.gitattributes

-AirBnB_Use_Berlin.ipynb !filter
-Das_Haus_vom_Nikolaus.ipynb !filter
-wikipedia_language_editions.ipynb !filter
+notebooks/AirBnB_Use_Berlin.ipynb !filter
+notebooks/Art.ipynb !filter
+notebooks/Das_Haus_vom_Nikolaus.ipynb !filter
+notebooks/DraCor.ipynb !filter
+notebooks/Drama_Speakers.ipynb !filter
+notebooks/FCA.ipynb !filter
+notebooks/FCA.ipynb !filter
+notebooks/goethes_words.ipynb !filter
+notebooks/Hamming.ipynb !filter
+notebooks/Video_Games.ipynb !filter
+notebooks/Weinbewertungen_Vivino.ipynb !filter
+notebooks/World_Risk_and_Happiness.ipynb !filter
+notebooks/distances.ipynb !filter
+notebooks/wikipedia_language_editions.ipynb !filter
+notebooks/wikipedia_regex.ipynb !filter
+notebooks/Wordsiblings.ipynb !filter

.gitignore

+_site
+_posts
+_tags
+public

.gitlab-ci.yml

+# from JK
+variables:
+  GEM_HOME: "$CI_PROJECT_DIR/.cache/vendor/ruby/gems"
+  PYTHON_VERSION: "3.8"
+default:
+  image: ruby:3.2
+  cache:
+    key: $CI_COMMIT_REF_SLUG
+    paths:
+      - $CI_PROJECT_DIR/.cache/vendor/ruby/bundle
+      - $CI_PROJECT_DIR/.cache/vendor/ruby/cache    
+  before_script:
+  # update only once, even if multiple commands are missing
+#  - '( command -v ssh-agent >/dev/null && command -v zip >/dev/null && command -v rsync >/dev/null) || apt-get update -y'
+#  - 'command -v ssh-agent >/dev/null || apt-get install openssh-client -y '
+#  - 'command -v zip >/dev/null || apt-get install zip -y '
+#  - 'command -v rsync >/dev/null || apt-get install rsync -y'
+  - '( command -v jupyter-nbconvert >/dev/null && command -v jupyter >/dev/null && command -v bundle >/dev/null) || apt-get update -y'
+  - 'command -v bundle >/dev/null || apt-get install ruby-bundler -y'
+  - 'command -v jupyter-nbconvert >/dev/null || apt-get install jupyter-nbconvert python3-nbconvert -y'
+# Should be automatically be installed by the above...
+#  - 'command -v jupyter >/dev/null || apt-get install jupyter-core -y'
+  - mkdir -p "$CI_PROJECT_DIR/.cache/vendor/ruby/bundle" "$CI_PROJECT_DIR/.cache/vendor/ruby/cache "  
+# Allow Caching  
+  - bundle config set --local path 'vendor/ruby/bundle'
+  - bundle config set --local cache_path 'vendor/ruby/cache'
+    
+# For Debugging  
+ # - ruby -v
+ # - gem -v
+ # - rsync -V
+
+pages:
+  stage: build
+  script:
+  - bundle install -j $(nproc)
+  - bundle exec jekyll build --config _config_prod.yml --strict_front_matter true -d public
+  artifacts:
+    paths:
+    - public
+  only:
+  - master
+

Art.ipynb

-%% Cell type:markdown id:framed-filename tags:
-
-# Computer-generated Art
-
-%% Cell type:markdown id:apart-death tags:
-
-## Unknown Pleasures
-
-This artwork is inspired by [Peter Saville's](https://en.wikipedia.org/wiki/Peter_Saville_(graphic_designer)) cover of the [Joy Division](https://en.wikipedia.org/wiki/Joy_Division) album [Unknown Pleasures](https://en.wikipedia.org/wiki/Unknown_Pleasures). The original [is based on some interesting real data](https://theconversation.com/joy-division-40-years-on-from-unknown-pleasures-astronomers-have-revisited-the-pulsar-from-the-iconic-album-cover-119861) but here we try to imitate it with random data (creating unique pictures). The code demonstrates some mathematical approaches like translation, scaling and composition of functions.
-
-%% Cell type:code id:desperate-hands tags:
-
-``` 
-import numpy as np
-import matplotlib.pyplot as plt
-
-k = 50 # number of horizontal points
-n = 80 # number of vertical lines
-
-plt.rcParams['figure.figsize'] = (15, 15)
-plt.rcParams['figure.dpi'] = 140
-plt.style.use('dark_background')
-plt.axis('off')
-plt.xlim(-100, 200)
-plt.ylim(-60, 240)
-
-xs = np.linspace(0, 100, k)
-
-def sq(x, minx, maxx, miny, maxy, invert=False):
-    x = (x - minx) / (maxx - minx)
-    if invert:
-        x = 1 - x
-    if x < 0.5:                 # ascent
-        fx = 2*x**2
-    else:                       # descent
-        fx = (1 - 2*(1-x)**2)
-    return fx * (maxy - miny) + miny
-
-def f(x, left, right, width, height):
-    if x < left or x > right:   # left + right
-        return np.abs(np.random.normal(0, 0.5))
-    if x < left + width:        # ascent
-        return np.abs(np.random.normal(0, 1.0)) + sq(x, left, left + width, 0, height)
-    if x > right - width:       # descent
-        return np.abs(np.random.normal(0, 1.0)) + sq(x, right - width, right, 0, height, True)
-    else:                       # middle
-        return np.random.exponential(2) + height
-
-for i in range(1, n+1):
-    data = [f(x, 25, 75, 10, 5) + i*2 for x in xs]
-    plt.plot(xs, data, color="white", zorder=n-i, linewidth=1)
-    plt.fill_between(xs, data, color="black", zorder=n-i)
-
-plt.show()
-```

