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Various conceptual frameworks are used to design and structure M&E evaluation criteria. For instance, these include: (i) the logic model, (ii) results-chain framework, and (iii) balanced scorecard approach. In using the logic model, the following key variables are considered: inputs, outputs and outcomes. The model also considers the logical linkages to external influences, environmental and related programmes as well as the situational context (problem) the motivates the introduction of the intervention (inputs and outputs) to achieve a specific impact (outcome) (Millar, Simeone and Carnevale, 2001). Oftentimes, the logic model is critiqued for being a linear model that aims to monitor and evaluate a multi-dimensional process. When planning to build a logic model the following questions can be posed: (i) what is the current situation that needs to be tackled? (ii) what will it look like when the desired outcome has been achieved? (iii) what behaviours need to change for that outcome to be achieved? (iv) what knowledge or skills do people need before the behaviour changes? (v) what activities need to be performed to cause the necessary change? (vi) what resources will be required to achieve the desired outcome? (Millar et al., 2001). The results-chain framework on the other hand, is a M&E tool that is used by the World Bank (2012) to measure effectiveness. This framework aims to establish and link strategic development objectives to interventions and intermediate outcomes and results. In developing such a framework that demonstrates effectivess, the following guiding questions can be discussed:

The critical aspects of a robust management plan includes the: (i) physical infrastructure; (ii) services and utilities; (iii) safety and security; (iv) insurance arrangements; (v) alternate power supply; (vi) maintenance; (vii) access and training; (viii) having appropriately skilled instrument staff in place, and (ix) a clear data management plan (Fig. 7.1). From the aspects highlighted above, the following key areas must Management Plan Infrastructure (building) Services & Utilities Safety & Security Insurance Alternate Power supply Maintenance Access and Training (cost recoverybench fees) Instrument Staff Data Management

Skilled instrument staff ranging from scientists, operators, technicians and engineers can enhance safety, productivity and extend the lifespan of equipment, as well as its components in addition to generating new knowledge and innovation. Skilled staff remains a critical and scarce resource in many countries including South Africa. For instance, discussions across the globe on this subject have revealed that instrumentation staff are not only a scarce skill, but are the hardest positions to fill in any research institution. Considering the scarcity of skillful staff, it becomes increasingly difficult to attract and retain such experts. 5.1 Staff Scientists Staff scientists are usually responsible for managing labs, facilities (e.g. radio telescope), or specific equipment (e.g. microscopes.) with the intent to acquire new knowledge through research. These experts tend to be academically qualified, typically with a doctoral qualification and tend to be employed on academic grades, such as associate or full professors. In general, staff scientist lead research programmes and investigations, generate data, interpret the data and publish manuscripts. Additionally, staff scientists (i) train students; (ii) pursue grant applications; (iii) generate publications; (iv) develop new innovations, techniques, systems and methodological protocols; and (v) operate and maintain the research equipment. 5.2 Operators In addition to the staff scientist, an operator plays a key role in managing the research equipment. One of the key roles of an operator is that of training and advising postgraduate students, researchers, postdoctoral fellows and other users on how to utilise

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