update whitespace and formatting. reorg sections

Author: TimidRobot

README.md

@@ -1,57 +1,17 @@
 # Quantifying
-  Quantifying the Commons: measure the size and diversity of the commons--the collection of works that are openly licensed or in the public domain
+
+Quantifying the Commons: measure the size and diversity of the commons--the
+collection of works that are openly licensed or in the public domain
 
 
 ## Overview
-This project seeks to quantify the size and diversity of the creative commons legal tools. We aim to track the collection of works (articles, images, publications, etc.) that are openly licensed or in the public domain. The project automates data collection from multiple data sources, processes the data, and generates meaningful reports.
 
+This project seeks to quantify the size and diversity of the creative commons
+legal tools. We aim to track the collection of works (articles, images,
+publications, etc.) that are openly licensed or in the public domain. The
+project automates data collection from multiple data sources, processes the
+data, and generates meaningful reports.
 
-### The three phases of generating a report
-- **1-Fetch**: This phase involves collecting data from a particular source using its API. Before writing any code, we plan the analyses we want to perform by asking meaningful questions about the data. We also consider API limitations (such as query limits) and design a query strategy to work within these limitations. Then we write a python script that gets the data, it is quite important to follow the format of the scripts existing in the project and use the modules and functions where applicable. It ensures consistency in the scripts and we can easily debug issues might arise.
-   - **Meaningful questions**
-     - The reports generated by this project (and the data fetched and processed to support it) seeks to be meaningful. We hope this project will provide data and analysis that helps inform discussions about the commons--the collection of works that are openly licensed or in the public domain.
-     The goal of this project is to help answer questions like:
-     - How has the world's use of the commons changed over time?
-       - How is the knowledge and culture of the commons distributed?
-     - Who has access (and how much) to the commons?
-       - What significant trends can be observed in the commons?
-     - Which public domain dedication or licenses are the most popular?
-       - What are the correlations between public domain dedication or licenses and
-         region, language, domain/endeavor, etc.?
-   - **Limitations of an API**
-      - Some data sources provide APIs with query limits (it can be daily or hourly) depending on what is given in the documentation. This restricts how many requests that can be made in the specified period of time. It is important to plan a query strategy and schedule fetch jobs to stay within the allowed limits.
-   - **Headings of data in 1-fetch**
-      - [Tool identifier](https://creativecommons.org/share-your-work/cclicenses/): A unique identifier used to distinguish each Creative Commons legal tool within the dataset. This helps ensure consistency when tracking tools across different data sources.
-      - [SPDX identifier](https://spdx.org/licenses/): A standardized identifier maintained by the Software Package Data Exchange (SPDX) project. It provides a consistent way to reference licenses in applications.
-- **2-Process**: In this phase, the fetched data is transformed into a structured and standardized format for analysis. The data is then analyzed and categorized based on defined criteria to extract insights that answer the meaningful questions identified during the 1-fetch phase.
-- **3-report**: This phase focuses on presenting the results of the analysis. We generate graphs and summaries that clearly show trends, patterns, and distributions in the data. These reports help communicate key insights about the size, diversity, and characteristics of openly licensed and public-domain works.
-
-
-### Automation scripts
-For automating these phases, the project uses Python scripts to fetch, process, and report data. GitHub Actions is used to automatically run these scripts on a defined schedule and on code updates. It handles script execution, manages dependencies, and ensures the workflow runs consistently.
-- **Script assumptions**
-  - Execution schedule for each quarter:
-    - 1-Fetch: first month, 1st half of second month
-    - 2-Process: 2nd half of second month
-    - 3-Report: third month
-- **Script requirements**
-   - *Must be safe*
-      - Scripts must not make any changes with default options
-      - Easiest way to run script should also be the safest
-      - Have options spelled out
-      - Must be timely
-   - *Scripts should complete within a maximum of 45 minutes*
-      - *Scripts shouldn't take longer than 3 minutes with default options*
-      - That way there’s a quicker way to see what is happening when it is running; see execution, without errors, etc. Then later in production it can be run with longer options
-   - *Must be idempotent (Idempotence: [Wikipedia](https://en.wikipedia.org/wiki/Idempotence))*
-      - This applies to both the data fetched and the data stored.
-        If the data changes randomly, we can't draw meaningful conclusions.
-   - *Balanced use of third-party libraries*
-      - Third-party libraries should be leveraged when they are:
-           - API specific (google-api-python-client, internetarchive, etc.)
-   - File formats
-      - CSV: the format is well supported (rendered on GitHub, etc.), easy to use, and the data used by the project is simple enough to avoid any shortcomings.
-      - YAML: prioritizes human readability which addresses the primary costs and risks associated with configuration files.
 
