Evidence-based nomenclature and taxonomy of research impact indicators

Research Impact (RI) is a broad topic of scientometrics to support the progress of science and monitoring the influence of efforts made by the government, institutions, societies, programs and individual researchers. There are several documented and popular RI assessment methods developed by individuals and organizations for evaluating the research of a particular programme or general purpose. This intent has created the diversity in evaluation methods, frameworks and scope. Some methods focus only on the impacts related to academic recognition and use such as Bibliometric Measures. However, with the growing technology, academic networking, effective and targeted research strategies, and regular monitoring of RI are reducing the gap between the research producers and consumers. As a result, the horizon of RI is being broadened and covering other areas of impacts such as on economy, society, and environment. Many individuals and organizations have introduced measures and indicators for assessing the RI. However, due to diversity in nature and scale of RI, not a single method is considered robust and complete (Vinkler, 2010). Therefore, new measures and indicators are being introduced on a time to time basis according to the interest and availability of resources of the method designers (CAHS, 2009). Additionally, higher availability of national and international funding for health sciences is critically influencing the science of RI assessment (Heller and de Melo-Martin, 2009). It means there are more indicators, measures, and frameworks for health-related research than any other areas of science. Resultantly, there is a huge gap available for generalizability and transformability of health related efforts to rest of the science. This study aims to discover the evidence-based diversity of RI indicators and to develop a method, which can lead the generalizability and transformability of previous efforts. Nomenclature of RI indicators is developed based on divide and rule principal to achieve the generalizability. Additionally, taxonomical analysis is presented based on the nomenclature. This effort is a step forward to develop a robust and inclusive RI assessment method