Deployed 42ea883 with MkDocs version: 1.6.0

technologies/index.html

@@ -989,9 +989,9 @@
 </li>
 
           <li class="md-nav__item">
-  <a href="#deepzoom" class="md-nav__link">
+  <a href="#deep-zoom" class="md-nav__link">
     <span class="md-ellipsis">
-      DeepZoom
+      Deep Zoom
     </span>
   </a>
 
@@ -1069,9 +1069,9 @@
 
 
 <h1 id="technologies">Technologies</h1>
-<p>Celldega is utilizing a suite of complementary technologies to develop an efficient web-based spatial-omics analysis and visualization toolkit.</p>
+<p>Celldega utilizes a suite of complementary technologies to develop an efficient web-based spatial-omics analysis and visualization toolkit.</p>
 <h2 id="visualization-technologies">Visualization Technologies</h2>
-<p>Spatial transcriptomics (ST) datasets can be very large and difficult for researchers to analyze and visualize collaboratively. Additionally, visualization that is easily linked to analysis is key to extracting biological insights. To address these issues, we built the Celldega <a href="../python/viz/"><code>viz</code></a> module to help researchers interactively visualize large ST datasets within notebook-based workflows on the cloud (e.g., Terra.bio).</p>
+<p>Spatial transcriptomics (ST) datasets can be very large and difficult for researchers to analyze and visualize collaboratively. Additionally, visualization that is linked to analysis is key to extracting biological insights. To address these issues, we built the Celldega <a href="../python/viz/"><code>viz</code></a> module to help researchers interactively visualize large ST datasets within notebook-based workflows on the cloud (e.g., Terra.bio).</p>
 <p>The Celldega Landscape visualization method (see <a href="../gallery">Gallery</a>) utilizes novel vector tiling approaches to enable interactive visualization of large ST datasets in a notebook environment or as a stand-alone webpage. This approach allows Celldega to visualize larger datasets than currently available open-source tools (e.g., datasets with hundreds of millions of transcripts). We also utilize modern web image data formats (<a href="#webp">WebP</a>) to reduce the data storage burden for interactive visualization. The resulting <a href="../overview/file_formats/#landscapefiles">LandscapeFiles</a> data format serves as a compact and highly performant visualization-specific data format.</p>
 <h3 id="terrabio">Terra.bio</h3>
 <p>Terra.bio is a cloud-based compute and data storage platform that is being developed by the <a href='https://www.broadinstitute.org/spatial-technology-platform' target='_blank'>Broad Institute of MIT and Harvard</a>. We are utilizing Terra.bio to help <a href='https://www.broadinstitute.org/spatial-technology-platform' target='_blank'>Spatial Technology Platform</a> clients access, analyze, and visualize their ST data.</p>
@@ -1080,11 +1080,13 @@ <h3 id="jupyter-widget">Jupyter Widget</h3>
 <h3 id="deckgl">Deck.gl</h3>
 <p>Celldega uses the GPU-powered data visualization library <a href='https://deck.gl/' target='_blank'>deck.gl</a> to create high-performance spatial- and data-visualizations.</p>
 <h3 id="apache-parquet">Apache Parquet</h3>
-<p>Celldega uses the <a href='https://parquet.apache.org/' target='_blank'>Apache Parquet</a> file format for storing spatial data. This file format in combination with the JavaScript library <a href="#parquetwasm-and-apache-arrow">ParquetWASM</a> and Apache Arrow in memory representation is used to build Celldega's high-performance vector tiling spatial visualization functionality (see <a href='https://observablehq.com/@kylebarron/geoarrow-and-geoparquet-in-deck-gl' target='_blank'>GeoArrow and GeoParquet in deck.gl</a>).</p>
+<p>Celldega uses the <a href='https://parquet.apache.org/' target='_blank'>Apache Parquet</a> file format for storing vectorized spatial data and metadata. This file format in combination with the JavaScript library <a href="#parquetwasm-and-apache-arrow">ParquetWASM</a> and Apache Arrow in memory representation is used to build Celldega's high-performance vector tiling spatial visualization functionality (see <a href='https://observablehq.com/@kylebarron/geoarrow-and-geoparquet-in-deck-gl' target='_blank'>GeoArrow and GeoParquet in deck.gl</a>).</p>
 <h3 id="parquetwasm-and-apache-arrow">ParquetWASM and Apache Arrow</h3>
-<p>ParquetWASM is a JavaScript library for reading Parquet files into Apache Arrow memory and utilizes Web Assembly (WASM) to run Rust in a browser environment. The Apache Arrow in-memory format is a complementary columnar in-memory format for storing data from Apache Parquet files. See <a href='https://observablehq.com/@kylebarron/geoarrow-and-geoparquet-in-deck-gl' target='_blank'>GeoArrow and GeoParquet in deck.gl</a> for more information.</p>
+<p>ParquetWASM is a JavaScript library for reading Parquet files into Apache Arrow memory and utilizes Web Assembly (WASM) to run Rust in a browser environment. The Apache Arrow in-memory format is a columnar in-memory format that is used for storing data from Apache Parquet files and efficiently passing to deck.gl. For more information please see <a href='https://observablehq.com/@kylebarron/geoarrow-and-geoparquet-in-deck-gl' target='_blank'>GeoArrow and GeoParquet in deck.gl</a>.</p>
 <h3 id="webp">WebP</h3>
-<h3 id="deepzoom">DeepZoom</h3>
+<p>A modern image format developed by Google, offering efficient lossless compression and designed specifically for the web.</p>
+<h3 id="deep-zoom">Deep Zoom</h3>
+<p>We utilize the Deep Zoom image schema, developed by Microsoft, to enable efficient visualization of large multi-channel microscopy image. Deep Zoom tile images are stored using the WebP image format.</p>
 <h3 id="clustergrammer-visualization">Clustergrammer Visualization</h3>
 <h2 id="data-analysis-technologies">Data Analysis Technologies</h2>
 <h3 id="geopandas">GeoPandas</h3>

