Low-Power Wide-Area Networks (LPWANs) are an emerging Internet-of-Things (IoT) paradigm marked by low-power and long-distance communication. Among them, LoRa is widely deployed for its unique characteristics and open-source technology.By adopting the Chirp Spread Spectrum (CSS) modulation, LoRa enables low signal-to-noise ratio (SNR) communication. Standard LoRa demodulation methods use the dechirp method to condense the power of the whole chirp into a power peak on the frequency domain, in order to provide decoding in low SNR scenarios, and can support communication even when SNR is lower than -15 db.However, the standard demodulation method does not fully exploit the properties of chirp signals, yielding space for improvement in all kinds of communication scenarios. Recently, neural network based methods have been applied on LoRa demodulation and have achieved significant improvements in low SNR demodulation scenarios and has become a new reasearch topic. However, neural network training needs large amounts of data, and the collection of such dataset needs dedicated software and tedious work.To support research in the decoding of LoRa symbols, this paper presents a comprehensive LoRa dataset gathered by real life equipment. The dataset composes of LoRa signals with a spreading factor from 7 to 10, with a total of 27329 symbols. Furthermore, we use this dataset to train a neural network and evaluate its performance in low SNR demodulation scenarios. The results show that the neural-based method achieves 1.84-2.35 dB SNR gain over the baseline. The dataset and code for neural network based LoRa demodulation can be found at https://github.com/daibiaoxuwu/NeLoRa_Dataset.