CITATION.cff

+# This CITATION.cff file was generated with cffinit.
+# Visit https://bit.ly/cffinit to generate yours today!
+
+cff-version: 1.2.0
+title: Notebooks
+message: >-
+  If you use this software, please cite it using the
+  metadata from this file.
+type: software
+authors:
+  - name: >-
+      Institute of Library and Information Science at
+      Humboldt-Universität zu Berlin
+    city: Berlin
+    address: Dorotheenstraße 26
+    post-code: '10117'
+    country: DE
+    alias: IBI
+repository-code: 'https://scm.cms.hu-berlin.de/ibi/notebooks'
+abstract: >-
+  A repository to collect and share Jupyter notebooks.
+
+  All notebooks should ideally work without any extra files, use only
+  standard Python libraries (pandas, scikit-learn, etc.), and gather
+  their data from the web.
+license: Apache-2.0
+commit: ef8a0fbb06130c7bab0ab4881be86c343950c62e
+date-released: '2024-08-22'

Gemfile

+source "https://rubygems.org"
+# Hello! This is where you manage which Jekyll version is used to run.
+# When you want to use a different version, change it below, save the
+# file and run `bundle install`. Run Jekyll with `bundle exec`, like so:
+#
+#     bundle exec jekyll serve
+#
+# This will help ensure the proper Jekyll version is running.
+# Happy Jekylling!
+
+# This is the default theme for new Jekyll sites. You may change this to anything you like.
+gem 'jekyll', '4.3.3'
+gem 'jekyll-sass-converter', '3.0.0'
+gem 'sass-embedded', '1.69.5'
+gem 'google-protobuf', '3.25.2'
+gem 'jekyll-jupyter-notebook'
+gem 'jekyll-datapage-generator'
+gem 'jekyll-tagging'
+gem "minima", "~> 2.5"
+#gem "jekyll-jupyter-notebook"
+# If you want to use GitHub Pages, remove the "gem "jekyll"" above and
+# uncomment the line below. To upgrade, run `bundle update github-pages`.
+# gem "github-pages", group: :jekyll_plugins
+# If you have any plugins, put them here!
+group :jekyll_plugins do
+  gem "jekyll-feed", "~> 0.12"
+end
+
+# Windows and JRuby does not include zoneinfo files, so bundle the tzinfo-data gem
+# and associated library.
+platforms :mingw, :x64_mingw, :mswin, :jruby do
+  gem "tzinfo", ">= 1", "< 3"
+  gem "tzinfo-data"
+end
+
+# Performance-booster for watching directories on Windows
+gem "wdm", "~> 0.1.1", :platforms => [:mingw, :x64_mingw, :mswin]
+
+# Lock `http_parser.rb` gem to `v0.6.x` on JRuby builds since newer versions of the gem
+# do not have a Java counterpart.
+gem "http_parser.rb", "~> 0.6.0", :platforms => [:jruby]

Gemfile.lock

+GEM
+  remote: https://rubygems.org/
+  specs:
+    addressable (2.8.6)
+      public_suffix (>= 2.0.2, < 6.0)
+    colorator (1.1.0)
+    concurrent-ruby (1.2.3)
+    em-websocket (0.5.3)
+      eventmachine (>= 0.12.9)
+      http_parser.rb (~> 0)
+    eventmachine (1.2.7)
+    ffi (1.16.3)
+    forwardable-extended (2.6.0)
+    google-protobuf (3.25.2-x86_64-linux)
+    http_parser.rb (0.8.0)
+    i18n (1.14.1)
+      concurrent-ruby (~> 1.0)
+    jekyll (4.3.3)
+      addressable (~> 2.4)
+      colorator (~> 1.0)
+      em-websocket (~> 0.5)
+      i18n (~> 1.0)
+      jekyll-sass-converter (>= 2.0, < 4.0)
+      jekyll-watch (~> 2.0)
+      kramdown (~> 2.3, >= 2.3.1)
+      kramdown-parser-gfm (~> 1.0)
+      liquid (~> 4.0)
+      mercenary (>= 0.3.6, < 0.5)
+      pathutil (~> 0.9)
+      rouge (>= 3.0, < 5.0)
+      safe_yaml (~> 1.0)
+      terminal-table (>= 1.8, < 4.0)
+      webrick (~> 1.7)
+    jekyll-datapage-generator (1.4.0)
+    jekyll-feed (0.17.0)
+      jekyll (>= 3.7, < 5.0)
+    jekyll-jupyter-notebook (0.0.5)
+      jekyll
+    jekyll-sass-converter (3.0.0)
+      sass-embedded (~> 1.54)
+    jekyll-seo-tag (2.8.0)
+      jekyll (>= 3.8, < 5.0)
+    jekyll-tagging (1.1.0)
+      nuggets
+    jekyll-watch (2.2.1)
+      listen (~> 3.0)
+    kramdown (2.4.0)
+      rexml
+    kramdown-parser-gfm (1.1.0)
+      kramdown (~> 2.0)
+    liquid (4.0.4)
+    listen (3.8.0)
+      rb-fsevent (~> 0.10, >= 0.10.3)
+      rb-inotify (~> 0.9, >= 0.9.10)
+    mercenary (0.4.0)
+    minima (2.5.1)
+      jekyll (>= 3.5, < 5.0)
+      jekyll-feed (~> 0.9)
+      jekyll-seo-tag (~> 2.1)
+    nuggets (1.6.1)
+    pathutil (0.16.2)
+      forwardable-extended (~> 2.6)
+    public_suffix (5.0.4)
+    rake (13.1.0)
+    rb-fsevent (0.11.2)
+    rb-inotify (0.10.1)
+      ffi (~> 1.0)
+    rexml (3.2.6)
+    rouge (4.2.0)
+    safe_yaml (1.0.5)
+    sass-embedded (1.69.5)
+      google-protobuf (~> 3.23)
+      rake (>= 13.0.0)
+    terminal-table (3.0.2)
+      unicode-display_width (>= 1.1.1, < 3)
+    unicode-display_width (2.5.0)
+    webrick (1.8.1)
+
+PLATFORMS
+  x86_64-linux
+
+DEPENDENCIES
+  google-protobuf (= 3.25.2)
+  http_parser.rb (~> 0.6.0)
+  jekyll (= 4.3.3)
+  jekyll-datapage-generator
+  jekyll-feed (~> 0.12)
+  jekyll-jupyter-notebook
+  jekyll-sass-converter (= 3.0.0)
+  jekyll-tagging
+  minima (~> 2.5)
+  sass-embedded (= 1.69.5)
+  tzinfo (>= 1, < 3)
+  tzinfo-data
+  wdm (~> 0.1.1)
+
+BUNDLED WITH
+   2.5.5

