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Friday 1 March 2013 University of Warwick, Data Management Seminar. Challenges and Opportunities in Research Data Management. Simon Hodson JISC Programme Manager, Managing Research Data. Why is managing research data important?.

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slide1

Friday 1 March 2013

University of Warwick, Data Management Seminar

  • Challenges and Opportunities in Research Data Management

Simon HodsonJISC Programme Manager, Managing Research Data

why is managing research data important
Why is managing research data important?

JISC considers it a priority to support universities in improving the way research data is managed and, where appropriate, made available for reuse.

  • Research funder policies, legislative frameworks, good practice, open data agenda
    • The outputs of publicly funded research should be publicly available.
    • The evidence underpinning research findings should be available for validation
  • Good data management is good for research
    • More efficient research process, avoidance of data loss, benefits of data reuse
  • Alignment with university missions.
    • Universities want to provide excellent research infrastructure.
    • Universities want to have better oversight of research outputs.
what is jisc doing
What is Jisc doing?
  • Jisc Managing Research Data Programme: developing capacity and good practice
    • First MRD Programme, 2009-11: http://bit.ly/jiscmrd2009-11
    • Selected outputs from the first programme: http://bit.ly/jiscmrd2009-11-outputs
    • Second JISC MRD Programme, 2011-13: http://bit.ly/jiscmrd2011-13
    • Programme Manager Blog: http://researchdata.jiscinvolve.org/
  • Digital Curation Centre: ‘because good research needs good data’
    • Advice, guidance, advocacy, training in RDM: http://www.dcc.ac.uk/
    • How to Guides: http://www.dcc.ac.uk/resources/how-guides
  • Janet Brokerage: Collaborative purchasing, B2B brokerage.
    • Suite of services (generic research tools, cloud storage): https://www.ja.net/products-services/janet-brokerage
structure
Structure
  • Drivers: why is this important? Changing practice, funder policies, journal policies.
  • Discussion on managing and sharing research data.
  • Jisc Managing Research Data Programme, structure and useful outputs.
  • Activity: data management planning.
data management and good research practice
Data management and good research practice
  • Good data management is good practice
    • Effective research: file naming, annotation etc.
    • How do you find your data, how do you understand it?
    • ‘The first person with whom you share your data is your future self’!
    • Avoidance of data loss.
slide6

DUDs

  • The data centre under the desk (or in a back pack) is not adequate.
can we quantify the benefits of reducing data loss
Can we quantify the benefitsof reducing data loss?
  • Jisc Managing Research Data Programme project surveys have uncovered evidence of data loss.
  • One survey found that 23.3% of respondents had lost research data
    • 0.5 % had suffered catastrophic loss of all their research data as it had not been backed up.
    • 7.5 % had lost one week’s work
    • 8 % had lost one day’s work
why policies on data availability sharing
Why Policies on Data Availability/Sharing?
  • Data sharing / data publication is good for research
    • Verification of research findings / Deterrence of fraud
    • Reproducibility of research / Science is a self-correcting process
    • Benefits of data reuse: asking new questions of old data.
    • Return on investment.
    • Metastudies/systematic review: greater statistical value of integrated results.
    • Integration of data in interdisciplinary research: the grand challenges require multiple data sets
combining data from disparate sources
Combining data from disparate sources
  • ‘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.’

http://www.rin.ac.uk/our-work/using-and-accessing-information-resources/physical-sciences-case-studies-use-and-discovery-

