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<section id="references">
<h1>References<a class="headerlink" href="#references" title="Permalink to this heading"></a></h1>
<section id="papers">
<h2>Papers<a class="headerlink" href="#papers" title="Permalink to this heading"></a></h2>
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<li><p><a class="reference external" href="https://dspace.mit.edu/handle/1721.1/131933">Ueckermann, M., Bieszczad, J., Entekhabi, D., Shapiro, M. L., Callender, D., Sullivan, D. and Milloy, J., “PODPAC: Open-Source Python Software for Enabling Harmonized, Plug-and-Play Processing of Disparate Earth Observation Data Sets and Seamless Transition Onto the Serverless Cloud by Earth Scientists,” Earth Science Informatics, Vol. 13, No. 3, 2020, pp. 1507–1521.</a></p></li>
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<section id="presentations">
<h2>Presentations<a class="headerlink" href="#presentations" title="Permalink to this heading"></a></h2>
<ul class="simple">
<li><p>Scipy 2020: <a class="reference external" href="https://www.youtube.com/watch?v=BXI6w9BECgs&amp;t=959s">Geospatial Analysis in the Cloud Using PODPAC and JupyterLab</a></p></li>
<li><p>AMS 2020: <a class="reference external" href="https://ams.confex.com/ams/2020Annual/meetingapp.cgi/Paper/365046">PODPAC: The Easy Way to Analyze Earth Science Data in the Cloud</a></p></li>
<li><p>AGU 2019: <a class="reference external" href="https://agu.confex.com/agu/fm19/meetingapp.cgi/Paper/505588">Building Web Browser Apps for On-Demand Retrieval and Processing of Cloud-Optimized Earth Science Data using the Open-Source WebESD Toolkit</a></p></li>
<li><p>AMS 2018: <a class="reference external" href="https://ams.confex.com/ams/98Annual/webprogram/Paper332957.html">A RESTful API for Python-Based Server-Side Analysis of High-Resolution Soil Moisture Downscaling Data</a></p></li>
<li><p>AGU 2018: <a class="reference external" href="https://agu.confex.com/agu/fm18/meetingapp.cgi/Paper/381197">Use of the Open Source PODPAC Library for Remote, Cloud-Based Data Analysis, Visualization, and Collaboration in a Web Browser</a></p></li>
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<h2>Posters<a class="headerlink" href="#posters" title="Permalink to this heading"></a></h2>
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<li><p>AMS 2023: Open Source Serverless Architecture for Sharing Earth Science Data</p></li>
<li><p>AGU 2021: <a class="reference external" href="https://agu.confex.com/agu/fm21/meetingapp.cgi/Paper/951494">General Server for Rapid Publishing of OGC-Compliant Earth Science Data Products</a></p></li>
<li><p>AMS 2020: <a class="reference external" href="https://ams.confex.com/ams/2020Annual/meetingapp.cgi/Paper/365046">PODPAC: The Easy Way to Analyze Earth Science Data in the Cloud</a></p></li>
<li><p>AMS 2019: <a class="reference external" href="https://ams.confex.com/ams/2019Annual/meetingapp.cgi/Paper/352145">PODPAC: A Python Library for Automatic Geospatial Data Harmonization and Seamless Transition to Cloud-Based Processing</a></p></li>
<li><p>AGU 2018: <a class="reference external" href="https://www.essoar.org/doi/abs/10.1002/essoar.10500684.1">Seamless Transition of Data Analyses and Analytics from a Local Workstation to Scalable, Massively Distributed Processing on the Cloud Using the Open Source PODPAC Library</a></p></li>
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