deploy: f448409

_sources/lesson_design.md

@@ -0,0 +1,166 @@
+# Lesson design (English version)
+
+## Lesson Scope (what is covered and what is not)
+
+## Participant requirements
+
+The following are the requirements needed for the course:
+
+
+### Desirable:
+
+- Have completed the NASA Open Science 101 course.
+
+- Have basic programming knowledge.
+
+
+### Required:
+
+- A free NASA EarthData user account:
+
+  - To access the data used in this course, you will need a free NASA EarthData user account. You can create an account on the NASA EarthData website ([https://urs.earthdata.nasa.gov/ ](https://urs.earthdata.nasa.gov/)).
+
+* A free user account on GitHub:
+
+  - To download the course source code, you will need a free user account on GitHub. You can create an account on the GitHub website (<https://github.com/>).
+
+- A 2i2c hub user account:
+
+  - To run the course notebooks, you will need a free 2i2c hub account. You can create an account on the 2i2c website (Link).
+
+* Basic knowledge of spatial data.
+
+
+## Module Objectives
+
+### Overall Objective: 
+
+Use NASA's open products called Dynamic Surface Water eXtent (DSWx) - Landsat Sentinel-2 harmonized (HLS) to map the extent of flooding resulting from the September 2022 monsoon in Pakistan.
+
+
+### Specific Objectives:
+
+1. Access the Jupyter Hub environment provided by 2i2c.
+
+2. Log in to EarthData from within the Jupyter Notebook.
+
+3. Access the DSWx-HLS products.
+
+4. Query the DSWx-HLS Provisional Data collection for a specific region of interest and according to a specific date.
+
+5. Display a map showing the area of ​​interest and the intersecting DSWx tiles.
+
+6. Create a table of search results.
+
+7. Generate a flood map highlighting flooded areas, indicating the extent of inundation.
+
+
+### Outputs:
+
+- Visualization of a flood map of Pakistan for September 2022.
+
+
+### Outcomes (medium-term impact):
+
+- Participants will be able to independently choose and filter the desired dataset from the DSWx collection, enabling them to obtain and visualize flood map extents.
+
+- Participants will be capable of designing and applying reproducible workflows, ensuring that their analyses and processes can be replicated by others or in similar situations.
+
+- Participants will have the ability to contribute to the community by sharing their workflows, scripts, or acquired knowledge, fostering collaboration and the exchange of best practices.
+
+
+## Learner personas:
+
+ ### 1. Martín
+
+Martin is a 35-year-old geographer, father of twins and living in Puerto Rico, who works in public management and needs to learn techniques that allow him to enrich their action proposals, such as how to take advantage of open databases with remote sensing data. He will not perform the analyses himself, but he wants to know what can be done with these open data.
+
+#### Objectives
+
+- Become familiar with NASA’s remote sensing data and understand the types of data they provide and the potential products that can be generated.
+
+- Identify and understand practical applications of remote sensing data in the context of public management.
+
+- Learn what online platforms and tools are available to access and analyze remote sensing data.
+
+#### Challenges
+
+2. Limited budget
+
+3. Lack of trained human resources
+
+4. Limited time to learn the necessary concepts.
+
+
+### 2. Marcia
+
+Marcia is a 30-year-old woman who lives in the basement of her parent’s house in the suburbs while she finishes her MBA in Public Affairs. She leads a small team of 5 people working in a local government office. She oversees a project to improve water distribution in a rural region. She needs to learn how to analyze remote sensing data to identify areas with water shortages. As her resources are limited, she wants to take advantage of open databases storing satellite images and data. She will perform the analyses directly together with her team and will advise her supervisor in deciding which strategies to follow.
+
+#### Objectives
+
+- Learn how to access and process NASA’s remote-sensing data
+
+- Inspect libraries and tools to process the different types of data provided by satellites
+
+- Identify and understand practical applications of remote sensing data in the context of public management.
+
+- Generate insights that can be used to drive decision-making.
+
+#### Challenges
+
+- Competency in Python programming but lacking specific technical knowledge about remote sensing data
+
+- Limited paid time to learn the necessary concepts
+
+- Needs approval to implement solutions
+
+
+### 3. Ana
+
+Ana is an agronomist who has recently completed her Master's in Hydrology, during which she focused on studying the seasonal variability of water resources in the inter-Andean valleys region in Bolivia. Additionally, she is a member of a community of practice dedicated to analysing and making geospatial data available that promotes open science. She is interested in designing a water resources management course with open data, specifically targeting individuals familiar with geospatial data but in need of learning how to analyze satellite images for flood zone detection.
+
+#### Objectives:
+
+- Acquire effective teaching practices for delivering a course on satellite data analysis.
+
+- Learn about available online platforms and tools for accessing and analyzing remote sensing data.
+
+- Develop skills to integrate case studies into the context of water resources management, providing practical applications of satellite image analysis.
+
+#### Challenges:
+
+- Teach data analysis in an easy and user-friendly manner to individuals without programming knowledge.
+
+- Work with data, materials, and workflows that adhere to the principles of open science.
+
+
+## Assessment of the learner's progress:
+
+- **Activity 1: Getting Started (5 min)**
+
+    - Access the 2i2c hub and log in with your Earth Data credentials.
+
+- **Activity 2: Selecting your area of interest (5 min)**
+
+  - Choose the region you want to analyze for potential flood risks.
+
+- **Activity 3: Retrieving relevant data (5 minutes)**
+
+  - Find the DSWx-HLS collection data corresponding to your selected area of interest.
+
+- **Activity 4: Creating a data summary (10 minutes)**
+
+  - Create a table with the new search results and export it.
+
+- **Activity 5: Visualizing flood map extents (10 minutes)**
+
+  -  With the new values, visualize the extent of the flood map.
+
+
+
+
+
+
+
+
+

_static/documentation_options.js

@@ -8,7 +8,7 @@ var DOCUMENTATION_OPTIONS = {
     LINK_SUFFIX: '.html',
     HAS_SOURCE: true,
     SOURCELINK_SUFFIX: '',
-    NAVIGATION_WITH_KEYS: true,
+    NAVIGATION_WITH_KEYS: false,
     SHOW_SEARCH_SUMMARY: true,
     ENABLE_SEARCH_SHORTCUTS: false,
 };

_static/scripts/bootstrap.js.LICENSE.txt

@@ -1,5 +1,5 @@
 /*!
-  * Bootstrap v5.2.3 (https://getbootstrap.com/)
-  * Copyright 2011-2022 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
+  * Bootstrap v5.3.2 (https://getbootstrap.com/)
+  * Copyright 2011-2023 The Bootstrap Authors (https://github.com/twbs/bootstrap/graphs/contributors)
   * Licensed under MIT (https://github.com/twbs/bootstrap/blob/main/LICENSE)
   */