Beiwe

Beiwe: A data collection platform for high-throughput digital phenotyping - Published in JOSS (2021)

https://github.com/onnela-lab/beiwe-backend

Science Score: 95.0%

This score indicates how likely this project is to be science-related based on various indicators:

  • CITATION.cff file
  • codemeta.json file
    Found codemeta.json file
  • .zenodo.json file
    Found .zenodo.json file
  • DOI references
    Found 1 DOI reference(s) in JOSS metadata
  • Academic publication links
  • Committers with academic emails
    2 of 27 committers (7.4%) from academic institutions
  • Institutional organization owner
  • JOSS paper metadata
    Published in Journal of Open Source Software

Keywords from Contributors

mesh

Scientific Fields

Computer Science Computer Science - 63% confidence
Last synced: 4 months ago · JSON representation

Repository

Beiwe is a smartphone-based digital phenotyping research platform. This is the Beiwe backend code

Basic Info
  • Host: GitHub
  • Owner: onnela-lab
  • License: bsd-3-clause
  • Language: Python
  • Default Branch: main
  • Homepage: https://www.beiwe.org/
  • Size: 8.13 MB
Statistics
  • Stars: 69
  • Watchers: 15
  • Forks: 48
  • Open Issues: 67
  • Releases: 1
Created almost 10 years ago · Last pushed 4 months ago
Metadata Files
Readme License Security

README-IntroToBeiwe.md

Beiwe logo

Beiwe Research Platform (www.beiwe.org)

The Beiwe Research Platform is an open-source digital phenotyping platform designed for the collection and analysis of research-grade raw data from smartphone sensors and usage logs. It was developed for biomedical research and funded largely by a 2013 NIH Director’s New Innovator Award to Dr. Jukka-Pekka Onnela at the Harvard T.H. Chan School of Public Health. More than just the Android and iOS apps, the Beiwe Research Platform also consists of three cloud-based components for collecting data, managing studies and performing data analysis which we call the backend. Beiwe was released as open source at the end of 2017 and this document is to help you understand how Beiwe works, what's available and what's planned. For background information, please see https://www.hsph.harvard.edu/onnela-lab/research/ and www.beiwe.org.

Of the Beiwe open source repositories, users need only implement the backend since apps, called Beiwe2, are ready to go on the iOS App Store and the Google Play Store. The Beiwe2 apps will prompt your study participants for your server name & will connect them to the correct study on your platform.

Beiwe consists of five main github repositories as follows.

Repositories

beiwe

This repository has no code but includes the wiki with the Beiwe documentation. Please visit the Beiwe wiki here: https://github.com/onnela-lab/beiwe/wiki

beiwe-backend

The Beiwe backend is the AWS-based source code for collecting data, managing studies and performing data analysis. The backend is a modular, scalable system where:

  • studies are created, configured and managed
  • patient IDs are created for enrolling study participants in individual studies
  • surveys, if used, are set up for each study
  • data is collected from all study participants' phones
  • data can be analysed using the Onnela Lab Forest codebase.
  • data is downloaded for analysis

For documentation on the Beiwe back-end and building your own backend server, start here.

For user documentation on Beiwe, start here.

beiwe-android

This repository contains the Beiwe Android app code. The Beiwe2 app is also available on the Google Play store to use with open source builds of the Beiwe backend. The Beiwe2 app prompts for your server name and patient IDs from studies on your server.

beiwe-ios

This repository contains the Beiwe iOS app code. The Beiwe2 app is also available on the Apple App store to use with open source builds of the Beiwe backend. The Beiwe2 app prompts for your server name and patient IDs from studies on your server.

beiwedata (https://github.com/onnela-lab/beiwedata)

This repository is provided by a doctoral student and contains a set of Python scripts designed to help analyze, and manipulate data generated by the Beiwe application.

Written with StackEdit.

