Gold fell to 3,333.04 USD/t.oz on July 2, 2025, down 0.16% from the previous day. Over the past month, Gold's price has fallen 0.61%, but it is still 41.30% 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.

Dataset of historical annual gold prices from 1970 to 2024, including significant events and acts that impacted gold prices.

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

Context

Gold, the yellow shiny metal, has been the fancy of mankind since ages. From making jewelry to being used as an investment, gold covers a huge spectrum of use cases. Gold, like other metals, is also traded on the commodities indexes across the world. For better understanding time series in a real-world scenario, we will work with gold prices collected historically and predict its future value.

Content

Metals such as gold have been traded for years across the world. Prices of gold are determined and used for trading the metal on commodity exchanges on a daily basis using a variety of factors. Using this daily price-level information only, our task is to predict future price of gold.

Data

For the purpose of implementing time series forecasting technique , i will utilize gold pricing from Quandl. Quandl is a platform for financial, economic, and alternative datasets. To access publicly shared datasets on Quandl, we can use the pandas-datareader library as well as quandl (library from Quandl itself). The following snippet shows a quick one-liner to get your hands on gold pricing information since 1970s.

import quandl gold_df = quandl.get("BUNDESBANK/BBK01_WT5511")

The time series is univariate with date and time feature

Starter Kernel(s)

-Start with Fundamentals: TSA & Box-Jenkins Methods

This notebook is an overview of TSA and traditional methods

Acknowledgements

For this dataset and tasks, i will depend upon Quandl. The premier source for financial, economic, and alternative datasets, serving investment professionals. Quandl’s platform is used by over 400,000 people, including analysts from the world’s top hedge funds, asset managers and investment banks.

Inspiration

• Forecast gold price

*If you find the data useful your upvote is an explicit feedback for future works, Have fun exploring data!*

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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.

Rhodium traded flat at 5,475 USD/t oz. on July 2, 2025. Over the past month, Rhodium's price has risen 2.82%, and is up 18.38% 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.

Silver rose to 37.04 USD/t.oz on July 3, 2025, up 1.30% from the previous day. Over the past month, Silver's price has risen 7.35%, and is up 21.97% 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.

The Geological Survey of South Australia has used SA Geodata to compile cleaned datasets of selected maximum downhole geochemistry for state-wide display on SARIG. Geochemical maps consist of drill hole locations, and sampled geochemical data... The Geological Survey of South Australia has used SA Geodata to compile cleaned datasets of selected maximum downhole geochemistry for state-wide display on SARIG. Geochemical maps consist of drill hole locations, and sampled geochemical data transformed from single element values (obtained from whole rock ppm/ppb conversion) normalised to times average crustal abundance. The maximum gold value from each drill hole has then been selected and displayed on SARIG.

jats:pThe detection of wheat heads in plant images is an important task for estimating pertinent wheat traits including head population density and head characteristics such as health, size, maturity stage, and the presence of awns. Several studies have developed methods for wheat head detection from high-resolution RGB imagery based on machine learning algorithms. However, these methods have generally been calibrated and validated on limited datasets. High variability in observational conditions, genotypic differences, development stages, and head orientation makes wheat head detection a challenge for computer vision. Further, possible blurring due to motion or wind and overlap between heads for dense populations make this task even more complex. Through a joint international collaborative effort, we have built a large, diverse, and well-labelled dataset of wheat images, called the Global Wheat Head Detection (GWHD) dataset. It contains 4700 high-resolution RGB images and 190000 labelled wheat heads collected from several countries around the world at different growth stages with a wide range of genotypes. Guidelines for image acquisition, associating minimum metadata to respect FAIR principles, and consistent head labelling methods are proposed when developing new head detection datasets. The GWHD dataset is publicly available at

This data release contains the analytical results and evaluated source data files of geospatial analyses for identifying areas in Alaska that may be prospective for different types of lode gold deposits, including orogenic, reduced-intrusion-related, epithermal, and gold-bearing porphyry. The spatial analysis is based on queries of statewide source datasets of aeromagnetic surveys, Alaska Geochemical Database (AGDB3), Alaska Resource Data File (ARDF), and Alaska Geologic Map (SIM3340) within areas defined by 12-digit HUCs (subwatersheds) from the National Watershed Boundary dataset. The packages of files available for download are: 1. LodeGold_Results_gdb.zip - The analytical results in geodatabase polygon feature classes which contain the scores for each source dataset layer query, the accumulative score, and a designation for high, medium, or low potential and high, medium, or low certainty for a deposit type within the HUC. The data is described by FGDC metadata. An mxd file, and cartographic feature classes are provided for display of the results in ArcMap. An included README file describes the complete contents of the zip file. 2. LodeGold_Results_shape.zip - Copies of the results from the geodatabase are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file. 3. LodeGold_SourceData_gdb.zip - The source datasets in geodatabase and geotiff format. Data layers include aeromagnetic surveys, AGDB3, ARDF, lithology from SIM3340, and HUC subwatersheds. The data is described by FGDC metadata. An mxd file and cartographic feature classes are provided for display of the source data in ArcMap. Also included are the python scripts used to perform the analyses. Users may modify the scripts to design their own analyses. The included README files describe the complete contents of the zip file and explain the usage of the scripts. 4. LodeGold_SourceData_shape.zip - Copies of the geodatabase source dataset derivatives from ARDF and lithology from SIM3340 created for this analysis are also provided in shapefile and CSV formats. The included README file describes the complete contents of the zip file.