Hamming.ipynb

-%% Cell type:markdown id: tags:
-
-# Graphing a kind of "Hamming similarity" for strings
-
-This notebook explores a (probably slightly weird) similarity measure for strings.
-
-## Equal characters in strings
-
-Given two strings, our idea is to consider the positions where their characters match:
-
-%% Cell type:code id: tags:
-
-``` 
-v = "Wiesbaden"
-w = "Potsdam"
-#       s a     – the matching characters of the two strings
-```
-
-%% Cell type:markdown id: tags:
-
-We can extract those characters with a loop:
-
-%% Cell type:code id: tags:
-
-``` 
-m = []                                 # resulting equal characters
-for i in range(min(map(len, [v, w]))): # loop over the shortest word's length
-    if v[i] == w[i]:                   # equal characters at this position?
-        m.append(v[i])                 # collect equal character
-m
-```
-
-%% Cell type:markdown id: tags:
-
-Let's create a function that, given two strings, returns their equal characters:
-
-%% Cell type:code id: tags:
-
-``` 
-def equal_chars(v, w):
-    m = []                                 # resulting equal characters
-    for i in range(min(map(len, [v, w]))): # loop over the shortest word's length
-        if v[i] == w[i]:                   # check character equality
-            m.append(v[i])                 # add character
-    return m
-```
-
-%% Cell type:markdown id: tags:
-
-By the way: thanks to Python's [list comprehensions](https://docs.python.org/3/howto/functional.html#generator-expressions-and-list-comprehensions) we can write the body of the function in one line:
-
-%% Cell type:code id: tags:
-
-``` 
-def equal_chars(v, w):
-    return [v[i] for i in range(min(map(len, [v, w]))) if v[i] == w[i]]
-```
-
-%% Cell type:markdown id: tags:
-
-Let's test our newly defined function:
-
-%% Cell type:code id: tags:
-
-``` 
-equal_chars(v, w)
-```
-
-%% Cell type:markdown id: tags:
-
-And with two different words:
-
-%% Cell type:code id: tags:
-
-``` 
-equal_chars("Washington", "Massachusetts")
-```
-
-%% Cell type:markdown id: tags:
-
-## Similarity
-
-Now we regard the number of equal characters in two strings as a similarity measure. For example, the similarity of our two strings is:
-
-%% Cell type:code id: tags:
-
-``` 
-len(equal_chars(v, w))
-```
-
-%% Cell type:markdown id: tags:
-
-## Graph
-
-Now given a set of strings, for example, the 16 capitals of all German states:
-
-%% Cell type:code id: tags:
-
-``` 
-capitals_de = ["Berlin", "Bremen", "Dresden", "Düsseldorf", "Erfurt",
-         "Hamburg", "Hannover", "Kiel", "Magdeburg", "Mainz", "München",
-         "Potsdam", "Saarbrücken", "Schwerin", "Stuttgart", "Wiesbaden"]
-```
-
-%% Cell type:markdown id: tags:
-
-or the names of the 16 German states:
-
-%% Cell type:code id: tags:
-
-``` 
-states_de = ["Baden-Württemberg", "Bayern", "Berlin", "Brandenburg",
-             "Bremen", "Hamburg", "Hessen", "Mecklenburg-Vorpommern",
-             "Niedersachsen", "Nordrhein-Westfalen", "Rheinland-Pfalz",
-             "Saarland", "Sachsen", "Sachsen-Anhalt",
-             "Schleswig-Holstein", "Thüringen"]
-```
-
-%% Cell type:markdown id: tags:
-
-we can create a graph with the strings as nodes by connecting strings whose similarity is larger than zero, that is, they have at least one position with equal characters:
-
-%% Cell type:code id: tags:
-
-``` 
-import networkx as nx
-
-def sim_graph(words):
-    G = nx.Graph()                                                  # resulting graph
-
-    for k, v in enumerate(words):                                   # first node
-        for l, w in enumerate(words):                               # second node
-            if k > l:                                               # avoid reverse duplicates
-                ec = equal_chars(v, w)                              # equal characters
-                sim = len(ec)                                       # similarity
-                if sim > 0:                                         # ignore dissimilar words
-                    G.add_edge(v, w, label="".join(ec), weight=sim) # add edge
-    return G
-```
-
-%% Cell type:markdown id: tags:
-
-Let's compute the graph for our set of capitals or states:
-
-%% Cell type:code id: tags:
-
-``` 
-g = sim_graph(states_de)
-```
-
-%% Cell type:markdown id: tags:
-
-A good way to understand a graph is to visualise it:
-
-%% Cell type:code id: tags:
-
-``` 
-%matplotlib inline
-from networkx.drawing.nx_agraph import graphviz_layout
-import matplotlib.pyplot as plt
-
-pos = graphviz_layout(g, prog='dot')
-nx.draw(g, pos, with_labels=True, arrows=False)
-nx.draw_networkx_edge_labels(g, pos, edge_labels=nx.get_edge_attributes(g, 'label'), font_color='blue')
-plt.show()
-```
-
-%% Cell type:markdown id: tags:
-
-This layout is not the best so it's better to use graphviz:
-
-%% Cell type:code id: tags:
-
-``` 
-from networkx.drawing.nx_pydot import write_dot
-import pydot
-from IPython.display import HTML, display
-import random
-
-write_dot(g, "graph.dot")
-graph = pydot.graph_from_dot_file("graph.dot")
-graph[0].write_svg("graph.svg")
-
-display(HTML('<img src="graph.svg?{0}">'.format(random.randint(0,2e9))))
-```

LICENSE

                     GNU GENERAL PUBLIC LICENSE
                        Version 3, 29 June 2007
 
- Copyright (C) 2007 Free Software Foundation, Inc. <http://fsf.org/>
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
  Everyone is permitted to copy and distribute verbatim copies
  of this license document, but changing it is not allowed.
 
