<jupyter_start><jupyter_text>Introduction to Jupyter Notebooks  Why use Jupyter?  Bridge the gap between biology and code“We used to speak two different languages. I would talk about the biology and she would talk about coding. Now we have common ground; we can communicate to each other better. This accelerates our research,”* <NAME>. "[Interactive notebooks: Sharing the code.](http://doi.org/10.1038/515151a)" Nature 515.7525 (2014): 151.  Use your favorite languages* [Python](https://www.python.org/)* [R](https://irkernel.github.io/)* [MATLAB](http://nbviewer.jupyter.org/github/Calysto/matlab_kernel/blob/master/matlab_kernel.ipynb)* [SciJava Jupyter kernel](https://github.com/scijava/scijava-jupyter-kernel) – scripting on the JVM* [Many others](https://github.com/jupyter/jupyter/wiki/Jupyter-kernels)  Run in the cloud, free, reproducible  Here to stay* [JULY 7, 2015 - Project Jupyter gets $6M to expand collaborative data-science software](http://news.berkeley.edu/2015/07/07/jupyter-project/)  Any notebook can be a presentation[Reveal.js](https://revealjs.com/) is an HTML presentation framework.The [RISE extension for Jupyter](https://damianavila.github.io/RISE/) makes any Jupyter notebook into a LIVE reveal.js presentation.Like this one! 🤩  Example notebooks * The [gallery of interesting Jupyter notebooks](https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooks) spans a broad range of scientific disciplines including Computer Science, Statistics, Machine Learning, Data Science, Mathematics, Physics, Chemistry, Biology, Earth Science and Geo-Spatial, Linguistics and Text Mining, Signal Processing and more.  In particular, see the [reproducible academic publications](https://github.com/jupyter/jupyter/wiki/A-gallery-of-interesting-Jupyter-Notebooksreproducible-academic-publications) section. * The ImageJ Tutorial Notebooks demonstrate how to use ImageJ in a notebook with either the SciJava Jupyter kernel or with the Python 3 kernel.These technologies should be considered alpha quality, but we are actively working to improve them. Finally, ___an awesome walkthrough___ of the Python data analysis pipeline from start to finish:* [An example machine learning notebook](https://github.com/rhiever/Data-Analysis-and-Machine-Learning-Projects/blob/master/example-data-science-notebook/Example%20Machine%20Learning%20Notebook.ipynb)<jupyter_code>#@ ImageJ ij

ij.io().open("https://imagej.net/images/FluorescentCells.jpg")<jupyter_output>[INFO] Populating metadata
[INFO] Populating metadata
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