royal society science as an open enterprise report 2012
Royal SocietyScience as an Open Enterprise Report, 2012
  • ‘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, http://royalsociety.org/policy/projects/science-public-enterprise/report/
science as an open enterprise report six key changes
Science as an Open Enterprise Report:six key changes
  • a shift away from a research culture where data is viewed as a private preserve;
  • expanding the criteria used to evaluate research to give credit for useful data communication and novel ways of collaborating;
  • the development of common standards for communicating data;
  • mandating intelligent openness for data relevant to published scientific papers;
  • strengthening the cohort of data scientists needed to manage and support the use of digital data (which will also be crucial to the success of private sector data analysis and the government’s Open Data strategy);
  • the development and use of new software tools to automate and simplify the creation and exploitation of datasets.
  • Royal Society 2012, Science as an Open Enterprise, http://royalsociety.org/policy/projects/science-public-enterprise/report/
drivers research funder policies
Drivers: Research Funder Policies
  • RCUK Common Principles on Data Policy: http://www.rcuk.ac.uk/research/Pages/DataPolicy.aspx
    • Public good: Publicly funded research data are produced in the public interest should be made openly available with as few restrictions as possible
    • Planning for preservation: Institutional and project specific data management policies and plans needed to ensure valued data remains usable
    • Discovery: Metadata should be available and discoverable; Published results should indicate how to access supporting data
    • Confidentiality: Research organisation policies and practices to ensure legal, ethical and commercial constraints assessed; research process should not be damaged by inappropriate release
    • First use: Provision for a period of exclusive use, to enable research teams to publish results
    • Recognition: Data users should acknowledge data sources and terms & conditions of access
    • Public funding: Use of public funds for RDM infrastructure is appropriate and must be efficient and cost-effective.
slide13

DCC Overview of Funder Data Policies: http://www.dcc.ac.uk/resources/policy-and-legal/overview-funders-data-policies

epsrc research data policy expectations
EPSRC Research Data Policy Expectations
  • Policy and expectations: http://www.epsrc.ac.uk/about/standards/researchdata/Pages/policyframework.aspx
  • Research organisations to have RDM policy, advocacy and support functions. (i, iii)
  • Research data to be effectively managed and curated throughout the life-cycle (viii)
  • Research organisations to maintain public catalogue of research data holdings, adequate metadata and permanent identifier (v)
  • Publications to indicate how research data can be accessed (ii)
  • Data to be retained for 10 years from last access (vii)
  • Research data management to be adequately resourced from appropriate funding streams (ix)
  • Roadmap in place by 1 May 2012
  • Compliance by 1 May 2015
data sharing and research disciplines
Data Sharing and Research Disciplines
  • DNA and Protein Sequences
    • Bermuda (1996) principles for immediate (pre-publication) sharing of human genome data.
    • Led to journal policies requiring data deposit making accession numbers for DNA sequences a requirement in the late nineties (e.g. Nature, 1998).
  • Acceleration in numbers of journal data polices from 2004/5. Since 2010 becoming more widespread (subjects) and specific (requirements).
  • Growing number of data repositories.
    • ICSU World Data System: http://www.icsu-wds.org/
    • Research Council Funded Data Centres: e.g. UKDA http://data-archive.ac.uk/; ADS http://archaeologydataservice.ac.uk/; BADC http://badc.nerc.ac.uk/home/index.html
    • DataBib Directory of Research Data Repositories: http://databib.org/
    • figshare: http://figshare.com/
    • Dryad Data Repository: http://datadryad.org/
dryad data repository jdap joint data archiving policy
Dryad Data RepositoryJDAP: Joint Data Archiving Policy
  • Joint Data Archiving Policy: http://datadryad.org/jdap
  • Joint declarations, Feb 2010, in American Naturalist, Evolution, the Journal of Evolutionary Biology, Molecular Ecology, Heredity, and other key journals in evolution and ecology: http://www.journals.uchicago.edu/doi/full/10.1086/650340
  • This journal requires, as a condition for publication, that data supporting the results in the paper should be archived in an appropriate public archive, such as GenBank, TreeBASE, Dryad, or the Knowledge Network for Biocomplexity.
  • Allows embargos of up to one year; allows exceptions for, e.g., sensitive information such as human subject data or the location of endangered species.
  • ‘Data that have an established standard repository, such as DNA sequences, should continue to be archived in the appropriate repository, such as GenBank. For more idiosyncratic data, the data can be placed in a more flexible digital data library such as the National Science Foundation-sponsored Dryad archive at http://datadryad.org.'
slide17

‘Some BioMed Central journals now additionally encourage or require authors, as a condition of publication, to include in some article types a section that provides a permanent link to the data supporting the results reported in the article. … The aim is to provide links in a consistent place within an article to supporting data - regardless of the location or format of the data - and to make it clear to readers when they can also access the data as well as the article.’