@@ -631,8 +631,8 @@ to attach them to the start of each source file to most effectively
 state the exclusion of warranty; and each file should have at least
 the "copyright" line and a pointer to where the full notice is found.
 
-    Notebooks
-    Copyright (C) 2020  Institut für Bibliotheks- und Informationswissenschaft
+    <one line to give the program's name and a brief idea of what it does.>
+    Copyright (C) 2024  Prof. Dr. Robert Jäschke
 
     This program is free software: you can redistribute it and/or modify
     it under the terms of the GNU General Public License as published by
@@ -645,14 +645,14 @@ the "copyright" line and a pointer to where the full notice is found.
     GNU General Public License for more details.
 
     You should have received a copy of the GNU General Public License
-    along with this program.  If not, see <http://www.gnu.org/licenses/>.
+    along with this program.  If not, see <https://www.gnu.org/licenses/>.
 
 Also add information on how to contact you by electronic and paper mail.
 
   If the program does terminal interaction, make it output a short
 notice like this when it starts in an interactive mode:
 
-    Notebooks  Copyright (C) 2020  Institut für Bibliotheks- und Informationswissenschaft
+    <program>  Copyright (C) 2024  Prof. Dr. Robert Jäschke
     This program comes with ABSOLUTELY NO WARRANTY; for details type `show w'.
     This is free software, and you are welcome to redistribute it
     under certain conditions; type `show c' for details.
@@ -664,11 +664,11 @@ might be different; for a GUI interface, you would use an "about box".
   You should also get your employer (if you work as a programmer) or school,
 if any, to sign a "copyright disclaimer" for the program, if necessary.
 For more information on this, and how to apply and follow the GNU GPL, see
-<http://www.gnu.org/licenses/>.
+<https://www.gnu.org/licenses/>.
 
   The GNU General Public License does not permit incorporating your program
 into proprietary programs.  If your program is a subroutine library, you
 may consider it more useful to permit linking proprietary applications with
 the library.  If this is what you want to do, use the GNU Lesser General
 Public License instead of this License.  But first, please read
-<http://www.gnu.org/philosophy/why-not-lgpl.html>.
+<https://www.gnu.org/licenses/why-not-lgpl.html>.

