Using the labels table create a dashboard that illustrates the inflows and outflows of assets from NEAR to CEX and vice-versa.

Look at items such as number of users and volume by CEX.

Another thing to look at is which CEXes bring in the most active users to the ecosystem?

Lastly what dApps in the ecosystem are users who onboarded funds from CEXes are using?

In this dashboard, we are analyzing the activity of users on centralized exchanges in the Near network. The analysis is based on the data provided by Flipside Crypto. Overall, the analysis in this dashboard can be divided into the following sections:

Asset Flow:
In this section, we have examined the transfer of NEAR tokens from centralized exchange wallets to the NEAR network and vice versa. In the conducted analysis, the flow of transfers from specific exchanges and the volume of transactions performed, along with other statistical information, have been determined. Some noteworthy points include:

1. Users have transferred tokens from the wallets of eight centralized exchanges to the NEAR network, and vice versa.
2. KuCoin has the largest number of exchange wallets.
3. The highest volume of inputs to exchanges from the NEAR network is associated with the wallets of OKEx.
4. The highest volume of outputs from exchanges to the NEAR network is associated with the wallets of Binance.

Active Users:
In this section, we have provided a definition for determining active users and based on that, we have examined the transaction data of transfers from centralized exchanges to the NEAR network and vice versa. We have attempted to display users' activities based on criteria such as the number of transactions, transaction volume, and the number of days on which the user has performed transactions. Some of the most important findings of this section include:
• KuCoin ranks first in the number of users who have performed over 100 transactions.
• OKEx has the highest transfer volume among users who have performed over 100 transactions (referring to the total sum of transfers by active users of this exchange).

To further investigate users, we have presented another definition of active users, considering the number of days a user has conducted transactions as the level of their activity. We have divided users into four categories based on the number of days of activity and then examined various criteria to demonstrate the behavior of users in different categories. By doing so, we have presented four categories of users for each exchange and shown the behavioral differences among users in different categories and different exchanges.

This analysis is important for several reasons. Firstly, it provides insight into the behavior of users on centralized exchanges in the NEAR network, which can be useful for understanding market trends and making informed decisions regarding investments in NEAR tokens. Secondly, it helps to identify which exchanges are most active and which have the highest transaction volumes, which can be useful for traders and investors who want to trade NEAR tokens on centralized exchanges. Finally, by examining the activity of users on exchanges, this analysis can help to identify potential risks and vulnerabilities, which can be addressed to improve the security and stability of the Near network, which can ultimately lead to a better user experience and increased adoption.