Dr. McEachern is Director of the Australian Data Archive at the Australian National University, and has research interests in data management and archiving, community and social attitude surveys, new data collection methods, and reproducible research methods. This talk was given for the Monthly Tech Talks event hosted by Australian data infrastructure groups ANDS, NeCTAR, RDS and others.

1. ADA, DDI and the Data
Lifecycle
Dr. Steve McEachern
Director, ADA
Tech Talk
April 2017

2. ADA in Brief
• The Social Science Data Archive (now ADA) was set up
in 1981, housed in the Research School of Social
Sciences at ANU, with a mission to collect and preserve
Australian social science data on behalf of the social
science research community
• The Archive holds over 5000 datasets from around
1500 studies, including national election studies; public
opinion polls; social attitudes surveys, censuses,
aggregate statistics, administrative data and many
other sources.
• Data holdings are sourced from academic, government
and private sectors.

3. The Data Documentation
Initiative standard
http://www.ddialliance.org

4. About DDI
• A structured metadata specification of and for the
community
• Two major development lines – XML Schemas
– DDI Codebook
– DDI Lifecycle
• Additional specifications:
– Controlled vocabularies
– RDF vocabularies for use with Linked Data
• Model based version is in development
– with serialisations in XML and RDF
– Includes support for provenance and process models
• Managed by the DDI Alliance
– http://www.ddialliance.org

5. DDI-Codebook
• XML based, first published in 2000
• Four sections:
1. Document description: characteristics of the DDI XML
document itself
2. Study description: characteristics of the Study (project) that
the DDI is describing (including Related Materials:
documents associated with the project, such as
questionnaires, codebooks, etc.)
3. File description: characteristics of the physical data files
4. Variable description: characteristics of the variables in the
data file

6. DDI Lifecycle Model
6
Metadata Reuse

7. Why can DDI Lifecycle
do more?
• It is machine-actionable – not just documentary
• It’s more complex with a tighter structure
• It manages metadata objects through a structured
identification and reference system that allows
sharing between organizations
• It has greater support for related standards
• Reuse of metadata within the lifecycle of a study and
between studies
7

8. Managing and Depositing Data:
ADA and DDI

9. Approach
• Core archive website:
– http://www.ada.edu.au
• Sub-archives focussed on specialised thematic or
methodological areas
- eg. http://www.ada.edu.au/indigenous/home
• “Add-on” systems for complex analysis or
visualisation tasks:
– Nesstar
– GIS: http://gis-test.ada.edu.au
– Longitudinal visualisation: Panemalia
– Historical census data: http://hccda.ada.edu.au

10. OAIS architecture

11. Data deposit: ADAPT

13. Archival processing
Manual system with some automation tools
1. Deposit:
– Review of ADAPT submission
– Storage via ADAPT to file store
2. Data processing:
– File format conversion (usually to SPSS for processing)
– Privacy/confidentiality review
– Data cleaning (in consultation with depositor)
3. Metadata processing:
– DDI-C metadata creation in Nesstar Publisher
4. Publishing:
– Archival storage and access format creation
– Data publication to Nesstar server
– Metadata publication to Nesstar and ADA CMS

14. The ADA study page
Study information is available through the tabs at the top of the
study:
• Study: information including the investigators, abstract,
sample, data collection methods, and access requirements.
• Variables: a list of variables available in a quantitative dataset
• Related Materials: additional documentation, links and other
related studies (eg. others in the series) that may interest you
The study page is also the access point for the ADA Nesstar
system, for:
• Analysis of quantitative data online,
• Download of data to your own computer.

15. The ADA Study Page

16. Future plans: Dataverse
• http://dataverse.org/
• “Dataverse is an open source web application to share,
preserve, cite, explore, and analyze research data. It
facilitates making data available to others, and allows you
to replicate others' work more easily. Researchers, data
authors, publishers, data distributors, and affiliated
institutions all receive academic credit and web visibility.
• A Dataverse repository is the software installation, which
then hosts multiple dataverses. Each dataverse contains
datasets, and each dataset contains descriptive metadata
and data files (including documentation and code that
accompany the data). As an organizing method,
dataverses may also contain other dataverses.”

17. Harvard Dataverse

18. Features
• One installation, multiple logins
• Multiple hosting options: Bare metal, VMWare, AWS,
OpenStack, …
• Login options: Native, ORCID, Shibboleth, …
• API and GUI access
• Client libraries: R, Python, Java
• OAI-PMH harvesting
• Open and Restricted data access
• New implications for data archiving, curation,
management and dissemination

19. Questions?
Steven McEachern
steven.mceachern@anu.edu.au
ada@anu.edu.au