Gold rose to 3,354.76 USD/t.oz on July 11, 2025, up 0.92% from the previous day. Over the past month, Gold's price has fallen 0.92%, but it is still 39.14% 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 rose to 38.37 USD/t.oz on July 11, 2025, up 3.65% from the previous day. Over the past month, Silver's price has risen 5.59%, and is up 24.68% 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.

Context

SPDR Gold Shares (GLD) This fund buys gold bullion. The only time it sells gold is to pay expenses and honor redemptions​. Because of the ownership of bullion, this fund is extremely sensitive to the price of gold and will follow gold price trends closely.

One upside to owning gold bars is that no one can loan or borrow them. Another upside is that each share of this fund represents more gold than shares in other funds that do not buy physical gold. However, the downside is taxes. The Internal Revenue Service (IRS) considers gold a collectible, and taxes on long-term gains are high. (For more, see: The Most Affordable Way to Buy Gold: Physical Gold or ETFs?)

Content

Fund overview: CategoryCommodities Precious Metals Fund familySPDR State Street Global Advisors

Acknowledgements

Yahoo Finance

Inspiration

Dataset will be helpful for people who are looking to start playing the Time Series Analysis. What always got my attention was, when Dollar goes down DowJones and Nasdaq goes up and vice-versa. Can this dataset be used for creating a Causal Model?

Rhodium traded flat at 5,700 USD/t oz. on July 11, 2025. Over the past month, Rhodium's price has risen 3.64%, and is up 23.91% 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.

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.

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.

Does price of gold go up with inflation?

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

Gold Reserves in China increased to 2292.31 Tonnes in the first quarter of 2025 from 2279.56 Tonnes in the fourth quarter of 2024. This dataset provides - China Gold Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

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.

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.

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

About this dataset

Context

The dataset tabulates the Gold Bar population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Gold Bar across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Gold Bar was 2,411, a 0.46% increase year-by-year from 2022. Previously, in 2022, Gold Bar population was 2,400, a decline of 0.37% compared to a population of 2,409 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Gold Bar increased by 345. In this period, the peak population was 2,412 in the year 2020. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Gold Bar is shown in this column.
• Year on Year Change: This column displays the change in Gold Bar population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

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 Population by Year. You can refer the same here

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).

Copper fell to 5.45 USD/Lbs on July 14, 2025, down 1.52% from the previous day. Over the past month, Copper's price has risen 13.70%, and is up 20.47% compared to the same time last year, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Copper - values, historical data, forecasts and news - updated on July of 2025.

*description edited on April 16 2018*

Versions in order of publication.

20180331_OpenAIRE2020_FP7PostGrantPilot_data.xlsx : These datasets contain all datasets related to the publications funded within the OpenAIRE FP7 Post-Grant Open Access Pilot from its start in May 1st 2015 until February 28th 2018. The final report of the Pilot will be based on this dataset.

20170831_DatasetD5.4.xlsx: This dataset contains all data related to the publications funded within the OpenAIRE FP7 Post-Grant Open Access Pilot in the two first years of its run, from May 2015 until April 30, 2017. The Pilot has received an extension of 10 months. This dataset will receive an update after the Pilot has come to a definitive end (February 2018).

A final version will be added to this record once all remaining accounting is wrapped up (July 2018). However, we are confident that the numbers presented here show trends and averages adequately, even if the absolute numbers will still change slightly.

The Pilot website can be found at : https://postgrantoapilot.openaire.eu

To be eligible, a publication needed to comply with the following conditions:

• Part of FP7 project that has ended, but no longer than two years ago at the moment of acceptance of publication P
• ublished in an Open Access Journal - we do not accept publications in so-called hybrid journals, which are journals that make individual articles Open Access but remain subscription based as a whole
• Maximum 3 publications per project
• The funding cap is € 2000 for articles and € 6000 for monographs.

Some author fees exceed this cap slightly due to currency conversion. Any other anomalies in this dataset are to be ascribed to human error. Some adjustments might have been made on accounting level that are not reflected in this dataset.

This dataset is available under CC0 (https://creativecommons.org/publicdomain/zero/1.0/), which means that it is dedicated to the public domain. You are free to copy, modify and distribute this dataset, even for commercial purposes, all without asking permission. Although it is not obliged, a credit to the OpenAIRE project is appreciated. At the OpenAIRE project, we are very interested in hearing how you have used this dataset. Feel free to ping us when you make use of it.
author: Gwen Franck gwen.franck@kb.nlOpenAIRE on Twitter @openaire_euinfo@openaire.eu

Abstract: First released in 2006, DrugBank (https://go.drugbank.com) has grown to become the 'gold standard' knowledge resource for drug, drug-target and related pharmaceutical information. DrugBank is widely used across many diverse biomedical research and clinical applications, and averages more than 30 million views/year. Since its last update in 2018, we have been actively enhancing the quantity and quality of the drug data in this knowledgebase. In this latest release (DrugBank 6.0), the number of FDA approved drugs has grown from 2646 to 4563 (a 72% increase), the number of investigational drugs has grown from 3394 to 6231 (a 38% increase), the number of drug-drug interactions increased from 365 984 to 1 413 413 (a 300% increase), and the number of drug-food interactions expanded from 1195 to 2475 (a 200% increase). In addition to this notable expansion in database size, we have added thousands of new, colorful, richly annotated pathways depicting drug mechanisms and drug metabolism. Likewise, existing datasets have been significantly improved and expanded, by adding more information on drug indications, drug-drug interactions, drug-food interactions and many other relevant data types for 11 891 drugs. We have also added experimental and predicted MS/MS spectra, 1D/2D-NMR spectra, CCS (collision cross section), RT (retention time) and RI (retention index) data for 9464 of DrugBank's 11 710 small molecule drugs. These and other improvements should make DrugBank 6.0 even more useful to a much wider research audience ranging from medicinal chemists to metabolomics specialists to pharmacologists.

