Download as PDF, PPTX
Uploaded byLouise Corti
Incentivising the uptake of reusable metadata in the survey production process
This document discusses incentivizing the uptake of reusable metadata in survey production. It notes that there is no universal language used to document survey questions and variables, leading to wasted resources. The Data Documentation Initiative (DDI) is proposed as a standard. Barriers to adopting metadata best practices include legacy systems, manual processes, and reluctance to change. The document outlines ideas to incentivize metadata use such as specifying documentation requirements in funding calls and improving documentation tools and workflows. Showing tangible benefits through applications like question banks and data exploration systems is also suggested.
More Related Content
Similar to Incentivising the uptake of reusable metadata in the survey production process
Incentivising the uptake of reusable metadata in the survey production process
- 1. Incentivising the uptakeof reusable metadata in the survey production process ESRA15 Reykjavik July 2015 Louise Corti Collections Development and Producer Support
- 2. Why worry aboutmetadata? • 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.
- 4. Show how toexploit 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 publishingrich 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 • Hardto 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 • Greatturn 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 ofXML 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 implementationtoday • 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 forsharable 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 datadocumentation requirements in the commissioning tender for fieldwork • Mapping between questions and data outputs • Improved readable questionnaire for end users
- 21. CLOSER project • Fundedvariable/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 CLOSERPIs? • 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 surveydesign • 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 • Surveyowners/producers - documentation online • Question banks • Journals - supporting data with sufficient metadata • Use DDI 3.2 Questionnaire profile, not bespoke schemas
- 25. Self-deposit expectations? • Peerreview 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 onincentivising • 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 Universityof Essex Wivenhoe Park Colchester Essex CO4 3SQ • ……………..…..……… ……………….. T +44 (0)1206 872145 E corti@essex.ac.uk