1. An enterprise approach to research outputs collection, management and reporting at the University of South Australia: collaborating to innovate ALIA Information Online 2017 Conference
Cathryn Mahar Jenny Quilliam
Research Connections Librarian Manager, Information Resources & Technology

3. Background & Drivers Open Access policy Process improvement
Systems utilisation
Enhanced end user services
Research metadata reuse
Automated harvesting
Research activity benchmark measures
Data quality

6. Use of Create Bib API
From harvested WoS / Scopus / manually submitted metadata  MARCXML ->Alma record
Staff upgrade & verify records in Alma

7. Send immediately or 6 hour delay
Services Page
Creating handles
Uploading outputs
Publishing records
Templates for:
Requesting post-prints
Acknowledging supplied post-prints
Acknowledging Gold Open Access outputs
Send immediately or 6 hour delay

8. DOI Minting 1. Retrieve Bib API returns metadata for review
2. Validates against constraints:
No existing DOI
Eligible resource type
All mandatory metadata elements present
3. Calls the DOI minting API
4. Returns newly minted DOI
5. Update Bib API adds the DOI

9. Cites harvesting, updating counts, surfacing
Via WoS and Scopus cites APIs
Weekly – harvests counts for ca. 10,000 records
Counts and date harvested pushed into Alma
Published in Primo and Staff home page

10. Author, ORCiD, Affiliation
Times Cited Counts
Funding & Grants
Author, ORCiD, Affiliation
Persistent Link
DOI & Link to publisher
Research Dataset .

11. Data Quality Assurance & Reporting
A comprehensive suite of data quality assurance measures and activities are in place and include the following:
Use of Alma’s controlled vocabulary and mandatory metadata fields for record validation
Normalisatiaon rules and Indication rules
Alma Analytics reporting
Checklists and comprehensive processing documentation
Record review by staff prior to publishing to BIP
BIP Exception reporting for mandatory and non-mandatory data elements
Review of records happens in two ways. Every record is reviewed by a second team member ensuring each output description is seen by two separate people within the team. There is also a daily Exceptions report provided by BIP that details any records that fail to meet mandatory and non-mandatory requirements.
Workflows are constantly under review to ensure that Alma’s functionality is fully exploited.
Data Quality Assurance & Reporting
Use of Alma’s controlled vocabulary and mandatory metadata fields for record validation
Use of Alma’s MARC Extensions pack
Alma Analytics reporting
Checklists and comprehensive processing documentation
Record review by staff prior to publishing to the Enterprise Data Warehouse
Exception reporting for mandatory and non-mandatory data elements

12. CRO & ROR

14. notifications

15. Claiming output Accept and claim your research output
Identify if work is Open Access
Upload a post-print of your work
Submit any comments
Confirm information is correct and claim your research

17. Data reuse – Staff Home Pages

18. How long does it takes researchers to claim an output?
82% < 5 mins.
In 2016 Researchers self-submitted 3,855 outputs via Appian

19. Lessons Learned Collaborate for success
 Set achievable milestones for the project
 Seek expert advice as required
 Clear communication
 Online support is fine but sometimes face to face is better
 Maximise staff buy-in
 Remember that cultural change takes time
 Acknowledge ambiguities
 Be pragmatic and work with available resources.

20. Future Plans
Review workflows to fully exploit Alma functionality and ORCiD
Future proof the system for new compliance and reporting requirements
Communicate regularly with key stakeholders
Embrace the mantras:
Enter once, re-use many
One Team

21. Questions?
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