Research Data Management


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Matthew Dovey from Jisc Technologies presented at the ULCC / Arkivum Briefing on Monday 24th February 2014

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  • Research Data Management

    1. 1. Matthew Dovey, JiscTechnologies Research Data Management
    2. 2. The growing pressure of growing data
    3. 3. 2009 Research Data Management 3
    4. 4. 2010 Research Data Management 4 Develop an international framework for a Collaborative Data Infrastructure Earmark additional funds for scientific einfrastructure Develop and use new ways to measure data value, and reward those who contribute it Train a new generation of data scientists, and broaden public understanding Create incentives for green technologies in the data infrastructure Establish a high-level, inter-ministerial group on a global level to plan for data infrastructure
    5. 5. Knowledge Exchange – A Surfboard for Riding theWave Research Data Management 5 Type of requirement Actions Research behaviours and new models of research output •development of incentives for researchers e.g. citation & metrics •new forms of publication e.g. enhanced & data publications •training & advice in data curation to researchers, data scientists; data librarians with appropriate career structures. Practice & policy and provision of advice & guidance •use of data management plans with support & monitoring •advice, guidance & best practice to universities to develop policies, solutions & to plan capacity •standards for interoperability & codes of practice in legal & ethical issues • improved understanding of the value, benefits & costs RDM infrastructure & provision of services •national research data infrastructure including registries, portals, preservation, storage & security •local infrastructure at universities to intersect with national & global repositories & portals, standards and deposit •sustainability & business models.
    6. 6. Science as Open Enterprise Report, 2012 Research Data Management 6 • ‘how the conduct and communication of science needs to adapt to this new era of information technology’. • ‘As a first step towards this intelligent openness, data that underpin a journal article should be made concurrently available in an accessible database. We are now on the brink of an achievable aim: for all science literature to be online, for all of the data to be online and for the two to be interoperable.’ • Royal Society June 2012, Science as an Open Enterprise, ence-public-enterprise/report/
    7. 7. 2012 Research Data Management 7 Innovation and Research Strategy for Growth, DBIS – research data & public sector data seen as key to innovation – it should therefore be open & accessible. RCUK research data policies/roadmaps - “Publicly funded research data should be made openly available with as few restrictions as possible. Institutional and project specific data management policies and plans … Data with …long-term value should be preserved and remain accessible and usable for future research. “ OECD Principles and Guidelines for Access to Research Data from Public Funding – research data should be open access & supported by public funding. “ It is important to place a higher value upon the position of “scientific programmer” and also “data scientist” in the academic environment and to offer more career opportunities to these staff. “e-Infrastructure vision for the UK, DBIS, 2012
    8. 8. 2012 Research Data Management 8 Government Open Data White Paper – June 2012 'We must also consider how we open up publicly funded research data in a way that maximises public benefit' (page 27).
    9. 9. G8 Science Ministers Statement London UK, 12 June 2013 Research Data Management 9 Open Scientific Research Data › To the greatest extent and with the fewest constraints possible publicly funded scientific research data should be open, while at the same time respecting concerns in relation to privacy, safety, security and commercial interests, whilst acknowledging the legitimate concerns of private partners. › Open scientific research data should be easily discoverable, accessible, assessable, intelligible, useable, and wherever possible interoperable to specific quality standards. › To maximise the value that can be realised from data, the mechanisms for delivering open scientific research data should be efficient and cost effective, and consistent with the potential benefits. › To ensure successful adoption by scientific communities, open scientific research data principles will need to be underpinned by an appropriate policy environment, including recognition of researchers fulfilling these principles, and appropriate digital infrastructure.
