Gold fell to 3,336.24 USD/t.oz on July 17, 2025, down 0.32% from the previous day. Over the past month, Gold's price has fallen 0.96%, but it is still 36.66% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Gold - values, historical data, forecasts and news - updated on July of 2025.

Silver fell to 38.14 USD/t.oz on July 18, 2025, down 0.02% from the previous day. Over the past month, Silver's price has risen 4.79%, and is up 30.54% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Silver - values, historical data, forecasts and news - updated on July of 2025.

Banking and stock markets consider gold to be an important component of their economic and financial status. There are various factors that influence the gold price trend and its fluctuations. Accurate and reliable prediction of the gold price is an essential part of financial and portfolio management. Moreover, it could provide insights about potential buy and sell points in order to prevent financial damages and reduce the risk of investment. In this paper, different architectures of deep neural network (DNN) have been proposed based on long short-term memory (LSTM) and convolutional-based neural networks (CNN) as a hybrid model, along with automatic parameter tuning to increase the accuracy, coefficient of determination, of the forecasting results. An illustrative dataset from the closing gold prices for 44 years, from 1978 to 2021, is provided to demonstrate the effectiveness and feasibility of this method. The grid search technique finds the optimal set of DNNs’ parameters. Furthermore, to assess the efficiency of DNN models, three statistical indices of RMSE, RMAE, and coefficient of determination (R2), were calculated for the test set. Results indicate that the proposed hybrid model (CNN-Bi-LSTM) outperforms other models in total bias, capturing extreme values and obtaining promising results. In this model, CNN is used to extract features of input dataset. Furthermore, Bi-LSTM uses CNN’s outputs to predict the daily closing gold price.

The average monthly prices for gold increased worldwide between January 2014 and May 2025, although with some fluctuations. In January 2014, the average monthly price for gold worldwide stood at ******** nominal U.S. dollars per troy ounce. Significant jumps in the gold prices were observed, especially in the periods of uncertainty, as the investors tend to see gold as a safe investment option. For instance, the Corona pandemic acted as a shock to the economy, resulting in substantial increases in gold prices in 2020. As of May 2025, gold valued at ******** U.S. dollars per ounce, the highest value reported during this period.

Dataset of historical annual silver prices from 1970 to 2022, including significant events and acts that impacted silver prices.

This dataset is designed for the commodity market, featuring over 10,000 manually annotated news headlines. It aims to provide deep insights into news sentiment and its implications for commodity prices. The headlines were collected from various news sources and evaluated by three subject experts over a period of more than 20 years, from 2000 to 2021. Each news item has been assessed across multiple dimensions, including implied price direction (up, down, or constant), whether the news discusses past or future events, and if it involves asset comparisons. This dataset is particularly valuable for developing machine learning models that can understand commodity news, which can then serve as an additional input for both short-term and long-term price forecasting models. It is also useful for creating news-based indicators for commodities. Researchers focused on text analytics and classification problems will find this dataset beneficial, although some classes are highly imbalanced, which may present challenges for machine learning algorithms.

Columns

The dataset includes the following columns:

• Dates: The date of the news headline.
• URL: The URL where the news headline was published.
• News: The actual news headline text.
• Price Direction Up: A binary indicator (1 for Yes, 0 for No) if the news headline suggests an increase in price.
• Price Direction Constant: A binary indicator (1 for Yes, 0 for No) if the news headline suggests a stable price (no change).
• Price Direction Down: A binary indicator (1 for Yes, 0 for No) if the news headline suggests a decrease in price.
• Asset Comparison: A binary indicator (1 for Yes, 0 for No) if the news headline compares different assets.
• Past Information: A binary indicator (1 for Yes, 0 for No) if the news headline refers to past events.
• Future Information: A binary indicator (1 for Yes, 0 for No) if the news headline refers to future events.
• Price Sentiment: The overall sentiment of the gold commodity price based on the headline, categorised as positive, negative, or other.