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.

Wind energy is produced from wind power, thanks to a wind turbine, which transforms the mechanical energy of the wind into electrical energy. Connected to a generator, it consists of a mast on which is attached a propeller that turns the wind. Onshore wind is distinguished from offshore wind — or offshore wind (having more frequent, stronger and more regular winds than on land). Depending on its height, a wind turbine may be subject to a building permit (for wind turbines over 12 m high), an impact assessment (for wind turbines over 50 m high) or a notice (for wind turbines less than 50 m high). This table contains all the wind turbines, regardless of their condition, with an accuracy on the status of the building permit (PC). Since the “Grenelle II” National Environmental Commitment Act, a minimum distance threshold has been introduced between future wind turbine installations and homes. In the absence of precision in the building permit, the rules of retreat from public roads or right-of-way and separative limits apply at any point of the wind turbine, at the end of the blade in a horizontal position. In addition, the operation of wind turbines will be subject to the authorisation scheme for installations classified for environmental protection (ICPE).’Wind energy is produced from wind power, thanks to a wind turbine, which transforms the mechanical energy of the wind into electrical energy. Connected to a generator, it consists of a mast on which is attached a propeller that turns the wind.Onshore wind is distinguished from offshore wind — or offshore wind (having more frequent, stronger and more regular winds than on land). Depending on its height, a wind turbine may be subject to a building permit (for wind turbines over 12 m high), an impact assessment (for wind turbines over 50 m high) or a notice (for wind turbines less than 50 m high). This table contains all the wind turbines, regardless of their condition, with an accuracy on the status of the building permit (PC). Since the “Grenelle II” National Environmental Commitment Act, a minimum distance threshold has been introduced between future wind turbine installations and homes. In the absence of precision in the building permit, the rules of retreat from public roads or right-of-way and separative limits apply at any point of the wind turbine, at the end of the blade in a horizontal position. In addition, the operation of wind turbines will be subject to the authorisation scheme for installations classified for environmental protection (ICPE).’Connected to a generator, it consists of a mast on which is attached a propeller that turns the wind. Onshore wind is distinguished from offshore wind — or offshore wind (having more frequent, stronger and more regular winds than on land). Depending on its height, a wind turbine may be subject to a building permit (for wind turbines over 12 m high), an impact assessment (for wind turbines over 50 m high) or a notice (for wind turbines less than 50 m high). This table contains all the wind turbines, regardless of their condition, with an accuracy on the status of the building permit (PC).

Since the “Grenelle II” National Environmental Commitment Act, a minimum distance threshold has been introduced between future wind turbine installations and homes.

In the absence of precision in the building permit, the rules of retreat from public roads or right-of-way and separative limits apply at any point of the wind turbine, at the end of the blade in a horizontal position.

In addition, the operation of wind turbines will be subject to the authorisation scheme for installations classified for environmental protection (ICPE).’

We propose a newly-created gold standard data set for citation-based tasks. This gold standard is based on all computer science papers in arXiv.org.

Abstract. Analyzing and recommending citations with their specific citation contexts have recently received much attention due to the growing number of available publications. Although data sets such as CiteSeerX have been created for evaluating approaches for such tasks, those data sets exhibit striking defects. This is understandable if one considers that both information extraction and entity linking as well as entity resolution need to be performed. In this paper, we propose a new evaluation data set for citation-dependent tasks based on arXiv.org publications. Our data set is characterized by the fact that it exhibits almost zero noise in the extracted content and that all citations are linked to their correct publications. Besides the pure content, available on a sentence-basis, cited publications are annotated directly in the text via global identifiers. As far as possible, referenced publications are further linked to DBLP. Our data set consists of over 15M sentences and is freely available for research purposes. It can be used for training and testing citation-based tasks, such as recommending citations, determining the functions or importance of citations, and summarizing documents based on their citations.

