Add claudecode_assistant D4D files and extraction reports

Added claudecode_assistant concatenated D4D files:
- AI_READI_d4d.yaml
- CHORUS_d4d.yaml
- CM4AI_d4d.yaml
- VOICE_d4d.yaml

Updated extraction reports:
- data/raw/organized_extraction_report.md
- data/raw/organized_extraction_summary.json

The claudecode_assistant method represents in-session synthesis
with direct user interaction during generation.

🤖 Generated with [Claude Code](https://claude.com/claude-code)

Author: realmarcin

data/d4d_concatenated/claudecode_assistant/AI_READI_d4d.yaml

@@ -0,0 +1,394 @@
+# D4D Datasheet for AI-READI Dataset
+# Generated by: Claude Code Assistant (In-Session Synthesis)
+# Source: data/preprocessed/concatenated/AI_READI_preprocessed.txt (13 source files)
+# Schema: data_sheets_schema_all.yaml
+# Generation Date: 2025-12-06
+
+id: https://fairhub.io/datasets/2
+name: AI-READI Dataset
+title: Flagship Dataset of Type 2 Diabetes from the AI-READI Project
+description: 'The AI-READI (Artificial Intelligence Ready and Exploratory Atlas for Diabetes Insights)
+  dataset
+
+  consists of data collected from individuals with and without Type 2 Diabetes Mellitus (T2DM),
+
+  harmonized across 3 data collection sites (University of Washington, University of Alabama at
+
+  Birmingham, and University of California San Diego). The composition was designed with future
+
+  AI/Machine Learning studies in mind, including recruitment sampling procedures aimed at achieving
+
+  triple-balanced distribution (race/ethnicity, diabetes severity, biological sex) and a multi-domain
+
+  data acquisition protocol (survey data, physical measurements, clinical data, imaging data, wearable
+
+  device data, environmental sensors) to enable downstream AI/ML analyses. Target enrollment is 4,000
+
+  participants with approximately 400 participants in longitudinal follow-up. The goal is to better
+
+  understand salutogenesis (the pathway from disease to health) in T2DM through pseudotime manifold
+
+  analysis.'
+page: https://fairhub.io/datasets/2
+language: en
+keywords:
+- Type 2 Diabetes
+- T2DM
+- AI-READI
+- Machine Learning
+- multimodal
+- harmonized
+- multi-site
+- salutogenesis
+- survey data
+- clinical data
+- imaging data
+- wearable device data
+- retinal images
+- ECG
+- blood glucose
+- laboratory results
+- environmental data
+- FAIR principles
+- Bridge2AI
+purposes:
+- description: '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. Develop a
+
+    foundational dataset in diabetes, agnostic to existing classification criteria, which can
+
+    be used to reconstruct a temporal atlas of T2DM development and reversal towards health.'
+tasks:
+- description: 'Enable downstream AI/ML analyses including pseudotime manifold analysis to predict disease
+
+    trajectories across survey, clinical, imaging, wearable, and environmental domains related
+
+    to T2DM that may not be feasible with existing data sources such as claims or electronic
+
+    health records data.'
+addressing_gaps:
+- description: 'Address the lack of well-designed, high quality, and large multimodal datasets needed
+    to
+
+    understand and affect the course of complex, multi-organ diseases such as T2DM. Provide
+
+    a harmonized, multi-site, multi-domain dataset with triple-balanced recruitment (race/ethnicity,
+
+    diabetes severity, biological sex) enabling AI/ML analyses not feasible with existing sources.'
+creators:
+- name: AI-READI Consortium
+  description: 'Multi-institutional team led by Contact PI Aaron Lee (University of Washington, Department
+
+    of Ophthalmology), with teams across eight institutions working on six cross-disciplinary
+
+    project modules.'
+instances:
+- description: 'Individual participants with and without Type 2 Diabetes Mellitus (T2DM) with multi-domain
+
+    measurements. Each participant represents an instance with cross-sectional data including
+
+    survey responses, physical and clinical measurements, blood and urine lab results, retinal
+
+    imaging, ECG, wearable device time-series (10 days), continuous blood glucose monitoring
+
+    (10 days), and environmental sensor data. Target enrollment 4,000 participants across three
+
+    sites, with approximately 400 participants (10%) in longitudinal follow-up.'
+subsets:
+- id: aireadi:public-dataset
+  name: Public dataset
+  description: 'Includes survey data, blood and urine lab results, fitness activity levels from Garmin
+
