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<h1 id="welcome-to-rapids-documentation">Welcome to RAPIDS documentation<a class="headerlink" href="#welcome-to-rapids-documentation" title="Permanent link">&para;</a></h1>
<p>Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to <a href="features/feature-introduction/">extract</a> and <a href="features/add-new-features/">create</a> <strong>behavioral features</strong> (a.k.a. digital biomarkers), <a href="visualizations/data-quality-visualizations/">visualize</a> mobile sensor data, and <a href="workflow-examples/analysis/">structure</a> your analysis into reproducible workflows.</p>
<p>RAPIDS is open source, documented, multi-platform, modular, tested, and reproducible. At the moment, we support <a href="datastreams/data-streams-introduction">data streams</a> logged by smartphones, Fitbit wearables, and Empatica wearables in collaboration with the <a href="https://dbdp.org/">DBDP</a>. </p>
<p>RAPIDS is open source, documented, multi-platform, modular, tested, and reproducible. At the moment, we support <a href="datastreams/data-streams-introduction">data streams</a> logged by smartphones, Fitbit wearables, and Empatica wearables (the latter in collaboration with the <a href="https://dbdp.org/">DBDP</a>). </p>
<div class="admonition tip">
<p class="admonition-title">Where do I start?</p>
<p><span class="twemoji"><svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 24 24"><path d="M13 3h-2v10h2V3m4.83 2.17-1.42 1.42A6.944 6.944 0 0 1 19 12a7 7 0 0 1-7 7A6.995 6.995 0 0 1 7.58 6.58L6.17 5.17a9.001 9.001 0 0 0-1.03 12.69c3.22 3.78 8.9 4.24 12.69 1.02A9.003 9.003 0 0 0 21 12c0-2.63-1.16-5.13-3.17-6.83z"/></svg></span> New to RAPIDS? Check our <a href="setup/overview/">Overview + FAQ</a> and <a href="workflow-examples/minimal">minimal example</a></p>
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<ol>
<li><strong>Consistent analysis</strong>. Every participant sensor dataset is analyzed in the same way and isolated from each other.</li>
<li><strong>Efficient analysis</strong>. Every analysis step is executed only once. Whenever your data or configuration changes, only the affected files are updated.</li>
<li><strong>Parallel execution</strong>. Thanks to Snakemake, your analysis can be executed over multiple cores without changing your code.</li>
<li><strong>Parallel execution</strong>. Thanks to <a href="https://snakemake.github.io/">Snakemake</a>, your analysis can be executed over multiple cores without changing your code.</li>
<li><strong>Code-free features</strong>. Extract any of the behavioral features offered by RAPIDS without writing any code.</li>
<li><strong>Extensible code</strong>. You can easily add your own data streams or behavioral features in R or Python, share them with the community, and keep authorship and citations.</li>
<li><strong>Time zone aware</strong>. Your data is adjusted to one or more time zones per participant.</li>
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<li><strong>Private</strong>. All your data is processed locally.</li>
</ol>
<h2 id="users-and-contributors">Users and Contributors<a class="headerlink" href="#users-and-contributors" title="Permanent link">&para;</a></h2>
<details class="quote"><summary>Community Contributors</summary><p>Many thanks to our community contributions and the <a href="../team">whole team</a>:</p>
<details class="quote"><summary>Community Contributors</summary><p>Many thanks to the <a href="./team">whole team</a> and our community contributions:</p>
<ul>
<li>Agam Kumar (CMU)</li>
<li>Yasaman S. Sefidgar (University of Washington)</li>
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<li>Stephen Price (CMU)</li>
<li>Neil Singh (University of Virginia)</li>
</ul>
<p>Many thanks to the researchers that made <a href="../citation">their work</a> open source:</p>
<p>Many thanks to the researchers that made <a href="./citation">their work</a> open source:</p>
<ul>
<li>Panda et al. <a href="https://pubmed.ncbi.nlm.nih.gov/31657854/">paper</a></li>
<li>Stachl et al. <a href="https://www.pnas.org/content/117/30/17680">paper</a></li>
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<li>Predicting Depression from Smartphone Behavioral Markers Using Machine Learning Methods, Hyper-parameter Optimization, and Feature Importance Analysis: An Exploratory Study <a href="https://preprints.jmir.org/preprint/26540">link</a></li>
<li>Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study <a href="https://cancer.jmir.org/2021/2/e27975/">link</a></li>
<li>An Automated Machine Learning Pipeline for Monitoring and Forecasting Mobile Health Data <a href="https://edas.info/showManuscript.php?m=1570708269&amp;random=750318666&amp;type=final&amp;ext=pdf&amp;title=PDF+file">link</a></li>
<li>Mobile Footprinting: Linking Individual Distinctiveness in Mobility Patterns to Mood, Sleep, and Brain Functional Connectivity <a href="https://www.biorxiv.org/content/10.1101/2021.05.17.444568v1.abstract">link</a></li>
</ul>
</details>
<div class="users">

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