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Posts, likes, retweets, and metadata from all collected public tweets and profiles.
This dataset is structured into two main tables: profiles and tweets.
The profiles table contains information about Twitter user profiles, with the following columns:
| Column Name | Description |
|---|---|
| collect_timestamp | Timestamp when the profile data was collected |
| id | Unique identifier for the Twitter user profile |
| name | Name of the Twitter user |
| display_name | Display name of the Twitter user |
| bio | Biography of the Twitter user |
| is_blue_verified | Indicates if the user is blue verified |
| created_timestamp | Timestamp when the Twitter account was created |
| location | Location of the Twitter user |
| profession_type | Type of profession of the Twitter user |
| profession_categories | Categories related to the user’s profession |
| following | Number of accounts the user is following |
| followers | Number of followers the user has |
| tweets_count | Total number of tweets made by the user |
| links | Links associated with the Twitter user |
| banner_image_url | URL of the user’s banner image |
| profile_image_url | URL of the user’s profile image |
| pinned_tweet_ids | IDs of tweets pinned by the user |
The tweets table contains information about tweets made by users, with the following columns:
| Column Name | Description |
|---|---|
| collect_timestamp | Timestamp when the tweet data was collected |
| id | Unique identifier for the tweet |
| poster_id | ID of the user who posted the tweet |
| text | Content of the tweet |
| timestamp | Timestamp when the tweet was posted |
| is_note | Indicates if the tweet is a note |
| images | Images associated with the tweet |
| videos | Videos associated with the tweet |
| hashtags | Hashtags used in the tweet |
| tagged_users | Users tagged in the tweet |
| replies | Number of replies to the tweet |
| reposts | Number of times the tweet has been reposted |
| likes | Number of likes the tweet has received |
| views | Number of views the tweet has received |
| quotes | Number of quotes of the tweet |
| bookmarks | Number of bookmarks of the tweet |
| quote_parent_id | ID of the parent tweet if this tweet is a quote |
| quote_poster_id | ID of the user who posted the parent tweet |
| reply_conversation_id | ID of the conversation this tweet is part of |
| reply_parent_id | ID of the parent tweet in the reply conversation |
| reply_poster_id | ID of the user who posted the parent tweet |
| retweet_parent_id | ID of the parent tweet if this tweet is a retweet |
| retweet_poster_id | ID of the user who posted the parent tweet |
Utilities for the Twitter dataset
- Tweet Id to URL: https://twitter.com/i/web/status/{tweetId}
- Profile Id to URL: https://twitter.com/intent/user?user_id={userId}
CoinGecko
Historical and real-time data for all assets in the CoinGecko universe. This dataset is structured into two main tables: history and projects.
The history table contains historical data for cryptocurrencies, with the following columns:
| Column Name | Description |
|---|---|
| COINGECKO_ID | Unique identifier for the cryptocurrency |
| DATE | Date of the recorded data |
| FDMC | Fully Diluted Market Cap |
| H24_VOLUME | 24-hour trading volume |
| MARKET_CAP | Market capitalization |
| PRICE | Price of the cryptocurrency |
| SYMBOL | Symbol of the cryptocurrency |
The projects table provides detailed information about cryptocurrency projects, with the following columns:
| Column Name | Description |
|---|---|
| CIRCULATING_SUPPLY | Current circulating supply of the cryptocurrency |
| COINGECKO_ID | Unique identifier for the cryptocurrency |
| FDMC | Fully Diluted Market Cap |
| HIGH_24H | Highest price in the last 24 hours |
| LOW_24H | Lowest price in the last 24 hours |
| MARKET_CAP | Market capitalization |
| MARKET_CAP_CHANGE_24H | Change in market cap over the last 24 hours |
| MARKET_CAP_CHANGE_PERCENTAGE_24H | Percentage change in market cap over the last 24 hours |
| MAX_SUPPLY | Maximum supply of the cryptocurrency |
| NAME | Name of the cryptocurrency project |
| PRICE | Current price of the cryptocurrency |
| PRICE_ATH | All-time high price |
| PRICE_ATH_CHANGE_PERCENTAGE | Percentage change from all-time high price |
| PRICE_ATH_DATE | Date of the all-time high price |
| PRICE_ATL | All-time low price |
| PRICE_ATL_CHANGE_PERCENTAGE | Percentage change from all-time low price |
| PRICE_ATL_DATE | Date of the all-time low price |
| PRICE_CHANGE_24H | Price change over the last 24 hours |
| PRICE_CHANGE_PERCENTAGE_24H | Percentage change in price over the last 24 hours |
| SYMBOL | Symbol of the cryptocurrency |
| TOTAL_SUPPLY | Total supply of the cryptocurrency |
| VOLUME_24H | 24-hour trading volume |
DePIN Ninja
Contains project characteristics, financial metrics, and historical revenue data for all DePIN.Ninja datasets.
