The Winners of the 2019 IEEE DataPort Dataset Contest
IEEE DataPort is excited to announce the 2019 Dataset Upload competition winners, selected by a panel of IEEE volunteers based on technical merit and level of engagement among the IEEE DataPort global technical community.
- 1st Place: Rabindra Lamsal, Twitter Sentiment Analysis
- 2nd Place: Ana-Cosmina Popescu, PRECIS HAR
- 3rd Place: Manjunath Matam, Detecting Corrupt Data in a Plant Database
Researchers around the globe rely on IEEE DataPort to safely and easily store, share, and manage their research data. Lamsal, a researcher at Jawaharlal Nehru University in New Delhi, uses this research data platform because of its high storage capacity – up to 2TB – and the ability to directly connect with dataset owners. Popescu, a researcher at University Politechnica of Bucharest uses IEEE DataPort because it is an affordable and stable platform to make her research available to the computer vision research community. And Matam, a researcher at the University of Central Florida, uses IEEE DataPort to reach a greater audience with his research data.
Check out these winning datasets below and upload your own research data to IEEE DataPort.
Keep watching for announcements regarding the next IEEE DataPort Dataset Upload Contest coming in 2020!
To learn more about how to upload your own datasets visit ieee-dataport.org.
1st Place: Rabindra Lamsal
Each database (*.db) contain three columns. First column: date and time of the tweet, second column: tweet, third column: sentiment score for the particular tweet within the range [-1,1] with -1 being the most negative, 0 being the neutral and +1 being the most positive sentiment.
2nd Place: Ana-Cosmina Popescu
PRECIS HAR represents a RGB-D dataset for human activity recognition, captured with the 3D camera Orbbec Astra Pro. It consists of 16 different activities (stand up, sit down, sit still, read, write, cheer up, walk, throw paper, drink from a bottle, drink from a mug, move hands in front of the body, move hands close to the body, raise one hand up, raise one leg up, fall from bed, and faint), performed by 50 subjects.
3rd Place: Manjunath Matam
This folder contains two csv files and one .py file. One csv file contains NIST ground PV plant data imported from https://pvdata.nist.gov/. This csv file has 902 days raw data consisting PV plant POA irradiance, ambient temperature, Inverter DC current, DC voltage, AC current and AC voltage. Second csv file contains user created data. The Python file imports two csv files. The Python program executes four proposed corrupt data detection methods to detect corrupt data in NIST ground PV plant data.
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