diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
new file mode 100644
index 0000000..01d44f3
--- /dev/null
+++ b/CONTRIBUTING.md
@@ -0,0 +1,54 @@
+# How to Contribute to D4D Tutorials
+
+We're very happy you want to contribute! This repository is a community effort, and your help is essential to keeping it relevant and useful.
+
+There are two main ways to contribute:
+
+1. **Request a Tutorial**
+2. **Add a Tutorial**
+
+---
+
+## 💡 1. Request a Tutorial
+
+If you want to learn something that isn't covered here, or have a great idea for a tutorial, the best way to suggest it is:
+
+1. Go to the [**Issues**](https://github.com/Data4Democracy/tutorials/issues) tab of our repository.
+
+2. Check if someone hasn't already made the same suggestion.
+
+3. Click on "New Issue".
+
+4. Give it a clear title (e.g., `[REQUEST] Web Scraping Tutorial with Scrapy`).
+
+5. Describe what you would like to see in the tutorial.
+6. Add the `tutorial-request` label, if possible.
+
+## ✍️ 2. Adding a Tutorial
+
+If you have written a tutorial or found an excellent external resource that fits here, the process for adding it is through a Pull Request.
+
+### The GitHub Workflow
+
+We use the standard "Fork & Pull" workflow:
+
+1. **Fork:** Create a "fork" (copy) of this repository in your own GitHub account.
+
+2. **Clone:** Clone *your* fork to your local machine (`git clone ...`).
+
+3. **Branch:** Create a new "branch" for your changes (`git checkout -b my-new-tutorial`).
+
+4. **Add Your Tutorial:**
+* Create a new folder with a descriptive name (e.g., `Web_Scraping/`).
+* Add your files (preferably `.ipynb` or `.md`).
+* **Important:** Go back to the main `README.md` and add a link to your new tutorial in the appropriate section.
+
+5. **Commit:** Commit your changes (`git commit -m "Add Scrapy tutorial"`).
+
+6. **Push:** Send your changes to *your* fork (`git push origin my-new-tutorial`).
+
+7. **Pull Request:** Go back to the original `Data4Democracy/tutorials` repository on GitHub. You will see a button for "Compare & Pull Request". Click it, give it a title and a clear description of what you did, and submit.
+
+One of the project maintainers will review your contribution and, if everything is correct, will merge it.
+
+Thank you for helping build Data for Democracy!
diff --git a/README.md b/README.md
index 8cb4d6f..e66a525 100644
--- a/README.md
+++ b/README.md
@@ -1,58 +1,75 @@
-# Tutorials
-
-**Slack:** [#tutorials](https://datafordemocracy.slack.com/messages/tutorials/)
-
-**Project Description:** A place for tutorials relevant to D4D projects.
-
-**Project Leads:**
-* [@alarcj](https://datafordemocracy.slack.com/messages/@alarcj/)
-* [@grichardson](https://datafordemocracy.slack.com/messages/@grichardson/)
-
-# List of Tutorials
-## External Resources
-* [External Learning Resources](https://github.com/Data4Democracy/tutorials/blob/master/External%20Resources/learning-resources.md)
- Reference of resources that members of D4D have found useful in learning some of the languages, platforms, libraries, and methods applicable to D4D projects.
-
-## AWS
-* [AWS S3 using boto3](https://github.com/Data4Democracy/tutorials/blob/master/aws/AWS_Boto3_s3_intro.ipynb)
- How to interact with S3 bucket from python using boto3 library.
-
-        - helpful tutorial for setting up first IAM user and adding permissions before beginning tutorial above: (https://linuxacademy.com/howtoguides/posts/show/topic/14209-automating-aws-with-python-and-boto3)
-
-## Twitter
-* [Intro Collecting Tweets](https://github.com/Data4Democracy/tutorials/blob/master/Twitter/Intro_Collecting_Tweets.ipynb)
- Getting started using the Twitter API for downloading Tweets.
-* [Twitter Getting Past the 32K Limit](https://github.com/Data4Democracy/tutorials/blob/master/Twitter/Twitter_Gettingpast_32K_Limit.ipynb)
- How to obtain all the Tweets from a given user.
-* [Streaming Tweets From Twitter](https://github.com/Data4Democracy/tutorials/blob/master/Twitter/StreamingTweetsFromTwitter.ipynb)
- Great example on how to Stream (listen to live events) Tweets.
-* [Basic Twitter Analysis](https://github.com/Data4Democracy/tutorials/blob/master/Twitter/Basic_Twiter_Analysis.ipynb)
- Simple word frequency analysis and tokenization.
