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@@ -160,8 +159,9 @@ <h1 class="text-white">NASA TOPS-T: Cloud Native Geospatial in R &amp; Python</h
     <ul class="list-unstyled mt-1">
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-1" aria-expanded="true">
- <span class="menu-text">R Tutorials</span></a>
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-1" aria-expanded="true"><i class="bi bi-mortarboard-fill" role="img">
+</i> 
+ <span class="menu-text">Tutorials in R</span></a>
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             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
@@ -169,15 +169,13 @@ <h1 class="text-white">NASA TOPS-T: Cloud Native Geospatial in R &amp; Python</h
       <ul id="quarto-sidebar-section-1" class="collapse list-unstyled sidebar-section depth1 show">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./contents/intro-R.html" class="sidebar-item-text sidebar-link"><i class="bi bi-play-btn" role="img">
-</i> 
- <span class="menu-text">Introduction (R)</span></a>
+  <a href="./tutorials/R/1-intro-R.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Introduction</span></a>
   </div>
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./contents/earthdata.html" class="sidebar-item-text sidebar-link"><i class="bi bi-rocket" role="img">
-</i> 
+  <a href="./tutorials/R/2-earthdata.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">NASA EarthData</span></a>
   </div>
 </li>
@@ -185,8 +183,9 @@ <h1 class="text-white">NASA TOPS-T: Cloud Native Geospatial in R &amp; Python</h
   </li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" aria-expanded="true">
- <span class="menu-text">Python Tutorials</span></a>
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" aria-expanded="true"><i class="bi bi-mortarboard-fill" role="img">
+</i> 
+ <span class="menu-text">Tutorials in Python</span></a>
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@@ -194,33 +193,33 @@ <h1 class="text-white">NASA TOPS-T: Cloud Native Geospatial in R &amp; Python</h
       <ul id="quarto-sidebar-section-2" class="collapse list-unstyled sidebar-section depth1 show">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="./contents/intro-python.html" class="sidebar-item-text sidebar-link"><i class="bi bi-play-btn" role="img">
-</i> 
- <span class="menu-text">Introduction (python)</span></a>
+  <a href="./tutorials/python/intro-python.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Introduction</span></a>
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- <span class="menu-text">Recipes</span></span>
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         <li class="sidebar-item sidebar-item-section">
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-            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="true">
- <span class="menu-text">Background</span></a>
-          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="true" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="true"><i class="bi bi-pc-display-horizontal" role="img">
+</i> 
+ <span class="menu-text">Platforms</span></a>
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="true" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
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-      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 show">  
+      <ul id="quarto-sidebar-section-3" class="collapse list-unstyled sidebar-section depth1 show">  
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   <div class="sidebar-item-container"> 
-  <a href="./contents/computing-environment.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Portable Environments</span></a>
+  <a href="./tutorials/computing-environment.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Computing Environments</span></a>
   </div>
 </li>
       </ul>
+  </li>
+        <li class="sidebar-item sidebar-item-section">
+      <span class="sidebar-item-text sidebar-link text-start">
+ <span class="menu-text">Recipes</span></span>
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     </ul>
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@@ -270,7 +269,7 @@ <h2 class="anchored" data-anchor-id="acknowledgements">Acknowledgements</h2>
 </div>
 
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+<script src="./assets/js/material-kit.js"></script>
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diff --git a/search.json b/search.json
index 8f58bc5..d8abb38 100644
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   {
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-    "title": "From NDVI to Environmental Justice",
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+    "title": "Exploring the Legacy of Redlining",
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-    "text": "# Examining Environmental Justice through Open Source, Cloud-Native Tools: Python\nThis executable notebook provides an opening example to illustrate a cloud-native workflow. Pedagogy research emphasizes the importance of “playing the whole game” before breaking down every pitch and hit. We intentionally focus on powerful high-level tools (STAC API, COGs, datacubes) to illustrate how a few chunks of code can perform a task that would be far slower and more verbose in a traditional file-based, download-first workflow. Note the close parallels between R and Python syntax. This arises because both languages wrap the same underlying tools (the STAC API and GDAL warper) and handle many of the nuisances of spatial data – from re-projections and resampling to mosaic tiles – without us noticing.\n\nfrom pystac_client import Client\nimport odc.stac\nimport pystac_client\nimport rioxarray\nimport geopandas as gpd\nfrom rasterstats import zonal_stats \n\n# suppress warning messages\nimport warnings\nwarnings.filterwarnings('ignore')\n\n\nThe first step in many workflows involves discovering individual spatial data files covering the space, time, and variables of interest. Here we use a STAC Catalog API to recover a list of candidate data. We dig deeper into how this works and what it returns in later recipes. This example searches for images in a lon-lat bounding box from a collection of Cloud-Optimized-GeoTIFF (COG) images taken by Sentinel2 satellite mission. This function will not download any imagery, it merely gives us a list of metadata about available images, including the access URLs.\n\nbox = [-122.51, 37.71, -122.36, 37.81]\nitems = (\n  Client.\n  open(\"https://earth-search.aws.element84.com/v1\").\n  search(\n    collections = ['sentinel-2-l2a'],\n    bbox = box,\n    datetime = \"2022-06-01/2022-08-01\",\n    query={\"eo:cloud_cover\": {\"lt\": 20}}).\n  item_collection()\n)\n\nWe pass this list of images to a high-level utilty (gdalcubes in R, odc.stac in python) that will do all of the heavy lifting. Using the URLs and metadata provided by STAC, these functions can extract only our data of interest (given by the bounding box) without downloading unnecessary regions or bands. While streaming the data, these functions will also reproject it into the desired coordinate reference system – (an often costly operation to perform in R) and can potentially resample or aggregate the data to a desired spatial resolution. (The R code will also resample from images in overlapping areas to replace pixels masked by clouds)\n\ndata = odc.stac.load(\n    items,\n    crs=\"EPSG:32610\",\n    bands=[\"nir08\", \"red\"],\n    bbox=box\n)\n# For some reason, requesting reprojection in EPSG:4326 gives only empty values\n\nWe can do arbitrary calculations on this data as well. Here we calculate NDVI, a widely used measure of greenness that can be used to determine vegetation cover. (Note that the R example uses lazy evaluation, and can thus perform these calculations while streaming)\n\nndvi = (((data.nir08 - data.red) / (data.red + data.nir08)).\n        resample(time=\"MS\").\n        median(\"time\", keep_attrs=True).\n        compute()\n)\n\n# mask out bad pixels\nndvi = ndvi.where(ndvi &lt;= 1)\n\nAnd we plot the result. The long rectangle of Golden Gate Park is clearly visible in the North-West.