README.org

@@ -21,35 +21,74 @@ contributing!*
 
 ** List of Notebooks
 
-So far, notebooks are listed alphabetically and stars shall indicate
-their difficulty (☆ = simple, ☆☆ = advanced, ☆☆☆ = sophisticated):
-
-- [[file:AirBnB_Use_Berlin.ipynb][AirBnB Use in Berlin]] :: exemplary (and excellent) term paper for the
-     module "Datenanalyse & -auswertung" (☆☆)
-- [[file:amazon_reviews.ipynb][Amazon reviews]] :: crawling web sites with [[https://scrapy.org/][Scrapy]], processing JSON
-     data, basic statistics and visualisation (☆☆)
-- [[file:Art.ipynb][Computer-generated art]] :: translation, scaling and composition of
-  functions (☆☆)
-- [[file:classification.ipynb][Classification]] :: basic machine learning classification example (☆)
-- [[file:community_detection.ipynb][Community detection]] :: applying community detection algorithms to
-     network graphs (☆☆)
-- [[file:crawling_a_blog.ipynb][Crawling a blog]] :: crawling web sites, basic text mining, basic
-     statistics and visualisation (☆☆)
-- [[file:distances.ipynb][Distances]] :: comprehensive interactive simulation of recovering
-     information from noisy data (namely, point positions given their
-     noisy distance matrix) (☆☆☆)
-- [[file:Dracor.ipynb][DraCor]] :: retrieving data from a REST API, text transformation and
-     classification (☆☆)
-- [[file:exponential_smoothing.ipynb][Exponential smoothing]] :: using [[https://ipywidgets.readthedocs.io/en/latest/examples/Widget%2520Basics.html][Jupyter's interactive widget]] to
-     explore [[https://en.wikipedia.org/wiki/Exponential_smoothing][exponential smoothing]] (☆)
-- [[file:Hamming.ipynb][Hamming]] :: a graph visualising a strange type of word similarity (☆)
-- [[file:Jupyter-Demo.ipynb][Jupyter demo]] :: demo of some Jupyter features useful for creating
-     learning material (☆)
-- [[file:Mondrian.ipynb][Mondrian]] :: turtle graphics, recursion, art (☆☆)
-- [[file:Das_Haus_vom_Nikolaus.ipynb][Nikolaus]] :: graph [[https://en.wikipedia.org/wiki/Graph_traversal][traversal]] and drawing (☆☆☆)
-- [[file:statistics_top50faculty.ipynb][Statistics top 50 faculty]] :: exploratory statistical analysis of the
-     [[http://cs.brown.edu/people/apapouts/faculty_dataset.html][dataset of 2200 faculty in 50 top US computer science graduate
-     programs]] (☆☆)
-- [[file:Twitter.ipynb][Twitter]] :: analysing Twitter data (raw JSON from Twitter's API) (☆)
-- [[file:wikipedia_language_editions.ipynb][Wikipedia]] :: plotting the depth and number of articles of different
-  Wikipedia language editions (☆)
+So far, notebooks are listed by difficulty, indicated by stars (☆ =
+simple, ☆☆ = advanced, ☆☆☆ = sophisticated), then alphabetically:
+
+- [[file:notebooks/classification.ipynb][Classification]] :: basic machine learning classification example (☆)
+- [[file:notebooks/exponential_smoothing.ipynb][Exponential smoothing]] :: using [[https://ipywidgets.readthedocs.io/en/latest/examples/Widget%2520Basics.html][Jupyter's interactive widget]] to
+  explore [[https://en.wikipedia.org/wiki/Exponential_smoothing][exponential smoothing]] (☆)
+- [[file:notebooks/Hamming.ipynb][Hamming]] :: a graph visualising a strange type of word similarity (☆)
+- [[file:notebooks/Jupyter-Demo.ipynb][Jupyter-Demo]] :: demo of some Jupyter features useful for creating
+  learning material (☆)
+- [[file:notebooks/Twitter.ipynb][Twitter]] :: analysing Twitter data (raw JSON from Twitter's API) (☆)
+- [[file:notebooks/wikipedia_language_editions.ipynb][Wikipedia language editions]] :: plotting the depth and number of
+  articles of different Wikipedia language editions (☆)
+- [[file:notebooks/wikipedia_regex.ipynb][Regular expressions]] :: simple information extraction from Wikipedia
+  articles (☆)
+- [[file:notebooks/amazon_reviews.ipynb][Amazon reviews]] :: crawling web sites with [[https://scrapy.org/][Scrapy]], processing JSON
+  data, basic statistics and visualisation (☆☆)
+- [[file:notebooks/Art.ipynb][Art]] :: Creating computer-generated art by translation, scaling and
+  composition of functions (☆☆)
+- [[file:notebooks/community_detection.ipynb][Community detection]] :: applying community detection algorithms to
+  network graphs (☆☆)
+- [[file:notebooks/crawling_a_blog.ipynb][Crawling a blog]] :: crawling web sites, basic text mining, basic
+  statistics and visualisation (☆☆)
+- [[file:notebooks/Dashboard.ipynb][Dashboard]] :: building data exploration dashboards using plotly and
+  dash (☆☆)
+- [[file:notebooks/DraCor.ipynb][DraCor]] :: retrieving data from a REST API, text transformation and
+  classification (☆☆)
+- [[file:notebooks/FCA.ipynb][FCA]] :: analysing characters in plays using Formal Concept Analysis
+  (☆☆)
+- [[file:notebooks/goethes_words.ipynb][Goethes Wörter]] :: finding the most frequent nouns (verbs,
+  adjectives, ...) in Goethe's works (here: Faust) (☆☆)
+- [[file:notebooks/machine_learning.ipynb][Machine Learning]] :: recipes for common machine learning tasks (☆☆)
+- [[file:notebooks/Mondrian.ipynb][Mondrian]] :: turtle graphics, recursion, art (☆☆)
+- [[file:notebooks/statistics_top50faculty.ipynb][Statistics top 50 faculty]] :: exploratory statistical analysis of the
+  [[htotp://cs.brown.edu/people/apapouts/faculty_dataset.html][dataset of 2200 faculty in 50 top US computer science graduate
+  programs]] (☆☆)
+- [[file:notebooks/Wordsiblings.ipynb][Word Siblings]] :: Visualizing a graph of words that are related to
+  each other (☆☆)
+- [[file:notebooks/Prefix_search.ipynb][Prefix search]] :: Implementing
+  prefix search using a Trie and comparing runtime with a naive approach (☆☆)
+- [[file:notebooks/distances.ipynb][Distances]] :: comprehensive interactive simulation of recovering
+  information from noisy data (namely, point positions given their
+  noisy distance matrix) (☆☆☆)
+- [[file:notebooks/Das_Haus_vom_Nikolaus.ipynb][Das Haus vom Nikolaus]] :: graph [[https://en.wikipedia.org/wiki/Graph_traversal][traversal]] and drawing (☆☆☆)
+- [[file:notebooks/scrape_review_blog.ipynb][Scrape review blog]] :: Here, we use the python package scrapy to
+  download all reviews of a literature blog (☆☆☆)
+- [[file:notebooks/Optimization.ipynb][Optimization]] :: Solving an optimization problem using the example
+  of assigning reviewers of papers to time slots (☆☆☆)
+
+*** Module "Datenanalyse & -auswertung"
+
+Exemplary (and excellent) computational essays from students of our module:
+
+- [[file:notebooks/AirBnB_Use_Berlin.ipynb][AirBnB Use in Berlin]] :: /Untersuchung der AirBnB Nutzung in Berlin/
+  by Juliane Köhler
+- [[file:notebooks/Drama_Speakers.ipynb][Gender of Characters in Drama]] :: /Die Repräsentanz von weiblichen
+  Sprecherinnen in den Theaterstücken der deutschen und französischen
+  DramaCorpora/ by Janina Pingel und Vivian Schlosser
+- [[file:notebooks/Video_Games.ipynb][Video Games Sales]] :: /Analysis of Video Games Sales Data/ by Jan
+  Raoul Weber
+- [[file:notebooks/Weinbewertungen_Vivino.ipynb][Weinbewertungen Vivino]] :: /Untersuchung von Weinbewertungen des
+  Online-Weinmarktplatzes Vivino/ by Heike Wilhelm
+- [[file:notebooks/World_Risk_and_Happiness.ipynb][World Risk and Happiness]] :: /World Risk Poll 2021 and World
+  Happiness Report 2021/ by Helene Hellmich
+
+** Wishlist for new Notebooks
+
+- Creating a word list is not trivial but we need it for [[file:notebooks/Wordsiblings.ipynb][the word
+  siblings notebook]]. We have [[file:notebooks/Prefix_search.ipynb][a prefix search notebook]]
+  which can be used to filter words which are a prefix of other words. This can help in filtering
+  invalid words which are the stem of other words and should not be in the list on their own but
+  this would also remove valid words if there are part of a compound word.
\ No newline at end of file

