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.

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.

This dataset offers a comprehensive view of global gold-related trends and metrics for the period 2020–2023. It is organized into several interrelated components, making it highly valuable for analyzing the role of gold in different sectors and its relationship with broader economic trends. The dataset contains the following files: 1. Gold Demand: Demand per Sector: Tracks gold demand in key sectors, including jewelry, investment, central banks, and technology, over time. Demand per Quarter: Provides quarterly demand data globally for greater temporal granularity. Yearly Demand by Country: Breaks down annual gold demand by individual countries. 2. Gold Reserves: Gold Reserves in Tonnes per Country: Highlights gold holdings by Central Banks, expressed in tonnes, for countries worldwide during the period. 3. Gold Jewelry: Gold Jewelry Demand by Country: Focuses on country-specific demand for gold jewelry, providing insights into cultural and economic patterns. The dataset is sourced from reliable and recognized industry databases and is designed to support a wide range of analyses, including demand trends, international comparisons, and the relationship between gold reserves and other economic indicators. Licensing: This dataset is sourced from the World Gold Council's website - Gold Demand & Supply by Country | World Gold Council The data is provided for general informational and educational purposes only. You are permitted to save, display, or print out this dataset strictly for personal, non-commercial use. Modifying, copying, scraping, distributing, reproducing, or using this dataset for commercial purposes is prohibited without prior written authorization from WGC. To request authorization, please contact WGC at info@gold.org.

Global Total Reserves Including Gold by Country, 2023 Discover more data with ReportLinker!

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 dataset presents a detailed country-wise record of Olympic medals from the first modern Olympics in 1896 to the most recent games in 2024. It provides insights into how different nations have performed over time, including their gold, silver, and bronze medal counts, overall rankings, and total medal tally.

The dataset is useful for sports analysts, data scientists, and researchers interested in studying trends in Olympic performance, country-wise dominance, and medal progression over the years.

Potential Applications: 🏅 Medal Prediction Models – Predict future Olympic performances based on historical trends. 📊 Data Visualization – Create interactive graphs and heatmaps of medal distributions. 🌍 Country-wise Performance Analysis – Compare Olympic dominance among nations. 📈 Time-Series Analysis – Identify trends in medal-winning performances over different decades.

In the history of the Summer Olympics, the United States has been the most successful nation ever, with a combined total of 2,761 medals in 29 Olympic Games. More than one thousand of these were gold, with almost 900 silver medals, and nearly 800 bronze medals. The second most successful team in Summer Olympic history was the Soviet Union**, who took home 440 golds and more than 1,100 total medals in ten Olympic Games between 1952 and 1992. When the total medal hauls of the Soviet Union, Russia and the Russian Empire are combined, they still fall short of the U.S. tally by over one thousand medals. Meanwhile, Great Britain sat in fifth place, with 299 golds and 980 medals in total. Emerging nations While European and Anglophone nations have traditionally dominated the medals tables, recent decades have seen the emergence and increased participation from athletes representing developing nations, such as Kenya, Jamaica, and particularly China. Although China has competed in just 12 Summer Olympics, they have the fifth most gold medals across a variety of events, despite only developing a significant Olympic presence in the 1980s. Athletes from African and Caribbean nations have also developed a more formidable presence since this time, by focusing their resources on specific sports; for example, Kenyan athletes have established a lasting legacy in distance running events, while Jamaicans have dominated sprinting events in recent years. Despite this increased investment, the past three Olympic Games have seen a record number of African-born athletes representing high-income countries in the Arabian Gulf; most notably, athletes born in Kenya and Ethiopia competing for Bahrain. The influence of money, politics and drugs As mentioned above, European and Anglophone countries have dominated the medals tables in the past; this is because they had the financial resources to send athletes around the world to compete, and, until 1964, the host cities were always in these countries, which caused financial and logistical difficulties for African, Asian and Latin American countries. Financial difficulties have caused some countries to refuse invitations to the Olympics as recently as the 1980s, for example, many African and Latin American countries joined in the U.S.-led boycott of the 1980 Moscow Games (due to the Soviet invasion of Afghanistan), saving face by citing the boycott and not financial problems as the reason. This boycott also contributed to the Soviet Union and East Germany's high medal tally, as both nations took over sixty percent of all available gold medals. In retaliation, the Soviet Union led a boycott of the following Games in Los Angeles, opening the way for the United States to win almost half of all available golds in 1984. Recent years have seen doping scandals replace financial and political factors as the main external-influence on the medals table. The World Anti-Doping Agency (WADA) was founded by the International Olympic Committee in 1999, to combat the increasing use of performance-enhancing substances in sports. Since then, it has had a major impact on the Olympic medal table, and has helped rescind and redistribute more than one hundred Olympic medals. Athletes from Russia and former-Soviet countries have been particularly affected by these measures, which follows a legacy of state-sponsored doping programs dating back to the 1980s. In 2019, WADA banned all Russian athletes from the 2020 Games in Tokyo due to yet another state-sponsored doping scandal; athletes from Russia could only compete if they have been cleared by WADA prior to the games, while representing the Russian Olympic Committee, rather than the country itself. Paris 2024 was also shadowed by the issue of doping, with some delegations criticizing WADA for clearing 11 Chinese swimmers to participate in the Games, despite testing for a banned substance in 2021.

