1. Wednesday 6 March 2013
KAPTUR Project Conference, RIBA, London
Supporting Research Data Management in Universities:
the Jisc Managing Research Data Programme
Simon Hodson
JISC Programme Manager, Managing Research Data
2. 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.
3. 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
5. STOP
What do we mean by
research data?
The digital and other
artifacts that are created
during the process of
research, and which
through analysis form the
evidence that underpins
research findings.
6. Data management and good research practice
Good data management is good practice
– Avoidance of data loss.
– 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’!
Data sharing / data publication is good for research
– Verification of research findings / Deterrence of fraud
– Reproducibility of research / Science as 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
7. DUDs
The data centre
under the desk (or
in a back pack) is
not adequate.
8. Can we quantify the benefits
of 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
9. Royal Society
Science 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/sci
ence-public-enterprise/report/
10. Science as an Open Enterprise Report:
six key changes
1. a shift away from a research culture where data is viewed as a private
preserve;
2. expanding the criteria used to evaluate research to give credit for useful
data communication and novel ways of collaborating;
3. the development of common standards for communicating data;
4. mandating intelligent openness for data relevant to published scientific
papers;
5. 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);
6. 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/
11. Drivers: Research Funder Policies
RCUK Common Principles on Data Policy:
http://www.rcuk.ac.uk/research/Pages/DataPolicy.aspx
1. Public good: Publicly funded research data are produced in the public interest should be
made openly available with as few restrictions as possible
2. Planning for preservation: Institutional and project specific data management policies
and plans needed to ensure valued data remains usable
3. Discovery: Metadata should be available and discoverable; Published results should
indicate how to access supporting data
4. Confidentiality: Research organisation policies and practices to ensure legal, ethical and
commercial constraints assessed; research process should not be damaged by
inappropriate release
5. First use: Provision for a period of exclusive use, to enable research teams to publish
results
6. Recognition: Data users should acknowledge data sources and terms & conditions of
access
7. Public funding: Use of public funds for RDM infrastructure is appropriate and must be
efficient and cost-effective.
12. DCC Overview of Funder Data Policies: http://www.dcc.ac.uk/resources/policy-and-
legal/overview-funders-data-policies
13. 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
14. 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
15. Professional benefits of data sharing
“48% of trials with “We find strong and consistent evidence that
data sharing, both formal and informal,
publicly available increases research productivity across a wide
microarray data range of publication metrics. Data archiving,
received 85% of the in particular, yields the greatest returns on
aggregate citations” investment with research productivity
(number of publications) being greater when
-- Piwowar HA, Day RS, data are archived. Not sharing data, either
Fridsma DB (2007) Sharing formally or informally, limits severely the
Detailed Research Data Is number of publications tied to research data.”
Associated with Increased –
Citation Rate. PLoS ONE
2(3): e308. Pienta, Alter, Lyle (2010) The Enduring Value of
Science Research: The Use and Reuse of Primary
Research Data.
“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)
Slide credit, Joss Winn, University of Lincoln
16. Research data are an asset!
Imagine the significance of
the research collections of
key departments/research
groups, departed alumni.
Don’t underestimate the
research value of the stuff
that underpins your
research, that you make
during your research.
17. Building Institutional Capacity:
Second MRD Programme, 2011-13
Encouraged to reuse Ownership: High level
outputs from first RDM ownership of the problem,
Training senior manager on
programme and
elsewhere. 5 projects steering .
Mix of pilot projects and Sustainability: Large
embedding projects. institutional contributions.
Holistic institutional Develop business cases
approach to RDM. to sustain work.
Institutional
RDM
Infrastructure
Services
Data 17 Projects RDM
Publication Planning
3 projects 10 projects
Second JISC MRD Programme, 2011-13: http://bit.ly/jiscmrd2011-13
19. Components of research data management support services
Business Plan and
RDM Policy and Roadmap
Sustainability
Research Data
Registry Data Management
Planning
Data
Repositories/Catalogu Managing Active Data
es
Processes for
Deposit / Handover selection and
retention
Guidance, Training and Support
20. Guidance
Research Data RDM Policy and Business Plan and
Roadmap Good Practice
Registry Sustainability
Coordination
DMPonline
Archival Data Management
Guidance
Storage Planning
Templates
Metadata DataStage
Data Managing Active Academic
Identifiers
Repositories/Catalo Data Dropbox
Guidance gues
Coordination Active Storage
SWORD Guidance
Selection and
Protocol Deposit / Handover Good Practice
Retention
Easy Uploader Case Studies
Jisc / Jisc-mediated
Products Training and
Advocacy, Guidance, Training and Support Advocacy
Products map to Resources
components of RDM
support services.
Arrows in indicate products
delivered.
Red arrows out indicates Jisc / Jisc-mediated
Institutional RDM Support Service Products
data hosting or metadata
transfer to external service.
21. University RDM
Guidance Pages
http://www.gla.ac.uk/services/data
management/
24. University RDM
http://www.southampton.ac.uk/library/research/researchdata/
Guidance Pages
25. University
RDM
Guidance
Pages
http://www2.le.ac.uk/services/research-data
26. Institutional Policies and Roadmaps
Institutional Research Data Management Policies:
http://www.dcc.ac.uk/resources/policy-and-legal/institutional-data-policies/uk-
institutional-data-policies
Institutional Roadmaps to meet EPSRC Expectations on Research Data:
http://www.dcc.ac.uk/resources/policy-and-legal/epsrc-institutional-roadmaps
27. 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/FundersData
PlanReqs_v4%204.pdf
DCC How to Develop a Data Management and Sharing Plan:
http://www.dcc.ac.uk/resources/how-guides/develop-data-plan
DCC DMPonline tool: https://dmponline.dcc.ac.uk/
28. 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.
29. 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/
30. Lincoln Orbital Project: Joining up Institutional Systems:
http://orbital.blogs.lincoln.ac.uk/2012/12/06/orbital-deposit-of-dataset-records-to-the-lincoln-
repository-workflow/
31. University
Data Repositories
https://ore.exeter.ac.uk/repository/handle/10871/502
32. University
Data Repositories
http://data.bris.ac.uk/datasets/12mjtnrtsdjfs17sl4pq2ucqrk/
33. University
Data Repositories
https://databank.ouls.ox.ac.uk/general/datasets/Tick1AudioCor
pus
34. Metadata Schema for Institutional Data Repositories
http://www.data-archive.ac.uk/media/375386/rde_eprints_metadataprofile.pdf
35. 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]
36. You and research
data/research outputs…
1. Does your institution have an
RDM policy and a set of guidance
pages supporting it?
2. Does your institution provide
support for data management
during your research?
3. Does your institution have a
repository for research data?
4. Do you know how to prepare a
data management plan?
5. Which data do you retain at the
end of a research project?
6. Would you reference data in your
published research?
7. Which data would you retain at
the end of a project and how
would you make this available?
