citation_vis

Notebook visualizing citation data for scholarly articles
https://github.com/jesseangelis/citation_vis
Science Score: 54.0%

This score indicates how likely this project is to be science-related based on various indicators:
✓
CITATION.cff file
Found CITATION.cff file
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codemeta.json file
Found codemeta.json file
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.zenodo.json file
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DOI references
Found 2 DOI reference(s) in README
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Academic publication links
Links to: arxiv.org
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Academic email domains
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Institutional organization owner
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JOSS paper metadata
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Scientific vocabulary similarity
Low similarity (7.1%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·
Repository

Notebook visualizing citation data for scholarly articles
Basic Info

Host: GitHub
Owner: jesseangelis
License: mit
Language: Jupyter Notebook
Default Branch: main
Size: 2.29 MB
Statistics

Stars: 0
Watchers: 1
Forks: 0
Open Issues: 0
Releases: 0
Created over 1 year ago · Last pushed over 1 year ago
Metadata Files

Readme License Citation
README.md

Citation Visualization Notebook

This notebook visualizes citation data for scholarly articles by retrieving and processing citation counts from the OpenAlex API based on each articles's DOI.
Overview

Data Loading: Reads tool names and DOIs from a CSV file.
Data Retrieval: Fetches citation data from OpenAlex for each DOI.
Plotting: Creates a scatter plot with histograms showing citations by year and total citations for each tool.
Requirements

Install required packages with:
bash pip install requests matplotlib pandas
Usage

Prepare CSV Input: Create a file (e.g., example.csv) with columns Name and DOI.
Run Notebook: The script generates and saves a visualization as a .png file.
Sample CSV

csv Name,DOI Name1,10.1234/example1 Name2,10.5678/example2
Citation

If using this notebook or OpenAlex data, please cite:
Priem, J., Piwowar, H., & Orr, R. (2022). OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts. ArXiv. https://arxiv.org/abs/2205.01833
Owner

Name: Jesse Angelis
Login: jesseangelis
Kind: user
Repositories: 1
Profile: https://github.com/jesseangelis
Citation (citation_vis.ipynb)