This dataset is structured into two main tables: projects and historical_revenue.
The projects table provides information about different projects, including their category, chain, and financial metrics.
| Column Name | Description |
|---|---|
projectId | Unique identifier for the project. |
name | Name of the project. |
category | Category of the project (e.g., AI, COMPUTE, SENSORS, etc.). |
chain | Blockchain network on which the project operates (e.g., ETHEREUM, SOLANA). |
arr | Annual Recurring Revenue (ARR) of the project. |
mrr | Monthly Recurring Revenue (MRR) of the project. |
createdAt | Date when the project was created. |
| Project ID | Name | Category | Chain | ARR | MRR | Created At |
|---|---|---|---|---|---|---|
| wmad1s | PAAL AI | AI | ETHEREUM | 1846345 | 1005192 | 2025-01-23 |
| 2a1uk3 | Virtual | AI | BASE | 231378903 | 16065510 | 2025-01-23 |
| … | … | … | … | … | … | … |
The historical_revenue table contains historical revenue data for various projects, allowing for analysis of revenue trends over time.
| Column Name | Description |
|---|---|
createdAt | Timestamp of when the revenue data was recorded. |
date | Specific date for the revenue data. |
name | Name of the project associated with the revenue data. |
projectId | Unique identifier for the project. |
revenue | Revenue generated by the project on the specified date. |
| Created At | Date | Name | Project ID | Revenue |
|---|---|---|---|---|
| 2025-01-23 | 2025-01-01 | PAAL AI | wmad1s | 50000 |
| 2025-01-23 | 2025-01-01 | Virtual | 2a1uk3 | 75000 |
| … | … | … | … | … |
This dataset can be used for various analyses, including:
- Financial performance evaluation of DePIN projects.
- Trend analysis of revenue over time.
- Comparison of projects across different categories and chains.
Slice Analytics
Contains user counts and scores of quality users for top DEXs, NFT exchange, and Perp DEX platforms.
It 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
This dataset is structured into three main tables: dex_trader_aggregates, nft_trader_aggregates, and perps_trader_aggregates.
The dex_trader_aggregates table contains aggregated data for decentralized exchange traders, with the following columns:
| Column Name | Description |
|---|---|
| avg_score | Average score of the traders |
| blockchain | Blockchain platform used |
| id | Unique identifier for the record |
| last_modified | Timestamp of the last modification |
| project | Name of the project |
| user_count | Total number of users |
| users_over_50 | Number of users over 50 score user quality |
| users_over_60 | Number of users over 60 score user quality |
| users_over_70 | Number of users over 70 score user quality |
| users_over_80 | Number of users over 80 score user quality |
| users_over_90 | Number of users over 90 score user quality |
| users_over_95 | Number of users over 95 score user quality |
The nft_trader_aggregates table provides aggregated data for NFT traders, with the following columns:
| Column Name | Description |
|---|---|
| avg_score | Average score of the traders |
| blockchain | Blockchain platform used |
| id | Unique identifier for the record |
| last_modified | Timestamp of the last modification |
| project | Name of the project |
| user_count | Total number of users |
| users_over_50 | Number of users over 50 score user quality |
| users_over_60 | Number of users over 60 score user quality |
| users_over_70 | Number of users over 70 score user quality |
| users_over_80 | Number of users over 80 score user quality |
| users_over_90 | Number of users over 90 score user quality |
| users_over_95 | Number of users over 95 score user quality |
The perps_trader_aggregates table contains aggregated data for perpetual contract traders, with the following columns:
| Column Name | Description |
|---|---|
| avg_score | Average score of the traders |
| blockchain | Blockchain platform used |
| id | Unique identifier for the record |
| last_modified | Timestamp of the last modification |
| project | Name of the project |
| user_count | Total number of users |
| users_over_50 | Number of users over 50 score user quality |
| users_over_60 | Number of users over 60 score user quality |
| users_over_70 | Number of users over 70 score user quality |
| users_over_80 | Number of users over 80 score user quality |
| users_over_90 | Number of users over 90 score user quality |
| users_over_95 | Number 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.