Mock Paper for CUNY GCDI Digital Research bootcamp

This paper can be edited by multiple authors (similar to a google doc). It can also be cloned as a git repo and edited on your computer. You can then push your code to github and view your changes on Authorea. PI’s need not fear the document can also be downloaded as a Word doc or PDF for review (although as more academics read content in web browsers it may make sense to learn to write your work in a web friendly manner using tools like Authorea).

Hope to see CUNY digital fellows, and the grad students and faculty from the bootcamp again. For those of you with an interest in science, the New York Genome Center host excellent public lectures most Wednesdays. We are also currently hiring. Find out more at http://www.nygenome.org.

Intro from Authorea:

At Authorea, we want to change the way scientists communicate and share their research. This includes giving all the information behind figures a place to live: by letting readers and reviewers access your data and code, your results can be easily reproduced and extended.

It’s really easy to incorporate IPython Notebooks in your articles. First, upload a figure to your article. You can do so by dragging and dropping an image or by clicking Insert Figure at the bottom of the block you want it under.

When your figure is in place you can attach data to it. We take json, csv, dat files and IPython Notebooks.

If you add a dataset in json, csv, or dat format, the data will be included in the figure folder. Find it by going to the folder view, and then browsing to the figures folder.

If you attach an IPython Notebook, you also get the notebook to be included in the figure folder. And in addition to that, your readers can also:

  1. Launch IPython directly in their browsers (by clicking on the link below the figure);

  2. see your annotated code and data;

  3. test it;

  4. adjust it to their pleasing;

  5. download it.

Beyond the obvious advantages this provides for streamlining the scientific process, imagine implementing this to facilitate classroom learning or centralizing repeated analysis in a lab setting. What’s more, it gives you a place to share and be very descriptive with your code.

In the predator-prey modeling example below (no data, just a model), a detailed walk-through is given in the IPython Notebook. The hope is that anyone so inclined could modify or fork it, perhaps adding a third organism or other environmental constraints. Or, if they had relevant ecological data, to test to see how well the model fits. Go check it out!

An example using Lotka-Volterra equations to model population dynamics.

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