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Citation_vis\n",
    "\n",
    "A notebook to visualize citations over time using the openalex [1] API\n",
    "\n",
    "[1] Priem, J., Piwowar, H., & Orr, R. (2022). OpenAlex: A fully-open index of scholarly works, authors, venues, institutions, and concepts. ArXiv. https://arxiv.org/abs/2205.01833"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import required packages\n",
    "import requests\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_citation_data(doi, cutoff_years=(2010,2024)):\n",
    "    \"\"\"\n",
    "    Retrieve citation data from the OpenAlex API for a specific DOI.\n",
    "\n",
    "    Parameters:\n",
    "        doi (str): Digital Object Identifier (DOI) of the publication\n",
    "        cutoff_years (tuple): Range of years to consider for citation data\n",
    "\n",
    "    Returns:\n",
    "        date (str): Publication date\n",
    "        years (list): List of years with citation counts\n",
    "        counts (list): Citation counts corresponding to each year\n",
    "    \"\"\"\n",
    "    url = f\"https://api.openalex.org/works/doi/{doi}\"\n",
    "    response = requests.get(url)  # Fetch data from the API\n",
    "    data = response.json() # Parse JSON response\n",
    "    id = data[\"id\"]\n",
    "    date = data['publication_date']\n",
    "\n",
    "    url = f\"https://api.openalex.org/works?group_by=publication_year&per_page=200&filter=cites:{id}\"\n",
    "    response = requests.get(url)\n",
    "    data = response.json()\n",
    "    \n",
    "    # Extract publication date and citation data by year\n",
    "    years = [int(x[\"key\"]) for x in data['group_by'] if (int(x[\"key\"])>=cutoff_years[0] and int(x[\"key\"])<=cutoff_years[1])]\n",
    "    counts = [x[\"count\"] for x in data['group_by'] if (int(x[\"key\"])>=cutoff_years[0] and int(x[\"key\"])<=cutoff_years[1])]\n",
    "    \n",
    "    return date, years, counts\n",
    "\n",
    "\n",
    "def get_plot_data(publications):\n",
    "    \"\"\"\n",
    "    Process and aggregate citation data for each tool for plotting.\n",
    "\n",
    "    Parameters:\n",
    "        publications (DataFrame): DataFrame with columns 'Name' and 'DOI' for each tool\n",
    "\n",
    "    Returns:\n",
    "        plot_data (DataFrame): Processed DataFrame with aggregated citation counts by year for each tool\n",
    "    \"\"\"\n",
    "    name, years, counts = [], [], []\n",
    "    date = {}  # Dictionary to store each tool's publication date\n",
    "\n",
    "    # Loop through each row of the publications DataFrame\n",
    "    for row in publications.iterrows():\n",
    "        row = row[1]\n",
    "        name_tool = row[\"Name\"]\n",
    "\n",
    "        # Get citation data for each tool\n",
    "        date_tool, years_tool, counts_tool = get_citation_data(row[\"DOI\"])\n",
    "\n",
    "        # If there are no citations, initialize year and count with publication year\n",
    "        if len(years_tool) == 0:\n",
    "            years_tool += [int(date_tool.split('-')[0])]\n",
    "            counts_tool += [0]\n",
    "\n",
    "        # Add citation data for the tool\n",
    "        name += len(years_tool) * [name_tool]\n",
    "        years += years_tool\n",
    "        counts += counts_tool\n",
    "\n",
    "        # Store the earliest publication date for each tool\n",
    "        if name_tool in date.keys():\n",
    "            if int(date[name_tool].split('-')[0]) < int(date_tool.split('-')[0]):\n",
    "                continue\n",
    "        date[name_tool] = date_tool  \n",
    "    \n",
    "    # Create DataFrame with collected data\n",
    "    plot_data = pd.DataFrame({\"Name\": name, \"Year\": years, \"Count\": counts})\n",
    "\n",
    "    # Filter out data points from before the publication year of each tool\n",
    "    for tool, pub_date in date.items():\n",
    "        plot_data = plot_data[~((plot_data['Name'] == tool) & (plot_data['Year'] < int(pub_date.split('-')[0])))]\n",
    "\n",
    "    # Aggregate citation counts for each tool by year\n",
    "    plot_data = plot_data.groupby(['Name', 'Year'], as_index=False).agg({'Count': 'sum'})\n",
    "\n",
    "    # Sort tools by publication date\n",
    "    sorted_names = sorted(date, key=date.get)\n",
    "    plot_data['Name'] = pd.Categorical(plot_data['Name'], categories=sorted_names, ordered=True)\n",
    "    plot_data = plot_data.sort_values('Name')\n",
    "\n",
    "    return plot_data\n",
    "\n",
    "\n",
    "def draw_plot(plot_data, custom_colors=True):\n",
    "    \"\"\"\n",
    "    Generate a scatter plot with marginal histograms for citation counts over time.\n",
    "\n",
    "    Parameters:\n",
    "        plot_data (DataFrame): Processed DataFrame with citation counts for each tool over time\n",
    "\n",
    "    Returns:\n",
    "        fig (Figure): Matplotlib figure containing the scatter plot with marginal histograms\n",
    "    \"\"\"\n",
    "    # Initialize a square figure\n",
    "    fig = plt.figure(figsize=(8, 6))\n",
    "\n",
