My data, your data, our data - increasing data value through reuse (Eurocris2014 keynote)

Editor's Notes

• #2: This is a keynote delivered at CRIS2014 in Rome, 2014-05-14
• #3: This is an outline of what I’ll be talking about. I hope to persuade you of the value of data reuse and. Having done that, to examine why we sometimes find it difficult. I’ll look at how we can overcome these barriers and what you as research administrators can do. I’lll then return to my opening themes in reverse order. Returning to the themes in this way means that this talk follows sonata form, the classic structure of the first movement of a symphony. I don’t claim my talk will be as beautiful but I do hope it will be enlightening.
• #4: I’m from an organisation in the UK called the digital curation centre. We are funded to help UK universities improve their own research data management practices. But that task is international in nature so we do much work in collaboration with others outside the UK.
• #5: Data curation is an odd term, combining vocabulary from the world of museums and science. It doesn’t always transate well to languages other than English. So this is an attempt to define what it means. It’s about preservation, but it’s more than that. It involves destruction and it involves adding value.
• #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.
• #12: 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.
• #15: 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.
• #16: 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.
• #18: 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.
• #37: Mu ch as I enjoy the JIR, it isn’t the publication most of us aim for. But it brings home one compelling argument for making data available, that of research integrity. Almost all fraud, and other less clear-cut cases of bad research, can be associated with the unavailability of research data. There are real consequences, including human suffering and death – of which more later. And did I mention that making your data available makes it more likely to be cited? Don’t worry, I will again.
• #38: These are just a few examples, some of outright fraud and others of simply dodgy research all of which would have been uncovered far more quickly had the data been made routinely available. The Duke case in particular roused the suspicions of many in the field but took many years to get to the bottom of because data was locked away. It is just one example of a set of practices described very clearly by Ben Goldacre in ‘Bad Pharma’. Missing data is the largest section in his book, although he has other justified concerns with research relating to medial treatments. It has led to a global movement to ensure that all clinical trial data is made available. But medicine is by no means the only area affected.
• #39: 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.
• #41: Getting better at managing research data isn’t just about keeping more stuff for longer. It’s also about being more selective about what we do keep and documenting the decision-making process that we use. Reports such as this make clear that technological advances means that the cost of producing data is dropping more rapidly than the cost of retention. Some arguments show that if we attempt to retain everything it won’t be long before we’re spending the entire GDP purely on data storage. That’s an extreme analysis, but the problem is real as CERN know well. In some disciplines it really is wiser to just generate the data again when it is needed. But for many observational disciplines, that opportunity isn’t open to us.
• #44: 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.
• #45: 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.
• #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!