Owner

  • Name: onnela-lab
  • Login: onnela-lab
  • Kind: organization

JOSS Publication

Beiwe: A data collection platform for high-throughput digital phenotyping
Published
December 15, 2021
Volume 6, Issue 68, Page 3417
Authors
Jukka-Pekka Onnela ORCID
Department of Biostatistics, Harvard T.H. Chan School of Public Health, Harvard University
Caleb Dixon
Zagaran, Inc.
Keary Griffin
Rocket Farm Studios
Tucker Jaenicke
Zagaran, Inc.
Leila Minowada
Zagaran, Inc.
Sean Esterkin
Zagaran, Inc.
Alvin Siu
Zagaran, Inc.
Josh Zagorsky
Zagaran, Inc.
Eli Jones
Zagaran, Inc.
Editor
Frederick Boehm ORCID
Tags
high-throughput digital phenotyping multi-language smartphone AWS

GitHub Events

Total
  • Issues event: 66
  • Watch event: 5
  • Delete event: 19
  • Member event: 3
  • Issue comment event: 111
  • Push event: 342
  • Gollum event: 17
  • Pull request event: 1
  • Fork event: 5
  • Create event: 22
Last Year
  • Issues event: 66
  • Watch event: 5
  • Delete event: 19
  • Member event: 3
  • Issue comment event: 111
  • Push event: 342
  • Gollum event: 17
  • Pull request event: 1
  • Fork event: 5
  • Create event: 22

Committers

Last synced: 5 months ago

All Time
  • Total Commits: 4,481
  • Total Committers: 27
  • Avg Commits per committer: 165.963
  • Development Distribution Score (DDS): 0.622
Past Year
  • Commits: 460
  • Committers: 3
  • Avg Commits per committer: 153.333
  • Development Distribution Score (DDS): 0.107
Top Committers
Name Email Commits
Eli Jones e****i@z****m 1,692
Eli Jones b****i@g****m 1,537
zagorsky j****y@g****m 344
Aaron Klein a****2@g****m 185
Dor Samet d****t@g****m 174
Reyva Babtista r****6@g****m 94
Caleb c****n@g****m 85
Alvin Siu a****n@z****m 67
tjaeni01 t****e@g****m 64
kfantastic k****n@g****m 46
Sean Esterkin s****n@z****m 39
Leila Minowada l****a@L****l 25
JP Onnela j****a 24
Ali Abdullah a****i@z****m 21
Keary Griffin k****y@m****m 17
Chris McCarthy m****y@z****m 10
Daniel Norton 4****n 8
Erinn Looney-Triggs e****s@g****m 8
Kevin Fan k****n@K****l 7
Caleb c****b@z****m 7
dependabot[bot] 4****] 6
Reyva Babtista r****1@g****m 5
kfantastic k****n@c****u 4
Noah Houghton N****n 4
msimoneau m****u@h****u 4
Jake Klingensmith j****e@z****m 3
Ubuntu u****u@i****l 1

Issues and Pull Requests

Last synced: 4 months ago

All Time
  • Total issues: 117
  • Total pull requests: 19
  • Average time to close issues: 11 months
  • Average time to close pull requests: 19 days
  • Total issue authors: 11
  • Total pull request authors: 2
  • Average comments per issue: 1.25
  • Average comments per pull request: 1.74
  • Merged pull requests: 6
  • Bot issues: 0
  • Bot pull requests: 11
Past Year
  • Issues: 41
  • Pull requests: 2
  • Average time to close issues: about 2 months
  • Average time to close pull requests: N/A
  • Issue authors: 5
  • Pull request authors: 1
  • Average comments per issue: 0.8
  • Average comments per pull request: 2.5
  • Merged pull requests: 0
  • Bot issues: 0
  • Bot pull requests: 0
Top Authors
Issue Authors
  • biblicabeebli (105)
  • hydawo (2)
  • Ilurusheshasai (2)
  • russellkgw (1)
  • CMLivermore (1)
  • alvinsiu (1)
  • zadking (1)
  • demarrid (1)
  • MMel099 (1)
  • yh0903 (1)
  • leonhhhhhhe (1)
Pull Request Authors
  • dependabot[bot] (11)
  • reyvababtista (8)
Top Labels
Issue Labels
Bug (25) enhancement (20) feature (14) UI/UX (8) Not A Bug. (3) documentation (3) discussion (2) ANNOUNCEMENT (2) dependencies... (1) Mano (1) Infrastructure (1) wish list (1) wontfix (1)
Pull Request Labels
dependencies... (11) feature (4) Infrastructure (4) enhancement (2)

Dependencies

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requirements_data_processing.txt pypi
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requirements_testing.txt pypi
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