+    tracker, clinical measurements (monofilament and cognitive function testing), retinal
+
+    images, ECG, continuous blood sugar levels from Dexcom CGM, and environmental variables
+
+    such as home air quality from environmental sensors. Available for public download upon
+
+    agreement with a license that defines how the data can be used.'
+- id: aireadi:controlled-access-dataset
+  name: Controlled-access dataset
+  description: 'Includes 5-digit zip code, sex, race, ethnicity, genetic sequencing data (future),
+
+    past health records, medications, and traffic and accident reports (environmental data).
+
+    Accessible by entering into a data use agreement.'
+sampling_strategies:
+- description: 'Recruitment sampling procedures aimed at achieving triple-balanced distribution across
+
+    four race/ethnic groups (Asian, Black, White, Hispanic), four categories of T2DM severity
+
+    (no diabetes, pre-diabetes/lifestyle-controlled, diabetes treated with oral/non-insulin
+
+    injectable medications, insulin-controlled diabetes), and biological sex (male, female).
+
+    Recruitment in waves with monitoring and adjustment through under- and oversampling as needed.'
+  is_sample:
+  - true
+  is_random:
+  - false
+  is_representative:
+  - false
+  strategies:
+  - Triple-balanced targeted recruitment across race/ethnicity, diabetes severity, and biological sex
+  - Wave-based recruitment with ongoing monitoring and adjustment
+  - Screening electronic health records using ICD-10 codes (R73.09 for pre-diabetes, E11.X for T2DM)
+  - Personalized invitation letters and emails with REDCap recruitment interface
+subpopulations:
+- description: 'Participants with and without Type 2 Diabetes Mellitus, stratified by diabetes severity.
+
+    Four T2DM categories: no diabetes, pre-diabetes/lifestyle-controlled, diabetes treated
+
+    with medications, insulin-controlled diabetes. Four race/ethnicity groups: Asian, Black,
+
+    White, Hispanic. Equal distribution by biological sex.'
+anomalies:
+- description: 'As enrollment is ongoing (began July 18, 2023, continues through November 30, 2026),
+
+    early data releases may not have achieved balanced distribution across all groups due
+
+    to wave-based recruitment and ongoing enrollment.'
+  anomaly_details:
+  - Early releases may exhibit unbalanced distributions across diabetes severity, race/ethnicity, or sex
+    groups
+  - Balanced distribution target is for final complete dataset
+external_resources:
+- name: AI-READI Dataset Documentation
+  description: 'Comprehensive documentation for the AI-READI dataset on the FAIRhub data portal,
+
+    including dataset landing page, data dictionary, and versioned documentation.'
+  external_resources:
+  - https://docs.aireadi.org
+  - https://fairhub.io/datasets/2
+  future_guarantees:
+  - Documentation versions correspond to dataset versions
+- name: Related Publications
+  description: AI-READI publications describing study protocol and design
+  external_resources:
+  - https://doi.org/10.1038/s42255-024-01165-x
+  - https://doi.org/10.1136/bmjopen-2024-097449
+- name: Zenodo Archive
+  description: Archived dataset record on Zenodo
+  external_resources:
+  - https://doi.org/10.5281/zenodo.10642459
+- name: NIH RePORTER
+  description: NIH project record for Bridge2AI Salutogenesis Data Generation
+  external_resources:
+  - https://reporter.nih.gov/project-details/10471118
+confidential_elements:
+- description: 'Contains protected health information elements under controlled access including
+
+    past health records, medications, genetic sequencing data (future), and 5-digit zip codes.'
+  confidential_elements_present: true
+  confidentiality_details:
+  - 5-digit zip code held under controlled access
+  - Genetic sequencing data (future) will be held under controlled access
+  - Past health records and medications held under controlled access
+  - Sex, race, ethnicity held under controlled access
+sensitive_elements:
+- description: 'Sensitive demographic and health data held under controlled access to protect
+
+    participant privacy.'
+  sensitive_elements_present: true
+  sensitivity_details:
+  - Sex, race, ethnicity held under controlled access
+  - Genetic sequencing data (future)
+  - Past health records and medications
+  - Traffic and accident reports (environmental data)
+acquisition_methods:
+- description: 'Harmonized, multi-domain data acquisition across three collection sites (University
