EU Riding the wave report
The last in my trio of belated blog posts is based on the report: Riding the wave – How Europe can gain from the rising tide of scientific data – Final report of the High Level Expert Group on Scientific Data, published in October 2010.
Once again I have extracted from this report some content that seems most relevant to discussions of data citation and attribution. First up is noting that “Develop and use new ways to measure data value, and reward those who contribute it” (P. 5-6. Point 3) was selected as one of six in a short-list of actions for EU institutions, listed in the Executive summary:
“If we are to encourage broader use, and re-use, of scientific data we need more, better ways to measure its impact and quality. We urge theEuropean Commission to lead the study of how to create meaningful metrics, in collaboration with the ‘power users’ in industry and academia, and in cooperation with international bodies.”
In scenarios of the future, scenario 3 (p. 14) describes how an academic creates a cleaned results data set and makes it publicly available; she imagines the result set becoming as popular as Top-40 song, with the consequence that “her chances for tenure rise”. The report goes on to explore incentives for contributing data:
“How can we get researchers – or individuals – to contribute to the global data set? Only if the data infrastructure becomes representative of the work of all researchers will it be useful; and for that, a great many scientists and citizens will have to decide it is worth their while to share their data, within the constraints they set. To start with, this will require that they trust the system to preserve, protect and manage access to their data; an incentive can be the hope of gain from others’ data, without fear of losing their own data. But for more valuable information, more direct incentives will be needed – from career advancement, to reputation to cash. Devising the right incentives will force changes in how our universities are governed and companies organised. This is social engineering, not to be undertaken haphazardly.” (P. 19)
The report then describes some milestones that need to be achieved to realise their vision of a data infrastructure for 2030, where data is a valuable asset and the infrastructure supports ‘seamless access, use, re-use, and trust of data’, and the expected impact of each milestone. One of the main milestones in this vision is that of producers of data who share it openly, and with confidence in the sharing infrastructure. The expected impact of this milestone is predicted to be “Researchers are rewarded, by enhanced professional reputation at the very least, for making their data available to others. Confidence that their data cannot be corrupted or lost reassures them to share even more. Data sharing, with appropriate access control, is the rule, not the exception. Data are peer-reviewed by the community of researchers re-using and re-validating them. The outcome: A data-rich society with information that can be used for new and unexpected purposes.” The report also describes the risk of inaction if that milestone is not achieved: “Information stays hidden. The researcher who created it in the hope it can yield more publications or patents in the future holds on to it. Other researchers who need that information are unable to get at it, or waste time re- creating it. The outcome: A world of fragmented data sources – in fact, a world much like today.” (P.25)
As mentioned earlier, in their call to action, the report writers selected Develop and use new ways to measure data value, and reward those who contribute it as one of six first steps that need to be acted upon. The need for universal metrics is described – although the report stops short of exploring the potential role for data linking and citation in the developing of these metrics.
“Who contributes the most or best to the data commons? Who uses the most? What is the most valuable kind of data – and to whom? How efficiently is the data infrastructure being used and maintained? These are all measurement questions. At present, we have lots of different ways of answering them – but we need better, more universal metrics. If we had them, funding agencies would know what they are getting for their money – who is using it wisely. Researchers would know the most efficient pathways to get whatever information they are seeking. Companies would be able to charge more easily for their services.” (P. 32)