_config.yml

+# config file 
+
+title: Notebooks am IBI
+email: robert.jaeschke@hu-berlin.de
+description: 
+  Humboldt-Universität zu Berlin
+  Unter den Linden 6
+  10099 Berlin 
+baseurl: "/notebooks" # the subpath of your site, e.g. /blog
+url: "" # the base hostname & protocol for your site, e.g. http://example.com
+twitter_username: IBI_HU
+# github_username:  jekyll
+
+# Build settings
+# source: notebooks
+#collections:
+#  notebooks:
+#    output: true
+
+theme: minima
+include: 
+  - notebooks
+  - jekyll-feed
+  - jekyll-seo-tag
+plugins:
+  - jekyll-feed
+  - jekyll-jupyter-notebook
+  - jekyll-datapage-generator
+
+# datapage gen
+page_gen-dirs: false
+
+page_gen:
+  - data: 'notebooks'
+    name: 'notebook_name'
+    dir: 'notebooks'
+
+# tagcloud
+tag_page_layout: tag_page
+tag_page_dir: tag
+
+
+# exclude:
+#   - .sass-cache/
+#   - .jekyll-cache/
+#   - gemfiles/
+#   - Gemfile
+#   - Gemfile.lock
+#   - node_modules/
+#   - vendor/bundle/
+#   - vendor/cache/
+#   - vendor/gems/
+#   - vendor/ruby/
+

_config_prod.yml

+# config file 
+
+title: Notebooks am IBI
+email: robert.jaeschke@hu-berlin.de
+description: 
+  Humboldt-Universität zu Berlin
+  Unter den Linden 6
+  10099 Berlin 
+baseurl: "/ibi/notebooks" # the subpath of your site, e.g. /blog
+url: "https://pages.cms.hu-berlin.de/" # the base hostname & protocol for your site, e.g. http://example.com
+twitter_username: IBI_HU
+# github_username:  jekyll
+
+# Build settings
+# source: notebooks
+#collections:
+#  notebooks:
+#    output: true
+
+theme: minima
+include: 
+  - notebooks
+  - jekyll-feed
+  - jekyll-seo-tag
+plugins:
+  - jekyll-feed
+  - jekyll-jupyter-notebook
+  - jekyll-datapage-generator
+
+# datapage gen
+page_gen-dirs: false
+
+page_gen:
+  - data: 'notebooks'
+    name: 'notebook_name'
+    dir: 'notebooks'
+
+# tagcloud
+tag_page_layout: tag_page
+tag_page_dir: tag
+
+
+# exclude:
+#   - .sass-cache/
+#   - .jekyll-cache/
+#   - gemfiles/
+#   - Gemfile
+#   - Gemfile.lock
+#   - node_modules/
+#   - vendor/bundle/
+#   - vendor/cache/
+#   - vendor/gems/
+#   - vendor/ruby/
+

