Benchmark Dataset for Mid-Price Prediction of Limit Order Book data

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Tensor representation in high-frequency financial data for price change prediction (2017, methodology): “In [29], a 144 -dimensional feature vector was extracted for every 10 events, leading to 453 , 975 feature vector samples.”
Forecasting Stock Prices from the Limit Order Book Using Convolutional Neural Networks (2017, methodology): “The time period used for collecting that data ranges from the 1st to the 14th June 2010 (only business days are included), while the data are provided by the Nasdaq Nordic data feeds [14] [15].”
Applications of deep learning in stock market prediction: recent progress (2020, methodology): “For the mid-price prediction of limit order book data, there is a benchmark dataset provided by Ntakaris et al. (2017) and has been used in the following studies (Tran et al., 2018).”
Time-series classification using neural Bag-of-Features (2017, methodology): “The dataset is provided by Nasdaq Nordic [22], [23].”
DeepLOB: Deep Convolutional Neural Networks for Limit Order Books (2018, methodology): “We test our model on a publicly available LOB dataset, known as FI-2010 [1], and our method remarkably outperforms all existing state-of-the-art algorithms.”