introduction to digital curation

http://www.ukoln.ac.uk/

An introduction to digital curation and preservation

Michael DayDigital Curation CentreUKOLN, University of [email protected]

Information and Library Management,University of the West of England, Bristol, 24 March 2009

Slides available on SlideShare: http://www.slideshare.net/michaelday


Information and Library Management, UWE, Bristol, 24 March 2009

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

ArchivalStorage

Access

DataManagement

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



What is needed for open science?– 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



What is needed for open science?– 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/