\n\nimport matplotlib as plt\ncmap = plt.colormaps.get_cmap('viridis')  # viridis is the default colormap for imshow\ncmap.set_bad(color='black')\n\nndvi.plot.imshow(row=\"time\", cmap=cmap, add_colorbar=False, size=4)\n\n&lt;xarray.plot.facetgrid.FacetGrid at 0x7fa18b685fc0&gt;\n\n\n\n\n\n\n\nWe examine the present-day impact of historic “red-lining” of US cities during the Great Depression using data from the Mapping Inequality project. All though this racist practice was banned by federal law under the Fair Housing Act of 1968, the systemic scars of that practice are still so deeply etched on our landscape that the remain visible from space – “red-lined” areas (graded “D” under the racist HOLC scheme) show systematically lower greenness than predominately-white neighborhoods (Grade “A”). Trees provide many benefits, from mitigating urban heat to biodiversity, real-estate value, to health.\n\n\nIn addition to large scale raster data such as satellite imagery, the analysis of vector shapes such as polygons showing administrative regions is a central component of spatial analysis, and particularly important to spatial social sciences. The red-lined areas of the 1930s are one example of spatial vectors. One common operation is to summarise the values of all pixels falling within a given polygon, e.g. computing the average greenness (NDVI)\n\n# make sure we are in the same projection.\n# zonal_stats method understands paths but not xarray\n(   ndvi.\n    rio.reproject(\"EPSG:4326\").\n    rio.to_raster(raster_path=\"ndvi.tif\", driver=\"COG\")   \n)\n\nsf_url = \"/vsicurl/https://dsl.richmond.edu/panorama/redlining/static/citiesData/CASanFrancisco1937/geojson.json\"\nmean_ndvi = zonal_stats(sf_url, \"ndvi.tif\", stats=\"mean\")\n\nWe plot the underlying NDVI as well as the average NDVI of each polygon, along with it’s textual grade. Note that “A” grades tend to be darkest green (high NDVI) while “D” grades are frequently the least green. (Regions not zoned for housing at the time of the 1937 housing assessment are not displayed as polygons.)\n\nsf = gpd.read_file(sf_url)\nsf[\"ndvi\"] = [x[\"mean\"] for x in mean_ndvi]\nsf.plot(column=\"ndvi\", legend=True)\n\n&lt;Axes: &gt;\n\n\n\n\n\nAre historically redlined areas still less green?\n\nimport geopolars as gpl\nimport polars as pl\n\n(gpl.\n  from_geopandas(sf).\n  group_by(\"grade\").\n  agg(pl.col(\"ndvi\").mean()).\n  sort(\"grade\")\n)\n\n\nshape: (5, 2)\n\n\n\ngrade\nndvi\n\n\nstr\nf64\n\n\n\n\nnull\n0.159305\n\n\n\"A\"\n0.337976\n\n\n\"B\"\n0.247661\n\n\n\"C\"\n0.236407\n\n\n\"D\"\n0.232125"
+    "text": "This executable notebook provides an opening example to illustrate a cloud-native workflow. Pedagogy research emphasizes the importance of “playing the whole game” before breaking down every pitch and hit. We intentionally focus on powerful high-level tools (STAC API, COGs, datacubes) to illustrate how a few chunks of code can perform a task that would be far slower and more verbose in a traditional file-based, download-first workflow. Note the close parallels between R and Python syntax. This arises because both languages wrap the same underlying tools (the STAC API and GDAL warper) and handle many of the nuisances of spatial data – from re-projections and resampling to mosaic tiles – without us noticing.\n\nfrom pystac_client import Client\nimport odc.stac\nimport pystac_client\nimport rioxarray\nimport geopandas as gpd\nfrom rasterstats import zonal_stats \n\n# suppress warning messages\nimport warnings\nwarnings.filterwarnings('ignore')\n\n\n\n\nThe first step in many workflows involves discovering individual spatial data files covering the space, time, and variables of interest. Here we use a STAC Catalog API to recover a list of candidate data. We dig deeper into how this works and what it returns in later recipes. This example searches for images in a lon-lat bounding box from a collection of Cloud-Optimized-GeoTIFF (COG) images taken by Sentinel2 satellite mission. 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(The R code will also resample from images in overlapping areas to replace pixels masked by clouds)\n\ndata = odc.stac.load(\n    items,\n    crs=\"EPSG:32610\",\n    bands=[\"nir08\", \"red\"],\n    bbox=box\n)\n# For some reason, requesting reprojection in EPSG:4326 gives only empty values\n\nWe can do arbitrary calculations on this data as well. Here we calculate NDVI, a widely used measure of greenness that can be used to determine vegetation cover. (Note that the R example uses lazy evaluation, and can thus perform these calculations while streaming)\n\nndvi = (((data.nir08 - data.red) / (data.red + data.nir08)).\n        resample(time=\"MS\").\n        median(\"time\", keep_attrs=True).\n        compute()\n)\n\n# mask out bad pixels\nndvi = ndvi.where(ndvi &lt;= 1)\n\nAnd we plot the result. The long rectangle of Golden Gate Park is clearly visible in the North-West.\n\nimport matplotlib as plt\ncmap = plt.colormaps.get_cmap('viridis')  # viridis is the default colormap for imshow\ncmap.set_bad(color='black')\n\nndvi.plot.imshow(row=\"time\", cmap=cmap, add_colorbar=False, size=4)\n\n&lt;xarray.plot.facetgrid.FacetGrid at 0x7fa18b685fc0&gt;\n\n\n\n\n\n\n\n\n\nWe examine the present-day impact of historic “red-lining” of US cities during the Great Depression using data from the Mapping Inequality project. All though this racist practice was banned by federal law under the Fair Housing Act of 1968, the systemic scars of that practice are still so deeply etched on our landscape that the remain visible from space – “red-lined” areas (graded “D” under the racist HOLC scheme) show systematically lower greenness than predominately-white neighborhoods (Grade “A”). Trees provide many benefits, from mitigating urban heat to biodiversity, real-estate value, to health.\n\n\nIn addition to large scale raster data such as satellite imagery, the analysis of vector shapes such as polygons showing administrative regions is a central component of spatial analysis, and particularly important to spatial social sciences. The red-lined areas of the 1930s are one example of spatial vectors. One common operation is to summarise the values of all pixels falling within a given polygon, e.g. computing the average greenness (NDVI)\n\n# make sure we are in the same projection.\n# zonal_stats method understands paths but not xarray\n(   ndvi.\n    rio.reproject(\"EPSG:4326\").\n    rio.to_raster(raster_path=\"ndvi.tif\", driver=\"COG\")   \n)\n\nsf_url = \"/vsicurl/https://dsl.richmond.edu/panorama/redlining/static/citiesData/CASanFrancisco1937/geojson.json\"\nmean_ndvi = zonal_stats(sf_url, \"ndvi.tif\", stats=\"mean\")\n\nWe plot the underlying NDVI as well as the average NDVI of each polygon, along with it’s textual grade. Note that “A” grades tend to be darkest green (high NDVI) while “D” grades are frequently the least green. (Regions not zoned for housing at the time of the 1937 housing assessment are not displayed as polygons.)\n\nsf = gpd.read_file(sf_url)\nsf[\"ndvi\"] = [x[\"mean\"] for x in mean_ndvi]\nsf.plot(column=\"ndvi\", legend=True)\n\n&lt;Axes: &gt;\n\n\n\n\n\nAre historically redlined areas still less green?\n\nimport geopolars as gpl\nimport polars as pl\n\n(gpl.\n  from_geopandas(sf).\n  group_by(\"grade\").\n  agg(pl.col(\"ndvi\").mean()).\n  sort(\"grade\")\n)\n\n\nshape: (5, 2)\n\n\n\ngrade\nndvi\n\n\nstr\nf64\n\n\n\n\nnull\n0.159305\n\n\n\"A\"\n0.337976\n\n\n\"B\"\n0.247661\n\n\n\"C\"\n0.236407\n\n\n\"D\"\n0.232125"
