Document: D4D Collection - AI-READI FAIRHub - All.docx
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Text: Prompt

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Text: Create a json record based on the schema below, filling out values based on the text further below:

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Text: id: https://w3id.org/bridge2ai/data-sheets-schema/collection

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Text: name: d4d-collection

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Text: title: "D4D Collection Subset"

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Text: description: >

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Text: A LinkML schema subset for capturing how data was collected, including

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Text: who collected it, over what timeframe, whether consent was gathered,

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Text: notifications were provided, etc. Based on the “Collection” subset

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Text: from the full Data Sheets for Datasets (D4D) schema.

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Text: license: MIT

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Text: prefixes:

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Text: linkml: https://w3id.org/linkml/

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Text: # You can add other prefixes as needed, e.g., schema, dcterms, etc.

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Text: # If you have a "core" module or a "D4D-Metadata" module that defines NamedThing or DatasetProperty,

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Text: # you can import it here instead of redefining them:

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Text: # imports:

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Text: #   - https://w3id.org/bridge2ai/data-sheets-schema/core

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Text: subsets:

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Text: Collection:

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Text: description: >

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Text: The "Collection" subset focuses on who collected the data, how and when it was collected,

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Text: whether participants were notified or gave consent, and related ethical considerations.

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Text: #------------------------------------------------------------------------------

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Text: # Minimal "foundation" classes (you can omit if imported from a core schema)

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Text: #------------------------------------------------------------------------------

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Text: classes:

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Text: NamedThing:

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Text: description: "A generic superclass for all identifiable entities."

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Text: DatasetProperty:

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Text: is_a: NamedThing

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Text: description: >

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Text: Represents a single property or aspect of a dataset.

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Text: Many specialized dataset elements will subclass DatasetProperty.

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Text: #------------------------------------------------------------------------------

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Text: # Collection-Related Classes

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Text: #------------------------------------------------------------------------------

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Text: InstanceAcquisition:

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Text: is_a: DatasetProperty

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Text: description: >

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Text: Describes how data associated with each instance was acquired

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Text: (e.g., directly observed, reported by subjects, inferred).

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Text: in_subset:

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Text: - Collection

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Text: attributes:

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Text: description:

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Text: description: Free-text description of the acquisition process

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Text: range: string

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Text: multivalued: true

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Text: was_directly_observed:

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Text: description: Whether the data was directly observed

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Text: range: boolean

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Text: was_reported_by_subjects:

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Text: description: Whether the data was reported directly by the subjects themselves

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Text: range: boolean

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Text: was_inferred_derived:

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Text: description: Whether the data was inferred or derived from other data

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Text: range: boolean

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Text: was_validated_verified:

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Text: description: Whether the data was validated or verified in any way

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Text: range: boolean

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Text: CollectionMechanism:

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Text: is_a: DatasetProperty

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Text: description: >

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Text: What mechanisms or procedures were used to collect the data (e.g., hardware,

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Text: manual curation, software APIs)? Also covers how these mechanisms were validated.

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Text: in_subset:

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Text: - Collection

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Text: attributes:

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Text: description:

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Text: description: Free-text description of the collection mechanism(s)

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Text: range: string

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Text: multivalued: true

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Text: DataCollector:

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Text: is_a: DatasetProperty

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Text: description: >

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Text: Who was involved in the data collection (e.g., students, crowdworkers, contractors),

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Text: and how they were compensated.

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Text: in_subset:

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Text: - Collection

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Text: attributes:

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Text: description:

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Text: description: Free-text details about the data collectors and compensation

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Text: range: string

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Text: multivalued: true

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Text: CollectionTimeframe:

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Text: is_a: DatasetProperty

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Text: description: >

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Text: Over what timeframe was the data collected, and does this timeframe match the

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Text: creation timeframe of the underlying data?

