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The SDOBenchmark Dataset
许可协议: CC-BY-SA 4.0

Overview

Context

Solar flares are intense bursts of radiation which can disrupt the power grids of a continent, shut down the GPS system or irradiate people exposed in space.
Developing systems for predicting solar flares would allow us to precisely aim our observation instruments at upcoming events, and eventually enable countermeasures against such worst-case scenarios.

Content

The SDOBenchmark dataset has a dedicated webpage at i4ds.github.io/SDOBenchmark, where you will find plenty of information. And if things are still unclear, please don't hesitate to ask questions in the Discussion tab!

Acknowledgements

This dataset was created by Roman Bolzern and Michael Aerni from the Institute for Data Science, FHNW, Switzerland. We owe our thanks to the SDO satellite mission, and to JSOC Stanford for providing the raw data.

Inspiration

The prediction of solar flares proves to be a challenging problem, some even compare it to weather forecasting. And regarding Machine Learning, we find this dataset to be particularly challenging because of the complexity of a single sample (up to 40 images), the relatively small size of samples (8'000 for training), and the fact that it is a regression problem. Yet Kagglers have proven time and time again that predictions can be made on the most complex of data. By providing this dataset, we hope to encourage Kaggle machine learners to push the envelope of solar flare predictions.

数据概要
数据格式
image,
数据量
398.271K
文件大小
792.14MB
发布方
Institute for Data Science, FHNW Switzerland
| 数据量 398.271K | 大小 792.14MB
The SDOBenchmark Dataset
许可协议: CC-BY-SA 4.0

Overview

Context

Solar flares are intense bursts of radiation which can disrupt the power grids of a continent, shut down the GPS system or irradiate people exposed in space.
Developing systems for predicting solar flares would allow us to precisely aim our observation instruments at upcoming events, and eventually enable countermeasures against such worst-case scenarios.

Content

The SDOBenchmark dataset has a dedicated webpage at i4ds.github.io/SDOBenchmark, where you will find plenty of information. And if things are still unclear, please don't hesitate to ask questions in the Discussion tab!

Acknowledgements

This dataset was created by Roman Bolzern and Michael Aerni from the Institute for Data Science, FHNW, Switzerland. We owe our thanks to the SDO satellite mission, and to JSOC Stanford for providing the raw data.

Inspiration

The prediction of solar flares proves to be a challenging problem, some even compare it to weather forecasting. And regarding Machine Learning, we find this dataset to be particularly challenging because of the complexity of a single sample (up to 40 images), the relatively small size of samples (8'000 for training), and the fact that it is a regression problem. Yet Kagglers have proven time and time again that predictions can be made on the most complex of data. By providing this dataset, we hope to encourage Kaggle machine learners to push the envelope of solar flare predictions.

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