Harvard GDP: Glaucoma Detection and Progression Dataset

1,000 patients with OCT RNFLT maps (225×225), visual field measurements, and demographics for glaucoma detection (binary) and longitudinal progression forecasting (6 definitions). First public glaucoma progression dataset from Harvard Ophthalmology AI Lab.

At a glance

Field	Value
Short name	harvard_gdp
Full name	Harvard GDP: Glaucoma Detection and Progression Dataset
Primary category	oct
Contained modalities	oct, visual_field, tabular
Tasks	classification, progression
Samples	1,000
Classes	2 (Non-Glaucoma, Glaucoma)
Splits	train, val, test
Size	2.0 GB
Source-stated terms	CC BY-NC-ND 4.0
Normalized terms	cc-by-nc-nd
Descriptive screening label	Explicit noncommercial clause recorded; check source
Terms scope	dataset_files
Access friction	anonymous_direct
Route backend	Google Drive
Availability	available (checked 2026-07-21)
Acquisition support	guided_instructions_only
Legacy sample-loader status	Standard loader included

Access preflight and acquisition

CLI

# Read-only preflight
eyehub download harvard_gdp --data-dir ./data --dry-run --json

# Explicit transfer, only when preflight reports supported behavior
eyehub download harvard_gdp --data-dir ./data

Python

from eyedatahub.acquisition import preflight_dataset
from eyedatahub.datasets.registry import REGISTRY

ds = REGISTRY.get_dataset('harvard_gdp')
print(preflight_dataset(ds, './data'))  # no transfer

Upstream page: drive.google.com/drive

Source-term evidence: drive.google.com/drive

Loader example

This entry includes a standard DatasetSample loader.

from pathlib import Path
from eyedatahub.datasets.registry import REGISTRY

data_dir = Path('~/.eyedatahub/data').expanduser()
ds = REGISTRY.get_dataset('harvard_gdp')
samples = ds.load(data_dir, split='test')
for s in samples[:5]:
    print(s.sample_id, s.label, s.image_path)

Citation

BibTeX

@misc{harvard_gdp,
  title  = { Harvard GDP: Glaucoma Detection and Progression Dataset },
  note   = { Luo Z et al., 'Glaucoma Progression Prediction Using Retinal Thickness via Deep Learning', arXiv 2308.13411, 2023. https://github.com/Harvard-Ophthalmology-AI-Lab/Harvard-GDP },
  year   = { 2023 },
  url    = { https://drive.google.com/drive/folders/1JMi_HCql113uc9X0DOaMkNfEWfxaDlEz },
}

Plain text

Luo Z et al., 'Glaucoma Progression Prediction Using Retinal Thickness via Deep Learning', arXiv 2308.13411, 2023. https://github.com/Harvard-Ophthalmology-AI-Lab/Harvard-GDP

Source-stated terms

• Raw source string: CC BY-NC-ND 4.0
• Normalized category: cc-by-nc-nd
• Apparent scope: dataset_files
• Descriptive screening label: Explicit noncommercial clause recorded; check source

⚠️ Source-stated terms, scope, and normalized labels are curation metadata, not legal advice or a permission finding. Review the current official source before transfer or reuse.

Related datasets with shared modalities

• grape: GRAPE: Glaucoma Real-world Appraisal Progression Ensemble (1,115 records, cc0)
• lmod_plus: LMOD+ Multimodal Ophthalmology Benchmark (32,633 records, unknown)
• olives: OLIVES: Ophthalmic Labels for Investigating Visual Eye Semantics (9,408 records, cc-by)
• dryad_namd_oct_quant: Moorfields nAMD Quantitative OCT Biomarker Dataset (2,966 records, cc0)
• dryad_namd_visual_prediction: Moorfields nAMD Visual-Change Prediction Dataset (926 records, cc0)
• dryad_glaucoma_rnfl_vf: RNFL and Visual-Field Glaucoma Diagnosis Dataset (499 records, cc0)
• stage_task1: STAGE 2023 Task 1 — Mean Deviation Prediction from OCT (400 records, research-only)
• stage_task2: STAGE 2023 Task 2 — Visual Field Sensitivity Map Prediction (400 records, research-only)