# healthnexus tab files d4d

Datasheet for Dataset - Human Readable Format

🎯

## Motivation

Why was the dataset created?

Funders

Grantor | Grant Name | Grant Number
--- | --- | ---
National Institutes of Health | Bridge2AI: Voice as a Biomarker of Health - Building an ethically sourced, bioaccoustic database to understand disease like never before | 3OT2OD032720-01S1

Purposes

- Response
    Create an ethically sourced, diverse, multi-institutional dataset of voice linked to clinical and questionnaire data to enable AI research on voice as a biomarker of health and support clinically meaningful insights.

📊

## Composition

What do the instances represent?

Instances

Counts | Data Type | Instance Type | Missing Information | Representation | Sampling Strategies
--- | --- | --- | --- | --- | ---
12523 | Derived features (spectrograms, acoustic, phonetic/prosodic); no raw audio waveforms in v1.0 | Audio-derived instance | {'missing': ['Original audio waveforms'], 'why_missing': ['Omitted in initial low-risk release; planned for future releases with additional safeguards']}, {'missing': ['Transcripts of free speech audio'], 'why_missing': ['Removed during de-identification']} | Voice-derived recordings (spectrograms/features) per recording | {'is_sample': ['yes'], 'is_random': ['no'], 'source_data': ['Patients presenting at specialty clinics across five North American sites'], 'is_representative': ['no'], 'why_not_representative': ['Participants were selected based on membership in predefined disease cohorts'], 'strategies': ['Targeted enrollment by predefined disease categories']}
306 | Demographics, clinical, and validated questionnaire responses | Participant |  | Participant-level phenotype records | {'is_sample': ['yes'], 'is_random': ['no'], 'source_data': ['Specialty clinics at five North American sites'], 'is_representative': ['no'], 'why_not_representative': ['Cohort-based selection for voice-relevant conditions']}

Subpopulations

- Identification
    - Adult cohort (v1.0); disease categories include voice disorders, neurological/neurodegenerative disorders, mood/psychiatric disorders, respiratory disorders
  Distribution
    - Participants selected based on membership in predefined disease cohorts

Distribution Formats

- Description
    - Parquet
    - TSV
    - JSON

Distribution Dates

- Description
    - 2024-11-27

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## Collection Process

How was the data acquired?

ID *
bridge2ai-voice-v1.0
Name
Bridge2AI-Voice
Title
Bridge2AI-Voice: An ethically-sourced, diverse voice dataset linked to health information v1.0
Description
Bridge2AI-Voice is a multi-site, ethically sourced dataset of human voice linked to clinical and questionnaire information to enable research on voice as a biomarker of health. Version 1.0 includes 12,523 recordings from 306 participants across five North American sites, focusing on cohorts with known voice-relevant conditions (voice disorders, neurological/neurodegenerative disorders, mood/psychiatric disorders, and respiratory disorders). The initial release contains only low-risk, derived data (e.g., spectrograms and acoustic features) and de-identified phenotype tables; original audio waveforms and free-speech transcripts are not included. Data were collected via a standardized protocol using a custom tablet application, with preprocessing that included resampling to 16 kHz with a Butterworth anti-aliasing filter, STFT-based spectrogram generation, extraction of acoustic and phonetic/prosodic features (OpenSMILE, Parselmouth/Praat), and automatic transcription using OpenAI Whisper Large. Documentation: "https://docs.b2ai-voice.org."
Page
https://docs.b2ai-voice.org
Issued
2024-11-27
Keywords

- voice
- bridge2ai
- audio
- VOICE

Creators

Name
Alistair Johnson
Jean-Christophe Bélisle-Pipon
David Dorr
Satrajit Ghosh
Philip Payne
Maria Powell
Anaïs Rameau
Vardit Ravitsky
Alexandros Sigaras
Olivier Elemento
Yael Bensoussan

Addressing Gaps

- Response
    Addresses the lack of large, high-quality, standardized, and demographically diverse voice datasets linked to clinical and questionnaire data across multiple institutions.