-* [Interactive Maps with Python's Folium](https://github.com/Data4Democracy/tutorials/blob/master/Twitter/Python_and_maps.ipynb)
- Interactive Maps of Tweets (excuse the sample size for this tutorial).
-* [Clustering Twitter](https://github.com/Data4Democracy/tutorials/blob/master/Twitter/Clustering_twitter.ipynb)
- Clustering a user's followers by using KMeans.
-* [Building a Graph with Twitter](https://github.com/Data4Democracy/tutorials/blob/master/Twitter/Building_a_Graph_Twitter.ipynb)
- Short tutorial on how to build a Graph of Twitter friends and followers using Networkx.
-
-## Tutorials by Project
-* [Assemble](https://github.com/Data4Democracy/assemble)
-    * [Twitter](https://github.com/Data4Democracy/tutorials/tree/master/Twitter) All of it!
-* [Internal Displacement](https://github.com/Data4Democracy/internal-displacement)
-    * [Interactive Maps with Python's Folium](https://github.com/Data4Democracy/tutorials/blob/master/Twitter/Python_and_maps.ipynb)
-* [USA Dashboard](https://github.com/Data4Democracy/usa-dashboard)
-    * [Interactive Maps with Python's Folium](https://github.com/Data4Democracy/tutorials/blob/master/Twitter/Python_and_maps.ipynb)
-* [Immigration Connect](https://github.com/Data4Democracy/immigration-connect)
-    * [Interactive Maps with Python's Folium](https://github.com/Data4Democracy/tutorials/blob/master/Twitter/Python_and_maps.ipynb)
-
-## git and GitHub
-* If you're new to using git and GitHub, [download git](https://git-scm.com) and create a GitHub account, then head over to our [GitHub playground repo](https://github.com/Data4Democracy/github-playground). Follow the instructions in the `README` to learn how it's done!
-
-# Looking for Something?
-* If you have some cool ideas for tutorial or want to see a tutorial that is not yet present please feel free to add it to our [Wish List](https://docs.google.com/spreadsheets/d/1o_821rVkR-8yz_dMBEN6Srl7tgXzrw-K8Nsqk-xkAmU/edit#gid=0)
-
-# Contributing
-* If you want to contribute your own tutorials or have any comments please open up an [issue](https://github.com/Data4Democracy/tutorials/issues).
-This way we can avoid duplicates.
-
-* If you you happen to have a cool idea for a tutorial or want to request a tutorial on a given subject/tool please feel free to open up an [issue](https://github.com/Data4Democracy/tutorials/issues) requesting it.
+# Data for Democracy (D4D) - Tutorials
+
+Welcome to the central repository of Data for Democracy tutorials! Our mission is to empower volunteers with the data skills needed to make a positive impact on civic projects and promote transparency.
+
+This repository is a living, community-maintained resource designed to help new and existing members learn the tools, techniques, and platforms we use in our projects.
+
+## 📌 Table of Contents
+
+* [How to Get Started (For New Members)](#how-to-get-started-for-new-members)
+* [How to Contribute](#how-to-contribute)
+* [Tutorial Topics](#tutorial-topics)
+* [External Learning Resources](#external-learning-resources)
+* [AWS (Amazon Web Services)](#aws-amazon-web-services)
+
+* [Twitter API](#twitter-api)
+* [Tutorials by D4D Project](#tutorials-by-d4d-project)
+
+---
+
+## 👋 How to Get Started (For New Members)
+
+If you are new to Data for Democracy or collaborative development, we recommend starting here:
+
+1. **Learn Git and GitHub:** If you are not If you're familiar with Git, we have a [**"playground" repository**](https://github.com/Data4Democracy/github-playground) where you can learn the basics of forks, commits, and pull requests without fear of breaking anything.
+
+2. **Explore External Resources:** We've compiled a [**learning resource list**](https://github.com/Data4Democracy/tutorials/blob/master/External%20Resources/learning-resources.md) that D4D members have found useful for learning about the languages, platforms, and methods we use.
+
+---
+
+## 🙋 How to Contribute
+
+This repository is made by *you*! There are two main ways to contribute:
+
+* **Request a Tutorial:** Want to learn something that's not here? Have an idea for a new tutorial? [**Open an Issue**](https://github.com/Data4Democracy/tutorials/issues/new?template=tutorial_request.md) and use the `tutorial-request` tag.
+
+* **Adding a Tutorial:** You created a tutorial? Fantastic! Please read our [**Contribution Guide (CONTRIBUTING.md)**](https(link_to_your_CONTRIBUTING.md)) for formatting guidelines and how to submit a Pull Request.