More Information:

Paper: DrugBank 6.0: the DrugBank Knowledgebase for 2024 PMID: 37953279 PMCID: PMC10767804 DOI: 10.1093/nar/gkad976

Knox, C., Wilson, M., Klinger, C. M., Franklin, M., Oler, E., Wilson, A., Pon, A., Cox, J., Chin, N. E. L., Strawbridge, S. A., Garcia-Patino, M., Kruger, R., Sivakumaran, A., Sanford, S., Doshi, R., Khetarpal, N., Fatokun, O., Doucet, D., Zubkowski, A., Rayat, D. Y., … Wishart, D. S. (2024). DrugBank 6.0: the DrugBank Knowledgebase for 2024. Nucleic acids research, 52(D1), D1265–D1275. https://doi.org/10.1093/nar/gkad976

AbstractPopulation surveys are vital for wildlife management, yet traditional methods often demand excessive time and resources, leading to data gaps for many species. Modern technologies such as drones can facilitate field surveys, but may also increase data analysis challenges. Citizen Science (CS) can address this issue by engaging non-specialists for data collection and analysis. We assess the applicability of CS for population monitoring using the endangered Galápagos marine iguana as a case study, analysing online volunteers' ability to detect and count animals in aerial images. Comparing against a Gold Standard dataset — comprising a consensus of expert counts in 4345 images — we investigated which aggregation methods produced optimal results from CS inputs, as well as the influence of image quality and filtering data from infrequent and anonymous participants. During three phases of our project — hosted on the Zooniverse platform — over 13,000 volunteers made 1,375,201 classifications from 57,838 aerial images; each image being independently classified 20 (phase 1 & 2) or 30 (phase 3) times. Volunteers achieved 68% to 94% accuracy in detecting iguanas, with more false negatives than false positives. Image quality strongly influenced accuracy; by excluding data from suboptimal pilot-phase images, volunteers counted with 90% to 92% of accuracy. For detecting presence or absence of iguanas, the commonly used ‘majority vote' aggregation approach (where the answer selected by the majority of individual inputs) produced less accurate results than when a minimum threshold of five (from the 20/30 independent classifications) was used. For aggregating results on iguana counts, the HDBSCAN clustering method yielded the best results. Removing inputs from anonymous and inexperienced volunteers reduced accuracy, emphasizing the importance of considering all volunteer contributions. We conclude that with sufficiently good aerial images, online volunteers can accurately identify and count marine iguanas from drone images, though a tendency to underestimate warrants further consideration. Finally, although CS-based data analysis is quicker than manual counting, it still requires significant time resources, thus we recommend the development of a Machine Learning approach to address this issue.MethodsWe created a citizen science project, named Iguanas from Above, in Zooniverse.org. There, we uploaded 'sliced' images from drone imagery belonging to several colonies of the Galápagos marine iguana. Citizen scientists (CS) were asked to classify the images doing two tasks: First to say yes or no for iguana presence in the image and second to count the individuals when present. Each image was classified by 20 or 30 volunteers. Once all the images, corresponding to three phases launched were classified, we downloaded the data from the Zooniverse portal and used the Panoptes Aggregation python package to extract and aggregate CS data.We ramdomly selected 5–10% of all the images to create a Gold Standard (GS) dataset. Three experts from the research team identified presence and absence of marine iguanas in the images and count them. The concensus answers are presented in this dataset and is referred as expert data. The aggregated CS data from Task 1 (a total number of yes and no answers per image) was analyzed as accepted for iguana presence when 5 or more volunteers (from the 20–30) selected yes (a minimum threshold rule), otherwise absence was accepted. Then, we compared all CS accepted answers against the expert data, as correct or incorrect, and calculated a percentage of CS accuracy regarding marine iguana detection.For Task 2, we selected all the images identied by the volunteers to have iguanas with this minimum threshold rule and aggregate (summarize) all classifications into one value (count) per image by using the statistical metrics median and mode and the spatial clustering methods DBSCAN and HDBSCAN. The rest of the images obtained 0 counts. CS data was incorporated into this dataset. We then compared total counts in this GS dataset calculated by the expert and all the aggregating methods used in terms of percentages of agreement towards the expert data. These percentages showed CS accuracy regarding marine iguana counting. We also investigated number of marine iguanas under and overestimated with all aggregating methods.Finally, by applying generalized linear models, we used this dataset to explore statistical differences among the different methods used to count marine iguanas (expert, median, mode and HDBSCAN) in the images and how the factors: phase analyzed, quality of the imges (assessed by the experts) and number of marine iguanas present in the image, could affect CS accuracy.

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

Gold Reserves in the United States remained unchanged at 8133.46 Tonnes in the first quarter of 2025 from 8133.46 Tonnes in the fourth quarter of 2024. This dataset provides - United States Gold Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Lithium rose to 63,750 CNY/T on July 11, 2025, up 0.16% from the previous day. Over the past month, Lithium's price has risen 5.11%, but it is still 29.56% 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.