Merge branch 'main' into abhicc_ROC

.github/workflows/build-and-deploy.yaml

@@ -1,4 +1,4 @@
-on: [push, pull_request]
+on: [push, pull_request, workflow_dispatch]
 
 name: Build and Deploy Glossary Website
 
@@ -7,7 +7,7 @@ jobs:
     runs-on: ubuntu-latest
     steps:
     - name: Checkout
-      uses: actions/checkout@v2.3.1
+      uses: actions/checkout@v3
       with:
           persist-credentials: false
 
@@ -22,7 +22,8 @@ jobs:
 
     - name: Deploy to site
       if: ${{ github.event_name == 'push' && github.ref == 'refs/heads/main' && github.repository == 'carpentries/glosario' }}
-      uses: JamesIves/github-pages-deploy-action@4.1.5
+      uses: JamesIves/github-pages-deploy-action@v4
       with:
         branch: site
         folder: _gh-site
+        token: ${{ secrets.GITHUB_TOKEN }}

.github/workflows/yaml-lint.yaml

@@ -4,16 +4,15 @@ on: [push, pull_request]
 
 jobs:
   build:
-
     runs-on: ubuntu-latest
     strategy:
       matrix:
         python-version: [3.8]
 
     steps:
-    - uses: actions/checkout@v2
+    - uses: actions/checkout@v3
     - name: Set up Python ${{ matrix.python-version }}
-      uses: actions/setup-python@v2
+      uses: actions/setup-python@v3
       with:
         python-version: ${{ matrix.python-version }}
     - name: Install dependencies

README.md

@@ -12,7 +12,7 @@ in any of several (human) languages.
 
 ## Contributing
 
-You do not need to know any particular programming language to contribute to Glosario: anyone possessing a basic familiarity with the GitHub web interface can get involved! We have prepared a [detailed and accessible guide for contributing](https://docs.google.com/document/d/18gTFR1Pw2Mk3PeNTMS0IHgFnJy-F4PBCGJQ1aMrDHPE/edit?usp=sharing), which has been translated into several languages. Contributions are welcome in any language, not only those represented in that document. If you need help with your contribution, feel free to come ask questions on the [#glosario](https://swcarpentry.slack.com/archives/C01G4HYGAQ6) Slack channel (if you are not a member of The Carpentries Slack you can join by filling [this form](https://swc-slack-invite.herokuapp.com/)).
+You do not need to know any particular programming language to contribute to Glosario: anyone possessing a basic familiarity with the GitHub web interface can get involved! We have prepared a [detailed and accessible guide for contributing](https://docs.google.com/document/d/18gTFR1Pw2Mk3PeNTMS0IHgFnJy-F4PBCGJQ1aMrDHPE/edit?usp=sharing), which has been translated into several languages. Contributions are welcome in any language, not only those represented in that document. If you need help with your contribution, feel free to come ask questions on the [#glosario](https://carpentries.slack.com/archives/C01G4HYGAQ6) Slack channel (if you are not a member of The Carpentries Slack you can join by filling [this form](https://slack-invite.carpentries.org/)).
 
 ## Lessons
 

glossary.yml

@@ -8657,6 +8657,16 @@
     term: "kies"
     def: >
       Om volledige kolomme of rye in 'n tabel volgens naam of plek te kies (selekteer).
+
+- slug: selecting on the dependent variable bias
+  en:
+    term: "selecting on the dependent variable bias"
+    def: >
+      A study that only includes cases where the dependent variable shows the same value, instead of cases with different values in the dependent variable, is a study affected by selecting on the dependent variable bias.
+  es:
+    term: "sesgo de selección en la variable dependiente"
+    def: >
+     Un estudio que solamente incluye casos en los que la variable dependiente tiene el mismo valor, en lugar de casos con variación en la variable dependiente, es un estudio con sesgo de selección en la variable dependiente.
       
 
 - slug: self_join
@@ -8935,7 +8945,7 @@
   en:
     term: "specificity"
     def: >
-      Statistical measure of a classification model which gives the False Negative rate. 
+      Statistical measure of a classification model which gives the True Negative rate. 
       For example, the proportion of people who do not have a disease that test negative. 
       Calculated as Specificity = TN/(TN+FP).
 