Relationships

- Description
    - Recordings are linked to participants and sessions via participant_id and session_id; tasks identified by task_name

Is Deidentified

Description
  - HIPAA Safe Harbor identifiers removed (e.g., names, contact details, precise dates, etc.)
  - State and province removed; country retained
  - Free speech transcripts removed; original audio waveforms omitted in v1.0

Sensitive Elements

- Description
    - Contains de-identified health-related information (demographics, clinical variables, questionnaire responses)

Confidential Elements

- Description
    - De-identified clinical and questionnaire data; identifiable communications removed

Acquisition Methods

1. Description
     - Standardized protocol with voice tasks (e.g., sustained vowel), demographics, health and targeted questionnaires
     - Custom tablet application; headset used when possible
     - Data exported from REDCap via open-source tooling
   Was Directly Observed
     yes
   Was Reported By Subjects
     yes
   Was Inferred Derived
     yes

Collection Mechanisms

- Description
    - Custom tablet app and headset for data capture; REDCap used for data entry/export

Data Collectors

- Description
    - Project investigators at five North American sites; patients presenting at specialty clinics were screened and consented

Ethical Reviews

- Description
    - Data collection and sharing approved by the University of South Florida IRB; submitted for review to the University of Toronto Research Ethics Board

Preprocessing Strategies

- Description
    - Raw audio converted to mono and resampled to 16 kHz with a Butterworth anti-aliasing filter
    - Spectrograms computed via short-time FFT (25 ms window, 10 ms hop, 512-point FFT)
    - Acoustic features extracted with OpenSMILE
    - Phonetic and prosodic features computed with Parselmouth and Praat
    - Transcriptions generated with OpenAI Whisper Large model
  Used Software

    Name | URL
    --- | ---
    OpenSMILE |
    Parselmouth |
    Praat |
    torchaudio |
    OpenAI Whisper Large |
    b2aiprep | https://github.com/sensein/b2aiprep

Cleaning Strategies

- Description
    - HIPAA Safe Harbor de-identification (removal of identifiers and fine-grained dates)
    - Removal of free speech transcripts
    - Omission of original audio waveforms from initial release

Labeling Strategies

- Description
    - Automatic Transcription Using Openai Whisper Large (note
        free speech transcripts are not included in v1.0)

Raw Sources

- Description
    - Original audio waveforms collected; not distributed in v1.0. Future releases aim to include voice data with additional precautions.

Maintainers

- Description
    - Health Data Nexus
    - Temerty Centre for AI Research and Education in Medicine

Is Tabular
mixed (Parquet dense arrays and tabular TSV/JSON)
Subsets

Description | Format | ID | Media Type | Name | Path | Title
--- | --- | --- | --- | --- | --- | ---
Parquet dataset containing time-frequency spectrograms derived from voice recordings; includes parti... |  | spectrograms-parquet | application/x-parquet | spectrograms.parquet | spectrograms.parquet | Spectrograms (derived from voice waveforms)
Tab-delimited file with one row per participant containing demographics, acoustic confounders, and v... |  | phenotype-tsv | text/tab-separated-values | phenotype.tsv | phenotype.tsv | Phenotype table
JSON data dictionary describing columns in phenotype.tsv; includes a one-sentence description for ea... | JSON | phenotype-json | application/json | phenotype.json | phenotype.json | Phenotype data dictionary
Tab-delimited file with one row per recording containing features derived from OpenSMILE, Praat/Pars... |  | static-features-tsv | text/tab-separated-values | static_features.tsv | static_features.tsv | Static audio-derived features
JSON data dictionary describing features in static_features.tsv; includes a description for each fea... | JSON | static-features-json | application/json | static_features.json | static_features.json | Static features data dictionary

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## Uses

What (other) tasks could the dataset be used for?

Tasks

- Response
    Research and development of AI/ML methods for analyzing voice-derived features and their associations with health conditions; exploratory and hypothesis-driven studies on voice as a biomarker.

License And Use Terms

Description
  - Bridge2AI Voice Registered Access License
  - Bridge2AI Voice Registered Access Agreement (DUA)
  - Access Policy
      Only credentialed users who sign the DUA can access the files
  - Required Training
      TCPS 2: CORE 2022

📤

## Distribution

How will the dataset be distributed?

DOI
doi:10.57764/qb6h-em84
Version Access

Description
  - Latest Version DOI
      https://doi.org/10.57764/3sg0-7440

🔄

## Maintenance

How will the dataset be maintained?

Version
1.0
Updates

Description
  - Future releases aim to include voice data with additional precautions to ensure data security

Generated on 2025-11-09 10:17:34 using Bridge2AI Data Sheets Schema