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Pascal Heus Open Data Foundation pheus@opendatafoundation opendatafoundation

Workshop on Metadata Standards and Best Practices November 19-20 th , 2007 Session 1 Leveraging Metadata Standards in RDC. Pascal Heus Open Data Foundation pheus@opendatafoundation.org http://www.opendatafoundation.org. Outline. PART 1: General issues & ODaF

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Pascal Heus Open Data Foundation pheus@opendatafoundation opendatafoundation

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  1. Workshop on Metadata Standards and Best PracticesNovember 19-20th, 2007Session 1Leveraging Metadata Standards in RDC Pascal Heus Open Data Foundation pheus@opendatafoundation.org http://www.opendatafoundation.org

  2. Outline • PART 1: General issues & ODaF • Needs and challenges in statistical data and metadata management • Metadata and XML solutions • Selecting specifications • Need for tools • Open Data Foundation • PART 2: RDC Specific issues • Metadata in RDCs • Solutions and benefits • Tools and ongoing initiatives • Conclusions / Q&A Open Data Foundation – IZA 2007/11

  3. What is Metadata? Labeled stuff Unlabeled stuff The bean example is taken from: A Manager’s Introduction to Adobe eXtensible Metadata Platform, http://www.adobe.com/products/xmp/pdfs/whitepaper.pdf • Common definition: Data about Data Open Data Foundation – IZA 2007/11

  4. Managing data and metadata is challenging! Academic Producers Users Librarians Sponsors Business Media/Press Policy Makers General Public Government • We are in charge of the data. We support our users but also need to protect our respondents! We have an information management problem • We want easy access to high quality and well documented data! • We need to collect the information from the producers, preserve it, and provide access to our users! Open Data Foundation – IZA 2007/11

  5. XML to the rescue! • XML is driving today’s web service oriented architecture of the Internet and Intranets • Using XML, we can capture, structure, transform, discover, exchange, query, edit and secure metadata and data • XML is platform & language independent and can be used by everyone • XML is both machine and human readable • XML is non-proprietary, public domain and many open tools exist • Domain specific standards are available! Open Data Foundation – IZA 2007/11

  6. XML Technical Overview Open Data Foundation – IZA 2007/11

  7. XML Solutions XML Specs Academic Producers Users Librarians Sponsors Media/Press Policy Makers Business General Public Government • Well documented data, here we come! • Great, I can provide public metadata! • Use our specifications and your will be happy! It will harmonize everything. • Now we can talk to each other! Open Data Foundation – IZA 2007/11

  8. Let’s use XML, but…. XML Specs Open Data Foundation Producers Users Librarians ? • Which specifications should we adopt? • How do we do this? Where are the tools and guidelines? Open Data Foundation – IZA 2007/11

  9. Open Data Foundation (ODaF) • US Based non-profit organization, established 2006 • Directors, advisors and managers from statistical and ICT communities • Project oriented • Mission • Focus on socio-economic data • Adoption of global metadata standards • Coordinated development of open-source tools • Capacity building • Improving data and metadata accessibility and overall quality • Operate at the global level Open Data Foundation – IZA 2007/11

  10. Selecting XML specifications • A single specification is not enough! • XML specifications commonly focus on a specific area of knowledge and/or set of functionalities • Cannot answer the needs of all actors • XML mappings between specifications are possible • Information can be converted from one domain to another and be carried across communities • Which ones should we use? • Fit for purpose • Widely accepted and supported • Can be mapped to a cross-domain family Open Data Foundation – IZA 2007/11

  11. A suggested set for socio-economic data • Statistical Data and Metadata Exchange (SDMX) • Macrodata, time series, indicators, registries • http://www.sdmx.org • Data Documentation Initiative (DDI) • Microdata (surveys, studies) • http://www.ddialliance.org • ISO 11179 • Semantic modeling, concepts, registries • http://metadata-standards.org/11179/ • ISO 19115 • Geography • http://www.isotc211.org/ • Dublin Core • Resources (documentation, images, multimedia) • http://www.dublincore.org Open Data Foundation – IZA 2007/11

  12. The need for Tools XML Specs Open Data Foundation Producers Users Librarians • We set specifications and standards. Tools are not our mandate • We produce data not tools! We don’t have the expertise. • We preserve and disseminate data not software! We don’t have the expertise • We use data and software but we don’t build tools! We don’t have the expertise Open Data Foundation – IZA 2007/11

