A myriad of metrics is available, compounded by many similar versions of the same metric. It is difficult to know which metric will give the most useful insights, whether a metric is being calculated appropriately, or whether other institutions are looking at things in the same way.
Snowball Metrics are best practice, built by the sector sharing its knowledge and experience. They are a manageable set of metrics that aim eventually to inform all areas of research activity. Agreeing methodologies, which can be consistently applied to research management information, creates consistency and facilitates benchmarking between peer institutions. This helps to establish a reliable foundation for institutional strategic decision making to complement existing approaches.
The Snowball Metrics initiative invites all interested organizations to apply the framework: research institutions, funding agencies, government groups and suppliers. Adopting a common framework will drive efficiencies throughout the sector.
Snowball Metrics is a response to common frustrations voiced by universities:
- Informed decisions depend on data, as well as on expert opinion and peer review. Lack of an evidence-base prevents universities making the best decisions for themselves
- University systems and the data that they collect are often determined in response to frequent demands from funders and agencies, rather than what would be most useful to address their own questions
- Universities are poor at collaborating with each other, and especially with funders and agencies
- The commercial systems and tools available have not effectively addressed all the needs of a university, which has led to the proliferation of independent bespoke institutional systems and little best practice
- To enable informed evidence-based decision-making by agreeing a single method to calculate metrics that will provide input to institutional strategies by ensuring the comparison of apples with apples. Snowball Metrics are based on all the data sources available to a university, including institutional data sources, and do not depend on a particular data source or supplier
- For research-intensive universities to own the definition of these metrics, rather than them being imposed by funders and agencies
- For institutions to collaborate with each other to agree a common solution, and to try to influence funders and agencies to adopt this as a common solution
- For research-intensive universities to work with a commercial supplier of research information who can learn about their needs at first hand, and build systems and tools that enable institutions to effectively and efficiently store their information and provide unambiguous, rigorously defined metrics based on consistent data