From 96d7b6e17090d223cc2ecc072949ac3f0107e436 Mon Sep 17 00:00:00 2001 From: JulioV Date: Mon, 12 Jul 2021 18:15:27 -0400 Subject: [PATCH] Fix links in home page --- docs/index.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/docs/index.md b/docs/index.md index f14dc56d..8cd80158 100644 --- a/docs/index.md +++ b/docs/index.md @@ -2,7 +2,7 @@ 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](workflow-examples/analysis.md) your analysis into reproducible workflows. -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 in collaboration with the [DBDP](https://dbdp.org/). +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?" @@ -25,7 +25,7 @@ RAPIDS is open source, documented, multi-platform, modular, tested, and reproduc 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, your analysis can be executed over multiple cores without changing your code. +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. @@ -37,7 +37,7 @@ RAPIDS is open source, documented, multi-platform, modular, tested, and reproduc ## Users and Contributors ??? quote "Community Contributors" - Many thanks to our community contributions and the [whole team](../team): + Many thanks to the [whole team](./team) and our community contributions: - Agam Kumar (CMU) - Yasaman S. Sefidgar (University of Washington) @@ -46,7 +46,7 @@ RAPIDS is open source, documented, multi-platform, modular, tested, and reproduc - Stephen Price (CMU) - Neil Singh (University of Virginia) - Many thanks to the researchers that made [their work](../citation) open source: + 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) @@ -60,6 +60,7 @@ RAPIDS is open source, documented, multi-platform, modular, tested, and reproduc - Predicting Depression from Smartphone Behavioral Markers Using Machine Learning Methods, Hyper-parameter Optimization, and Feature Importance Analysis: An Exploratory Study [link](https://preprints.jmir.org/preprint/26540) - 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://edas.info/showManuscript.php?m=1570708269&random=750318666&type=final&ext=pdf&title=PDF+file) + - 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)
carnegie mellon university