01_inspecting.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 1: Inspecting the Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this first assignment, we will get to know the dataset that we will be using throughout. You can find the assignment tasks at the bottom of this document.\n",
    "\n",
    "Our dataset consists of short texts (article abstracts) from the [PubMed](https://www.ncbi.nlm.nih.gov/pubmed/) database of scientific publications in the Life Science domain. As the full dataset consists of millions of documents, we are using just a small subset, namely all publications that contain the word \"cognition\" in their title or abstract. You can download that dataset in the form of four files (`cognition_Summaries.pkl.bz2`, etc.) from this Github (data folder). Save these four files in a directory called `data` (do not unpack them, but save them as .bz2 files!), which should be a sub-directory of the one that contains this notebook file."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Importing some Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import namedtuple, defaultdict, Counter\n",
    "import pickle, bz2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Loading the Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "Summaries_file = 'data/cognition_Summaries.pkl.bz2'\n",
    "Summaries = pickle.load( bz2.BZ2File( Summaries_file, 'rb' ) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To make it easier to access the data, we convert here paper entries into [named tuples](http://docs.python.org/3/library/collections.html#collections.namedtuple). This will allow us to refer to fields by keyword (like `var.year`), rather than index (like `var[2]`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "paper = namedtuple( 'paper', ['title', 'authors', 'year', 'doi'] )\n",
    "\n",
    "for (id, paper_info) in Summaries.items():\n",
    "    Summaries[id] = paper( *paper_info )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "paper(title='Pollinator cognition: Framing bee memories in\\xa0an\\xa0ecological context.', authors=['Giurfa M'], year=2022, doi='10.1016/j.cub.2022.08.043')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Summaries[36220089]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Pollinator cognition: Framing bee memories in\\xa0an\\xa0ecological context.'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Summaries[36220089].title"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Statistics"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The two lines below will install [matplotlib](http://matplotlib.org/) and [NumPy](http://www.numpy.org/), if they are not installed already (if that doesn't work, you might have to download and install them from [here](http://matplotlib.org/downloads.html) and [here](http://sourceforge.net/projects/numpy/files/NumPy/1.10.1/)). We will need these libraries for drawing diagrams and analyzing the data below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: matplotlib in c:\\python310\\lib\\site-packages (3.5.2)\n",
      "Requirement already satisfied: pillow>=6.2.0 in c:\\python310\\lib\\site-packages (from matplotlib) (9.1.1)\n",
      "Requirement already satisfied: cycler>=0.10 in c:\\python310\\lib\\site-packages (from matplotlib) (0.11.0)\n",
      "Requirement already satisfied: fonttools>=4.22.0 in c:\\python310\\lib\\site-packages (from matplotlib) (4.33.3)\n",