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Text: in_subset:

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Text: - Collection

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Text: attributes:

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Text: description:

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Text: description: Free-text description of the data collection timeframe

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Text: range: string

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Text: multivalued: true

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Text: DirectCollection:

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Text: is_a: DatasetProperty

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Text: description: >

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Text: Indicates whether the data was collected directly from the individuals in question

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Text: or obtained via third parties/other sources.

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Text: in_subset:

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Text: - Collection

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Text: attributes:

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Text: description:

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Text: description: Free-text description of data collection source(s)

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Text: range: string

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Text: multivalued: true

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Text: CollectionNotification:

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Text: is_a: DatasetProperty

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Text: description: >

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Text: Whether individuals were notified about data collection, and if so, how they were notified.

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Text: in_subset:

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Text: - Collection

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Text: attributes:

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Text: description:

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Text: description: Details of the notification method and language

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Text: range: string

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Text: multivalued: true

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Text: CollectionConsent:

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Text: is_a: DatasetProperty

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Text: description: >

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Text: Whether individuals consented to the collection and use of their data, and how consent

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Text: was requested/provided.

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Text: in_subset:

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Text: - Collection

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Text: attributes:

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Text: description:

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Text: description: Details of the consent process and exact language

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Text: range: string

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Text: multivalued: true

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Text: ConsentRevocation:

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Text: is_a: DatasetProperty

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Text: description: >

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Text: If consent was obtained, was there a mechanism to revoke that consent in the future?

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Text: in_subset:

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Text: - Collection

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Text: attributes:

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Text: description:

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Text: description: Details or evidence of how consent can be revoked

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Text: range: string

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Text: multivalued: true

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Text: EthicalReview:

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Text: is_a: DatasetProperty

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Text: description: >

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Text: Whether any ethical review or IRB process was conducted, and what the outcomes were.

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Text: in_subset:

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Text: - Collection

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Text: attributes:

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Text: description:

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Text: description: Details or references for ethical review, outcomes, or documentation

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Text: range: string

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Text: multivalued: true

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Text: DataProtectionImpact:

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Text: is_a: DatasetProperty

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Text: description: >

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Text: Whether a data protection impact assessment (or similar) was conducted to evaluate risks

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Text: to the data subjects, including outcomes or references to documentation.

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Text: in_subset:

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Text: - Collection

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Text: attributes:

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Text: description:

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Text: description: Free-text description of the impact assessment

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Text: range: string

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Text: multivalued: true

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Text: Text:

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Text: FAIRhub

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Text: Find Datasets

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Text: Share datasets

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Text: About

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Text: Contact

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Text: Flagship Dataset of Type 2 Diabetes from the AI-READI Project

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Text: AI-READI Consortium

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Text: Flagship Dataset of Type 2 Diabetes from the AI-READI Project

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Text: About

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Text: Healthsheet

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Text: Study Dashboard

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Text: Study Metadata

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Text: Dataset Metadata

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Text: Dataset Structure Preview

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Text: Dataset Quality Dashboard

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Text: Dataset Uses

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Text: Info

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Text: This page provides an overview of the dataset and associated study.

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Text: Overview of the study

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Text: The Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights (AI-READI) project seeks to create a flagship ethically-sourced dataset to enable future generations of artificial intelligence/machine learning (AI/ML) research to provide critical insights into type 2 diabetes mellitus (T2DM), including salutogenic pathways to return to health. The ability to understand and affect the course of complex, multi-organ diseases such as T2DM has been limited by a lack of well-designed, high quality, large, and inclusive multimodal datasets. The AI-READI team of investigators will aim to collect a cross-sectional dataset of 4,000 people and longitudinal data from 10% of the study cohort across the US. The study cohort will be balanced for self-reported race/ethnicity, gender, and diabetes disease stage. Data collection will be specifically designed to permit downstream pseudo-time manifold analysis, an approach used to predict disease trajectories by collecting and learning from complex, multimodal data from participants with differing disease severity (normal to insulin-dependent T2DM). The long-term objective for this project is to develop a foundational dataset in T2DM, agnostic to existing classification criteria or biases, which can be used to reconstruct a temporal atlas of T2DM development and reversal towards health (i.e., salutogenesis). Data will be optimized for downstream AI/ML research and made publicly available. This project will also create a roadmap for ethical and equitable research that focuses on the diversity of the research participants and the workforce involved at all stages of the research process (study design and data collection, curation, analysis, and sharing and collaboration).