+
+    of Washington, University of Alabama at Birmingham, University of California San Diego)
+
+    using surveys, clinical exams, imaging devices, wearable sensors, and environmental
+
+    monitors. In-person study visit for clinical assessments, followed by 10-day at-home
+
+    monitoring with wearable devices.'
+  was_directly_observed: true
+  was_reported_by_subjects: true
+  acquisition_details:
+  - Physical and clinical measurements directly observed during in-person visit
+  - Survey data self-reported by participants via REDCap
+  - Retinal imaging directly captured during visit
+  - ECG directly measured during visit
+  - Wearable device data passively collected over 10 days at home (Garmin fitness tracker)
+  - Continuous glucose monitoring over 10 days at home (Dexcom CGM)
+  - Environmental sensor data directly measured over 10 days at home
+  - Blood and urine laboratory tests from samples collected during visit
+collection_mechanisms:
+- description: 'Multi-modal data collection using hardware devices, clinical procedures, and
+
+    software-driven capture. REDCap used for patient-reported questionnaires and
+
+    clinical data entry.'
+  mechanism_details:
+  - REDCap for survey data and clinical measurements entry
+  - Retinal imaging devices exporting to DICOM format
+  - ECG devices exporting raw format
+  - Garmin fitness tracker for 10-day activity and heart rate monitoring
+  - Dexcom Continuous Glucose Monitor for 10-day glucose tracking
+  - Environmental sensors for home air quality monitoring
+  - Laboratory equipment for blood and urine analysis
+  - Clinical procedures (monofilament testing, cognitive function testing, visual acuity, contrast sensitivity)
+data_collectors:
+- description: 'Three data collection sites with trained study coordinators and clinical staff.
+
+    Community Advisory Board of 11 persons from three sites contributes to protocol development.'
+  collector_details:
+  - University of Washington (Seattle, WA)
+  - University of Alabama at Birmingham (Birmingham, AL)
+  - University of California San Diego (San Diego, CA)
+  - Study coordinators and clinical staff at each site
+  - Community Advisory Board representation from all three sites
+collection_timeframes:
+- description: 'Enrollment began July 18, 2023 and will continue through November 30, 2026.
+
+    Data collected in waves to facilitate efficient sampling. Periodic data releases
+
+    planned as enrollment proceeds.'
+  timeframe_details:
+  - 'Enrollment start: July 18, 2023'
+  - 'Enrollment end: November 30, 2026'
+  - Wave-based recruitment with periodic releases
+  - 'Each participant: single in-person visit plus 10-day at-home monitoring'
+  - 'Longitudinal cohort (10% of participants): follow-up visits'
+preprocessing_strategies:
+- description: 'Domain-specific processing and harmonization described in the Dataset Documentation
+
+    for each data domain. Images exported from devices in raw format with some requiring
+
+    conversion to DICOM standard format prior to upload. Data from wearable devices and
+
+    sensors exported in their raw formats.'
+  preprocessing_details:
+  - File formats, data standards, metadata, and example outputs provided per domain
+  - Harmonization across three collection sites
+  - Retinal images converted to DICOM format where needed
+  - Wearable device data exported in device-native formats
+  - Environmental sensor data exported in raw format
+cleaning_strategies:
+- description: 'Harmonization and processing across three sites with domain-specific details
+
+    provided in the documentation. Quality control procedures for clinical measurements
+
+    and imaging data.'
+  cleaning_details:
+  - Cross-site harmonization procedures
+  - Domain-specific data cleaning as documented
+  - Quality control for retinal images and ECG data
+  - Validation of laboratory results
+labeling_strategies:
+- description: 'Domain-specific labeling and annotation where applicable, as described in the
+
+    documentation for each data domain. Clinical test outputs and imaging outputs
+
+    labeled according to clinical standards.'
+  labeling_details:
+  - Clinical test outputs annotated per domain protocols
+  - Retinal images labeled with image quality and clinical findings
+  - Diabetes classification based on clinical criteria and ICD-10 codes
+human_subject_research:
+  involves_human_subjects: true
+  irb_approval:
