Future of Scholarly Communications

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UKSG 37th Annual Conference and Exhibition: Harrogate, UK, 14 Apr 2014

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  • ESRC was allocated 64m and much of this is being used to set up the ESRC Big Data Network. The ESRC’s Big Data Network will support the development of a network of innovative investments which will strengthen the UK’s competitive advantage in Big Data for the social sciences. The core aim of this network is to facilitate access to different types of data and thereby stimulate innovative research and develop new methods to undertake that research. Although you should note that diagram it is only illustrative in terms of how the UKDS and ADS will work across – that is still under discussion; and only illustrative in the number of Business and Local Government Data Research.This network has been divided into three phases. In Phase 1 of the Big Data Network the ESRC has invested in the development of the Administrative Data Research Network (ADRN) which will provide access to de-identified administrative data collected by government departments for research use – focus of this meeting and all your grants.A few words about Phase 2 and 3 before we pass to Vanessa to talk about the ADRN some more. Phase 2is currently bring commissioned and will deal primarily with business data and/ or local government data. Phase 3, further details of which will be released in the last autumn / winter and will focus primarily on third sector data and social media data. It is expected that there will be opportunities for interaction across all elements of the ESRC Big Data Network and that they will all work together around the wider objectives of facilitating access to different forms of data and of ensuring maximum impact is generated from the use of that data for the mutual benefit of data owners and researchers, and through the research facilitated by the Network, benefit society and the economy more generally.
  • ESRC Cities Expert Group
  • Thanks to Simon Hettrick for additional input to this slide.
  • Future of Scholarly Communications

    1. 1. David De Roure The Future of Scholarly Communications
    2. 2. A revolutionary idea… Open Science! rstl.royalsocietypublishing.org
    3. 3. Overview 1. Shifts in scholarship 2. End of the article 3. Research Objects 4. Social Machines
    4. 4. The Big Picture More people Moremachines Big Data Big Compute Conventional Computation “Big Social” Social Networks e-infrastructure Online R&D (Science 2.0) Information Society @dder (Social Machines)
    5. 5. Edwards, P. N., et al. (2013) Knowledge Infrastructures: Intellectual Frameworks and Research Challenges. Ann Arbor: Deep Blue. http://hdl.handle.net/2027.42/97552
    6. 6. ChristineBorgman
    7. 7. F i r s t
    8. 8. Big Data Network
    9. 9. New Social Process http://www.theguardian.com/uk/series/reading-the-riots
    10. 10. Interdisciplinary and “in the wild” * * “in it” versus “on it”
    11. 11. www.zooniverse.org
    12. 12. Scientists Talk Forum Image Classification data reduction Citizen Scientists
    13. 13. http://www.scilogs.com/eresearch/pages-of-history/ David De Roure
    14. 14. http://www.scilogs.com/eresearch/pages-of-history/DavidDeRoure
    15. 15. 1. It was no longer possible to include the evidence in the paper – container failure! “A PDF exploded today when a scientist tried to paste in the twitter firehose…”
    16. 16. 2. It was no longer possible to reconstruct a scientific experiment based on a paper alone
    17. 17. 3. Writing for increasingly specialist audiences restricted essential multidisciplinary re-use Grand Challenge Areas: • Energy • Living with Environmental Change • Global Uncertainties • Lifelong Health and Wellbeing • Digital Economy • Nanoscience • Food Security • Connected Communities • Resilient Economy
    18. 18. 4. Research records needed to be readable by computer to support automation and curation A computationally-enabled sense-making network of expertise, data, models and narratives.
    19. 19. 5. Single authorship gave way to casts of thousands
    20. 20. 6. Quality control models scaled poorly with the increasing volume Filter, Publish, Filter, Publish, … Like big data, publishing has increasing volume, variety and velocity But what about veracity?
    21. 21. 7. Alternative reporting necessary for compliance with regulations One piece of research may have multiple reports and multiple narratives for multiple readerships, in multiple formats and languages (Computer are readers too!)
    22. 22. 8. Research funders frustrated by inefficiencies in scholarly communication An investment is only worthwhile if • Outputs are discoverable • Outputs are reusable …and preferably outputs accrue value through use Using an obsolete scholarly communication system impedes innovation and hence return on investment What are we doing about it? Trying to fix it using an obsolete scholarly communication system!
    23. 23. data method script program workflow model protocol …
    24. 24. NeilChueHong
    25. 25. www.myexperiment.org
    26. 26. Research Objects Computational Research Objects The Evolution of myExperiment Workflows Packs OAI ORE W3CPROV Social Objects
    27. 27. The R Dimensions Research Objects facilitate research that is reproducible, repeatable, replicable, reusable, referenceable, retrievable, reviewable, replayable, re-interpretable, reprocessable, recomposable, reconstructable, repurposable, reliable, respectful, reputable, revealable, recoverable, restorable, reparable, refreshable?” @dder 14 April 2014 sci method access understand new use social curation Research Object Principles
    28. 28. www.researchobject.org JunZhao
    29. 29. Real life is and must be full of all kinds of social constraint – the very processes from which society arises. Computers can help if we use them to create abstract social machines on the Web: processes in which the people do the creative work and the machine does the administration... The stage is set for an evolutionary growth of new social engines. The ability to create new forms of social process would be given to the world at large, and development would be rapid. Berners-Lee, Weaving the Web, 1999 (pp. 172–175) Social Machines
    30. 30. SOCIAM: The Theory and Practice of Social Machines is funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant number EPJ017728/1 and comprises the Universities of Southampton, Oxford and Edinburgh. See sociam.org
    31. 31. Scholarly Machines EcosystemDavid De Roure, JCDL 2013
    32. 32. 1. Shifts in scholarship – A “turn” or ongoing transformation? 2. End of the article – Don’t retrofit digital, think post-digital 3. Research Objects – Inevitable with automation – How do we cite them, how are they curated? 4. Social Machines – Humans in the loop, empowered – Can you view your projects as social machines?
    33. 33. Thanks to Christine Borgman, Iain Buchan, Neil Chue Hong, Jun Zhao, Carole Goble, FORCE11, myExperiment, Software Sustainability Institute, wf4ever and SOCIAM david.deroure@oerc.ox.ac.uk www.oerc.ox.ac.uk/people/dder @dder www.oerc.ox.ac.uk www.force11.org www.researchobject.org www.software.ac.uk sociam.org
    34. 34. www.oerc.ox.ac.uk david.deroure@oerc.ox.ac.uk @dder

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