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Sharing of social science and humanities data

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Sharing of social science and humanities data Professor Denise Lievesley Head of School of Social Science and Public Policy, King s College London and – PowerPoint PPT presentation

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Title: Sharing of social science and humanities data


1
Sharing of social science and humanities data
  • Professor Denise Lievesley
  • Head of School of Social Science and Public
    Policy,
  • Kings College London and
  • Chair, European Statistical Advisory Committee

2
  • Principles
  • Policy
  • Practice
  • Partnerships

3
Principles
  • Scientific principle research findings together
    with the data should be available for others to
    refute, confirm, clarify, or extend the results
    part of public accountability
  • Responsibility to funders and to society to use
    resources efficiently (data are often
    under-exploited)
  • Important to reduce response burden
  • Increasing international responsibilities

4
Scientific paradigm
  • Many codes of professional conduct espouse these
    principles eg
  • The International Statistical Institutes
    declaration on professional ethics states that A
    principle of all scientific work is that it
    should be open to scrutiny, assessment and
    possible validation by fellow scientists.

5
Two important publications
  • Fienberg S., Martin and Straf (1985) Sharing
    research data National Academy Press
  • Arzberger P., Schroeder, Beaulieu, Bowker, Casey,
    Laaksonen, Moorman, Uhlir, Wouters (2004)
    Promoting Access to Public Research Data for
    Scientific, Economic, and Social Development
    Data Science Journal

6
  • Publicly funded research data are a public
    good, produced in the public interest. As such
    they should remain in the public realm.
    Availability should be restricted only by
    legitimate considerations of national security
    restrictions protection of confidentiality and
    privacy intellectual property rights and
    time-limited exclusive use by principal
    investigators.

7
  • In recent years, the debate on e-science has
    tended to focus on the open access to the
    digital output of scientific research, namely,
    the results of research published by researchers
    as the articles in the scientific journals. This
    focus on publications often overshadows the
    issues of access to the input of research - the
    research data, the raw material at the heart of
    the scientific process and the object of
    significant annual public investments. In terms
    of access, availability of research data
    generally poses more serious problems than access
    to publications. Arzberger et al (2004)

8
Reduction of response burden
  • Compliance costs important especially in small
    countries and in surveys of elites, businesses,
    institutions
  • Fresh data collection takes time and resources
  • Secondary data analysis can take place in
    resourceconstrained (including a
    time-constrained) environment

9
conclusion
  • Deliberate replication is to be encouraged
  • Duplication in ignorance of previous research is
    to be abhored
  • There is growing awareness that failure to
    exploit the full potential of data has costs for
    society and many institutions and agencies now
    espouse the aim of ensuring that data are used as
    extensively as possible.

10
Importance of establishing policies on data
access, sharing and preservation
  • by
  • funding agencies
  • universities or university consortia
  • professional societies
  • data producers
  • Policies need an implementation plan which
    must pay attention to the sticks and carrots and
    to the means of achieving the plan

11
Example policy UK Economic and Social Research
Council
  • limits new data collection
  • encourages secondary analysis
  • requires deposit of new data and derived data in
    UK data archive
  • determines the date for deposit
  • sets standards for documentation
  • provides resources for data access and
    preservation
  • builds data commons
  • funds data use workshops.

12
Barriers to data access
  • legal obstacles especially with respect to
    confidentiality, commitments to respondents
  • technical and financial obstacles including
    in-house capacity to handle the complex aspects
    of micro-data dissemination such as data
    anonymization
  • political obstacles
  • psychological obstacles the tendency to control
    access perhaps because of concerns over its
    mis-interpretation or because data is power

13
Incentives in academic system
  • In 1985 the report of the US committee of
    national statistics pointed out that A scientist
    is recognised and rewarded through the scientific
    community and its institutions. Researchers will
    have greater incentives to share data if the
    community and its institutions foster the idea
    that the practice advances science and is part of
    what is recognised as necessary and proper
    scientific behaviour.
  • Competition, performance targets, etc

14
Policies must pay attention to the
responsibilities of data users
  • acknowledge and give credit
  • respect conditions of access
  • use data responsibly
  • provide feedback on use
  • Value and role of data intermediaries

15
Benefits to universities of sharing data
  • Development of knowledge
  • Encourage greater exploitation of data and
    therefore greater impact
  • Contribute to sound policy decisions
  • Foster multiple perspectives on data
  • Facilitate comparative research
  • Create knowledgeable data community
  • Provide feedback on data and improve data quality
  • Improve citations and competitiveness
  • Improve quality and relevance of teaching

16
Putting the plan into practice
  • Promotion of the plan
  • Clear guidance for data producers
  • Resources
  • for providing access
  • for preservation

17
Access one size doesnt fit all
  • Needs of users/usages differ
  • especially in relation to their sophistication
    and the need for individual level data
  • Data sets vary especially in relation to
    sensitivity of content and possibility of
    disclosure
  • Particular challenges are posed by
  • Integrated, longitudinal data
  • Qualitative data
  • Administrative data
  • Cross-national data

