Statistical Research Data on the Semantic Web

1. Statistical Research Data on the Semantic Web Daniel BahlsLeibniz Information Centre for Economics (ZBW) SWIB 2012 Cologne, Germany

2. Outline • Introduction • Research data in economics and scientific practices • Thoughts on data representation • Repeatability of research results • Outlook • Data access and retrieval • Proxies and empirical models

3. MaWiFo Project Management of Economic Research Data

4. „What researchers want“ • Tools and services must be in tune with researchers’ workflows, which are often discipline-specific • They must be easy to use • “Cafeteria model”: researchers can pick and choosefrom a set of tools and services • Benefits must be clearly visible – not in three years’time, but now Source: Feijen (2011)

5. Research Dataas Bibliographic Artefacts • Re-useData Sharing gives more opportunities for research • Citation • Data acquisition and assignement of Persistent Identifiers • TransparencyReproducibility: Fundamental criteria for good scientific practice

6. Research data in economics and scientific practices • Target Group: Researchers in Economics • Community Building for Knowledge Exchange: Economists – Data Librarians – Computer Scientists • Interviews on • Data Management Sharing Sources Publishing • Processing

7. How does Research Data look like in Economics?

8. Interviews with Researchers in Economics Data Agencies Own Surveys & Studies Local File System Statistical Offices Sources Data Management Backup Server Trusted Institutesand Researchers DVD, External HD, ... not includedin review process Execution Times: seconds, minutes, hours SPSSStata Matlab ... Publishing Processing Zip Files practiced sometimes ProgrammingLanguages Sharing On Request (?) High PerformanceComputing Trusted Colleagues Within Teams Seite 8

9. Particular Findings • Research is driven by the availability of data (to some extent) • Some research is based on external data, • Some research is based on self-conducted studies • Combining and Merging of data sets in average, 66% ofthe data comes from external sources (estimated)

10. Particular Findings • Data Usage Rights – e.g. Thomson-Reuters Datastream • Data Protection • on-site access, virtual access • sample data to understand structure • analysis scripts • aggregation • protection maintained? Copy to third party?

11. Thoughts on Data Representation Often, the legal situation does not allow for publishing the entire data set as was used ? transparency re-use data review repeatability curation

12. Interim Conclusion • A model based on copying is insufficient • We suggest fine-grained referencing • single data items must be referenceable (merging, curation) • highly distributable (distributed data sources) • extensible (heterogeneous long tail data, curation) • LOD-based approach

13. DataSet type external dataset Data Items UserDataSet includesData type Data Itemsfrom own survey

14. RDF-Representation for Statistical Data used for our example SourceData Cube vocabulary StatsWales: Life Expectancy, Dataset 003311

15. DataSet Dimension Item DimValue dataProperty example: label time A 2005-7 rdf:value X label region B 83.7 Cardiff gender label C Female

16. RDF-Representation for Statistical Data Using the semantic model, referencing of data at a very detailed level is possible - without need for the data itself to be public Challenge:Stable URIs required for every single data item label time A 2005-7 rdf:value X label region protected B 83.7 Cardiff gender label C Female you can omit single information itemssuch as the value itself, yet the data is still referenceable

17. SCOVO

18. RDF Data Cube Vocabulary (QB) source:http://publishing-statistical-data.googlecode.com/svn/trunk/specs/src/main/html/cube.html

19. Repeatability of research results • missing values • seasonal adjustment • purchasing power adjustment • plausibility tests • basket analyses • ... aggregationand data cleaning ? McCullough, B. D. Got Replicability? The _Journal of Money, Credit and Banking_ Archive Econ Journal Watch, 2007, 4, 326-337 Interesting read

20. Repeatability of research results • scripts (“do-files”) • working copies of data • change parameters, so thateffect can be shown clearly • no overall build process

21. A build script for empirical analyses • Maven-like, ANT-like

22. DataSet No gaps Trust Incentive type external dataset Data Items UserDataSet includesData type buildScript Data Itemsfrom own survey

23. Communication & Architecture Archive C Archive D Archive B Archive A Authenticate & Request Data Reference Model Digital Library DOI Client

24. Open Challenges (practical) • Researchers in economics would love to re-use data from others. • Researchers in economics hesitate to share their data. • Competitive advantage: • “We put too much effort into data production, • so we want to be the ones to publish on it.” • “The code discloses too much of our know-how.” • Incentives needed: • Data citation • Trust in research results (no gaps from data sources to results)

25. Open Challenges (technical) • Precise referencing: • A unique URI for every data item / table cell ? How about curation and data versioning ? • Maven-like build scripts: • How to specify entire system environments and software modules? • Vocabulary extensions: • Specific data needs specific description, • where do the necessary rdf:Properties come from?

26. Summing up • Reference model for exact reconstruction of research data sets • Build scripts and dependency management for repeatability • Transparency of data sources and processes • “executable paper”, learning from others, data reviews,.... • rerun analysis – with curated values – with latest data

27. Thank you