{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "347y_Tu1024o",
      "metadata": {
        "id": "347y_Tu1024o"
      },
      "source": [
        "# SciKGTeX 1. User Test Result Analysis\n",
        "\n",
        "This notebook details the calculation and analysis which has been executed to achieve the evaluation results in my master thesis on SciKGTeX; a LaTeX package to mark main contributions in scientific publications."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 495,
      "id": "68a6dd52",
      "metadata": {
        "id": "68a6dd52"
      },
      "outputs": [],
      "source": [
        "%matplotlib inline\n",
        "import glob\n",
        "import re\n",
        "import matplotlib\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "from ast import literal_eval\n",
        "from collections import defaultdict\n",
        "from scipy import stats\n",
        "from matplotlib import pyplot as plt\n",
        "from xml.etree import ElementTree as ET\n",
        "from difflib import SequenceMatcher\n",
        "from itertools import permutations\n",
        "\n",
        "\n",
        "#matplotlib.rcParams['figure.figsize'] = (10.0, 8.0)\n",
        "matplotlib.rcParams.update({'font.size': 18})\n",
        "\n",
        "LIKERT_TO_NUM = {'Strongly agree':5, 'Agree':4, 'Neutral':3, 'Disagree':2, 'Strongly disagree':1}\n",
        "PUBLICATION_TO_NUM = {'None':1, '1-5':2, '6-10':3, '11-20':4, '20 or more':5}\n",
        "COMPARE_TO_NUM = {'Never':1, 'Rarely':2, 'Sometimes':3, 'Often':4, 'Always':5}\n",
        "EXP_TO_NUM = {'No experience':1, 'Less than 1 year':2, '1-2 years':3, '3-4 years':4, '5 years or more':5}"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8dd94be8",
      "metadata": {},
      "source": [
        "### Loading the data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 358,
      "id": "d1e62dbe",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d1e62dbe",
        "outputId": "c55ed84e-7676-4523-e4d7-b49f88efce78"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "(26, 31)\n"
          ]
        }
      ],
      "source": [
        "df = pd.read_csv('responses.csv')\n",
        "print(df.shape)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "qsLlM4Db1Ie-",
      "metadata": {
        "id": "qsLlM4Db1Ie-"
      },
      "source": [
        "The evaluation has a sample size of 26 participants and 31 datapoints were collected per participant.\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 359,
      "id": "3a05494c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3a05494c",
        "outputId": "b42b6090-70ab-42f5-8db6-23fda8c67cb3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0 Timestamp\n",
            "1 User ID (provided)\n",
            "2 time task 1\n",
            "3 How old are you?\n",
            "4 What is your gender?\n",
            "5 What is your highest degree?\n",
            "6 Which organisation are you affiliated with? (name of university, company or other institution)\n",
            "7 What is your position?\n",
            "8 How many scientific publications (published or not) have you approximately produced or collaborated on?\n",
            "9 I am familiar with the LaTeX document system.\n",
            "10 How many years of experience do you have with LaTeX?\n",
            "11 Compared to other tools, how often do you use the LaTeX document preparation system to write publications, reports or similar documents?\n",
            "12 I prefer LaTeX to produce scientific documents.\n",
            "13 I am familiar with the concept of the Semantic Web.\n",
            "14 I have worked with Semantic Web technologies (Knowledge Graphs, ontologies, etc.).\n",
            "15 I  know the Resource Description Framework (RDF).\n",
            "16 I know the difference between Uniform Resource Identifiers (URIs) and Uniform Resource Location (URLs).\n",
            "17 I am familiar with the FAIR (Findable, Accessible, Interoperable, Reusable) principles for data management.\n",
            "18 I think that I would like to use this system frequently.\n",
            "19 I found the system unnecessarily complex.\n",
            "20 I thought the system was easy to use.\n",
            "21 I think that I would need the support of a technical person to be able to use this system.\n",
            "22 I found the various functions in this system were well integrated.\n",
            "23 I thought there was too much inconsistency in this system.\n",
            "24 I would imagine that most people would learn to use this system very quickly.\n",
            "25 I found the system very cumbersome to use.\n",
            "26 I felt very confident using the system.\n",
            "27  I needed to learn a lot of things before I could get going with this system.\n",
            "28 How long did it take you to complete task 1?\n",
            "29 Further remarks on the usability of the annotation package.\n",
            "30 time\n"
          ]
        }
      ],
      "source": [
        "for i,c in enumerate(df.columns):\n",
        "    print(i, c)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e7556c12",
      "metadata": {},
      "source": [
        "### Calculate the System Usability Score (SUS)\n",
        "\n",
        "https://en.wikipedia.org/wiki/System_usability_scale"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 360,
      "id": "f27b89f3",
      "metadata": {
        "id": "f27b89f3"
      },
      "outputs": [],
      "source": [
        "def calculate_sus(sus_responses):\n",
        "    \"\"\"\n",
        "    For each of the odd numbered questions, subtract 1 from the score.\n",
        "    For each of the even numbered questions, subtract their value from 5.\n",
        "    Take these new values which you have found, and add up the total score. Then multiply this by 2.5.\n",
        "    \"\"\"\n",
        "    # 10 questions\n",
        "    assert(sus_responses.shape[1] == 10)\n",
        "    for i, question in enumerate(sus_responses):\n",
        "        question_number = i+1\n",
        "        numeric_responses = sus_responses[question].map(LIKERT_TO_NUM)\n",
        "        if (question_number%2 == 0):\n",
        "            sus_responses[question] = 5 - numeric_responses\n",
        "        else:\n",
        "            sus_responses[question] = numeric_responses - 1\n",
        "    sums = sus_responses.sum(axis=1)\n",
        "    sus_scores = sums*2.5\n",
        "    return sus_scores"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 361,
      "id": "8c3b5772",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8c3b5772",
        "outputId": "76e5367d-5284-4094-d793-676fd405b3ef"
      },
      "outputs": [],
      "source": [
        "sus_columns = df.iloc[:,18:28]\n",
        "df['sus'] = calculate_sus(sus_columns)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 362,
      "id": "0db48d47",
      "metadata": {
        "id": "0db48d47"
      },
      "outputs": [],
      "source": [
        "def statistics_overview(data):\n",
        "    print('{:16s}: {:d}'.format('number of samples', len(data)))\n",
        "    print('-'*30)\n",
        "    print('{:20s}:{:6.2f}'.format('mean', data.mean()))\n",
        "    print('{:20s}:{:6.2f}'.format('standard deviation', data.std()))\n",
        "    print('{:20s}:{:6.2f}'.format('median', data.median()))\n",
        "    print('{:20s}:{:6.2f}'.format('10th percentile', data.quantile(.1)))\n",
        "    print('{:20s}:{:6.2f}'.format('25th percentile', data.quantile(.25)))\n",
        "    print('{:20s}:{:6.2f}'.format('75th percentile', data.quantile(.75)))\n",
        "    print('{:20s}:{:6.2f}'.format('90th percentile', data.quantile(.9))) \n",
        "    plt.figure(figsize = (3,7))\n",
        "    ax = sns.boxplot(y = data)\n",
        "    ax.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a40097a2",
      "metadata": {
        "id": "a40097a2"
      },
      "source": [
        "### General statistic values of the usability study\n",
        "The mean SUS score is 79.8 ($\\sigma$ = 11.6) and the median is 81.25.\n",
        "\n",
        "The 10th percentile is 62.5.\n",
        "\n",
        "The first quartile is 75.0.\n",
        "\n",
        "The third quartile is 85.0.\n",
        "\n",
        "The 90th percentile is 95.0.\n",
        "\n",
        "80% of scores lie between 62.5 and 95.0."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 363,
      "id": "47d4997c",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "47d4997c",
        "outputId": "25a03b32-079b-4b9c-f2f1-ae9678b540df"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "number of samples: 26\n",
            "------------------------------\n",
            "mean                : 79.81\n",
            "standard deviation  : 11.60\n",
            "median              : 81.25\n",
            "10th percentile     : 62.50\n",
            "25th percentile     : 75.00\n",
            "75th percentile     : 85.00\n",
            "90th percentile     : 95.00\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 216x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "statistics_overview(df['sus'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "0f4c0141",
      "metadata": {
        "id": "0f4c0141"
      },
      "source": [
        "### The usability scores for different groups of the participants \n",
        "\n",
        "1. grouped by the job position\n",
        "2. grouped by the affiliation"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "89e93db0",
      "metadata": {
        "id": "89e93db0"
      },
      "source": [
        "#### Grouped by job position"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 364,
      "id": "eq6fzkUCQk0V",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 592
        },
        "id": "eq6fzkUCQk0V",
        "outputId": "d89f1fa2-ff70-4c58-9c9e-1734117d367f"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 364,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize = (15,8))\n",
        "df['position'] = df[df.columns[7]].map(lambda x: x if x in ['Master Student', 'Post-Doctoral Researcher', 'PhD Student'] else 'Other')\n",
        "ax = sns.boxplot(x='position', y='sus', data=df)\n",
        "sns.stripplot(x=\"position\", y=\"sus\", data=df,\n",
        "              size=10, color=\".3\", linewidth=0)\n",
        "ax.set_xticklabels(ax.get_xticklabels(),rotation = 30)\n",
        "ax.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8310d7ee",
      "metadata": {
        "id": "8310d7ee"
      },
      "source": [
        "PhD students"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 365,
