London's Population Over Time

For trend prediction London's population

By Eva Murray [source]

About this dataset

This file contains data on the projected population of London from 2011 to 2050. The data comes from the London Datastore and offers a glimpse into the future of one of the world's most populous cities

How to use the dataset

Research Ideas

• Predicting crime rates based on population growth
• Determining which areas of London will need more infrastructure to accommodate the growing population
• Planning for different marketing and advertising strategies based on demographics

Acknowledgements

Data Source

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: central_trend_2017_base.csv | Column name | Description | |:--------------|:------------------------------------| | gss_code | The GSS code for the area. (String) | | district | The name of the district. (String) | | component | The population component. (String) | | sex | The sex of the population. (String) | | age | The age of the population. (String) | | 2011 | The population in 2011. (Integer) | | 2012 | The population in 2012. (Integer) | | 2013 | The population in 2013. (Integer) | | 2014 | The population in 2014. (Integer) | | 2015 | The population in 2015. (Integer) | | 2016 | The population in 2016. (Integer) | | 2017 | The population in 2017. (Integer) | | 2018 | The population in 2018. (Integer) | | 2019 | The population in 2019. (Integer) | | 2020 | The population in 2020. (Integer) | | 2021 | The population in 2021. (Integer) | | 2022 | The population in 2022. (Integer) | | 2023 | The population in 2023. (Integer) | | 2024 | The population in 2024. (Integer) | | 2025 | The population in 2025. (Integer) | | 2026 | The population in 2026. (Integer) | | 2027 | The population in 2027. (Integer) | | 2028 | The population in 2028. (Integer) | | 2029 | The population in 2029. (Integer) | | 2030 | The population in 2030. (Integer) | | 2031 | The population in 2031. (Integer) | | 2032 | The population in 2032. (Integer) | | 2033 | The population in 2033. (Integer) | | 2034 | The population in 2034. (Integer) | | 2035 | The population in 2035. (Integer) | | 2036 | The population in 2036. (Integer) | | 2037 | The population in 2037. (Integer) | | 2038 | The population in 2038. (Integer) | | 2039 | The population in 20 |

Acknowledgements

If you use this dataset in your research, please credit Eva Murray.

The total population in the United States was estimated at 341.2 million people in 2024, according to the latest census figures and projections from Trading Economics. This dataset provides - United States Population - actual values, historical data, forecast, chart, statistics, economic calendar and news.

The QS World University Rankings for 2025 is a list of universities from all over the world, organized to show which ones are the best in various areas. It is widely recognized as one of the most reliable ways to compare higher education institutions. This ranking helps students, researchers, and decision-makers understand how well universities perform in terms of academics, teaching, research, and global connections. Let’s break it down into simple parts so that you can understand it easily.

What’s in the Ranking? The ranking includes several key pieces of information about each university:

University Name: This is simply the name of the school. For example, Harvard University or Oxford University. Ranking Position: This tells you the university’s position on the list, like 1st, 50th, or 200th. A lower number means the university is ranked higher. Country/Region: This shows where the university is located, like the USA, the UK, or Japan. Academic Reputation Score: This score is based on surveys of professors and researchers. They give their opinions on which universities are best for studying and learning. Employer Reputation Score: Employers are asked which universities produce the most skilled graduates. This score shows how good a university is at preparing students for jobs. Faculty-Student Ratio: This measures how many students there are per teacher. A lower number means smaller classes and more personal attention for students. Citations per Faculty: This is about research. It shows how often the university’s studies are mentioned in other research papers. The more citations, the better. International Faculty & Students: This looks at how many teachers and students come from different countries, showing how global and diverse the university is. Why Is This Ranking Useful? There are many ways this ranking can help people:

For Students: It helps students decide where they might want to study. For example, if someone wants a university with a good reputation for teaching and research, they can use this ranking to find the best options. For Universities: Schools can use the rankings to see how they compare to others. If one university is ranked lower than another, it can look at the scores to find ways to improve. For Researchers: Researchers can study the ranking to learn about trends in global education. For example, they might explore why certain regions, like Asia or Europe, have universities that are improving quickly. For Policymakers: Governments and organizations can use the rankings to decide where to invest in education. They can also study which areas of education are most important for the future. What Can We Learn from It? The QS World University Rankings help us learn which universities are leading in academics and research. It also shows us how important global diversity is in education. By understanding these rankings, people can make smarter decisions about studying, teaching, or improving education systems. It’s like a guidebook for the world of universities, helping everyone find the best options and learn from the best practices.