    10. 10. 2013 Principle 1: Open Data by Default Principle 2: Quality and Quantity Principle 3: Usable by All Principle 4: Releasing Data for Improved Governance Principle 5: Releasing Data for Innovation Research Data Management 10 G8 Open Data Charter
    11. 11. Big Data Research Data Management 11
    12. 12. The Changing Research Process
    13. 13. Data as New Output of Research Research Data Management 13 ‘technology has enabled data to become the prevalent material and currency of research. Data, not information, not publications, is rapidly becoming the accepted deliverable of research.’ Graham Pryor, Observations on the RLUK Reskilling for Research Report 2012
    14. 14. Research Outputs Research Data Management 14 Result DataSoftware ValueTransition 'Software is the Modern Language of Science‘ Ed Seidel, NSF
    15. 15. Changing Research Methods and Requirements Research Data Management 15 Wide variety of research practices in sub- disciplines. Unequal distribution of generic information skills. Importance of generic and bespoke data analysis tools; importance of programming skills. ‘New technologies for sharing data and for combining data from disparate sources are particularly valuable in multidisciplinary fields such as earth science and nanoscience. ... The challenge of federating, mining, analysing and interpreting these data will be a key focus in coming years.’ case-studies-use-and-discovery-
    16. 16. Research Data Management 16 “Its not just curation, retrieving and integrating data – its also what we do with it!” Jim Gray, Microsoft “When you go and look at what scientists are doing, day in and day out, in terms of data analysis, it is truly dreadful. We are embarrassed by our data!” So what are the priorities? 1. Ensuring scientifically valid processing 2. Innovative manipulation to create new information 3. Effective management of research data There is a serious issue of education, training and support at undergraduate, doctoral and post-doctoral levels Geoffrey Boulton (University of Edinburgh)
    17. 17. Research Data Management 17 “Science is broken” Examples: psychology academics making up data, anaesthesiologistYoshitaka Fujii with 172 faked articles Nature - rise in biomedical retraction rates overtakes rise in published papers This week, “economists have been astonished to find that a famous academic paper often used to make the case for austerity cuts contains major errors. Another surprise is that the mistakes, by two eminent Harvard professors, were spotted by a student doing his homework”
    18. 18. Opening Science Research Data Management 18 Tim Gowers - crowd-sourced mathematics An unsolved problem posed on his blog. 32 days – 27 people – 800 substantive contributions Emerging contributions rapidly developed or discarded Problem solved! “Its like driving a car whilst normal research is like pushing it” Citizen Science Galaxy Zoo: Hubble Solar StormWatch Old Weather Whale FM Ancient Lives Fold It (creating protein molecules) SETI (extra terrestrial intelligence) Etc. What inhibits such processes? -The criteria for credit and promotion.
    19. 19. Research Data Management
    20. 20. Why Research Data Management? Research Data Management 20 •Research Excellence & Impact – data will be cited; used by others including peers, other disciplines, the public, industry, in learning – ability to meet global challenges; innovate & create new research areas. •Research integrity - replication, verification of research, improvement of methods & results. •Efficiency - save duplication of research effort, data creation & therefore costs; ease of access & re-use. •Managing risks – ability to meet FOI requests; protect reputation.
    21. 21. In reality… Research Data Management 21 Social Scientist- “I have notes, photos & video and audio of subjects it would take way too long to anonymise it ” Informatics researcher- “ yes I will share my data but people should register; & why change to the Open Data Commons licence I have a bespoke licence I have always used” Philosopher- “I don’t have data, I annotate books” Bio-scientist- “why would I put my data in a repository? I share it informally with my peers, no-one else would understand it. “ Engineer “ I have lots of data but you need a licence to this bespoke software to use it”
    22. 22. DUDs The data centre under the desk (or in a back pack) is not adequate. 22
    23. 23. Evidence that significant data loss occurs Research Data Management 23 ‘Departments typically don’t have guidelines or norms for personal back- up and researcher procedure, knowledge and diligence varies tremendously. Many have experienced moderate to catastrophic data loss.’ » Incremental Project Scoping Study and Implementation Plan .pdf ‘The current environment is such that responsibility for good data management is devolved to individual researchers and in practice PIs set the 'rules' and establish the cultural practices of the research groups and this means there is good data management practice going on in pockets but no consistency across groups. There is also consequently a high risk of data losses by a number of means’. » MaDAM Project RequirementsAnalysis FINAL.pdf
    24. 24. Jisc Research Data Management Programme Research Data Management 24
    25. 25. Improved RDM skills Improved DM planning Improved metadata Improved storage decisions Improved access control Improved institutional support for RDM Etc. Greater visibility / use of institution research data Improved compliance with funder requirements Time / costs saved by improved RDM infrastructure Improved use / uptake of RDM infrastructure Raised understanding and awareness of RDM Higher profile for researchers Improved metrics for REF etc. Institutional reputation enhancement Higher bidding success rate Improved productivity / effectiveness Minimised risk of data loss More cohesive practice across campus Improved motivation for good RDM practice Improved availability of RDM infrastructure Enhanced potential for new knowledge creation Data policy formation and compliance Etc. Key: Overall benefit of programme Benefits addressed by the current evidence- gathering activity Potential further benefits of programme activity Training attendance Data policy promotion Enhanced opportunities for collaboration
    26. 26. Second RDM Programme, 2011-2013 Research Data Management 26 Second JISC MRD Programme, 2011-13: Institutional RDM Infrastructure Services 17 Projects RDM Training 5 projects RDM Planning 10 projects Data Publication 3 projects Ownership: High level ownership of the problem, senior manager on steering . Sustainability: Large institutional contributions. Develop business cases to sustain work. Encouraged to reuse outputs from first programme and elsewhere. Mix of pilot projects and embedding projects. Holistic institutional approach to RDM.
