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highlighted the thorny issue of failed reproducibility in social science. But as the researcher points out: ‘At the same time, we also made all of our data available and it has since generated more than 20 publications, with other researchers using those data for new purposes.


‘Some of these purposes have been


quite different to what our original research was about,’ he adds. ‘Just making that data available has created possibilities that couldn’t have been anticipated in advance.’ Nosek also points to OpenNeuro, formerly known as OpenfMRI, an open platform for sharing neuroimaging data. ‘We’ve seen a number of different publications that have come out of [OpenNeuro] that wouldn’t have happened without this,’ he says. ‘So this long tail of research is getting more exposed to data sharing and we’re seeing investigations that no one could have imagined, as a result of smaller datasets being aggregated.’


Causes for concern Yet, amid the data-sharing success stories, myriad worries remain. Top of the pile is the potential for data misuse, but as Nosek highlights: ‘Yes, someone could misuse your data but what’s the relative risk of someone using it well?’ ‘Are you certain you have taken the best approach?’ he adds: ‘Originators of the dataset don’t always think of all the


different ways that that data could be used, and I’ve seen so many new research applications emerge from the same data.’ Inappropriate sharing of data is another


key concern, which Nosek agrees is ‘very real’. He believes researchers should take appropriate steps to comply with ethical guidelines, adding: ‘This is just an essential part of moving towards an open data environment.’ But beyond data-misuse and unethical sharing, the thorny issue of credit remains. While data citation is necessary if researchers are to share datasets, tensions around credit for data sharing clearly emerged in the recent State of Open Data survey. Results indicated that a mighty 58


per cent of respondents felt they do not receive sufficient credit for sharing data, while only nine per cent felt they do. Grace Baynes, VP for research data and


new product development, open research, at Springer Nature, is keen to encourage data sharing, but believes credit is critical. As she writes in this year’s State of Open Data report: ‘Researchers would share data more routinely, and more openly, if they genuinely believed they would get proper credit for their work, that counted in advancing their academic standing and success in career development and grant applications, and for subsequent work that builds on their data.’ For her part, she believes published, citable datasets should be valued in


“A mighty 58 per cent of respondents felt they do not receive sufficient credit for sharing data”


the same way as research articles but concedes: ‘Routine inclusion of datasets, their citations and impact in grant assessments and CV evaluation is probably still years away.’ In the meantime, she reckons citable


data articles should be encouraged, and also highlights the importance of initiatives that promote dataset use and citation (see ‘FAIR principles’). Key examples include pan-European research data management initiative, GO FAIR, the FAIR data project from The Netherlands Institute’s Data Archiving and Networked Services, and the Alfred P Sloan Foundation-funded project, MakeDataCount. Meanwhile, pertinent community initiatives also include DataCite, which provides DOIs for research data and FORCE, already implementing a Data Citation Roadmap with publishers and other organisations. ‘Perhaps there is more we can do to


make it easier for researchers to write and publish data articles, and see the benefits to their research in doing so,’ she adds. Digital Science’s Hook agrees and is


Easing data-sharing


While more and more publishers, funders and institutions are providing data sharing policies that recommend or mandate that data from an article be made available upon publication, compliance with these policies is often low. Many authors remain unsure as to which datasets they should be sharing, while stakeholders cannot always tell when the authors have shared the right data. However, proposed software from the Collaborative Knowledge Foundation – Coko – could change this. Coko recently won funding


from the Sloan Foundation to build DataSeer, an online service that will use Natural Language Processing to identify datasets that are


associated with a particular article. Pioneered by Dr Tim Vines, from Origin Editorial, the software aims to plug, what Vines sees as, a huge implementation gap between general policy and actually sharing data. ‘Researchers are struggling to get funding, are overloaded with reviews and data sharing is just another thing they need to do,’ he says. ‘But I believe that DataSeer will change the game and transform data- sharing, as this process will now become easy and almost automatic. The software will use artificial intelligence to read articles and work out which dataset fields should be provided.’ According to Vines, the


ultimate goal is to provide a 8 Research Information December 2018/January 2019


service that guides authors through the data sharing process for their article, with reports for publishers, funders, and institutions, so they can easily assess policy compliance by comparing what should be shared, with what was shared. Initial partners are the University of California Curation Center (UC3), PLOS, and the University of California Press. ‘We will initially work with publishers to get DataSeer into a journal’s editorial workflow,’ said Vines. ‘We will release this as free open source software and it will be freely available to all potential users as a standalone online service or a component of Coko’s journal management software, PubSweet.’


also adamant that professorships should be awarded to researchers that produce ‘brilliant work with data’. ‘Until you make someone a professor


for creating a dataset, you are not going to see the change that we need, and you are not going to see researchers being promoted for the right reasons,’ he says. In addition to concerns over misuse, ethics and citations, a pressing need clearly exists to provide researchers with more credit for generating and sharing data. And as part of this, Hook believes that researchers now should be recognised for much more than published results. ‘It’s a great conceit of modern research to feel that you are the best person to come up with the idea, get the grant funding, work on experimental design, perform the experiment, collect, analyse and interpret the data, write up the paper and get it published,’ he says. ‘It’s like playing midfield, defence,


goalkeeper and striker all at the same time, and in today’s increasingly complex research problems, you just can’t do this,’ he adds. ‘We need to recognise people for the very different roles that they now play in research.’


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