      "Requirement already satisfied: pyparsing>=2.2.1 in c:\\python310\\lib\\site-packages (from matplotlib) (3.0.9)\n",
      "Requirement already satisfied: kiwisolver>=1.0.1 in c:\\python310\\lib\\site-packages (from matplotlib) (1.4.2)\n",
      "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\thomas\\appdata\\roaming\\python\\python310\\site-packages (from matplotlib) (2.8.2)\n",
      "Requirement already satisfied: packaging>=20.0 in c:\\python310\\lib\\site-packages (from matplotlib) (21.3)\n",
      "Requirement already satisfied: numpy>=1.17 in c:\\python310\\lib\\site-packages (from matplotlib) (1.22.3)\n",
      "Requirement already satisfied: six>=1.5 in c:\\python310\\lib\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -p (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ip (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution - (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -p (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ip (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution - (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -p (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ip (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution - (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -p (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ip (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution - (c:\\python310\\lib\\site-packages)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in c:\\python310\\lib\\site-packages (1.22.3)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: Ignoring invalid distribution -p (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ip (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution - (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -p (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ip (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution - (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -p (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ip (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution - (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -p (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution -ip (c:\\python310\\lib\\site-packages)\n",
      "WARNING: Ignoring invalid distribution - (c:\\python310\\lib\\site-packages)\n"
     ]
    }
   ],
   "source": [
    "! pip install --user matplotlib\n",
    "! pip install --user numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# show plots inline within the notebook\n",
    "%matplotlib inline\n",
    "# set plots' resolution\n",
    "plt.rcParams['savefig.dpi'] = 100"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Papers per Year"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First, we will try to find out how many papers in the dataset were published per year. We are using the [Counter](https://docs.python.org/3/library/collections.html#collections.Counter) class for this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of papers in the dataset per year for the past decade:\n",
      "[(2014, 5455), (2015, 6000), (2016, 6494), (2017, 7049), (2018, 7431), (2019, 7920), (2020, 8757), (2021, 10568), (2022, 9781), (2023, 10)]\n"
     ]
    }
   ],
   "source": [
    "paper_years = [ p.year for p in Summaries.values() ]\n",
    "papers_per_year = sorted( Counter(paper_years).items() )\n",
    "\n",
