# healthnexus tab usage-notes d4d

Datasheet for Dataset - Human Readable Format

🎯

## Motivation

Why was the dataset created?

Purposes

1. ID
     purpose:voice-biomarker-research
   Name
     Voice as a biomarker of health
   Description
     Enable AI research into health-related acoustic markers using ethically sourced, diverse voice data linked to clinical information.
   Used Software
   Response
     Enable future research in artificial intelligence using voice as a biomarker of health.

Funders

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

📊

## Composition

What do the instances represent?

Instances

1. ID
     instance:recordings-and-derived-data
   Name
     Voice recordings (derived) and participant data
   Description
     Instances represent derived data from voice recordings (e.g., spectrograms and static acoustic/phonetic/prosodic features) linked to participant-level phenotype and clinical questionnaire data.
   Used Software
   Representation
     Derived voice data linked to clinical and demographic information.
   Instance Type
     Participants, recording sessions, and derived data per recording.
   Data Type
     Derived spectrograms (513 x N), static acoustic/phonetic/prosodic features, and participant phenotype/clinical questionnaire responses.
   Counts
     12,523
   Label
     Participants selected from predefined disease cohorts; no explicit classification labels provided in v1.0.
   Sampling Strategies
     1. ID
          sampling:clinical-cohorts
        Name
          Cohort-based sampling at specialty clinics
        Description
          Non-random sample of patients selected from five predetermined clinical groups at specialty clinics and institutions.
        Used Software
        Is Sample
          - yes
        Is Random
          - no
        Source Data
          - Specialty clinics in North America with predefined disorder cohorts
        Is Representative
          - Not intended to be representative of the general population
        Why Not Representative
          - Purposeful enrichment for conditions known to manifest in the voice waveform
        Strategies
          - Deterministic cohort-based inclusion from predefined groups
   Missing Information
     1. ID
          missing:raw-audio
        Name
          Raw audio and free-speech transcripts removed
        Description
          Raw audio waveforms and transcripts of free speech are not included in v1.0 to reduce risk and protect privacy.
        Used Software
        Missing
          - Raw audio waveforms
          - Transcripts of free speech audio
        Why Missing
          - Privacy and de-identification; low-risk initial release with only derived data

Subpopulations

1. ID
     subpops:cohorts
   Name
     Disorder cohorts
   Description
     Identification
       - Voice disorders
       - Neurological and neurodegenerative disorders
       - Mood and psychiatric disorders
       - Respiratory disorders
       - Pediatric voice and speech disorders (not included in v1.0)
     Distribution
       - v1.0 contains adult cohort data only
   Used Software

Distribution Formats

Description | ID | Name | Used Software
--- | --- | --- | ---
Restricted-access database; files available to credentialed users who sign the DUA and complete required training. | distfmt:portal | Credentialed access via Health Data Nexus |
Spectrograms stored as Parquet | distfmt:parquet | Parquet |
phenotype.tsv and static_features.tsv | distfmt:tsv | TSV |
phenotype.json and static_features.json data dictionaries | distfmt:json | JSON |

Distribution Dates

1. ID
     distdate:2024-11-27
   Name
     Initial release date
   Description
     - 2024-11-27
   Used Software

🔍

## 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
Description
The human voice contains complex acoustic markers which have been linked to important health conditions including dementia, mood disorders, and cancer. When viewed as a biomarker, voice is a promising characteristic to measure as it is simple to collect, cost-effective, and has broad clinical utility. The Bridge2AI-Voice project seeks to create an ethically sourced flagship dataset to enable future research in artificial intelligence and support critical insights into the use of voice as a biomarker of health. Bridge2AI-Voice v1.0, the initial release, provides 12,523 recordings for 306 participants collected across five sites in North America. Participants were selected based on known conditions which manifest within the voice waveform including voice disorders, neurological disorders, mood disorders, and respiratory disorders. The initial release contains data considered low risk, including derivations such as spectrograms but not the original voice recordings. Detailed demographic, clinical, and validated questionnaire data are also made available.
Language
English
Page
https://doi.org/10.57764/qb6h-em84
Issued
2024-11-27
Created On
2024-11-27
Keywords

