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diff --git a/Chapter2-DataManipulation/2.1_Data_Definitions.html b/Chapter2-DataManipulation/2.1_Data_Definitions.html
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@@ -685,22 +685,23 @@ <h2>Data modality in Geosciences<a class="headerlink" href="#data-modality-in-ge
 <ul class="simple">
 <li><p><strong>In-situ Data</strong>: Measurements taken directly at the site of interest. In-situ data often comes as time series. Examples include:</p>
 <ul>
-<li><p>Temperature readings from weather stations.</p></li>
-<li><p>Seismic wave data from seismographs.</p></li>
-<li><p>Soil moisture content from field sensors.</p></li>
+<li><p>Temperature data from weather stations.</p></li>
+<li><p>Ground motion data from seismometer.</p></li>
+<li><p>Soil moisture data from <em>in-situ</em> probes.</p></li>
 </ul>
 </li>
 <li><p><strong>Remote Sensing Data</strong>: Collected from instruments not in direct contact with the object of study, often using satellites, drones, or aircraft. <em>Geospatial Data</em> are tied to specific locations on Earth’s surface, often represented as maps or grids (e.g., GIS data). Examples include:</p>
 <ul>
-<li><p>Spectral data (e.g., multispectral or hyperspectral images) from satellites.</p></li>
+<li><p>Images at multiple wavelengths (e.g., multispectral or hyperspectral images) from satellites.</p></li>
 <li><p>Topography data using LiDAR or radar systems.</p></li>
 <li><p>Sea surface temperature from satellites.</p></li>
 </ul>
 </li>
 <li><p><strong>Model Data</strong>: Simulated data generated from computational models. For example:</p>
 <ul>
-<li><p>Climate models predicting future temperatures or precipitation.</p></li>
+<li><p>Climate models predicting future temperatures, precipitation, chemistry.</p></li>
 <li><p>Hydrological models simulating water flow in river basins.</p></li>
+<li><p>Wavefield simulations that solve the wave equation in complex media</p></li>
 </ul>
 </li>
 <li><p><strong>Geophysical Data</strong>: Subsurface measurements derived through indirect methods like seismic surveys, gravity, or magnetic studies.</p></li>
@@ -714,7 +715,7 @@ <h2>Data Formats in Geosciences<a class="headerlink" href="#data-formats-in-geos
 <p><strong>CSV</strong> (Comma-Separated Values): A simple format for tabular data. It’s human-readable and widely supported across software, but less efficient for large or multidimensional datasets.</p>
 <p><strong>GeoTIFF</strong>: A popular format for raster geospatial data, often used in remote sensing and GIS applications.</p>
 <p><strong>Shapefiles</strong>: A vector data format for geographic information system (GIS) software, which contains geometric locations and attribute information of spatial features.</p>
-<p>Most of these files are not cloud optimized, and we will explore next new format to accomodate large cloud storage systems.</p>
+<p><strong>Cloud Optimized Formats</strong>: As large archives are moving to cloud systems, other formats are becoming useful: COGT (Cloud Optimized GeoTiffs), Zarr, TileDB among others.</p>
 </section>
 <section id="arrays">
 <h2>Arrays<a class="headerlink" href="#arrays" title="Permalink to this headline">#</a></h2>

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