      "id": "959455be",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "959455be",
        "outputId": "b1100a75-c1ea-4e6f-bd0a-41af27845106"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "number of samples: 14\n",
            "------------------------------\n",
            "mean                : 82.32\n",
            "standard deviation  : 12.34\n",
            "median              : 83.75\n",
            "10th percentile     : 68.00\n",
            "25th percentile     : 75.00\n",
            "75th percentile     : 90.62\n",
            "90th percentile     : 97.50\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 216x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "phd_students_group = df.loc[df['position']=='PhD Student']\n",
        "statistics_overview(phd_students_group['sus'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5c7629f0",
      "metadata": {
        "id": "5c7629f0"
      },
      "source": [
        "Master Students"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 366,
      "id": "4d5b8bfc",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "4d5b8bfc",
        "outputId": "d95e920c-fbc9-48d2-d132-08d13d490366"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "number of samples: 5\n",
            "------------------------------\n",
            "mean                : 75.50\n",
            "standard deviation  : 15.45\n",
            "median              : 85.00\n",
            "10th percentile     : 58.50\n",
            "25th percentile     : 60.00\n",
            "75th percentile     : 85.00\n",
            "90th percentile     : 88.00\n"
          ]
        },
        {
          "data": {
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAAOkAAAGOCAYAAABysSS1AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjMuNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8QVMy6AAAACXBIWXMAAAsTAAALEwEAmpwYAAAWRklEQVR4nO3df7BXdZ3H8edLSBQR0YEMSWSVxLbCGq/5M8cfszuik7jbbKOuo5RIOeaYpabtruPWrrpsM+IyNnpFTW2qzUI21kyjsnFEY6+OC5puXhUtFAMF5McFUt/7xzlf+Xb5gnzhe7/n7ZfXY+bOh/s5n3PO+zK8OJ/z456vIgIzy2uXqgsws61zSM2Sc0jNknNIzZJzSM2Sc0jNkhtcdQHtNHLkyBg3blzVZZg19Nhjjy2PiFH9+3eqkI4bN46enp6qyzBrSNKLjfo93TVLziE1S84hNUvOITVLziE1S84hNUvOITVLziE1S84hNUuu0pBK2lfSTZJ+L2mjpJck3SBpRIOxEyTNkbRC0lpJD0k6sYKyzdqqsscCJb0f+A2wH3Az8CTwUeAC4DhJx0TEunLsQcB84E1gOrAKOB+4X9KkiJhXwY9g1hZVPrv7deAA4KyI+H6tU9J84HvAV4B/KbuvBUYAh0XEE+W4O4GngBslHRJ+WZN1qCqnuycAfcAP+vX/J7Ae+ByApD2A04AHawEFiIg1wCzgYODwNtRrVokqQzoEWN//CBgRb1OE90BJI4GJ5dhHGmzj0bJ1SK1jVTndfQqYIOnj9UdISR8H9i6/HUtxzgqwpME2an1jBqjGdGbOnElvb2/VZbBkSfFXP2ZMtX/148eP56KLLqq0hoFW5ZF0BvA28ENJp0gaK2kSxXT3T+WYoeUXwIYG21hfN64hSdMk9UjqWbZsWWsqN/r6+ujr66u6jJ1CZUfSiHhI0hnAfwD3lt1vUZxnPgX8DfAGsK5cNqTBZnYr23UNltX20w10A3R1db3nLy5lOWpcfPHFANxwww0VV9L5Kn0zQ0TcLWk28DFgT+D/IuKPkhZQ3G7pBfYohzeaV9X6Gk2FzTpC5a9PiYi3gCdq30v6APAJ4NcRsU7SIoqp7lENVj+ybP1OFOtYqR4LlLQLxfR3EPCv8M6tlrnA8ZIOrRs7DJgKPAssaH+1Zu1R5RNHwyjCdQ/wArAXcCZwGPAPEfGruuFXAicBD0i6nuJc9XyK6e6pfpDBOlmV092NwELgLGA0xcWf/wFOjoj76wdGRK+kY4DrgCuAXYHHy7F+JNA6WpVXdzcCZzQx/mlg8sBVZJZTqnNSM9ucQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapZcpSGVNEzS1yUtkrRa0nJJ8yVNkaS6cVdLii18XVrlz2A20AZXtWNJuwD3AUcDdwAzgaHAmcDtwIeBr/Vb7RJgeb++xwa2UrNqVRZS4AjgWGBGRFxS65T0beAZ4AtsHtI5EbG4bRWaJVDldHd42b5c3xkRGymOlmsbrSRpuKQq/3Mxa6sq/7EvAFYCl0taDPwG2B2YAhwGfLHBOguBPYG3JC0AvhkR97WjWICpU6fyyiuvtGt3qfX19QFw6qmnVlxJDqNHj2bWrFkDsu3KQhoRKySdBswCfli3aDXwmYiYU9e3EugG5gMrgAnAl4F7JX0+Ir6zpf1ImgZMAxg7duwO1bxy5UrWrF0Hg3wgJ4pmzfqN1daRwVtvsnLlygHbfNX/2tYATwI/oQjgPsCFwPckTY6InwNExIz+K0q6rVz3ekk/iog1jXYQEd0UAaerqyt2pNgxY8awdMNg+g45ZUc2Yx1m92d+ypgx+w7Y9is7J5X0MYpg/jwiLouIeyLiVoqLSUuBWyQN2tL6EfEacBMwguIKsVlHqvLC0SXAbsDd9Z0RsQ64FzgAGPcu21hctiNbXJtZGlWGdEzZNjpaDu7XbsmHyvbVllRkllCVIf1t2U6p75Q0AphMcYHoOUmDJe3Vf2VJ+wMXAK9RTJvNOlKVF45mAOcA15Xnpw9TXDg6HxgNXBgRb5ahfUHSHOBpNl3dnQoMA86MiL62V2/WJlXegnlR0ieBq4CTgDOAPuAJ4KsRMbsc2gf8mOIJpdMpgrkcmAdMj4gF7a3crL0qvQUTEc8B577LmA0UR02znZJ/Vc0sOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cy5SkMqaZikr0taJGm1pOWS5kuaIkn9xk6QNEfSCklrJT0k6cSqajdrl8FV7VjSLsB9wNHAHcBMYChwJnA78GHga+XYg4D5wJvAdGAVcD5wv6RJETGv7T+AWZtUFlLgCOBYYEZEXFLrlPRt4BngC5QhBa4FRgCHRcQT5bg7gaeAGyUdEhHRvtLN2qfK6e7wsn25vjMiNgLLgbUAkvYATgMerAW0HLcGmAUcDBzehnrNKlHlkXQBsBK4XNJi4DfA7sAU4DDgi+W4icAQ4JEG23i0bA8vt2fWcSoLaUSskHQaxdHwh3WLVgOfiYg55ff7le2SBpup9Y0ZkCLNEqj6Fswa4EngW8DfAlOBXuB7kv6qHDO0bDc0WH99vzGbkTRNUo+knmXLlrWmarM2qiykkj5GccX25xFxWUTcExG3UlxMWgrcImkQsK5cZUiDzexWtusaLAMgIrojoisiukaNGtXCn8CsPao8kl5CEbK76zsjYh1wL3AAMI5NF5YaTWlrfY2mwmYdocqQ1gI2qMGywXXtIoqp7lENxh1Ztj2tLc0sjypD+tuynVLfKWkEMBlYATxX3mqZCxwv6dC6ccMozmGfxVd2rYNVeQtmBnAOcF15fvowsA/Fk0SjgQsj4s1y7JXAScADkq4H3ijHjQFO9YMM1smqvAXzoqRPAldRBPAMoA94AvhqRMyuG9sr6RjgOuAKYFfgceBkPxJona7KIykR8Rxw7jaOfZpiGmy2U6n6PqmZvQuH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsuUpDKulqSbGVrz9t49hLq/w5zAbS4Ir3PxvobdA/EbgMmNtg2SXA8n59j7W4LrM0Kg1pRCwEFvbvl3Rz+cdbG6w2JyIWD2RdZpmkOyeVNBQ4A1gC/GwLY4ZLqnoWYNYW6UIKfBYYDtweEW81WL4QWAWslzRf0qS2VmfWZhmPRucBAdzWr38l0A3MB1YAE4AvA/dK+nxEfKd9JZq1T6qQSpoAHAv8IiJeqF8WETMajL8NeBK4XtKPImJNgzHTgGkAY8eOHYiyzQZUS6a7kkZK+lALNnVe2c7alsER8RpwEzACOHoLY7ojoisiukaNGtWCEs3aq6mQSjpHUne/vmuBV4FnJD0sac/tKaS8EHQO8DpwTxOrLi7bkduzX7Psmj2SfoG6KbKkLuBrwEPALcAnga9sZy2fBvYF7oqIDU2sVzuCv7qd+zVLrdlz0vHA3XXf/x3Fke+vI2KjpKC4OvvP21FLbaq72b3R8ii7R0Ss6te/P3AB8BrFBSWzjtNsSPeiuP1RcxIwLyI2lt/3AGc3W4Sk/YCTgQURsajBkGHAC5LmAE+z6eru1HLZmRHR1+x+zd4Lmg3pUsrppaRRwMeB2+uWDwMa3dt8N1OAQWz5glEf8GPgCOD0cj/LgXnA9IhYsB37NHtPaDakvwQulPQ6cALF/cx765ZPoHhSqCkRcQ1wzVaWb6A4aprtdJoN6VUUtzqml9//S+052vK88TMURzwza5GmQhoRf5D0EeAvgVUR8VLd4qEUDw38bwvrM9vpNf3EUfk87WYXdyLiDeC/WlGUmW3SVEglbdNzdf2OsGa2A5o9ki6muFj0bgY1X4qZNdJsSL/B5iEdDBwETKaYBt/XgrrMrNTshaOrt7RM0oHAIxQPNJhZi7Tsl74j4nngZrbvkUAz24JWv5lhCcXtGTNrkVaH9HSK52rNrEWavQVz1RYW7QOcCHyUTU8jmVkLNHt19+qtLFsK/CPwb9tdjZltptmQ/kWDvgBeb/R+ITPbcc3egnmxf1/5YP1kSXsD/x0RS1tVnJk1f046HTghIg4vvxfF73R+ChDwmqQjI+K5lldqtpNq9uruyRTvM6r5NHAc8O/AWWXfFS2oy8xKzZ6T7g88W/f9p4EXIuIKgPLX2P6+RbWZGc0fSXflz1+PcgLFdLfmeWD0jhZlZps0G9LfA0fCO0fNA4Ff1y1/P+CrvGYt1Ox09wfAP0l6P/AR4A3gp3XLPwH4opFZCzV7JL0W+A5wFMX90XMiYiWApL2A04BftLA+s51es/dJN1C8xPq8BotXU5yPrmtBXWZWatmnqkXE2/z5i7PNrAUyfoiwmdVxSM2Sc0jNknNIzZJzSM2Sc0jNknNIzZJzSM2Sc0jNknNIzZJzSM2Sc0jNknNIzZJzSM2Sc0jNknNIzZKrNKSSrpYUW/n6U7/xEyTNkbRC0lpJD0k6sar6zdqhZW9m2E6zgd4G/ROBy4C5tQ5JBwHzgTcpPrltFXA+cL+kSRExr8F2zN7zKg1pRCwEFvbvl3Rz+cdb67qvBUYAh0XEE+W4O4GngBslHRIRMaAFm1Ug3TmppKHAGRSfGv6zsm8PijcRPlgLKED5SW6zgIOBw9terFkbpAsp8FlgOHB7RNTelj8RGAI80mD8o2XrkFpHyhjS8yje6XtbXd9+Zbukwfha35iBLMqsKqlCKmkCcCzwy4h4oW7R0LLd0GC19f3G9N/mNEk9knqWLVvWumLN2iRVSNn00u1Z/fprL9we0mCd3fqN+TMR0R0RXRHRNWrUqBaUaNZeaUJafmL4OcDrwD39Fr9cto2mtLW+RlNhs/e8NCGl+KzTfYG7yo+zqLeIYqp7VIP1jizbngGszawymUJam+re2n9BeatlLnC8pENr/ZKGAVMpPth4QTuKNGu3qp84AkDSfsDJwIKIWLSFYVcCJwEPSLqe4mMXz6eY7p7qBxmsU6UIKTAFGMTmF4zeERG9ko4BrgOuoPjU8ceBk/1IoHWyFCGNiGuAa7Zh3NPA5IGvyCyPTOekZtaAQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapacQ2qWnENqlpxDapZc5SGVtI+kb0nqlbRe0jJJv5L0qboxV0uKLXxdWmX9ZgNtcJU7l3QA8CAwDLgV+B2wFzARGNNglUuA5f36HhvAEs0qV2lIge+WNUyMiFe2YfyciFg8sCWZ5VLZdFfSccCxwPSIeEXS+yQN3Yb1hkuq+j8Xs7ap8pz0lLJ9SdJcoA9YK+l3ks7ewjoLgVXAeknzJU1qR6FmVaoypBPK9hZgH+Bc4DxgI3CXpM/VjV0JdAMXAZOBK4EDgHslTdnaTiRNk9QjqWfZsmUt/QHM2qHKaeOeZbsaOCEiNgJIugd4HrhG0h0R8XZEzOi/sqTbgCeB6yX9KCLWNNpJRHRTBJyurq5o/Y9hNrCqPJL2le33awEFiIgVwE+AD7DpaLuZiHgNuAkYARw9cGWaVavKkP6hbJc2WFa70rv3u2xjcdmObEVBZhlVGdIFZfvBBstqfX98l218qGxfbUlFZglVGdI5FOejZ0saVuuUNBo4HXg2InolDZa0V/+VJe0PXAC8BsxvS8VmFajswlFErCgf6bsZeLS8ELQrRfB2Bb5UDh0GvCBpDvA0sILiXHVquezMiOjDrENV+lBARHRLWg5cDnwTeBt4BDgrIh4uh/UBPwaOoDjCDqN4NHAexYMQC/pv16yTVP7kTkTMBmZvZfkGiqOm2U6p8t+CMbOtc0jNknNIzZJzSM2Sc0jNknNIzZJzSM2Sc0jNknNIzZJzSM2Sc0jNknNIzZJzSM2Sc0jNknNIzZJzSM2Sc0jNknNIzZJzSM2Sc0jNknNIzZJzSM2Sc0jNknNIzZJzSM2Sc0jNknNIzZJzSM2Sc0jNknNIzZJzSM2Sc0jNknNIzZJzSM2Sc0jNknNIzZJzSM2Sc0jNknNIzZKrPKSS9pH0LUm9ktZLWibpV5I+1W/cBElzJK2QtFbSQ5JOrKpus3YZXOXOJR0APAgMA24FfgfsBUwExtSNOwiYD7wJTAdWAecD90uaFBHz2lu5WftUGlLgu2UNEyPila2MuxYYARwWEU8ASLoTeAq4UdIhEREDXKtZJSqb7ko6DjgWmB4Rr0h6n6ShDcbtAZwGPFgLKEBErAFmAQcDh7enarP2q/JIekrZviRpLjAJGCTpWeAbEfHdcvlEYAjwSINtPFq2hwMLBrLYmkHrXmf3Z37ajl2ltsv6NwB4e7fhFVdSvUHrXgf2HbDtVxnSCWV7C/AscC5FGL8C3CXpfRFxO7BfOW5Jg23U+sY0WAaApGnANICxY8fuUMHjx4/fofU7SW/vagDGHzhw/zjfO/Yd0H8bVYZ0z7JdDZwQERsBJN0DPA9cI+kOoDYF3tBgG+vLdrNpck1EdAPdAF1dXTt03nrRRRftyOod5eKLLwbghhtuqLiSzlflLZi+sv1+LaAAEbEC+AnwAYqj7bpy0ZAG29itbNc1WGbWEaoM6R/KdmmDZbUrvXsDL5d/bjSlrfU1mgqbdYQqQ1q70PPBBstqfX8EFlFMdY9qMO7Isu1pbWlmeVQZ0jkU56NnSxpW65Q0GjgdeDYiestbLXOB4yUdWjduGDCV4qJTW67smlWhsgtHEbFC0qXAzcCjkm4DdgUuKNsv1Q2/EjgJeEDS9cAbFE8cjQFO9YMM1skqfeIoIrolLQcuB74JvE1xP/SsiHi4blyvpGOA64ArKEL8OHCyHwm0Tlf1Y4FExGxg9jaMexqYPPAVmeVS+W/BmNnWOaRmyTmkZsk5pGbJOaRmyTmkZsk5pGbJOaRmyTmkZsk5pGbJOaRmyTmkZsk5pGbJOaRmyTmkZsk5pGbJOaRmyVX+ZgZrzsyZM+nt7a26jHdqqL0kuyrjx4/v+JeWO6S2XXbfffeqS9hpOKTvMZ1+1LDN+ZzULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOYfULDmH1Cw5h9QsOUVE1TW0jaRlwItV19FBRgLLqy6igxwQEaP6d+5UIbXWktQTEV1V19HpPN01S84hNUvOIbUd0V11ATsDn5OaJecjqVlyDqlZcg6pWXIOqVlyDqlZcv8P8/hmAmhPWKAAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 216x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "master_students_group = df.loc[df[df.columns[7]]=='Master Student']\n",
        "statistics_overview(master_students_group['sus'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "309b685c",
      "metadata": {
        "id": "309b685c"
      },
      "source": [
        "Post Doctoral Researchers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 367,
      "id": "f2c0a413",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "f2c0a413",
        "outputId": "810d37ce-5662-4baf-fcce-ba310d5fa7a3"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "number of samples: 3\n",
            "------------------------------\n",
            "mean                : 75.00\n",
            "standard deviation  :  6.61\n",
            "median              : 72.50\n",
            "10th percentile     : 70.50\n",
            "25th percentile     : 71.25\n",
            "75th percentile     : 77.50\n",
            "90th percentile     : 80.50\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 216x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "postdoc_group = df.loc[df[df.columns[7]]=='Post-Doctoral Researcher']\n",
        "statistics_overview(postdoc_group['sus'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "a0c8d363",
      "metadata": {
        "id": "a0c8d363"
      },
      "source": [
        "#### Grouped by affiliation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 368,
      "id": "916b9fa2",
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "execution_count": 368,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "aff_map  = {\n",
        "    'LUH':'LUH',\n",
        "    'L3S/LUH University Hannover':'LUH', \n",
