Introduction to digital curation

An introduction to digital curation and preservation Michael Day Digital Curation Centre UKOLN, University of Bath [email_address] Information and Library Management, University of the West of England, Bristol, 24 March 2009 Slides available on SlideShare: http://www.slideshare.net/michaelday

Presentation outline:
The DCC digital curation lifecycle
Some definitions
OAIS concepts
Roles and responsibilities
Reasons for preserving research data
Digital preservation challenges and strategies
A taxonomy of research data collections
Infrastructures for preservation and curation
Some comments on curation and “Open Science”

Learning outcomes
An greater awareness of the factors that need to be taken into account when considering how to preserve research data (and other materials) over time
A deeper understanding of the preservation options currently available
Part of the “digital curation lifecycle” (Digital Curation Centre)

Preservation in the curation lifecycle
Lifecycle includes:
Creation 
Appraisal and selection 
Ingest 
Preservation 
Storage 
Access, use and reuse 
Transformation 
Generic tasks:
Preservation planning
Community watch
Metadata (Descriptive Information, Representation Information)

Preservation in the curation lifecycle
There are major dependencies on the rest of the curation process
The creation stage is normally the best time to ensure that data are fit-for-purpose and “preservable”
Need to document both explicit and implicit knowledge, contexts (part of the metadata issue)
Preservation Planning informs ingest strategies as well as preservation actions and transformations

Definitions (1)
Preservation:
A management function
“Its objective is to ensure that information survives in usable form for as long as it is wanted” - John Feather (1991)
Not primarily about:
Conservation or restoration
Storage media or backup regimes
Concepts of “permanence”

Definitions (2)
Digital preservation:
Digital information is different
Technical problems with ensuring continued access (more of this later)
But also (primarily) a managerial problem
“ ... the planning, resource allocation, and application of preservation methods and technologies to ensure that digital information of continuing value remains accessible and usable” - Margaret Hedstrom (1998)

Definitions (3)
Digital curation:
General concept (data curation) originates in the scientific data world (e.g. bioinformatics, astronomy)
Is used to mean something more than just the preservation of objects
"The activity of managing and promoting the use of data from its point of creation, to ensure it is fit for contemporary purpose, and available for discovery and reuse" - Philip Lord, et al. (2004)
"Maintaining and adding value to a trusted body of information for current and future use" -- DCC presentation at CNI (2005)

The OAIS reference model
Reference Model for an Open Archival Information System (OAIS)
Fundamental standard, defines key concepts
Development managed by the Consultative Committee on Space Data Systems (CCSDS)
CCSDS Blue Book 650.0-B-1 (2002)
ISO 14721:2003
Recently reviewed - no major changes proposed
Has established a common framework of terms and concepts
Information model has been influential on the design of some preservation metadata schemas
It is still uncertain what 'conformance' might mean

OAIS mandatory responsibilities
Negotiating and accepting information
Obtaining sufficient control of the information to ensure long-term preservation
Determining the "designated community"
Ensuring that information is independently understandable , i.e. can be (re)used without the assistance of those who produced it
Following documented policies and procedures
Making the preserved information available

OAIS Functional Model (1)
Six entities
Ingest
Archival Storage
Data Management
Administration
Preservation Planning
Access
Described using UML diagrams

OAIS Functional Model (2) Administration Ingest Archival Storage Access Data Management Descriptive info. PRODUCER CONSUMER MANAGEMENT queries result sets Descriptive info. Preservation Planning orders OAIS Functional Entities (Figure 4-1) SIP SIP SIP DIP DIP AIP AIP

OAIS Information Model
Defines the “Information Packages” required
Ingest (Submission Information Package)
Storage (Archival Information Package)
Access (Dissemination Information Package)
General principle of Information Packages:
All objects are wrapped in layers of metadata (Representation Information, Descriptive Information, Packaging, etc.)