+
+---
+
+## 📚 Tutorial Topics
+
+### External Learning Resources
+* [Useful Resources](https://github.com/Data4Democracy/tutorials/blob/master/External%20Resources/learning-resources.md): A reference list of resources that D4D members have found useful for learning the languages, platforms, and methods applicable to D4D projects.
+
+### AWS (Amazon Web Services)
+* [AWS S3 using boto3](https://github.com/Data4Democracy/tutorials/blob/master/aws/AWS_Boto3_s3_intro.ipynb): How to interact with an S3 bucket using Python and the `boto3` library.
+
+*Note:* This [guide on IAM configuration](https://linuxacademy.com/howtoguides/posts/show/topic/14209-automating-aws-with-python-and-boto3) is a good prerequisite.
+
+### Twitter API
+* [Introduction to Collecting Tweets](https://github.com/Data4Democracy/tutorials/blob/master/Twitter/Intro_Collecting_Tweets.ipynb): Getting started with the Twitter API to download Tweets.
+
+* [Bypassing the 32K Limit](https://github.com/Data4Democracy/tutorials/blob/master/Twitter/Twitter_Gettingpast_32K_Limit.ipynb): How to get *all* the Tweets from a given user.
+
+* [Tweet Streaming](https://github.com/Data4Democracy/tutorials/blob/master/Twitter/StreamingTweetsFromTwitter.ipynb): A great example of how to "listen" to live (streaming) Tweet events.
+
+* [Basic Twitter Analysis](https://github.com/Data4Democracy/tutorials/blob/master/Twitter/Basic_Twiter_Analysis.ipynb): Simple word frequency analysis and tokenization.
+
+* [Interactive Maps with Folium](https://github.com/Data4Democracy/tutorials/blob/master/Twitter/Python_and_maps.ipynb): Creating interactive Tweet maps (sample size is small). * [Clustering Twitter (KMeans)](https://github.com/Data4Democracy/tutorials/blob/master/Twitter/Clustering_twitter.ipynb): Grouping a user's followers with KMeans.
+
+## 🔗 Tutorials by D4D Project
+
+Want to contribute to a specific project? See which tutorials are most relevant for each one:
+
+* **[Assemble](https://github.com/Data4Democracy/assemble)**
+
+* All tutorials in the [Twitter](#twitter-api) section are highly relevant.
+
+* **[Internal Displacement](https://github.com/Data4Democracy/internal-displacement)**
+* **[USA Dashboard](https://github.com/Data4Democracy/usa-dashboard)**
+* **[Immigration Connect](https://github.com/Data4Democracy/immigration-connect)**
+
+* [Building a Graph with Networkx](https://github.com/Data4Democracy/tutorials/blob/master/Twitter/Building_a_Graph_Twitter.ipynb): A brief tutorial on how to build a graph of Twitter friends and followers.
+
+---
diff --git a/streamlit_dashboard/README.md b/streamlit_dashboard/README.md
new file mode 100644
index 0000000..758cefa
--- /dev/null
+++ b/streamlit_dashboard/README.md
@@ -0,0 +1,32 @@
+# Tutorial: From Jupyter to Interactive Dashboard with Streamlit
+
+**Objective:** To teach how to transform a static data analysis (such as a Jupyter Notebook) into an interactive web application that anyone can use, without needing knowledge of HTML/CSS/JavaScript.
+
+**Practical Example:** Use a municipal spending dataset to create a dashboard where the user can filter by municipality and see spending over time.
+
+---
+
+### 1. Concept: Why a Dashboard?
+
+* **The Problem:** A Jupyter Notebook is great for *analysts*, but terrible for the *public*. Nobody will download your notebook, install Jupyter, and run your cells.
+
+* **The Solution:** A web dashboard allows the end user to **interact** with the data (filtering, selecting) and see the results (graphs) in real time, directly in the browser.
+
+**The Tool (Streamlit):** It's a Python library that transforms Python scripts into web applications. You write pure Python; Streamlit magically draws the buttons, menus, and graphics.
+
+### 2. Installation and Execution
+
+For this tutorial, you will need the following libraries:
+
+```bash
+pip install streamlit pandas plotly
+```
+
+To run the application: Open your terminal, navigate to this folder and run:
+
+
+```bash
+streamlit run app.py
+```
+
+Your browser will automatically open with the dashboard.