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 dataset includes Instagram user characteristics of those Olympic athletes who won gold medals in the individual events of Rio2016. The name of all these gold medalists of individual events are in the dataset (226 athletes), however only 144 athletes (83 men and 61 women) had a publicly available Instagram account in all of the observations during the 4 months period of data gathering. The first round of data gathering (first observation, i.e. OlympicAthletesData_1) took place 9-Aug-2019 to 12-Aug-2019, the second round of data gathering (second observation, i.e. OlympicAthletesData_2) took place 9-Sep-2019 to 12-Sep-2019, the third round of data gathering (third observation, i.e. OlympicAthletesData_3) took place 9-Oct-2019 to 12-Oct-2019, the fourth round of data gathering (fourth observation, i.e. OlympicAthletesData_4) took place 9-Nov-2019 to 12-Nov-2019. The data gathered for each user (in each observation) consists of: 1- Name of the individual event 2- Country 3- Name 4- Gender 5- Instagram ID 6- Number of Posts 7- Number of followers 8- Number of followings 9- Maximum Number of likes (in the last 10 photo posts) 10- Number of comments for the post with Maximum Number of likes (in the last 10 photo posts) 11- Number of self-presenting posts in the last 10 photo posts (those posts in which the athlete is present) 12- Number of pure self-presenting posts in the last 10 photo posts (those posts in which the athlete is the only person who is present) 13- Age 14- Date of data crawling

About this dataset

Context

The dataset presents the distribution of median household income among distinct age brackets of householders in Gold Bar. 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 Bar. 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 Bar, the median household income stands at $107,656 for householders within the 45 to 64 years age group, followed by $87,448 for the 25 to 44 years age group. Notably, householders within the 65 years and over age group, had the lowest median household income at $52,647.

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

About this dataset

Context

The dataset tabulates the Gold Bar population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Gold Bar. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 to 64 years with a poulation of 1,468 (62.79% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2018-2022 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Gold Bar population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Gold Bar is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Gold Bar is shown in the following column.

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 Age. You can refer the same here

Monthly gold prices in USD since 1833 (sourced from the World Gold Council). The data is derived from historical records compiled by Timothy Green and supplemented by data provided by the World Bank...

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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The dataset is published with:

MultiEURLEX - A multi-lingual and multi-label legal document classification dataset for zero-shot cross-lingual transfer. Ilias Chalkidis, Manos Fergadiotis, and Ion Androutsopoulos. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing. 2021. Punta Cana, Dominican Republic.

Documents: MultiEURLEX comprises 65k EU in 23 official EU languages. Each EU law has been annotated with EUROVOC concepts (labels) by the Publication Office of EU. Each EUROVOC label ID is associated with a Label descriptor, e.g., [60, `agri-foodstuffs'], [6006, `plant product'], [1115, `fruit']. The descriptors are also available in 23 languages. Chalkidis et al. (2019) published a monolingual (English) version of this dataset, called EURLEX57K, comprising 57k EU laws with the originally assigned gold labels.

Languages: MultiEURLEX covers 23 languages from 7 families. EU laws are published in all official EU languages, except for Irish for resource-related reasons (Read more: https://europa.eu/european-union/about-eu/eu-languages_en). This wide coverage makes the dataset a valuable testbed for cross-lingual transfer. All languages use the Latin script, except for Bulgarian (Cyrillic script) and Greek.

Multi-granular Labeling: EUROVOC has eight levels of concepts. Each document is assigned one or more concepts (labels). If a document is assigned a concept, the ancestors and descendants of that concept are typically not assigned to the same document. The documents were originally annotated with concepts from levels 3 to 8. We created three alternative sets of labels per document, by replacing each assigned concept by its ancestor from levels 1, 2, or 3, respectively. Thus, we provide four sets of gold labels per document, one for each of the first three levels of the hierarchy, plus the original sparse label assignment.

