Written by IEEE | August 6, 2020   |   Updated: November 11, 2020

This is the third in a series of articles highlighting new tools and resources offered by IEEE in support of the Open Science movement.

COVID-19 isn’t stopping technologists from designing research and dataset projects — it’s actually driving the innovation of new and exciting ways for collaboration — and IEEE DataPort is helping them along the way. IEEE DataPort is an accessible online data platform that allows users to store, search, access and manage datasets up to 2TB across a broad scope of topics. This IEEE platform also helps technologists analyze datasets while supporting Open Data initiatives that keep referenceable data available for reproducible research.

We connected with three technologists currently using IEEE DataPort to learn how the platform has helped them move forward with their data collection and research.

1. Research Tracks COVID-19 Real-Time Communications

Rabindra Lamsal has released five sets of data to IEEE DataPort, with the majority focused on studying how much real-time information and language around the pandemic is being used on the social media platform, Twitter.

“Out of these five, Coronavirus (COVID-19) Tweets Dataset [1] and Coronavirus (COVID-19) Geo-tagged Tweets Dataset [2] have more than 59,000 and 12,000 accesses respectively,” says Lamsal. “Both of these datasets contain tweet IDs and respective sentiment scores for each tweet ID. These datasets are being used by thousands of technical and non-technical (social science) researchers worldwide.”

Lamsal chose IEEE DataPort because, “I strongly support the idea of having healthy communication with the dataset owner to understand the data completely. And I find IEEE DataPort is nailing this point.”

2. Datasets for Cybersecurity of IoT Networks

Hyunjae Kang is working on research to detect IoT intrusions by analyzing network packets.

“In order to conduct the research, it was necessary to collect packets generated by the actual IoT device, as well as packets generated during the process of attacking the devices,” says Kang. “Ultimately, the dataset was used to extract features from traffic and create a deep-learning based detection model.”

Kang chose DataPort because, “the formal citation phrase including digital object identifier (DOI) provided by IEEE Dataport helped our project to meet [certain] goals such as number of citation records. As a person who studies security models based on deep learning, we felt that there are [few] open datasets in the field of cybersecurity. We wanted to give a little help to other cybersecurity researchers.”

3. Understanding Diabetic Retinopathy Imaging

Prasanna Porwal created the Indian Diabetic Retinopathy Image Dataset (IDRiD) as a part of a Ph.D. project. Diabetic retinopathy is an eye condition that can cause loss of vision and blindness in people who have diabetes. According to the project description, the dataset can be used “for development and evaluation of image analysis algorithms for early detection of diabetic retinopathy.”

“Relevant data and ground truths are essential for the development, evaluation and comparison of automated algorithms,” says Porwal. “We created this dataset as a first step to attaining our research objectives.”

DataPort has helped Porwal because, “The popularity of IEEE has helped with increasing visibility of our work by making data available to researchers on a common data-sharing platform.”

Learn more about IEEE DataPort and how to get involved.


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