Distribution

The dataset contains over 10,000 unique news headlines and corresponding metadata. Data files are typically provided in CSV format. Key distribution statistics for some dimensions are as follows:

• Dates: 3,761 unique values.
• URL: 10,570 unique values.
• News: 10,570 unique values.
• Price Direction Up: 6,158 headlines do not imply up, 4,412 imply up.
• Price Direction Constant: 10,126 headlines do not imply constant, 444 imply constant.
• Price Direction Down: 6,658 headlines do not imply down, 3,912 imply down.
• Asset Comparison: 8,569 headlines do not compare assets, 2,001 compare assets.
• Past Information: 318 headlines do not discuss past information, 10,252 discuss past information.
• Future Information: 10,251 headlines do not discuss future information, 319 discuss future information.
• Price Sentiment: Approximately 42% positive, 36% negative, and 22% other sentiment.

Usage

This dataset is ideally suited for:

• Developing machine learning models that understand commodity news for price forecasting.
• Creating news-based indicators for commodity markets.
• Evaluating text classification models in the context of news analytics.
• Research into the impact of news on commodity market volatility.

Coverage

The dataset has a global regional coverage. It spans a significant time range of over 20 years, from 2000 to 2021, with headlines collected across this period. There are no specific demographic notes beyond the focus on gold commodity news.

License

CC-BY-NC

Who Can Use It

This dataset is primarily intended for:

• Researchers and practitioners specialising in news analytics for commodities, who can leverage it for building predictive models.
• Data scientists and machine learning engineers working on text classification and natural language processing tasks, especially those dealing with imbalanced datasets.
• Financial analysts and market strategists interested in incorporating news sentiment into their commodity market analysis.

Dataset Name Suggestions

• Gold News Sentiment Analysis Dataset
• Commodity Market News Classifier
• Financial News Headline Sentiment
• Gold Price Direction News Data
• Annotated Commodity News for ML

Attributes

Original Data Source: Sentiment Analysis of Commodity News (Gold)

Bullion Price: Monthly Average: Mumbai: Gold: Standard data was reported at 84,995.000 INR/10 g in Feb 2025. This records an increase from the previous number of 79,079.000 INR/10 g for Jan 2025. Bullion Price: Monthly Average: Mumbai: Gold: Standard data is updated monthly, averaging 9,691.000 INR/10 g from Apr 1990 (Median) to Feb 2025, with 419 observations. The data reached an all-time high of 84,995.000 INR/10 g in Feb 2025 and a record low of 3,285.000 INR/10 g in Jul 1990. Bullion Price: Monthly Average: Mumbai: Gold: Standard data remains active status in CEIC and is reported by Reserve Bank of India. The data is categorized under Global Database’s India – Table IN.PG002: Memo Items: Bullion Price.

This document contains statistical data and analysis of global gold demand and prices from 2010 to 2024, presented by Dojipedia, a website focused on Forex investment information. The data is organized quarterly and includes various categories of gold demand such as jewelry fabrication, technology use, investment, and central bank purchases. It also provides the LBMA gold price in US dollars per ounce for each quarter.The document highlights significant events that influenced gold prices and demand during this period. These events include major economic crises, geopolitical tensions, and market shifts. For instance, it mentions the European debt crisis in 2010, the U.S. credit rating downgrade in 2011, the Federal Reserve's quantitative easing tapering signals in 2013, and the COVID-19 pandemic's impact starting in 2020.The data shows how gold demand and prices often increase during times of economic uncertainty or political instability, as investors view gold as a safe-haven asset. For example, gold prices reached record highs in 2024 amid global economic and geopolitical uncertainties.Dojipedia presents itself as a platform with five years of Forex market investment experience. The site offers free educational content on technical analysis methods such as Elliott Wave, ICT Trading, and Smart Money Concept. It also mentions plans to publish free books on technical analysis.The document includes a disclaimer stating that the information provided is for general purposes only and not financial advice. It warns about the high risks associated with investing in financial markets like CFDs, Forex, cryptocurrencies, and gold. The disclaimer emphasizes that leveraged products may not be suitable for all investors due to the high risk to capital.Overall, this document serves as a comprehensive resource for those interested in gold market trends and their relationship to global economic events over the past decade and a half.