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+    "text": "The first step in many workflows involves discovering individual spatial data files covering the space, time, and variables of interest. Here we use a STAC Catalog API to recover a list of candidate data. We dig deeper into how this works and what it returns in later recipes. This example searches for images in a lon-lat bounding box from a collection of Cloud-Optimized-GeoTIFF (COG) images taken by Sentinel2 satellite mission. This function will not download any imagery, it merely gives us a list of metadata about available images, including the access URLs.\n\nbox = [-122.51, 37.71, -122.36, 37.81]\nitems = (\n  Client.\n  open(\"https://earth-search.aws.element84.com/v1\").\n  search(\n    collections = ['sentinel-2-l2a'],\n    bbox = box,\n    datetime = \"2022-06-01/2022-08-01\",\n    query={\"eo:cloud_cover\": {\"lt\": 20}}).\n  item_collection()\n)\n\nWe pass this list of images to a high-level utilty (gdalcubes in R, odc.stac in python) that will do all of the heavy lifting. Using the URLs and metadata provided by STAC, these functions can extract only our data of interest (given by the bounding box) without downloading unnecessary regions or bands. While streaming the data, these functions will also reproject it into the desired coordinate reference system – (an often costly operation to perform in R) and can potentially resample or aggregate the data to a desired spatial resolution. (The R code will also resample from images in overlapping areas to replace pixels masked by clouds)\n\ndata = odc.stac.load(\n    items,\n    crs=\"EPSG:32610\",\n    bands=[\"nir08\", \"red\"],\n    bbox=box\n)\n# For some reason, requesting reprojection in EPSG:4326 gives only empty values\n\nWe can do arbitrary calculations on this data as well. Here we calculate NDVI, a widely used measure of greenness that can be used to determine vegetation cover. (Note that the R example uses lazy evaluation, and can thus perform these calculations while streaming)\n\nndvi = (((data.nir08 - data.red) / (data.red + data.nir08)).\n        resample(time=\"MS\").\n        median(\"time\", keep_attrs=True).\n        compute()\n)\n\n# mask out bad pixels\nndvi = ndvi.where(ndvi &lt;= 1)\n\nAnd we plot the result. The long rectangle of Golden Gate Park is clearly visible in the North-West.\n\nimport matplotlib as plt\ncmap = plt.colormaps.get_cmap('viridis')  # viridis is the default colormap for imshow\ncmap.set_bad(color='black')\n\nndvi.plot.imshow(row=\"time\", cmap=cmap, add_colorbar=False, size=4)\n\n&lt;xarray.plot.facetgrid.FacetGrid at 0x7fa18b685fc0&gt;"
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     "text": "In addition to large scale raster data such as satellite imagery, the analysis of vector shapes such as polygons showing administrative regions is a central component of spatial analysis, and particularly important to spatial social sciences. The red-lined areas of the 1930s are one example of spatial vectors. One common operation is to summarise the values of all pixels falling within a given polygon, e.g. computing the average greenness (NDVI)\n\n# make sure we are in the same projection.\n# zonal_stats method understands paths but not xarray\n(   ndvi.\n    rio.reproject(\"EPSG:4326\").\n    rio.to_raster(raster_path=\"ndvi.tif\", driver=\"COG\")   \n)\n\nsf_url = \"/vsicurl/https://dsl.richmond.edu/panorama/redlining/static/citiesData/CASanFrancisco1937/geojson.json\"\nmean_ndvi = zonal_stats(sf_url, \"ndvi.tif\", stats=\"mean\")\n\nWe plot the underlying NDVI as well as the average NDVI of each polygon, along with it’s textual grade. Note that “A” grades tend to be darkest green (high NDVI) while “D” grades are frequently the least green. (Regions not zoned for housing at the time of the 1937 housing assessment are not displayed as polygons.)\n\nsf = gpd.read_file(sf_url)\nsf[\"ndvi\"] = [x[\"mean\"] for x in mean_ndvi]\nsf.plot(column=\"ndvi\", legend=True)\n\n&lt;Axes: &gt;\n\n\n\n\n\nAre historically redlined areas still less green?\n\nimport geopolars as gpl\nimport polars as pl\n\n(gpl.\n  from_geopandas(sf).\n  group_by(\"grade\").\n  agg(pl.col(\"ndvi\").mean()).\n  sort(\"grade\")\n)\n\n\nshape: (5, 2)\n\n\n\ngrade\nndvi\n\n\nstr\nf64\n\n\n\n\nnull\n0.159305\n\n\n\"A\"\n0.337976\n\n\n\"B\"\n0.247661\n\n\n\"C\"\n0.236407\n\n\n\"D\"\n0.232125"
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+    "text": "The NASA EarthData program provides access to an extensive collection of spatial data products from each of its 12 Distributed Active Archive Centers (‘DAACs’) on the high-performance S3 storage system of Amazon Web Services (AWS). We can take advantage of range requests with NASA EarthData URLs, but unlike the previous examples, NASA requires an authentication step. NASA offers several different mechanisms, including netrc authentication, token-based authentication, and S3 credentials, but only the first of these works equally well from locations both inside and outside of AWS-based compute, so there really is very little reason to learn the other two.\nThe earthdatalogin package in R or the earthaccess package in Python handle the authentication. The R package sets up authentication behind the scenes using environmental variables.\n\nearthdatalogin::edl_netrc()\n\n(A default login is supplied though users are encouraged to register for their own individual accounts.) Once this is in place, EarthData’s protected URLs can be used like any other:\n\nterra::rast(\"https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-protected/HLSL30.020/HLS.L30.T56JKT.2023246T235950.v2.0/HLS.L30.T56JKT.2023246T235950.v2.0.SAA.tif\",\n     vsi=TRUE)\n\nclass       : SpatRaster \ndimensions  : 3660, 3660, 1  (nrow, ncol, nlyr)\nresolution  : 30, 30  (x, y)\nextent      : 199980, 309780, 7190200, 7300000  (xmin, xmax, ymin, ymax)\ncoord. ref. : WGS 84 / UTM zone 56N (EPSG:32656) \nsource      : HLS.L30.T56JKT.2023246T235950.v2.0.SAA.tif \nname        : HLS.L30.T56JKT.2023246T235950.v2.0.SAA"
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+    "text": "This project seeks to introduce cloud-native approaches to geospatial analysis in R & Python through the lens of environmental justice applications. This is not meant as a complete course in geospatial analysis – though we encourage interested readers to consider Geocomputation in R or Python as an excellent resource. We present opinionated recipes meant to empower users with the following design goals:\n\nOpen science: open source software, open data, open standards, and reproducibility are emphasized.\nRecipes are presented as reproducible computational notebooks (Quarto and Jupyter) set in narrative analysis.\nAccess to cloud-based compute is not required. The high speed networks of commercial cloud providers can accelerate network-bound operations, but can run from anywhere.\nAccess to a powerful laptop or desktop is not required either. Examples can run from a browser interface on free compute resources such as Codespaces.\nPortable software environments: a Docker container with precise software used to produce these examples. The container is build from the included Dockerfile using the included GitHub Action and is deployed by the Codespaces instance.\nAlignment between R and Python: recipes will try to show approaches in both languages and seek to utilize common interfaces (such as STAC and GDAL) that can be shared across these platforms.\n\nThis is a work in progress! Check back often, and feedback welcome! Test these modules, file an issue or pull request, launch into a Codespaces environment, or reach out on the discussion board.\nWhat is “cloud-native” anyway? We define cloud-native to mean simply that data is accessed over http range request methods, rather than downloading entire files. Code-based examples will develop why this is important and how it differs from renting cloud-based compute. The core philosophy is that what many users already know how to do locally translates pretty seamlessly here, and then a bit extra is required to coerce certain software to stay in ‘range request’ mode and not get greedy trying to download everything. Some authors define this concept somewhat differently, and certainly not all range requests give the same performance, nor are http range requests best in all cases.\n\n\nThanks to input, suggestions, feedback and ideas from so many amazing folks in the OpenScapes community and input and financial support from the NASA TOPS community."