    "print('Number of papers in the dataset per year for the past decade:')\n",
    "print(papers_per_year[-10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Filtering results, to obain only papers since 1940:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of papers in the dataset published since 1940:\n",
      "102110\n"
     ]
    }
   ],
   "source": [
    "papers_per_year_since_1940 = [ (y,count) for (y,count) in papers_per_year if y >= 1940 ]\n",
    "years_since_1940 = [ y for (y,count) in papers_per_year_since_1940 ]\n",
    "nr_papers_since_1940 = [ count for (y,count) in papers_per_year_since_1940 ]\n",
    "\n",
    "print('Number of papers in the dataset published since 1940:')\n",
    "print(sum(nr_papers_since_1940))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Creating a bar plot to visualize the results (using [matplotlib.pyplot.bar](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.bar)):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(x=years_since_1940, height=nr_papers_since_1940, width=1.0)\n",
    "plt.xlim(1940, 2021)\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('number of papers');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternatively, we can get the same result by plotting it as a histogram with [matplotlib.pyplot.hist](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.hist):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist( x=[p.year for p in Summaries.values()], bins=range(1940,2023) );\n",
    "plt.xlim(1940, 2022)\n",
    "plt.xlabel('year')\n",
    "plt.ylabel('number of papers');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Papers per Author"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here, we will obtain the distribution characterizing the number of papers published by an author."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# flattening the list of lists of authors:\n",
    "flat_author_list = [ auth for paper in Summaries.values() for auth in paper.authors ]\n",
    "\n",
    "nr_papers_by_author = Counter( flat_author_list )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of authors in the dataset with distinct names:\n",
      "232358\n"
     ]
    }
   ],
   "source": [
    "print('Number of authors in the dataset with distinct names:')\n",
    "print(len(nr_papers_by_author))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Top 50 authors with greatest number of papers:\n",
      "[('Wang Y', 661), ('Zhang Y', 580), ('Wang J', 523), ('Li Y', 485), ('Zhang J', 441), ('Wang X', 434), ('Liu Y', 392), ('Li X', 370), ('Zhang X', 368), ('Wang L', 347), ('Bennett DA', 345), ('Chen Y', 341), ('Zhang L', 318), ('Li J', 313), ('Liu X', 292), ('Liu J', 288), ('Wang H', 284), ('Wang Z', 279), ('Chen J', 274), ('Li H', 272), ('Chen X', 268), ('Zhang Z', 246), ('Zhang H', 240), ('Yang Y', 234), ('Zhang W', 218), ('Wang C', 218), ('Xu Y', 217), ('Wang S', 203), ('Alzheimer’s Disease Neuroimaging Initiative.', 202), ('Li Z', 199), ('Lee J', 199), ('Li S', 198), ('Yaffe K', 194), ('Harvey PD', 192), ('Yang J', 184), ('Li C', 184), ('Li W', 183), ('Li L', 181), ('Wang Q', 176), ('Wang W', 176), ('Chen L', 174), ('Zhou Y', 174), ('Chen H', 173), ('Wu Y', 173), ('Chen C', 172), ('Wilson RS', 172), (\"Alzheimer's Disease Neuroimaging Initiative.\", 170), ('Zhang M', 170), ('Xu X', 167), ('Li M', 166)]\n"
     ]
    }
   ],
   "source": [
    "print('Top 50 authors with greatest number of papers:')\n",
