simon hodson
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Supporting Research Data Management in Universities:
the Jisc Managing Research Data Programme
Simon Hodson
JISC Programme Manager, Managing Research Data
Wednesday 6 March 2013
KAPTUR Project Conference, RIBA, London
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?
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
STOP
What do we mean by research data?
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.
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
DUDs
The data centre under the desk (or in a back pack) is
not adequate.
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
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/
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/
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.
DCC Overview of Funder Data Policies: http://www.dcc.ac.uk/resources/policy-and-legal/overview-funders-data-policies
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
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
“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)
“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.
“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.
Slide credit, Joss Winn, University of Lincoln
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.
Building Institutional Capacity:
Second MRD Programme, 2011-13
Second JISC MRD Programme, 2011-13: http://bit.ly/jiscmrd2011-13
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.
Jisc MRD RDM Infrastructure Projects
Data Management Planning
Managing Active Data
Processes for selection and
retention Deposit / Handover
Data Repositories/Catalogu
es
Components of research data management support services
RDM Policy and Roadmap Business Plan and
Sustainability
Guidance, Training and Support
Research Data Registry
Data Management Planning
Selection and Retention Deposit / Handover
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 / Jisc-mediated Products
Guidance
Good Practice
Coordination
Research Data Registry
Training and Advocacy Resources
Institutional RDM Support Service Jisc / Jisc-mediated
Products
Archival Storage
Data Repositories/Catalo
gues
Managing Active Data
Products map to components of RDM support services. Arrows in indicate products delivered. Red arrows out indicates data hosting or metadata transfer to external service.
University RDM Guidance Pages
http://www.gla.ac.uk/services/datamanagement/
University RDM Guidance Pages
http://www.admin.ox.ac.uk/rdm/
University RDM Guidance Pages
http://www.bath.ac.uk/research/data
University RDM Guidance Pages
http://www.southampton.ac.uk/library/research/researchdata/
University RDM Guidance Pages
http://www2.le.ac.uk/services/research-data
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
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/
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
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/
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/
University Data Repositories
https://ore.exeter.ac.uk/repository/handle/10871/502
University Data Repositories
http://data.bris.ac.uk/datasets/12mjtnrtsdjfs17sl4pq2ucqrk/
University Data Repositories
https://databank.ouls.ox.ac.uk/general/datasets/Tick1AudioCorpus
Metadata Schema for Institutional Data Repositories
http://www.data-archive.ac.uk/media/375386/rde_eprints_metadataprofile.pdf
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]
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?
Thank You
JISC Managing Research Data Programme: http://bit.ly/jiscmrd2009-11
JISC MRD Programme Blog: http://researchdata.jiscinvolve.org/wp/
Jisc MRD Programme List: https://www.jiscmail.ac.uk/cgi-
bin/webadmin?A0=JISCMRD
RESEARCH-DATAMAN Discussion List: https://www.jiscmail.ac.uk/cgi-
bin/webadmin?A0=RESEARCH-DATAMAN
E-mail: s.hodson@jisc.ac.uk
Twitter: @simonhodson99 ; #jiscmrd
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