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     "text": "This executable notebook provides an opening example to illustrate a cloud-native workflow in both R and python. Pedagogy research emphasizes the importance of “playing the whole game” before breaking down every pitch and hit. We intentionally focus on powerful high-level tools (STAC API, COGs, datacubes) to illustrate how a few chunks of code can perform a task that would be far slower and more verbose in a traditional file-based, download-first workflow. Note the close parallels between R and Python syntax. 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While streaming the data, these functions will also reproject it into the desired coordinate reference system – (an often costly operation to perform in R) and can potentially resample or aggregate the data to a desired spatial resolution. (The R code will also resample from images in overlapping areas to replace pixels masked by clouds)\n\ncol &lt;- stac_image_collection(items$features, asset_names = c(\"B08\", \"B04\", \"SCL\"))\n\ncube &lt;- cube_view(srs =\"EPSG:4326\",\n                  extent = list(t0 = start_date, t1 = end_date,\n                                left = box[1], right = box[3],\n                                top = box[4], bottom = box[2]),\n                  dx = 0.0001, dy = 0.0001, dt = \"P1D\",\n                  aggregation = \"median\", resampling = \"average\")\n\nmask &lt;- image_mask(\"SCL\", values=c(3, 8, 9)) # mask clouds and cloud shadows\n\ndata &lt;-  raster_cube(col, cube, mask = mask)\n\nWe can do arbitrary calculations on this data as well. 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All though this racist practice was banned by federal law under the Fair Housing Act of 1968, the systemic scars of that practice are still so deeply etched on our landscape that the remain visible from space – “red-lined” areas (graded “D” under the racist HOLC scheme) show systematically lower greenness than predominately-white neighborhoods (Grade “A”). Trees provide many benefits, from mitigating urban heat to biodiversity, real-estate value, to health.\n\n\nIn addition to large scale raster data such as satellite imagery, the analysis of vector shapes such as polygons showing administrative regions is a central component of spatial analysis, and particularly important to spatial social sciences. The red-lined areas of the 1930s are one example of spatial vectors. One common operation is to summarise the values of all pixels falling within a given polygon, e.g. computing the average greenness (NDVI)\n\nsf &lt;- \"/vsicurl/https://dsl.richmond.edu/panorama/redlining/static/citiesData/CASanFrancisco1937/geojson.json\" |&gt;\n  st_read() |&gt;\n  st_make_valid() |&gt;\n  select(-label_coords)\n\n\npoly &lt;- ndvi |&gt; extract_geom(sf, FUN = mean, reduce_time = TRUE)\nsf$NDVI &lt;- poly$NDVI\n\nWe plot the underlying NDVI as well as the average NDVI of each polygon, along with it’s textual grade, using tmap. Note that “A” grades tend to be darkest green (high NDVI) while “D” grades are frequently the least green. 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     "text": "The first step in many workflows involves discovering individual spatial data files covering the space, time, and variables of interest. Here we use a STAC Catalog API to recover a list of candidate data. We dig deeper into how this works and what it returns in later recipes. This example searches for images in a lon-lat bounding box from a collection of Cloud-Optimized-GeoTIFF (COG) images taken by Sentinel2 satellite mission. This function will not download any imagery, it merely gives us a list of metadata about available images, including the access URLs.\n\nbox &lt;- c(xmin=-122.51, ymin=37.71, xmax=-122.36, ymax=37.81) \nstart_date &lt;- \"2022-06-01\"\nend_date &lt;- \"2022-08-01\"\nitems &lt;-\n  stac(\"https://earth-search.aws.element84.com/v0/\") |&gt;\n  stac_search(collections = \"sentinel-s2-l2a-cogs\",\n              bbox = box,\n              datetime = paste(start_date, end_date, sep=\"/\"),\n              limit = 100) |&gt;\n  ext_query(\"eo:cloud_cover\" &lt; 20) |&gt;\n  post_request()\n\nWe pass this list of images to a high-level utilty (gdalcubes in R, odc.stac in python) that will do all of the heavy lifting. Using the URLs and metadata provided by STAC, these functions can extract only our data of interest (given by the bounding box) without downloading unnecessary regions or bands. While streaming the data, these functions will also reproject it into the desired coordinate reference system – (an often costly operation to perform in R) and can potentially resample or aggregate the data to a desired spatial resolution. (The R code will also resample from images in overlapping areas to replace pixels masked by clouds)\n\ncol &lt;- stac_image_collection(items$features, asset_names = c(\"B08\", \"B04\", \"SCL\"))\n\ncube &lt;- cube_view(srs =\"EPSG:4326\",\n                  extent = list(t0 = start_date, t1 = end_date,\n                                left = box[1], right = box[3],\n                                top = box[4], bottom = box[2]),\n                  dx = 0.0001, dy = 0.0001, dt = \"P1D\",\n                  aggregation = \"median\", resampling = \"average\")\n\nmask &lt;- image_mask(\"SCL\", values=c(3, 8, 9)) # mask clouds and cloud shadows\n\ndata &lt;-  raster_cube(col, cube, mask = mask)\n\nWe can do arbitrary calculations on this data as well. Here we calculate NDVI, a widely used measure of greenness that can be used to determine tree cover. (Note that the R example uses lazy evaluation, and can thus perform these calculations while streaming)\n\nndvi &lt;- data |&gt;\n  select_bands(c(\"B04\", \"B08\")) |&gt;\n  apply_pixel(\"(B08-B04)/(B08+B04)\", \"NDVI\") |&gt;\n  reduce_time(c(\"mean(NDVI)\"))\n\nndvi_stars &lt;- st_as_stars(ndvi)\n\nAnd we plot the result. The long rectangle of Golden Gate Park is clearly visible in the North-West.\n\nmako &lt;- tm_scale_continuous(values = viridisLite::mako(30))\nfill &lt;- tm_scale_continuous(values = \"Greens\")\n\ntm_shape(ndvi_stars) + tm_raster(col.scale = mako)"
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     "text": "Good reproducibility is like an onion – it comes in many layers. There’s a purpose to containerized environments deployed on cloud-hosted virtual machines. And we believe students should be able to leverage those things, easily and rapidly deploying cloud-hosted images, and will get to that here. But most of the time, we just want to copy-paste a few lines of code and expect it to work. Many layers of the onion can be found between these two extremes – from package dependencies and system dependencies to containers, orchestration, metadata, even hardware requirements.\nIn the examples here, copy-pasting the code blocks into your preferred environment should work in most cases. Sometimes it may be necessary to install specific libraries or specific versions of those libraries. And for fastest setup and maximum reproducibility, users can deploy the fully containerized environment. 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     "text": "We can sidesteps elements specific to the VSCode editor defined in the devcontainer.json configuration while still leveraging the same system libraries and pre-built packages. For example, a user could also choose to run (or extend) the underlying docker container independently, e.g.\ndocker run --rm -ti ghcr.io/boettiger-lab/nasa-tops:latest bash\nwhich opens a bash terminal inside the container. This approach is also compatible with most HPC setups using singularity instead of docker.\nSome users may not be familiar with editing and running code entirely from a bash shell, so the container also includes RStudio server and thus can be run to launch RStudio in an open port instead,\ndocker run -d -p 8787:8787 --user root -e DISABLE_AUTH=true \\\n  ghcr.io/boettiger-lab/nasa-tops:latest\nand visit http://localhost:8787 to connect."