    "print(sorted(nr_papers_by_author.items(), key=lambda i:i[1], reverse=True)[:50])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can create a histogram to visualize the results:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist( x=list(nr_papers_by_author.values()), bins=range(101), log=True )\n",
    "plt.xlabel('number of papers authored')\n",
    "plt.ylabel('number of authors');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Authors per Paper"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then we can make a similar analysis for the number of authors per paper:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x=[ len(p.authors) for p in Summaries.values() ], bins=range(20), align='left', density=True)\n",
    "plt.xlabel('number of authors in one paper')\n",
    "plt.ylabel('fraction of papers')\n",
    "plt.xlim(-0.5, 15.5);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Words in Titles"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And then we can have a first look into the words we find in titles (applying a very naive word splitting method for now):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of distinct words in the paper titles:\n",
      "75212\n"
     ]
    }
   ],
   "source": [
    "words = [ word.lower() for paper in Summaries.values() for word in paper.title.split(' ') ]\n",
    "word_counts = Counter(words)\n",
    "\n",
    "print('Number of distinct words in the paper titles:')\n",
    "print(len(word_counts))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "----------"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Tasks"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Your name:** Thomas Norton"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 1\n",
    "\n",
    "Create a Python dictionary object that returns a set of all names of first authors (that is, the authors that appear first in the list of authors) for a given year. Name this dictionary `first_authors_at_year`. (You can use a [*defaultdict*](https://docs.python.org/3/library/collections.html#collections.defaultdict) with a default value of [*set*](https://docs.python.org/3/library/stdtypes.html#set-types-set-frozenset).) Demonstrate the working of this dictionary by showing the set of first authors for the year 1970."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Maleev N', 'Ishii S', 'Loprieno M', 'Abramov VG', 'Reitan RM', 'Ganner H', 'Jarmuzek Z', 'Dawson ME'}\n"
     ]
    }
   ],
   "source": [
    "first_authors_at_year = defaultdict(set)\n",
    "for paper in Summaries.values():\n",
    "    if len(paper.authors) > 1:\n",
    "        author = paper.authors[0]\n",
    "    else:\n",
    "        author = str(paper.authors)[2:-2] #remove the square brackets and extra quotation marks\n",
    "    first_authors_at_year[paper.year].add(author)\n",
    "print(first_authors_at_year[1970])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 2\n",
    "\n",