The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly, reaching *** zettabytes in 2024. Over the next five years up to 2028, global data creation is projected to grow to more than *** 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 * 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 **** percent over the forecast period from 2020 to 2025. In 2020, the installed base of storage capacity reached *** zettabytes.

🌍 Countries and Dependencies by Population (2025)

This dataset provides a comprehensive list of countries and dependent territories worldwide, along with their most recent population estimates.The data is sourced from the Wikipedia page List of countries and dependencies by population, which compiles figures from national statistical offices and the United Nations Population Division

📄 Dataset Overview

• Country/Territory Name Includes sovereign states, dependent territories, and regions with limited recognition.
• Population Latest available estimates, primarily from national censuses or UN projection.
• Percentage of World Population Each country's population as a percentage of the global total.
• Date of Estimate The reference date for the population figure.
• Notes Additional information, such as inclusion or exclusion of certain region.

🧠 Potential Use Cases

• Analyzing global population distribution and trends.- Creating visualizations like choropleth maps.- Normalizing other datasets by population for per capita analysis.- Educational purposes in demographics and geography.

📌 Notes

• The dataset includes territories and regions with limited recognition to provide a complete global perspective.
• Population figures are based on the most recent estimates available as of 225.
• Data may be subject to revisions as new census information becomes available.

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

How much time do people spend on social media? As of 2025, the average daily social media usage of internet users worldwide amounted to 141 minutes per day, down from 143 minutes in the previous year. Currently, the country with the most time spent on social media per day is Brazil, with online users spending an average of 3 hours and 49 minutes on social media each day. In comparison, the daily time spent with social media in the U.S. was just 2 hours and 16 minutes. Global social media usageCurrently, the global social network penetration rate is 62.3 percent. Northern Europe had an 81.7 percent social media penetration rate, topping the ranking of global social media usage by region. Eastern and Middle Africa closed the ranking with 10.1 and 9.6 percent usage reach, respectively. People access social media for a variety of reasons. Users like to find funny or entertaining content and enjoy sharing photos and videos with friends, but mainly use social media to stay in touch with current events friends. Global impact of social mediaSocial media has a wide-reaching and significant impact on not only online activities but also offline behavior and life in general. During a global online user survey in February 2019, a significant share of respondents stated that social media had increased their access to information, ease of communication, and freedom of expression. On the flip side, respondents also felt that social media had worsened their personal privacy, increased a polarization in politics and heightened everyday distractions.

Historical chart and dataset showing World death rate by year from 1950 to 2025.

Historical chart and dataset showing World life expectancy by year from 1950 to 2025.

Historical chart and dataset showing U.S. birth rate by year from 1950 to 2025.

This Dataset containes the details of the AI, ML, Data Science Salary (2020- 2025). Salary data is in USD and recalculated at its average fx rate during the year for salaries entered in other currencies.

The data is processed and updated on a weekly basis so the rankings may change over time during the year.

Attribute Information

• work_year: The year the salary was paid.
• experience_level: The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director
• employment_type: The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance
• job_title: The role worked in during the year.
• salary: The total gross salary amount paid.
• salary_currency: The currency of the salary paid as an ISO 4217 currency code.
• salary_in_usd: The salary in USD (FX rate divided by avg. USD rate of respective year) via statistical data from the BIS and central banks.
• employee_residence: Employee's primary country of residence in during the work year as an ISO 3166 country code.
• remote_ratio : The overall amount of work done remotely, possible values are as follows: 0 No remote work (less than 20%) 50 Partially remote/hybird 100 Fully remote (more than 80%)
• company_location: The country of the employer's main office or contracting branch as an ISO 3166 country code.
• company_size: The average number of people that worked for the company during the year: S less than 50 employees (small) M 50 to 250 employees (medium) L more than 250 employees (large)

Acknowledgements

https://aijobs.net/

Photo by Anastassia Anufrieva on Unsplash

The World Health Organization reported 766440796 Coronavirus Cases since the epidemic began. In addition, countries reported 6932591 Coronavirus Deaths. This dataset provides - World Coronavirus Cases- actual values, historical data, forecast, chart, statistics, economic calendar and news.

Historical chart and dataset showing World population growth rate by year from 1961 to 2023.

