add numbering

Author: e-marshall

book/_config.yml

@@ -24,7 +24,7 @@ latex:
     targetname: book.tex
 
 bibtex_bibfiles:
-  - "paper.bib"
+  - "book_refs.bib"
 
 # Information about where the book exists on the web
 repository:
@@ -76,18 +76,18 @@ sphinx:
       part4_title: "Summary + Conclusion"
 
       #tutorial 1 nb titles
-      title_its_nb1: "# 1. Accessing cloud-hosted ITS_LIVE data"
-      title_its_nb2: "# 2. Working with larger than memory data"
-      title_its_nb3: "# 3. Handling raster and vector data"
-      title_its_nb4: "# 4. Exploratory data analysis of a single glacier"
-      title_its_nb5: "# 5. Exploratory data analysis of multiple glaciers"
+      title_its_nb1: "# 3.1 Accessing cloud-hosted ITS_LIVE data"
+      title_its_nb2: "# 3.2 Working with larger than memory data"
+      title_its_nb3: "# 3.3 Handling raster and vector data"
+      title_its_nb4: "# 3.4 Exploratory data analysis of a single glacier"
+      title_its_nb5: "# 3.5 Exploratory data analysis of multiple glaciers"
 
       #tutorial 2 nb titles
-      title_s1_1: "# 1. Read Sentinel-1 data processed by ASF"
-      title_s1_2: "# 2. Wrangle metadata"
-      title_s1_3: "# 3. Exploratory analysis of ASF S1 imagery"
-      title_s1_4: "# 4. Read Sentinel-1 RTC data from Microsoft Planetary Computer"
-      title_s1_5: "# 5. Comparing Sentinel-1 RTC datasets"
+      title_s1_1: "# 4.1 Read Sentinel-1 data processed by ASF"
+      title_s1_2: "# 4.2 Wrangle metadata"
+      title_s1_3: "# 4.3 Exploratory analysis of ASF S1 imagery"
+      title_s1_4: "# 4.4 Read Sentinel-1 RTC data from Microsoft Planetary Computer"
+      title_s1_5: "# 4.5 Comparing Sentinel-1 RTC datasets"
       #title_s1_6: "# 6. Example of Sentinel-1 RTC time series analysis"
 
       #global nb sections

book/_toc.yml

@@ -2,43 +2,38 @@
 format: jb-book
 root: introduction
 parts:
-  - caption: Introduction
-    #numbered: 2
+  - caption: Part 1. Introduction
     chapters:
     - file: intro/getting_started
     - file: intro/learning_objectives
     - file: intro/open_source_setting
-  - caption: Background
-    #numbered: 2
+  - caption: Part 2. Background
     chapters:
     - file: background/context_motivation
     - file: background/data_cubes
     - file: background/tutorials_overview
     - file: background/tutorial_data
-    - file: intro/software
+    - file: background/software
     - file: background/relevant_concepts
-  - caption: Part 1
-    #numbered: 2
+  - caption: Part 3. ITS_LIVE Tutorial
     chapters:
     - file: itslive/itslive_intro
-    - file: itslive/nbs/1_accessing_itslive_s3_data
-    - file: itslive/nbs/2_larger_than_memory_data
-    - file: itslive/nbs/3_combining_raster_vector_data
-    - file: itslive/nbs/4_exploratory_data_analysis_single
-    - file: itslive/nbs/5_exploratory_data_analysis_group
-  - caption: Part 2
-    #numbered: 2
+    - file: itslive/nbs/accessing_itslive_s3_data
+    - file: itslive/nbs/larger_than_memory_data
+    - file: itslive/nbs/combining_raster_vector_data
+    - file: itslive/nbs/exploratory_data_analysis_single
+    - file: itslive/nbs/exploratory_data_analysis_group
+  - caption: Part 4. Sentinel-1 RTC Tutorial
     chapters:
     - file: sentinel1/s1_intro
-    - file: sentinel1/nbs/1_read_asf_data
-    - file: sentinel1/nbs/2_wrangle_metadata
-    - file: sentinel1/nbs/3_asf_exploratory_analysis
-    - file: sentinel1/nbs/4_read_pc_data
-    - file: sentinel1/nbs/5_comparing_s1_rtc_datasets
-  - caption: Conclusion
-    #numbered: 2
+    - file: sentinel1/nbs/read_asf_data
+    - file: sentinel1/nbs/wrangle_metadata
+    - file: sentinel1/nbs/asf_exploratory_analysis
+    - file: sentinel1/nbs/read_pc_data
+    - file: sentinel1/nbs/comparing_s1_rtc_datasets
+  - caption: Part 5. Conclusion
     chapters:
-    - file: conclusion/conclusion
+    - file: conclusion/wrapping_up
     - file: conclusion/summary
     - file: conclusion/datacubes_revisited
   - caption: Additional material