_data/notebooks.yml

+- notebook_name: AirBnB_Use_Berlin
+  tags: [datavis, statistics, tabular, daa, student]
+  title: Airbnb Use Berlin
+  author: Juliane Köhler
+  difficulty: 3
+  language: German
+  description: Untersuchung der Einflüsse auf Übernachtungspreise, Bewertungen, Absagen, etc. von Airbnb-Inseraten in Berlin.
+
+- notebook_name: amazon_reviews
+  tags: [crawling, datavis, web, tabular]
+  title: Amazon Reviews
+  author: Michael Paris
+  difficulty: 3
+  language: English
+  description: Learning to analyze web data by analyzing Amazon reviews with Python.
+
+- notebook_name: Art
+  tags: [image, art]
+  title: Art
+  author: Robert Jäschke
+  difficulty: 2
+  language: English
+  description: Creating an artwork inspred by Peter Saville's cover of the Joy Division album Unknown Pleasures.
+
+- notebook_name: Das_Haus_vom_Nikolaus
+  tags: [graph, search]
+  title: Das Haus vom Nikolaus
+  author: Robert Jäschke
+  difficulty: 3
+  language: German
+  description: Wir suchen alle möglichen Lösungen für das Zeichenspiel "Das Haus vom Nikolaus".
+
+- notebook_name: Dashboard
+  tags: [datavis]
+  title: Dashboard
+  author: Robert Jäschke
+  difficulty: 2
+  language: English
+  description: This notebooks shows an example of how to create a dashboard for data exploration using plotly dash.
+
+- notebook_name: DraCor
+  tags: [classification, literature, text, dracor]
+  title: Textklassifikation mit DraCor
+  author: Robert Jäschke
+  difficulty: 2
+  language: German
+  description: In diesem Notebook testen wir, inwiefern sich die Autor:innen von Dramen anhand ihrer Texte identifizieren lassen.
+
+- notebook_name: Drama_Speakers
+  tags: [datavis, literature, tabular, dracor, student, daa, statistics]
+  title: Gender of Characters in Dramas
+  author: Janina Pingel & Vivian Schlosser
+  difficulty: 3
+  language: German
+  description: Untersuchung der Beschaffenheit der weiblichen Repräsentanz anhand der landesspezifischen DramaCorpora Deutschlands und Frankreichs.
+
+- notebook_name: FCA
+  tags: [fca, literature, dracor, datavis, clustering]
+  title: FCA & DraCor
+  author: Robert Jäschke
+  difficulty: 2
+  language: English
+  description: We want to analyse which patterns can be discovered when we consider which characters speak in scenes of different plays. Therefore, we extract the corresponding information from DraCor and analyse it using Formal Concept Analysis (FCA).
+
+- notebook_name: Hamming
+  tags: [similarity, text, graph]
+  title: Hamming Similarity
+  author: Robert Jäschke
+  difficulty: 1
+  language: English
+  description: This notebook explores a (probably slightly weird) similarity measure for strings.
+
+- notebook_name: Jupyter-Demo
+  tags: [jupyter]
+  title: Jupyter Demo
+  author: Robert Jäschke
+  difficulty: 1
+  language: German
+  description: Dieses Notebook enthält Beispiele für verschiedene Anwendungsfälle von Jupyter-Notebooks.
+
+- notebook_name: Mondrian
+  tags: [image, art, turtle]
+  title: Digital Mondrian
+  author: Robert Jäschke
+  difficulty: 2
+  language: English
+  description: This notebook aims to automatically create images similar to those of Piet Mondrian.
+
+- notebook_name: Twitter
+  tags: [text, web]
+  title: Analyzing Twitter Data
+  author: Robert Jäschke
+  difficulty: 1
+  language: English
+  description: Different examples of Twitter data analysis for raw JSON fom Twitter's API.
+
+- notebook_name: Video_Games
+  tags: [datavis, statistics, tabular, daa, student]
+  title: Video Games Sales
+  author: Jan Raoul Weber
+  difficulty: 3
+  language: English
+  description: Examine the sales of video games across different regions of the world to find out what video games are sold where and how the regional markets differ regarding preferences for e.g. Publishers.
+
+- notebook_name: Weinbewertungen_Vivino
+  tags: [datavis, statistics, tabular, daa, student]
+  title: Weinbewertungen Vivino
+  author: Heike Wilhelm
+  difficulty: 3
+  language: German
+  description: Untersuchung von Weinbewertungen des Online-Weinmarktplatzes Vivino und ihrer Zusammenhänge mit Preis, Herkunftsland und Sorte der Weine.
+
+- notebook_name: World_Risk_and_Happiness
+  tags: [datavis, statistics, tabular, daa, student]
+  title: World Risk and Happiness
+  author: Helene Hellmich
+  difficulty: 3
+  language: English
+  description: A closer look at the World Worry Index of the "World Risk Poll 2021" to determine if there is a relationship between responses or demographic factors of the respondents and the worry index, then comparison to the demographic's ranking in the "World Happiness Report".
+
+- notebook_name: classification
+  tags: [classification, datamining]
+  title: Classification
+  author: Robert Jäschke
+  difficulty: 1
+  language: English
+  description: This notebook shows the basic steps to apply a machine learning classification algorithm. The example uses a decision tree but the procedure is very similar for other algorithms.
+
+- notebook_name: community_detection
+  tags: [datamining, graph, clustering]
+  title: Community Detection in Networks
+  author: Michel Schwab
+  difficulty: 2
+  language: English
+  description: In this notebook we want to detect community in network graphs.
+
+
+- notebook_name: crawling_a_blog
+  tags: [crawling, web, text]
+  title: Crawling a Blog
+  author: Robert Jäschke
+  difficulty: 2
+  language: English
+  description: This Jupyter notebook shows how to crawl a web blog and extract information from it for later analysis.
+
+- notebook_name: distances
+  tags: [similarity, datavis]
+  title: Distances
+  author: Robert Jäschke
+  difficulty: 3
+  language: English
+  description:  If we have a number of devices in a room which can communicate with each other and measure the distance to each other (e.g., using round-trip time or signal strength), can we use that information to reconstruct the positions of the devices?
+
+- notebook_name: exponential_smoothing
+  tags: [jupyter, datavis, statistics]
+  title: Exponential Smoothing
+  author: Robert Jäschke
+  difficulty: 1
+  language: English
+  description: Using Jupyter's interactive widget to explore exponential smoothing.
+
+- notebook_name: goethes_words
+  tags: [literature, text, dracor]
+  title: Wörter im Faust
+  author: Robert Jäschke
+  difficulty: 2
+  language: German
+  description: Finding the most frequent nouns (verbs, adjectives, …) in Goethe's works (Faust in our example).
+
+- notebook_name: machine_learning
+  tags: [datamining, classification, tabular]
+  title: Machine Learning Recipes
+  author: Robert Jäschke
+  difficulty: 2
+  languge: English
+  description: This notebook contains recipes for common machine learning tasks.
+
+- notebook_name: Optimization
+  tags: [optimization, tabular, integerlinearprogramming]
+  title: Optimization Example
+  author: Frederik Arnold
+  difficulty: 3
+  language: English
+  description: This notebook shows an example of how to use linear programming to solve the following optimization problem using Python and the PuLP library.
+
+- notebook_name: scrape_review_blog
+  tags: [crawling, web, text]
+  title: Scraping Reviews
+  author: Michel Schwab
+  difficulty: 3
+  language: English
+  description: We use the Python package scrapy to download all reviews of a literature blog.
+
+- notebook_name: statistics_top50faculty
+  tags: [datavis, tabular, statistics]
+  title: Statistics Top 50 Faculty
+  author: Robert Jäschke
+  difficulty: 2
+  language: English
+  description: Basic statistics using the top 50 faculty dataset.
+
+- notebook_name: wikipedia_language_editions
+  tags: [datavis, web, wikipedia]
+  title: Comparing Wikipedia Language Editions
+  author: Robert Jäschke
+  difficulty: 1
+  language: English
+  description: Wikipedia (as of January 2022) has more than 300 active language editions. We can compare (some of) these editions quantitatively and qualitatively using the table from https://meta.wikimedia.org/wiki/Wikipedia_article_depth.
+
+- notebook_name: wikipedia_regex
+  tags: [crawling, web, wikipedia]
+  title: Wikipedia RegEx
+  author: Robert Jäschke
+  difficulty: 1
+  language: German
+  description: Anwendung von regulären Ausdrücken am Beispiel von Wikipedia-Seiten.
+
+- notebook_name: Wordsiblings
+  tags: [datavis, text, graph]
+  title: Visualizing Word Siblings
+  author: Robert Jäschke
+  difficulty: 2
+  language: English
+  description: Visualizing a graph of words that are related (similar) to each other.
+
+- notebook_name: Prefix_search
+  tags: [text, trie, search, graph]
+  title: Prefix Search
+  author: Frederik Arnold
+  difficulty: 2
+  language: English
+  description: Implementing prefix search using a Trie and comparing runtime with a naive approach.