About this dataset

Context

The dataset illustrates the median household income in Gold Bar, spanning the years from 2010 to 2021, with all figures adjusted to 2022 inflation-adjusted dollars. Based on the latest 2017-2021 5-Year Estimates from the American Community Survey, it displays how income varied over the last decade. The dataset can be utilized to gain insights into median household income trends and explore income variations.

Key observations:

From 2010 to 2021, the median household income for Gold Bar increased by $9,942 (13.43%), as per the American Community Survey estimates. In comparison, median household income for the United States increased by $4,559 (6.51%) between 2010 and 2021.

Analyzing the trend in median household income between the years 2010 and 2021, spanning 11 annual cycles, we observed that median household income, when adjusted for 2022 inflation using the Consumer Price Index retroactive series (R-CPI-U-RS), experienced growth year by year for 5 years and declined for 6 years.

https://i.neilsberg.com/ch/gold-bar-wa-median-household-income-trend.jpeg" alt="Gold Bar, WA median household income trend (2010-2021, in 2022 inflation-adjusted dollars)">

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2022-inflation-adjusted dollars.

Years for which data is available:

• 2010, 2011, 2012, 2013, 2014, 2015, 2016, 2017, 2018, 2019, 2020, 2021

Variables / Data Columns

• Year: This column presents the data year from 2010 to 2021
• Median Household Income: Median household income, in 2022 inflation-adjusted dollars for the specific year
• YOY Change($): Change in median household income between the current and the previous year, in 2022 inflation-adjusted dollars
• YOY Change(%): Percent change in median household income between current and the previous year

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Gold Bar median household income. You can refer the same here

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.

What happens to gold if CPI increases?

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

Rhodium traded flat at 5,775 USD/t oz. on July 17, 2025. Over the past month, Rhodium's price has risen 5.48%, and is up 24.19% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Rhodium - values, historical data, forecasts and news - updated on July of 2025.

Gold prices in , June, 2025 For that commodity indicator, we provide data from January 1960 to June 2025. The average value during that period was 600.07 USD per troy ounce with a minimum of 34.94 USD per troy ounce in January 1970 and a maximum of 3352.66 USD per troy ounce in June 2025. | TheGlobalEconomy.com

Open access (OA) can be defined as the practice of providing on-line access to scientific information that is free of charge to the user and that is re-usable. A distinction is usually made between OA to scientific peer reviewed publications and research data. In Horizon 2020 open access to peer-reviewed scientific publications (primarily articles) is mandatory; however, researchers can choose between the open access route most appropriate to them.

For open access publishing (gold open access), researchers can publish in open access journals, or in journals that sell subscriptions and also offer the possibility of making individual articles openly accessible (hybrid journals). In that case, publishers often charge an article processing charge (APC). These costs are eligible for reimbursement during the duration of the Horizon 2020 grant. For APCs incurred after the end of the grant agreement, a mechanism for reimbursing some of these costs is being piloted and implemented through the OpenAIRE project. Note that in case of gold open access publishing, a copy must also be deposited in an open access repository.

For self-archiving (green open access), researchers deposit the final peer-reviewed manuscript in a repository of their choice. In this case, they must ensure open access to the publication within six months of publication (12 months in case of the social sciences and humanities).

This page provides an overview of the state of play as regards the uptake of open access to scientific publications in Horizon 2020 from 2014 to 2017, updating information from 2016.

Two datasets have been used for the analysis presented in this note: one dataset from the EU funded OpenAIRE project for FP7 and H2020 and one dataset from CORDA for H2020, which also provides supplementary information on article processing charges and embargo periods. The datasets are from September and August 2017 respectively.

