Welcome to IEEE Dataport

IEEE DataPort™ is an easily accessible repository of datasets, including Big Data datasets.  IEEE DataPort™ is designed to make storage of datasets easier, provide access to valuable datasets across a broad scope of topics, facilitate analysis of datasets, and retain referenceable data for reproducible research.

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  • CREATE: Multimodal Dataset for Unsupervised Learning and Generative Modeling of Sensory Data from a Mobile Robot

    The CREATE database is composed of 14 hours of multimodal recordings from a mobile robotic platform based on the iRobot Create.

  • Tracking Neurons in a Moving and Deforming Brain Dataset

    Advances in optical neuroimaging techniques now allow neural activity to be recorded with cellular resolution in awake and behaving animals.  Brain motion in these recordings pose a unique challenge. The location of individual neurons must  be tracked in 3D over time to accurately extract single neuron activity traces. Recordings from small invertebrates like C.

  • Indian Diabetic Retinopathy Image Dataset (IDRiD)

    Diabetic Retinopathy is the most prevalent cause of avoidable vision impairment, mainly affecting working age population in the world. Recent research has given a better understanding of requirement in clinical eye care practice to identify better and cheaper ways of identification, management, diagnosis and treatment of retinal disease.

  • IEEE Brain Data Bank Hackathon at COMPSAC 2018 Conference

    Cognitive control is defined by a set of neural processes that allow us to interact with our complex environment in a goal-directed manner. Humans regularly challenge these control processes when attempting to simultaneously accomplish multiple goals (multitasking).


    Remote sensing of environment research has explored the benefits of using synthetic aperture radar imagery systems for a wide range of land and marine applications since these systems are not affected by weather conditions and therefore are operable both daytime and nighttime.

  • ECG signals (744 fragments)

    For research purposes, the ECG signals were obtained from the PhysioNet service (http://www.physionet.org) from the MIT-BIH Arrhythmia database. The created database with ECG signals is described below. 1) The ECG signals were from 29 patients: 15 female (age: 23-89) and 14 male (age: 32-89).

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IEEE DataPort hosts all types of datasets and all data owners are encouraged to upload their datasets without cost. The goal is to provide a sustainable platform to all data owners so datasets can be stored and retrieved at any time to support your research and/or research of other technical experts. Need help? View detailed instructions on uploading a dataset.

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