  13. The need for Tools Open Data Foundation Mandated to develop tools Provide cross-domain expertise in ICT and statistics Provide umbrella for coordinated development Ensure inter-operability Outline harmonized architecture and environment Promote open source / maximize reusability Foster global registries Resources/Fund raising … Open Data Foundation – IZA 2007/11

  14. ODaF Vision • Promote and facilitate the production and use of “open data” • Public metadata, high quality, fully documented, respondent protected, easy to find, accessible in accordance to statistical principles and legislations • Foster a global harmonized framework • Facilitate the flow of data and metadata • Promotes dialog between all stakeholders Unlock the Data! Open Data Foundation – IZA 2007/11

  15. Some Projects & Ideas • Guidelines for an harmonized architecture and development environment • Roadmap for tools development • XML mappings • Facility to host development of open source projects (GForge) • Provide hosting services for agencies • Implement registries / catalogs • Produce training and reference material • Technical support & capacity building • Advocacy Open Data Foundation – IZA 2007/11

  16. ODaF partners / clients • Statistical agencies / producers • Data Archives • Academic & Research communities • Standard settings agencies & consortiums • Governmental organizations • International organizations • Open source community • Software developers • IT Vendors Open Data Foundation – IZA 2007/11

  17. Growing solutions in a complex environment XML-DB Programming XSLT XPath SOAP Databases Warehouse Web SDMX GIS XML ISO 11179 Infrastructure DDI ISO 19115 TECHNOLOGY SAS METADATA DCMI Excel Stata Registries Accessibility ANALYSIS DISSEMINATION SPSS Legal DISCOVERY Privacy Disclosure Toolkit PRESERVATION Access SECURITY SDDS Blaise PRODUCTION QUALITY GDDS CSPro USE DQAF What are we concerned with? Open Data Foundation – IZA 2007/11

  18. Growing solutions in a complex environment XML-DB Programming XSLT XPath SOAP Databases Warehouse Web SDMX GIS XML ISO 11179 Infrastructure DDI ISO 19115 TECHNOLOGY SAS METADATA DCMI Excel Stata Registries Accessibility ANALYSIS DISSEMINATION SPSS Legal DISCOVERY Privacy Disclosure Toolkit PRESERVATION Access SECURITY SDDS Blaise PRODUCTION QUALITY GDDS CSPro USE DQAF CHALLENGE We need a set of tools that work together in an harmonized framework. This requires coordinated efforts and expertise from the various communities • OPEN DATA FOUNDATION • Provide cross-domain & IT expertise • Coordinate and support development • Knowledge sharing • Capacity Building • Provide global vision and guidance Open Data Foundation – IZA 2007/11

  19. Challenges • The technology is available today • The right people are available today • The need and the will are there • The real challenges are: • Tools availability • Awareness / Understanding of technology • Change management • Coordination & Guidance • Focused resources and funding • Institutional commitment • Learn for the past for a better future • It’s not about data, it’s about people Open Data Foundation – IZA 2007/11

  20. Summary • Managing data and metadata is challenging • Solutions exist to make it easier and provide better information to unlock the data • Adopt a set of specifications that answer your requirements and can connect across domains • DDI, SDMX, ISO 11179, Dublin Core, ISO 19115 • Promote the use and development of open tools, do not work in isolation, get the appropriate expertise • Open Data Foundation Open Data Foundation – IZA 2007/11

  21. PART 2: Metadata & RDCs

  22. PART 2 • RDC metadata perspective • List of stakeholders / initiatives • Benefits of adopting metadata • Challenges • Tools demo (IHSN Toolkit) Open Data Foundation – IZA 2007/11

  23. RDC Objectives • Provide a secure environment for the researcher to perform the in depth analysis of sensitive/confidential data in a cost effective way • Facilitate the capture, sharing and dissemination of research knowledge • Provide feedback to the producer on data usage and quality • Exchange information with other RDC’s / agencies / public • Overall: benefit all stakeholders: producers, librarians, researcher, general public, etc. Open Data Foundation – IZA 2007/11

  24. RDC metadata • Simple access to data file and codebook is insufficient. Researcher need high quality comprehensive metadata and a collaborative environment to promote dynamic research • Traditionally, survey metadata has focused on archiving/preservation (current DDI 1/2.x) • This however insufficient and should extended into both the survey production process and the secondary use of the data • New DDI 3.0 meets such requirements • RDC ideal environment for capture of researcher metadata Open Data Foundation – IZA 2007/11

