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int64 35
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Dataset Card for "NFT-70M_image"
Dataset summary
The NFT-70M_image dataset is a companion for our released NFT-70M_transactions dataset, which is the largest and most up-to-date collection of Non-Fungible Tokens (NFT) transactions between 2021 and 2023 sourced from OpenSea.
As we also reported in the "Data anonymization" section of the dataset card of NFT-70M_transactions, the URLs of NFT images data were replaced by identifiers to numerical vectors that represent an encrypted representation (i.e., embeddings) of the image contents obtained via the google/vit-base-patch16-224 neural network model.
Note about this dataset version: embedded-image data in this dataset only include jpg and png formats and correspond to the Collection headers (i.e., collection_image field in the NFT-70M_transactions dataset). Upcoming versions will include all NFT embedded-image data.
Ethical use of data and informed consent
This data repository is made available for research and informational purposes only.
Any finding that might be drawn from the data provided within this repository should be intended to support decision-making regarding actions made on NFTs, and not to replace the human specialists.
The authors are not responsible for any issues related to trading failures based on the data provided within this repository.
Terms of Usage
Please cite the following papers in any research product whose findings are based on the data provided within this repository:
- L. La Cava, D. Costa, A. Tagarelli: SONAR: Web-based Tool for Multimodal Exploration of Non-Fungible Token Inspiration Networks. In: Proc. ACM SIGIR 2023. Taipei, Taiwan, July 23-27 2023. DOI: https://doi.org/10.1145/3539618.3591821
- L. La Cava, D. Costa, A. Tagarelli: Visually Wired NFTs: Exploring the Role of Inspiration in Non-Fungible Tokens. CoRR abs/2303.17031 (2023). DOI: https://doi.org/10.48550/arXiv.2303.17031
- D. Costa, L. La Cava, A. Tagarelli: Show me your NFT and I tell you how it will perform: Multimodal representation learning for NFT selling price prediction. In: Proc. ACM WebConf 2023, pp. 1875-1885. Austin, TX, USA, 30 April 2023 – 4 May 2023. DOI: https://doi.org/10.1145/3543507.3583520
Data within this repository were fetched using the REST APIs provided by OpenSea. You should also acknowledge OpenSea API.
Liability statement
The authors hereby declare that they are not responsible for any harmful or objectionable content that may be contained within the data provided within this repository. Users of the dataset are expected to exercise due diligence and responsibility when using the data, including but not limited to: (i) Content Review: Users should review the dataset's contents carefully and assess its suitability for their intended purposes; (ii) Compliance: Users are responsible for ensuring that their use of the dataset complies with all applicable laws, regulations, and ethical standards; (iii) Data Processing: Users may need to apply data preprocessing, filtering, or other techniques to remove or address any objectionable or harmful content as needed.
The authors of this dataset disclaim any liability for the accuracy, completeness, or suitability of the data and shall not be held responsible for any consequences resulting from the use or misuse of the dataset.
By accessing and using this dataset, users acknowledge and accept this disclaimer.
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