Data Community

The Elden Ring Weapons dataset includes information about all of the available weapons in Elden Rings. The data includes the following breakdown for each weapon: Name - name of weaponType - type of weaponPhy - physical damageMag - magical damageFir - fire damageLit - light damageHol - holy damageCri - critical damageSta - stamina usageStr - strength scalingDex - dexterity scalingInt - intelligence scalingFai - faith scalingArc - arcane scalingAny - special effect damagePhy - physical blocking damageMag - magical blocking damageFir - fire blocking damageLit - light blocking damageHol - holy blocking damageBst - boostWgt - weight of weaponUpgrade - which stone should be used to upgrade the weaponThis data was collected from Kaggle, an online community of data scientists and machine learning practitioners, and can be found here.This data is extremely useful for players looking to maximize the quality of their builds. Having the ability to understand all aspects of a weapon, and more importantly to compare weapons to other weapons can really make the difference, especially in such a difficult game. Before going out to search for a new weapon, check this table to ensure the weapon fits your build perfectly.

All historic open, high, low, close, trading volume and market cap info for all cryptocurrencies as of the 21st of May, 2018. This dataset includes 1,584 unique crypto currencies, and over 900,000 observations. With this information, one can get a much deeper understanding of the crypto market. While this is not financial advice, one could certainly use this dataset for more informed trading decisions. We can also use this dataset with others to determine how what's happening in the world affects the prices of crypto.

This data includes name, number of appearances, page ID, wiki URL, ID (secrete or not), Align (good or bad character), eye color, hair color, sex, first appearnce, and more for each character in the DC universe. With this information, you can really understand a lot about any character, or even groups of characters. We could look at only the older characters, or even compare the older ones to the newer ones. We can look at how physical attributes like eye and hair color are used to signify whether a character is good or bad. We can also look at how these attributes change over time. We gathered this data here.

This dataset was built using the Philadelphia Federal Reserve's State Coincident Indices and the Bry-Boschan Method for business cycle dating. It then became available here. In the tradition of Owyang, Piger, et al. business cycles are calculated on the state level which provides interesting analysis opportunities for looking at recession timing for different regions or sectors present in different states. With this information, we could look to predict future recessions, or try to understand why they occurred in the past.

This data includes daily data of gold rates from 1st Jan 1985 to 11th Feb 2022. This data was collected from gold.org and then cleaned, becoming publically available here. With this information, one can have a much better understanding on what is going on in the gold market. We can use this data with other information to understand what events may cause the price of gold to increase or decrease. We can also look at the other currencies/prices to determine the effect of other countries on the gold market. Lastly, we can analyze the rises and falls in price to determine an optimal time to purchase.

This data includes name, team, position, age, height, weight, college, and salary of all current NBA players from the 2021-2022 season. This data was found here.With this information, we can look at all sorts of things. We can look at how height, or weight, can affect a player's salary. We can look at how different teams select players based on physical attributes differently. We can also look at what schools produce the best players, what schools produce the tallest players, etc. This information can also be compared to that of previous year to understand how the league is changing.

This data was downloaded here from RedFin. It has weekly data on housing sales by region and includes region ID, region name, region type, period begin and end, duration, total homes sold, median sale price, homes sold year over year, and so much more. With all of this information, we can get a better understanding of what happens in the housing market. Where homes are being sold, what areas have homes selling for the highest and lowest prices. With this, we can determine where the cheapest place to live is. We can also start looking at possible investments, or look for places where a decrease in price may occur

This dataset was created via Python using the requests, json, and pandas libraries. The information was pulled on January 16, 2022, and represents all time information for the top NFT collections. As an example, the Sales column represents all sales under a specified NFT collection from its creation up until January 16, 2022.This data was scraped from https://coinmarketcap.com/nft/collections/The data became available hereThe dataset consists of the following information:•Index: The index of the file.•Name: The name of the NFT collection.•Volume: The volume of sales from the NFT collection in Solana (SOL).•Volume_USD: The volume of sales from the NFT collection in United States Dollar (USD).•Market_Cap: The market capitalization—total value of the collection's items in circulation—in Solana (SOL).•MarketCapUSD: The market capitalization—total value of the collection's items in circulation—in United States Dollar (USD).•Sales: The number of sales from the NFT collection.•Floor_Price: The lowest price of any NFT in the collection in Solana (SOL).•FloorPriceUSD: The lowest price of any NFT in the collection in United States Dollar (USD).•Average_Price: The average price of an NFT in the collection in Solana (SOL).•AveragePriceUSD: The average price of an NFT in the collection in United States Dollar (USD).•Owners: The number of owners of NFT's in the collection.•Assets: The number of items in the collection.•OwnerAssetRatio: The ownership percentage of all items in the collection.•Category: The category of the NFT collection.•Website: The associated website of the NFT collection.•Logo: The associated image of the NFT collection.With this information, we hope to better understand what is going on in the NFT world. What projects are doing, how much they are selling for, how many people are buying them, etc. Understanding aspects of the crypto market can also help us understand where people’s money is going and why. From this, you can learn more about where to invest your money, or even what NFT project to create next!

This data was scraped from the google play store, and became available here. The data consists of over 2.3 million apps within the app store. The data includes name, App ID, category, rating, number of installs, rating count, price, size, release date, and SO much more. With this information, we can have a much better understanding of what apps succeed and why. We can also look at how people choose to spend their time and money, and what type of apps people enjoy more than others.