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-    "text": "This project seeks to introduce cloud-native approaches to geospatial analysis in R & Python through the lens of environmental justice applications. This is not meant as a complete course in geospatial analysis – though we encourage interested readers to consider Geocomputation in R or Python as an excellent resource. We present opinionated recipes meant to empower users with the following design goals:\n\nOpen science: open source software, open data, open standards, and reproducibility are emphasized.\nRecipes are presented as reproducible computational notebooks (Quarto and Jupyter) set in narrative analysis.\nAccess to cloud-based compute is not required. The high speed networks of commercial cloud providers can accelerate network-bound operations, but can run from anywhere.\nAccess to a powerful laptop or desktop is not required either. Examples can run from a browser interface on free compute resources such as Codespaces.\nPortable software environments: a Docker container with precise software used to produce these examples. The container is build from the included Dockerfile using the included GitHub Action and is deployed by the Codespaces instance.\nAlignment between R and Python: recipes will try to show approaches in both languages and seek to utilize common interfaces (such as STAC and GDAL) that can be shared across these platforms.\n\nThis is a work in progress! Check back often, and feedback welcome! Test these modules, file an issue or pull request, launch into a Codespaces environment, or reach out on the discussion board.\nWhat is “cloud-native” anyway? We define cloud-native to mean simply that data is accessed over http range request methods, rather than downloading entire files. Code-based examples will develop why this is important and how it differs from renting cloud-based compute. The core philosophy is that what many users already know how to do locally translates pretty seamlessly here, and then a bit extra is required to coerce certain software to stay in ‘range request’ mode and not get greedy trying to download everything. Some authors define this concept somewhat differently, and certainly not all range requests give the same performance, nor are http range requests best in all cases.\n\n\nThanks to input, suggestions, feedback and ideas from so many amazing folks in the OpenScapes community and input and financial support from the NASA TOPS community."
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-    "href": "index.html#acknowledgements",
-    "title": "",
-    "section": "",
-    "text": "Thanks to input, suggestions, feedback and ideas from so many amazing folks in the OpenScapes community and input and financial support from the NASA TOPS community."
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\ No newline at end of file
diff --git a/sitemap.xml b/sitemap.xml
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-    <lastmod>2024-02-09T17:41:33.376Z</lastmod>
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-    <lastmod>2024-02-09T17:41:32.672Z</lastmod>
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-    <lastmod>2024-02-09T17:41:32.112Z</lastmod>
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+    <lastmod>2024-02-09T21:59:42.087Z</lastmod>
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+    <loc>https://github.com/boettiger-lab/nasa-topst-env-justice/tutorials/computing-environment.html</loc>
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similarity index 91%
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+++ b/tutorials/R/1-intro-R.html
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+<script src="../../site_libs/quarto-html/popper.min.js"></script>
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   <meta name="generator" content="pandoc">
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-    <a class="nav-link" href="../index.html" rel="" target=""><i class="bi bi-house" role="img">
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@@ -170,7 +170,7 @@
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+      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../../tutorials/R/1-intro-R.html">Tutorials in R</a></li><li class="breadcrumb-item"><a href="../../tutorials/R/1-intro-R.html">Introduction</a></li></ol></nav>
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 <!-- content -->
 <header id="title-block-header" class="bg-gradient-dark">
-<div class="page-header min-vh-45" style="background-image: url(../assets/img/blue-marble.jpg);">
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+<p class="lead mb-4 text-white opacity-8">Cloud-Native Data in R</p>
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+</i> 
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+ <span class="menu-text">Introduction</span></a>
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+  <a href="../../tutorials/R/2-earthdata.html" class="sidebar-item-text sidebar-link">
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@@ -220,8 +219,9 @@ <h1 class="text-white">Introduction</h1>
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+</i> 
+ <span class="menu-text">Tutorials in Python</span></a>
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+ <span class="menu-text">Computing Environments</span></a>
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 <div class="cell-output-display">
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 </div>
 </div>
 <p>Are historically redlined areas still less green?</p>
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-      <a href="../contents/earthdata.html" class="pagination-link">
+      <a href="../../tutorials/R/2-earthdata.html" class="pagination-link">
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similarity index 100%
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similarity index 86%
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rename to tutorials/R/2-earthdata.html
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+<link href="../../site_libs/bootstrap/bootstrap-icons.css" rel="stylesheet">
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   <meta name="generator" content="pandoc">
   <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
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+    <a class="nav-link" href="../../index.html" rel="" target=""><i class="bi bi-house" role="img">
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+      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../../tutorials/R/1-intro-R.html">Tutorials in R</a></li><li class="breadcrumb-item"><a href="../../tutorials/R/2-earthdata.html">NASA EarthData</a></li></ol></nav>
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@@ -179,7 +178,7 @@
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 <!-- content -->
 <header id="title-block-header" class="bg-gradient-dark">
-<div class="page-header min-vh-45" style="background-image: url(../assets/img/blue-marble.jpg);">
+<div class="page-header min-vh-45" style="background-image: url(../../assets/img/blue-marble.jpg);">
 <span class="mask bg-gradient-dark opacity-3"></span>
 <div class="container">
 <div class="row justify-content-center">
@@ -196,8 +195,9 @@ <h1 class="text-white">NASA EarthData</h1>
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- <span class="menu-text">R Tutorials</span></a>
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+</i> 
+ <span class="menu-text">Tutorials in R</span></a>
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@@ -205,15 +205,13 @@ <h1 class="text-white">NASA EarthData</h1>
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- <span class="menu-text">Introduction (R)</span></a>
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+ <span class="menu-text">Introduction</span></a>
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+  <a href="../../tutorials/R/2-earthdata.html" class="sidebar-item-text sidebar-link active">