        "    'L3S Research Center, Leibniz University Hannover':'LUH',\n",
        "    'Leibniz Uni':'LUH',\n",
        "    'leibniz university hannover':'LUH',\n",
        "    'Leibniz University Hannover':'LUH',\n",
        "    'University of Innsbruck':'UIBK',\n",
        "    'UIBK':'UIBK',\n",
        "    'TIB':'TIB',\n",
        "    'TIB Hannover':'TIB'\n",
        "}\n",
        "aff = defaultdict(lambda: 'Other', aff_map)\n",
        "df['affiliation'] = df[df.columns[6]].map(aff)\n",
        "plt.figure(figsize = (15,8))\n",
        "ax = sns.boxplot(x='affiliation', y='sus', data=df)\n",
        "sns.stripplot(x=\"affiliation\", y=\"sus\", data=df,\n",
        "              size=10, color=\".3\", linewidth=0)\n",
        "ax.set_xticklabels(ax.get_xticklabels(),rotation = 30)\n",
        "ax.plot()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "07bda290",
      "metadata": {
        "id": "07bda290"
      },
      "source": [
        "LUH participants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 369,
      "id": "a397b714",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "a397b714",
        "outputId": "36129baa-a4cc-45fa-f932-ae07cc29673b"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "number of samples: 6\n",
            "------------------------------\n",
            "mean                : 80.42\n",
            "standard deviation  : 11.11\n",
            "median              : 81.25\n",
            "10th percentile     : 68.75\n",
            "25th percentile     : 74.38\n",
            "75th percentile     : 84.38\n",
            "90th percentile     : 91.25\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 216x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "def luh_group(df):\n",
        "    return df.loc[df[df.columns[6]].isin([\n",
        "        'LUH',\n",
        "        'L3S/LUH University Hannover', \n",
        "        'L3S Research Center, Leibniz University Hannover',\n",
        "        'Leibniz Uni',\n",
        "        'leibniz university hannover',\n",
        "        'Leibniz University Hannover'\n",
        "    ])]\n",
        "statistics_overview(luh_group(df)['sus'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "6f8018c9",
      "metadata": {
        "id": "6f8018c9"
      },
      "source": [
        "UIBK participants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 370,
      "id": "4417c140",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "4417c140",
        "outputId": "7a4d8836-db41-4931-fe68-b6a53f935dd9"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "number of samples: 7\n",
            "------------------------------\n",
            "mean                : 81.79\n",
            "standard deviation  : 11.06\n",
            "median              : 85.00\n",
            "10th percentile     : 69.00\n",
            "25th percentile     : 80.00\n",
            "75th percentile     : 87.50\n",
            "90th percentile     : 91.00\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 216x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "def uibk_group(df):\n",
        "    return  df.loc[df[df.columns[6]].isin([\n",
        "        'University of Innsbruck',\n",
        "        'UIBK'\n",
        "    ])]\n",
        "statistics_overview(uibk_group(df)['sus'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "903182ad",
      "metadata": {
        "id": "903182ad"
      },
      "source": [
        "TIB participants"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 371,
      "id": "bf92ebcd",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 554
        },
        "id": "bf92ebcd",
        "outputId": "b1bdce89-f851-4e2a-921d-b2e365da697f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "number of samples: 5\n",
            "------------------------------\n",
            "mean                : 84.00\n",
            "standard deviation  : 10.40\n",
            "median              : 82.50\n",
            "10th percentile     : 75.00\n",
            "25th percentile     : 75.00\n",
            "75th percentile     : 87.50\n",
            "90th percentile     : 95.00\n"
          ]
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 216x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "def tib_group(df):\n",
        "    return df.loc[df[df.columns[6]].isin([\n",
        "        'TIB',\n",
        "        'TIB Hannover'\n",
        "    ])]\n",
        "statistics_overview(tib_group(df)['sus'])"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "XJrby4uqJVad",
      "metadata": {
        "id": "XJrby4uqJVad"
      },
      "source": [
        "### Time for the first task\n",
        "Task 1 of the evaluation was: Assign _background_, _research problem_, _method_, _result_ and _conclusion_ to parts of the text.\n",
        "The time to completion was measured for all the participants. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 372,
      "id": "apDy9r4XJVJA",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "apDy9r4XJVJA",
        "outputId": "5ad94297-8c51-4d73-9998-cc433850896f"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "mean                :  00:07:34\n",
            "median              :  00:07:46\n",
            "10th percentile     :  00:03:23\n",
            "25th percentile     :  00:04:41\n",
            "75th percentile     :  00:10:25\n",
            "90th percentile     :  00:11:25\n"
          ]
        }
      ],
      "source": [
        "df[\"time task 1\"] = pd.to_timedelta(df[\"time task 1\"])\n",
        "print('{:20s}: {:s}'.format('mean', str(df[\"time task 1\"].mean())[6:15]))\n",
        "print('{:20s}: {:s}'.format('median',  str(df[\"time task 1\"].median())[6:15]))\n",
        "print('{:20s}: {:s}'.format('10th percentile', str(df[\"time task 1\"].quantile(.1))[6:15]))\n",
        "print('{:20s}: {:s}'.format('25th percentile', str(df[\"time task 1\"].quantile(.25))[6:15]))\n",
        "print('{:20s}: {:s}'.format('75th percentile', str(df[\"time task 1\"].quantile(.75))[6:15]))\n",
        "print('{:20s}: {:s}'.format('90th percentile', str(df[\"time task 1\"].quantile(.9))[6:15]))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dbdf6056",
      "metadata": {},
      "source": [
        "### Explore some possible correlations "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 373,
      "id": "cz7QgaT63CdJ",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "id": "cz7QgaT63CdJ",
        "outputId": "1092bbab-419b-4226-edff-b44468cb3e79"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<Figure size 1080x576 with 0 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 639.15x360 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "plt.figure(figsize = (15,8))\n",
        "df['time first task seconds'] = df['time task 1'].map(lambda x: x.seconds)\n",
        "ax = sns.relplot(x=\"sus\", y=\"time first task seconds\", hue=df.columns[5], data=df)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "5w52P5lhsnuj",
      "metadata": {
        "id": "5w52P5lhsnuj"
      },
      "source": [
        "### Hypothesis testing\n",
        "\n",
        "The Nullhypothesis H<sub>0</sub> is that participants use more than or equal to 10 minutes to execute task 1.\n",
        "The Alternate Hypothesis is that participants use less than 10 minutes to execute task 1.\n",
        "\n",
        "We test this hypothesis by a one-tailed one sample t-test"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "52345273",
      "metadata": {},
      "source": [
        "We reject the Nullhypothesis with a p-value of 0.00057."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 374,
      "id": "4056547c",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "0.000575401697838523\n"
          ]
        }
      ],
      "source": [
        "test_hyp1 = stats.ttest_1samp(a=df['time first task seconds'], popmean=600, alternative='less')\n",
        "print(test_hyp1.pvalue)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "28fe5c85",
      "metadata": {
        "id": "28fe5c85"
      },
      "source": [
        "### Correlation between independent variables and usability score\n",
        "##### Hypothetical correlation between LaTeX knowledge and usability score\n",
        "Determine Pearson coefficients. Plot regression analysis."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 375,
      "id": "9ac42428",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 330
        },
        "id": "9ac42428",
        "outputId": "a58aa769-1ee5-4fe1-9674-ad300d5dde23"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "number of samples: 26\n",
            "------------------------------\n",
            "mean                : 76.15\n",
            "standard deviation  : 18.67\n",
            "median              : 80.00\n",
            "10th percentile     : 60.00\n",
            "25th percentile     : 71.25\n",
            "75th percentile     : 85.00\n",
            "90th percentile     : 92.50\n",
            "Correlation between LaTeX familiarity and usability score: 0.2915\n",
            "Correlation between LaTeX preference and usability score: 0.1120\n",
            "Correlation between LaTeX usage frequency and usability score: 0.0689\n",
            "Correlation between LaTeX experience and usability score: 0.2760\n",
            "Correlation between number of publications and usability score: 0.0813\n",
            "Correlation between LaTeX score and usability score: 0.1997\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='latex_score', ylabel='sus'>"
            ]
          },
          "execution_count": 375,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 216x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df['latex_knowledge'] = df[df.columns[9]].map(LIKERT_TO_NUM)\n",
        "df['latex_preference'] = df[df.columns[12]].map(LIKERT_TO_NUM)\n",
        "df['latex_freq'] = df[df.columns[11]].map(COMPARE_TO_NUM)\n",
        "df['latex_experience'] = df[df.columns[10]].map(EXP_TO_NUM)\n",
        "df['pub_num'] = df[df.columns[8]].map(PUBLICATION_TO_NUM)\n",
        "\n",
        "df['latex_score'] = (df['pub_num'] + df['latex_knowledge'] + df['latex_preference'] + df['latex_freq'] + df['latex_experience'] - 5) * 5\n",
        "statistics_overview(df['latex_score'])\n",
        "\n",
        "print(f\"Correlation between LaTeX familiarity and usability score: {df['latex_knowledge'].corr(df['sus']):.4f}\")\n",
        "print(f\"Correlation between LaTeX preference and usability score: {df['latex_preference'].corr(df['sus']):.4f}\")\n",
        "print(f\"Correlation between LaTeX usage frequency and usability score: {df['latex_freq'].corr(df['sus']):.4f}\")\n",