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Text: Description of the dataset

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Text: This dataset contains data from 1067 participants that was collected between July 19, 2023 and July 31, 2024. Data from multiple modalities are included. A full list is provided in the Data Standards section below. The data in this dataset contain no protected health information (PHI). Information related to the sex and race/ethnicity of the participants as well as medication used has also been removed.

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Text: The dataset contains 165,051 files and is around 2.01 TB in size.

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Text: A detailed description of the dataset is available in the AI-READI documentation for v2.0.0 of the dataset at docs.aireadi.org.

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Text: Protocol

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Text: The protocol followed for collecting the data can be found in the AI-READI documentation for v2.0.0 of the dataset at docs.aireadi.org.

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Text: Dataset access/restrictions

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Text: Accessing the dataset requires several steps, including:

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Text: Login in through a verified ID system

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Text: Agreeing to use the data only for type 2 diabetes related research.

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Text: Agreeing to the license terms which set certain restrictions and obligations for data usage (see License section below).

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Text: Data standards followed

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Text: This dataset is organized following the Clinical Dataset Structure (CDS) v0.1.1. We refer to the CDS documentation for more details. Briefly, data is organized at the root level into one directory per datatype (c.f. Table below). Within each datatype folder, there is one folder per modality. Within each modality folder, there is one folder per device used to collect that modality. Within each device folder, there is one folder per participant. Each datatype, modality, and device folder is named using a name that best defines it. Each participant folder is named after the participant's ID number used in the study. For each datatype, the data files follow the standards listed in the Table below. More details are available in the dataset_structure_description.json metadata file included in this dataset.

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Text: Datatype directory name	Description	File format standard followed

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Text: cardiac_ecg	This directory contains electrocardiogram data collected by a 12 lead protocol (the current standard), Holter monitor, or smartwatch. The terms ECG and EKG are often used interchangeably.	WaveForm DataBase (WFDB)

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Text: clinical_data	This directory contains clinical data collected through REDCap. Each CSV file in this directory is a one-to-one mapping to the OMOP CDM tables.	Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM)

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Text: environment	This directory contains data collected through an environmental sensor device custom built for the AI-READI project.	Earth Science Data Systems (ESDS) format

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Text: retinal_flio	This directory contains data collected through fluorescence lifetime imaging ophthalmoscopy (FLIO), an imaging modality for in vivo measurement of lifetimes of endogenous retinal fluorophores.	Digital Imaging and Communications in Medicine (DICOM)

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Text: retinal_oct	This directory contains data collected using optical coherence tomography (OCT), an imaging method using lasers that is used for mapping subsurface structure.	Digital Imaging and Communications in Medicine (DICOM)

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Text: retinal_octa	This directory contains data collected using optical coherence tomography angiography (OCTA), a non-invasive imaging technique that generates volumetric angiography images.	Digital Imaging and Communications in Medicine (DICOM)

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Text: retinal_photography	This directory contains retinal photography data, which are 2D images. They are also referred to as fundus photography.	Digital Imaging and Communications in Medicine (DICOM)

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Text: wearable_activity_monitor	This directory contains data collected through a wearable fitness tracker.	Open mHealth

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Text: wearable_blood_glucose	This directory contains data collected through a continuous glucose monitoring (CGM) device.	Open mHealth

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Text: Resources

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Text: All of our data files are in formats that are accessible with free software commonly used for such data types so no specific software is required. Some useful resources related to this dataset are listed below:

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Text: Documentation of the dataset: docs.aireadi.org (see Dataset v2.0.0 for this version of the dataset)

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Text: AI-READI project website: aireadi.org

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Text: Zenodo community of the AI-READI project: zenodo.org/communities/aireadi

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Text: GitHub organization of the AI-READI project: github.com/AI-READI

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Text: Suggested split

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Text: The suggested split for training, validating, and testing AI/ML models is included in the participants.tsv file that will be included in the dataset. A summary is provided in the table below.

