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This dataset utilizes Slice’s proprietary methodology for segmenting users into quality groups. Slice builds a score for each wallet based on life-time history of value, historic onchain volume, and fees paid

Table Schemas

dex_trader_aggregates

Column NameTypeNullableDescription
avg_scorefloattrueAverage score of the traders
blockchaintextfalseBlockchain platform used
idtextfalseUnique identifier for the record
last_modifiedtimestamp with timezonetrueTimestamp of the last modification
projecttextfalseName of the project
user_countbiginttrueTotal number of users
users_over_50biginttrueNumber of users over 50 score user quality
users_over_60biginttrueNumber of users over 60 score user quality
users_over_70biginttrueNumber of users over 70 score user quality
users_over_80biginttrueNumber of users over 80 score user quality
users_over_90biginttrueNumber of users over 90 score user quality
users_over_95biginttrueNumber of users over 95 score user quality

nft_trader_aggregates

Column NameTypeNullableDescription
avg_scorefloattrueAverage score of the traders
blockchaintextfalseBlockchain platform used
idtextfalseUnique identifier for the record
last_modifiedtimestamp with timezonetrueTimestamp of the last modification
projecttextfalseName of the project
user_countbiginttrueTotal number of users
users_over_50biginttrueNumber of users over 50 score user quality
users_over_60biginttrueNumber of users over 60 score user quality
users_over_70biginttrueNumber of users over 70 score user quality
users_over_80biginttrueNumber of users over 80 score user quality
users_over_90biginttrueNumber of users over 90 score user quality
users_over_95biginttrueNumber of users over 95 score user quality

perps_trader_aggregates

Column NameTypeNullableDescription
avg_scorefloattrueAverage score of the traders
blockchaintextfalseBlockchain platform used
idtextfalseUnique identifier for the record
last_modifiedtimestamp with timezonetrueTimestamp of the last modification
projecttextfalseName of the project
user_countbiginttrueTotal number of users
users_over_50biginttrueNumber of users over 50 score user quality
users_over_60biginttrueNumber of users over 60 score user quality
users_over_70biginttrueNumber of users over 70 score user quality
users_over_80biginttrueNumber of users over 80 score user quality
users_over_90biginttrueNumber of users over 90 score user quality
users_over_95biginttrueNumber of users over 95 score user quality
This dataset is ideal for analyzing quality users. DAU (daily active users) is an incomplete metric that is easily botted - this dataset provides more nuance and can allow researchers to understand the quality of the composing users.