        "print(f\"Correlation between LaTeX experience and usability score: {df['latex_experience'].corr(df['sus']):.4f}\")\n",
        "print(f\"Correlation between number of publications and usability score: {df['pub_num'].corr(df['sus']):.4f}\")\n",
        "print(f\"Correlation between LaTeX score and usability score: {df['latex_score'].corr(df['sus']):.4f}\")\n",
        "plt.figure(figsize = (15,8))\n",
        "sns.regplot(x=\"latex_score\", y=\"sus\", data=df)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "dd435ec0",
      "metadata": {
        "id": "dd435ec0"
      },
      "source": [
        "##### Hypothetical correlation between semantic web affinity and usability score"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 426,
      "id": "ddf67a36",
      "metadata": {
        "id": "ddf67a36"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "number of samples: 26\n",
            "------------------------------\n",
            "mean                : 62.50\n",
            "standard deviation  : 31.31\n",
            "median              : 57.50\n",
            "10th percentile     : 27.50\n",
            "25th percentile     : 35.00\n",
            "75th percentile     : 90.00\n",
            "90th percentile     :100.00\n",
            "Correlation between Semantic Web familiarity and usability score: 0.1579\n",
            "Correlation between having worked with knowledge graphs and usability score: 0.0786\n",
            "Correlation between knowing RDF and usability score: 0.2126\n",
            "Correlation between knowing the difference between URI and URL and usability score: 0.1036\n",
            "Correlation between knowing the FAIR data principles and usability score: 0.0515\n",
            "Correlation between Semantic Web affinity score and usability score: 0.1350\n"
          ]
        },
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:xlabel='semweb_score', ylabel='sus'>"
            ]
          },
          "execution_count": 426,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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RL9L4ZQoz64CyN8r2B77WN9AAEfEr0rjsA7b6lJl1TNlQP016U2pb1pGmPDKzipQN9XXAcZK26rZL2pF0XX1dKwozs+b0e01dzOtd75+Bg4EfF680LiNNNrgvaYKEYUUbM6vI9m6UPUwKbb3a+9Hf3sb+e0nhNrMKbC/UF7B1qM1sCOs31BFxfofqMLMW8QsdZpkpHWpJfyDpXEn/LWm5pOnF/rHF/snb+w4za5+yw0R7gP8mvcjRW2xHAUTEGkknkFab/HRryzSzgSo7TPRvSSthHAQ8Sppnu96/k2bxNLOKlO1+vxe4PCLupvFd8RXA6wddlZk1rWyox5K63duyhRfXqzKzCpQN9Spg736Ov5nULTezipQN9QLg5GKpm5eQdBDwMdJ1tZlVpGyovwA8D9wDzCZdV58g6VukxecfB77c0grNrJRSoY6IVaSVI38KnEQa7/1R4H+TlrF9e0Q82eoizWzgmlnK9jHgmGKZ2kmkYPc6zGZDQ9OziUbEOuBnLazFzFqg7Iiye4EfAD8CbomIp9pRlJk1r+yNsg3AJ0irTq6WtFjSxZKOlLRT68szs7LK3ig7CNiVtETsXNJkCJ8hPep6snjJ4wstr9LMBqz0W1oR8UxEzI+IT0fEm4Ee0h3w5aSpjj7f4hrNrISmbpRJ2gGYCswgvcBxMGl46CrAE/mbVajsjbJPkkL8TmBn0sJ3twJnAT+MiAdaXqGZlVL2TH0p8AJwLTAHuCciPIeZ2RBS9pp6IWkdrY8C/wF8Q9LJkvZseWVm1pRSZ+qI+JNi0v5ppG74YcBlwI6SHiVdT/8gIq5teaVmNiDN3P3+XUTcFhHnR8Q7gVeSVubYCJwIXNPaEs2sjGbvfo8C3ka6+z0DeAvpmfUWYEmrijOz8sre/T6X1O0+CNiR9DLHA6Sldn4I/MhDR82qVfZMfT7wEKmL/UPSY6wnWl2UmTWvbKgnRISnKzIbwsqO/XagzYa4Zm+UvQv4I+BVvLjaZU1ExIWDLczMmlP2Rtlk4AZgH7YOc00ADrVZRcqeqb9Gmqz/U8BtpLHfZjaElA31W4GLImJuO4oxs8ErO6Lst8CadhRiZq1RNtTfBv60DXWYWYuU7X5/DviupBuAfwAeJr2K+RJ+9GVWnbKh/h1wH2lShKP7aTes6YrMbFDKhvrLpAXl7yEtPu+732ZDTNlQfwy4ISL+vB3FmNnglb1RNpq0ZpaZDVFlQ70I2K8dhZhZa5QN9WeAD0p6fzuKMbPBa2Y20WeA6yStJL1b3feRVkTE4S2ozcyaUDbUe5Fe2Kg9hx7f2nLMbLDKziY6oU11mFmLlJ5N1MyGtmYnSdiTNAHha4BvRsTDkoYDrwVWRcTmFtZoZiWUPlNL+jLwS2AecAHpOhvSAnn3k9avNrOKlAq1pL8kjfu+DPgT6mY/iYh1wPeA97WyQDMrp+yZ+hOkYaKfIo3/7uvnwKTBFjVQkkZLekhSSPrHBscnSbpR0lpJGyTdJmlGp+ozq0LZUO8DfL+f46uBsc2XU9oF2/rnSdobuB2YDlxM6mGMAW6SdETHKjTrsLKh3gTs1M/xPYCnmq6mBElvIc2Vdt42mswGdgHeHRGzI+Jy4O3A48BlkrY1caJZVysb6juBP2t0QNJI0hK3PxlsUdsjaRhwBfBfwPUNju9Eet/7lohYUtsfEeuBK0k9jqntrtOsCmVD/XfAdEnXAPsX+14r6d3ALcDrgK+0rrxtOhOYDJyxjeP7AyOAOxocW1RsHWrLUtkRZQsl/RUwB/hwsbu2dO1m4NSIaBSklimekX8BuKB4Pj6hQbPdi+3KBsdq+8a1oTyzypUefBIR8yR9D/gA6WwpYDnwnYhoFKJW+yfSiyR/30+b0cX2uQbHNvVp8xKSTgNOAxg/3kPbrfs0NaIsIlYBcwEk7Ui6fn2PpPnFsbaQdDzp+fg7IuJ3/TTdWGxHNDg2sk+bl4iIeaSBNUyZMiWaLNWsMmUHn1ws6Wd1P4v0iOs7pNU7lhaPklpO0gjS2XkBsErSREkTSXfcAf6w2LcL6Q43NO5i1/Z1oldh1nFlb5QdSVpup+Z9wDtIN9Bq19hnt6CuRkYBPcBRpO5+7c8txfHji59PAZaSut7TG3zPtGK7uE11mlWqbPf79aTg1LwPeCgizgaQtB/wkRbV1tcG0nV8Xz3A5aTHW/8C/Dwi1kuaD7xf0gER8T9FfWNIoV9Oejxnlp2yoR7OS2c6OQxYWPfzCmC3wRbVSHEN/d2+++vufj8YEfXHzyG9SXazpEuAdcCppO73URHh62XLUtnu92MU3dfirLwXcGvd8VcD61tT2uBERC9wCOm59Nmk5+cbgCMj4qYqazNrp7Jn6m8D/1fSq0mziq4j3biqeTPwYItqG5CIeJhtrJUdEQ8Ax3SyHrOqlT1Tzwb+H+kGVAAfi4inACT9IenR1g9aWJ+ZlVR2RNlzwMnFn76eIV1PN3z+a2ad0dTgk0YiYgvwdKu+z8ya44kHzTLjUJtlxqE2y4xDbZYZh9osMw61WWYcarPMtOw5tTU2bOOTjFq2YPsNM7fDpnUAbBm5c8WVVG/YxidJK1a1h0PdRhMnTqy6hCGjt/cZACbu1b5/mbvHa9r674ZD3UYzZ86suoQhY9asWQDMmTOn4kry52tqs8w41GaZcajNMuNQm2XGoTbLjENtlhmH2iwzDrVZZhxqs8w41GaZcajNMuNQm2XGoTbLjENtlhmH2iwzDrVZZhxqs8w41GaZcajNMuNQm2XGoTbLjENtlhmH2iwzDrVZZhxqs8w41GaZcajNMuNQm2XGoTbLjENtlhmH2iwzDrVZZhxqs8w41GaZcajNMuNQm2XGoTbLjENtlhmH2iwzDrVZZhxqs8w41GaZcajNMuNQm2XGoTbLjENtlhmH2iwzDrVZZhxqs8w41GaZ6ZpQS9pH0gWSFklaLekZSUskfU7STg3aT5J0o6S1kjZIuk3SjCpqN+ukrgk1cBJwJvAgcAFwFvAL4G+B2yWNqjWUtDdwOzAduLhoOwa4SdIRHa7brKNeUXUBJXwXmB0RT9ft+2dJy4HPAScD/1jsnw3sAhwYEUsAJF0N3AdcJmlyRESnCjfrpK45U0fE4j6Brvm3YvtGgKIrfjRwSy3QxefXA1cC+wBT21utWXW6JtT9eF2xfaLY7g+MAO5o0HZRsXWoLVtdHWpJw4BzgeeBa4vduxfblQ0+Uts3rs2lmVWmq0MNXApMA86NiF8U+0YX2+catN/Up81WJJ0mabGkxatXr25ZoWad0rWhlnQhcAYwLyJm1x3aWGxHNPjYyD5tthIR8yJiSkRM6enpaU2xZh3UlaGWdD7weeDrwMf7HH682DbqYtf2Neqam2Wh60It6TzgPOBq4JQGj6aWkrre0xt8fFqxXdy+Cs2q1VWhlnQucD5wDfAXEbGlb5vi0dV84FBJB9R9dgxwCrAcuLMjBZtVoGsGn0g6HfgC8CiwEPiwpPomT0TE94u/nwMcDtws6RJgHXAqqft9lAeeWM66JtS8+Gx5PHBVg+O3At8HiIheSYcAFwFnA8OBu4EjI2JhB2o1q0zXhDoiTgROLNH+AeCYdtVjNlR11TW1mW2fQ22WGYfaLDMOtVlmHGqzzDjUZplxqM0y41CbZcahNsuMQ22WGYfaLDMOtVlmHGqzzDjUZplxqM0y41CbZcahNsuMQ22WGYfaLDMOtVlmHGqzzDjUZplxqM0y41CbZcahNsuMQ22WGYfaLDMOtVlmHGqzzHTNqpfWnLlz59Lb21t1Gb+vYdasWZXWMXHiRGbOnFlpDe3mUFtHjBo1quoSXjYc6szlflayrfma2iwzDrVZZhxqs8w41GaZcajNMuNQm2XGoTbLjENtlhmH2iwzDrVZZhxqs8w41GaZcajNMuNQm2XGoTbLjCKi6hqGLEmrgUeqriMjY4E1VReRiT0ioqfRAYfaOkbS4oiYUnUduXP32ywzDrVZZhxq66R5VRfwcuBrarPM+ExtlhmH2iwzDrVZZhxqs8w41GaZ+f+DeCws8nB4QQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 216x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df['semweb1'] = df[df.columns[13]].map(LIKERT_TO_NUM)\n",
        "df['semweb2'] = df[df.columns[14]].map(LIKERT_TO_NUM)\n",
        "df['semweb3'] = df[df.columns[15]].map(LIKERT_TO_NUM)\n",
        "df['semweb4'] = df[df.columns[16]].map(LIKERT_TO_NUM)\n",
        "df['semweb5'] = df[df.columns[17]].map(LIKERT_TO_NUM)\n",
        "\n",
        "df['semweb_score'] = (df['semweb1'] + df['semweb2'] + df['semweb3'] + df['semweb4'] + df['semweb5'] - 5) * 5\n",
        "statistics_overview(df['semweb_score'])\n",
        "\n",
        "print(f\"Correlation between Semantic Web familiarity and usability score: {df['semweb1'].corr(df['sus']):.4f}\")\n",
        "print(f\"Correlation between having worked with knowledge graphs and usability score: {df['semweb2'].corr(df['sus']):.4f}\")\n",
        "print(f\"Correlation between knowing RDF and usability score: {df['semweb3'].corr(df['sus']):.4f}\")\n",
        "print(f\"Correlation between knowing the difference between URI and URL and usability score: {df['semweb4'].corr(df['sus']):.4f}\")\n",
        "print(f\"Correlation between knowing the FAIR data principles and usability score: {df['semweb5'].corr(df['sus']):.4f}\")\n",
        "print(f\"Correlation between Semantic Web affinity score and usability score: {df['semweb_score'].corr(df['sus']):.4f}\")\n",
        "plt.figure(figsize = (15,8))\n",
        "sns.regplot(x=\"semweb_score\", y=\"sus\", data=df)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "9ab611da",
      "metadata": {
        "id": "9ab611da"
      },
      "source": [
        "## Similarity of Annotations\n",
        "Similarity of annotation was measured through the use of Gestalt Pattern Matching on the basis of words and through the Fleiss kappa measure of inter-annotator agreement.\n",
        "\n",
        "https://en.wikipedia.org/wiki/Gestalt_Pattern_Matching\n",
        "\n",
        "https://en.wikipedia.org/wiki/Fleiss%27_kappa\n",
        "\n",
        "The raw annotations produced in the user test were collected and appended to the 'results.tsv' file (for details see appendix of notebook). "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 553,
      "id": "02aa3eb8",
      "metadata": {},
      "outputs": [],
      "source": [
        "result_df = pd.read_csv('results.tsv', delimiter=\"\\t\")\n",
        "for prop in PROPERTY_NAMES:\n",
        "    result_df[prop] = result_df[prop].apply(literal_eval)\n",
        "    result_df[prop+'_vector'] = result_df[prop+'_vector'].apply(lambda x: np.fromstring(x.replace('\\n','')[1:-1], sep=' '))\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 554,
      "id": "b8ea3469",
      "metadata": {},
      "outputs": [],
      "source": [
        "def compute_inter_similarity(data):\n",
        "    all_similarities = defaultdict(list)\n",
        "    for prop in PROPERTY_NAMES:\n",
        "        sims = []\n",
        "        for i, ann1 in enumerate(data[prop]):\n",
        "            similarities = []\n",
        "            for j, ann2 in enumerate(data[prop]):\n",
        "                if not i==j:\n",
        "                    ann1_cleaned = re.split(r'\\W+',\" \".join(ann1))[:-1]\n",
        "                    ann2_cleaned = re.split(r'\\W+',\" \".join(ann2))[:-1]\n",
        "                    similarity = SequenceMatcher(None, ann1_cleaned, ann2_cleaned).ratio()\n",
        "                    similarities.append(similarity)\n",
        "                    all_similarities[prop].append(similarity)\n",
        "            sims.append(np.mean(similarities))\n",
        "        data['inter_similarity_'+prop] = sims"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 555,
      "id": "e01ba8af",
      "metadata": {},
      "outputs": [],
      "source": [
        "compute_inter_similarity(result_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 556,
      "id": "1ef32e75",
      "metadata": {},
      "outputs": [],
      "source": [
        "def report_mean_similarity_per_property(data):\n",
        "    for prop in PROPERTY_NAMES:\n",
        "        print(prop)\n",
        "        print(data['inter_similarity_'+prop].mean())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 557,
      "id": "8343551e",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "researchproblem\n",
            "0.28197368625751174\n",
            "background\n",
            "0.44211552739260784\n",
            "method\n",
            "0.3142994391670738\n",
