The code will run on an installation of R with the add on packages lattice, dplyr, lattice, and latticeExtra. The output is a graph (Fig. 2) and a table showing likelihood ratios of run chart rules for identification of non-random variation in simulated run charts of different length with or without a shift in process mean. (R)

Gold increased 393.93 USD/t oz. or 15.01% since the beginning of 2025, 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 March of 2025.

To create the dataset, the top 10 countries leading in the incidence of COVID-19 in the world were selected as of October 22, 2020 (on the eve of the second full of pandemics), which are presented in the Global 500 ranking for 2020: USA, India, Brazil, Russia, Spain, France and Mexico. For each of these countries, no more than 10 of the largest transnational corporations included in the Global 500 rating for 2020 and 2019 were selected separately. The arithmetic averages were calculated and the change (increase) in indicators such as profitability and profitability of enterprises, their ranking position (competitiveness), asset value and number of employees. The arithmetic mean values of these indicators for all countries of the sample were found, characterizing the situation in international entrepreneurship as a whole in the context of the COVID-19 crisis in 2020 on the eve of the second wave of the pandemic. The data is collected in a general Microsoft Excel table. Dataset is a unique database that combines COVID-19 statistics and entrepreneurship statistics. The dataset is flexible data that can be supplemented with data from other countries and newer statistics on the COVID-19 pandemic. Due to the fact that the data in the dataset are not ready-made numbers, but formulas, when adding and / or changing the values in the original table at the beginning of the dataset, most of the subsequent tables will be automatically recalculated and the graphs will be updated. This allows the dataset to be used not just as an array of data, but as an analytical tool for automating scientific research on the impact of the COVID-19 pandemic and crisis on international entrepreneurship. The dataset includes not only tabular data, but also charts that provide data visualization. The dataset contains not only actual, but also forecast data on morbidity and mortality from COVID-19 for the period of the second wave of the pandemic in 2020. The forecasts are presented in the form of a normal distribution of predicted values and the probability of their occurrence in practice. This allows for a broad scenario analysis of the impact of the COVID-19 pandemic and crisis on international entrepreneurship, substituting various predicted morbidity and mortality rates in risk assessment tables and obtaining automatically calculated consequences (changes) on the characteristics of international entrepreneurship. It is also possible to substitute the actual values identified in the process and following the results of the second wave of the pandemic to check the reliability of pre-made forecasts and conduct a plan-fact analysis. The dataset contains not only the numerical values of the initial and predicted values of the set of studied indicators, but also their qualitative interpretation, reflecting the presence and level of risks of a pandemic and COVID-19 crisis for international entrepreneurship.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching 149 zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than 394 zettabytes. In 2020, the amount of data created and replicated reached a new high. The growth was higher than previously expected, caused by the increased demand due to the COVID-19 pandemic, as more people worked and learned from home and used home entertainment options more often. Storage capacity also growing Only a small percentage of this newly created data is kept though, as just two percent of the data produced and consumed in 2020 was saved and retained into 2021. In line with the strong growth of the data volume, the installed base of storage capacity is forecast to increase, growing at a compound annual growth rate of 19.2 percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached 6.7 zettabytes.

Inflation Rate in the United States decreased to 2.80 percent in February from 3 percent in January of 2025. This dataset provides - United States Inflation Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

Money Supply M2 in the United States increased to 21447.60 USD Billion in November from 21311.20 USD Billion in October of 2024. This dataset provides - United States Money Supply M2 - actual values, historical data, forecast, chart, statistics, economic calendar and news.

