assignment.ipynb

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   "source": [
    "In this repository, you will find JSON representing a <i>subset</i> of the data for the <a href=\"http://modeldb.yale.edu\">ModelDB</a> repository of computational neuroscience models."
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   "source": [
    "<h1>Getting started</h1>"
   ]
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   "source": [
    "Begin by cloning this repository. Create a private repository on github and push your local copy to there.<br/><br/>Connect to MongoDB and create a database for this assignment."
   ]
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   "source": [
    "Using the <tt>json</tt> module and Python file operations, load the data from <tt>modelcollection.json</tt> and <tt>papercollection.json</tt> into Python."
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   "source": [
    "Put the loaded data into two collections in your database. I recommend calling them <tt>models</tt> and <tt>papers</tt>."
   ]
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   "source": [
    "<h1>Explore the database</h1>"
   ]
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  {
   "cell_type": "markdown",
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   "source": [
    "Use MongoDB to answer the following questions. Run your code in the spaces provided."
   ]
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   "source": [
    "<b>Q: How many models are there?</b>"
   ]
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   "source": [
    "<b>What are the field names (keys) for the model entry with <tt>_id</tt> = 87284?</b>"
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   "cell_type": "markdown",
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   "source": [
    "Note: this data is not completely denormalized: references in both collections are given in terms of the <tt>_id</tt> field of the paper collection.<br/><br/><b>How many distinct cell types are in the models collection?</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "markdown",
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   "source": [
    "<b>Find the list of model ids for models that contain a Hippocampus CA3 pyramidal cell.</b>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "markdown",
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   "source": [
    "<b>What other cells appear in models with a Hippocampus CA3 pyramidal cell? Sort them in alphabetical order. How many such cells are there?</b>"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Use aggregation</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "How many models are there for each cell type? Display the results in a formatted table, sorted from most commonly appearing cell type to least commonly appearing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "markdown",
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   "source": [
    "Find the model titles (not paper titles) for models that (1) involve a Hippocampus CA3 pyramidal cell, and (2) have an associated reference where one of the authors is \"Migliore M\"."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "markdown",
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   "source": [
    "Find all the authors who were on a paper associated with a model that involved a Hippocampus CA3 pyramidal cell. Sort them in alphabetical order; give this list and state its length."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h1>Modify the database</h1>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rename the Hippocampus CA1 pyramidal cell to be the Hippocampus CA1 pyramidal neuron. (Note: here we're using CA1 instead of CA3.) Make sure that this is consistent across all documents in the models collection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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  {
   "cell_type": "markdown",
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   "source": [
    "Add a new entry (make up the data, but keep it appropriate) to the models collection. Associate it with two references, one that already exists and one that you also add to the papers collection."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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