    "Based on the dictionary `first_authors_at_year` from exercise 1 above, create a plot for the years from 1970 until now that shows how many authors published at least one paper as first author in the given particular year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of First Authors Published')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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SVpjpcNKq02RhZgPDxcfM7PPA/wJN7ds1TbiI9CVb9uzjB0+s5D0TB/Phk0ZnOpy0S3RlsYjgkaftnYevidvmQO+arF1EpBPuzn/+cRkxh+998NheOVFgMp0mC3efAGBmRe7eGL/NzHrP459ERJJ4bMlWnl25g2+eN4UxA4szHU5GRBln8VLEMhGRXsfdufXZNUweXsan3jU+0+FkTKI2i+HAKKCfmZ3IgdtR5UDfTK0i0ue8urGaVdvr+MGHjiW3l0/pkUiiNouzgE8Bo4H4Bx3VAV9PYUwiIj3GvfM3UlqYxweOH5npUDIqUZvFPGCemV3o7n9IY0wiIj3Cnr3N/GnJVi6aPpqSwigjDXqvKLWf2tHzK9z9uymIR0Skx/jDq5tpbo3x8RnjMh1KxkVJFvFPxisCzgPeSE04IiI9g7tz7/wNnDi2P1NGlmc6nIyLMpHgT+LXzezHwFMpi0hEpAeYv76KdTsb+PFHjs90KD1ClK6zBysmaPQWEem17pu/kfKiPM47bkSmQ+kRosw6u5RgxDZALsHzLG5IZVAiIpm0u76JJ5Zt5ZKZ4yjKz810OD1ClDaL8+KWW4Ht7t6aonhERDLuoUWVtLQ5l8wcm+lQeoykt6HcfUP7C9gDfMzM/pzyyEREMiAWc+5bsJEZ4wcycVhZpsPpMZImCzMrMLMPmtnvga3AGQTPtxAR6XVeWrubDbv38nFdVbxNouk+zgQuBs4EngN+A5zs7pelKTYRkbS7b8EGBhTnc/bUvvMUvCgSXVk8STAN+Snu/gl3fwyIpScsEZH021HXyF+Wb+fDJ41Ww/ZBEjVwTwM+BvzVzNYBDxD0hhIR6ZUeWlRJa8y5eIZuQR2s0ysLd3/N3a9193cA1wMnAPlm9oSZXZGuAEVE0uX5lTs5dlQFRwwpzXQoPU6kQXnu/pK7f5FgMN5PgVkpjUpEJM0aW9p4bdMeZh0xMPnOfVCXplF09xjwl/AlItJrLN64h+a2GDMnDMp0KD3SoUz3ISLS68xfvxszOHmCriw6kvJkYWa5ZrbYzP4Urk8ws/lmtsbMfmdmBWF5Ybi+Jtw+Pu4c14Xlq8zsrFTHLCJ9z/x1VRw9vJyKfvmZDqVHijIo754oZQlczdunNL8J+Km7HwlUA3PD8rlAdVj+03A/zGwKQa+sY4CzgdvMTL2yRKTbNLW28erGamaqvaJTUa4s3vbgo/AX9UlRTm5mo4H3A3eE6wacDjwU7jIPuCBcnh2uE24/I9x/NvCAuze5+3pgDTAjyueLiESxpLKGptYYs45Qe0VnOk0W4a2fOuA4M6sNX3XADuCRiOf/GfBVDgzmGwTsiZuIsBIYFS6PAjYBhNtrwv33l3dwTHy8V5jZQjNbuHPnzojhiYjAy2t3AzBjvK4sOpNonMX33b0M+JG7l4evMncf5O7XJTuxmZ0H7HD3Rd0ZcGfc/XZ3n+7u04cMGZKOjxSRXmL++iomDy9jQElBpkPpsaLchvqTmZUAmNknzOxmM4vyQNp3A+eb2VsEo79PB34O9Dez9i67o4HN4fJmYEz4OXlABbA7vryDY0REDktLW4xFG6qZqV5QCUVJFr8E9prZ8cCXgbUEkwom5O7Xuftodx9P0ED9rLtfQjAp4YfD3eZw4JbWo+E64fZn3d3D8o+FvaUmABOBBVEqJyKSzJLKGva1tDFT7RUJRUkWreEv7dnAf7v7rcDhTPL+NeBLZraGoE3izrD8TmBQWP4l4FoAd18OPAisIJjc8Ep3bzuMzxcR2W/++rC9QlcWCUUZwV1nZtcBnwBONbMcoEsdkd39eeD5cHkdHfRmcvdG4COdHH8jcGNXPlNEJIr566o4cmgpg0sLMx1KjxblyuKjQBMw1923EbQZ/CilUYmIpEFrW4yFb1VpPqgIEl5ZhGMq7nf397aXuftGIrRZiIj0dMu31NLQ3Kb5oCJIeGURtg3EzKwiTfGIiKRNe3uFRm4nF6XNoh5YamZPAw3the5+VcqiEhFJg/nrqjhicAlDy4oyHUqPFyVZPBy+RER6jbaYs2B9FecdPyLToWSFpMnC3ecl20dEJNu8sbWWuqZWtVdE1GmyMLMH3f0iM1sK+MHb3f24lEYmIpJCL69Te0VXJLqy+HL4fl46AhERSaf566sYN6iYERX9Mh1KVkjUG+oRAHffAHzF3TfEv9ITnohI94vFnFfeqtJ8UF2QKFlY3PK7Ux2IiEi6rNpex569LWqv6IJEyeKf2ilERHoDtVd0XaI2i8lmtoTgCuMd4TLhuquBW0SyUV1jC7e/sI6jR5QzekBxpsPJGomSxdFpi0JEJE1+9NQqttU2cusl0zIdSlbpNFmoEVtEepuFb1Vxz8sbmPPO8UwbOyDT4WSVKLPOiohkvabWNq59eCkjK/rxlbOOynQ4WSfKdB8iIlnvtufWsmZHPXdfdjKlhfrV11WdXlmY2TPh+03pC0dEpPu9ub2O255fw+wTRvLeo4ZmOpyslCi9jjCzdwHnm9kDvH3cBe7+akojExHpBm0x52t/WEJpYR7fOm9KpsPJWomSxbeAbxI8Ge/mg7Y5cHqqghIR6S6/fXkDizfu4eaLjmeQHp16yBL1hnoIeMjMvunuN6QxJhGRbrF5zz5++ORK3jNxMB88cVSmw8lqUaYov8HMzgdODYued/c/pTYsEZHD09oW4z9+9xoxh+998FjMLPlB0qmkXWfN7PvA1cCK8HW1mX0v1YGJiByOHz21igXrq/jeh6YyZqBGah+uKP3H3g+c4O4xADObBywGvp7KwEREDtWTy7bx6xfW8YlZY/ngiaMzHU6vEHVQXv+45YoUxCEi0i3W72rgmt+/zvGjK/imej91myhXFt8HFpvZcwTdZ08Frk1pVCIih2Bfcxuf++0icnONWy+ZRmFebqZD6jWiNHDfb2bPAyeHRV9z920pjUpEpIvcnW/8cSmrttdx96dO1oyy3SzSmHd33wo8muJYREQO2f0LNvHwq5v59/dN5DSN0u52mkhQRLLe4o3VfPvR5Zw6aQhXnT4x0+H0SkoWIpLVFm+s5tK7FjCsopCfffQEcnI0niIVEiYLM8s1s5XpCkZEpCsWvlXFJ+9cwIDiAh644p0MLCnIdEi9VsJk4e5twCozG5umeEREInl53W4uvWsBQ8sKefCz72RU/36ZDqlXi9LAPQBYbmYLgIb2Qnc/P2VRiYgk8I81u5g77xXGDCjm3s/MZGhZUaZD6vWiJItvpjwKEZGInl+1g8/es4gJg0v47adnMlgzyaZFlHEWfzOzccBEd/+rmRUDGukiImn35LJtXHX/YiYOK+W3c2cyQG0UaRNlIsHPAA8Bvw6LRgF/jHDcGDN7zsxWmNlyM7s6LB9oZk+b2erwfUBYbmZ2i5mtMbMlZjYt7lxzwv1Xm9mcQ6iniGQxd+f2F9byuXsXccyocu779CwlijSL0nX2SuDdQC2Au68Goox4aQW+7O5TgFnAlWY2hWCqkGfcfSLwDAemDjkHmBi+rgB+CUFyAa4HZgIzgOvbE4yI9H6tbTG+8cdlfO/xlZw7dQT3f2YWFcX5mQ6rz4mSLJrcvbl9xczyCJ6Ul5C7b21/9Kq71wFvEFyVzAbmhbvNAy4Il2cDv/HAy0B/MxsBnAU87e5V7l4NPA2cHaVyIpLd6hpbuHzeQu6bv5HPnfYOfnHxiRTl6y54JkRp4P6bmX0d6Gdm/wp8HnisKx9iZuOBE4H5wLBw+hCAbcCwcHkUsCnusMqwrLNyEenFKqv3Mvd/FrJ2Zz03XXgsHz1ZPfgzKUqyuBaYCywFPgs8DtwR9QPMrBT4A/Dv7l4b/7Qqd3czS3qVEvFzriC4fcXYsfqhEslmizZU82+/XURjcxv/c9kMTpk4ONMh9XlRekPFwgcezSe4/bTK3SP9gjezfIJEca+7PxwWbzezEe6+NbzNtCMs3wyMiTt8dFi2GTjtoPLnO4jzduB2gOnTp3dLAhKR9IrFnF+/sI4f/2UVI/sXce+nZzJpWFmmwxKi9YZ6P7AWuAX4b2CNmZ0T4TgD7gTecPeb4zY9CrT3aJoDPBJXfmnYK2oWUBPernoKONPMBoQN22eGZSLSi+yqb+JT//MKNz25krOPGc6fr3qPEkUPEuU21E+A97r7GgAzewfwZ+CJJMe9G/gksNTMXgvLvg78AHjQzOYCG4CLwm2PA+cCa4C9wGUA7l5lZjcAr4T7fdfdqyLELSJZ4qW1u7j6gdeo2dfCjR+cysdnjCX+lrVkXpRkUdeeKELrgLpkB7n7iwRP1uvIGR3s7wTddDs6113AXclDFZFs0hZzfv7Man7x7GomDC7hN5fP4OgR5ZkOSzrQabIwsw+FiwvN7HHgQYI2i49w4K98EZFDsmXPPv79d6+xYH0VF04bzXdnH0NJYaTnsUkGJPpmPhC3vB34l3B5J6DpHUXkkP1l+Ta++ocltLTGuPmi4/nQtNGZDkmS6DRZuPtl6QxERHq/xpY2vvf4G/zm/zZw7KgKbrn4RCYMLsl0WBJB0ms+M5sAfBEYH7+/pigXka5Ys6OOL9y3mJXb6vj0KRP46tmTKcjTwzqzRZQbhH8k6AL7GBBLaTQi0iv9ackWrvn9EooLcrn7spN571FRppeTniRKsmh091tSHomI9Druzi//tpYfPrmKk8cP4NaPT2NouR5UlI2iJIufm9n1wF+ApvbC9kkCRUQ60tIW45t/XMYDr2zi/ONH8sMPH6dJALNYlGRxLMHgutM5cBvKw3URkX9S29jClfe+yt9X7+IL7z2SL/3rJHJyNMgum0VJFh8BjoifplxEpDOb9+zj8rtfYe3Oen544XFcdPKY5AdJjxclWSwD+nNgwj8RkX/S1NrGn17fyk1PrmSfZovtdaIki/7ASjN7hbe3WajrrIiwq76Je1/eyD0vb2BXfROThpVyz9yZHDVckwD2JlGSxfUpj0JEss4bW2u568X1PPL6FppbY5x21BAuf/cE3jNxsCYB7IWiPM/ib+kIRESyw6sbq/nFM6t5btVO+uXnctH00XzqXRM4cmhppkOTFIoygruOA8/cLgDygQZ319SQIn3I/HW7+cWza3hxzS4GFOfzlTMn8YlZ4+hfXJDp0CQNolxZ7L/xGD7QaDYwK5VBiUjP4O68tHY3tzyzmvnrqxhcWsjXz53MJTPHaYbYPqZL33b4zIk/hoP0rk1NSCLSE8xft5ufPP0mC9ZXMay8kOs/MIWLZ4zVwLo+KsptqA/FreYA04HGlEUkIhm1eGM1Nz/9Jn9fvYuhZYV8d/YxXDR9jJJEHxflyiL+uRatwFsEt6JEpBdZtrmGnz79Js+s3MGgkgL+8/1H84lZ45QkBIjWZqHnWoj0Ym9sreVnf32Tp5Zvp6JfPtecdRSfetd4tUnI2yR6rOq3Ehzn7n5DCuIRkTRZua2Wn/91NU8s20ZZYR5XnzGRue+ZQHlRfqZDkx4o0Z8ODR2UlQBzgUGAkoVIFnF39rW0sW5nA7c9v4bHlwZJ4qozJjL33ROoKFaSkM4leqzqT9qXzawMuBq4DHgA+Elnx4lI5r22aQ8/+csqdtY1UdfYSn1T8GqLBUOmSgvz+OLpRzL3lAkaJyGRJLwpaWYDgS8BlwDzgGnuXp2OwESk65pbY/zi2dXc9vxaBpcWcMKY/pQW5lNWlEdJYS6lhfn0L87nnKnDlSSkSxK1WfwI+BBwO3Csu9enLSoR6bKV22r50u9eZ8XWWi6cNppvfWAKFf10a0m6R6Iriy8TzDL7n8A34iYGM4IGbk33IdIDtMWc219Yx0+ffpPyfnn8+pMncdYxwzMdlvQyidosctIZiIh0zfbaRp55Ywe/W7iJ1zft4Zypw/mvC6YyqLQw06FJL6SO1CJZwt1ZsbWWv67YwTMrt7OksgaAMQP78bOPnsDsE0ZqanBJGSULkR6koamVZ1fuoLJ6H1UNTeyub2ZXQzNVDU1sq2lkV30zZnDCmP5cc9ZRvO/oYUwaVqokISmnZCGSYe7OgvVV/H5RJY8v3cre5jYAivJzGFRSyODSAoaUFjJ5eDkzxg/kvZOHMqRMt5okvZQsRFKgLea8tbuBFVtqWbWtDjPoX1zAgOKg62r/4gKK8nJ55o3tPPRqJRt276WkIJfzjx/Jh08azZSR5RQX6L+n9Bz6aRQ5TA1NrazaXsfKrXWs2FrDii21rNxWt/8KITfHiLnj3vHxs44YyFWnT+ScY4crQUiPpZ9MkSRiMWfPvhZ21zexs76JXfXNrN1Rz8ptQVLYsHvv/n1LC/OYMqKci6aPYcrIco4ZWc7EoWXk5Ri1jS3s2dtC9d5m9uxroXZfCyeOGcDYQcUZrJ1INEoW0ifEYs6u+iYcGFBcQEHeP/cMb2pt481t9SzdXMOyLTUs31LLlj37qGpo3j9NRrscg/GDS5g6soILp41m8vAyJg8vZ/SAfuTkdNzY3L+4gP7FBYynJBVVFEkpJQvpVWr2tbB4YzVrdtSzsWovm6r2srFqL5XV+2hqje3fr6wwj4GlBQwoLmBgSQHbahp5c3sdrWFSKC/K45iRFbzv6KH7G5kHlRYyuDRYHj2gmH4Fes6D9B1KFtKjuTt7m9toaYthZuTmGLlmmAVtARur9rJoQzWvbqhm0YZqVu84MCtNWWEeYwYWM3FoGadPHsqYgcXkmFHd0Mzuhmaq9zZT1dDM9tpGBpUW8JmjjuDYURVMHVnBmIH91B1VJE7WJAszOxv4OZAL3OHuP8hwSEJw62ZHbRO1jS24Q8ydWPju7jS1xKjZ10LNvhZqG1v2L+9rjtEWi9Eac9pivv+9saWN2sYWave1UtvYQl1j6z/dAupIeVEeJ40bwOwTRjJt3ACOHl5O/+J8/cIX6SZZkSzMLBe4FfhXoBJ4xcwedfcVmY3s0NQ3tfLWrgbW72oI3nc3sKO2iaL8HEoK8ygpzKMsfO8XPtLSCXrTOOAOZpCXY+Tn5pCfm0NerlGQm4PjwS/afQd+Mdfsa2FfSxu5OUZejpFjRl6ukZuTQ8ydhqZWGppaqW9q27/c5k55UTBbaXlRPuX98vY/FGdHXRPbaxvZXttI9d6WLtU9N8coL8qjuCAvjOHtMRXl5TK0rIgjh+RR3u/A5xfk5dAW8/3JqC3mxGLOkLJCpo8fwBGDSzttKxCRw5cVyQKYAaxx93UAZvYAwXPAuzVZrNxWyxfuW0xrW/AXb2ub09r+129b+NetBTMphnFg4bqZhe/BTgfKwcJ1gJa2GLvqm9/2uSMqihheUURVQ4yG5lbqw+cPxN9jPxT98nOp6JdPRb98+hXk4n7gL/j2FxAmqFxG9S/an6xyzahvCpJObWMLb+3aS21jCzF3hpUXMWZgMdPHD2BYWRHDyoso75dPbo6RY5ATVj7HggRW0S+fiuIgjpKCXP21L5KFsiVZjAI2xa1XAjPjdzCzK4ArAMaOHXtIH9IvP5dJw0rJywn+Us/LMfJyc8jLCf4CBt7WV97d9/+lf/Bf/rSvh/u3b8/LNcYMLGbCoBLGDy5h/KCSThtKW9piNLa0tdfvbcnHCX7xt7TGaGlzWtpitLTFcKC8KPjF3FGPHxGRQ5EtySIpd7+d4NkbTJ8+PflN7g6MG1TCbZec1K1xHY72W0wiIpmWLb+JNgNj4tZHh2UiIpIG2ZIsXgEmmtkEMysAPgY8muGYRET6jKy4DeXurWb2BeApgq6zd7n78gyHJSLSZ2RFsgBw98eBxzMdh4hIX5Qtt6FERCSDlCxERCQpJQsREUlKyUJERJIy7+zxXVnMzHYCGw7jFIOBXd0UTk/Xl+oKqm9v1pfqCqmp7zh3H9LRhl6ZLA6XmS109+mZjiMd+lJdQfXtzfpSXSH99dVtKBERSUrJQkREklKy6NjtmQ4gjfpSXUH17c36Ul0hzfVVm4WIiCSlKwsREUlKyUJERJLqE8nCzO4ysx1mtiyu7Hgz+z8zW2pmj5lZeVh+iZm9FveKmdkJ4baTwv3XmNkt1kOfD9rF+uab2byw/A0zuy7umLPNbFVY32szUZdkuljXAjO7Oyx/3cxOizsmW77bMWb2nJmtMLPlZnZ1WD7QzJ42s9Xh+4Cw3ML6rDGzJWY2Le5cc8L9V5vZnEzVqTOHUNfJ4ffeZGZfOehc2fCz3NX6XhJ+p0vN7CUzOz7uXN1fX3fv9S/gVGAasCyu7BXgX8Lly4EbOjjuWGBt3PoCYBbB47WfAM7JdN0Ot77Ax4EHwuVi4C1gPMFU8GuBI4AC4HVgSqbrdph1vRK4O1weCiwCcrLsux0BTAuXy4A3gSnAD4Frw/JrgZvC5XPD+lhYv/lh+UBgXfg+IFwekOn6HWZdhwInAzcCX4k7T7b8LHe1vu9q/86Ac+K+25TUt09cWbj7C0DVQcWTgBfC5aeBCzs49GLgAQAzGwGUu/vLHnwjvwEuSEnAh6mL9XWgxMzygH5AM1ALzADWuPs6d28m+HeYnerYu6qLdZ0CPBsetwPYA0zPsu92q7u/Gi7XAW8QPKN+NjAv3G0eB+KfDfzGAy8D/cP6ngU87e5V7l5N8O90dvpqklxX6+ruO9z9FaDloFNly89yV+v7UvjdAbxM8ARRSFF9+0Sy6MRyDvwDfoS3P7a13UeB+8PlUUBl3LbKsCxbdFbfh4AGYCuwEfixu1cR1G1T3PHZVN/O6vo6cL6Z5ZnZBOCkcFtWfrdmNh44EZgPDHP3reGmbcCwcLmz7zGrvt+Ide1MVtUVDqm+cwmuICFF9e3LyeJy4PNmtojgkq85fqOZzQT2uvuyjg7OQp3VdwbQBowEJgBfNrMjMhNit+msrncR/MdZCPwMeImg7lnHzEqBPwD/7u618dvCq6Ne0ye+L9UVul5fM3svQbL4Wirjypon5XU3d18JnAlgZpOA9x+0y8c4cFUBsJkDl3mEy5tTGWN3SlDfjwNPunsLsMPM/gFMJ/jLJP5qK2vq21ld3b0V+I/2/czsJYL7wtVk0XdrZvkEv0zudfeHw+LtZjbC3beGt5l2hOWb6fh73AycdlD586mM+1B0sa6d6ezfoMfpan3N7DjgDoI2tt1hcUrq22evLMxsaPieA/wn8Ku4bTnARYTtFRDcTwRqzWxW2FPmUuCRtAZ9GBLUdyNwerithKARdCVBI/FEM5tgZgUEyfPRdMd9KDqrq5kVh3XEzP4VaHX3Fdn03Ybx3Qm84e43x216FGjv0TSHA/E/Clwa9oqaBdSE9X0KONPMBoS9a84My3qMQ6hrZ7LiZ7mr9TWzscDDwCfd/c24/VNT30z3AEjHi+AKYStBw1clwSXb1QR/Vb4J/IBwNHu4/2nAyx2cZzqwjKCnwX/HH9OTXl2pL1AK/J7gPv8K4Jq485wb7r8W+Eam69UNdR0PrCJoOPwrwXTM2fbdnkJwG2IJ8Fr4OhcYBDwDrA7rNjDc34Bbw3otBabHnetyYE34uizTdeuGug4PfwZqCTovVBJ0XMiWn+Wu1vcOgqvi9n0Xxp2r2+ur6T5ERCSpPnsbSkREolOyEBGRpJQsREQkKSULERFJSslCRESSUrIQ6QbhOIYXzeycuLKPmNmTmYxLpLuo66xINzGzqQRjVk4kmB1hMXC2u689hHPleTDiXKRHULIQ6UZm9kOCiRlLwvdxwFQgH/i2uz8SThJ3T7gPwBfc/SULnq9xA8FAq8nuPim90Yt0TslCpBuF04m8SjB54Z+A5e7+WzPrT/DMjBMJRunG3L3RzCYC97v79DBZ/BmY6u7rMxG/SGf67ESCIqng7g1m9jugnmB+sQ/EPbWtCBgLbAH+24InMLYRPH+j3QIlCumJlCxEul8sfBlwobuvit9oZt8GtgPHE3QyaYzb3JCmGEW6RL2hRFLnKeCL4WyimNmJYXkFsNXdY8AnCR6DKdKjKVmIpM4NBA3bS8xsebgOcBswx8xeByajqwnJAmrgFhGRpHRlISIiSSlZiIhIUkoWIiKSlJKFiIgkpWQhIiJJKVmIiEhSShYiIpLU/wcMF6l/j5cQMgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# list(first_authors_at_year.items())\n",
    "x = []\n",
    "y = []\n",
    "for i in range(1970,2022):\n",
    "    x.append(i)\n",
    "    y.append(len(first_authors_at_year[i]))\n",
    "plt.plot(x,y)\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Number of First Authors Published')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 3\n",