Implementing OAIS
Fundamentals:
OAIS is a reference model (conceptual framework), NOT a blueprint for system design
It informs the design of system architectures, the development of systems and components
It provides common definitions of terms … a common language, means of making comparison
But it does NOT ensure consistency or interoperability between implementations
Conformance only relates to mandatory responsibilities and following information model

Repository audit and certification
Building on OAIS concepts ... but focusing on requirements for helping to ensure that repositories meet identified criteria:
Trustworthy Repositories Audit & Certification: Criteria and Checklist (TRAC)
Center for Research Libraries, OCLC, NARA, et al .
http://www.crl.edu/
DRAMBORA (Digital Repository Audit Method Based on Risk Assessment)
Self-assessment tool developed by: Digital Curation Centre, Digital Preservation Europe
http://www.repositoryaudit.eu/

Who undertakes preservation?
Researchers
Indirectly - they have most direct contact with creation stage, and understand how data can be used
Directly - sometimes responsible for maintaining community data collections
Information professionals
Sometimes, but it depends on the context
IT professionals
Primarily informaticians working with scientists

Roles and responsibilities (1)
Dealing with data (JISC)
Scientist
Institution
Data centre
User
Funder
Publisher
Long-lived data collections (NSB)
Data authors
Data managers
Data scientists
Data users
Funding agencies

Scientists
Initial creation and use of data
Expectation of first use and in gaining appropriate credit and recognition
Responsible for:
Managing data for life of project
For using standards (where possible)
For complying with data policies
For making the data available in a form that can (easily?) be used by others

Institutions:
Role less clear
Institutional policies may require short-term management of data
Advocacy and training
Some institutions are developing repository services
Are rarely currently used for research data
Federated approaches maintain disciplinary involvement

Data centres
Undertakes curation and provides access
Responsible for:
Selection and ingest
Participating in the development of standards
Protecting the rights of data creators
Supporting ingest and metadata capture
Supporting re-use (tools and services)
Training

Users:
Users of third-party data
Responsible for:
Adhering to any licenses and restrictions on use
Acknowledging data creators and curators
Managing any derived data
Provide feedback to scientists and data centres

Funding bodies:
Acting at policy level
Responsible for:
Considering wider policy perspectives
Developing policies in co-operation with other stakeholders
Monitoring and enforcing data policies
Support for long-term data management
Support for data curation

What is research data?
An extremely broad category of material:
“... any information that can be stored in digital form, including text, numbers, images, video or movies, audio, software, algorithms, equations, animations, models, simulations, etc.” (National Science Board, Long-lived digital data collections, 2005)
In practice, it can mean almost anything

Why curate research data? (1)
Part of the normal research process:
The need for others to validate and replicate research
In some disciplines, supporting data is routinely made available to reviewers and linked from journal papers
Principles of sharing and openness are firmly embedded in some disciplines

Extrinsic and intrinsic value;
High investment in research
Data can be very expensive to capture and analyse
Data is impossible to recreate once lost
Observational data (by definition) is irreplaceable
Current generations of instruments can gather more data than can be analysed

The potential for creating 'new' knowledge from existing data:
Re-use, re-analysis, data mining
Annotation, e.g. in molecular biology astronomy
Combining datasets in innovative ways, e.g. mapping biodiversity data onto ecological GIS
“Science 2.0”

It is increasingly a requirement of some research funding bodies
Some have quite mature data retention policies (not necessarily for permanent retention)
Increasing expectation of access to data from publicly-funded research
OECD Principles and guidelines for access to research data from public funding (2007)

Institutional asset management:
Universities and other research organisations invest very large sums of money into research activities
Research data is a key output of this activity
It is, therefore, an institutional asset that needs stewardship

Promoting the institution, research group or individual:
Re-use helps promote visibility and 'impact'
Institutions become acknowledged 'centres of competence'

Preservation challenges (1)
Media (1)
Currently magnetic or optical tape and disks, some devices (e.g., memory sticks)
Examples include: CD, DVD (optical), DAT, DLT, laptop hard drives (magnetic)
Unknown lifetimes
Subject to differences in quality or storage conditions
But relatively short lifetimes compared to paper or good quality microform
Lifetimes measured in years rather than decades

Media (2)
Technical solutions
Longer lasting media:
e.g. Norsam's High Density Rosetta system - analogue storage on nickel plates
COM (output to good-quality microform)
Keeping paper copies!
Periodic copying of data bits on to new media (refreshing) - data management solution
Principle of active management

Hardware and software dependence
Most digital objects are dependent on particular configurations of hardware and software
Relatively short obsolescence cycles for:
Hardware
Scientific instrumentation, peripherals (e.g. floppy disk drives)
Software
e.g., word-processing files, CAD

Conceptual problems (1)
What is an digital object?
Some are analogues of traditional objects, e.g. meeting minutes, research papers
Others are not, e.g. Web pages, GIS, 3D models of chemical structures
Complexity
Dynamic nature

