Abstract

A CRIS is only as effective as the data contained within. A simple statement, yet tightly bound with the risk and opportunities research managers encounter daily.

In principle, a CRIS is a tool to ingest, process and report on research-related activities and outcomes – all vital information to support research management decision processes across multiple purposes in the research ecosystem. These purposes have grown over time and, as new measures of activity and outcomes appear, will continue to evolve. Concurrently, as noted in a recent EARMA press release [1], “The role of research management has undergone dramatic changes, in response to the ever-changing demands of the social and political context of public-funded research. Research managers are now an integral and vital part of the research ecosystem, and take many forms, including policy advisers, project managers, financial support, data stewards, business developers and knowledge brokers.”

Underlying this, the need for high quality data in a CRIS is paramount. However, managing data quality is too often thought of as an unavoidable cost of ownership or, worse, not thought of at all.

Data quality, however, is not an absolute measure – it’s relative, based on the use cases and purposes of its users. As an example, for research managers, the concept of completeness plays a factor in data quality. It may not be vital for all possible information on a record to be present; rather only the information needed to support stakeholders should be available and accessible. A simple maxim, one that other data quality concepts such as correct, connected, current, and compliant (the 5Cs of data quality) follow, and one a CRIS should support.

As a further example of completeness in regards to data quality; an organisation may use multiple publication index sources, with each source having distinct advantages – cost, scope, or update schedule for example. Why rely on only one source to create the final record? A CRIS should be able to take advantage of these distinguishing factors to supplement any data gaps. Similarly, for the concept of correctness: if multiple sources have overlapping coverage, a more reliable or trusted source can be used to provide data for specific fields where correctness is more valued. In regards to connectedness, the ability of a CRIS to correctly relate researchers to their publications and patents - including any associated metrics, and associated applications and grants - supports several value propositions for research managers and their stakeholders. And, lastly, the concepts of currency and compliance ensure that records submitted to, for example, national assessment exercises contain all the necessary metadata and meet any open access requirements.

The 5Cs of data quality provide research managers with a framework to evaluate the effectiveness of their CRIS and the data contained therein. It’s a framework for research managers to ensure they remain, as mentioned in the press release, “a vital part of delivering research impact, strategic planning and bridging between leadership groups in complex university and research organisations.”

[1] EARMA Press release “Mapping the future of Research Management”. Accessed 15 September 2022. https://earma.org/news/press-release-mapping-future-research-management/