    "\n",
    "Print out the top 10 most often occurring words in the papers' titles together with their frequency, in descending order with respect to the frequency. Filter out words that are only one or two characters long, like `a` or `of`, to only show words that have three or more characters. Use the data structures created above (in the sections before the tasks)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('and', 69010),\n",
       " ('the', 42637),\n",
       " ('cognitive', 24256),\n",
       " ('with', 21544),\n",
       " ('for', 13569),\n",
       " ('cognition', 10935),\n",
       " ('brain', 6789),\n",
       " ('effects', 6607),\n",
       " ('study.', 5600),\n",
       " ('patients', 5563)]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new = []\n",
    "for word in words:\n",
    "    if len(word) > 2:\n",
    "        new.append(word)\n",
    "save = Counter(new)\n",
    "save.most_common(10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 4\n",
    "\n",
    "Calculate and plot (e.g. using [plt.plot](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.plot)) a graph of the frequency of the 100 most frequent words in titles of papers, from most frequent to least frequent, again only counting words of three or more characters. (For readability, don't show the actual words.) Make again use of the data structures created above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Number of Occurences')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "array = []\n",
    "idx = []\n",
    "i = 1\n",
    "for word, cnt in save.most_common(100):\n",
    "    i += 1\n",
    "    array.append(cnt)\n",
    "    idx.append(i)\n",
    "plt.plot(idx,array)\n",
    "plt.xlabel('Index of Most Frequent Words')\n",
    "plt.ylabel('Number of Occurences')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Task 5\n",
    "\n",
    "What do you notice when looking at the top 10 most frequent words? Explain all types of words we find in this top-10 and why they show up here."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the top 10 most frequent words still contain a few connecting words which are \"and\", \"the\", \"with\" and \"for\" even though we removed the two letter ones. We can also see the other most frequent words are \"cognitive\", \"cognition\", \"brain\", \"effects\", \"study\" and \"patients\". These are words that are commonly used in the Life-Science domain particularly those that include emotion in the title or abstract. What is interesting to notice is that emotion is not one of the most frequent words."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Submission"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Submit the answers to the assignment via Canvas as a modified version of this Notebook file (file with `.ipynb` extension) that includes your code and your answers.\n",
    "\n",
    "Before submitting, restart the kernel and re-run the complete code (**Kernel > Restart & Run All**), and then check whether your assignment code still works as expected.\n",
    "\n",
    "Don't forget to add your name, and remember that the assignments have to be done **individually**, and that code sharing or copying are **strictly forbidden** and will be punished."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.11.0 64-bit",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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