We update the data monthly, so it's accurate up to the end of the previous month.Registration is the process where we add a vehicle’s details to the MVR and issue its number plates. It is not the same thing as vehicle licensing, also called ‘rego’.To give you a quick overview of the data, see the charts in the ‘Attributes’ section below. These will give you information about each of the attributes (variables) in the dataset.Each chart is specific to a variable, and shows all data (without any filters applied).Motor Vehicle Register data - field descriptionsDue to the size of the data we recommend using the following for direct downloads of the data.Download this data as zipped CSV filesFuel consumption (litres / 100km) is for cars driving in urban areas (Column FC_Urban), on motorways (Column FC_Extra_Urban) and a combination of both (Column FC_Combined). Values range from 1 to 60.Greenhouse gas emissions are the SGGs (synthetic greenhouse gases) that airconditioning units produce.The data is added to the MVR when a new or used vehicle is first registered in New Zealand.Description of attributes for fuel consumption (27) and synthetic greenhouse gas (34)Data reuse caveatsAs per license.We’ve taken reasonable care in compiling this information, and provide it on an ‘as is, where is’ basis. We are not liable for any action taken on the basis of the information. For further information see the Waka Kotahi website, as well as the terms of the CC-BY 4.0 International license under which we publish this data.CC-BY 4.0 International licence detailsVariables in the dataset are formatted for analytical use. This can result in attribute charts that may not appear meaningful, and are not suitable for broader analysis or use. In addition, some variables are not mutually exclusive and should not be considered in isolation. As such, these charts should not be taken and used directly as analysis of the overall data.Data quality statement:This data relates to vehicles, not people.An entry certifier enters the data manually into the MVR when someone first registers a new or used vehicle in New Zealand.We have included some information about vehicle registered owners live. This is based on the most recent information we have about their physical address. To make sure it isn’t possible to identify a person in the data, we have provided this at Territorial Authority (TA) level. A TA is a broad geographical area defined under the Local Government Act 2002 as a city council or district council. There are 67 TAs consisting of 12 city councils, 53 districts, Auckland Council and Chatham Island Council.We haven’t included vehicles that belong to people with a confidential listing.We have restricted the Vehicle Identification Number (VIN) to the first 11 characters – these are generic and don’t identify specific vehicles.Data quality caveats:Many of the fields in the (MVR) are free text fields, which means there may be spelling mistakes and other human errors. The data is verified at time of entry, but there is potential for data to be entered incorrectly.We have algorithmically cleaned the data to correct identified errors (particularly with respect to a vehicle’s make and model). However, due to the large number of vehicles on the Register we may not have corrected some information.Additionally, some variables may be subject to differences in how people have recorded details – for example, manufacturers release a variety of sub-models and these may not be referred to, or put into the system, in the same way.We have made our cleaning code open source.Vehicle make and model cleansing code (GitHub)The below links are used to determine fuel consumption and CO2 emissions that are then entered when registering vehicle. This is mandatory and not optional. Data is first added to landata.importer.fuelsaver.govt.nz/certifier/www.greenvehicleguide.gov.au/www.fueleconomy.gov/www.vcacarfueldata.org.uk/UPDATE: The Motor Vehicle Register (MVR) dataset now contains information on fuel consumption and greenhouse gas emissions.

View data of the S&P 500, an index of the stocks of 500 leading companies in the US economy, which provides a gauge of the U.S. equity market.

Graph and download economic data for 15-Year Fixed Rate Mortgage Average in the United States (MORTGAGE15US) from 1991-08-30 to 2025-03-20 about 15-year, fixed, mortgage, interest rate, interest, rate, and USA.

Graph and download economic data for Federal Debt: Total Public Debt (GFDEBTN) from Q1 1966 to Q4 2024 about public, debt, federal, government, and USA.

Corn decreased 3.39 USd/BU or 0.74% since the beginning of 2025, according to trading on a contract for difference (CFD) that tracks the benchmark market for this commodity. Corn - values, historical data, forecasts and news - updated on March of 2025.

The USDMXN increased 0.2404 or 1.20% to 20.3451 on Thursday March 27 from 20.1047 in the previous trading session. Mexican Peso - values, historical data, forecasts and news - updated on March of 2025.

In 2023, the number of data compromises in the United States stood at 3,205 cases. Meanwhile, over 353 million individuals were affected in the same year by data compromises, including data breaches, leakage, and exposure. While these are three different events, they have one thing in common. As a result of all three incidents, the sensitive data is accessed by an unauthorized threat actor. Industries most vulnerable to data breaches Some industry sectors usually see more significant cases of private data violations than others. This is determined by the type and volume of the personal information organizations of these sectors store. In 2022, healthcare, financial services, and manufacturing were the three industry sectors that recorded most data breaches. The number of healthcare data breaches in the United States has gradually increased within the past few years. In the financial sector, data compromises increased almost twice between 2020 and 2022, while manufacturing saw an increase of more than three times in data compromise incidents. Largest data exposures worldwide In 2020, an adult streaming website, CAM4, experienced a leakage of nearly 11 billion records. This, by far, is the most extensive reported data leakage. This case, though, is unique because cyber security researchers found the vulnerability before the cyber criminals. The second-largest data breach is the Yahoo data breach, dating back to 2013. The company first reported about one billion exposed records, then later, in 2017, came up with an updated number of leaked records, which was three billion. In March 2018, the third biggest data breach happened, involving India’s national identification database Aadhaar. As a result of this incident, over 1.1 billion records were exposed.