  25. DDI 3.0 and the Survey Life Cycle • A survey is not a static process • It dynamically evolved across time and involves many agencies/individuals • DDI 2.x is about archiving, DDI 3.0 extends to life cycle • 3.0 is a modular framework available for multiple purposes (use cases) • Metadata is key to comprehensive capture of knowledge Open Data Foundation – IZA 2007/11

  26. RDC issues • Without producer metadata • researchers can’t work discover data or perform efficient work • Without researcher metadata • producer don’t know about data usage and quality issues • Other researcher are not aware of what has been done • Without standards • Information can’t be properly managed and exchanged between agencies or with the public • Without tools: • Can’t capture and preserve/share knowledge Open Data Foundation – IZA 2007/11

  27. When to capture metadata? • Metadata must be captured at the time the event occurs! • Documenting after the facts leads to considerable loss of information • This is true for producers and researchers Open Data Foundation – IZA 2007/11

  28. RDC Metadata Framework Producers Researcher RDC RDC RDC External users 1. Producer provide data & basic docs 2. Need to enhance existing metadata 3. Start capturing researcher metadata 4. Knowledge grows and gets reused 5. Provides usage and quality feedback to producer / RDC 6. Repeat across surveys/topics 7. Metadata facilitates output Research Output 8. Public metadata facilitates data discovery / fosters global knowledge Research Metadata 9. Metadata exchange between agencies Public Use metadata Producer/Archive Metadata Data Open Data Foundation – IZA 2007/11

  29. Metadata Components • Producer metadata: • Codebook, questionnaires, reports, methodologies, processing, scripts, quality, admin, etc. • Research metadata • Recodes, analysis, table, scripts, papers, references, logs, quality, usage • Activities, discussions, knowledge base • Outputs • Papers, presentations, tables • Public metadata • Metadata stripped out of sensitive information (summary statistics, sensitive variables, etc.) • Metadata capture can be manual, semi-automated, automated Open Data Foundation – IZA 2007/11

  30. RDC Solutions • Metadata management • Adopt standards and provide researcher with comprehensive metadata • Use related tools to capture research process • Metadata mining and reporting utilities • Collaborative environment • Used web technologies to foster a dynamic research environment • Connected and Remote enclaves • Connect RDCs through secure networks • Consider virtual data enclave or batch analysis • Data disclosure • Protect respondent through sound data disclosure techniques (using metadata as well) • Train producers/researchers (methods and data) Open Data Foundation – IZA 2007/11

  31. Solution Examples • Simple solutions: use good practices • File and variable naming conventions, sound statistical methods • Comment source code • Document the work • Metadata solutions: • DDI tools, citation database, source code level metadata capture, variable recodes, table disclosure, data quality feedback, comparability • Web based collaboration environment • Wiki, blogs, discussion groups, events/todo Open Data Foundation – IZA 2007/11

  32. Benefits (1) • Comprehensive data documentation • Through good metadata practices, comprehensive documentation is available to the researchers • Preservation, integration and sharing of knowledge • Research process is captured and preserved in harmonized formats • Research knowledge becomes integrant part of the survey and available to all • Reduce duplication of efforts and facilitates reuse • Producer gets feedback from the data users (usage, quality issues) Open Data Foundation – IZA 2007/11

  33. Benefits (2) • Research outputs and dissemination • Facilitate production of research outputs • Facilitate dissemination and fosters broader visibility of research outputs • Exchange of information • Metadata exchange between RDC, producers, librarians • Importance of public metadata for sensitive datasets • Facilitate data discovery (inside and outside RDC) • Advanced metadata mining / comparability Open Data Foundation – IZA 2007/11

  34. Answering the tools challenge • Metadata standards are available but there is a lack of tools for metadata management • Several efforts are ongoing • DDI Alliance, International Household Survey Network, UK Data archive, NORC Data Enclave, Canada RDC, Open Data Foundation • DDI Foundation Tools Program, UK DExT, Canada RDC, EU Framework 7 • Joint efforts will minimize costs, maximize reusability and foster tool harmonization / interoperability • Open source model: availability & sustainability Open Data Foundation – IZA 2007/11

  35. RDC challenges • Adopting good metadata management framework takes effort • Survey metadata must first be compiled • ICT capacity building and tools development • Producer and researchers need to be trained • Not only a technological challenge • change management, training • Leads to better research, shared knowledge, better user/producer dialog, improved data quality • Meets the mandate of RDC Open Data Foundation – IZA 2007/11

  36. IHSN Toolkit Quick Demo 1 Import data and compile metadata 3 Generate HTML based CD-ROM 2 Import metadata and prepare CD-ROM Open Data Foundation – IZA 2007/11

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