The fit-for-purpose CGIAR Evaluation Framework and revised Evaluation Policy aim to support the evolving needs and demands of CGIAR in transformation, as stated in the 2030 CGIAR Research and Innovation Strategy and accompanying Performance and Results Management Framework. Consistent with the Quality of Research for Development (QoR4D) framework, one of the six evaluation criteria found in the Policy is Quality of Science (QoS). In process and performance evaluations, among other evaluation criteria, such dimensions as research inputs, processes, and outputs would be analyzed towards evaluating QoS. Given the amplitude of aspects covered by QoS, a range of different methods and tools is required; many relying on CGIAR-wide monitoring, evaluation, and learning (MEL) systems. This guest blog explains how network analysis can complement other methods and metrics used to monitor and assess the QoS. The example shows how integrating different dimensions like geographical focus, gender and duration of the collaborations and bibliometrics into the network analysis, brings value to analyzing the QoS inputs and processes, under the QoR4D.
Engaging in collaborations is beneficial for scientific discovery and accelerating science leading to impact (Fortunato et al., 2018). Multidisciplinary science is particularly crucial especially in agricultural research for development (AR4D), which aims to achieve the Sustainable Development Goals (SDGs). In this context, CGIAR is a unique international research community developing innovations and working locally to make research demand driven.
In 2021, the International Center for Agricultural Research in Dry Areas (ICARDA), CGIAR Research Programs on Grain Legumes and Dryland Cereals (GLDC) and Roots, Tubers and Bananas (RTB), and the University of Florida (UF) teamed up to analyze the collaboration networks occurring within each of the two CGIAR Research Program. The work aimed to explore the potential of using network analysis to understand existing data and support decision-making for One CGIAR. Discussions related to this study were shared during the development of the recently published technical note ‘Bibliometric Analysis to Evaluate Quality of Science in the Context of One CGIAR’ developed by Science-Metrix and CGIAR Advisory Services (CAS) Secretariat Evaluation Function.
During the study, we asked ourselves: how can we best visualize and describe the networks of GLDC and RTB based on the metadata from their published journal articles? What would these networks look like? Is there evidence about the influence of internal organizational structures in these networks? These and other questions are addressed while exploring the scientific collaborations and team composition, based on peer-reviewed journal articles and organizational structures in the two selected Programs from 2017-2018 throughout the end of 2020.