The OpenAIRE sample includes primarily peer-reviewed scientific articles but also some other forms of publications such as conference papers, book chapters and reports or pre-prints. It is based on information obtained from Open Access repositories, pre-print servers, OA journals and project reports and contains some underreporting since OpenAIRE has difficulties tracking hybrid publications and publications in repositories which are not OpenAIRE compliant. The CORDA sample contains only peer-reviewed scientific articles and is based on project self-reporting. The figures in this note measure open access in a broad sense and not the compliance with the specifics of article 29.2. of the Model Grant Agreement.

The 2017 analysis of open access during the entirety of Horizon 2020 so far shows an overall open access rate of 63,2% from OpenAIRE data (+2,4% compared with the sample from 2016). Internal project reporting through SYGMA shows a total of 80,6% open access for Horizon 2020 scientific peer reviewed articles and 75% for all peer-reviewed publications (including also conference procedures, book chapter, monographs and the like); however, since this data is based on beneficiary self-reporting it may contain some over-reporting.

According to the OpenAIRE sample 75% of publications are green open access and 25% gold open access. Internal figures are similar although they show a slightly higher amount of gold OA with a split of 70% green and 30% gold.

For gold OA internal project reporting suggests than an average of 1500 € is spent per article (median: 1200 €), an increase from the average of 1006 € in the previous sample. A more detailed analysis reveals that 27% percent of articles have a price tag of between 1000 to 1999 €. It is also important to note that 26% of all publications are in gold OA but without any APC charges. Very high APCs of 4000€ or more only concerns a tiny fraction of Horizon 2020 publications (3%).

The average embargo period of green OA publications is 10 months, that is a decrease of 1 month from the 2016 sample. 40% of articles have an embargo period of 11-12 months, followed by 575 articles (or 33% with no embargo period at all. 302 articles, that is 17% have an embargo period of 12,1-24 months and 162 articles or 9% of 0,1 to 6 months. Finally, 12 articles, that is 1%, have an embargo period that is longer than 36 months.

This 2017 analysis thus broadly confirms the earlier findings from summer 2016, but is based on a larger and more robust sample. In the 2017 sample overall open access rates have gone up in all the datasets and cohorts. The distribution between gold and green open access remains similar to the 2016 dataset; for gold OA, average APCs have increased, for green OA embargo periods have slight decreased.

Please consult the background note for a more detailed analysis. Note also that these files only refer to open access to publications. Information on open access to research data is made available on the open data portal on a diffe

Rhodium price data, historical values, forecasts, and news provided by Money Metals Exchange. Rhodium prices and trends updated regularly to provide accurate market insights.

About This Dataset: Copper Prices and Market Trends

    This dataset provides **insights into copper prices**, including current rates, historical trends, and key factors affecting price fluctuations. Copper is essential in **construction**, **electronics**, and **transportation** industries. Investors, traders, and analysts use accurate copper price data to guide decisions related to **trading**, **futures**, and **commodity investments**.

    ### **Key Features of the Dataset**

    #### **Live Market Data and Updates**
    Stay updated with the latest **copper price per pound** in USD. This data is sourced from exchanges like the **London Metal Exchange (LME)** and **COMEX**. Price fluctuations result from **global supply-demand shifts**, currency changes, and geopolitical factors.

    #### **Interactive Copper Price Charts**
    Explore **dynamic charts** showcasing real-time and historical price movements. These compare copper with **gold**, **silver**, and **aluminium**, offering insights into **market trends** and inter-metal correlations.

    ### **Factors Driving Copper Prices**

    #### **1. Supply and Demand Dynamics**
    Global copper supply is driven by mining activities in regions like **Peru**, **China**, and the **United States**. Disruptions in production or policy changes can cause **supply shocks**. On the demand side, **industrial growth** in countries like **India** and **China** sustains demand for copper.

    #### **2. Economic and Industry Trends**
    Copper prices often reflect **economic trends**. The push for **renewable energy** and **electric vehicles** has boosted long-term demand. Conversely, economic downturns and **inflation** can reduce demand, lowering prices.

    #### **3. Impact of Currency and Trade Policies**
    As a globally traded commodity, copper prices are influenced by **currency fluctuations** and **tariff policies**. A strong **US dollar** typically suppresses copper prices by increasing costs for international buyers. Trade tensions can also disrupt **commodity markets**.

