Introduction

Data sets in natural, physical, and computer sciences are now so massive that their analysis, interpretation, and visualization often cannot keep up with the rate at which they can be routinely produced \cite{Labrinidis_2012}. This data explosion means scientists across disciplines must work together to build tools, models, and visualizations capable of exploring that massive, complex, multi-faceted landscape. This cooperation will lead to the development of new tools that will allow us to observe aspects of our universe that have previously been hidden or beyond our reach. These new tools have the potential to transform the foundations of how we understand our universe analogous to the development of the microscope centuries ago \cite{w3bezo} .
Education and training within the natural and physical sciences do not sufficiently equip students with the necessary experience and skills to meet emerging data processing and analysis needs and experts across scientific disciplines are raising this issue \cite{Lau_2012,Lu_2021,Skuse_2019,Pevzner_2009}. To this end, we created the Digital Imaging and Vision Analysis in Science (DIVAS) project.  
DIVAS secured funds in 2016 from the National Science Foundation’s Improving Undergraduate Science Education division \cite{4yzxmo}. The program aims to engage novice learners (mostly first- and second-year college students) from biological and chemical sciences in computational thinking and coding using image data as a ‘hook’. Images are widely used for diagnostics, phenotyping, and analytical measurements. They are also easy to obtain and novice learners understand them. When students analyze image data they engage in all of the aspects of computational thinking including recognition of a problem, analysis of solutions, design of a solution, and implementation and testing.  
Of note, we saw the computational confidence and skills improve in students not normally well represented in computer science courses. Our pilot study indicated that the scaffolding of interventions that make up the program and the context of a community of practice were important elements contributing to the gains we observed \cite{Durham_Brooks_2021}. The pilot study motivated us to see how broadly these results can be replicated. In this report, we discuss the key elements of the DIVAS project. We will also discuss what we have learned about fostering self-efficacy in computing more broadly across the natural and physical sciences.

DIVAS Project Overview

The DIVAS program targets first-year students enrolled in introductory biology and chemistry courses. The program name, its leaders, and the recruiting strategies used resulted in a high percentage of individuals who identify as women joining the program (76% of all program scholars) [8]. The program begins in the spring with a one-credit seminar and continues five to eight weeks into the following summer. The program ends with a capstone seminar the following fall or spring. From the first DIVAS seminar, students join a community of practice that will support them throughout the rest of the program. In that seminar, scholars learn about images as a form of data; do basic coding; meet professionals who use coding in their work; and explore working environments where coding occurs.