            "result\n",
            "0.7818068411654193\n",
            "conclusion\n",
            "0.8649300684857815\n"
          ]
        }
      ],
      "source": [
        "report_mean_similarity_per_property(result_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "84a36e42",
      "metadata": {},
      "source": [
        "### Similarities among groups"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 558,
      "id": "d97a20e6",
      "metadata": {},
      "outputs": [],
      "source": [
        "tib_group_df = result_df.loc[result_df['affiliation']=='TIB'].copy()\n",
        "luh_group_df = result_df.loc[result_df['affiliation']=='LUH'].copy()\n",
        "uibk_group_df = result_df.loc[result_df['affiliation']=='UIBK'].copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 559,
      "id": "8d5c5a24",
      "metadata": {},
      "outputs": [],
      "source": [
        "compute_inter_similarity(tib_group_df)\n",
        "compute_inter_similarity(luh_group_df)\n",
        "compute_inter_similarity(uibk_group_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 560,
      "id": "3978a0a1",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "researchproblem\n",
            "0.47318181818181815\n",
            "background\n",
            "0.6079840222719846\n",
            "method\n",
            "0.2521712942479957\n",
            "result\n",
            "0.8336656022693919\n",
            "conclusion\n",
            "0.8486415541402941\n"
          ]
        }
      ],
      "source": [
        "report_mean_similarity_per_property(tib_group_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f6a5ba9e",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "researchproblem\n",
            "0.28817115251897857\n",
            "background\n",
            "0.43648318369380984\n",
            "method\n",
            "0.21801357549889347\n",
            "result\n",
            "0.8072133388824244\n",
            "conclusion\n",
            "0.880945797549571\n"
          ]
        }
      ],
      "source": [
        "report_mean_similarity_per_property(luh_group_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 561,
      "id": "c54e6b94",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "researchproblem\n",
            "0.31367457987388564\n",
            "background\n",
            "0.20734510765809594\n",
            "method\n",
            "0.22430672926886244\n",
            "result\n",
            "0.6925727811421835\n",
            "conclusion\n",
            "0.7393458393458393\n"
          ]
        }
      ],
      "source": [
        "report_mean_similarity_per_property(uibk_group_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 562,
      "id": "47fc2d44",
      "metadata": {},
      "outputs": [],
      "source": [
        "master_students_group = result_df.loc[result_df['position']=='Master Student'].copy()\n",
        "phd_students_group = result_df.loc[result_df['position']=='PhD Student'].copy()\n",
        "postdoc_group = result_df.loc[result_df['position']=='Post-Doctoral Researcher'].copy()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 563,
      "id": "baa03e91",
      "metadata": {},
      "outputs": [],
      "source": [
        "compute_inter_similarity(master_students_group)\n",
        "compute_inter_similarity(phd_students_group)\n",
        "compute_inter_similarity(postdoc_group)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 564,
      "id": "dba5d4ff",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "researchproblem\n",
            "0.32142660448069493\n",
            "background\n",
            "0.2585065710872162\n",
            "method\n",
            "0.3295566784809113\n",
            "result\n",
            "0.47970777725303615\n",
            "conclusion\n",
            "0.9927272727272728\n"
          ]
        }
      ],
      "source": [
        "report_mean_similarity_per_property(master_students_group)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 565,
      "id": "bf49542b",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "researchproblem\n",
            "0.2862133746497483\n",
            "background\n",
            "0.5174978475676462\n",
            "method\n",
            "0.34516033122380196\n",
            "result\n",
            "0.8789435257590238\n",
            "conclusion\n",
            "0.7784177349790636\n"
          ]
        }
      ],
      "source": [
        "report_mean_similarity_per_property(phd_students_group)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 566,
      "id": "4d5c87e6",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "researchproblem\n",
            "0.022988505747126436\n",
            "background\n",
            "0.7419354838709676\n",
            "method\n",
            "0.37707444286391656\n",
            "result\n",
            "0.6313163044597796\n",
            "conclusion\n",
            "1.0\n"
          ]
        }
      ],
      "source": [
        "report_mean_similarity_per_property(postdoc_group)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "4df55be3",
      "metadata": {},
      "source": [
        "### Correlations between prior knowledge and similarity to others"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 567,
      "id": "5ee27e17",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Correlation between LaTeX score and inter annotator similarity of researchproblem: -0.2393\n",
            "Correlation between Semantic Web affinity score and inter annotator similarity of researchproblem: -0.2902\n",
            "Correlation between LaTeX score and inter annotator similarity of background: 0.1909\n",
            "Correlation between Semantic Web affinity score and inter annotator similarity of background: 0.2402\n",
            "Correlation between LaTeX score and inter annotator similarity of method: -0.0736\n",
            "Correlation between Semantic Web affinity score and inter annotator similarity of method: 0.1843\n",
            "Correlation between LaTeX score and inter annotator similarity of result: 0.1899\n",
            "Correlation between Semantic Web affinity score and inter annotator similarity of result: -0.0054\n",
            "Correlation between LaTeX score and inter annotator similarity of conclusion: 0.0465\n",
            "Correlation between Semantic Web affinity score and inter annotator similarity of conclusion: -0.1869\n"
          ]
        }
      ],
      "source": [
        "for prop in PROPERTY_NAMES:\n",
        "    print(f\"Correlation between LaTeX score and inter annotator similarity of {prop}: {result_df['latex_score'].corr(result_df['inter_similarity_'+prop]):.4f}\")\n",
        "    print(f\"Correlation between Semantic Web affinity score and inter annotator similarity of {prop}: {result_df['semweb_score'].corr(result_df['inter_similarity_'+prop]):.4f}\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "e6801732",
      "metadata": {},
      "source": [
        "### Similarity of group with prior knowledge on Semantic Web"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 568,
      "id": "29cdc619",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "10\n",
            "researchproblem\n",
            "0.16514070435722114\n",
            "background\n",
            "0.5558745610801152\n",
            "method\n",
            "0.34599910705465453\n",
            "result\n",
            "0.8369078913152004\n",
            "conclusion\n",
            "0.7663446226975639\n"
          ]
        }
      ],
      "source": [
        "semantic_group = result_df.loc[result_df['semweb_score']>80].copy()\n",
        "print(len(semantic_group))\n",
        "compute_inter_similarity(semantic_group)\n",
        "report_mean_similarity_per_property(semantic_group)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "279987b5",
      "metadata": {},
      "source": [
        "### Fleiss kappa calculations\n",
        "\n",
        "Every word of the abstract can be seen as a possible annotation of the text."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 569,
      "id": "7bdcfb91",
      "metadata": {},
      "outputs": [],
      "source": [
        "original_abstract_words = []\n",
        "with open('raw_text_eval_task.txt') as infile:\n",
        "    file_str = infile.read().replace('\\n',' ')\n",
        "    original_abstract_words = re.split(r'\\W+',file_str)[:-1]\n",
        "    original_abstract_words_str = ' '.join(original_abstract_words)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "56eeca6a",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "To provide privacy-aware software systems, it is crucial to consider privacy from the very beginning of the development. However, developers do not have the expertise and the knowledge required to embed the legal and social requirements for data protection into software systems. We present an approach to decrease privacy risks during agile software development by automatically detecting privacy-related information in the context of user story requirements, a prominent notation in agile Requirement Engineering (RE). The proposed approach combines Natural Language Processing (NLP) and linguistic resources with deep learning algorithms to identify privacy aspects into User Stories. NLP technologies are used to extract information regarding the semantic and syntactic structure of the text. This information is then processed by a pre-trained convolutional neural network, which paved the way for the implementation of a Transfer Learning technique. We evaluate the proposed approach by performing an empirical study with a dataset of 1680 user stories. The experimental results show that deep learning algorithms allow to obtain better predictions than those achieved with conventional (shallow) machine learning methods. Moreover, the application of Transfer Learning allows to considerably improve the accuracy of the predictions, ca. 10\\%. Our study contributes to encourage software engineering researchers in considering the opportunities to automate privacy detection in the early phase of design, by also exploiting transfer learning models.\n"
          ]
        }
      ],
      "source": [
        "print(file_str)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 570,
      "id": "6afad03e",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "222\n"
          ]
        }
      ],
      "source": [
        "print(len(original_abstract_words))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "21854cce",
      "metadata": {},
      "source": [
        "### Let's find out if some annotations do not occur in the original text (made up by participant)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 571,