@@ -10139,11 +10149,12 @@
     - perceptron
     - neural_network
     - machine_learning
-  term: "convolutional neural network (cnn)"
-  def: >
-    A class of artificial neural network that is primarily used to analyze images. A CNN has layers
-    that perform convolutions, where a filter is shifted over the data, instead of the general 
-    matrix multiplications that we see in fully connected neural network layers.
+  en:
+    term: "convolutional neural network (cnn)"
+    def: >
+      A class of artificial neural network that is primarily used to analyze images. A CNN has layers
+      that perform convolutions, where a filter is shifted over the data, instead of the general 
+      matrix multiplications that we see in fully connected neural network layers.
 
 - slug: rnn
   ref:
@@ -10152,11 +10163,12 @@
     - perceptron
     - neural_network
     - machine_learning
-  term: recurrent neural network
-  def: >
-    A class of [artificial neural networks](#neural_network) where connections between nodes can 
-    create a cycle. This allows the network to exhibit behavior that is dynamic over time. This 
-    type of network is applicable to tasks like speech and handwriting recognition.
+  en:
+    term: recurrent neural network
+    def: >
+      A class of [artificial neural networks](#neural_network) where connections between nodes can 
+      create a cycle. This allows the network to exhibit behavior that is dynamic over time. This 
+      type of network is applicable to tasks like speech and handwriting recognition.
 
 - slug: epoch_dl
   ref:
@@ -10165,11 +10177,12 @@
     - perceptron
     - neural_network
     - machine_learning
-  term: epoch (deep learning)
-  def: >
-    In [deep learning](#deep_learning), an epoch is one cycle in the deep learning process where all
-    the training data has been fed to the algorithm once. Training a deep neural networks usually
-    consists of multiple epochs.
+  en:
+    term: epoch (deep learning)
+    def: >
+      In [deep learning](#deep_learning), an epoch is one cycle in the deep learning process where all
+      the training data has been fed to the algorithm once. Training a deep neural networks usually
+      consists of multiple epochs.
 
 - slug: learning_rate
   ref:
@@ -10178,30 +10191,36 @@
     - perceptron
     - neural_network
     - machine_learning
-  def: >
-    In [artificial neural networks](#neural_network), the learning rate is a hyper-parameter that
-    determines the pace at which the network adjusts the weights to move down the loss gradient. 
-    A large learning rate can speed up training, but the network might overshoot and miss the 
-    minimum. A small learning rate will overshoot less, but will be slower. It can also get more 
-    easily stuck in local minima.
+  en:
+    term: learning rate (deep learning)
+    def: >
+      In [artificial neural networks](#neural_network), the learning rate is a hyper-parameter that
+      determines the pace at which the network adjusts the weights to move down the loss gradient. 
+      A large learning rate can speed up training, but the network might overshoot and miss the 
+      minimum. A small learning rate will overshoot less, but will be slower. It can also get more 
+      easily stuck in local minima.
 
 - slug: class_imbalance
   ref:
     - machine_learning
-  def: >
-    Class imbalance refers to the problem in [machine learning](machine_learning) where there is an 
-    unequal distribution of classes in the dataset.
+  en:
+    term: class imbalance
+    def: >
+      Class imbalance refers to the problem in [machine learning](machine_learning) where there is an 
+      unequal distribution of classes in the dataset.
 
 - slug: hidden_layer
   ref:
     - neural_network
     - machine_learning
     - deep_learning
     - perceptron
-  def: >
-    A hidden layer in a [neural network](#neural_network) refers to the layers of neurons that are
-    not directly connected to input or output. The layers are "hidden" because you do not directly
-    observe their input and output values.
+  en:
+    term: hidden layer (deep learning)
+    def: >
+      A hidden layer in a [neural network](#neural_network) refers to the layers of neurons that are
+      not directly connected to input or output. The layers are "hidden" because you do not directly
+      observe their input and output values.
 
 - slug: roc_curve
   ref:
@@ -10214,4 +10233,4 @@
       of a binary classifier at different [classification](#classification) thresholds. The curve is 
       obtained by plotting the True Positive Rate (also known as Recall or [Sensitivity](#sensitivity))
       along the vertical axis and the False Positive Rate along the horizontal axis.
-   
+