Kevin Ashley_Sharing research data: benefits for the researcher, benefits for society

Editor's Notes

• #8: There are many such stories of unexpected data reuse; these are a few examples. The last, exemplified in the Old Weather project, is seeing the original data being reused for at least the third time and in doing so is helping both climatologist and family historians through a single piece of transcription work. An impressive result.
• #9: Medicine does, however, provide some clear reasons why we can’t just stick all research data on the internet for anyone to trawl through. When human subjects are involved there are real concerns about confidentiality. Yet what alltrials.net and other initiatives make clear is that the *existence* of the data should never be hidden. That allows it to be discovered and for negotiations to take place about its use. It avoids costly replication, which can delay scientific discovery and involve human suffering when the replication takes the form of a clinical trial.
• #11: There are many such stories of unexpected data reuse; these are a few examples. The last, exemplified in the Old Weather project, is seeing the original data being reused for at least the third time and in doing so is helping both climatologist and family historians through a single piece of transcription work. An impressive result.
• #12: Kevin Ashley, DCC, UKSG Glasgow. CC-BY
• #13: For an audience such as this, I shouldn’t have to explain why data reuse is important. But just in case, and to explain why some things have happened the way they have, I’ll describe some of the drivers. Ensuring that all research data is discoverable and reusable increases the quality of the research that we do. It can add to the data we collect ourselves and can improve the statistical rigour of our results. Exposing data to scrutiny makes it more straightforward to validate or challenge the findings of others. Making data available also improves the speed with which we can do research. If someone else has already gathered the data we need (perhaps for a different end use), we can move directly to the analysis stage of our work, saving both time and money. And saving money increases the efficiency of research. We hope that the money saved lets us do more research, but even if it doesn’t society as a whole will gain. There’s evidence behind this that I’ll come to later, but it is an effective counter to those in some universities who feel that increasing funder requirements for data management simply leads to additional costs with no gain. There is a gain in all these areas, and hence every one of the actors – researchers, their employers, their funders, and society, should be motivated to make this happen.
• #27: Did I mention that making data available increases citations? This is a win all round. If you don’t believe me, here are three studies from three different areas that all show robust, positive correlations. The effect size varies with discipline, but we have enough evidence now that anyone who says that their area is different needs to come up with evidence to show why.
• #30: Yet some researchers still aren’t convinced by the rhetoric. Carly Strasser at CDL has listed some of the reasons for not sharing data that she’s encountered – and here are some of my one-line responses. I’m not saying that the concerns aren’t sincere or reasonable but they can all be dealt with and some are positively misguided. The purpose of data centres, for instance, is to make data independently reusable (as stated in the OAIS standard) which relieves researchers of the burden of dealing with questions about it, at the same time as increasing the likelihood that their data will be cited.
• #33: In the last four years, we have investigated and understood the challenges of the UK research community. Anecdotally, we had a lot of evidence for people working in this area that researchers relied on software, but there had been no studies conducted. So we did this ourselves. Two areas of interest, do you use software and possibly more important, what would happen to your research without software – this is 170,000 researchers in the UK who could not conduct their software without software. This is more than just a reliance on Word or web browsers – specialist software is written into the research workflows of people from psychology to physics, from the life sciences to literature. The reliance isn’t confined to the “traditionally” computationally intensive subjects, it’s a feature of all disciplines. This means that 140,000 researchers are relying on their own coding skills.
• #34: http://www.flickr.com/photos/esva/2364906768
• #37: All of these “excuses” can be seen as reasons to do the various things – ir will benefit you as well as your readers.
• #38: FIONA method of training researcher how to code, but is taught by researchers for researchers using real research data and real problem. Researchers are desperate for more skills which allow them to craft their data in research specific ways. They don’t need to build highly scalable services (i.e. they don’t need - or want - to be professional software “engineers”), but they do need to be able to craft specific parts of their data so they can get to solution for their research (i.e. they do need to do a bit of software “carpentry”). By way of analogy, researchers need to know about as much about computing as they do about statistics. Most researchers don't have a degree in statistics, but at some point they've learned what correlation means, what statistical significance is, and so on.
• #39: A metajournal which encourages the publication of information that encourages the reuse of software. A way of using the current tools and practices to make software better recognised.
• #48: Many of you may be familiar with this graph from the Hubble Space telescope data archive. It tells the same story in a different way, and also tells a story about the transformation of astronomy as a discipline. In the days of photographic plates, sharing (analogue) astronomical data was difficult. Digital instruments transformed this, and some time around 2000, more research was being done with old data than with new data. I could be more specific about this if the data behind this graph was made available, incidentally!