Three layers:
Physical: the bits stored on a particular medium
Logical: defines how the bits are used by a software application, based on data types (e.g. ASCII); in order to understand (or preserve) the bits, we need to know how to process this
Conceptual: things that we deal with in the real world
From: Ken Thibodeau, “Overview of technological approaches to digital preservation and challenges in coming years.” In: The state of digital preservation: an international perspective. CLIR, 2002. http://www.clir.org/

On which of these layers should preservation activities focus?
We need to preserve the ability to reproduce the objects, not just the bits
In fact, we can change the bits and logical representation and still reproduce an authentic conceptual object (e.g. converting into PDF)
Authenticity and integrity
How can we trust that an object is what it claims to be?
Digital information can easily be changed by accident or design

Some general principles (1)
Most of the technical problems associated with long-term digital preservation can be solved if a life-cycle management approach is adopted
i.e. a continual programme of active management
Ideally, combines both managerial and technical processes, e.g., as in the OAIS Model
Many current systems are attempting to support this approach
Preservation strategies need to be seen in this wider context
Preservation needs to be considered at a very early stage in an object's life-cycle

There is a need to identify 'significant properties'
Recognises that preservation is context dependent
Helps with choosing an acceptable preservation strategy
Consider encapsulation
Surrounding the digital object - at least conceptually - with all of the information needed to decode and understand it (including software)
Produces autonomous 'self-describing' objects, reduces external dependencies (linked to the Information Package concept in the OAIS Reference Model)
Keep the original byte-stream

Metadata and documentation is vitally important
Relates to the OAIS concepts like Representation Information and Preservation Description Information
Functions
Records scientific meaning
Records the research context
Enables the development of finding aids
Standards are being developed that support digital preservation activities (e.g., the PREMIS Data Dictionary)

Digital preservation strategies
Three main families:
Technology preservation
Technology emulation
Information migration
Also:
Digital archaeology (rescue)

Technology preservation
The preservation of an information object together with all of the hardware and software needed to interpret it
Successfully preserves the look, feel and behaviour of the whole system (at least while the hardware and software still functions)
May have a role for historically important hardware
Severe problems with storage and ongoing maintenance, missing documentation
Would inevitably lead to 'museums' of “ageing and incompatible computer hardware” -- Mary Feeney
May have a shorter-term role for supporting the rescue of digital objects (digital archaeology)

Technology emulation (1)
Preserving the original bit-streams and application software; running this on emulator programs that mimic the behaviour of obsolete hardware
Emulators change over time
Chaining, rehosting
Emulation Virtual Machines
Running emulators on simplified 'virtual machines' that can be run on a range of different platforms
Virtual machines are migrated so the original bit-streams do not have to be

Benefits:
Technique already widely used, e.g. for emulating different hardware, computer games
Preserves (and uses) the original bits
Reduces the need for regular object transformations (but emulators and virtual machines may themselves need to be migrated)
Retains ‘look-and-feel’
May be the only approach possible where objects are complex or dependent on executable code
Less 'understanding' of formats is needed; little incremental cost in keeping additional formats

Challenges:
Do organisations have the technical skills necessary to implement the strategy?
Preserving 'look and feel' may not be needed for all objects
It will be difficult to know definitively whether user experience has been accurately preserved
Conclusions:
Promising family of approaches
Needs further practical application and research, e.g.
Dioscuri software (National Library of the Netherlands (KB), Nationaal Archief and Planets project)

Information migration (1)
Managed transformations:
A set of organised tasks designed to achieve the periodic transfer of digital information from one hardware and software configuration to another, or from one generation of computer technology to a subsequent one - CPA/RLG report (1996)
Abandons attempts to keep old technology (or substitutes for it) working
A 'known' solution used by data archives and software vendors (e.g., a linear migration strategy is used by software vendors for some data types, e.g. Microsoft Office files)
Focuses on the content (or properties) of objects

Main types (from OAIS Model):
Refreshment
Replication
Repackaging
Transformation
Challenges:
Labour intensive
There can be problems with ensuring the 'integrity and authenticity' of objects
Transformations need to be documented (part of the preservation metadata)

Uses:
Seems to be most suitable for dealing with large collections of similar objects
Migration can often be combined with some form of standardisation process, e.g., on ingest
ASCII
Bit-mapped-page images
Well-defined XML formats
Some variations: migration on Request (CAMiLEON project)
Keep original bits, migrate the rendering tools