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Text: Train				Val				Test				Total

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Text: Hispanic	Asian	Black	White	Hispanic	Asian	Black	White	Hispanic	Asian	Black	White	Hispanic	Asian	Black	White

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Text: Race/ethnicity (count)	144	167	211	225	40	40	40	40	40	40	40	40	224	247	291	305

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Text: Male	Female			Male	Female			Male	Female			Male	Female

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Text: Sex (count)	302	445			80	80			80	80			462	605

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Text: No DM	Lifestyle	Oral	Insulin	No DM	Lifestyle	Oral	Insulin	No DM	Lifestyle	Oral	Insulin	No DM	Lifestyle	Oral	Insulin

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Text: Diabetes status (count)	292	162	235	58	40	40	47	33	40	40	41	39	364	242	331	130

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Text: Mean age (years ± sd)	60.3 ± 11.13				60.2 ± 10.5				60.4 ± 11.0				60.3 ± 11.1

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Text: Total	747				160				160				1067

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Text: No DM : Participants who do not have Type 1 or Type 2 Diabetes

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Text: Lifestyle: Participants with pre-Type 2 Diabetes and those with Type 2 Diabetes whose blood sugar is controlled by lifestyle adjustments

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Text: Oral: Participants with Type 2 Diabetes whose blood sugar is controlled by oral or injectable medications other than insulin

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Text: Insulin: Participants with Type 2 Diabetes whose blood sugar is controlled by insulin

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Text: Changes between versions of the dataset

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Text: Changes between the current version of the dataset and the previous one are provided in details in the CHANGELOG file included in the dataset (also visible at docs.aireadi.org). A summary of the major changes is provided in the table below.

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Text: Dataset	v1.0.0 pilot	year 2 data	v2.0.0 main study

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Text: Participants	204	863	1067

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Text: Data types	15+ data types	+1 image device	15+ data types

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Text: Processing	custom / ad hoc	automated + custom	automated + custom

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Text: Release date	5/3/2024	included in v2.0.0	11/8/2024

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Text: License

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Text: This work is licensed under a custom license specifically tailored to enable the reuse of the AI-READI dataset (and other clinical datasets) for commercial or research purposes while putting strong requirements around data usage, security, and secondary sharing to protect study participants, especially when data is reused for artificial intelligence (AI) and machine learning (ML) related applications. More details are available in the License file included in the dataset and also available at https://doi.org/10.5281/zenodo.10642459.

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Text: How to cite

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Text: If you use this dataset for any purpose, please cite the resources specified in the AI-READI documentation for version 2.0.0 of the dataset at https://docs.aireadi.org.

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Text: Contact

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Text: For any questions, suggestions, or feedback related to this dataset, please go to https://aireadi.org/contact. We refer to the study_description.json and dataset_description.json metadata files included in this dataset for additional information about the contact person/entity, authors, and contributors of the dataset.

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Text: Acknowledgement

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Text: The AI-READI project is supported by NIH grant 1OT2OD032644 through the NIH Bridge2AI Common Fund program.