book/background/context_motivation.md

@@ -1,8 +1,8 @@
-# Context & Motivation
+# 2.1 Context & Motivation
 
 This book demonstrates scientific workflows using publicly-available, cloud-optimized geospatial datasets and open-source scientific software tools in order to address the need for educational resources related to new technologies and reduce barriers to entry to working with earth observation data. The tutorials in this book focus on the complexities inherent to working with n-dimensional, gridded datasets and use the core stack of software packages built on and around the Xarray data model.
 
-### *I. Moving away from the 'download model' of scientific data analysis*
+### *Moving away from the 'download model' of scientific data analysis*
 
 Technological developments in recent decades have engendered fundamental shifts in the nature of scientific data and how it is used for analysis.
 
@@ -11,7 +11,7 @@ Technological developments in recent decades have engendered fundamental shifts
 -- {cite}`abernathey_2021_cloud`
 ```
 
-### *II. Increasingly large, cloud-optimized data means new tools and approaches for data management*
+### *Increasingly large, cloud-optimized data means new tools and approaches for data management*
 
 The increase in publicly available earth observation data has transformed scientific workflows across a range of fields, prompting analysts to gain new skills in order to work with larger volumes of data in new formats and locations, and to use distributed cloud-computational resources in their analysis ({cite:t}`abernathey_2021_cloud,gentemann_2021_science,mathieu_2017_esas,ramachandran_2021_open,Sudmanns_2020_big,wagemann_2021_user`). 
 
@@ -21,7 +21,7 @@ The increase in publicly available earth observation data has transformed scient
 Volume of NASA Earth Science Data archives, including growth of existing-mission archives and new missions, projected through 2029. Source: [NASA EarthData - Open Science](https://www.earthdata.nasa.gov/about/open-science).
 ```
 
-### *III. Asking questions of complex datasets*
+### *Asking questions of complex datasets*
 
 Scientific workflows involve asking complex questions of diverse types of data. Earth observation and related datasets often contain two types of information: measurements of a physical observable (e.g. temperature) and metadata that provides auxiliary information that required in order to interpret the physical observable (time and location of measurement, information about the sensor, etc.). With the increasingly complex and large volume of earth observation data that is currently available, storing, managing and organizing these types of data can very quickly become a complex and challenging task, especially for students and early-career analysts {cite}`mathieu_esas_2017,palumbo_2017_building,Sudmanns_2020_big,wagemann_2021_user`. 
 

book/background/data_cubes.md

@@ -1,8 +1,8 @@
-# Data cubes
+# 2.2 Data cubes
 
 The term **data cube** is used frequently throughout this book. This page contains an introduction of ***what*** a data cube is and ***why*** it is useful. 
 
-## *I. Anatomy of a data cube*
+## *Anatomy of a data cube*
 
 The key object of analysis in this book is a [raster data cube](https://openeo.org/documentation/1.0/datacubes.html). Raster data cubes are n-dimensional objects that store continuous measurements or estimates of physical quantities that exist along given dimension(s). Many scientific workflows involve examining how a variable (such as temperature, windspeed, relative humidity, etc.) varies over time and/or space. Data cubes are a way of organizing geospatial data that let us ask these questions.
 
