Given a short query with keyphrases about a research idea in 2016 in the domain of artificial intelligence, recommend the relevant datasets to validate the research idea from options provided. A short description of the datasets is also provided to help you.

Query: A computational model for retinal hemodynamics accounting for ocular curvature is presented.

Keyphrases: retinal hemodynamics

Dataset description:
G1020: A large publicly available retinal fundus image dataset for glaucoma classification called G1020. The dataset is curated by conforming to standard practices in routine ophthalmology and it is expected to serve as standard benchmark dataset for glaucoma detection.
EyeQ: Dataset with 28,792 retinal images from the EyePACS dataset, based on a three-level quality grading system (i.e., Good',Usable' and `Reject') for evaluating RIQA methods.
DRIVE: The Digital Retinal Images for Vessel Extraction (DRIVE) dataset is a dataset for retinal vessel segmentation. It consists of a total of JPEG 40 color fundus images; including 7 abnormal pathology cases.
ROSE: Retinal OCTA SEgmentation dataset (ROSE) consists of 229 OCTA images with vessel annotations at either centerline-level or pixel level.

Options:- A: G1020  B: EyeQ  C: DRIVE  D: ROSE
DRIVE