Nowadays, it is not uncommon for neuroscientists to collect >500 GB of data for each experiment. The ease of collecting biological data requires tools to seamlessly clean, process, and share the data. Here, I will present the ways in which neuroscientists use Jupyter tools (such as Jupyter notebook) for experimental workflow and collaboration. Furthermore, I will focus on how Python packages assist with data analysis and visualization. While this presentation will feature specifically neuroscience data, the analysis can be extended to any network scientist or those interested in drawing biological inspiration for development of artificial intelligence.
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