This study presents a capacity framework for measuring community readiness for supporting big data science projects during cyberinfrastructure (CI) diffusion. CI projects are academic big data science projects driven by data-intensive research efforts. CI projects are an interesting example of understanding big data science projects from a scientific and academic perspective. In this paper, we advanced the argument that in order for CI projects to succeed, they need to draw from five dimensions of community readiness. More specifically, we present a capacity framework of community readiness consisting of the five dimensions of national support networks, peer-to-peer support networks, CI opinion leaders, curriculum and training, and student workforce. We proposed composite scale items developed to quantitatively define and measure these five dimensions, which can be administered using a questionnaire. The overall average score and the composite scores of the five dimensions can be utilized as reflexive assessment and feedback for CI projects about the academic and professional community they belong to. Future research could statistically validate the framework via factor analyses.

This paper presents an organizational framework for measuring institutional stakeholder engagement for big data science teams toward cyberinfrastructure (CI) diffusion. CI projects are an academic example of big data science projects in data-intensive research efforts. CI projects provide a unique case for understanding big data science teams from an important context, which is scientific and academic in nature. We argue that the capacity of a big data science team needs to take into consideration several institutional stakeholder engagement factors, such as having a pro-CI administration, institutional CI investments, campus CI tech support, and a non-traditional research culture. We proposed composite scale items designed to quantitatively measure these four dimensions, using a self-reported questionnaire. The overall mean score and the four individual composite scores of the main dimensions can be used as reflexive feedback and assessment for teams about the macro institutional environment in which they are embedded. Future research should statistically validate the framework via (exploratory and confirmatory) factor analyses.

This paper presents a socio-technical framework for measuring organizational capacity for cyberinfrastructure (CI) implementation, adoption, and diffusion at the team’s level. CI implementation is an example of big data science project in data-intensive projects funded by the US National Science Foundation (NSF), providing a unique case for understanding big data science teams from an important field that is academic and scientific in nature. We argue that organizational capacity can be defined by the three dimensions of foundational technical expertise, daily social interactions, and enduring organizational qualities. We provide scale items for measuring these three dimensions, using a questionnaire in a self-reported and self-reflexive fashion. The overall average score and the individual composite scores of the three dimensions (and their sub-dimensions) can be used as feedback and capacity building activities as intervention strategies. Future research will statistically validate the framework using exploratory and confirmatory factor analyses.