_includes/listing.html

+  <ul class="notebooks">
+    {% for notebook in site.data.notebooks %}
+      <li class="notebook" data-tags="{{ notebook.tags | join: ' ' }}">
+        <a class="title" href="{{ notebook.notebook_name | datapage_url: 'notebooks' }}">{{ notebook.title }}</a>
+	{% include stars.html %}
+        <span class="language"> {{ notebook.language }}</span>
+        <p class="author">by <em>{{ notebook.author }}</em></p>
+        <p class="description">{{ notebook.description }}</p>
+      </li>
+    {% endfor %}
+  </ul>

_includes/stars.html

+<span class="stars">{% for i in (1..3) %}{% if i <= notebook.difficulty %}★{% else %}☆{% endif %}{% endfor %}</span>
+&nbsp; <!-- TODO: workaround → better use flexbox or the like -->

_includes/tagbar.js

+document.addEventListener('DOMContentLoaded', () => {
+  // Show all notebooks by default
+  showAllNotebooks();
+});
+
+function filterByTag(tag, element) {
+  const tags = document.querySelectorAll('.tag');
+  const allTag = document.querySelector('.tag.all');
+
+  // If the clicked tag is active and not "All", deactivate it and activate the "All" tag
+  if (element.classList.contains('active')) {
+    tags.forEach(tag => tag.classList.remove('active'));
+    allTag.classList.add('active');
+    showAllNotebooks();
+  } else {
+    // Otherwise, remove the active class from all tags and activate the clicked tag
+    tags.forEach(tag => tag.classList.remove('active'));
+    element.classList.add('active');
+
+    // If "All" tag is clicked, show all notebooks
+    if (tag === 'all') {
+      showAllNotebooks();
+    } else {
+      filterNotebooksByTag(tag);
+    }
+  }
+}
+
+function showAllNotebooks() {
+  const notebooks = document.querySelectorAll('.notebook');
+  notebooks.forEach(notebook => {
+    notebook.style.display = 'list-item';
+  });
+}
+
+function filterNotebooksByTag(tag) {
+  const notebooks = document.querySelectorAll('.notebook');
+  notebooks.forEach(notebook => {
+    const tags = notebook.getAttribute('data-tags').split(' ');
+    if (tags.includes(tag)) {
+      notebook.style.display = 'list-item';
+    } else {
+      notebook.style.display = 'none';
+    }
+  });
+}
+

_includes/tagcloud.html

+<div class="word-cloud">
+  <!-- Add an "All" tag to reset the filter and make it permanently highlighted -->
+  <span class="tag all active" onclick="filterByTag('all', this)">All</span>
+    {% assign tags = "" | split: "" %}
+    {% for notebook in site.data.notebooks %}
+      {% for tag in notebook.tags %}
+        {% unless tags contains tag %}
+          {% assign tags = tags | push: tag %}
+        {% endunless %}
+      {% endfor %}
+    {% endfor %}
+    {% assign tags = tags | sort %}
+    {% for tag in tags %}
+  <span class="tag" onclick="filterByTag('{{ tag }}', this)">{{ tag }}</span>
+    {% endfor %}
+</div>

_layouts/base_no-nav.html

+<!DOCTYPE html>
+<html lang="{{ page.lang | default: site.lang | default: 'en' }}">
+
+  {%- include head.html -%}
+
+  <body>
+
+    <main class="page-content" aria-label="Content">
+      <div class="wrapper">
+        {{ content }}
+      </div>
+    </main>
+
+    <footer class="site-footer">
+      <div class="wrapper">
+        <div class="footer-content">
+          <div class="footer-left">
+            <p><b>{{ site.title }}</b></p>
+            <p><a href="https://www.ibi.hu-berlin.de/de">https://www.ibi.hu-berlin.de/de</a></p>
+            <div class="social-links">
+              {%- include social.html -%}
+            </div>
+          </div>
+          <div class="footer-right">
+            <p>Humboldt-Universität zu Berlin<br>
+              Institut für Bibliotheks- und Informationswissenschaft<br>
+              Unter den Linden 6<br>
+              10099 Berlin</p>
+          </div>
+        </div>
+      </div>
+    </footer>
+
+  </body>
+
+</html>

_layouts/notebooks.html

+---
+layout: base_no-nav
+---
+<article class="post">
+
+<head>
+  <meta charset="UTF-8">
+  <meta name="viewport" content="width=device-width, initial-scale=1.0">
+  <title>{{ page.title }}</title>
+  <style>
+    .stars {
+      color: gold;
+      font-size: 1.2em;
+    }
+  </style>
+</head>
+
+  <div>
+    <span><strong>Author:</strong> {{ page.author }}</span> |
+    <span class="stars">{% for i in (1..3) %}{% if i <= page.difficulty %}★{% else %}☆{% endif %}{% endfor %}</span>
+    <span><strong>Language:</strong> {{ page.language }}</span>
+    <!--  <br> -->
+    <!-- <span><strong>Description:</strong> {{ page.description }}</span> -->
+  </div>
+  
+  <div class="post-content">
+    <div class="post-content e-content" itemprop="articleBody">
+      <p><a href="https://scm.cms.hu-berlin.de/ibi/notebooks/-/raw/master/notebooks/{{ page.notebook_name }}.ipynb?ref_type=heads&inline=false">Download Notebook</a></p>
+
+      <div class="jupyter-notebook" style="position: relative; width: 100%; margin: 0 auto;">
+        <div class="jupyter-notebook-iframe-container">
+          <iframe src="{{ page.notebook_name }}.ipynb.html" style="position: absolute; top: 0; left: 0; border-style: none;" width="100%" height="100%" onload="this.parentElement.style.paddingBottom = (this.contentWindow.document.documentElement.scrollHeight + 10) + 'px'"></iframe>
+        </div>
+      </div>
+    </div>
+  </div>
+
+  <a class="gitlab-link" href="https://scm.cms.hu-berlin.de/ibi/notebooks/-/blob/master/notebooks/{{ page.notebook_name }}.ipynb?ref_type=heads">GitLab Link</a>
+  
+</article>
+

_plugins/ext.rb

+# _plugins/ext.rb allows you to define custom plugins and/or require
+# plugins that otherwise wouldn't work with Jekyll
+
+# Activate jekyll-tagging
+# https://github.com/pattex/jekyll-tagging
+require 'jekyll/tagging'