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-</i> 
- <span class="menu-text">Introduction (python)</span></a>
+  <a href="../../tutorials/python/intro-python.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Introduction</span></a>
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- <span class="menu-text">Background</span></a>
-          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="true" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="true"><i class="bi bi-pc-display-horizontal" role="img">
+</i> 
+ <span class="menu-text">Platforms</span></a>
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="true" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 show">  
+      <ul id="quarto-sidebar-section-3" class="collapse list-unstyled sidebar-section depth1 show">  
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   <div class="sidebar-item-container"> 
-  <a href="../contents/computing-environment.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Portable Environments</span></a>
+  <a href="../../tutorials/computing-environment.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Computing Environments</span></a>
   </div>
 </li>
       </ul>
+  </li>
+        <li class="sidebar-item sidebar-item-section">
+      <span class="sidebar-item-text sidebar-link text-start">
+ <span class="menu-text">Recipes</span></span>
   </li>
     </ul>
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@@ -291,7 +290,7 @@ <h1 class="text-white">NASA EarthData</h1>
 dimensions  : 3660, 3660, 1  (nrow, ncol, nlyr)
 resolution  : 30, 30  (x, y)
 extent      : 199980, 309780, 7190200, 7300000  (xmin, xmax, ymin, ymax)
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+coord. ref. : WGS 84 / UTM zone 56N (EPSG:32656) 
 source      : HLS.L30.T56JKT.2023246T235950.v2.0.SAA.tif 
 name        : HLS.L30.T56JKT.2023246T235950.v2.0.SAA </code></pre>
 </div>
@@ -305,7 +304,7 @@ <h1 class="text-white">NASA EarthData</h1>
 </div>
 
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-<script src="assets/js/material-kit.js"></script>
+<script src="../../assets/js/material-kit.js"></script>
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 <script id="quarto-html-after-body" type="application/javascript">
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@@ -542,13 +541,13 @@ <h1 class="text-white">NASA EarthData</h1>
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   <div class="nav-page nav-page-previous">
-      <a href="../contents/intro-R.html" class="pagination-link">
-        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">Introduction (R)</span>
+      <a href="../../tutorials/R/1-intro-R.html" class="pagination-link">
+        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">Introduction</span>
       </a>          
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-      <a href="../contents/intro-python.html" class="pagination-link">
-        <span class="nav-page-text">Introduction (python)</span> <i class="bi bi-arrow-right-short"></i>
+      <a href="../../tutorials/python/intro-python.html" class="pagination-link">
+        <span class="nav-page-text">Introduction</span> <i class="bi bi-arrow-right-short"></i>
       </a>
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@@ -560,7 +559,7 @@ <h1 class="text-white">NASA EarthData</h1>
     </div>   
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       &nbsp;
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+    <div class="toc-actions"><div><i class="bi bi-github"></i></div><div class="action-links"><p><a href="https://github.com/boettiger-lab/nasa-topst-env-justice/edit/main/tutorials/R/2-earthdata.qmd" class="toc-action">Edit this page</a></p><p><a href="https://github.com/boettiger-lab/nasa-topst-env-justice/issues/new" class="toc-action">Report an issue</a></p></div></div></div>
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diff --git a/contents/computing-environment.html b/tutorials/computing-environment.html
similarity index 94%
rename from contents/computing-environment.html
rename to tutorials/computing-environment.html
index 66ef182..5ba6976 100644
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+<link href="../tutorials/python/intro-python.html" rel="prev">
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 <script src="../site_libs/quarto-html/popper.min.js"></script>
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+      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../tutorials/computing-environment.html">Platforms</a></li><li class="breadcrumb-item"><a href="../tutorials/computing-environment.html">Computing Environments</a></li></ol></nav>
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- <span class="menu-text">R Tutorials</span></a>
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-1" aria-expanded="true"><i class="bi bi-mortarboard-fill" role="img">
+</i> 
+ <span class="menu-text">Tutorials in R</span></a>
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-</i> 
- <span class="menu-text">Introduction (R)</span></a>
+  <a href="../tutorials/R/1-intro-R.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Introduction</span></a>
   </div>
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../contents/earthdata.html" class="sidebar-item-text sidebar-link"><i class="bi bi-rocket" role="img">
-</i> 
+  <a href="../tutorials/R/2-earthdata.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">NASA EarthData</span></a>
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 </li>
@@ -220,8 +218,9 @@ <h1 class="text-white">Computing Environments</h1>
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       <div class="sidebar-item-container"> 
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- <span class="menu-text">Python Tutorials</span></a>
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" aria-expanded="true"><i class="bi bi-mortarboard-fill" role="img">
+</i> 
+ <span class="menu-text">Tutorials in Python</span></a>
           <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" aria-expanded="true" aria-label="Toggle section">
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@@ -229,33 +228,33 @@ <h1 class="text-white">Computing Environments</h1>
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   <div class="sidebar-item-container"> 
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-</i> 
- <span class="menu-text">Introduction (python)</span></a>
+  <a href="../tutorials/python/intro-python.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Introduction</span></a>
   </div>
 </li>
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-  </li>
-        <li class="sidebar-item sidebar-item-section">
-      <span class="sidebar-item-text sidebar-link text-start">
- <span class="menu-text">Recipes</span></span>
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         <li class="sidebar-item sidebar-item-section">
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-            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="true">
- <span class="menu-text">Background</span></a>
-          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="true" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="true"><i class="bi bi-pc-display-horizontal" role="img">
+</i> 
+ <span class="menu-text">Platforms</span></a>
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="true" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
           </a> 
       </div>
-      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 show">  
+      <ul id="quarto-sidebar-section-3" class="collapse list-unstyled sidebar-section depth1 show">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../contents/computing-environment.html" class="sidebar-item-text sidebar-link active">
- <span class="menu-text">Portable Environments</span></a>
+  <a href="../tutorials/computing-environment.html" class="sidebar-item-text sidebar-link active">