      "id": "d3d15f44",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "background 12 ['Deep', 'learning', 'based']\n",
            "conclusion 12 ['The', 'study', 'contributes', 'to', 'encourage', 'software', 'engineering', 'researchers', 'in', 'considering', 'the', 'opportunities', 'to', 'automate', 'privacy', 'detection', 'in', 'the', 'early', 'phase', 'of', 'design', 'by', 'also', 'exploiting', 'transfer', 'learning', 'models']\n"
          ]
        }
      ],
      "source": [
        "for prop in PROPERTY_NAMES:\n",
        "    for id, user_annotation in zip(result_df['id'],result_df[prop]):\n",
        "        for single_annotation in user_annotation:\n",
        "            cleaned_annotation = re.split(r'\\W+',single_annotation)[:-1]\n",
        "            if ' '.join(cleaned_annotation) not in original_abstract_words_str:\n",
        "                print(prop, id, cleaned_annotation)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2691a6ad",
      "metadata": {},
      "source": [
        "#### Participant 12 did not completely understand the task apparently. Only two annotations total. They will be handled as wrong."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 572,
      "id": "3c6a1fc5",
      "metadata": {},
      "outputs": [],
      "source": [
        "def add_annotation_vector(data):\n",
        "    for prop in PROPERTY_NAMES:\n",
        "        vectors = []\n",
        "        for user_annotation in data[prop]:\n",
        "            vector = np.zeros(len(original_abstract_words), dtype=int)\n",
        "            for single_annotation in user_annotation:\n",
        "                cleaned_annotation = re.split(r'\\W+', single_annotation)[:-1]\n",
        "                if ' '.join(cleaned_annotation) not in original_abstract_words_str:\n",
        "                    print('no match!:,', prop, cleaned_annotation)\n",
        "                else:\n",
        "                    for i in range(len(original_abstract_words)):\n",
        "                        if original_abstract_words[i:i+len(cleaned_annotation)] == cleaned_annotation:\n",
        "                            vector[i:i+len(cleaned_annotation)] = 1\n",
        "                            break\n",
        "            vectors.append(vector)\n",
        "        data[prop+'_vector'] = vectors\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 573,
      "id": "f1411235",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "no match!:, background ['Deep', 'learning', 'based']\n",
            "no match!:, conclusion ['The', 'study', 'contributes', 'to', 'encourage', 'software', 'engineering', 'researchers', 'in', 'considering', 'the', 'opportunities', 'to', 'automate', 'privacy', 'detection', 'in', 'the', 'early', 'phase', 'of', 'design', 'by', 'also', 'exploiting', 'transfer', 'learning', 'models']\n"
          ]
        }
      ],
      "source": [
        "add_annotation_vector(result_df)"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1c284101",
      "metadata": {},
      "source": [
        "### Now we assigned a vector to the annotations which is 1 for all the annotated words and 0 for the left out ones."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 574,
      "id": "22b2d1e0",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "To provide privacy aware software systems it is crucial to consider privacy from the very beginning of the development However developers do not have the expertise and the knowledge required to embed the legal and social requirements for data protection into software systems We present an approach to decrease privacy risks during agile software development by automatically detecting privacy related information in the context of user story requirements a prominent notation in agile Requirement Engineering RE The proposed approach combines Natural Language Processing NLP and linguistic resources with deep learning algorithms to identify privacy aspects into User Stories NLP technologies are used to extract information regarding the semantic and syntactic structure of the text This information is then processed by a pre trained convolutional neural network which paved the way for the implementation of a Transfer Learning technique We evaluate the proposed approach by performing an empirical study with a dataset of 1680 user stories The experimental results show that deep learning algorithms allow to obtain better predictions than those achieved with conventional shallow machine learning methods Moreover the application of Transfer Learning allows to considerably improve the accuracy of the predictions ca 10 Our study contributes to encourage software engineering researchers in considering the opportunities to automate privacy detection in the early phase of design by also exploiting transfer learning models\n",
            "0 ** 0 ******* 0 ******* 0 ***** 0 ******** 0 ******* 0 ** 0 ** 0 ******* 0 ** 0 ******** 0 ******* 0 **** 0 *** 0 **** 0 ********* 0 ** 0 *** 0 *********** 0 ******* 0 ********** 0 ** 0 *** 0 **** 0 *** 0 ********* 0 *** 0 *** 0 ********* 0 ******** 0 ** 0 ***** 0 *** 0 ***** 0 *** 0 ****** 0 ************ 0 *** 0 **** 0 ********** 0 **** 0 ******** 0 ******* 0 ** 0 ******* 0 ** 0 ******** 0 ** 0 ******** 1 privacy 1 risks 1 during 1 agile 1 software 0 *********** 0 ** 0 ************* 0 ********* 0 ******* 0 ******* 0 *********** 0 ** 0 *** 0 ******* 0 ** 0 **** 0 ***** 0 ************ 0 * 0 ********* 0 ******** 0 ** 0 ***** 0 *********** 0 *********** 0 ** 0 *** 0 ******** 0 ******** 0 ******** 0 ******* 0 ******** 0 ********** 0 *** 0 *** 0 ********** 0 ********* 0 **** 0 **** 0 ******** 0 ********** 0 ** 0 ******** 0 ******* 0 ******* 0 **** 0 **** 0 ******* 0 *** 0 ************ 0 *** 0 **** 0 ** 0 ******* 0 *********** 0 ********* 0 *** 0 ******** 0 *** 0 ********* 0 ********* 0 ** 0 *** 0 **** 0 **** 0 *********** 0 ** 0 **** 0 ********* 0 ** 0 * 0 *** 0 ******* 0 ************* 0 ****** 0 ******* 0 ***** 0 ***** 0 *** 0 *** 0 *** 0 *** 0 ************** 0 ** 0 * 0 ******** 0 ******** 0 ********* 0 ** 0 ******** 0 *** 0 ******** 0 ******** 0 ** 0 ********** 0 ** 0 ********* 0 ***** 0 **** 0 * 0 ******* 0 ** 0 **** 0 **** 0 ******* 0 *** 0 ************ 0 ******* 0 **** 0 **** 0 **** 0 ******** 0 ********** 0 ***** 0 ** 0 ****** 0 ****** 0 *********** 0 **** 0 ***** 0 ******** 0 **** 0 ************ 0 ******* 0 ******* 0 ******** 0 ******* 0 ******** 0 *** 0 *********** 0 ** 0 ******** 0 ******** 0 ****** 0 ** 0 ************ 0 ******* 0 *** 0 ******** 0 ** 0 *** 0 *********** 0 ** 0 ** 0 *** 0 ***** 0 *********** 0 ** 0 ********* 0 ******** 0 *********** 0 *********** 0 ** 0 *********** 0 *** 0 ************* 0 ** 0 ******** 0 ******* 0 ********* 0 ** 0 *** 0 ***** 0 ***** 0 ** 0 ****** 0 ** 0 **** 0 ********** 0 ******** 0 ******** 0 ******\n"
          ]
        }
      ],
      "source": [
        "compare = [(word, '1 ' + word) if vec_val==1 else (word,'0 '+'*'*len(word)) for vec_val, word in zip(result_df['researchproblem_vector'][2],original_abstract_words)]\n",
        "print(' '.join([w for w,_ in compare]))\n",
        "print(' '.join([w for _,w in compare]))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 575,
      "id": "a216665d",
      "metadata": {},
      "outputs": [],
      "source": [
        "def get_coincidence_matrix(reliability_data):\n",
        "    coincidence_matrix = np.zeros((2,2))\n",
        "    permuts = list(permutations(range(26),2))\n",
        "    for word in range(reliability_data.shape[1]):\n",
        "        for i, j in permuts:\n",
        "            if reliability_data[i][word] == 0 and reliability_data[j][word] == 1:\n",
        "                coincidence_matrix[0,1] += 1\n",
        "            elif reliability_data[i][word] == 1 and reliability_data[j][word] == 0:\n",
        "                coincidence_matrix[1,0] += 1\n",
        "            elif reliability_data[i][word] == 1 and reliability_data[j][word] == 1:\n",
        "                coincidence_matrix[1,1] += 1\n",
        "            elif reliability_data[i][word] == 0 and reliability_data[j][word] == 0:\n",
        "                coincidence_matrix[0,0] += 1\n",
        "        \n",
        "    coincidence_matrix /= reliability_data.shape[0]-1\n",
        "    return coincidence_matrix"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 576,
      "id": "9429023b",
      "metadata": {},
      "outputs": [],
      "source": [
        "def calculate_krippendorff_alpha(data):\n",
        "    \"\"\"\n",
        "    alpha = 1 - D_o/D_e\n",
        "    \"\"\"\n",
        "    for prop in PROPERTY_NAMES:\n",
        "        print(prop)\n",
        "        rel_data =  np.array([v for v in data[prop+'_vector']])\n",
        "        coincidence_matrix = get_coincidence_matrix(rel_data)\n",
        "        len_x, len_y = coincidence_matrix.shape\n",
        "        assert len_x == len_y\n",
        "        D_o = np.sum([coincidence_matrix[i,j] for i in range(len_x) for j in range(len_y) if j!=i])\n",
        "        D_e = (1/(np.sum(coincidence_matrix)-1)) * np.sum([np.sum(coincidence_matrix[i,:]) * np.sum(coincidence_matrix[:,j]) for i in range(len_x) for j in range(len_y) if j!=i]) \n",
        "        alpha = 1-(D_o/D_e)\n",
        "        print(alpha)\n",
        "        df['krippendorf_alpha_'+prop] = alpha\n",
        "            \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 577,
      "id": "8934b200",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "researchproblem\n",
            "0.21157923320531535\n",
            "background\n",
            "0.4387684511461398\n",
            "method\n",
            "0.2409103909703454\n",
            "result\n",
            "0.7442711257645526\n",
            "conclusion\n",
            "0.8182228277167584\n"
          ]
        }
      ],
      "source": [
        "calculate_krippendorff_alpha(result_df)\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "67e8d8b9",
      "metadata": {},
      "outputs": [],
      "source": [
        "def calculate_fleiss_kappa(data):\n",
        "    \"\"\"\n",
        "    Fleiss' kappa, or rather K, intersects with Krippendorff's alpha in that special situation\n",
        "    in which a fixed number of m coders code all of N units (no data are missing), \n",
        "    using nominal categories, and the sample size n = mN is very large, theoretically infinite.\n",
        "    k = (mean(P) - mean(p_e)) / (1 - mean(P_e))\n",
        "    \"\"\"\n",
        "    for prop in PROPERTY_NAMES:\n",
        "        print(prop)\n",
        "        rel_data =  np.array([v for v in data[prop+'_vector']])\n",
