Dataset Card for "NFT-70M_transactions"

Dataset summary

The NFT-70M_transactions dataset is the largest and most up-to-date collection of Non-Fungible Tokens (NFT) transactions between 2021 and 2023 sourced from OpenSea, the leading trading platform in the Web3 ecosystem. With more than 70M transactions enriched with metadata, this dataset is conceived to support a wide range of tasks, ranging from sequential and transactional data processing/analysis to graph-based modeling of the complex relationships between traders. Besides, the availability of textual and image contents further amplifies the modeling capabilities and usage opportunities of this dataset, making it a unique and comprehensive multimodal source of information for delving into the NFT landscape.

This dataset can serve as a benchmark for various innovative and impactful tasks within the crypto landscape, such as projecting NFT prices or detecting fraudolent and wash trading activities. Furthermore, the multimodal nature of the dataset fosters the development of classification models, as well as textual and visual generative models.

Data anonymization

We point out that the collected NFT transactions and metadata from OpenSea are publicly distributed on blockchain. For our purposes of re-distribution, we are also committed to ensure non-disclosure of information that might lead to identifying the NFT creators, in order to be compliant with privacy-preserving requirements and to avoid violation of data protection regulations and of property rights. In this respect, we carried out three actions:

• Values of all variables describing non-sensitive information were kept in their original form;
• Values of all variables describing sensitive information were anonymized, in a one-way, non-revertible mode;
• URLs of image data and textual contents (i.e., NFT images and their descriptions) were replaced by identifiers to numerical vectors that represent an encrypted representation (i.e., embeddings) of the image/text contents obtained via neural network models. Such embeddings are eventually provided in place of their original image and text data, and can be found in the NFT-70M_image and NFT-70M_text supplementary datasets, respectively.

Data Fields

Variable	Type	Description	Processing	Notes
token_id	String	The id of the NFT — this value is unique within the same collection	Anonymized	Original values were replaced by hash-codes
num_sales	Integer	A progressive integer indicating the number of successful transactions involving the NFT up to the current timestamp (cf. tx_timestamp)	Original	Not sensitive variable
nft_name	Vector ID	The name of the NFT	Anonymized	Original values were encrypted via neural textual embedding
nft_description	Vector ID	The description of the NFT as provided by the creator	Anonymized	Original values were encrypted via neural textual embedding
nft_image	Vector ID	The ID for accessing the NFT image vector	Anonymized	Original values were encrypted via neural visual embedding
collection_name	Vector ID	The ID for accessing the Collection name vector	Anonymized	Original values were encrypted via neural textual embedding
collection_description	Vector ID	The ID for accessing the Collection description vector	Anonymized	Original values were encrypted via neural textual embedding
collection_image	Vector ID	The ID for accessing the Collection image vector	Anonymized	Original values were encrypted via neural visual embedding
fees_seller	Float	The absolute amount of fees the seller has gained from this transaction expressed in token	Original	Not sensitive variable
fees_opensea	Float	The absolute amount of fees OpenSea has gained from this transaction expressed in token	Original	Not sensitive variable
fees_seller_usd	Float	The absolute amount of fees the seller has gained from this transaction expressed in US dollars (USD)	Original	Not sensitive variable
fees_opensea_usd	Float	The absolute amount of fees OpenSea has gained from this transaction expressed in US dollars (USD)	Original	Not sensitive variable
payout_collection_address	String	The wallet address where seller fees are deposited	Anonymized	Original values were replaced by hash-codes
tx_timestamp	String	Timestamp of the transaction expressed in yyyy-mm-ddTHH:MM:SS	Original	Not sensitive variable
price	Float	The price of the transaction expressed in token	Original	Not sensitive variable
gain	Float	The gain after fees (i.e., gain = price - fees_opensea * price - fees_seller * price)	Original	Not sensitive variable
usd_price	Float	The price of the transaction expressed in US dollars (USD)	Original	Not sensitive variable
usd_gain	Float	The difference between the price and the fees expressed in US dollars (USD)	Original	Not sensitive variable
token	Categorical	The token type used to pay the transaction	Original	Not sensitive variable
to_eth	Float	The conversion rate to convert tokens into Ethereum at the current timestamp, such that eth = price * to_eth	Original	Not sensitive variable
to_usd	Float	The conversion rate to convert tokens into US dollars (USD) at the current timestamp, such that usd = price * to_usd	Original	Not sensitive variable
from_account	String	The address that sends the payment (i.e., winner/buyer)	Anonymized	Original values were replaced by hash-codes
to_account	String	The address that receives the payment (it often corresponds to the contract linked to the asset)	Anonymized	Original values were replaced by hash-codes
seller_account	String	The address of the NFT seller	Anonymized	Original values were replaced by hash-codes
winner_account	String	The address of the NFT buyer	Anonymized	Original values were replaced by hash-codes
contract_address	String	The contract address on the blockchain	Anonymized	Original values were replaced by hash-codes
created_date	Timestamp	The date of creation of the contract	Original	Not sensitive variable
chain	Categorical	The blockchain where the transaction occurs	Original	Not sensitive variable
token_type	Categorical	The schema of the token, i.e., ERC721 or ERC1155	Original	Not sensitive variable
asset_contract_type	Categorical	The asset typology, i.e., non-fungible or semi-fungible	Original	Not sensitive variable
asset_type	Categorical	Whether the asset was involved in a simple or bundle transaction	Original	Not sensitive variable

How to use

Data provided within this repository can be straightforwardly loaded via the datasets library as follows:

from datasets import load_dataset
dataset = load_dataset("MLNTeam-Unical/NFT-70M_transactions")

Complementary data involving textual and visual embeddings can be integrated as follows:

from datasets import load_dataset
import numpy as np


transactions_dataset=load_dataset("MLNTeam-Unical/NFT-70M_transactions")
image_dataset=load_dataset("MLNTeam-Unical/NFT-70M_image")
text_dataset=load_dataset("MLNTeam-Unical/NFT-70M_text")


# Mapping from image_id to the row_index within the image dataset
image_id2row_index={int(id):k for k,id in enumerate(image_dataset["train"]["id"])}

# Mapping from text_id to row_index within the text dataset
text_id2row_index={int(id):k for k,id in enumerate(text_dataset["train"]["id"])}


def get_image_embedding(image_id,image_id2row_index,image_dataset):
  # If the mapping contains the image, the embedding exists
  idx_emb=image_id2row_index.get(int(image_id),None)
  
  if idx_emb:
    # If the embedding exists, return it
    return np.array(image_dataset["train"].select([idx_emb])["emb"][0])
  else:
    return None
  
def get_text_embedding(text_id,text_id2row_index,text_dataset):
  # If the mapping contains the text, the embedding exists
  idx_emb=text_id2row_index.get(int(text_id),None)

  if idx_emb:
    # If the embedding exists, return it
    return np.array(text_dataset["train"].select([idx_emb])["emb"][0])
  else:
    return None

### USAGE EXAMPLE ###

# Select transaction_id
transaction_id=120

# Get the image_id (e.g., collection_image or nft_image)
id_image=transactions_dataset["train"].select([transaction_id])["collection_image"][0]

# Get the image
image_embedding=get_image_embedding(id_image,image_id2row_index,image_dataset)

# Get the text_id
id_text=transactions_dataset["train"].select([transaction_id])["collection_description"][0]

# Get the text
text_embedding=get_text_embedding(id_text,text_id2row_index,text_dataset)

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.