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Text: Usage statistics

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Text: Views

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Text: 0

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Text: Cited by

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Text: 303

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Text: Access approved

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Text: All versions

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Text: Current version

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Text: 2.01 TB

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Text: 165,051 Files

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Text: License

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Text: Health Data License

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Text: Keywords

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Text: Diabetes mellitus

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Text: Machine Learning

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Text: Artificial Intelligence

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Text: Electrocardiography

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Text: Continuous Glucose Monitoring

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Text: Retinal imaging

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Text: Eye exam

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Text: Citation

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Text: When using this resource, please follow the citation instructions provided at https://docs.aireadi.org/docs/2/citation

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Text: Versions

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Text: Version 2.0.0

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Text: 10.60775/fairhub.2

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Text: Nov 8, 2024

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Text: Version 1.0.0

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Text: 10.60775/fairhub.1

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Text: May 3, 2024

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Text: Response

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Text: {

[333] Style: normal
Text: "id": "https://w3id.org/bridge2ai/data-sheets-schema/collection",

[334] Style: normal
Text: "name": "d4d-collection",

[335] Style: normal
Text: "title": "D4D Collection Subset",

[336] Style: normal
Text: "description": "A LinkML schema subset for capturing how data was collected, including who collected it, over what timeframe, whether consent was gathered, notifications were provided, etc. Based on the “Collection” subset from the full Data Sheets for Datasets (D4D) schema.",

[337] Style: normal
Text: "license": "MIT",

[338] Style: normal
Text: "prefixes": {

[339] Style: normal
Text: "linkml": "https://w3id.org/linkml/"

[340] Style: normal
Text: // Additional prefixes can be added here

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Text: },

[342] Style: normal
Text: "subsets": {

[343] Style: normal
Text: "Collection": {

[344] Style: normal
Text: "description": "The \"Collection\" subset focuses on who collected the data, how and when it was collected, whether participants were notified or gave consent, and related ethical considerations."

[345] Style: normal
Text: }

[346] Style: normal
Text: },

[347] Style: normal
Text: "classes": {

[348] Style: normal
Text: "NamedThing": {

[349] Style: normal
Text: "description": "A generic superclass for all identifiable entities."

[350] Style: normal
Text: },

[351] Style: normal
Text: "DatasetProperty": {

[352] Style: normal
Text: "is_a": "NamedThing",

[353] Style: normal
Text: "description": "Represents a single property or aspect of a dataset. Many specialized dataset elements will subclass DatasetProperty."

[354] Style: normal
Text: },

[356] Style: normal
Text: "InstanceAcquisition": {

[357] Style: normal
Text: "is_a": "DatasetProperty",

[358] Style: normal
Text: "description": "Describes how data associated with each instance was acquired (e.g., directly observed, reported by subjects, inferred).",

[359] Style: normal
Text: "in_subset": [

[360] Style: normal
Text: "Collection"

[361] Style: normal
Text: ],

[362] Style: normal
Text: "attributes": {

[363] Style: normal
Text: "description": {

[364] Style: normal
Text: "description": "Free-text description of the acquisition process",

[365] Style: normal
Text: "range": "string",

[366] Style: normal
Text: "multivalued": true,

[367] Style: normal
Text: "examples": [

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Text: {

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Text: "value": "Data were collected from 1067 participants in the AI-READI T2DM study between July 19, 2023 and July 31, 2024 using multiple modalities (e.g., ECG, retinal imaging, continuous glucose monitors, environment sensors). Some data were entered by participants via REDCap (self-report), and other data were directly captured by clinical or wearable devices."

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Text: }

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Text: ]

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Text: },

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Text: "was_directly_observed": {

[374] Style: normal
Text: "description": "Whether the data was directly observed",

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Text: "range": "boolean",

[376] Style: normal
Text: "examples": [

[377] Style: normal
Text: {

[378] Style: normal
Text: "value": true,

[379] Style: normal
Text: "description": "Physiological data from ECG, retinal scans, and wearable monitors were directly measured by devices."

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Text: }

[381] Style: normal
Text: ]

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Text: },

[383] Style: normal
Text: "was_reported_by_subjects": {

[384] Style: normal
Text: "description": "Whether the data was reported directly by the subjects themselves",

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Text: "range": "boolean",

[386] Style: normal
Text: "examples": [

[387] Style: normal
Text: {

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Text: "value": true,

[389] Style: normal
Text: "description": "Participants entered some information into REDCap forms (e.g., demographic or lifestyle data)."

