Add Phase II proposal

2024-landscape.md

@@ -6,16 +6,16 @@ site:
 
 # 2024 Landscape Analysis
 
-Python is widely adopted in data science, and its use for statistics is expanding rapidly---particularly in education and applied research.
+Python is widely adopted in data science, and its use for statistics is expanding rapidly—particularly in education and applied research.
 The statistical ecosystem in Python is currently anchored by six major libraries:
 
 - [numpy](https://www.numpy.org/), which provides fast, flexible array and numerical operations, and underpins nearly all statistical and scientific computing in Python.
   It supports descriptive statistics, correlation and covariance computations, random sampling, and tools for constructing histograms and binning data.
 - [pandas](https://www.pandas.org/), which offers intuitive, high-performance data structures for tabular and time series data, making data cleaning, wrangling, reshaping, aggregation, and exploratory analysis straightforward and efficient.
-- [scipy](https://www.scipy.org/), which builds on NumPy to deliver a broad range of scientific and statistical functionality---including, in its [`scipy.stats`](https://docs.scipy.org/doc/scipy/reference/stats.html) submodule, a comprehensive suite of probability distributions, summary statistics, and basic statistical tests.
+- [scipy](https://www.scipy.org/), which builds on NumPy to deliver a broad range of scientific and statistical functionality—including, in its [`scipy.stats`](https://docs.scipy.org/doc/scipy/reference/stats.html) submodule, a comprehensive suite of probability distributions, summary statistics, and basic statistical tests.
   It also provides modules for clustering, optimization, interpolation, and signal processing.
 - [matplotlib](https://matplotlib.org/), the foundational plotting library in Python, which enables the creation of high-quality static, animated, and interactive visualizations, and serves as the basis for many higher-level plotting and statistical graphics libraries.
-- [statsmodels](https://www.statsmodels.org/), which offers tools for econometrics, classical statistics, and statistical modeling---including linear and generalized linear models, time series analysis, survival analysis, and hypothesis testing, with extensive support for model diagnostics and statistical inference.
+- [statsmodels](https://www.statsmodels.org/), which offers tools for econometrics, classical statistics, and statistical modeling—including linear and generalized linear models, time series analysis, survival analysis, and hypothesis testing, with extensive support for model diagnostics and statistical inference.
 - [scikit-learn](https://scikit-learn.org/), which is best known for machine learning but also supports statistical modeling, offering a consistent API for regression, classification, clustering, model evaluation, statistical preprocessing, and dimensionality reduction.
 
 These core libraries are generally well-tested, reliable, and uphold high software engineering standards, making them trusted foundations for research and application.

about.md

@@ -7,12 +7,8 @@ site:
 # About
 
 The Statistical Python project was launched with support from a [grant from the NSF](https://nsf.elsevierpure.com/en/projects/pose-phase-1-an-open-source-ecosystem-for-statistical-python), titled _"POSE: Phase I: An open-source ecosystem for statistical Python."_
-We are now completing Phase I, which has focused on scoping activities to inform the transition into a sustainable open-source ecosystem.
-During this phase, we conducted interviews with stakeholders across the statistical and scientific Python communities, engaged with related domain-specific OSEs to learn from their experiences, led group discussions at national and international conferences, and organized a workshop to gather input on community needs and technical priorities.
-
-## Audience / Target Groups
 
-We help:
+Our aim is to help:
 
 - **Educators** teach statistics using a comprehensive, free computational ecosystem with clear user interfaces and accessible learning materials.
 - **Researchers** produce reliable results through an extensive collection of well-engineered and tested computational libraries, featuring intuitive APIs and comprehensive documentation.
@@ -21,6 +17,13 @@ We help:
 
 We foster a sustainable ecosystem, aiming to attract statisticians who actively participate in developing the tools they use daily.
 
-## Landscape Analysis
+## Phase I
+
+We have now completed Phase I, which focused on scoping activities to inform the transition into a sustainable open-source ecosystem.
+During this phase, we conducted interviews with stakeholders across the statistical and scientific Python communities, engaged with related domain-specific OSEs to learn from their experiences, led group discussions at national and international conferences, and organized a workshop to gather input on community needs and technical priorities.
+
+You may read our [2024 Landscape Analysis](2024-landscape), a primary output of the Phase I activities.
+
+## Phase II
 
-Read our [2024 Landscape Analysis](2024-landscape), a primary output from our Phase I activities.
+The above activities led to the development of a [Phase II proposal](static/Statistical-Python-POSE-Phase-II.pdf) to implement the formation of the Statistical Python Open Source Ecosystem.

footer.md

@@ -14,7 +14,7 @@
 
 ::::{div}
 
-```{image} images/statistical-python-logo.svg
+```{image} static/images/statistical-python-logo.svg
 :width: 60px
 :align: left
 ```

myst.yml

@@ -16,9 +16,9 @@ site:
     - title: Packages
       url: /packages
   options:
-    favicon: images/favicon.ico
-    logo: images/statistical-python-logo.svg
+    favicon: static/images/favicon.ico
+    logo: static/images/statistical-python-logo.svg
     logo_text: Statistical Python
-    style: ./site.css
+    style: static/site.css
   parts:
     footer: footer.md