    "    # Configure grid layout with two rows and two columns\n",
    "    gs = fig.add_gridspec(2, 2, width_ratios=(4, 1), height_ratios=(1, 4),\n",
    "                          left=0.1, right=0.9, bottom=0.1, top=0.9,\n",
    "                          wspace=0.05, hspace=0.05)\n",
    "\n",
    "    # Define main scatter plot and histograms for marginal distributions\n",
    "    ax = fig.add_subplot(gs[1, 0])  # Main scatter plot\n",
    "    ax_histx = fig.add_subplot(gs[0, 0], sharex=ax)  # Histogram for years\n",
    "    ax_histy = fig.add_subplot(gs[1, 1], sharey=ax)  # Bar plot for total citations\n",
    "\n",
    "    # Create a color map for unique tools\n",
    "    unique_names = plot_data['Name'].unique()\n",
    "    num_unique_names = len(unique_names)\n",
    "    if custom_colors:\n",
    "        hex_codes = ['#007FFF', '#B1DDF0', '#FF7800', '#2EC27E', '#8FF0A4', '#F8CECC', '#B266FF', '#A0522D', '#BAC8D3']*10\n",
    "        colors = plt.cm.colors.ListedColormap(hex_codes)\n",
    "    else:\n",
    "        colors = plt.get_cmap('tab20', num_unique_names) \n",
    "\n",
    "    # Map each name to a unique color\n",
    "    color_map = {name: colors(i) for i, name in enumerate(unique_names)}\n",
    "\n",
    "    # Add light gray horizontal lines\n",
    "    ax.set_axisbelow(True)  # Ensures grid lines are behind the scatter plot\n",
    "    ax.yaxis.grid(True, which='major', linestyle='--', linewidth=0.5, color='lightgray')\n",
    "\n",
    "    # Scatter plot with varying size based on citation counts\n",
    "    ax.scatter(plot_data['Year'], plot_data['Name'], \n",
    "                s=plot_data['Count'] / max(plot_data['Count']) * 1000, \n",
    "                alpha=0.7, \n",
    "                color=[color_map[name] for name in plot_data['Name']])\n",
    "    \n",
    "    ax.set_xlabel('Year')\n",
    "\n",
    "    # Aggregate total citations for each tool and plot on the side bar chart\n",
    "    tool_counts = plot_data.groupby('Name', observed=True)['Count'].sum().reindex(sorted(plot_data['Name'].unique())).values\n",
    "    ax_histy.barh(sorted(plot_data['Name'].unique()), tool_counts, \n",
    "                   color=[color_map[name] for name in sorted(plot_data['Name'].unique())], alpha=0.7)\n",
    "\n",
    "    # Create histogram for marginal distribution by years\n",
    "    year_counts = plot_data.groupby('Year', observed=True)['Count'].sum().reindex(sorted(plot_data['Year'].unique())).values\n",
    "    ax_histx.bar(sorted(plot_data['Year'].unique()), year_counts, color='black', alpha=0.5)\n",
    "\n",
    "    # Adjust axis limits and remove unnecessary borders\n",
    "    ax_histx.set_xlim(ax.get_xlim())\n",
    "    for histogram_ax in [ax_histx, ax_histy]:\n",
    "        histogram_ax.spines['top'].set_visible(False)\n",
    "        histogram_ax.spines['right'].set_visible(False)\n",
    "    ax_histy.spines['left'].set_visible(False)\n",
    "    ax_histx.spines['bottom'].set_visible(False)\n",
    "    ax_histx.get_xaxis().set_visible(False)\n",
    "    ax_histy.set_xlabel('Citations\\nper tool')\n",
    "    ax_histx.set_ylabel('Citations\\nper year')\n",
    "    ax_histy.get_yaxis().set_visible(False)\n",
    "\n",
    "    # Set x-axis ticks to full years\n",
    "    years = sorted(plot_data['Year'].unique())  # Extract unique years in sorted order\n",
    "    if len(years) > 20:\n",
    "        years = list(range(min(years), max(years)+1))[::-3]\n",
    "    elif len(years) > 10:\n",
    "        years = list(range(min(years), max(years)+1))[::-2]\n",
    "    else:\n",
    "        years = list(range(min(years), max(years)+1))\n",
    "    ax.set_xticks(years)                        # Set ticks to only those years\n",
    "    ax.set_xticklabels(years)                   # Ensure ticks are labeled properly\n",
    "\n",
    "    return fig\n",
    "\n",
    "\n",
    "def get_citation_vis(file, custom_colors=True):\n",
    "    \"\"\"\n",
    "    Generate and save a citation visualization for tools listed in the input file.\n",
    "\n",
    "    Parameters:\n",
    "        file (str): Path to the CSV file containing tool names and DOIs\n",
    "    \"\"\"\"\"\"\n",
    "    Generate and save a citation visualization for tools listed in the input file.\n",
    "\n",
    "    Parameters:\n",
    "        file (str): Path to the CSV file containing tool names and DOIs\n",
    "    \"\"\"\n",
    "    df = pd.read_csv(file)\n",
    "    name = file[:-4]\n",
    "    plot_data = get_plot_data(df)\n",
    "    plot_data.sort_values(['Name', 'Year'], ascending=[True, False]).to_csv(f'{name}_plot_data.csv', index=False)\n",
    "    fig = draw_plot(plot_data, custom_colors=custom_colors)\n",
    "    fig.savefig(name+\".png\", format=\"png\", bbox_inches=\"tight\", dpi=1000)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Execute the visualization function with a sample CSV file\n",
    "get_citation_vis(\"example.csv\")\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Citatoin_vis",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
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