Digital archaeology
Not so much a preservation strategy, but the default situation if there isn't one
Using various techniques to recover digital content from obsolete or damaged physical objects (media, hardware, etc.)
A time consuming process, needs specialised equipment and (in most cases) adequate documentation
Considered to be expensive (and risky)
Remains an option for content deemed to be of value

Choosing a strategy
Preservation strategies are not in competition (different strategies will work together)
It has been suggested that we should keep the original bits (with some documentation) in any case
But the strategy chosen has implications for:
The technical infrastructure required (and metadata)
Collection management priorities
Rights management
e.g, Owning the rights to re-engineer software
Costs
Planets project - PLATO preservation planning tool
Decision support tool

File formats and preservation
Formats can be identified and validated at ingest
JHOVE, PRONOM-DROID
Standardisation on ingest
Perceived wisdom suggests the adoption of open or non-proprietary standards, e.g. databases structured in XML, uncompressed images
However, we need more empirical data on how robust some of these standards are to random bit-rot

Rescue of BBC Domesday (1)
Case Study:
BBC Domesday project (1986)
Commemorated the 900th Anniversary of the original Domesday survey
Two interactive videodiscs (12")
Mixture of textual material (some produced by schools), maps, statistical data, images and video
Technical basis:
Hardware: BBC Master Series microcomputer and Philips Laservision (LV-ROM) player
Some software in ROM chip, others on the discs
System obsolete by end of 1990s; working hardware becoming more difficult to find

Rescue of BBC Domesday (2)
CAMiLEON project
Proof of concept for the emulation approach
Converted data into media-neutral form
Adapted an existing emulator for the BBC microcomputer to render Domesday content
The National Archives (and partners)
Reengineered the whole system for use on Windows PCs
Digital versions of images and video converted from original master tapes (still held by BBC)
Developed an improved interface
Web version: http://domesday1986.com/

Other preservation challenges
Scale (1):
The “digital deluge”
e-Science
New generations of instruments
Computer simulations
Many terabytes generated per day, petabyte scale computing (and growing)
Cory Doctorow, “Welcome to the petacentre.” Nature, 455, pp 17-21, 4 Sep 2008

Scale (2):
Problems of scale are particularly acute in traditional 'big-science' disciplines:
Particle physics (e.g., the Large Hadron Collider)
Astronomy (sky surveys, etc)
But “smaller experiments will grow the fastest” (Szalay & Gray, Nature, 440, 413-4, 23 Mar 2006)
Bioinformatics, crystallography, engineering design, and many others
In some cases it may be cheaper just to generate the data again, e.g. for computer simulations

Complexity (1)
Research data is extremely diverse - not really a single category of material
tabular data, images, GIS, etc.
raw machine output vs, derived data
varying levels of structure (XML, legacy formats, etc.)
many different standards
Research data is not homogeneous
No one-size-fits-all approach possible

Complexity (2):
Even wider range of social contexts in which data is used (and shared)
DCC SCARP project has been exploring disciplinary factors in curation practice
Practice even within single disciplines is very fragmented
Case studies ongoing
Big-science archives, medical and social sciences, architecutre and engineering, biological images

Diverse research cultures
Data practices vary widely, even within a single discipline
Gene sequence data is typically deposited in public databases
In proteomics sharing is not so widespread; partly driven by lack of standards, but there is also concern about who have exploitation rights
Role of commercial interests
Pharmaceuticals, architecture and engineering, geological prospecting

Costs
Recent JISC study (2008) - focusing on the institution level
Some findings:
The complex service requirements for curating research data means that institutions are setting-up federated approaches to repository development
Currently ingest costs are much higher than long-term storage and preservation costs
Start-up (and R&D) costs are high, but there can be economies of scale

Research data collections (1)
A typology (1):
From National Science Board report Long-lived digital data collections (2005)
Research data collections – the products of one or more focused research projects
Resource or community data collections – collections that emerge to serve particular subject sub-disciplines
Reference data collections – serve a broader and more diverse set of user communities

Research data collections (2)
Data in “research data collections” is most at risk
A modern version of the “file-drawer problem”
Data stored on personal hard-drives or on media; largely undocumented
Particular challenge when the data creator has retired or moved to another institution
Data creators not aways aware of its potential value
The reward structure of science is not always helpful

