1630 mon lomond ashley

Because good research needs good data

Funded by:

© Digital Curation Centre, 2009. Licensed under Creative Commons BY-NC-SA 2.5 Scotland:

http://creativecommons.org/licenses/by-nc-sa/2.5/scotland/

On data (and publications) – who does what?

Kevin Ashley

Director, DCC

[email protected]•High Heid Yin,

CC-BY

With thanks to

Liz Lyon

Director, UKOLN


2012-03-26 Kevin Ashley, DCC, UKSG Glasgow. CC-BY 2

“Data is the new oil.”

Andreas Weigend, Stanford (ex Amazon)

“The future belongs to companies and people that turn data into products”

Mike Loukides, O’Reilly Media



Overview• Why should we care ?• Things you could do• How you might get there• Things to avoid



Brian Aldiss – “The Secret of This Book (1995)

“Information… has become a saleable commodity like never before”

Yet – 33% don’t know Earth orbits the Sun (GB, 1999)



What is data curation ?• “Maintaining, preserving and adding value to

research data throughout its lifecycle”• More than preservation:

• Active management – dealing with change

• Less than preservation:• Lifecycle sometimes involves destruction

• Sometimes, not always, about sharing, publication or citation



Why care?• Data is expensive – an investment• Reuse:

• More research• Teaching & Learning• Planning

• Impact – with or without publication• Accountability• Legal & regulatory requirements



Without good RDM – BAD THINGS HAPPEN

With good RDM – GOOD STUFF HAPPENS



http://www.epsrc.ac.uk/about/standards/researchdata/Pages/expectations.aspx

EPSRC expects all those institutions it funds

•to develop a roadmap that aligns … with EPSRC’s expectations by 1st May 2012;

•to be fully compliant … by 1st May 2015.



• 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



Learning & Teaching workflows

Research & e-Science workflows

Aggregator services: national, commercial

Repositories : institutional, e-prints, subject, data, learning objects

Institutional presentation services: portals, Learning Management Systems, u/g, p/g courses, modules

Harvestingmetadata

Data creation / capture / gathering: laboratory experiments, Grids, fieldwork, surveys, media

Resource discovery, linking, embedding

Deposit / self-archiving

Peer-reviewed publications: journals, conference proceedings

Publication

Validation

Data analysis, transformation, mining, modelling


Deposit / self-archiving

Learning object creation, re-use

Searching , harvesting, embedding

Quality assurance bodies

Validation

Presentation services: subject, media-specific, data, commercial portals


The scholarly knowledge cycle.

Liz Lyon, Ariadne, July 2003.

This work is licensed under a Creative Commons LicenseAttribution-ShareAlike 2.0

© Liz Lyon (UKOLN, University of Bath), 2005



(e)-Research Life Cycle view of Data Curation?Formulate hypothesis / ideas, test,

experiment, observe: data creation, collection & capture

Adding value: Data linking, annotation,

visualisation, simulation

(New) knowledge extraction: data mining, modelling, analysis, synthesis

e-Infrastructure

Open access

Collaboration

Scholarly communications: data disclosure, publication, citation, discovery, re-use

Data management storage & validation: description, deposit,

self-archiving, preservation,

certification

Data processing

Data processingData processing

Data processing

Data processing

This work is licensed under a Creative Commons LicenseAttribution-ShareAlike 2.0 •Liz Lyon December 2005



Chris Rusbridge, DCC



OAIS



MoReq2Model Requirements for

Electronic Records Management 2

• Records Management Discipline

• No mention of DATA• Simple to explain• Easily used to organise

and present resources



E-Science Curation Report - 2003• E-science

discipline

• Appropriate for current focus

• Takes integrated look at higher education data curation problems

• Granularity on curation activities?



InterPARES - 2001



Sheila Corrall: Libraries, Librarians and Data Many action exemplars

RLUK/Mary Auckland: Reskilling for Research

9 areas are skill gaps for subject librarians



Some library roles• Leadership – coordinate action• Audit – who has what, where does it go?• Advice on access – data, wherever it is• Preservation – permanance• Citability• Data/publication linking• Promoting data in teaching



Understanding Data Requirements

http://www.dcc.ac.uk/



Data management plans



How to cite data

What data to keep



Data Licensing

• Bespoke licences• Standard licences• Multiple licensing• Licence mechanisms



Tools to track impact

http://total-impact.org/



Findable, citable data has value• Important to link publications to data (and vice

versa)• Increases citations – of data & publication• Increases reuse (hence value)• But effects exist even without publication• All benefit – researcher; institution; publisher

MORAL: build a data registry



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



4. Audit/Assessment

Dealing with Data: Rec 4

Benefits:

Prioritisation of resources

Capacity development and planning

Efficiency savings – move data to more cost-effective storage

Manage risks associated with data loss

Realise value through improved access & re-use

Scale:

Departments, institutions



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



“The role of the Library in data-intensive research is important and a strategic repositioning of the Library with respect to research support is now appropriate.”

“there are…not enough specialised data librarians yet”

“Recommendation: The research library community in the UK should work with universities and research institutes to define properly and to formalise the role of data librarians, and to develop a curriculum that ensures a suitable supply of librarians skilled in data handling.”

Dealing with Data : Rec 34

Only 5 in UK -

Only 5 in UK -

“accidental”??

“accidental”??

Cilip Update June 2008

Cilip Update June 2008



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

• Help promote data literacy

• Bridge between publishers & researchers



Observations• Role for national & institutional differs• BUILD on existing subject data centers• Datasets aren’t publications

• Indistinct boundaries• Continual change• Multi-dimensional• Non-linear



Clay Shirky

“Institutions will try to preserve the problem(s) to which they are the solution”



Summary• Data not just adjunct to publication• Data is often living – treat it as such (and be

ready to kill it)• There’s more to the world than scholarly

research• Hidden data is wasted data• Bad things happen without RDM• Great benefits accrue with it



Questions• How does data management align with

institutional mission?• When is library a coordinator, and when is it a

service provider?• What will you do alone, and what will you

coordinate with others?• What skills must you acquire?• What do you want from DCC?

1630 mon lomond ashley

Technology

good data data

good data searching

good data oais2012

good data overviewwhy

good datadata

good dataepsrc

good datainformation

good data2012