+ <span class="menu-text">Computing Environments</span></a>
   </div>
 </li>
       </ul>
+  </li>
+        <li class="sidebar-item sidebar-item-section">
+      <span class="sidebar-item-text sidebar-link text-start">
+ <span class="menu-text">Recipes</span></span>
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@@ -322,7 +321,7 @@ <h2 class="anchored" data-anchor-id="anywhere-docker">Anywhere: Docker</h2>
 </div>
 
 </main> <!-- /main -->
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+<script src="../assets/js/material-kit.js"></script>
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 <script id="quarto-html-after-body" type="application/javascript">
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@@ -559,8 +558,8 @@ <h2 class="anchored" data-anchor-id="anywhere-docker">Anywhere: Docker</h2>
 </script>
 <nav class="page-navigation">
   <div class="nav-page nav-page-previous">
-      <a href="../contents/intro-python.html" class="pagination-link">
-        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">Introduction (python)</span>
+      <a href="../tutorials/python/intro-python.html" class="pagination-link">
+        <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">Introduction</span>
       </a>          
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   <div class="nav-page nav-page-next">
@@ -574,7 +573,7 @@ <h2 class="anchored" data-anchor-id="anywhere-docker">Anywhere: Docker</h2>
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+    <div class="toc-actions"><div><i class="bi bi-github"></i></div><div class="action-links"><p><a href="https://github.com/boettiger-lab/nasa-topst-env-justice/edit/main/tutorials/computing-environment.qmd" class="toc-action">Edit this page</a></p><p><a href="https://github.com/boettiger-lab/nasa-topst-env-justice/issues/new" class="toc-action">Report an issue</a></p></div></div></div>
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diff --git a/contents/img/vscode-welcome.png b/tutorials/img/vscode-welcome.png
similarity index 100%
rename from contents/img/vscode-welcome.png
rename to tutorials/img/vscode-welcome.png
diff --git a/contents/intro-python.html b/tutorials/python/intro-python.html
similarity index 90%
rename from contents/intro-python.html
rename to tutorials/python/intro-python.html
index eb9d19a..3d33687 100644
--- a/contents/intro-python.html
+++ b/tutorials/python/intro-python.html
@@ -1,26 +1,26 @@
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   <meta name="generator" content="pandoc">
   <meta name="viewport" content="width=device-width, initial-scale=1.0, user-scalable=yes">
-  <title>Data discovery</title>
+  <title>Introduction to cloud-native data: Python</title>
   <style>
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@@ -68,9 +68,9 @@
     pre > code.sourceCode > span > a:first-child::before { text-decoration: underline; }
     }
   </style>
-  <link rel="stylesheet" href="../assets/css/nucleo-icons.css">
-  <link rel="stylesheet" href="../assets/css/quarto-material-kit.css">
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+  <link rel="stylesheet" href="../../assets/css/quarto-material-kit.css">
+  <link rel="stylesheet" href="../../assets/css/custom.css">
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@@ -94,7 +94,7 @@
   <style>
 
         .quarto-title-block .quarto-title-banner {
-          background-image: url(../assets/img/blue-marble.jpg);
+          background-image: url(../../assets/img/blue-marble.jpg);
   background-size: cover;
         }
   </style>
@@ -108,7 +108,6 @@
 
 
 <body class="nav-sidebar floating nav-fixed fullcontent">
-
 <div id="quarto-search-results"></div>
   <header id="quarto-header" class="headroom fixed-top">
     <nav class="navbar navbar-expand-lg navbar-dark ">
@@ -120,7 +119,7 @@
           <div class="collapse navbar-collapse" id="navbarCollapse">
             <ul class="navbar-nav navbar-nav-scroll ms-auto">
   <li class="nav-item">
-    <a class="nav-link" href="../index.html" rel="" target=""><i class="bi bi-house" role="img">
+    <a class="nav-link" href="../../index.html" rel="" target=""><i class="bi bi-house" role="img">
 </i> 
  <span class="menu-text">Home</span></a>
   </li>  
@@ -174,7 +173,7 @@
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+      <nav class="quarto-page-breadcrumbs" aria-label="breadcrumb"><ol class="breadcrumb"><li class="breadcrumb-item"><a href="../../tutorials/python/intro-python.html">Tutorials in Python</a></li><li class="breadcrumb-item"><a href="../../tutorials/python/intro-python.html">Introduction</a></li></ol></nav>
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@@ -182,12 +181,12 @@
 </header>
 <!-- content -->
 <header id="title-block-header" class="bg-gradient-dark">
-<div class="page-header min-vh-45" style="background-image: url(../assets/img/blue-marble.jpg);">
+<div class="page-header min-vh-45" style="background-image: url(../../assets/img/blue-marble.jpg);">
 <span class="mask bg-gradient-dark opacity-3"></span>
 <div class="container">
 <div class="row justify-content-center">
 <div class="col-lg-8 text-center mx-auto my-auto">
-<h1 class="text-white">Data discovery</h1>
+<h1 class="text-white">Introduction to cloud-native data: Python</h1>
 <p class="lead mb-4 text-white opacity-8"></p>
 </div>
 </div>
@@ -199,8 +198,9 @@ <h1 class="text-white">Data discovery</h1>
     <ul class="list-unstyled mt-1">
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-1" aria-expanded="true">
- <span class="menu-text">R Tutorials</span></a>
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-1" aria-expanded="true"><i class="bi bi-mortarboard-fill" role="img">
+</i> 
+ <span class="menu-text">Tutorials in R</span></a>
           <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-1" aria-expanded="true" aria-label="Toggle section">
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@@ -208,15 +208,13 @@ <h1 class="text-white">Data discovery</h1>
       <ul id="quarto-sidebar-section-1" class="collapse list-unstyled sidebar-section depth1 show">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../contents/intro-R.html" class="sidebar-item-text sidebar-link"><i class="bi bi-play-btn" role="img">
-</i> 
- <span class="menu-text">Introduction (R)</span></a>
+  <a href="../../tutorials/R/1-intro-R.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Introduction</span></a>
   </div>
 </li>
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../contents/earthdata.html" class="sidebar-item-text sidebar-link"><i class="bi bi-rocket" role="img">
-</i> 
+  <a href="../../tutorials/R/2-earthdata.html" class="sidebar-item-text sidebar-link">
  <span class="menu-text">NASA EarthData</span></a>
   </div>
 </li>
@@ -224,8 +222,9 @@ <h1 class="text-white">Data discovery</h1>
   </li>
         <li class="sidebar-item sidebar-item-section">
       <div class="sidebar-item-container"> 
-            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" aria-expanded="true">
- <span class="menu-text">Python Tutorials</span></a>
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" aria-expanded="true"><i class="bi bi-mortarboard-fill" role="img">
+</i> 
+ <span class="menu-text">Tutorials in Python</span></a>
           <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-2" aria-expanded="true" aria-label="Toggle section">
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@@ -233,33 +232,33 @@ <h1 class="text-white">Data discovery</h1>
       <ul id="quarto-sidebar-section-2" class="collapse list-unstyled sidebar-section depth1 show">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../contents/intro-python.html" class="sidebar-item-text sidebar-link active"><i class="bi bi-play-btn" role="img">
-</i> 
- <span class="menu-text">Introduction (python)</span></a>
+  <a href="../../tutorials/python/intro-python.html" class="sidebar-item-text sidebar-link active">
+ <span class="menu-text">Introduction</span></a>