Curation infrastructures (1)
Focus on the generic:
Need for a balance between:
The 'bottom-up' discipline-based drivers that promote the generation of research data
The policy level, looking to make cost effective investment in curation
When building Infrastructures, focus on the generic
Storage systems and middleware
Preservation services
Identifying the needs of the wider community

Curation infrastructures (2)
The need for collaboration:
Need for 'deep-infrastructure' recognised as far back as 1996 by the Task Force on Archiving of Digital Information
Digital preservation involves the "grander problem of organizing ourselves over time and as a society ... [to manoeuvre] effectively in a digital landscape" (p. 7)

Summing-up
Long-term preservation of digital research data (and other types of object) is a big ongoing challenge
Solutions are normally based on the active management of data
Decisions needed on whether to adopt standard formats, the identification of “significant properties,” preservation planning
Research disciplines and sub-disciplines are at different stages of maturity

The Future ...
“It is always a mistake for a historian to try and predict the future. Life, unlike science, is simply too full of surprises” - Richard J. Evans, In defence of history (1997, p. 62)

Readings (1)
Neil Beagrie and Maggie Jones , Preservation Management of Digital Materials: a Handbook (2001). Updated version available at: http://www.dpconline.org/
Council on Library and Information Resources, Building a National Strategy for Preservation: Issues in Digital Media Archiving (April 2002) http://www.clir.org/pubs/abstract/pub106abst.html
Council on Library and Information Resources , The state of digital preservation: an international perspective (July 2002) http://www.clir.org/pubs/abstract/pub107abst.html
Margaret Hedstrom , It's about time: research challenges in digital archiving and long-term preservation (2003) http://www.digitalpreservation.gov /
Margaret Hedstrom and Seamus Ross, Invest to save: report and recommendations of the NSF-DELOS Working Group on Digital Archiving and Preservation (2003) http://eprints.erpanet.org/archive/00000095/

Readings (2)
Philip Lord and Alison Macdonald , Data curation for e-Science in the UK: an audit to establish requirements for future curation and provision (2003) http://www.jisc.ac.uk/
Helen R. Tibbo, "On the nature and importance of archiving in the digital age." Advances in Computers 57 (2003): 1-67.
Brian Lavoie and Lorcan Dempsey, "Thirteen Ways of Looking at ... Digital Preservation." D-Lib Magazine 10, no. 7/8 (July/August 2004) http://www.dlib.org/dlib/july04/lavoie/07lavoie.html
National Science Board, Long-lived digital data collections: enabling research and education in the 21st century (2005) http://www.nsf.gov/pubs/2005/nsb0540/
DCC Digital Curation Manual (2005- ) http://www.dcc.ac.uk/resource/curation-manual/chapters/
Christine L. Borgman, Scholarship in the digital age: Information, infrastructure, and the Internet (Cambridge, MA: MIT Press, 2007)
Murtha Baca (Ed.), Introduction to metadata , v 3.0 (Los Angeles, CA: Getty Publications, 2008) http://www.getty.edu/research/conducting_research/standards/intrometadata/

Curation and “open science”

The UK research context (1)
Dual-support funding system
Splits funding of research from infrastructure
Research Councils (around EUR 4 billion pa)
Higher education funding bodies
Direct institutional support
Joint Information Systems Committee (JISC)
Data curation on the agenda of several of these
Research Councils UK
Higher Education Funding Council for England
National research data service study
JISC

The UK research context (2)
JISC has been very active in funding work on long-term digital preservation and curation:
Research projects
Over ten years
A major recent focus has been on institutional repositories)
Supporting studies
Dealing with Data (2007)
Keeping Research Data Safe (2008)
Studies of 'significant properties' of certain classes of content (ongoing)
The Digital Curation Centre (DCC)

The Digital Curation Centre (DCC)
Launched in 2004
Initial grant funding from:
Joint Information Systems Committee (JISC)
UK e-Science Core Programme (Engineering and Physical Sciences Research Council)
Main activities:
Development, services and outreach in digital curation
Research programme (2004-2008)
Consortium of four institutions
Now in second phase

Curation, not just preservation
Active management of data over life-cycle of scholarly and scientific interest
Reproducibility and reuse
Appreciation of differences between disciplines
Explored in separate DCC SCARP project
Big-science / small-science distinctions are becoming blurred
Importance of lifecycles
Conception, creation, use, re-use
Curation potentially involves a lifetime of endeavour

