Ether Price and Volume Dataset
Introduction
This is the market data of Ether in terms of price and volume from August 2015 (when Ether first appeared) to March 2019. The time interval of sampling is selected as four-hour, that is to say, we choose every kinds of price and volume every of four-hour as the original data.
The original market data of Ether are obtained from Poloniex,one of the most active crypto asset exchanges.
Data details
Citation
BibTeX
@article{han2020long,
title={Long-range dependence, multi-fractality and volume-return causality of Ether market},
author={Han, Qing and Wu, Jiajing and Zheng, Zibin},
journal={Chaos: An Interdisciplinary Journal of Nonlinear Science},
volume={30},
number={1},
pages={011101},
year={2020},
publisher={AIP Publishing LLC}
}IEEE
Q. Han, J. Wu and Z. Zheng, “Long-range dependence, multi-fractality and volume-return causality of Ether market,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 30, no. 1, pp. 011101, 2020.
ACM
Qing Han, Jiajing Wu and Zibin Zheng, “Long-range dependence, multi-fractality and volume-return causality of Ether market,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 30, no. 1, pp. 011101, 2020.
Bitcoin Price and Volume Dataset
Introduction
This is the market data of Bitcoin in terms of price and volume from August 2015 (when Ether first appeared) to March 2019. The time interval of sampling is selected as four-hour, that is to say, we choose every kinds of price and volume every of four-hour as the original data.
The original market data of Bitcoin are obtained from Poloniex,one of the most active crypto asset exchanges.
Data details
Citation
BibTeX
@article{han2020long,
title={Long-range dependence, multi-fractality and volume-return causality of Ether market},
author={Han, Qing and Wu, Jiajing and Zheng, Zibin},
journal={Chaos: An Interdisciplinary Journal of Nonlinear Science},
volume={30},
number={1},
pages={011101},
year={2020},
publisher={AIP Publishing LLC}
}IEEE
Q. Han, J. Wu and Z. Zheng, “Long-range dependence, multi-fractality and volume-return causality of Ether market,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 30, no. 1, pp. 011101, 2020.
ACM
Qing Han, Jiajing Wu and Zibin Zheng, “Long-range dependence, multi-fractality and volume-return causality of Ether market,” Chaos: An Interdisciplinary Journal of Nonlinear Science, vol. 30, no. 1, pp. 011101, 2020.
Mt.Gox Leaked Transaction
Introduction
This data set is the transaction data leaked by mt.gox exchange. First, we combine the buy and sell transaction fields of the same transaction, and then de duplicate them through transaction time, transaction account, etc. to ensure the uniqueness of each transaction data. This transaction data is very useful for analyzing the user behavior of bitcoin market.
We have done a market manipulation study using this data set. You can see related research for details.
Data details
Citation
BibTeX
@inproceedings{chen2019market,
title={Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network},
author={Chen, Weili and Wu, Jun and Zheng, Zibin and Chen, Chuan and Zhou, Yuren},
booktitle={IEEE Conference on Computer Communications},
pages={964--972},
year={2019},
organization={IEEE}
}IEEE
W. Chen, J. Wu, Z. Zheng, C. Chen, and Y. Zhou, “Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network,” Proc. - IEEE INFOCOM, vol. 2019-April, no. April 2011, pp. 964–972, 2019, doi: 10.1109/INFOCOM.2019.8737364.
ACM
Weili Chen, Jun Wu, Zibin Zheng, Chuan Chen, and Yuren Zhou. 2019. Market Manipulation of Bitcoin: Evidence from Mining the Mt. Gox Transaction Network. Proceedings - IEEE INFOCOM 2019-April, April 2011: 964–972. https://doi.org/10.1109/INFOCOM.2019.8737364
Activity Information of DApps
Introduction
Blockchain-based decentralized applications (DApp) is an application that enables end users to interact directly with blockchain. Generally, It consists of two parts: web pages as front end, and smart contracts as back end.
There are a number of DApps on platforms like Ethereum, EOS, Steem and so on. Some of them are always active (s.t., there are always new transactions corresponding to those DApps), while some of them are seemingly dead for a long time.
This dataset contains two files, one for DappRadar and the other for State of the Dapps.
The dataset provides information about dapps’ activity, such as their transaction counts, total transaction values and active users in 24 hours, 7 days and 30 days.
Data details
Citation
BibTeX
@INPROCEEDINGS{zheng2017overview,
author={Z. {Zheng} and S. {Xie} and H. {Dai} and X. {Chen} and H. {Wang}},
booktitle={2017 IEEE International Congress on Big Data (BigData Congress)},
title={An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends},
year={2017},
pages={557-564},
doi={10.1109/BigDataCongress.2017.85}
}IEEE
Z. Zheng, S. Xie, H. Dai, X. Chen, and H. Wang, “An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends,” Proc. - 2017 IEEE 6th Int. Congr. Big Data, BigData Congr. 2017, no. October, pp. 557–564, 2017, doi: 10.1109/BigDataCongress.2017.85.
ACM
Zibin Zheng, Shaoan Xie, Hongning Dai, Xiangping Chen, Huaimin Wang, “An Overview of Blockchain Technology: Architecture, Consensus, and Future Trends,” Proc. - 2017 IEEE 6th Int. Congr. Big Data, BigData Congr. 2017, no. October, pp. 557–564, 2017, doi: 10.1109/BigDataCongress.2017.85.