- voice
- bridge2ai
- audio

Creators

Role | Name | ORCID | Affiliation
--- | --- | --- | ---
Contributor | Bridge2AI-Voice Team | creator:bridge2ai-voice-team | -

Addressing Gaps

1. ID
     gap:multisite-diverse-standardized-voice
   Name
     Diverse, multi-institutional, standardized voice dataset
   Description
     Addresses the lack of large, diverse, multi-institutional voice datasets with standardized collection protocols and linked clinical/demographic data for AI research.
   Used Software
   Response
     Provide a large, high-quality, multi-institutional and diverse voice dataset with standardized protocols linked to health information.

Relationships

1. ID
     relationships:participant-session
   Name
     Participant-session linkage
   Description
     - Spectrogram entries include participant_id, session_id, and task_name linking recordings to participants and sessions.
   Used Software

Splits

1. ID
     splits:na
   Name
     Data splits (not specified)
   Description
     - No recommended train/validation/test splits are provided in v1.0.
   Used Software

Anomalies

1. ID
     anomalies:none-reported
   Name
     No anomalies reported
   Description
     - No specific errors, noise sources, or redundancies were reported in the release notes.
   Used Software

External Resources

Description | ID | Name | Used Software
--- | --- | --- | ---
external_resources: ['https://docs.b2ai-voice.org'] future_guarantees: ['Not stated'] archival: ['DOI versioning is provided'] | ext:docs | Documentation website |
external_resources: ['https://doi.org/10.5281/zenodo.14148755'] future_guarantees: ['Not stated'] archival: ['Zenodo DOI'] | ext:redcap-zenodo | Bridge2AI Voice REDCap (v3.20.0) |

Confidential Elements

1. ID
     confidential:clinical-linkage
   Name
     Clinical and demographic information
   Description
     - Dataset includes clinical and demographic information linked to recordings; v1.0 includes de-identified, low-risk derived data only.
   Used Software

Content Warnings
Sensitive Elements

1. ID
     sensitive:health-data
   Name
     Health-related data
   Description
     - Demographics, clinical information, and questionnaire responses related to health conditions
   Used Software

Acquisition Methods

1. ID
     acquisition:direct-recording-and-questionnaires
   Name
     Direct recording with standardized protocol and questionnaires
   Description
     - Raw audio recorded via a custom tablet application with headset when possible; demographic and disease-specific data collected via validated questionnaires and clinical instruments.
     - Multiple tasks including sustained vowel phonation; sessions per participant as needed.
   Used Software
   Was Directly Observed
     yes (raw audio recordings; derived data released)
   Was Reported By Subjects
     yes (validated questionnaires)
   Was Inferred Derived
     yes (derived spectrograms and features from raw audio; ASR transcripts initially generated then removed prior to release)
   Was Validated Verified
     yes (validated questionnaires; standardized collection protocol)

Collection Mechanisms

1. ID
     collection:tablet-headset-redcap
   Name
     Custom tablet application and headset; REDCap for data management
   Description
     - Standardized protocol; custom data collection app on tablet with headset when possible; REDCap used for clinical/phenotype data; export and conversion performed with an open-source library (b2aiprep).
   Used Software

     ID | Name | URL
     --- | --- | ---
     software:redcap | REDCap |
     software:b2aiprep | b2aiprep | https://github.com/sensein/b2aiprep

Data Collectors

1. ID
     collectors:project-investigators
   Name
     Project investigators at specialty clinics and institutions
   Description
     - Patients screened for inclusion/exclusion; investigators obtained consent and conducted data collection sessions.
   Used Software

Ethical Reviews

Description | ID | Name | Used Software
--- | --- | --- | ---
Data collection and sharing approved by the University of South Florida Institutional Review Board. | ethics:usf-irb | University of South Florida IRB |
Submission for review to the University of Toronto Research Ethics Board. | ethics:utoronto-reb | University of Toronto Research Ethics Board |

