# Welcome to RAPIDS documentation Reproducible Analysis Pipeline for Data Streams (RAPIDS) allows you to process smartphone and wearable data to [extract](features/feature-introduction.md) and [create](features/add-new-features.md) **behavioral features** (a.k.a. digital biomarkers), [visualize](visualizations/data-quality-visualizations.md) mobile sensor data, and [structure](analysis/complete-workflow-example.md) your analysis into reproducible workflows. Check out our [paper](https://www.frontiersin.org/article/10.3389/fdgth.2021.769823)! RAPIDS is open source, documented, multi-platform, modular, tested, and reproducible. At the moment, we support [data streams](datastreams/data-streams-introduction) logged by smartphones, Fitbit wearables, and Empatica wearables (the latter in collaboration with the [DBDP](https://dbdp.org/)). !!! tip "Where do I start?" :material-power-standby: New to RAPIDS? Check our [Overview + FAQ](setup/overview/) and [minimal example](analysis/minimal) :material-play-speed: [Install](setup/installation), [configure](setup/configuration), and [execute](setup/execution) RAPIDS to [extract](features/feature-introduction.md) and [plot](visualizations/data-quality-visualizations.md) behavioral features :material-github: Bugs should be reported on [Github issues](https://github.com/carissalow/rapids/issues) :fontawesome-solid-tasks: Questions, discussions, feature requests, and feedback can be posted on our [Github discussions](https://github.com/carissalow/rapids/discussions) :material-twitter: Keep up to date with our [Twitter feed](https://twitter.com/RAPIDS_Science) or [Slack channel](http://awareframework.com:3000/) :material-plus-network: Do you want to modify or add new functionality to RAPIDS? Check our [contributing guide](./contributing) :fontawesome-solid-sync-alt: Are you upgrading from RAPIDS `0.4.x` or older? Follow this [guide](migrating-from-old-versions) ## What are the benefits of using RAPIDS? 1. **Consistent analysis**. Every participant sensor dataset is analyzed in the same way and isolated from each other. 2. **Efficient analysis**. Every analysis step is executed only once. Whenever your data or configuration changes, only the affected files are updated. 5. **Parallel execution**. Thanks to [Snakemake](https://snakemake.github.io/), your analysis can be executed over multiple cores without changing your code. 6. **Code-free features**. Extract any of the behavioral features offered by RAPIDS without writing any code. 7. **Extensible code**. 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. 8. **Time zone aware**. Your data is adjusted to one or more time zones per participant. 9. **Flexible time segments**. You can extract behavioral features on time windows of any length (e.g., 5 minutes, 3 hours, 2 days), on every day or particular days (e.g., weekends, Mondays, the 1st of each month, etc.), or around events of interest (e.g., surveys or clinical relapses). 10. **Tested code**. We are continually adding tests to make sure our behavioral features are correct. 11. **Reproducible code**. If you structure your analysis within RAPIDS, you can be sure your code will run in other computers as intended, thanks to R and Python virtual environments. You can share your analysis code along with your publications without any overhead. 12. **Private**. All your data is processed locally. ## Users and Contributors ??? quote "Community Contributors" Many thanks to the [whole team](./team) and our community contributions: - Agam Kumar (CMU) - Yasaman S. Sefidgar (University of Washington) - Joe Kim (Duke University) - Brinnae Bent (Duke University) - Stephen Price (CMU) - Neil Singh (University of Virginia) Many thanks to the researchers that made [their work](./citation) open source: - Panda et al. [paper](https://pubmed.ncbi.nlm.nih.gov/31657854/) - Stachl et al. [paper](https://www.pnas.org/content/117/30/17680) - Doryab et al. [paper](https://arxiv.org/abs/1812.10394) - Barnett et al. [paper](https://doi.org/10.1093/biostatistics/kxy059) - Canzian et al. [paper](https://doi.org/10.1145/2750858.2805845) ??? quote "Publications using RAPIDS" - Predicting Symptoms of Depression and Anxiety Using Smartphone and Wearable Data [link](https://www.frontiersin.org/articles/10.3389/fpsyt.2021.625247/full) - Predicting Depression from Smartphone Behavioral Markers Using Machine Learning Methods, Hyperparameter Optimization, and Feature Importance Analysis: Exploratory Study [link](https://mhealth.jmir.org/2021/7/e26540) - Digital Biomarkers of Symptom Burden Self-Reported by Perioperative Patients Undergoing Pancreatic Surgery: Prospective Longitudinal Study [link](https://cancer.jmir.org/2021/2/e27975/) - An Automated Machine Learning Pipeline for Monitoring and Forecasting Mobile Health Data [link](https://ieeexplore.ieee.org/abstract/document/9483755/) - Mobile Footprinting: Linking Individual Distinctiveness in Mobility Patterns to Mood, Sleep, and Brain Functional Connectivity [link](https://www.biorxiv.org/content/10.1101/2021.05.17.444568v1.abstract)