        "        n_raters, n_units = rel_data.shape\n",
        "        coincidence_matrix = get_coincidence_matrix(rel_data)\n",
        "        len_x, len_y = coincidence_matrix.shape\n",
        "        assert len_x == len_y\n",
        "        P_o = np.trace(coincidence_matrix)/ (n_raters*n_units)\n",
        "        P_e = np.sum([n_c**2 / ((n_raters*n_units)**2) for n_c in np.sum(coincidence_matrix, axis = 0)])\n",
        "        fleiss_kappa = (P_o - P_e) /(1 - P_e)\n",
        "        print(fleiss_kappa)\n",
        "        df['fleiss_kappa_'+prop] = fleiss_kappa\n",
        "            \n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 578,
      "id": "1f3852b6",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "researchproblem\n",
            "0.2114426155018327\n",
            "background\n",
            "0.4386712008344342\n",
            "method\n",
            "0.240778855775573\n",
            "result\n",
            "0.744226813015595\n",
            "conclusion\n",
            "0.8181913293330666\n"
          ]
        }
      ],
      "source": [
        "calculate_fleiss_kappa(result_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 579,
      "id": "e2c49781",
      "metadata": {},
      "outputs": [],
      "source": [
        "### reference implementation taken from here https://github.com/grrrr/krippendorff-alpha\n",
        "def nominal_metric(a, b):\n",
        "    return a != b\n",
        "\n",
        "\n",
        "def interval_metric(a, b):\n",
        "    return (a-b)**2\n",
        "\n",
        "\n",
        "def ratio_metric(a, b):\n",
        "    return ((a-b)/(a+b))**2\n",
        "\n",
        "\n",
        "def krippendorff_alpha(data, metric=interval_metric, force_vecmath=False, convert_items=float, missing_items=None):\n",
        "    '''\n",
        "    Calculate Krippendorff's alpha (inter-rater reliability):\n",
        "    \n",
        "    data is in the format\n",
        "    [\n",
        "        {unit1:value, unit2:value, ...},  # coder 1\n",
        "        {unit1:value, unit3:value, ...},   # coder 2\n",
        "        ...                            # more coders\n",
        "    ]\n",
        "    or \n",
        "    it is a sequence of (masked) sequences (list, numpy.array, numpy.ma.array, e.g.) with rows corresponding to coders and columns to items\n",
        "    \n",
        "    metric: function calculating the pairwise distance\n",
        "    force_vecmath: force vector math for custom metrics (numpy required)\n",
        "    convert_items: function for the type conversion of items (default: float)\n",
        "    missing_items: indicator for missing items (default: None)\n",
        "    '''\n",
        "    \n",
        "    # number of coders\n",
        "    m = len(data)\n",
        "    \n",
        "    # set of constants identifying missing values\n",
        "    if missing_items is None:\n",
        "        maskitems = []\n",
        "    else:\n",
        "        maskitems = list(missing_items)\n",
        "    if np is not None:\n",
        "        maskitems.append(np.ma.masked_singleton)\n",
        "    \n",
        "    # convert input data to a dict of items\n",
        "    units = {}\n",
        "    for d in data:\n",
        "        try:\n",
        "            # try if d behaves as a dict\n",
        "            diter = d.items()\n",
        "        except AttributeError:\n",
        "            # sequence assumed for d\n",
        "            diter = enumerate(d)\n",
        "            \n",
        "        for it, g in diter:\n",
        "            if g not in maskitems:\n",
        "                try:\n",
        "                    its = units[it]\n",
        "                except KeyError:\n",
        "                    its = []\n",
        "                    units[it] = its\n",
        "                its.append(convert_items(g))\n",
        "\n",
        "\n",
        "    units = dict((it, d) for it, d in units.items() if len(d) > 1)  # units with pairable values\n",
        "    n = sum(len(pv) for pv in units.values())  # number of pairable values\n",
        "    \n",
        "    if n == 0:\n",
        "        raise ValueError(\"No items to compare.\")\n",
        "    \n",
        "    np_metric = (np is not None) and ((metric in (interval_metric, nominal_metric, ratio_metric)) or force_vecmath)\n",
        "    \n",
        "    Do = 0.\n",
        "    for grades in units.values():\n",
        "        if np_metric:\n",
        "            gr = np.asarray(grades)\n",
        "            Du = sum(np.sum(metric(gr, gri)) for gri in gr)\n",
        "        else:\n",
        "            Du = sum(metric(gi, gj) for gi in grades for gj in grades)\n",
        "        Do += Du/float(len(grades)-1)\n",
        "    Do /= float(n)\n",
        "\n",
        "    if Do == 0:\n",
        "        return 1.\n",
        "\n",
        "    De = 0.\n",
        "    for g1 in units.values():\n",
        "        if np_metric:\n",
        "            d1 = np.asarray(g1)\n",
        "            for g2 in units.values():\n",
        "                De += sum(np.sum(metric(d1, gj)) for gj in g2)\n",
        "        else:\n",
        "            for g2 in units.values():\n",
        "                De += sum(metric(gi, gj) for gi in g1 for gj in g2)\n",
        "    De /= float(n*(n-1))\n",
        "\n",
        "    return 1.-Do/De if (Do and De) else 1."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 441,
      "id": "86903807",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "researchproblem\n",
            "0.2115792332053147\n",
            "background\n",
            "0.43876845114614005\n",
            "method\n",
            "0.24091039097034472\n",
            "result\n",
            "0.7442711257645525\n",
            "conclusion\n",
            "0.8182228277167584\n"
          ]
        }
      ],
      "source": [
        "for prop in PROPERTY_NAMES:\n",
        "    print(prop)\n",
        "    rel_data =  np.array([v for v in result_df[prop+'_vector']])\n",
        "    print(krippendorff_alpha(rel_data, nominal_metric))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "40edcc9b",
      "metadata": {},
      "source": [
        "### What is the mean length of annotations of different categories?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 549,
      "id": "fe712f61",
      "metadata": {},
      "outputs": [],
      "source": [
        "def add_annotation_length(data):\n",
        "    for prop in PROPERTY_NAMES:\n",
        "        lens = []\n",
        "        print(prop)\n",
        "        for anno in result_df[prop+'_vector']:\n",
        "            lens.append(np.sum(anno))\n",
        "        data[prop + '_annotation_len'] = np.array(lens)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 550,
      "id": "31b70f33",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "researchproblem\n",
            "background\n",
            "method\n",
            "result\n",
            "conclusion\n"
          ]
        }
      ],
      "source": [
        "add_annotation_length(result_df)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 551,
      "id": "4ef00b19",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "researchproblem\n",
            "number of samples: 26\n",
            "------------------------------\n",
            "mean                : 17.27\n",
            "standard deviation  : 12.33\n",
            "median              : 19.00\n",
            "10th percentile     :  4.00\n",
            "25th percentile     :  5.25\n",
            "75th percentile     : 24.00\n",
            "90th percentile     : 32.00\n",
            "background\n",
            "number of samples: 26\n",
            "------------------------------\n",
            "mean                : 23.81\n",
            "standard deviation  : 13.42\n",
            "median              : 20.50\n",
            "10th percentile     :  8.00\n",
            "25th percentile     : 19.00\n",
            "75th percentile     : 38.25\n",
            "90th percentile     : 43.00\n",
            "method\n",
            "number of samples: 26\n",
            "------------------------------\n",
            "mean                : 42.54\n",
            "standard deviation  : 34.56\n",
            "median              : 37.50\n",
            "10th percentile     :  4.50\n",
            "25th percentile     : 16.00\n",
            "75th percentile     : 60.75\n",
            "90th percentile     : 95.50\n",
            "result\n",
            "number of samples: 26\n",
            "------------------------------\n",
            "mean                : 33.92\n",
            "standard deviation  : 13.15\n",
            "median              : 32.00\n",
            "10th percentile     : 16.50\n",
            "25th percentile     : 29.50\n",
            "75th percentile     : 39.00\n",
            "90th percentile     : 39.00\n",
            "conclusion\n",
            "number of samples: 26\n",
            "------------------------------\n",
            "mean                : 24.73\n",
            "standard deviation  :  6.41\n",
            "median              : 28.00\n",
            "10th percentile     : 17.00\n",
            "25th percentile     : 25.00\n",
            "75th percentile     : 28.00\n",
