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Wednesday Oct. 14, 2020, 4 p.m.–Oct. 14, 2020, 4:15 p.m. in Jupyter in Scientific Research

Jupyter Notebook as a Medium for Experimentalist and Data Scientist Collaboration in neuroImaging

Hawley Helmbrecht

Audience level:

Brief Summary

Traditional wet labs are embracing data science as a tool for high throughput data analysis. However, synthesizing experimentalists with an effective data science strategy can be a difficult task due to differences in expertise, terminology, and workflow habits. We utilize Jupyter Notebook to effectively collaborate with experimentalists and integrate experimental and data science.


Objective: Demonstrate the effectiveness of well-designed, experimental data informed Jupyter notebooks for collaboration between data scientists and experimantalists.

Central Thesis: Jupyter notebook is an approachable platform to support collaboration between trained data scientists with expert domain scientists in neuroimaging.

Outline of Talk:

  1. Introduction

    a. Brain cell morphology from a wet lab experimentalist perspective

    b. Data science from the wet lab experimentalist perspective

    c. Brain cell morphology from a data science perspective

  2. The Merger Between Data and Experimental Science

    a. Introduction of our work on FIBER, a Framework for neuroImage Based Experimental Routines

  3. The Creation of a Notebook

    a. Applicable steps from FIBER to design a data science project alongside the domain scientists

    b. Initial Set-Up of a notebook with domain scientists in mind

  4. Utilization of a Jupyter Notebook to Produce Cell Morphology results

    a. Demo and walk through as a Data Scientist

    b. Demo and walk through as a Experimentalist

  5. Conclusion

    a. Review of Jupyter Notebook as an avenue of merging data and experimental science

    b. Insight into additional and future possibilities

Key Takeaways: 1. An approach to discussing data science analysis with experimental scientists 2. A framework for creating Jupyter Notebooks that allow both sides to understand and utilize the analysis pipeline

Background Knowledge Expected from Attendees: None