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In many areas of science, the ability to use computers to process, analyze, and visualize large data sets has become essential. The mismatch between the ability to generate large data sets and the computing skill to analyze them is arguably the most striking within the life sciences. The Digital Image and Vision Applications in Science (DIVAS) project describes a scaffolded series of interventions implemented over the span of a year to build the coding and computing skill of undergraduate students majoring primarily in the natural sciences. The program is designed as a community of practice, providing support within a network of learners. The program focus, images as data, provides a compelling ‘hook’ for participating scholars. Scholars begin the program with a one-credit spring semester seminar where they are exposed to image analysis. The program continues in the summer with a one-week, intensive Python and image processing workshop. From there, scholars tackle image analysis problems using a pair programming approach and can finish the summer with independent research. Finally, scholars participate in a follow-up seminar the subsequent spring and help onramp the next cohort of incoming scholars. We observed promising growth in participant self-efficacy in computing that was maintained throughout the project as well as significant growth in key computational skills. DIVAS program funding was able to support seventeen DIVAS over three years, with 76% of DIVAS scholars identifying as women and 14% of scholars identifying as members of an underrepresented minority group. Most scholars (82%) entered the program as first year students, with 94% of DIVAS scholars retained for the duration of the program and 100% of scholars remaining a STEM major one year after completing the program. The outcomes of the DIVAS project support the efficacy of building computational skill through repeated exposure of scholars to relevant applications over an extended period within a community of practice. 

Development of latent prints employing cyanoacrylate ester (CA) can be a multistep process including CA fuming and subsequent fluorescent staining to produce fingerprints of sufficient contrast for comparison work. To enable a single‐step CA fuming—staining process, a selection of fluorophores have been developed as sublimation dyes in CA fuming. A greater array of such luminescent sublimation dyes would allow users greater flexibility in selecting a particular dye—CA combination to best suit their processing needs. Toward this end, six benzoic acid derivatives were evaluated for use as luminescent sublimation dyes under elementary CA fuming conditions using a single non‐porous surface type and an inexpensive handheld UV lamp for excitation. Two benzoic acid derivatives, 2‐hydroxybenzoic acid (salicylic acid) and 2‐aminobenzoic acid (anthranilic acid), were identified as new potential luminescent sublimation dyes with stained fingerprints excited at 254 nm. The fluorescence intensity and stability of prints produced via the sublimation of CA with 2‐hydroxybenzoic acid and 2‐aminobenzoic acid were evaluated over approximately six weeks using image and statistical analysis.