The Maine Geological Survey and the USGS coordinate the colletction of snow measurements each winter for the Maine River Flow Advisory Commission's flood prediction report. These measurements are sent to MGS monthly in January and February and weekly in March, April and May as long as there is snow on the ground. The dataset contains all the raw snow survey measurements (depth, water content, density), their locations, data quality and other qualitative comments or observations. These measurements are used to create the snow survey site summary graphs. These graphs show the water content measurements by defined date range for the current year and the complete historical mean, minimum, maximum, and percentiles

As of March 2025, there were a reported 5,426 data centers in the United States, the most of any country worldwide. A further 529 were located in Germany, while 523 were located in the United Kingdom. What is a data center? A data center is a network of computing and storage resources that enables the delivery of shared software applications and data. These facilities can house large amounts of critical and important data, and therefore are vital to the daily functions of companies and consumers alike. As a result, whether it is a cloud, colocation, or managed service, data center real estate will have increasing importance worldwide. Hyperscale data centers In the past, data centers were highly controlled physical infrastructures, but the cloud has since changed that model. A cloud data service is a remote version of a data center – located somewhere away from a company's physical premises. Cloud IT infrastructure spending has grown and is forecast to rise further in the coming years. The evolution of technology, along with the rapid growth in demand for data across the globe, is largely driven by the leading hyperscale data center providers.

MGD: Music Genre Dataset

Over recent years, the world has seen a dramatic change in the way people consume music, moving from physical records to streaming services. Since 2017, such services have become the main source of revenue within the global recorded music market.
Therefore, this dataset is built by using data from Spotify. It provides a weekly chart of the 200 most streamed songs for each country and territory it is present, as well as an aggregated global chart.

Considering that countries behave differently when it comes to musical tastes, we use chart data from global and regional markets from January 2017 to December 2019, considering eight of the top 10 music markets according to IFPI: United States (1st), Japan (2nd), United Kingdom (3rd), Germany (4th), France (5th), Canada (8th), Australia (9th), and Brazil (10th).

We also provide information about the hit songs and artists present in the charts, such as all collaborating artists within a song (since the charts only provide the main ones) and their respective genres, which is the core of this work. MGD also provides data about musical collaboration, as we build collaboration networks based on artist partnerships in hit songs. Therefore, this dataset contains:

• Genre Networks: Success-based genre collaboration networks
• Genre Mapping: Genre mapping from Spotify genres to super-genres
• Artist Networks: Success-based artist collaboration networks
• Artists: Some artist data
• Hit Songs: Hit Song data and features
• Charts: Enhanced data from Spotify Weekly Top 200 Charts

This dataset was originally built for a conference paper at ISMIR 2020. If you make use of the dataset, please also cite the following paper:

Gabriel P. Oliveira, Mariana O. Silva, Danilo B. Seufitelli, Anisio Lacerda, and Mirella M. Moro. Detecting Collaboration Profiles in Success-based Music Genre Networks. In Proceedings of the 21st International Society for Music Information Retrieval Conference (ISMIR 2020), 2020.

@inproceedings{ismir/OliveiraSSLM20,
 title = {Detecting Collaboration Profiles in Success-based Music Genre Networks},
 author = {Gabriel P. Oliveira and 
      Mariana O. Silva and 
      Danilo B. Seufitelli and 
      Anisio Lacerda and
      Mirella M. Moro},
 booktitle = {21st International Society for Music Information Retrieval Conference}
 pages = {726--732},
 year = {2020}
}

MusicOSet is an open and enhanced dataset of musical elements (artists, songs and albums) based on musical popularity classification. Provides a directly accessible collection of data suitable for numerous tasks in music data mining (e.g., data visualization, classification, clustering, similarity search, MIR, HSS and so forth). To create MusicOSet, the potential information sources were divided into three main categories: music popularity sources, metadata sources, and acoustic and lyrical features sources. Data from all three categories were initially collected between January and May 2019. Nevertheless, the update and enhancement of the data happened in June 2019.

The attractive features of MusicOSet include:

• Integration and centralization of different musical data sources
• Calculation of popularity scores and classification of hits and non-hits musical elements, varying from 1962 to 2018
• Enriched metadata for music, artists, and albums from the US popular music industry
• Availability of acoustic and lyrical resources
• Unrestricted access in two formats: SQL database and compressed .csv files

|    Data    | # Records |
|:-----------------:|:---------:|
| Songs       | 20,405  |
| Artists      | 11,518  |
| Albums      | 26,522  |
| Lyrics      | 19,664  |
| Acoustic Features | 20,405  |
| Genres      | 1,561   |

