Overview

Information of more than 110,000 games published on Steam. Maintained by Fronkon Games. This dataset has been created with this code (MIT) and use the API provided by Steam, the largest gaming platform on PC. Data is also collected from Steam Spy. Only published games, no DLCs, episodes, music, videos, etc. Here is a simple example of how to parse json information:

Simple parse of the 'games.json' file.

import os import json

dataset = {} if… See the full description on the dataset page: https://huggingface.co/datasets/FronkonGames/steam-games-dataset.

This dataset contains info of the current games on sale on Steam. The data is updated daily and it's propose is to track the sales in order to analyze them and find the best current deals.

The data is pulled using a spider with Scrapy framework for Python.

⚠️ Notice: This dataset has not been curated to exclude NSFW titles, discretion is advised.

The online gaming platform, Steam, was first released by the Valve Corporation in 2003. What started off as a small platform for Valve to provide updates to its games has turned into the largest computer gaming platform in the world. The platform initially released just 65 games in 2004, but this number has progressively risen in the ensuing years, reaching a staggering 15,422 in 2024, up from 9,204 in 2020. Steam’s PC dominance When you think of PC gaming, you automatically think of Steam. With such a wide range of games on offer, from traditional online multiplayer shooters to farming simulators, there is something for every gaming taste on the platform. As a result, gamers flock to Steam in their millions, with the platform registering over 132 million monthly active users in 2021. The global nature of the platform can be seen by the wide range of languages spoken by its users. Whilst English was the most spoken language for most of the platform's history, this changed as over 33 percent of users in October 2024 claimed Chinese as their platform language. Steam’s biggest games Counter Strike 2 was the most popular game on Steam during 2024. The first-person shooter averaged almost 685,000 players per hour, a significant lead over its successor, Counter-Strike 2. The game was also third among the 2024 list for peak number of concurrent players — CS2 reached over 1.74 million players in a single hour in its peak, with Black Myth: Wukong claiming first place with over 2.4 million peak concurrent players.

Context

I have generated this set of auxilary tables to complement the dataset of Kickstarter projects with the focus on videogames.

Content

Currently the set contains three tables:

SteamSpy table contains aggregate information on released games tracked by SteamSpy

KSreleased table links the Steam appid's with Kickstarter project IDs for those KS games, that after a successful campaign were finished and released on Steam

Currencies table shows historical currency exchange rates to USD($) for each week since the earliest campaign deadline among those in KSreleased

Acknowledgements

SteamSpy table was created using the site's API and I would like to take this opportunity to praise the site's creator Sergey Galyonkin

KSreleased table was generated by crawling Kickstarter "Play now" pages

Currencies table was generated using Fixer.io API

If you would like to know the details/see the code that I wrote to generate the data, I uploaded it as the "DEMO: generate data" kernel. It won't work online (otherwise I wouldn't have the need to create the dataset in the first place), but you can download the notebook and run it locally or just check my poor coding style :)

Inspiration

I intend to finalize my analysis on KS games that were released on Steam and publish it here, but of course I would like you to find more uses for this data beyond what I would have thought of. And again, I don't think this dataset is useful on its own, so please don't forget to connect to the KS projects dataset by Kemical

Societies have distinct social structures and individuals are positioned within the structure, or hierarchy, of the society. The broad consensus within social science is that an individual's position is partially ascribed by their social background and partially by their own achievements. The relative influence of social background and personal achievement remains an empirical question. in a society where social background is relatively more important it is plausible that an individual will attain a status position that is similar to their parents. Conversely, in a society where personal achievement is more influential it is plausible that a greater degree of discrepancy between status position and social background may be observed.This study examines the extent to which macro level developments have been able to shift the relative importance of background and achievement for status attainment in the Netherlands during the nineteenth and early twentieth centuries. The present thesis distinguishes six macro level developments that are sometimes referred to as 'modernization': industrialization, educational expansion, mass communication, mass transportation, urbanization and in-migration. For each of the developments hypotheses are derived on how they influence status attainment through marriage as well as intergenerational status attainment.The hypotheses are tested using hierarchical linear analyses. Large scale individual level datasets are augmented with contextual data on each of the macro level developments. The approach taken provides new insights in spatial and temporal variation in the status attainment process. Moreover, it allows hypotheses on 'modernization' to be tested on their home ground: in a period before and during industrialization.

Data Set Information: The dataset contains 9568 data points collected from a Combined Cycle Power Plant over 6 years (2006-2011), when the power plant was set to work with full load. Features consist of hourly average ambient variables Temperature (T), Ambient Pressure (AP), Relative Humidity (RH) and Exhaust Vacuum (V) to predict the net hourly electrical energy output (EP) of the plant. A combined cycle power plant (CCPP) is composed of gas turbines (GT), steam turbines (ST) and heat recovery steam generators. In a CCPP, the electricity is generated by gas and steam turbines, which are combined in one cycle, and is transferred from one turbine to another. While the Vacuum is colected from and has effect on the Steam Turbine, he other three of the ambient variables effect the GT performance.

Attribute Information: Features consist of hourly average ambient variables - Temperature (T) in the range 1.81°C and 37.11°C, - Ambient Pressure (AP) in the range 992.89-1033.30 milibar, - Relative Humidity (RH) in the range 25.56% to 100.16% - Exhaust Vacuum (V) in teh range 25.36-81.56 cm Hg - Net hourly electrical energy output (EP) 420.26-495.76 MW The averages are taken from various sensors located around the plant that record the ambient variables every second. The variables are given without normalization.

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

NPWR: Powering Up or Losing Steam?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data

This analysis presents a rigorous exploration of financial data, incorporating a diverse range of statistical features. By providing a robust foundation, it facilitates advanced research and innovative modeling techniques within the field of finance.

(JELstock) Jersey Electricity: Powering Up or Running Out of Steam?

Financial data:

• Historical daily stock prices (open, high, low, close, volume)

• Fundamental data (e.g., market capitalization, price to earnings P/E ratio, dividend yield, earnings per share EPS, price to earnings growth, debt-to-equity ratio, price-to-book ratio, current ratio, free cash flow, projected earnings growth, return on equity, dividend payout ratio, price to sales ratio, credit rating)

• Technical indicators (e.g., moving averages, RSI, MACD, average directional index, aroon oscillator, stochastic oscillator, on-balance volume, accumulation/distribution A/D line, parabolic SAR indicator, bollinger bands indicators, fibonacci, williams percent range, commodity channel index)

Machine learning features:

• Feature engineering based on financial data and technical indicators

• Sentiment analysis data from social media and news articles

• Macroeconomic data (e.g., GDP, unemployment rate, interest rates, consumer spending, building permits, consumer confidence, inflation, producer price index, money supply, home sales, retail sales, bond yields)

Potential Applications:

• Stock price prediction

• Portfolio optimization

• Algorithmic trading

• Market sentiment analysis

• Risk management

Use Cases:

• Researchers investigating the effectiveness of machine learning in stock market prediction

• Analysts developing quantitative trading Buy/Sell strategies

• Individuals interested in building their own stock market prediction models

• Students learning about machine learning and financial applications

Additional Notes:

• The dataset may include different levels of granularity (e.g., daily, hourly)

• Data cleaning and preprocessing are essential before model training

• Regular updates are recommended to maintain the accuracy and relevance of the data