More information can be found in our publication "A High-Quality Gold Standard for Citation-based Tasks" (LREC'18).

You can cite the data set as follows:

@inproceedings{DBLP:conf/lrec/0001TJ18, author = {Michael F{"{a}}rber and Alexander Thiemann and Adam Jatowt}, title = "{A High-Quality Gold Standard for Citation-based Tasks}", booktitle = "{Proceedings of the Eleventh International Conference on Language Resources and Evaluation}", series = "{LREC'18}", location = "{Miyazaki, Japan}", year = {2018}, url = {http://www.lrec-conf.org/proceedings/lrec2018/summaries/283.html} }

A brain MRI dataset to develop and test improved methods for detection and segmentation of brain metastases. The dataset includes 156 whole brain MRI studies, including high-resolution, multi-modal pre- and post-contrast sequences in patients with at least 1 brain metastasis accompanied by ground-truth segmentations by radiologists. About 2% of all patients with a primary neoplasm will be diagnosed with brain metastases at the time of their initial diagnosis. As we are getting better at controlling primary cancers, even more patients eventually present with such lesions. Given that brain metastases are often quite treatable with surgery or stereotactic radiosurgery, accurate segmentation of brain metastases is a common job for radiologists. Having algorithms to help detect and localize brain metastasis could relieve radiologists from this tedious but crucial task. Given the success of recent AI techniques on other segmentation tasks, we have put together this gold-standard, labeled MRI dataset to allow for the development and testing of new techniques in these patients with the hopes of spurring research in this area. This is a dataset of 156 pre- and post-contrast whole brain MRI studies in patients with at least 1 cerebral metastasis. Mean patient age was 63±12 years (range: 29–92 years). Primary malignancies included lung (n = 99), breast (n = 33), melanoma (n = 7), genitourinary (n = 7), gastrointestinal (n = 5), and miscellaneous cancers (n = 5). The specific primary malignancies for each case are included in an excel sheet that can be downloaded with the data. 64 (41%) had 1–3 metastases, 47 (30%) had 4–10 metastases, and 45 (29%) had >10 metastases. Lesion sizes varied from 2 mm to over 4 cm and were scattered in every region of the brain parenchyma, i.e., the supratentorial and infratentorial regions, as well as the cortical and subcortical structures. It includes 4 different 3D sequences (T1 spin-echo pre-contrast, T1 spin-echo post-contrast, T1 gradient-echo post (using an IR-prepped FSPGR sequence), T2 FLAIR post) in the axial plane, co-registered to each other, resampled to 256 x 256 pixels. The nominal in-plane resolution is 0.94 mm and the through-plane resolution is 1.0 mm. Standard dose (0.1 mmol/kg) gadolinium contrast agents were used for all cases. All the images have been skull-stripped by using the Brain Extraction Tool (BET) (Smith SM. Fast robust automated brain extraction. Hum Brain Map. 2002;17:143–155). The brain masks were generated from the precontrast T1-weighted 3D CUBE imaging series using the nordicICE software package (NordicNeuroLab, Bergen, Norway) and propagated to the other sequences. For 105 cases, we include radiologist-drawn segmentations of the metastatic lesions, stored in folder ‘mets_stanford_release_train’. The segmentations were based on the T1 gradient-echo post-contrast images. The remaining 51 cases are unlabeled and stored in ‘mets_stanford_release_test’. There are 5 folders for each subject in the training group – folder ‘0’ contains T1 gradient-echo post images; folder ‘1’ contains T1 spin-echo pre images; folder ‘2’ contains T1 spin-echo post images; folder ‘3’ contains T2 FLAIR post images; folder ‘seg’ contains a binary mask of the segmented metastases (0, 255). There are 4 folders for each subject in the testing group, which are labelled identically, except for the absence of folder ‘seg’. More detailed information on this dataset and the Stanford group’s initial performance on this data set can be found in Grøvik et al., Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multisequence MRI, JMRI 2019; 51(1):175-182. We would like to thank the team involved with labeling and preparing the data and for checking it for potential PHI: Darvin Yi, Endre Grovik, Elizabeth Tong, Michael Iv, Daniel Rubin, Greg Zaharchuk, and Ghiam Yamin, and the Division of Neuroimaging at Stanford for supporting this project. Grøvik et al., Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multisequence MRI, JMRI 2019; 51(1):175-182 also available on ArXiv (https://arxiv.org/abs/1903.07988).

Interactive chart of the S&P 500 stock market index since 1927. Historical data is inflation-adjusted using the headline CPI and each data point represents the month-end closing value. The current month is updated on an hourly basis with today's latest value.