    ### **Applications and Benefits**

    This dataset supports **commodity investors**, **traders**, and **industry professionals**:

    - **Investors** forecast price trends and manage **investment risks**. 
    - **Analysts** perform **market research** using price data to assess **copper futures**. 
    - **Manufacturers** optimize supply chains and **cost forecasts**.

    Explore more about copper investments on **Money Metals**:

    - [**Buy Copper Products**](https://www.moneymetals.com/buy/copper) 
    - [**95% Copper Pennies (Pre-1983)**](https://www.moneymetals.com/pre-1983-95-percent-copper-pennies/4) 
    - [**Copper Buffalo Rounds**](https://www.moneymetals.com/copper-buffalo-round-1-avdp-oz-999-pure-copper/297)

    ### **Copper Price Comparisons with Other Metals**

    Copper prices often correlate with those of **industrial** and **precious metals**:

    - **Gold** and **silver** are sensitive to **inflation** and currency shifts. 
    - **Iron ore** and **aluminium** reflect changes in **global demand** within construction and manufacturing sectors.

    These correlations help traders develop **hedging strategies** and **investment models**.

    ### **Data Variables and Availability**

    Key metrics include:

    - **Copper Price Per Pound:** The current market price in USD. 
    - **Copper Futures Price:** Data from **COMEX** futures contracts. 
    - **Historical Price Trends:** Long-term movements, updated regularly. 

    Data is available in **CSV** and **JSON** formats, enabling integration with analytical tools and platforms.

    ### **Conclusion**

    Copper price data is crucial for **monitoring global commodity markets**. From **mining** to **investment strategies**, copper impacts industries worldwide. Reliable data supports **risk management**, **planning**, and **economic forecasting**.

    For more tools and data, visit the **Money Metals** [Copper Prices Page](https://www.moneymetals.com/copper-prices).

Iron Ore rose to 97.18 USD/T on July 17, 2025, up 0.12% from the previous day. Over the past month, Iron Ore's price has risen 2.61%, but it is still 10.62% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Iron Ore - values, historical data, forecasts and news - updated on July of 2025.

Magic Chess: Go Go Hero & Card Reference Dataset

This repository contains reference datasets for heroes and cards used in Magic Chess: Go Go. These datasets are intended to complement the Magic Chess: Go Go Matches Dataset by providing structured information about hero attributes and card effects in order to enabling more meaningful and accurate analysis.

📂 Dataset Structure

The dataset consists of two csv files:

1. heroes

A dataset containing information about all heroes available in the game.

2. cards

A dataset describing the attributes and effects of various in-game cards.
Each card is encoded with binary features: 1 indicates that the card has that attribute, and 0 means it does not.

🎯 Purpose

These reference datasets are designed to support match-level analysis in the Magic Chess: Go Go Matches Dataset. They provide additional context such as:

• Hero characteristics for synergy or frontline/backline composition analysis
• Card effects for evaluating card impact on win rate or synergy boosts

By linking match data with this reference information, we can uncover deeper patterns and more accurate insights.

🧠 Use Cases

• Enhance feature engineering for match analysis
• Map hero usage to roles/factions for synergy analysis
• Analyze the strategic impact of specific card attributes
• Cluster or group cards/heroes by characteristics (e.g., all economy-boosting cards)

🏗️ Collection Methodology

All data was entered manually into a spreadsheet.

Cobalt traded flat at 33,335 USD/T on July 17, 2025. Over the past month, Cobalt's price has remained flat, but it is still 25.20% higher than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Cobalt - values, historical data, forecasts and news - updated on July of 2025.

Lithium traded flat at 64,950 CNY/T on July 17, 2025. Over the past month, Lithium's price has risen 7.44%, but it is still 24.04% lower than a year ago, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Lithium - values, historical data, forecasts and news - updated on July of 2025.

This dataset provides values for GOLD RESERVES reported in several countries. The data includes current values, previous releases, historical highs and record lows, release frequency, reported unit and currency.