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Text: }

[391] Style: normal
Text: ]

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Text: },

[393] Style: normal
Text: "was_inferred_derived": {

[394] Style: normal
Text: "description": "Whether the data was inferred or derived from other data",

[395] Style: normal
Text: "range": "boolean",

[396] Style: normal
Text: "examples": [

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Text: {

[398] Style: normal
Text: "value": false,

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Text: "description": "No explicit mention of derived data, though some analyses may create secondary variables."

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Text: }

[401] Style: normal
Text: ]

[402] Style: normal
Text: },

[403] Style: normal
Text: "was_validated_verified": {

[404] Style: normal
Text: "description": "Whether the data was validated or verified in any way",

[405] Style: normal
Text: "range": "boolean",

[406] Style: normal
Text: "examples": [

[407] Style: normal
Text: {

[408] Style: normal
Text: "value": false,

[409] Style: normal
Text: "description": "The text does not specify formal verification steps beyond standard clinical data collection procedures."

[410] Style: normal
Text: }

[411] Style: normal
Text: ]

[412] Style: normal
Text: }

[413] Style: normal
Text: }

[414] Style: normal
Text: },

[416] Style: normal
Text: "CollectionMechanism": {

[417] Style: normal
Text: "is_a": "DatasetProperty",

[418] Style: normal
Text: "description": "What mechanisms or procedures were used to collect the data (e.g., hardware, manual curation, software APIs)? Also covers how these mechanisms were validated.",

[419] Style: normal
Text: "in_subset": [

[420] Style: normal
Text: "Collection"

[421] Style: normal
Text: ],

[422] Style: normal
Text: "attributes": {

[423] Style: normal
Text: "description": {

[424] Style: normal
Text: "description": "Free-text description of the collection mechanism(s)",

[425] Style: normal
Text: "range": "string",

[426] Style: normal
Text: "multivalued": true,

[427] Style: normal
Text: "examples": [

[428] Style: normal
Text: {

[429] Style: normal
Text: "value": "Data were collected through hardware sensors (ECG machines, environmental monitors, continuous glucose monitors), manual entry into REDCap for clinical data, and standardized imaging devices for retinal scans. All data were organized following the CDS v0.1.1 structure."

[430] Style: normal
Text: }

[431] Style: normal
Text: ]

[432] Style: normal
Text: }

[433] Style: normal
Text: }

[434] Style: normal
Text: },

[436] Style: normal
Text: "DataCollector": {

[437] Style: normal
Text: "is_a": "DatasetProperty",

[438] Style: normal
Text: "description": "Who was involved in the data collection (e.g., students, crowdworkers, contractors), and how they were compensated.",

[439] Style: normal
Text: "in_subset": [

[440] Style: normal
Text: "Collection"

[441] Style: normal
Text: ],

[442] Style: normal
Text: "attributes": {

[443] Style: normal
Text: "description": {

[444] Style: normal
Text: "description": "Free-text details about the data collectors and compensation",

[445] Style: normal
Text: "range": "string",

[446] Style: normal
Text: "multivalued": true,

[447] Style: normal
Text: "examples": [

[448] Style: normal
Text: {

[449] Style: normal
Text: "value": "Data were collected by the AI-READI consortium across multiple clinical sites in the US with researchers funded by an NIH grant (1OT2OD032644). There is no specific mention of crowdsourcing or external contractors."