18
Shared resources?
  • Centralisation v. disseminated model
  • Specialised services v. generic
  • Delivering data remotely v. safe havens

19
Partnership - with data intermediaries
  • for both technical work and advocacy partnership
    across the data archiving, data librarian,
    statistical and research communities is to be
    encouraged
  • Preservation
  • Metadata and documentation
  • Providing access
  • Keeping records
  • Running user training

20
Preservation is essential
  • Having collected data at some cost to the
    taxpayer, it behoves us to manage them well.
  • Alongside dissemination, this entails data
    preservation.
  • Due to poor data management, human error as well
    as technical change and inadequate use of
    technology, many data sets are no longer
    readable.
  • Thus all that remains of this important legacy
    are the, often quite superficial, reports or
    papers that were produced at the time.
  • To this extent an important part of our heritage
    is lost and we are severely limited in our
    analysis of change.

21
  • Long term preservation of electronic material is
    not a straightforward task especially with data
    sets which have embedded software
  • It can be hard to persuade financial authorities
    to spend money on the preservation of data for
    historians and researchers of the future, when
    there are so many pressing problems today.

22
Partnerships- with government data agencies
  • to broaden data use and reuse
  • to foster diversity and deepen the quality of
    data analysis thereby extracting more information
    from the data
  • to add value to data by bringing subject-matter
    knowledge to data analysis
  • to improve data quality (Data analysts can and
    often do detect errors in data and when they
    provide feedback to statistical agencies, this
    can lead to improvements in future data
    collection.)

23
Such agencies aim to graduate from being data
producers to generators of information and
knowledge
  • attention to data collection at expense of
    generation of information and knowledge
  • collection costly and difficult
  • importance of quality of data
  • mountains of data insufficiently processed and
    analysed
  • most people not adept at understanding data
  • important for government agencies to get involved
    in interpretation and use of information

24
  • It is the responsibility of official agencies to
    ensure that the widest possible use is made of
    data consistent of course with the legal
    constraints and ethical undertakings.
  • Partnership with Universities is a key way of
    enabling them to deliver on this responsibility.

25
Case study building the secondary uses services
National Health Service in Englandindividual
patient care records
  • Conducting audits of clinical practice
  • Surveillance of infectious diseases
  • Management of the health system
  • Monitor equity of access and provision
  • Evidence-based health policy
  • Providing better information to the general
    public
  • Improving the quality and safety of care

26
Aim of SUS to promote the widest possible
informed use of the data whilst maintaining trust
in the system
  • Hierarchy of data access consistent with ensuring
    lowest risk of patient identification
  • Need to know
  • Role of honest brokers and safe havens
  • Development of virtual safe havens

27
Information governance of Secondary Uses
Service
  • aggregate data widely available
  • default anonymised
  • - or pseudonymised
  • if identifiers needed consent should be obtained
  • full justification in terms of benefits to be
    made for exceptions
  • exceptions assessed by transparent, equitable,
    replicable and open process involving patients
    representatives
  • requirement for safety and security of
    information (ie accountability)

28
Partnerships internationally
  • Data archives
  • Cochrane collaboration
  • Campbell collaboration
  • National Library of Health
  • Communities of practice
  • Principle of reciprocity

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Results of a meta-analysis
  • Collation of the results of many studies
    contradict this advice
  • Extract from publicity prepared for the UK
    Reduce the Risk Campaign (early 1990s)
  • The risk of cot death is reduced if babies are
    not put on the tummy to sleep. Place your baby on
    the back to sleep. .Healthy babies placed on
    their backs are not more likely to choke.

33
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34
Iain Chalmers
  • No doubt like millions of his other readers, I
    passed on and acted on this apparently rational
    and authoritative advice.
  • We now know that the advice promulgated so
    successfully in Spock's book led to thousands, if
    not tens of thousands, of avoidable cot deaths.
  • (Letter to BMJ)

35
Communities of practice
  • International social survey programme
  • CROP - the Comparative Research Programme on
    Poverty whose major aim is to produce sound and
    reliable knowledge, which can serve as a basis
    for poverty reduction
  • RENCORE - encourage and enhance comparative
    empirical research of individual, national and
    institutional level data from the states of
    western, central and eastern Europe
  • Cleveland conference on education research
  • African Programme on Rethinking Development
    Economics

36
  • Concluding remarks
  • Social scientists and humanities researchers
    are involved in the creation of a diverse range
    of datasets, many of which are unique, rich in
    information content and incapable of replication.
  • Sharing allows scientists to extend the value
    of these datasets through new, high quality,
    ethical research and exploitation.
  • It also reduces unnecessary duplication of
    data collection.
  • Building preservation systematically into
    routine data management is part of good research
    practice it strengthens quality, enables
    replication and audit, and provides a sound basis
    for data sharing.

37
  • Research data grow in value the more they are
    used, unlike most commodities which are
    diminished with use.
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