Supported Tasks: Similarly to EURLEX (Chalkidis et al., 2019), MultiEURLEX can be used for legal topic classification, a multi-label classification task where legal documents need to be assigned concepts (in our case, from EUROVOC) reflecting their topics. Unlike EURLEX57K, however, MultiEURLEX supports labels from three different granularities (EUROVOC levels). More importantly, apart from monolingual (one-to-one) experiments, it can be used to study cross-lingual transfer scenarios, including one-to-many (systems trained in one language and used in other languages with no training data), and many-to-one or many-to-many (systems jointly trained in multiple languages and used in one or more other languages).

Data Split and Concept Drift: MultiEURLEX is chronologically split in training (55k, 1958-2010), development (5k, 2010-2012), test (5k, 2012-2016) subsets, using the English documents. The test subset contains the same 5k documents in all 23 languages. The development subset also contains the same 5k documents in 23 languages, except Croatian. Croatia is the most recent EU member (2013); older laws are gradually translated. For the official languages of the seven oldest member countries, the same 55k training documents are available; for the other languages, only a subset of the 55k training documents is available. Compared to EURLEX57K (Chalkidis et al., 2019), MultiEURLEX is not only larger (8k more documents) and multilingual; it is also more challenging, as the chronological split leads to temporal real-world concept drift across the training, development, test subsets, i.e., differences in label distribution and phrasing, representing a realistic temporal generalization problem (Huang and Paul, 2019; Lazaridou et al., 2021). Recently, Søgaard et al. (2021) showed this setup is more realistic, as it does not overestimate real performance, contrary to random splits (Gorman and Bedrick, 2019).

These data sets include deposit description, location, and geochemistry of sedimentary rock-hosted (Carlin-type) Au deposits in P.R. China. Deposit data was compiled by U.S. Geological Survey and Tianjim Geological Academy into a series of tables (.xls and .csv) provided in Open-File Report 02-131, Appendix III and IV. Since the 1980s, Chinese geologists have devoted a large-scale exploration and research effort to the deposits. As a result, there are more than 20 million oz of proven Au reserves in sedimentary rock-hosted Au deposits in P.R. China. Additional estimated and inferred resources are present in over 160 deposits and occurrences, which are undergoing exploration. This makes China second to Nevada in contained ounces of Au in Carlin-type deposits. It is likely that many of the Carlin-type Au ore districts in China, when fully developed, could have resource potential comparable to the multi-1,000-tonne Au resource in northern Nevada.

About this dataset

Context

The dataset presents median income data over a decade or more for males and females categorized by Total, Full-Time Year-Round (FT), and Part-Time (PT) employment in Gold Bar. It showcases annual income, providing insights into gender-specific income distributions and the disparities between full-time and part-time work. The dataset can be utilized to gain insights into gender-based pay disparity trends and explore the variations in income for male and female individuals.

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Gold Bar, the median income for all workers aged 15 years and older, regardless of work hours, was $49,429 for males and $39,764 for females.

These income figures indicate a substantial gender-based pay disparity, showcasing a gap of approximately 20% between the median incomes of males and females in Gold Bar. With women, regardless of work hours, earning 80 cents to each dollar earned by men, this income disparity reveals a concerning trend toward wage inequality that demands attention in thecity of Gold Bar.

- Full-time workers, aged 15 years and older: In Gold Bar, among full-time, year-round workers aged 15 years and older, males earned a median income of $72,800, while females earned $66,417, resulting in a 9% gender pay gap among full-time workers. This illustrates that women earn 91 cents for each dollar earned by men in full-time positions. While this gap shows a trend where women are inching closer to wage parity with men, it also exhibits a noticeable income difference for women working full-time in the city of Gold Bar.

Interestingly, when analyzing income across all roles, including non-full-time employment, the gender pay gap percentage was higher for women compared to men. It appears that full-time employment presents a more favorable income scenario for women compared to other employment patterns in Gold Bar.

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.

Gender classifications include:

• Male
• Female

Employment type classifications include:

• Full-time, year-round: A full-time, year-round worker is a person who worked full time (35 or more hours per week) and 50 or more weeks during the previous calendar year.
• Part-time: A part-time worker is a person who worked less than 35 hours per week during the previous calendar year.

Variables / Data Columns

• Year: This column presents the data year. Expected values are 2010 to 2023
• Male Total Income: Annual median income, for males regardless of work hours
• Male FT Income: Annual median income, for males working full time, year-round
• Male PT Income: Annual median income, for males working part time
• Female Total Income: Annual median income, for females regardless of work hours
• Female FT Income: Annual median income, for females working full time, year-round
• Female PT Income: Annual median income, for females working part time

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 by race. You can refer the same here

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).