 ## Code of conduct
 
@@ -74,6 +34,93 @@ See [`CONTRIBUTING.md`][org-contrib].
 [org-contrib]: https://github.com/creativecommons/.github/blob/main/CONTRIBUTING.md
 
 
+### The three phases of generating a report
+- **1-Fetch**: This phase involves collecting data from a particular source
+  using its API. Before writing any code, we plan the analyses we want to
+  perform by asking meaningful questions about the data. We also consider API
+  limitations (such as query limits) and design a query strategy to work within
+  these limitations. Then we write a python script that gets the data, it is
+  quite important to follow the format of the scripts existing in the project
+  and use the modules and functions where applicable. It ensures consistency in
+  the scripts and we can easily debug issues might arise.
+  - **Meaningful questions**
+    - The reports generated by this project (and the data fetched and processed
+      to support it) seeks to be meaningful. We hope this project will provide
+      data and analysis that helps inform discussions about the commons--the
+      collection of works that are openly licensed or in the public domain.
+
+      The goal of this project is to help answer questions like:
+      - How has the world's use of the commons changed over time?
+      - How is the knowledge and culture of the commons distributed?
+      - Who has access (and how much) to the commons?
+      - What significant trends can be observed in the commons?
+      - Which public domain dedication or licenses are the most popular?
+      - What are the correlations between public domain dedication or licenses
+        and region, language, domain/endeavor, etc.?
+  - **Limitations of an API**
+    - Some data sources provide APIs with query limits (it can be daily or
+      hourly) depending on what is given in the documentation. This restricts
+      how many requests that can be made in the specified period of time. It is
+      important to plan a query strategy and schedule fetch jobs to stay within
+      the allowed limits.
+  - **Headings of data in 1-fetch**
+    - [Tool identifier][tool-identifier]: A unique identifier used to
+      distinguish each Creative Commons legal tool within the dataset. This
+      helps ensure consistency when tracking tools across different data
+      sources.
+    - [SPDX identifier][spdx-identifier]: A standardized identifier maintained
+      by the Software Package Data Exchange (SPDX) project. It provides a
+      consistent way to reference licenses in applications.
+- **2-Process**: In this phase, the fetched data is transformed into a
+  structured and standardized format for analysis. The data is then analyzed
+  and categorized based on defined criteria to extract insights that answer the
+  meaningful questions identified during the 1-fetch phase.
+- **3-report**: This phase focuses on presenting the results of the analysis. We  generate graphs and summaries that clearly show trends, patterns, and
+  distributions in the data. These reports help communicate key insights about
+  the size, diversity, and characteristics of openly licensed and public-domain
+  works.
+
+[tool-identifier]: https://creativecommons.org/share-your-work/cclicenses/
+[spdx-identifier]: https://spdx.org/licenses/
+
+
+### Automation phases
+
+For automating these phases, the project uses Python scripts to fetch, process,
+and report data. GitHub Actions is used to automatically run these scripts on a
+defined schedule and on code updates. It handles script execution, manages
+dependencies, and ensures the workflow runs consistently.
+- **Script assumptions**
+  - Execution schedule for each quarter:
+    - 1-Fetch: first month, 1st half of second month
+    - 2-Process: 2nd half of second month
+    - 3-Report: third month
+- **Script requirements**
+  - *Must be safe*
+    - Scripts must not make any changes with default options
+    - Easiest way to run script should also be the safest
+    - Have options spelled out
+    - Must be timely
+  - *Scripts should complete within a maximum of 45 minutes*
+    - *Scripts shouldn't take longer than 3 minutes with default options*
+    - That way there’s a quicker way to see what is happening when it is
+      running; see execution, without errors, etc. Then later in production it
+      can be run with longer options
+  - *Must be idempotent*
+    - [Idempotence - Wikipedia](https://en.wikipedia.org/wiki/Idempotence)
+    - This applies to both the data fetched and the data stored. If the data
+      changes randomly, we can't draw meaningful conclusions.
+  - *Balanced use of third-party libraries*
+    - Third-party libraries should be leveraged when they are:
+      - API specific (google-api-python-client, internetarchive, etc.)
+- File formats
+  - CSV: the format is well supported (rendered on GitHub, etc.), easy to use,
+    and the data used by the project is simple enough to avoid any
+    shortcomings.
+  - YAML: prioritizes human readability which addresses the primary costs and
+    risks associated with configuration files.
+
+
 ### Project structure
 
 Please note that in the directory tree below, all instances of `fetch`,