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:
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
The Merger Between Data and Experimental Science
a. Introduction of our work on FIBER, a Framework for neuroImage Based Experimental Routines
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
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
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