@@ -55,7 +55,7 @@ A data cube should be organized out of these building blocks adhering to the fol
 **Attributes** - Metadata that can be assigned to a given `xr.Dataset` or `xr.DataArray` that is ***static*** along that object's dimensions.   
 :::
 
-## *II. 'Analysis-ready' data*
+## *'Analysis-ready' data*
 The process described above is an example of preparing data for analysis. Thanks to development and collaboration across the earth observation community, analysis-ready for earth observation has a specific, technical definition:
 
 ```{epigraph}
@@ -67,15 +67,15 @@ The development and increasing adoption of analysis-ready specifications for sat
 
 However, many legacy datasets still require significant effort in order to be considered 'analysis-ready'. Furthermore, for analysts, 'analysis-ready' can be a subjective and evolving label. Semantically, from a user-perspective, analysis-ready data can be thought of as data whose structure is conducive to scientific analysis.
 
-## *III. Analysis-ready data cubes & this book*
+## *Analysis-ready data cubes & this book*
 The tutorials in this book contain examples of data at various degrees of 'analysis-ready'. [Tutorial 1: ITS_LIVE](../itslive/itslive_intro.md) uses a dataset of multi-sensor observations that is already organized as a `(x,y,time)` cube with a common grid. In [Tutorial 2: Sentinel-1](../sentinel1/s1_intro.md), we will see an example of a dataset that has undergone intensive processing to make it 'analysis-ready' but requires further manipulation to arrive at the `(x,y,time)` cube format that will be easist to work with. 
 
 ### References
 - {cite:t}`montero_2024_EarthSystemData`
 - {cite:t}`appel_2019_ondemand`
 - {cite:t}`giuliani_2019_EarthObservationOpen`
 - {cite:t}`truckenbrodt_2019_Sentinel1ARD`
-## Additional data cube resources
+### Additional data cube resources*
 - [OpenEO - Data Cubes](https://openeo.org/documentation/1.0/datacubes.html)
 - [Open Data Cube initiative](https://www.opendatacube.org/about-draft)
 - [The Datacube Manifesto](http://www.earthserver.eu/tech/datacube-manifesto/The-Datacube-Manifesto.pdf)

book/background/relevant_concepts.md

@@ -1,4 +1,4 @@
-# Relevant concepts
+# 2.6 Relevant concepts
 
 ## *Larger than memory data, parallelization and Dask*
 

book/intro/software.md book/background/software.mdbook/intro/software.md renamed to book/background/software.md

@@ -1,4 +1,4 @@
-# Software and computing environment
+# 2.5 Software and computing environment
 
 On this page you'll find information about the computing environment and datasets that will be used in both of the tutorials in this book.
 

book/background/tutorial_data.md

@@ -1,4 +1,4 @@
-# Data used in tutorials
+# 2.4 Data used in tutorials
 
 We use a many different datasts throughout these tutorials. While each tutorial is focused on a different raster time series (ITS_LIVE ice velocity data and Sentinel-1 imagery), we also use vector data to represent points of interest. 
 

book/background/tutorials_overview.md

@@ -1,4 +1,4 @@
-# Tutorials overview
+# 2.3 Tutorials overview
 
 This book contains two distinct tutorials, each of which focuses on a different cloud-optimized geospatial dataset and different cloud-computing resources. Read more about the datasets used [here](tutorial_data.md).
 

book/paper.bib book/book_refs.bibbook/paper.bib renamed to book/book_refs.bib

@@ -1,4 +1,4 @@
-# Data Cubes Revisited
+# 5.3 Data Cubes Revisited
 
 In this book, we saw a range of real-world datasets and the steps required to prepare them for analysis. Several guiding principles for assembling and using analysis-ready data cubes in Xarray can be drawn from these examples.