[450] Style: normal
Text: }

[451] Style: normal
Text: ]

[452] Style: normal
Text: }

[453] Style: normal
Text: }

[454] Style: normal
Text: },

[456] Style: normal
Text: "CollectionTimeframe": {

[457] Style: normal
Text: "is_a": "DatasetProperty",

[458] Style: normal
Text: "description": "Over what timeframe was the data collected, and does this timeframe match the creation timeframe of the underlying data?",

[459] Style: normal
Text: "in_subset": [

[460] Style: normal
Text: "Collection"

[461] Style: normal
Text: ],

[462] Style: normal
Text: "attributes": {

[463] Style: normal
Text: "description": {

[464] Style: normal
Text: "description": "Free-text description of the data collection timeframe",

[465] Style: normal
Text: "range": "string",

[466] Style: normal
Text: "multivalued": true,

[467] Style: normal
Text: "examples": [

[468] Style: normal
Text: {

[469] Style: normal
Text: "value": "Data for this version of the dataset (v2.0.0) were collected between July 19, 2023 and July 31, 2024 from 1067 participants."

[470] Style: normal
Text: }

[471] Style: normal
Text: ]

[472] Style: normal
Text: }

[473] Style: normal
Text: }

[474] Style: normal
Text: },

[476] Style: normal
Text: "DirectCollection": {

[477] Style: normal
Text: "is_a": "DatasetProperty",

[478] Style: normal
Text: "description": "Indicates whether the data was collected directly from the individuals in question or obtained via third parties/other sources.",

[479] Style: normal
Text: "in_subset": [

[480] Style: normal
Text: "Collection"

[481] Style: normal
Text: ],

[482] Style: normal
Text: "attributes": {

[483] Style: normal
Text: "description": {

[484] Style: normal
Text: "description": "Free-text description of data collection source(s)",

[485] Style: normal
Text: "range": "string",

[486] Style: normal
Text: "multivalued": true,

[487] Style: normal
Text: "examples": [

[488] Style: normal
Text: {

[489] Style: normal
Text: "value": "Most data (clinical, wearable, imaging) were collected directly from participants and their devices during study visits or via REDCap forms. No mention of third-party data acquisition."

[490] Style: normal
Text: }

[491] Style: normal
Text: ]

[492] Style: normal
Text: }

[493] Style: normal
Text: }

[494] Style: normal
Text: },

[496] Style: normal
Text: "CollectionNotification": {

[497] Style: normal
Text: "is_a": "DatasetProperty",

[498] Style: normal
Text: "description": "Whether individuals were notified about data collection, and if so, how they were notified.",

[499] Style: normal
Text: "in_subset": [

[500] Style: normal
Text: "Collection"

[501] Style: normal
Text: ],

[502] Style: normal
Text: "attributes": {

[503] Style: normal
Text: "description": {

[504] Style: normal
Text: "description": "Details of the notification method and language",

[505] Style: normal
Text: "range": "string",

[506] Style: normal
Text: "multivalued": true,

[507] Style: normal
Text: "examples": [

[508] Style: normal
Text: {

[509] Style: normal
Text: "value": "Participants were informed of data collection during the study’s consent process, in alignment with standard IRB protocols for clinical research."

[510] Style: normal
Text: }

[511] Style: normal
Text: ]

[512] Style: normal
Text: }

[513] Style: normal
Text: }

[514] Style: normal
Text: },

[516] Style: normal
Text: "CollectionConsent": {

[517] Style: normal
Text: "is_a": "DatasetProperty",

[518] Style: normal
Text: "description": "Whether individuals consented to the collection and use of their data, and how consent was requested/provided.",

[519] Style: normal
Text: "in_subset": [

[520] Style: normal
Text: "Collection"

[521] Style: normal
Text: ],

[522] Style: normal
Text: "attributes": {

[523] Style: normal
Text: "description": {

[524] Style: normal
Text: "description": "Details of the consent process and exact language",

[525] Style: normal
Text: "range": "string",

[526] Style: normal
Text: "multivalued": true,

[527] Style: normal
Text: "examples": [

[528] Style: normal
Text: {

[529] Style: normal
Text: "value": "Informed consent was obtained from all participants prior to data collection. The exact consent language and process are governed by IRB protocols and documented in the study’s protocol (see docs.aireadi.org)."