+  - Approved by University of Washington Institutional Review Board (approval number STUDY00016228)
+  - Reliance agreements with IRBs of University of Alabama at Birmingham and University of California
+    San Diego
+  ethics_review_board:
+  - University of Washington IRB (lead institution)
+  - University of Alabama at Birmingham IRB (reliance agreement)
+  - University of California San Diego IRB (reliance agreement)
+informed_consent:
+- consent_obtained: true
+  consent_type:
+  - Written informed consent provided by all participants
+  - Informed consent document available for download during recruitment
+  - Consent covers data collection and sharing of de-identified research data
+  description: 'Written informed consent is provided by all participants. Participants can review
+
+    informed consent document during REDCap recruitment interface before enrollment.
+
+    Consent covers participation in study visit, at-home monitoring, and data sharing.'
+is_deidentified:
+  description: 'Public dataset is de-identified. Controlled-access dataset contains limited identifiers
+
+    (5-digit zip code, demographic data) under data use agreement protections.'
+future_use_impacts:
+- description: 'Dataset designed for AI/ML research on T2DM and salutogenesis. Early-release
+
+    imbalance across groups may affect AI/ML model performance and fairness until
+
+    final balanced dataset is complete. Users should account for group balance and
+
+    potential distribution shifts. Sensitive elements must be handled under DUA to
+
+    mitigate privacy risks.'
+  impact_details:
+  - Potential bias from unbalanced early releases (will be resolved in final dataset)
+  - Privacy considerations for controlled-access data
+  - Designed for pseudotime manifold analysis and trajectory modeling
+  - Multi-modal data enables novel AI/ML approaches not feasible with single-domain datasets
+discouraged_uses:
+- description: 'Users must adhere to license terms for public data and the Data Use Agreement
+
+    for controlled-access data. Uses that would violate participant privacy protections
+
+    or re-identification attempts are prohibited.'
+  discouragement_details:
+  - Uses not permitted by license terms
+  - Uses that would violate participant privacy protections
+  - Attempts to re-identify participants
+  - Uses that would discriminate against participants or populations
+distribution_formats:
+- description: 'Public dataset downloadable upon agreement with a license; full dataset available
+
+    via controlled access through Data Use Agreement. Data formats vary by domain
+
+    as documented in dataset documentation.'
+  access_urls:
+  - https://fairhub.io/datasets/2
+license_and_use_terms:
+  description: 'Public data available for download upon agreement with a license defining permitted
+
+    uses. Full dataset access (including controlled-access components) contingent on
+
+    entering into a Data Use Agreement. Adheres to FAIR (Findable, Accessible,
+
+    Interoperable, Reusable) principles.'
+  license_terms:
+  - Public data under license with defined permitted uses
+  - Full dataset requires Data Use Agreement for controlled-access components
+  - FAIR principles for data sharing
+  - Attribution required
+maintainers:
+- name: AI-READI Project Team
+  description: 'Multi-institutional project team responsible for maintaining the dataset.
+
+    Contact PI: Aaron Lee, University of Washington, Department of Ophthalmology.'
+  maintainer_details:
+  - See Documentation site Contact Us and GitHub references
+  - Contact information available through FAIRhub portal
+updates:
+  description: 'Periodic updates to data releases are planned as enrollment proceeds (enrollment
+
+    through November 30, 2026). Documentation versions align with dataset versions
+
+    to ensure users can track changes across releases.'
+  update_details:
+  - Periodic data releases as enrollment continues
+  - Documentation versioned with dataset (e.g., v1.0.0, v2.0.0)
+  - 'Target completion: November 2026'
+version_access:
+  description: 'Separate documentation versions align to dataset versions. Users can navigate
+
+    between versions via the documentation site. FAIRhub provides version control
+
+    and DOI assignment for each release.'
+  version_details:
+  - Version dropdown available in documentation
+  - Each dataset version has corresponding documentation version
+  - DOI assigned to each major release
+  - Version history tracked on FAIRhub