DCC Curation Lifecycle Model

DCC vision
Centre of excellence in digital curation and preservation in the UK
Authoritative source of advocacy and expert advice and guidance to the community
Key facilitator of an informed research community with established collaborative networks of digital curators
Service provider of a wide range of resources, software, tools and support services

Selected DCC activities and outputs
User services
Curation Lifecycle Model
Curation manual and briefing papers
Tools for repository self-assessment (DRAMBORA)
Community Development
Website, journal (IJDC)
Events (regular workshops/training, annual international conference)
Liaison with JISC's repositories activities
Tools and infrastructure
Representation Information registries

Problem 1: who 'owns' curation?
Many potential stakeholders
Dealing with Data report (2007) identified: scientists, institutions, data centres, the users of data, funding bodies and publishers
Also ... data scientists, curation specialists
Different repository types (project-specific, community-driven, reference collections)
The potential for duplication of effort and confusion is high
All of these probably have some kind of role ... so how do we co-ordinate?

Problem 2: institutions v disciplines
A major focus in UK is on the institutional role in curation:
Building on the Institutional Repository paradigm
It is not clear, however, that the curation of data is best performed at this level
Keeping Research Data Safe (2008) report notes that data is more often dealt with by discipline-based consortia
Bottom-up approaches to curation work well in some domains – but not in all
Need to understand domain differences
Initial SCARP studies reveal much complexity

Problem 3: how much will it cost?
Keeping Research Data Safe (2008):
Report (with case studies) focused on identifying costs at the institutional level
Some findings:
The complex service requirements for curating research data means that institutions are setting-up federated approaches to repository development
Currently ingest costs are much higher than long-term storage and preservation costs
Start-up (and R&D) costs are high for first adopters

What is needed for open science?
Some challenges:
1. Being open is not enough
Data need to be made available in ways that facilitate high-throughput reuse
e.g., Peter Murray-Rust's comments on the amount of chemistry data captured in formats like PDF
2. How do we capture the context(s) of research?
Not just papers and data, but Web-sites, annotation services, blogs, wikis, etc.
Importance of recording provenance

3. Current scientific reward structures do not support either data curation or open science
Funding bodies can 'mandate' (and in some cases fund) Principal Investigators to maintain data and make it available
Without a sustainable infrastructure, however, this will be only a short term solution
We need to decide what infrastructure we need and how we pay for it

4. What will be the role of institutions?
They have traditionally had an important role (e.g., research libraries)
Currently are major supporters (and hosts) of Institutional Repositories
Potential skills gap WRT data:
We need to think about the status and skills of data curators (capacity building)
DCC Curation 101, DigCCurr project
What does the 'institution' mean in Open Science anyway?
Open Notebook Science, open grant proposals, loyalty to collaborators or to institution

Summing up
There are still many more questions than answers
There is a (widely acknowledged) need for better co-ordination:
The curation landscape is currently very fragmented, with no real clarity with regard to identifying (and owning) roles and responsibilities
Much is specific to particular domains
There is a need for infrastructure
But what should this include?
Are we really able to identify generic needs?

Further reading
National Science Board, Long-lived digital data collections: enabling research and education in the 21st century (NSF, 2005) http//www.nsf.gov/pubs/2005/nsb0540/
Liz Lyon, Dealing with data; roles, rights, responsibilities and relationships (JISC, 2007) http://www.jisc.ac.uk/whatwedo/programmes/digitalrepositories2005/dealingwithdata.aspx
Neil Beagrie, Jullia Chruszcz, and Brian Lavoie, Keeping research data safe: a cost model and guidance for UK universities (JISC, 2008) http://www.jisc.ac.uk/publications/publications/keepingresearchdatasafe.aspx

Thank you for your attention! “ Pigabyte” King Bladud’s Pigs in Bath (public art project), Summer 2008 http://www.kingbladudspigs.org/

Acknowledgments
UKOLN is funded by the Museums, Libraries and Archives Council (MLA), the Joint Information Systems Committee (JISC) of the UK higher and further education funding councils, as well as by project funding from the JISC, the European Union, and other sources. UKOLN also receives support from the University of Bath, where it is based.
More information: http://www.ukoln.ac.uk/

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Introduction to digital curation

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