   </div>
 </li>
       </ul>
-  </li>
-        <li class="sidebar-item sidebar-item-section">
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- <span class="menu-text">Background</span></a>
-          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-4" aria-expanded="true" aria-label="Toggle section">
+            <a class="sidebar-item-text sidebar-link text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="true"><i class="bi bi-pc-display-horizontal" role="img">
+</i> 
+ <span class="menu-text">Platforms</span></a>
+          <a class="sidebar-item-toggle text-start" data-bs-toggle="collapse" data-bs-target="#quarto-sidebar-section-3" aria-expanded="true" aria-label="Toggle section">
             <i class="bi bi-chevron-right ms-2"></i>
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-      <ul id="quarto-sidebar-section-4" class="collapse list-unstyled sidebar-section depth1 show">  
+      <ul id="quarto-sidebar-section-3" class="collapse list-unstyled sidebar-section depth1 show">  
           <li class="sidebar-item">
   <div class="sidebar-item-container"> 
-  <a href="../contents/computing-environment.html" class="sidebar-item-text sidebar-link">
- <span class="menu-text">Portable Environments</span></a>
+  <a href="../../tutorials/computing-environment.html" class="sidebar-item-text sidebar-link">
+ <span class="menu-text">Computing Environments</span></a>
   </div>
 </li>
       </ul>
+  </li>
+        <li class="sidebar-item sidebar-item-section">
+      <span class="sidebar-item-text sidebar-link text-start">
+ <span class="menu-text">Recipes</span></span>
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@@ -280,7 +279,8 @@ <h1 class="text-white">Data discovery</h1>
 <div class="container">
 <div class="row">
 <div class="col-12 mx-auto">
-<p># Examining Environmental Justice through Open Source, Cloud-Native Tools: Python</p>
+<section id="exploring-the-legacy-of-redlining" class="level1">
+<h1>Exploring the Legacy of Redlining</h1>
 <p>This executable notebook provides an opening example to illustrate a cloud-native workflow. Pedagogy research emphasizes the importance of “playing the whole game” before breaking down every pitch and hit. We intentionally focus on powerful high-level tools (STAC API, COGs, datacubes) to illustrate how a few chunks of code can perform a task that would be far slower and more verbose in a traditional file-based, download-first workflow. Note the close parallels between R and Python syntax. This arises because both languages wrap the same underlying tools (the STAC API and GDAL warper) and handle many of the nuisances of spatial data – from re-projections and resampling to mosaic tiles – without us noticing.</p>
 <div class="cell" data-tags="[]" data-execution_count="1">
 <div class="sourceCode cell-code" id="cb1"><pre class="sourceCode python code-with-copy"><code class="sourceCode python"><span id="cb1-1"><a href="#cb1-1" aria-hidden="true" tabindex="-1"></a><span class="im">from</span> pystac_client <span class="im">import</span> Client</span>
@@ -294,6 +294,8 @@ <h1 class="text-white">Data discovery</h1>
 <span id="cb1-9"><a href="#cb1-9" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> warnings</span>
 <span id="cb1-10"><a href="#cb1-10" aria-hidden="true" tabindex="-1"></a>warnings.filterwarnings(<span class="st">'ignore'</span>)</span></code><button title="Copy to Clipboard" class="code-copy-button"><i class="bi"></i></button></pre></div>
 </div>
+<section id="data-discovery" class="level2">
+<h2 class="anchored" data-anchor-id="data-discovery">Data discovery</h2>
 <!--
 Large geospatial data comes in many different formats and is frequently divided into many individual files or "assets" which may represent different points in space, time, sensor bands or variables. Many users are familiar with file-based workflows, where each file type is read into the computational environment by a specific tool and that workflows proceed file-by-file. However, the same data can be represented in many different formats (ncdf or tiff, say) and subdivided in different ways. Importantly, the file-based-divisions often do not reflect the way a user might want to work with the data.  For instance, a NASA ncdf product may provide sea-surface-temperature as one file per day, with each file covering the entire global extent, while a user wants to examine trends in the data over time but only in a certain regional area. In such cases, it is inefficient to download data for the whole globe over many files.  Just as end-users in high level languages are not expected to manage very low-level concepts like memory block sizes, geospatial data scientists need not worry about these file serialization details when they have good high-level abstractions that can do it for them.  
 -->
@@ -346,6 +348,8 @@ <h1 class="text-white">Data discovery</h1>
 <p><img src="intro-python_files/figure-html/cell-6-output-2.png" class="img-fluid"></p>
 </div>
 </div>
+</section>
+</section>
 <section id="from-ndvi-to-environmental-justice" class="level1">
 <h1>From NDVI to Environmental Justice</h1>
 <p>We examine the present-day impact of historic “red-lining” of US cities during the Great Depression using data from the <a href="https://dsl.richmond.edu/panorama/redlining">Mapping Inequality</a> project. All though this racist practice was banned by federal law under the Fair Housing Act of 1968, the systemic scars of that practice are still so deeply etched on our landscape that the remain visible from space – “red-lined” areas (graded “D” under the racist HOLC scheme) show systematically lower greenness than predominately-white neighborhoods (Grade “A”). Trees provide many benefits, from mitigating urban heat to biodiversity, real-estate value, to health.</p>
@@ -443,7 +447,7 @@ <h2 class="anchored" data-anchor-id="zonal-statistics">Zonal statistics</h2>
 </div>
 
 </main> <!-- /main -->
-<script src="assets/js/material-kit.js"></script>
+<script src="../../assets/js/material-kit.js"></script>
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 <script id="quarto-html-after-body" type="application/javascript">
 window.document.addEventListener("DOMContentLoaded", function (event) {
@@ -678,16 +682,15 @@ <h2 class="anchored" data-anchor-id="zonal-statistics">Zonal statistics</h2>
   }
 });
 </script>
-
 <nav class="page-navigation">
   <div class="nav-page nav-page-previous">
-      <a href="../contents/earthdata.html" class="pagination-link">
+      <a href="../../tutorials/R/2-earthdata.html" class="pagination-link">
         <i class="bi bi-arrow-left-short"></i> <span class="nav-page-text">NASA EarthData</span>
       </a>          
   </div>
   <div class="nav-page nav-page-next">
-      <a href="../contents/computing-environment.html" class="pagination-link">
-        <span class="nav-page-text">Portable Environments</span> <i class="bi bi-arrow-right-short"></i>
+      <a href="../../tutorials/computing-environment.html" class="pagination-link">
+        <span class="nav-page-text">Computing Environments</span> <i class="bi bi-arrow-right-short"></i>
       </a>
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@@ -699,7 +702,7 @@ <h2 class="anchored" data-anchor-id="zonal-statistics">Zonal statistics</h2>
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diff --git a/contents/intro-python_files/figure-html/cell-6-output-2.png b/tutorials/python/intro-python_files/figure-html/cell-6-output-2.png
similarity index 100%
rename from contents/intro-python_files/figure-html/cell-6-output-2.png
rename to tutorials/python/intro-python_files/figure-html/cell-6-output-2.png
diff --git a/contents/intro-python_files/figure-html/cell-8-output-2.png b/tutorials/python/intro-python_files/figure-html/cell-8-output-2.png
similarity index 100%
rename from contents/intro-python_files/figure-html/cell-8-output-2.png
rename to tutorials/python/intro-python_files/figure-html/cell-8-output-2.png