diff --git a/streamlit_dashboard/app.py b/streamlit_dashboard/app.py
new file mode 100644
index 0000000..86075db
--- /dev/null
+++ b/streamlit_dashboard/app.py
@@ -0,0 +1,90 @@
+import streamlit as st
+import pandas as pd
+import plotly.express as px
+
+# --- Page Configuration ---
+# Must be the first Streamlit command in the script
+st.set_page_config(
+    page_title="Municipal Spending Dashboard",
+    page_icon="📊",
+    layout="wide"
+)
+
+
+
+
+# --- Data Loading ---
+@st.cache_data
+def load_data():
+    # Diga ao Pandas para ignorar linhas que começam com '#'
+    df = pd.read_csv(
+        "municipal_spending.csv", 
+        comment='#'  # <--- Esta é a linha mágica
+    ) 
+    df['date'] = pd.to_datetime(df['date'])
+    return df
+
+# Load the data
+df = load_data()
+
+# --- Dashboard Title ---
+st.title("📊 Municipal Spending Dashboard")
+
+# --- Sidebar for Filters ---
+st.sidebar.header("Filters")
+
+# Get the list of unique municipalities
+municipalities_unique = sorted(df['municipality'].unique())
+
+# Create the selectbox in the sidebar
+selected_municipality = st.sidebar.selectbox(
+    "Select a Municipality:",
+    municipalities_unique
+)
+
+# --- Data Filtering ---
+# Filter the main dataframe based on user selection
+df_filtered = df[df['municipality'] == selected_municipality]
+
+# --- Main Layout (Charts) ---
+st.header(f"Analysis for: {selected_municipality}", divider="gray")
+
+# Create two columns for the charts
+col1, col2 = st.columns(2)
+
+# --- Chart 1: Spending Over Time (Line Chart) ---
+with col1:
+    st.subheader("Spending Over Time")
+    fig_line = px.line(
+        df_filtered,
+        x="date",
+        y="amount",
+        color="category", # Shows different lines for Health, Education, etc.
+        title=f"Spending Evolution"
+    )
+    # Use streamlit's theme and fit to column width
+    st.plotly_chart(fig_line, use_container_width=True, theme="streamlit")
+
+# --- Chart 2: Distribution by Category (Pie Chart) ---
+with col2:
+    st.subheader("Distribution by Category")
+    # Group data for the pie chart
+    df_grouped_category = df_filtered.groupby('category')['amount'].sum().reset_index()
+
+    fig_pie = px.pie(
+        df_grouped_category,
+        names="category",
+        values="amount",
+        title="Percentage of Spending by Category"
+    )
+    st.plotly_chart(fig_pie, use_container_width=True, theme="streamlit")
+
+
+# --- Show raw data (optional, with an expander) ---
+with st.expander("View Filtered Raw Data"):
+    st.dataframe(df_filtered)
+
+# To run this application:
+# 1. Save this file as 'app.py'
+# 2. Make sure 'municipal_spending.csv' is in the same folder
+# 3. Open your terminal and run: streamlit run app.py
diff --git a/streamlit_dashboard/municipal_spending.csv b/streamlit_dashboard/municipal_spending.csv
new file mode 100644
index 0000000..99d8a01
--- /dev/null
+++ b/streamlit_dashboard/municipal_spending.csv
@@ -0,0 +1,33 @@
+# ⚠️ Disclaimer: Sample Data
+# The municipal_spending.csv file included in this tutorial contains fictitious (false) data.
+# This data was created synthetically to ensure a clean format...
+id,municipality,date,category,amount
+1,Lisbon,2023-01-15,Health,150000
+2,Lisbon,2023-01-20,Education,220000
+3,Lisbon,2023-01-25,Transport,95000
+4,Porto,2023-01-17,Health,120000
+5,Porto,2023-01-22,Education,180000
+6,Porto,2023-01-28,Culture,50000
+7,Lisbon,2023-02-15,Health,165000
+8,Lisbon,2023-02-20,Education,230000
+9,Lisbon,2023-02-25,Transport,110000
+10,Porto,2023-02-17,Health,135000
+11,Porto,2023-02-22,Education,190000
+12,Porto,2023-02-28,Culture,55000
+13,Lisbon,2023-03-15,Health,170000
+14,Lisbon,2023-03-20,Education,240000
+15,Lisbon,2023-03-25,Transport,100000
+16,Porto,2023-03-17,Health,140000
+17,Porto,2023-03-22,Education,205000
+18,Porto,2023-03-28,Culture,60000
+19,Coimbra,2023-01-20,Health,80000
+20,Coimbra,2023-01-25,Education,110000
+21,Coimbra,2023-02-20,Health,85000
+22,Coimbra,2023-02-25,Education,115000
+23,Coimbra,2023-03-20,Health,90000
+24,Coimbra,2023-03-25,Education,120000
+25,Coimbra,2023-03-28,Transport,40000
+26,Lisbon,2023-04-15,Health,180000
+27,Lisbon,2023-04-20,Education,250000
+28,Porto,2023-04-17,Health,150000
+29,Coimbra,2023-04-20,Health,95000