Classification of research plays an integral role in the functioning of research funding organizations. As such it is important to have a classification system for efficient research data collection, use, analysis and reporting. Hear about lessons learned as well as key limitations and challenges for the implementation of a standard approach to classification through the analyses of different international standards currently in use with respect to their governance, development, implementation and maintenance

1. Research Classification System
October 28, 2015
Reconnect 15 – Conference
Sarah Moreault and Dr. Monica A. Valsangkar-Smyth

2. Agenda
1. Federal Funders Ecosystem
2. Project Background
3. Review of Existing Research Classification
Standards
Processes
Lessons learned
Challenges and Limitations
4. Conclusion for the Federal Funders
5. Next Steps

3. Federal Funders Ecosystem
 Who are the Federal Funders?
 Social Sciences and Humanities Research
Council (SSHRC)
 Natural Sciences and Engineering Research
Council of Canada (NSERC)
 Canadian Institute of Health Research (CIHR)
 Canada Foundation for Innovation (CFI)

4. Project Background
“Research classification is at the core of what
it is to fund research and report on the results”
“Good classification standard is essential to
the function of research organizations”
Jul 13 Oct 13 Jan 14 Apr 14 Jul 14 Oct 14 Jan 15 Apr 15 Jul 15 Oct 15 Jan 16 Apr 16
Jan 2015 - Mar 2015
Phase 2 - Establishing a
Data Governance
Structure and Designing a common
taxonomy framework
Sep 2013 - Mar 2014
Phase 1 – Common
RCS Feasibility Study
May 2013
Starting Point- Initial Discussions
between Funders
Sep 2015
Funders Decision
to Move Forward
with Next Phase Jan 2016 - Mar 2016
Phase 3
Reconnect15 Conference

5. Phase 1 - Feasibility Study

6. Phase 1 - Feasibility Study
 Tens of thousands of grant, chair, scholarship and
fellowship applications and thousands of on-going
awards every year
 Funders utilize a number of classification systems
within and between organizations
 Multiple process uses classification but data needs
partially met

7. Phase 1 – Benefits of a Common Approach
 Maximise consistency and efficiency of data
collection, analysis, use and reporting
 Enhance ability to:
 Support mandatory reporting
 Tell Science and Technology story
 Increase influence through better information
 Continuous improvement

8. Phase 2 - Data Governance and Designing a
common taxonomy framework
 Creation of an Executive Data Governance
Coordination Committee
 Review of existing Classification Standards

9. Review of Existing Research Classification
Standards
 Organization for Economic and Community
Development (OECD) Frascati Manual
 Australia and New Zealand Research Classification
2008 (ANZSRC 2008)
 Health Research Classification System (HRCS)
 German Research Federation Classification (DFG)

10. Processes involved in the Establishment of a
Common RCS
 Governance
 Establishment of policies and continuous monitoring of
their proper implementation
 Development
 Developing the RCS whether by modifying an existing
system or creating a brand new system
 Implementation
 Processes involved in the implementation of the RCS
such as user training, coding tools etc.
 Maintenance
 Monitoring the RCS and updating as required

11. Lessons Learned
 Governance
 Require research subject matter experts as well as
statisticians/evaluation officers
 Development
 Tight management of consultation process
 Need strong user support
 If possible, alignment to an existing classification
 Ensure equal treatment of all users
 Must adhere to the principles of a good classification system

12. Lessons Learned
 Implementation
 While different classification management systems are being
developed, manual coding remains the most thorough process
 Have supporting tools to assist implementation like coding
tools, mappings, operational guideline documentation
 Maintenance
 Have a clear process for maintaining the classification whether
this is a living register of issues or a review plan (every 5 or 10
years)

13. Challenges and Limitations
 No single ideal classification
 Human and technical resources
 Historical datasets
 Interdisciplinary research
 Training and guidance

14. Conclusion for the Federal Funders
“Developing, implementing and maintaining a
common research classification system for the
federal funders would require substantial human,
material and technical resources and would not be
feasible.”
So what’s next?

15. Next steps
 Quick-win approaches to improve the current
Research Classification System are preferred.
 Next steps:
 Assess the feasibility of using the CASRAI Model
 Explore the use of an evaluation platform.

16. Thank you!