Data Protection Impacts
Preprocessing Strategies

1. ID
     prep:audio-standardization
   Name
     Audio standardization and feature derivation
   Description
     - Raw audio converted to mono, resampled to 16 kHz with a Butterworth anti-aliasing filter; short-time FFT spectrograms computed (25 ms window, 10 ms hop, 512-point FFT).
     - Acoustic features extracted with OpenSMILE; phonetic and prosodic features computed using Parselmouth and Praat; ASR transcriptions generated using Whisper Large (transcripts of free speech removed before release).
   Used Software

     ID | Name | URL
     --- | --- | ---
     software:opensmile | OpenSMILE |
     software:parselmouth | Parselmouth |
     software:praat | Praat |
     software:torchaudio | Torchaudio |
     software:whisper | OpenAI Whisper Large |
     software:b2aiprep | b2aiprep | https://github.com/sensein/b2aiprep

Cleaning Strategies

1. ID
     cleaning:deid-and-removals
   Name
     De-identification and removal of sensitive fields
   Description
     - HIPAA Safe Harbor identifiers removed; state/province removed (country retained); transcripts of free speech removed; raw audio waveforms omitted from v1.0.
   Used Software

Labeling Strategies

1. ID
     labeling:asr-internal-then-removed
   Name
     ASR transcription (internal) then removal
   Description
     - Transcriptions were generated using Whisper Large during processing; transcripts of free speech audio were removed in the released dataset.
   Used Software
     - ID
         software:whisper
       Name
         OpenAI Whisper Large

Raw Sources

1. ID
     raw:audio
   Name
     Raw audio waveforms
   Description
     - Raw audio recorded during sessions; not included in v1.0 release. Future releases may include voice data with additional precautions for data security.
   Used Software

Ip Restrictions
Regulatory Restrictions
Maintainers
Errata
Retention Limit
Extension Mechanism
Is Deidentified

ID
  deid:hipaa-safe-harbor
Name
  HIPAA Safe Harbor de-identification
Description
  - Removal of HIPAA Safe Harbor identifiers
  - Removal of state/province (country retained)
  - Removal of transcripts of free speech
  - Omission of raw audio in v1.0 (derived data only)

Is Tabular
mixed
Subsets

Role | Name | ORCID | Affiliation
--- | --- | --- | ---
Contributor | spectrograms.parquet | subset:spectrograms-parquet | -
Contributor | phenotype.tsv | subset:phenotype-tsv | -
Contributor | phenotype.json | subset:phenotype-json | -
Contributor | static_features.tsv | subset:static-features-tsv | -
Contributor | static_features.json | subset:static-features-json | -

🚀

## Uses

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

Tasks

1. ID
     task:acoustic-feature-analysis
   Name
     Acoustic feature analysis and modeling
   Description
     Support AI/ML methods for extracting prognostic and diagnostic information from voice-derived spectrograms and features.
   Used Software
   Response
     AI/ML research on voice-derived spectrograms and features linked to health data.

Existing Uses
Use Repository
Other Tasks

1. ID
     othertasks:health-ai
   Name
     Additional health AI tasks
   Description
     - Potential use in screening, monitoring, and characterization of conditions that manifest in voice and speech.
   Used Software

Future Use Impacts

1. ID
     future:low-risk-derivatives
   Name
     Low-risk derivative-only release considerations
   Description
     - Initial release limits data to derived spectrograms and features (no raw audio or free-speech transcripts) to reduce privacy risks and support ethical use.
   Used Software

Discouraged Uses
License And Use Terms

ID
  terms:registered-access
Name
  Registered access license and DUA
Description
  - License
      Bridge2AI Voice Registered Access License
  - Data Use Agreement
      Bridge2AI Voice Registered Access Agreement
  - 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
License
Bridge2AI Voice Registered Access License
Version Access

ID
  versioning:doi-latest
Name
  DOI versioning
Description
  - Latest Version DOI
      https://doi.org/10.57764/3sg0-7440

🔄

## Maintenance

How will the dataset be maintained?

Last Updated On
2024-11-27
Version
1.0
Updates

ID
  updates:future-voice-inclusion
Name
  Planned future updates
Description
  - v1.0 is the first release; future releases aim to include voice audio with additional data security precautions.

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