Enhancing Research Performance Management at University of the Witwatersrand
Explore the key challenges and strategies for managing research performance at the University of the Witwatersrand, Johannesburg. Learn about data collection, staff specialization, funding, and more to boost research quality and impact.
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Presentation Transcript
Presentation at ARUA meeting in Addis Ababa Presentation by: University of the Witwatersrand, Johannesburg Tuesday, 27 February 2018
Brief Summary Our ability to collect, collate and verify data in the following areas is summarised as: 1. Research management infrastructure Well developed with considerably specialisation 2. Research policy and/or strategy Well developed with a new 5 year plan in place (2018) 3. Staff data Captured by the HR division, but must be defined carefully 4. Research income Difficult to separate from consulting and other services 5. Student data Captured by Registrar s division 6. Research publications Captured using an internal collections system 7. Technology transfer Captured by Wits Enterprise, a wholly own Wits company 8. Research performance data Used regularly to manage the research process 9. Research performance reporting Report regularly using a variety of means
Considerable specialisation has taken place in the Wits Research and Innovation Management Space Wits Health Consortium Clinical trial management Wits Enterprise Tech Transfer office University Research Office 23 people Research Income = $35 million 12 people Research Income = $10 million 30 people Research Income = $150 million
Business Intelligence Service (BIS) Student data We are blessed with but do we use it? And so forth Financial data BIS Staff data Research data
10. GENERAL What do you regard as your THREE most important challenges as far as research performance management are concerned? 1. Funding for direct and indirect research costs and PG student bursaries 2. Improving the quality of research produced and its impact 3. Ensuring productivity becomes more uniform across the university through the use of sensitive and effective workload models
THANK YOU THANK YOU
