data: institutional & data centre roles the dcc...
TRANSCRIPT
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Data: institutional & data centre roles The DCC experience
Kevin Ashley Digital Curation Centre
www.dcc.ac.uk @kevingashley
Reusable with attribution: CC-BY The DCC is supported by Jisc
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A summary
• Why data reuse ?
• What stops us ?
• How data management helps
• Barriers & costs
• The case for reuse - again
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My home – the DCC
• Mission – to increase capability and capacity for research data services in UK institutions
• Not just a UK problem – an international one
• Training, shared services, guidance, policy, standards, futures
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DCC guidance
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SWEDEN
DENMARK
CANADA
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Data reuse stories
• The palaeontologist who saved years of work with archaeological data
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What a paleontologist looks at
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Now 100 million years ago
25m 50m 75m
1m
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What a paleontologist looks at
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Now 100 million years ago
25m 50m 75m
1m Now 1 million years
750,000 500,000 100,000
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What an archaeologist looks at
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Now 1 million years
750,000 500,000 100,000
100,000 years ago
75,000 50,000 25,000
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Data reuse stories
• The palaeontologist who saved years of work with archaeological data
• The 19th-century ships logs that help us model climate change
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The Old weather project
Data for research, not from research
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Data reuse stories
• The palaeontologist who saved years of work with archaeological data
• The 19th-century ships logs that help us model climate change
• The ‘noise’ from research radar that mapped dust from Eyjafjallajökull
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Data reuse - messages
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Often your data tells stories that your
publications do not
Not all data comes from other researchers
One person’s noise is another person’s signal
Discipline-bounded data discovery doesn’t give us
all we need or want
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Why care?
• Data is expensive – an investment
• Reuse:
– More research
– Teaching & Learning
– Planning
• Impact – with or without publication
• Accountability
• Legal & regulatory requirements
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Why does this matter?
• Research quality – How close can we get to
the truth?
• Research speed – How quickly can we get
to the truth?
• Research finance – How much does the
truth cost?
• Improving one or more of these is of interest to all actors:
• Researchers as data creators
• Researchers as data reusers
• Research institutions
• Funders – hence government and society
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G8UK - Endorses
OA
Open Data
Charter
Policy Paper
18 June 2013
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G8UK - Billigt offenen Zugang
Eine offene Daten Charter
Strategiepapier.
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Funder requirements
• UK
• USA – NSF, NEH, NIH • Europe
• Most place burden on researcher – some on the institution
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http://www.epsrc.ac.uk/about/standards/researchdata/Pages/policyframework.aspx
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RCUK policy - The 1-minute version
• Research data are a public good – make openly available in timely & responsible way
• Have policies & plans. Data with long-term value should be preserved & usable
• Metadata for discovery & reuse. Link publications & data
• Sometimes law, ethics get in the way. We understand.
• Limited embargos OK. Recognition is important – always cite data sources
• OK to use public money to do this. Do it efficiently.
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EPSRC policy points
• Awareness of regulatory environment
• Data access statement
• Policies and processes
• Data storage
• Structured metadata descriptions
• DOIs for data
• Securely preserved for a minimum of 10 years from last use
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Compliance expected by 2016
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DCC Policy Summary
http://www.dcc.ac.uk/resources/policy-and-legal
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Compliance
Benefits
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Some institutional roles
• Leadership – coordinate action • Audit – who has what, where does it go? • Advice on access – data, wherever it is • Preservation – permanence • Citability • Data/publication linking • Promoting data in teaching • Selection • Education – early career researchers
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Who (in the UK) is addressing RDM ?
Library
IT
Research
Office
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How?
• Create policy – collaborate with others
• Develop existing digital services
• Learn about audit tools (DCC & others)
• Learn about data & sources
• Reskill subject librarians
• Learn about your own data
• Bridge between publishers & researchers
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Understanding Data Requirements
http://www.dcc.ac.uk/
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What stops data reuse
• Loss • Destruction • Pride • Gluttony • Ineptitude • Concealment • Bureaucracy • Complexity • Procrastination • Lack of potential
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“Departments 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 Report, June 2010
http://www.flickr.com/photos/mattimattila/3003324844/
2014-06-09
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Roles and Responsibilities
What data to keep
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Excuses – and responses • “People will ask questions”
– So use a data centre or repository
• “It will be misinterpreted” – Stuff happens. Also, openness encourages correction
• “It’s not interesting” – Let others be the judge – your noise is my signal
• “I might get another paper out of it” – Up to a point. We might get more research out of it
• “I don’t have permission” – A real problem. But solvable at senior level
• “It’s too bad/complicated” –see above • “It’s not a priority”
– Unfortunately, funders are making it so. But if you looked at the evidence, it would be your priority as well
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See e.g. Carly Strasser’s blog: http://datapub.cdlib.org/2013/04/24/closed-data-excuses-excuses/
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Data centres are good value!
• See Jisc reports on ADS, BADC, UKDA:
• Returns on investment between 400% and 1200%
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What about collaboration?
• Collaborate within the university
• Collaborate with partners
• Collaborate with regional, national services
• Not everything can be done well locally
• Infrastructure needed at research group, institution, national, (discipline) & international level
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Pimp your data –
make it findable & reusable
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Gking.harvard.edu/data
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On costs
• Costs of data curation relatively simple to measure: see work of 4C (4cproject.eu)
• Charging and payment are more complex
• Funder rules can lead to perverse, inefficient payment systems
• Fundamental question is ‘who pays’. This changes the answer to ‘what does it cost’
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Commercial services
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The UK funding model
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Research Council Higher Education
Funding Council
The complexities appear in every country, just in different ways
Annual funding, per institution
Project funding, to PI
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What it means
• Project funding can only be spent during projects on direct project costs
• Project funding comes with overheads, which universities must use for research infrastructure
• Ongoing (‘QR’) money is continuous, relates to research ranking
• Important to distinguish business-as-usual from exceptional requirements
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A research lifecycle
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Time
Resources
Exceptional zone
Normal zone
Project end point
Business as usual threshold
Eligible for project funding
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Funders view
We have money
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We have rules about how you use money to meet requirements
We have requirements
Over to you!
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Being clever with costs
• Ongoing costs beyond project end cannot be charged to a grant, but…
• ‘Pay once, store forever’ charges are acceptable.
• Thus, incentive to outsource long-term curation
• Yet universities are only acting as last-resort option in any case – discipline data archives preferred
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Many of these are run by
funders
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Closing thoughts
• Library/data centre roles: – selecting content – protecting it – enabling and encouraging reuse – Assisting with data management planning
• Library: – helping users find the most relevant content – much
research data does not come from research
• Data center: – setting standards – enabling uptake
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Infrastructure levels
• Truly international – instruments, standards
• National variation, international core:
– Training
– Data management planning
– Policy
– ..
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My message to researchers • The credit belongs to you
• The data belongs to all of us
• Share, and we all reap the benefits
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