            "90th percentile     : 28.00\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 216x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 216x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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X137VZl2Wc6U+FngE2G3wE2hJXwZ+UrXZt/3y1vLbavtIi2ONOjYHHq6+b3WL3djX6tbcrAg5nU92I818ss5HStW+c4Dd2y2shcaDrVe3ONbY9wfgTtKt99wW7Rp19ddbmtnokRPq8QzdY+zJqk3dLq9+70GSpjR2Svpz4F3APRExEBErgCuBvSTt0tRuCmmyxHvo0pNvs17ICfVdwPskrXPrXu3726pNrar3zv+HdAt9i6RPSTqa9DHVeNbu8HIMqRfZNZKOlvRx4MbqtfO70fHErFdyQv110i34DyTtJ2nb6utvSJMk7EZ6Ml67iDgbOJDUc+1E0ppdvyY9Db+mqd0AsAcp8EcDp5KmXdq3Q0/mzUaNnM+pz5W0Hemq2WqCwS9HxHltV7b+338ZcNkw2t3F2v3VzTYKWZ9TR8RnJZ1HCs22pOGW9wJXRMR/11ifmY1Q9swnVXi/XGMtZlYDL5BnVpjcbqJzSU+btwNeybqznXiBPLMeGXGoJX0IOB/4E2mSAi+GZzaK5FypGx8j7RMRD2+osZl1V8576m2ArzvQZqNTTqh/S+sRUGY2CuSE+p+BD0ryInhmo1DOe+rbSV01b5V0JnA/abmdtUTED9uszcwy5IT6B03fn8uahecbVO3zldysB3JCfUjtVZhZbXIGdFzQiULMrB7uJmpWmNxuoiItrzNUN9ET26zNzDLkdBPdjjS10CzWv8JlkCYxMLMuy7lSLwJmAJ8FrgMeq7UiM2tLTqjfDJweEafWXYyZtS/nQdlzpA4nZjYK5YT6atKkfmY2CuWE+lPAXEmfltSJ+b3NrA0576l/DEwGTgFOlvQw6/b99swnZj2SE+qHWLe/t5mNEjndRPfqQB1mVhN3EzUrjENtVpisUEvaQ9L/k7RM0vOSVg/6er7uQs1seEYcakl7AteTFsL7SXWO64HbSH3BfwFcVGONZjYCOVfqzwO/A/4ncHC170sRsTuwL2ltrXNrqc7MRiwn1G8Ezo2IZcALzeeplpO9CI/QMuuZnFBPAJZW3z9bbV/WdHwJsGsbNZlZG3JC/Tvg1QAR8TTwOPDapuOvBvygzKxHcnqU3cbaAzquAY6S9CDpH4kjSA/QzKwHcq7U5wGPSppY/fw54BngX4FvkG7J/76W6sxsxHK6iX4f+H7Tz/dJ2h7YmzSw40cR8UR9JZrZSNTSoywino6IKyLiO4MDLenlkr4haVYdv8vMhtaNbqITgQ8DW3Xhd5lt9LrV93t9s46aWc08oMOsMA61WWEcarPCONRmhXGozQrjUJsVphuhfg64AVjehd9lttHLWsp2JCJiOfDWTv8eM0s2GGpJ12WcNyJi74zXmVmbhnOlfg3rTt4/Gdii+v5xUo+xV1Q/PwqsqKM4Mxu5Db6njojpEbFt44s0GusZYCGwVURMjYjNSX27zwBWVm06TtIkSfdLCklfa3F8B0mXS1ou6WlJN0p6WzdqM+uVnAdlpwE3RcRREfFIY2dEPBIRRwK3VG264QTW3DGsRdIM4CZgLmndr88AU4CrJe3TpfrMui4n1HuRnmavz2K68GBM0l8ARwJfXE+Tk4DNgL+OiJMi4izgLcDDwJmSPMjEipQT6gB2HOL4TnR4AT1J44BzgO8Bl7U4PhnYH1gcEUsa+yNiBWn64u2BOZ2s0axXckJ9DfB3kj7UfLVT8mHg8KpNJx0FzCLNh9bKzqRZT29uceyWautQW5FyF51fCpwPLJV0g6TF1b5vkG5vP1VbhYNI2hY4HjghIh5YT7PGhAxLWxxr7Ju2nvPPk9QvqX/ZsmVt1WrWCyMOdUT8Fng98E+kXmJvJC3Bs7za9/qqTad8Hbgf+OoQbSZV22dbHFs1qM1aIuLsiJgdEbP7+vryqzTrkaweZdU8ZJ+rvrpG0kHAXwF7RsSfhmi6stpOaHFs00FtzIrS8W6idZE0gXR1vgp4RNLM6lDjNvoV1b5HSW8Bmo81a+xrdWtuNublLmU7WdLxkn4uaUX19XNJx1VPnjthItAH7Afc0/S1uDp+UPXzocCdpFvvuS3Os3u17e9QnWY9NeIrtaSpwI2kj7UeBX5aHdoeOBZ4r6S3RMQfa6syeRp4b4v9fcBZpI+3zgN+HhErJF0JvEfSLhHxs6r2KaTQ3wPcWnN9ZqNCzu33Caz5OOlfImI1vPjZ8TxgEXAc8MmaagSgeg/9n4P3S5pefXtvRDQfP4bUXfUaSacBTwKHkW6/94uIjn6WbtYrOaHen7SU7VnNO6twf13SG4B3UXOoRyoiBiTtAZwMHA2MB+4A9o2Ia7tRw9KlSxm38gkm3n1VN36djRHjVj7G0qWdW0MyJ9RbsuaWu5U7SJP3d0X1WXXLLp8RcRdwQLdqMRsNckL9e+ANQxx/Q9Vmozdt2jQeefYlPDPrnb0uxUaRiXdfxbRpW3bs/DlPv68EPirpcEkvvl7SJpLmAR8BrqirQDMbmZwr9bHA20lPnI+X9Otq/w6kJ9EDrH/klJl1WE430ceA2aQHUI+RBkbMIX28dRIwp2pjZj2Q2030SeDz1ZeZjSKe99usMFlX6moc9T7AdsArWfcjpYiIE9uszcwy5HQT3Q64nNSrbH1TAgXgUJv1QM6VehEwA/gscB3pYZmZjRI5oX4zcHpEnFp3MWbWvpwHZc+RZh4xs1EoJ9RXA3vUXYiZ1SN34sG5kj4taXzdBZlZe4azQN59LXZPIa16cbKkh4HVg45HRMyooT4zG6HhPCh7iA5Pzm9m9dlgqCNiry7UYWY1GfF7akl7SlrvhNiStpC0Z3tlmVmunAdl15OGXq7P3lUbM+uBnFBvaLXIccALGec1sxrkjtIa6sHZm0hjq82sB4bVTVTSAmBB067TJf1ji6abAy8nLZRnZj0w3L7fjwMPVt9PJw3iGDy5YAC/IC0Ve3r7pZlZjmGFOiIuAC4AkHQ/cHREeHJBs1FoxKO0ImLbThRiZvXIns6o+rz6HySdI2lWtW9KtX+z2io0sxHJ6XwyTtIlpM+iP0ea53ur6vDzpFlRPl5XgWY2MjlX6s8CB5JGa+1I0+fWEbEK+DbgJSnMeiQn1B8CLoyIhbT+PPou0nRHZtYDOaGeDtw8xPHHSZ9Xm1kP5IT6KWDqEMdnAsvyyjGzduWE+kfAQdXc32uRtDnpwZkHdJj1SE6o/5E0if91wN9U+3aRdDhpberJpHW2zKwHcjqf9Et6D3AecH61+1TSU/A/AO+OiF/VV6KZjUTuAnlXSZoO/BVrVuq4B7g6IlbWV56ZjVRWqAEi4lnSAvRX1leOmbUrq5uopA9I+rGkP0ha3eLr+boLNbPhyVkg7wvA8aShlzcBy+suyszy5dx+fxxYDOwbEX+qtxwza1fO7ffLgW850GajU06ofwr8j7oLMbN65IT6C8DHJP1F3cWYWftyOp/cIOmjwC2SbgYeoPVaWh+toT4zG6Gcp9+7Af9avfYt1ddgATjUZj2Qc/u9EPgTcAAwNSI2afE1rt4yzWy4cj7S2hk4LiLck8xsFMq5Uv8BeK7uQsysHjmh/gZpPHV2v3Ez65ycYP6INI76FklnAfez7tNvIuKHbdZmZhlyQn1t0/fnsu5iear21fqwTNL2wEGk4Z4zgE2Be4FLgdMj4ulB7XcA/gn4S2A8aQKHL0bEdXXWZTba5IT6kNqrGJ6PAJ8ArgC+SXoC/1bgH4D/JWn3iHgGQNIM0mCT54FTgCeAw4CrJb0jIq5tcX6zIuR0PrmgE4UMw38CJ0XEE037/lnSPcDnSZ+Lf63afxKwGbBrRCwBkHQh8EvgTEmzImKo5XjNxqzsZXe6LSL6BwW64ZJq+1oASZOB/YHFjUBXr19BeruwPTCns9Wa9c6YCfUQXl1tG0vr7gxMoPXc5LdUW4faijWmQy1pHHAs6b3zxdXuxrpeS1u8pLFv2hDnnCepX1L/smWevtzGnjEdatLi9rsDx0bEr6t9k6rtsy3arxrUZh0RcXZEzI6I2X19fbUVatYtYzbUkk4EjgDOjoiTmg41ZjOd0OJlmw5qY1acMRlqSceRxnWfD3xs0OGHq22rW+zGvla35mZFGHOhlvRF4IvAhcChLT6aupN06z23xct3r7b9navQrLfGVKglHQscB1wEHBIRLwxuU310dSWwl6Rdml47BTiUtOjArV0p2KwHxsygDEmfIE1N/BCpq+oHBq3R9/uI+H71/THA3sA1kk4DniT1KJsG7OeOJ1ayMRNq1ny2vDXQqlfbDcD3ASJiQNIepIX6jmZN3+993UXUSjdmQh0RBwMHj6D9XaTZWcw2KmPqPbWZbZhDbVYYh9qsMGPmPfVYNW7lH5l491W9LqPnNln1JAAvbPryHlfSe+NW/hHYsmPnd6g7aObMmb0uYdQYGHgKgJmv6dz/zGPHlh39f8Oh7qD58+f3uoRRY8GCBQAsXLiwx5WUz++pzQrjUJsVxqE2K4xDbVYYh9qsMA61WWEcarPCONRmhXGozQrjUJsVxqE2K4xDbVYYh9qsMA61WWEcarPCONRmhXGozQrjUJsVxqE2K4xDbVYYh9qsMA61WWEcarPCONRmhXGozQrjUJsVxqE2K4xDbVYYh9qsMA61WWEcarPCONRmhXGozQrjUJsVxqE2K4xDbVYYh9qsMA61WWEcarPCONRmhXGozQpTdKglbSLpKEl3S1ol6TeSviJpcq9rM+uUokMNnAZ8FfgVMB+4FPgkcKWk0v9220i9pNcFdIqknUhBviwiDmzafz9wBvA+4OIelWfWMSVfrd4PCDh90P5zgJXAQd0uyKwbSg71HOAF4NbmnRGxClhSHTcrTrG338BWwKMR8WyLY0uBN0kaHxHPdbmurlq0aBEDAwO9LuPFGhYsWNDTOmbOnMn8+fN7WkOnlRzqSUCrQAOsamqzVqglzQPmAWy99dYdK25jM3HixF6XsNFQRPS6ho6QdCfwqojYssWxbwHvBSYMdaWePXt29Pf3d7BKszySbo+I2a2Olfye+mFgC0kTWhybRro1L/rW2zZOJYf6NtLf98bmnZI2BV4P+BJsRSo51JcAARw5aP9hpPfS3+x2QWbdUOyDsoi4U9KZwBGSLgOuAnYk9Si7AXc8sUIVG+rKkcADpKfZ+wGPAouAYyPihd6VZdY5RYc6IlYDX6m+zDYKJb+nNtsoOdRmhXGozQrjUJsVxqE2K4xDbVYYh9qsMA61WWEcarPCFDueug6SlgEP9rqOgmxB6qpr7dsmIvpaHXCorWsk9a9vYL/Vx7ffZoVxqM0K41BbN53d6wI2Bn5PbVYYX6nNCuNQmxXGoTYrjENtVhiH2qww/x+OaZ0VMfw0cgAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 216x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 216x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        },
        {
          "data": {
            "image/png": "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",
            "text/plain": [
              "<Figure size 216x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "for prop in PROPERTY_NAMES:\n",
        "    print(prop)\n",
        "    statistics_overview(result_df[prop+'_annotation_len'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 552,
      "id": "017bfe15",
      "metadata": {},
      "outputs": [],
      "source": [
        "result_df.to_csv('results.tsv', sep='\\t')"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "c317a6bb",
      "metadata": {},
      "source": [
        "### Here are the processing steps to merge the annotations from the user test output files into the results file (Do not run again!)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 459,
      "id": "0c3f4506",
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "{http://orkg.org/property/}background developers do not have the expertise and the knowledge required to embed the legal and social requirements for data protection into software systems\n",
            "{http://orkg.org/property/}researchproblem privacy risks during agile software development\n",
            "{http://orkg.org/property/}method pre-trained convolutional neural network\n",
            "{http://orkg.org/property/}result deep learning algorithms allow to obtain better predictions than those achieved with conventional (shallow) machine learning methods\n",
            "{http://orkg.org/property/}result the application of Transfer Learning allows to considerably improve the accuracy of the predictions, ca. 10%\n",
            "{http://orkg.org/property/}conclusion encourage software engineering researchers in considering the opportunities to automate privacy detection in the early phase of design, by also exploiting transfer learning models\n"
          ]
        }
      ],
      "source": [
        "PROPERTY_NAMES = [\n",
        "    'researchproblem',\n",
        "    'background',\n",
        "    'method',\n",
        "    'result',\n",
        "    'conclusion'\n",
        "]\n",
        "tree = ET.parse('../user_test4/main_cs.xmp_metadata.xml')\n",
        "for p in tree.iter():\n",
        "    for prop_name in PROPERTY_NAMES:\n",
        "        if p.tag.endswith(prop_name):\n",
        "            if p.text:\n",
        "                print(p.tag, p.text)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 460,
      "id": "279d3f0f",
      "metadata": {},
      "outputs": [],
      "source": [
        "def extract_properties(xml_tree, previous_observations = None):\n",
        "    if not previous_observations: \n",
        "        property_observations = defaultdict(list)\n",
        "    else:\n",
        "        property_observations = previous_observations\n",
        "    for prop_name in PROPERTY_NAMES:\n",
        "        value = []\n",
        "        for p in xml_tree.findall(f'.//{{http://orkg.org/property/}}{prop_name}'):\n",
        "            if p.text:\n",
        "                value.append(p.text)\n",
        "        property_observations[prop_name].append(value)\n",
        "    return property_observations"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 461,
      "id": "125df87b",
      "metadata": {
        "id": "125df87b"
      },
      "outputs": [],
      "source": [
        "observations = defaultdict(list)\n",
        "user_ids = []\n",
        "for file in glob.glob('../user_test*/main_cs.xmp_metadata.xml'):\n",
        "    user_id = int(re.search('/user_test(\\d+)',file).group(1))\n",
        "    user_ids.append(user_id)\n",
        "    observations = extract_properties(ET.parse(file), observations)\n"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "47fa1f92",
      "metadata": {},
      "source": [
        "#### Some sanity checks"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 462,
      "id": "6a95a6c5",
      "metadata": {},
      "outputs": [],
      "source": [
        "for i in [1]+list(range(3,28)):\n",
        "    try:\n",
        "        assert i in user_ids\n",
        "    except AssertionError:\n",
        "        print(i)\n",
        "for id in user_ids:\n",
        "    try:\n",
        "        assert id in [1]+list(range(3,28))\n",
        "    except AssertionError:\n",
        "        print(id)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 463,
      "id": "f3af9124",
      "metadata": {},
      "outputs": [],
      "source": [
        "props = pd.DataFrame(observations)\n",
        "props['id'] = user_ids"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "71685a93",
      "metadata": {},
      "source": [
        "#### Merge the annotations into the results file."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 464,
      "id": "5cc2c049",
      "metadata": {},
      "outputs": [],
      "source": [
        "result_df = pd.merge(df, props, left_on=df.columns[1], right_on='id')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 382,
      "id": "2fc6cfbb",
      "metadata": {},
      "outputs": [],
      "source": [
        "result_df.to_csv('results.tsv', sep='\\t')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "6b178aa9",
      "metadata": {},
      "outputs": [],
      "source": [
        "def get_subsequences(iterable, all_subsequences = None):\n",
        "    if all_subsequences == None:\n",
        "        all_subsequences = [[]]\n",
        "    if len(iterable)==0:\n",
        "        return all_subsequences\n",
        "    else:\n",
        "        for i in range(len(iterable)):\n",
        "            all_subsequences.append(iterable[0:i+1])\n",
        "        return get_subsequences(iterable[1:], all_subsequences)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "analysis.ipynb",
      "provenance": []
    },
    "interpreter": {
      "hash": "f13b5283b5723cdbc74b17fc6b0f3471c504bd7ac5e8496bf1737ed0f04d0b63"
    },
    "kernelspec": {
      "display_name": "datascience_py37",
      "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": "3.7.10"
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  "nbformat_minor": 5
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