t just about releasing huge datasets to the public, it’s also about sharing tools and know how. I’m sure at some point in your career you’ve improved or adapted a standard protocol, or you’ve written a piece of code to speed up a tedious step of analysis. What did you do with that information? Is it buried in a hard drive somewhere? Or in an appendix of your thesis?
Michael Doube spoke about the collection of ImageJ plugins he wrote to speed up his own analysis of bone geometry. He made these plugins freely available and editable on GitHub and published a paper describing the design and application of the different tools. That paper has now been cited close to 500 times — BoneJ has saved researchers across the globe countless hours of analysis time. If you’ve written some code that helps your research, share it somewhere like GitHub. If you’ve got a great protocol, upload it to protocols.io. Someone will probably improve on your idea to make it even better, and you never know if it might spawn interesting collaborations.
4. Get creative
Making data open shouldn’t just benefit the scientific community – it should benefit the public too. Much research is funded by the public via taxes or charitable donations, as Aled Edwards pointed out in his keynote at #SciData17. That means we have the responsibility to be good stewards of that investment. I think that includes an obligation to communicate our findings to the public in an accessible way that improves their understanding. Dumping data into an online portal is unlikely to be enough to meet that obligation. Therefore, now is the time to think carefully about how to make your data accessible and relevant to those without a science background. If you can’t release all your data right now, start by communicating your main findings to a lay audience. Be creative about how you communicate, and try to understand how you will eventually need to package the data itself to encourage and enable the public at large to engage with it. For inspiration see ESASky, a beautiful web application that allows seamless access to the data from all ESA astronomy missions in a very accessible form.
5. Stay up to date
Tools and platforms for open data sharing are being improved all the time, so if you’re interested in this area the new research data website from Springer Nature could be helpful. Launched at #SciData17, it has resources, blogs, articles and information on upcoming open data events.
I’m convinced that a grassroots movement of early career researchers — managing their research with data sharing in mind and pushing for more transparency in their labs and institutions — can make a real difference to the arrival of the open data era in science. Most of the talks at #SciData17 this year were from lab heads and PIs. Perhaps next year PhD students will be sharing their experiences of bringing open data practices to their own labs.
Sarah Lemprière is a neuroscience PhD student at the University of Edinburgh, where she’s investigating the role of synaptic proteins in the action of ketamine. She loves to write and is interested in how scientists share their findings with each other, and with the public.