technologies/index.md

@@ -1,8 +1,8 @@
 # Technologies
-Celldega is utilizing a suite of complementary technologies to develop an efficient web-based spatial-omics analysis and visualization toolkit.
+Celldega utilizes a suite of complementary technologies to develop an efficient web-based spatial-omics analysis and visualization toolkit.
 
 ## Visualization Technologies
-Spatial transcriptomics (ST) datasets can be very large and difficult for researchers to analyze and visualize collaboratively. Additionally, visualization that is easily linked to analysis is key to extracting biological insights. To address these issues, we built the Celldega [`viz`](../python/viz/) module to help researchers interactively visualize large ST datasets within notebook-based workflows on the cloud (e.g., Terra.bio).
+Spatial transcriptomics (ST) datasets can be very large and difficult for researchers to analyze and visualize collaboratively. Additionally, visualization that is linked to analysis is key to extracting biological insights. To address these issues, we built the Celldega [`viz`](../python/viz/) module to help researchers interactively visualize large ST datasets within notebook-based workflows on the cloud (e.g., Terra.bio).
 
 The Celldega Landscape visualization method (see [Gallery](../gallery)) utilizes novel vector tiling approaches to enable interactive visualization of large ST datasets in a notebook environment or as a stand-alone webpage. This approach allows Celldega to visualize larger datasets than currently available open-source tools (e.g., datasets with hundreds of millions of transcripts). We also utilize modern web image data formats ([WebP](#webp)) to reduce the data storage burden for interactive visualization. The resulting [LandscapeFiles](../overview/file_formats.md#landscapefiles) data format serves as a compact and highly performant visualization-specific data format.
 
@@ -16,17 +16,20 @@ We utilize the Jupyter Widget ecosystem to build interactive spatial and data vi
 Celldega uses the GPU-powered data visualization library <a href='https://deck.gl/' target='_blank'>deck.gl</a> to create high-performance spatial- and data-visualizations.
 
 ### Apache Parquet
-Celldega uses the <a href='https://parquet.apache.org/' target='_blank'>Apache Parquet</a> file format for storing spatial data. This file format in combination with the JavaScript library [ParquetWASM](#parquetwasm-and-apache-arrow) and Apache Arrow in memory representation is used to build Celldega's high-performance vector tiling spatial visualization functionality (see <a href='https://observablehq.com/@kylebarron/geoarrow-and-geoparquet-in-deck-gl' target='_blank'>GeoArrow and GeoParquet in deck.gl</a>).
+Celldega uses the <a href='https://parquet.apache.org/' target='_blank'>Apache Parquet</a> file format for storing vectorized spatial data and metadata. This file format in combination with the JavaScript library [ParquetWASM](#parquetwasm-and-apache-arrow) and Apache Arrow in memory representation is used to build Celldega's high-performance vector tiling spatial visualization functionality (see <a href='https://observablehq.com/@kylebarron/geoarrow-and-geoparquet-in-deck-gl' target='_blank'>GeoArrow and GeoParquet in deck.gl</a>).
 
 ### ParquetWASM and Apache Arrow
-ParquetWASM is a JavaScript library for reading Parquet files into Apache Arrow memory and utilizes Web Assembly (WASM) to run Rust in a browser environment. The Apache Arrow in-memory format is a complementary columnar in-memory format for storing data from Apache Parquet files. See <a href='https://observablehq.com/@kylebarron/geoarrow-and-geoparquet-in-deck-gl' target='_blank'>GeoArrow and GeoParquet in deck.gl</a> for more information.
+ParquetWASM is a JavaScript library for reading Parquet files into Apache Arrow memory and utilizes Web Assembly (WASM) to run Rust in a browser environment. The Apache Arrow in-memory format is a columnar in-memory format that is used for storing data from Apache Parquet files and efficiently passing to deck.gl. For more information please see <a href='https://observablehq.com/@kylebarron/geoarrow-and-geoparquet-in-deck-gl' target='_blank'>GeoArrow and GeoParquet in deck.gl</a>.
 
 ### WebP
+A modern image format developed by Google, offering efficient lossless compression and designed specifically for the web.
 
-### DeepZoom
+### Deep Zoom
+We utilize the Deep Zoom image schema, developed by Microsoft, to enable efficient visualization of large multi-channel microscopy image. Deep Zoom tile images are stored using the WebP image format.
 
 ### Clustergrammer Visualization
 
+
 ## Data Analysis Technologies
 
 ### GeoPandas