Cross-Disciplinary Faculty Development in Data Science Principles for Classroom Integration

Marshall, Brandeis; Geier, Susan

doi:10.1145/3328778.3366801

Cited by 1 publication

(1 citation statement)

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“…An exciting way to solve the teacher shortage is joint interdisciplinary graduate-level training that brings together students from experimental and computational sciences and introduces both biological problems and quantitative approaches to tackle them (Saunders et al, 2018;von Arnim and Missra, 2017). Another potential solution is recruiting faculty from a neighboring computational department to jointly develop with biomedical faculty, a discipline-specific data science curriculum (Marshall and Geier, 2020). Resources to facilitate cross-disciplinary teaching have also begun to sprout.…”

Section: Training the Next Generation Of Data Scientists In Cell Biologymentioning

confidence: 99%

Data science in cell imaging

Driscoll

Zaritsky

2021

Journal of Cell Science

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Cell imaging has entered the ‘Big Data’ era. New technologies in light microscopy and molecular biology have led to an explosion in high-content, dynamic and multidimensional imaging data. Similar to the ‘omics’ fields two decades ago, our current ability to process, visualize, integrate and mine this new generation of cell imaging data is becoming a critical bottleneck in advancing cell biology. Computation, traditionally used to quantitatively test specific hypotheses, must now also enable iterative hypothesis generation and testing by deciphering hidden biologically meaningful patterns in complex, dynamic or high-dimensional cell image data. Data science is uniquely positioned to aid in this process. In this Perspective, we survey the rapidly expanding new field of data science in cell imaging. Specifically, we highlight how data science tools are used within current image analysis pipelines, propose a computation-first approach to derive new hypotheses from cell image data, identify challenges and describe the next frontiers where we believe data science will make an impact. We also outline steps to ensure broad access to these powerful tools – democratizing infrastructure availability, developing sensitive, robust and usable tools, and promoting interdisciplinary training to both familiarize biologists with data science and expose data scientists to cell imaging.

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Section: Training the Next Generation Of Data Scientists In Cell Biologymentioning

confidence: 99%