[530] Style: normal
Text: }

[531] Style: normal
Text: ]

[532] Style: normal
Text: }

[533] Style: normal
Text: }

[534] Style: normal
Text: },

[536] Style: normal
Text: "ConsentRevocation": {

[537] Style: normal
Text: "is_a": "DatasetProperty",

[538] Style: normal
Text: "description": "If consent was obtained, was there a mechanism to revoke that consent in the future?",

[539] Style: normal
Text: "in_subset": [

[540] Style: normal
Text: "Collection"

[541] Style: normal
Text: ],

[542] Style: normal
Text: "attributes": {

[543] Style: normal
Text: "description": {

[544] Style: normal
Text: "description": "Details or evidence of how consent can be revoked",

[545] Style: normal
Text: "range": "string",

[546] Style: normal
Text: "multivalued": true,

[547] Style: normal
Text: "examples": [

[548] Style: normal
Text: {

[549] Style: normal
Text: "value": "Not explicitly stated in the dataset description. Typically, participants can withdraw from NIH-funded studies at any time. Specific revocation procedures would be outlined in the IRB-approved consent form."

[550] Style: normal
Text: }

[551] Style: normal
Text: ]

[552] Style: normal
Text: }

[553] Style: normal
Text: }

[554] Style: normal
Text: },

[556] Style: normal
Text: "EthicalReview": {

[557] Style: normal
Text: "is_a": "DatasetProperty",

[558] Style: normal
Text: "description": "Whether any ethical review or IRB process was conducted, and what the outcomes were.",

[559] Style: normal
Text: "in_subset": [

[560] Style: normal
Text: "Collection"

[561] Style: normal
Text: ],

[562] Style: normal
Text: "attributes": {

[563] Style: normal
Text: "description": {

[564] Style: normal
Text: "description": "Details or references for ethical review, outcomes, or documentation",

[565] Style: normal
Text: "range": "string",

[566] Style: normal
Text: "multivalued": true,

[567] Style: normal
Text: "examples": [

[568] Style: normal
Text: {

[569] Style: normal
Text: "value": "The AI-READI study was conducted under an IRB-approved protocol (NIH-supported). The project explicitly aims to create a roadmap for ethical and equitable research. Outcome: Approval for data collection and sharing with restrictions."

[570] Style: normal
Text: }

[571] Style: normal
Text: ]

[572] Style: normal
Text: }

[573] Style: normal
Text: }

[574] Style: normal
Text: },

[576] Style: normal
Text: "DataProtectionImpact": {

[577] Style: normal
Text: "is_a": "DatasetProperty",

[578] Style: normal
Text: "description": "Whether a data protection impact assessment (or similar) was conducted to evaluate risks to the data subjects, including outcomes or references to documentation.",

[579] Style: normal
Text: "in_subset": [

[580] Style: normal
Text: "Collection"

[581] Style: normal
Text: ],

[582] Style: normal
Text: "attributes": {

[583] Style: normal
Text: "description": {

[584] Style: normal
Text: "description": "Free-text description of the impact assessment",

[585] Style: normal
Text: "range": "string",

[586] Style: normal
Text: "multivalued": true,

[587] Style: normal
Text: "examples": [

[588] Style: normal
Text: {

[589] Style: normal
Text: "value": "All protected health information (PHI) was removed from the dataset, and strong usage restrictions are enforced via a custom license. The text indicates the data is de-identified and only accessible under controlled conditions for T2DM research."

[590] Style: normal
Text: }

[591] Style: normal
Text: ]

[592] Style: normal
Text: }

[593] Style: normal
Text: }

[594] Style: normal
Text: }

[595] Style: normal
Text: }

[596] Style: normal
Text: }


TABLES:
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DOCUMENT PROPERTIES:
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Title: Word Document
Author: 
Created: None
Modified: 2025-04-16 17:44:58+00:00