Adopted by 20 BMC journals between Aug 2011 and Mar 2012

(most encourage…)

http://www.biomedcentral.com/about/supportingdata

data journals and data papers
Data Journals and Data Papers
  • Geoscience Data Journal, Wiley: http://onlinelibrary.wiley.com/journal/10.1002/(ISSN)2049-6060
  • Ubiquity Press data papers and research reports: http://metajnl.com/
  • Forthcoming initiatives from Nature…
  • Aim to provide researchers with a place to publish data
  • PREPARDE project list of data journals: http://proj.badc.rl.ac.uk/preparde/blog/DataJournalsList
  • Making Data Count: research data availability and research assessment, 11-12 April, Berlin: http://www.knowledge-exchange.info/Default.aspx?ID=576
  • Now and Future of Data Publishing, a symposium, 22 May, Oxford: http://researchdata.jiscinvolve.org/wp/2013/02/19/the-now-and-future-of-data-publishing-a-symposium-22-may-2013-oxford-uk/
barriers to data sharing
Barriers to data sharing…
  • Researchers concerns:
    • Concern that data may be misused or misunderstood.
    • Concern that will lose scientific edge if sharing before fully exploited.
    • Desire to retain control of a professional asset.
    • Concern that will not be credited.
    • Lack of career rewards for data publication.
  • See ODE report, using Parse.Insight findings: http://www.alliancepermanentaccess.org/wp-content/uploads/downloads/2011/11/ODE-ReportOnIntegrationOfDataAndPublications-1_1.pdf
  • RIN Report, ‘To Share or not to share’, http://www.rin.ac.uk/our-work/data-management-and-curation/share-or-not-share-research-data-outputs
professional benefits of data sharing
Professional benefits of data sharing

“We find strong and consistent evidence that data sharing, both formal and informal, increases research productivity across a wide range of publication metrics. Data archiving, in particular, yields the greatest returns on investment with research productivity (number of publications) being greater when data are archived. Not sharing data, either formally or informally, limits severely the number of publications tied to research data.” –

Pienta, Alter, Lyle (2010) The Enduring Value of Science Research: The Use and Reuse of Primary Research Data.

“48% of trials with publicly available microarray data received 85% of the aggregate citations”

-- Piwowar HA, Day RS, Fridsma DB (2007) Sharing Detailed Research Data Is Associated with Increased Citation Rate. PLoS ONE 2(3): e308.

Slide credit, Joss Winn, University of Lincoln

“authors who make data from their articles available are cited twice as frequently as articles with “no data but otherwise equivalent credentials, including degree of formalization.”” -- Glenditsch, Petter, Metelits, and Strand (2003: 92)

slide21

Friday 1 March 2013

University of Warwick, Data Management Seminar

  • How do you feel about managing and sharing research data?
  • Are you confident in your data practices?
  • Would you share data underpinning publications?
  • Would you publish key data outputs of a funded research project?
  • Do you have concerns? Do you think making data available is a good thing?
  • How would you go about publishing research data?
slide22

Supporting the Research Data Lifecycle

Store

Annotate

Describe

Identify

Access

Discard

Hand Over?

Select

supporting the research data lifecycle
Supporting the Research Data Lifecycle

Store

Annotate

Describe

Identify

Access

Select

Discard

Hand Over?

building institutional capacity second mrd programme 2011 13
Building Institutional Capacity:Second MRD Programme, 2011-13

Ownership: High level ownership of the problem, senior manager on steering committee.

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.

Second JISC MRD Programme, 2011-13: http://bit.ly/jiscmrd2011-13

slide25

RDM Policy and Roadmap

Components of researchdata management support services

Business Plan and Sustainability

Guidance, Training and Support

Research Data Registry

development of institutional rdm capacity
Development of Institutional RDM Capacity

The Royal Society Science as an Open Enterprise report recommended that the JISC Managing Research Data Programme ‘should be expanded beyond the pilot 17 institutions within the next five years.’

