Digital Curation 101: Preserve
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Digital Curation 101: Preserve

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Presentation given at: Digital Curation 101, National eScience Centre (NeSC), Edinburgh, 8 October 2008. The course was organised by the Digital Curation Centre (DCC), and ran from 6-9 October 2008

Presentation given at: Digital Curation 101, National eScience Centre (NeSC), Edinburgh, 8 October 2008. The course was organised by the Digital Curation Centre (DCC), and ran from 6-9 October 2008

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Digital Curation 101: Preserve Digital Curation 101: Preserve Presentation Transcript

  • Digital Curation 101: Preserve Michael Day UKOLN, University of Bath [email_address]
  • Presentation outline:
      • Preservation in the curation life-cycle
      • Roles and responsibilities
      • Reasons for preserving research data
      • Digital preservation challenges and strategies
      • Major types of research data collection
      • Infrastructures for preservation and curation
  • Module outline
    • This module will explore actions required to ensure long-term preservation and retention of the authoritative nature of data
    • Preservation actions should ensure that data remains authentic, reliable and usable while maintaining its integrity.
    • Actions include data cleaning, validation, assigning preservation metadata, assigning representation information and ensuring acceptable data structures or file formats
  • 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
  •  
  • Preservation in the DCC lifecycle
    • In the DCC Curation Lifecycle Model, the “Preservation Action” stage:
      • Immediately follows the “Ingest” stage
      • Is followed by the “Store” stage
      • Is directly linked with “Transform” and “Appraise and Select” stages
      • Includes major elements from the inner circles:
        • Description and Representation Information, Preservation Planning, Community Watch
  • Preservation in the DCC 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
  • 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)
    • Long-lived data collections (NSB)
      • Data authors
      • Data managers
      • Data scientists
      • Data users
      • Funding agencies
    • Dealing with data (JISC)
      • Scientist
      • Institution
      • Data centre
      • User
      • Funder
      • Publisher
  • Roles and responsibilities (2)
    • 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
  • Roles and responsibilities (3)
    • 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
  • Roles and responsibilities (3)
    • 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
  • Roles and responsibilities (4)
    • 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
  • Roles and responsibilities (5)
    • 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
  • Why curate research data? (2)
    • 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
  • Why curate research data? (3)
    • 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”
  • Why curate research data? (4)
    • 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)
  • Why curate research data? (5)
    • 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
  • Why curate research data? (6)
    • 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
  • Preservation challenges (2)
    • 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
  • Preservation challenges (3)
    • 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
  • Conceptual problems (2)
    • 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/
  • Conceptual problems (3)
    • 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
  • Some general principles (2)
    • 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
  • Some general principles (3)
    • 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
  • Technology emulation (2)
      • 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
  • Technology emulation (3)
      • 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
  • Information migration (2)
      • 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)
  • Information migration (3)
      • 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
  • 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
  • Other preservation challenges
    • 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
  • Other preservation challenges
    • 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
  • Other preservation challenges
    • 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
  • Other preservation challenges
    • 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
  • Other preservation challenges
    • 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 research data is a big ongoing challenge
    • Solutions are based on the active management of data
    • Decisions needed on whether to adopt standard formats, identifying 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)
  •  
  • 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/
  • Acknowledgements
    • 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/