    27. 27. Functions of an Institutional RDM Service Research Data Management 27 1) Requirements 2) Planning 3) Informatics 4) Citation 5) Training 6) Licensing 7) Appraisal 8) Storage 9) Access 10)Impact • Liz Lyon, ‘The Informatics Transform: Re-Engineering Libraries for the Data Decade’, International Journal of Digital Curation (2012), 7(1), 126–138; Institutional Coordination and Partnerships
    28. 28. Researcher identifiers Organisation identifiers Registries Librarians and research managers have three cohesive suites of services, leading to greater efficiencies Researchers have a more flexible, innovative and cohesive suite of research and research management servicesScholarly communications suite Repositories suite Research management suite There is a set of infrastructure components that underpin all three suites ResearchCouncils’Research OutcomesService Researchreporting sharedservice ResearchManagementand AdministrstionSystems REFsubmission system Usage statistics Institutional repository Repository Junction Sherpa / RoMEO Repository search UK PubMed Central arXiv Google Scholar Publisher platforms Citation databases Social networks Resource discovery services Repository shared services Key Established service Project Other supported JISC supported ResearchCouncils’ GatewaytoResearch Jointe-submission system Supporting repositories & research information management
    29. 29. Data Management Planning Selection and RetentionDepositTools Advocacy, Guidance,Training and Support RDM Policy and Roadmap Business Plan and Sustainability DMPonline Guidance Templates DataStage Academic Dropbox Active Storage Guidance Good Practice Case Studies SWORD Protocol Easy Uploader Metadata Identifiers Guidance Coordination Jisc-mediated Services/national Guidance Good Practice Coordination Research Data Registry Training and Advocacy Resources Institutional RDM Support Service Jisc-mediated Services/national Archival Storage Data Repositories/Catalo gues Managing Active Data
    30. 30. BRISKit Research Data Management 31 Biomedical Research Infrastructure Software Service kit: a cloud enabled translational IT platform
    31. 31. Jisc Research Data Current Activities Liaison with stakeholders to formulate a way forward for UK HEIs and research institutes – e.g. DBIS, HEFC, libraries, IT directors, RCUK , publishers etc Work with international initiatives: »Research Data Alliance »CODATA »Knowledge Exchange – at the moment through the KE we are exploring incentives to sharing & funding models for research data infrastructure Research Data Management 32 Two European projects – Sim4RDM and 4Cs. Both related to data. 4Cs is holding a workshop today to understand how people are costing the curation of their research data. Jisc CASRAI - UK pilot – development of related vocabularies and standards, for example data management plans vocabularies
    32. 32. Jisc Research Data future plans »Building on the research data registry pilot & developing a UK service »Taking forward BRISSKit and developing a Jisc/ University of Leicester service for the support of medical data sharing »Considering the development of a data base of Journal research data policies building on the Jord feasibility work »Continuing considerations of shared repository, storage and archiving solutions – via framework agreements, provision of services if there is demand, advice & guidance »Continuation of the Digital Curation Centre , advice, support for HEIs, DMP On Line tool & Cardio Research Data Management 33 Shared Services and Infrastructure
    33. 33. Jisc Research Data future plans Research Data Management 34 Developments in HEIs and shared standards & protocols »Hefce plan to support via Jisc further implementation projects in HEIs to develop infrastructure and capability – agreement being finalised »Data deposit & re-use experiments to increase interoperability and workflows – generic and disciplinary focused
    34. 34. Questions Research Data Management 35