[Royal Society 2012, Science as an Open Enterprise, p.73]

university rdm guidance pages
University RDM Guidance Pages
  • University of Glasgow: http://www.gla.ac.uk/services/datamanagement/
  • University of Oxford: http://www.admin.ox.ac.uk/rdm/
  • University of Southampton: http://www.southampton.ac.uk/library/research/researchdata/
  • University of Bath: http://www.bath.ac.uk/research/data
  • University of Leicester: http://www2.le.ac.uk/services/research-data
jiscmrd training projects phase 1 and 2
JISCMRD Training Projects Phase 1 and 2
  • Need for subject focussed research data management / curation training, integrated with PG studies
  • Five projects in the first programme to design and pilot (reusable) discipline-focussed training units for postgraduate courses: http://www.jisc.ac.uk/whatwedo/programmes/mrd/rdmtrain.aspx
  • Heath studies; creative arts; archaeology and social anthropology; psychological sciences; social sciences and geographical sciences: http://www.dcc.ac.uk/training/train-trainer/disciplinary-rdm-training/disciplinary-rdm-training
  • Four projects in the second programme: http://researchdata.jiscinvolve.org/wp/2012/08/23/research-data-management-training-five-new-jiscmrd-projects/
  • Psychology and computer science; digital music; physics and astronomy; subject and liaison librarians.
mantra training materials university of edinburgh
MANTRA Training Materials, University of Edinburgh
  • Online course built using OS Xerte toolkit.
  • Sections include:
    • DMPs
    • Organising Data
    • File Formats and Transformation
    • Documentation and Metadata
    • Storage and Security
    • Data Protection
    • Preservation, sharing and licensing
  • Also software practicals for users of SPSS, R, ArcGIS, Nvivo
  • Research Data MANTRA: http://datalib.edina.ac.uk/mantra/
dcc how to guides
DCC How-To Guides
  • DCC How-To Guides: http://www.dcc.ac.uk/resources/how-guides
    • Appraise and select research data for curation
    • How to license research data
    • How to develop a data management and sharing plan
    • Hot of the press: How to cite research data!
  • Further Guides in preparation.
appraisal and selection
Appraisal and selection
  • Relevance to mission
  • Scientific or historical value
  • Uniqueness
  • Potential for redistribution
  • Non-replicability
  • Economic case
  • Full documentation

Angus Whyte (DCC) and Andrew Wilson (ANDS), How to Appraise and Select Research Data for Curationhttp://www.dcc.ac.uk/node/9098

slide32

Supporting the Research Data Lifecycle

Store

Annotate

Describe

Identify

Access

Discard

Hand Over?

Select

questions around the research data lifecycle
Questions around the research data lifecycle
  • Do you know how to prepare a data management plan?
  • How do you look after your data during the research project?
  • Which data do you retain at the end of a research project?
  • Which data do you publish and how?
  • Where do you deposit your research data? Institution, national or international data archive?
  • How do you make these decisions?
  • How do you describe and identify data you make available?
  • Do you reuse other peoples’ data? If so, how do you find it and how do you cite it?
data management planning
Data Management Planning
  • Jez Cope, University of Bath, R360 Project http://opus.bath.ac.uk/30772/
  • Detailed guidance on funder requirements for DMPs from DCC: http://www.dcc.ac.uk/sites/default/files/documents/resource/policy/FundersDataPlanReqs_v4%204.pdf
  • DCC How to Develop a Data Management and Sharing Plan: http://www.dcc.ac.uk/resources/how-guides/develop-data-plan
slide35

Friday 1 March 2013

University of Warwick, Data Management Seminar

  • Consider a data management plan…
  • Five sections: defining your data, looking after your data, sharing your data, archiving your data, executing your plan…
  • Jez Cope, University of Bath, R360 Project http://opus.bath.ac.uk/30772/
thank you
Thank You!
  • First JISC MRD Programme, 2009-11: http://bit.ly/jiscmrd2009-11
  • JISC MRD Outputs Page: http://bit.ly/jiscmrd2009-11-outputs
  • Second JISC MRD Programme, 2011-13: http://bit.ly/jiscmrd2011-13
  • Programme Blog: http://researchdata.jiscinvolve.org/
  • E-mail: s.hodson@jisc.ac.uk
  • Twitter: @simonhodson99