Data DescriptionThe layer on this map contains population, employed labour force counts, private dwelling counts, and employment counts at a Census Tract geography from the 2006 Census. The definition of each variable is described next:Population counts: the total population aggregated from different ages in each census tract.Employment counts: the number of labour force aged 15 years and over having an usual work place or working at home at places of work in each census tract, excluding workers with a non-fixed place-of-work.Employed labour force counts: the number of employed labour force aged 15 years and over having a usual work place or working at home at places of residence in each census tract including workers with a non-fixed place-of-work.Private dwellings count: the number of households aggregated from different types of dwellings in each census tract.Note: Population counts are from long census survey forms, covering 25% of the population. The other three variables are from short census survey forms, covering 100% population.Note about the Legend: the Employment and Population values are normalized by Quantiles. Each colour has the same number of features and will not necessarily represent the same values in different layers.InstructionsZoom in and out of the map to update the bar charts. Use the Select Tool to select specific geographies to display on the bar chart.“Select by rectangle” allows you to draw a rectangle and select multiple geography to view in the chart.“Select by point” allows you select an area by clicking on its geography."Add Data" allows you add separate public data as need from ArcGIS Online, URL (an ArcGIS Server Web Service, a WMS OGC Web Service, a KML file, a GeoRSS file, a CSV file), and local files (shapefile, csv, kml, gpx, geojson)

Wikipedia is the largest and most read online free encyclopedia currently existing. As such, Wikipedia offers a large amount of data on all its own contents and interactions around them, as well as different types of open data sources. This makes Wikipedia a unique data source that can be analyzed with quantitative data science techniques. However, the enormous amount of data makes it difficult to have an overview, and sometimes many of the analytical possibilities that Wikipedia offers remain unknown. In order to reduce the complexity of identifying and collecting data on Wikipedia and expanding its analytical potential, after collecting different data from various sources and processing them, we have generated a dedicated Wikipedia Knowledge Graph aimed at facilitating the analysis, contextualization of the activity and relations of Wikipedia pages, in this case limited to its English edition. We share this Knowledge Graph dataset in an open way, aiming to be useful for a wide range of researchers, such as informetricians, sociologists or data scientists.

There are a total of 9 files, all of them in tsv format, and they have been built under a relational structure. The main one that acts as the core of the dataset is the page file, after it there are 4 files with different entities related to the Wikipedia pages (category, url, pub and page_property files) and 4 other files that act as "intermediate tables" making it possible to connect the pages both with the latter and between pages (page_category, page_url, page_pub and page_link files).

The document Dataset_summary includes a detailed description of the dataset.

Thanks to Nees Jan van Eck and the Centre for Science and Technology Studies (CWTS) for the valuable comments and suggestions.

FOCUSON**LONDON**2010:**INCOME**AND**SPENDING**AT**HOME**

Household income in London far exceeds that of any other region in the UK. At £900 per week, London’s gross weekly household income is 15 per cent higher than the next highest region. Despite this, the costs to each household are also higher in the capital. Londoners pay a greater amount of their income in tax and national insurance than the UK average as well as footing a higher bill for housing and everyday necessities. All of which leaves London households less well off than the headline figures suggest.

This chapter, authored by Richard Walker in the GLA Intelligence Unit, begins with an analysis of income at both individual and household level, before discussing the distribution and sources of income. This is followed by a look at wealth and borrowing and finally, focuses on expenditure including an insight to the cost of housing in London, compared with other regions in the UK.

See other reports from this Focus on London series.

REPORT:

To view the report online click on the image below. Income and Spending Report PDF

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PRESENTATION:

This interactive presentation finds the answer to the question, who really is better off, an average London or UK household? This analysis takes into account available data from all types of income and expenditure. Click on the link to access.

PREZI

The Prezi in plain text version

RANKINGS: https://londondatastore-upload.s3.amazonaws.com/fol/fol10-income-tableau-chart-thumb.jpg" alt="Alt text">

This interactive chart shows some key borough level income and expenditure data. This chart helps show the relationships between five datasets. Users can rank each of the indicators in turn.

Borough rankings Tableau Chart

MAP:

These interactive borough maps help to geographically present a range of income and expenditure data within London.

Interactive Maps - Instant Atlas

DATA:

All the data contained within the Income and Spending at Home report as well as the data used to create the charts and maps can be accessed in this spreadsheet.

Report data

FACTS:

Some interesting facts from the data…

● Five boroughs with the highest median gross weekly pay per person in 2009:

-1. Kensington & Chelsea - £809

-2. City of London - £767

-3. Westminster - £675

-4. Wandsworth - £636

-5. Richmond - £623

-32. Brent - £439

-33. Newham - £422

● Five boroughs with the highest median weekly rent for a 2 bedroom property in October 2010:

-1. Kensington & Chelsea - £550

-2. Westminster - £500

-3. City of London - £450

-4. Camden - £375

-5. Islington - £360

-32. Havering - £183

-33. Bexley - £173

● Five boroughs with the highest percentage of households that own their home outright in 2009:

-1. Bexley – 38 per cent

-2. Havering – 36 per cent

-3. Richmond – 32 per cent

-4. Bromley – 31 per cent

-5. Barnet – 28 per cent

-31. Tower Hamlets – 9 per cent

-32. Southwark – 9 per cent