Incentivising the uptake of reusable metadata in the survey production process

1. Incentivising the uptake of reusable
metadata in the survey
production process
ESRA15
Reykjavik
July 2015
Louise Corti
Collections Development and
Producer Support

2. Why worry about metadata?
• No universal language used to document
questions and variables
• Too many bespoke systems and vocabularies
around
• Massive waste of human resource in the survey
data lifecycle
• Interoperability saves money
• Why don’t we all use the Data Documentation
Initiative (DDI)?

3. Who needs incentivising?

4. Show how to exploit metadata for surveys
• Challenge – to get established survey
operations to recognise the benefits of reusable
metadata
• Midlife study in the US (MIDUS) quite unique!
• Help funders, owners and producers ‘See the
light’
• For this we need to show something very cool
• Some good experimental stuff happening

5. Benefits of publishing rich survey metadata
• Survey documentation systems
• Question banks
• Survey data exploration systems
• Nesstar
• SDA
• Bespoke visualisation systems

9. Published outputs – question bank

10. Published outputs – online access

11. The reality
• Hard to match up Question and Variable
information
• Too much manual data entry involved in publishing
• Must do better
• Gain rich reusable metadata from the survey
design and production process

12. Survey production lifecycle
• Beset with manual processes
• Legacy systems
• Reluctancy to change or adapt systems
• Hard to embrace new ways – disruptive,
expensive

13. Typical process – worst case scenario
• Manual questionnaire entry
(doc/excel/database)
• Export in word format
• Deliver to survey agency
• Manual transfer to IBM Data Collection
• Export SPSS and PDF/word questionnaire

14. Survey Metadata: Barriers & Opportunities
Workshop: 26 June 2014

15. Meeting outcomes
• Great turn out and knowledge exchange!
• Quick turn around of principles into a ‘campaign’
document and a published ‘Questionnaire profile’
• Some very positive responses – shared problem
• Be an advocate!

17. Increasing use of XML for survey design and
publishing
Such as:
• Social science data archive published survey
metadata (DDI 2.5)
• Essex panel studies - bespoke XML Questionnaire
Specification Language for survey design
• UK LifeStudy – survey design instrument – XML

18. Discussing DDI implementation today
• CLOSER cohorts portal using DDI 3.2 Questionnaire
Profile
• DASHISH DDI 3.2 use
• Blaise – import by Michigan Questionnaire
Documentation System (MQDS) DDI 3
• IBM Data Collection DDI experiments

19. Short brochure for sharable survey products
• Work closely with data owners and producers
• Existing information on data sharing complex
• What is really expected!
• Transferrable information
• Not a bible

20. Sticks?
• Specifying data documentation requirements in the
commissioning tender for fieldwork
• Mapping between questions and data outputs
• Improved readable questionnaire for end users

21. CLOSER project
• Funded variable/question discovery service
• Long-running birth cohorts & longitudinal studies
• Drivers for project
• Harmonisation (biomedical, socio-economic)
• Capacity building
• Data Linkage
• Impact
• Discovery
• Encourage use of existing data resources
• Tools for enhancing survey metadata

22. Incentives for CLOSER PIs?
• Large award to get prestigious cohort studies on
board £££
• Reduce burden - enhancement work done centrally
• Survey data managers
 happy to be part of peer group
 rewarding to to go back and look at data
 liked a shared controlled vocabulary
 Received training
 variable to questionnaire mappings useful
 liked visibility of their study in the search platform

23. Forward looking survey design
• Think upfront about reusability of questionnaire
metadata
• New studies – new opportunities
• Legacy work to get old messy survey design metadata
into a new environment – may be worth investing in
• Can make harmonisation work so much easier – XML
schema allow formal linkages of variables across time,
equivalence, differences etc.

24. Data publishers
• Survey owners/producers - documentation online
• Question banks
• Journals - supporting data with sufficient metadata
• Use DDI 3.2 Questionnaire profile, not bespoke
schemas

25. Self-deposit expectations?
• Peer review of data by data centres for all data
published – includes quality of metadata
• Journals – no unified standard for data description
or documentation
• Start with minimal metadata expectations:
• data collection description
• provenance
• data description: file and variable names, labels,
• relationships between tables/files

26. Some tips on incentivising
• Speak a common language
• On DDI, don’t drown in detail; use existing profiles
• Start with the lowest common denominator. Baby steps
• Show value – shiny interfaces and examples!
• Provide supporting tools where possible e.g. metadata
entry
• Integrate into everyday workflows and research tools

27. CONTACT
UK Data Service
University of Essex
Wivenhoe Park
Colchester
Essex CO4 3SQ
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T +44 (0)1206 872145
E corti@essex.ac.uk