I have created this dataset for people interested in League of Legends who want to approach the game from a more analytical side.

Most of the data was acquired from Games of Legends (https://gol.gg/tournament/tournament-stats/LEC%20Spring%20Season%202024/) and also from official account of the League of Legends EMEA Championship (https://www.youtube.com/c/LEC)

Dataset Contents:

• Player: Name of the player.
• Role: Role of the player (e.g., TOP, JUNGLE, MID, ADC, SUPPORT)
• Team: Name of the player's team
• Opponent Team: Name of the opposing team
• Opponent Player: Name of the opposing player
• Date: Date of the match
• Week: Week of the tournament
• Day: Specific day of the tournament
• Patch: Version of the game patch during the match
• Stage: Stage of the tournament
• No Game: Game number in the series
• all Games: Total number of games in the series
• Format: Format of the match (e.g., Best of 1, Best of 3)
• Game of day: Number of the game that day
• Side: Side of the map the team started on (Blue/Red)
• Time: Duration of the match

Team Performance Metrics:

• Kills Team: Total kills by the team
• Turrets Team: Total turrets destroyed by the team
• Dragon Team: Total dragons killed by the team
• Baron Team: Total barons killed by the team

Player Performance Metrics:

• Level: Final level of the player
• Kills: Number of kills by the player
• Deaths: Number of deaths of the player
• Assists: Number of assists by the player
• KDA: Kill/Death/Assist ratio
• CS: Creep Score (minions killed)
• CS in Team's Jungle: Creep Score in the team's jungle
• CS in Enemy Jungle: Creep Score in the enemy's jungle
• CSM: Creep Score per Minute
• Golds: Total gold earned
• GPM: Gold Per Minute
• GOLD%: Percentage of team's total gold earned by the player

Vision and Warding:

• Vision Score: Total vision score
• Wards placed: Number of wards placed
• Wards destroyed: Number of wards destroyed
• Control Wards Purchased: Number of control wards purchased
• Detector Wards Placed: Number of detector wards placed
• VSPM: Vision Score Per Minute
• WPM: Wards Placed per Minute
• VWPM: Vision Wards Placed per Minute
• WCPM: Wards Cleared per Minute
• VS%: Vision Score percentage

Damage Metrics:

• Total damage to Champion: Total damage dealt to champions
• Physical Damage: Total physical damage dealt
• Magic Damage: Total magic damage dealt
• True Damage: Total true damage dealt
• DPM: Damage Per Minute
• DMG%: Percentage of team’s total damage dealt by the player

Combat Metrics:

• K+A Per Minute: Kills and Assists per Minute
• KP%: Kill Participation percentage
• Solo kills: Number of solo kills
• Double kills: Number of double kills
• Triple kills: Number of triple kills
• Quadra kills: Number of quadra kills
• Penta kills: Number of pentakills

Early Game Metrics:

• GD@15: Gold Difference at 15 minutes
• CSD@15: Creep Score Difference at 15 minutes
• XPD@15: Experience Difference at 15 minutes
• LVLD@15: Level Difference at 15 minutes

Objective Control:

• Objectives Stolen: Number of objectives stolen
• Damage dealt to turrets: Total damage dealt to turrets
• Damage dealt to buildings: Total damage dealt to buildings

Healing and Mitigation:

• Total heal: Total healing done
• Total Heals On Teammates: Total healing done on teammates
• Damage self mitigated: Total damage self-mitigated
• Total Damage Shielded On Teammates: Total damage shielded on teammates

Crowd Control Metrics:

• Time ccing others: Time spent crowd controlling others
• Total Time CC Dealt: Total crowd control time dealt

Survival and Economy:

• Total damage taken: Total damage taken
• Total Time Spent Dead: Total time spent dead
• Consumables purchased: Number of consumables purchased
• Items Purchased: Number of items purchased
• Shutdown bounty collected: Total shutdown bounty collected
• Shutdown bounty lost: Total shutdown bounty lost

This dataset is a meta-data evaluation of the public datasets on the Open Data portal. Each public dataset is evaluated based on a variety of topics, and assigned a score between 0 and 100.
The datasets are assigned a meta data attribute based on the following scores: • 0-70: Bronze • 71-80: Silver • 81-100: Gold For more information about the method by which the score is calculated, please visit the following PDF: http://wpgopendata.blob.core.windows.net/documents/Data-Quality-Score-Documentation.pdf

Gold Reserves in Ghana increased to 31.01 Tonnes in the first quarter of 2025 from 30.53 Tonnes in the fourth quarter of 2024. This dataset provides - Ghana Gold Reserves - actual values, historical data, forecast, chart, statistics, economic calendar and news.