Update paper.md

Author: jponnela

paper/paper.md

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 # Summary
 
-Beiwe is a high-throughput data collection platform for smartphone-based digital phenotyping. It has been in development and use since 2013. Beiwe consists of two native front-end applications: one for Android (written in Java and Kotlin) and one for iOS (written in Swift and Objective-C). The Beiwe back-end, which is based on Amazon Web Services (AWS), has been implemented primarily in Python 3.6, but it also makes use of Django for ORM and Flask for API and web servers. It uses several AWS services, such as S3 for flat file storage, EC2 virtual servers for data processing, Elastic Beanstalk for orchestration, and RDS for PostgreSQL database engine. Most smartphone applications use software development kits (SDKs) that generate unvalidated behavioral summary measures using closed proprietary algorithms. These applications do not meet the high standards of reproducible science, and often require researchers to modify their scientific questions based on what data happens to be available. In contrast, Beiwe collects raw sensor and phone use data, and its data collection parameters can be customized to address specific scientific questions of interest. Collection of raw data also improves reproducibility of studies and enables re-analyses of data and pooling of data across studies. Every aspect of Beiwe data collection is fully customizable, including which sensors to sample, how frequently to sample them, whether to add Gaussian noise to GPS location, whether to use Wi-Fi or cellular data for uploads, how frequently to upload data, specification of surveys and their response options, and skip logic. All study settings are captured in a JSON-formatted configuration file, which can be exported from and imported to Beiwe to enhance transparency and reproducibility of studies.
+Forest is a data analysis library for raw high-throughput digital phenotyping data. It is intended to integrate direcly with its companion software--the Beiwe plaform--which is a high-throughput data collection platform for smartphone data from Android and iOS phones. Beiwe is in digital phenotyping to collect active data (e.g., surveys and audio samples) and passive data (e.g., accelerometer data) from Android and iOS phones. Most smartphone applications use software development kits (SDKs) that generate unvalidated behavioral summary measures using closed proprietary algorithms. These applications do not meet the high standards of reproducible science, and often require researchers to modify their scientific questions based on what data happens to be available. In contrast, Beiwe collects raw sensor and phone use data, and its data collection parameters can be customized to address specific scientific questions of interest. The quantitative methods for analyzing such data have been developed and published elsewhere. The purpose of the Forest is to provide software implementations of these methods in Python. Each method within Forest is named after a tree; for example, the method that implements our sparse online Gaussian process imputation of missing GPS trajectories is called Python. We expect Forest to grow by the addition of new trees as more methods become available in the future.
 
 # Statement of need
 
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 # Acknowledgements
 
-The Principal Investigator, Jukka-Pekka Onnela, is extremely grateful for his NIH Director’s New Innovator Award in 2013 (DP2MH103909) for enabling the crystallization of the concept of digital phenotyping and the construction of the Beiwe platform. He is also grateful to the members of the Onnela Lab. The authors also wish to acknowledge the contributions of the following individuals at Zagaran: Aaron Klein, Kevin Fan, Christopher McCarthy, Dor Samet, Alicia Fan, Rebecca Magazine Malamud, Jacob Klingensmith, and Benjamin Zagorsky.
+The Principal Investigator, Jukka-Pekka Onnela, is extremely grateful for his NIH Director’s New Innovator Award in 2013 (DP2MH103909) for enabling the crystallization of the concept of digital phenotyping and the construction of the Beiwe platform. He is also grateful to the members of the Onnela Lab.
 
 # References