At least 150 people have died in hazardous pits abandoned by the gold mines that are decimating Cameroon's forests. Such deaths occur regularly in the eastern region and Adamaoua, the country's two main gold mining regions, according to the civil society organization Forêts et Développement Rural (FODER). Between 2014 and 2020, FODER counted more than 150 deaths at abandoned mining sites in Cameroon, more than 90% of which occurred in the East region, the country's largest forest region.

Gold Reserves in Russia decreased to 2329.63 Tonnes in the first quarter of 2025 from 2332.74 Tonnes in the fourth quarter of 2024. This dataset provides - Russia Gold Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.

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

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Gold Hill. Based on the latest 2019-2023 5-Year Estimates from the American Community Survey, it displays how income varies among householders of different ages in Gold Hill. It showcases how household incomes typically rise as the head of the household gets older. The dataset can be utilized to gain insights into age-based household income trends and explore the variations in incomes across households.

Key observations: Insights from 2023

In terms of income distribution across age cohorts, in Gold Hill, householders within the under 25 years age group have the highest median household income at $76,250, followed by those in the 25 to 44 years age group with an income of $71,161. Meanwhile householders within the 45 to 64 years age group report the second lowest median household income of $38,750. Notably, householders within the 65 years and over age group, had the lowest median household income at $36,304.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates. All incomes have been adjusting for inflation and are presented in 2023-inflation-adjusted dollars.

Age groups classifications include:

• Under 25 years
• 25 to 44 years
• 45 to 64 years
• 65 years and over

Variables / Data Columns

• Age Of The Head Of Household: This column presents the age of the head of household
• Median Household Income: Median household income, in 2023 inflation-adjusted dollars for the specific age group

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 Hill median household income by age. You can refer the same here

This is a cross-disciplinary work at the physics/life science interface which addresses an important question in the use of gold nanoparticles (AuNPs) conjugated to fluorescent molecules for cell biology, namely whether the fluorophore is a faithful reporter of the nanoparticle location.AuNPs are among the most widely investigated systems in nano-medicine research for applications in intracellular imaging and sensing, drug delivery and photothermal therapy, owing to their small sizes, biocompatibility, ease of surface functionalisation and bio-conjugation.In this context, a particularly interesting system is that of a AuNP-fluorophore conjugate, whereby a fluorescently labelled biomolecule (e.g. a protein ligand, nucleotide, peptide, antibody) is attachedonto the AuNP surface, and its uptake and intracellular fate is followed in situ in real time by fluorescence microscopy. AuNPs are historically well known to biologists as markers for electron microscopy due to their high electron density; hence these conjugate are specifically useful probes for correlative light electron microscopy. However, an important question that has remained elusive to answer is whether the fluorescence readout is actually a reliable reporter of the AuNP location. This is because it is challenging with current optical techniques to directly visualise a single small AuNP against the endogenous scattering, absorption and phase contrast in a highly heterogeneous three-dimensional cellular environment.These data demonstrate the application of a novel optical microscopy technique developed in our lab (four-wave mixing (FWM) interferometry) to directly image single small AuNPs background-free inside cells with high 3D spatial resolution. The data show four different AuNP-fluorophore conjugates imaged inside two different cell types. By correlative fluorescence-FWM microscopy, the data show that, in most cases, fluorescence emission originated from unbound fluorophores rather than from fluorophores attachedto nanoparticles. Fluorescence detection was also severely limited by photobleaching, quenching and autofluorescence background.The datasets consist of images and numerical data. Images consist of two groups: experimental and calculated datasets.Experimental images are optical microscopy datasets obtained using: i) Differential Interference Contrast Microscopy (DIC), 2) FWM microscopy, 3) Confocal fluorescence microscopy, 4) extinction microscopy, 5) wide-field epifluorescence microscopy. Calculated datasets are images of the cross correlation coefficient as a function of relative translation coordinates, calculated from the experimental images. Numerical data consist of:1) One dimensional cut profiles along images. Data are provided as Origin plots where original datasets can be retrieved.2) Plots of representative values of extinction cross-sections. Data are provided as Origin plots where original datasets can be retrieved.3) Scatter plot from a two-channel/colour fluorescence image, showing the intensity of one colour channel in a given pixel as the x-coordinate and the fluorescence intensity of the second channel at the same pixel as the y-coordinate. Data are provided as Origin plots where original datasets can be retrieved.4) Scatter plot showing the fluorescence flux (in units of detected photoelectrons/s) versus extinction cross-section of nanoparticles. Data are provided as Origin plots where original datasets can be retrieved.Research results based upon these data are published at https://doi.org/10.1039/C9NR08512B