Market Summary of Database Security Market:

• The Global Database Security market size in 2023 was XX Million. The Database Security Industry's compound annual growth rate (CAGR) will be XX% from 2024 to 2031. • The database security industry is growing faster and is expected to expand at a faster rate due to these strict regulatory frameworks. Also, the increase in advanced technology for better protection of data is driving the growth of the Database security market. • The dominating segment is the software. It includes encryption, auditing, tokenization, data masking, and access control management. • Due to the increase in internet users, remote working demand, and risk of data breaches, the COVID-19 pandemic has had a beneficial effect on the market for data security solutions. • The database security market is dominated by North America in terms of both revenue and market share. This can be attributed to the region's concentration of significant industry participants and increasing technical advancements in their product line.

Market Dynamics of Database Security Market:

Key Drivers of Database Security Market:

The Database Security Market is driven by the strict regulatory framework to address information security

Regulatory frameworks can establish standards that developers and users must follow to guarantee a secure database. The market is growing as a result of increasingly stringent regulations enforced globally to protect sensitive data by governments and other relevant authorities in numerous nations. Currently, rules for data localization are in place in most of the countries. These have significant effects on how businesses interact with local regulations as well as their IT footprints, data governance, and data architectures. The general goals of localization regulations are to stop cybercrimes (such as identity theft), boost local economies, and possibly most importantly address growing privacy concerns. The desire of firms to utilize data for their commercial advantage is often the most contentious topic, with different governments arriving at different judgments about how to balance it. Data must be processed and stored within a specific nation or region to comply with geographic constraints on data export. For each of these, businesses must build a unique infrastructure, set of computing resources, and teams. For Instance, two sets of standard contractual agreements were adopted by the European Commission, one was meant for use between controllers and processors, and the other was meant for the transfer of personal data to third parties. They ensure a high level of data privacy for citizens by reflecting new standards under the General Data Privacy Regulation (GDPR) and taking the Court of Justice's Schrems II ruling into consideration. These new tools will provide European firms with greater legal stability and aid SMEs in particular to maintain compliance with secure data transfer standards while enabling unrestricted cross-border data movement. (Source: https://ec.europa.eu/commission/presscorner/detail/en/ip_21_2847) Furthermore, Federal Financial Institutions, the Sarbanes-Oxley Act (SOX), the Payment Card Industry Data Security Standard (PCI-DSS), Examination Council (FFIEC), etc are some strict regulatory measures for protection. So, the database security industry is growing faster and is expected to expand at a faster rate due to these strict regulatory frameworks.

Key Restraints of Database Security Market

Lack of skills and technological knowledge in database security market

Inadequate information security may lead to disruptions in socially significant activities, inability to do business appropriately and efficiently, and loss of personal integrity protection. Nevertheless, obstacles including unclear career routes, out-of-date education, expensive certifications, and high levels of job stress deter people from choosing cybersecurity as a vocation. For Instance, As of May 2023, there were 40,000 cybersecurity job openings in India; however, a significant lack of skilled workers prevented 30% of these positions from being filled, according to TeamLease Digital, a division of TeamLease Services. The need for cybersecurity experts has outpaced supply, making it difficult for m...

This dataset contains counts of deaths for California counties based on information entered on death certificates. Final counts are derived from static data and include out-of-state deaths to California residents, whereas provisional counts are derived from incomplete and dynamic data. Provisional counts are based on the records available when the data was retrieved and may not represent all deaths that occurred during the time period. Deaths involving injuries from external or environmental forces, such as accidents, homicide and suicide, often require additional investigation that tends to delay certification of the cause and manner of death. This can result in significant under-reporting of these deaths in provisional data.

The final data tables include both deaths that occurred in each California county regardless of the place of residence (by occurrence) and deaths to residents of each California county (by residence), whereas the provisional data table only includes deaths that occurred in each county regardless of the place of residence (by occurrence). The data are reported as totals, as well as stratified by age, gender, race-ethnicity, and death place type. Deaths due to all causes (ALL) and selected underlying cause of death categories are provided. See temporal coverage for more information on which combinations are available for which years.

The cause of death categories are based solely on the underlying cause of death as coded by the International Classification of Diseases. The underlying cause of death is defined by the World Health Organization (WHO) as "the disease or injury which initiated the train of events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury." It is a single value assigned to each death based on the details as entered on the death certificate. When more than one cause is listed, the order in which they are listed can affect which cause is coded as the underlying cause. This means that similar events could be coded with different underlying causes of death depending on variations in how they were entered. Consequently, while underlying cause of death provides a convenient comparison between cause of death categories, it may not capture the full impact of each cause of death as it does not always take into account all conditions contributing to the death.

A meticulously compiled dataset providing deep insights into the global fitness industry in 2025. This dataset covers high-demand topics such as the exponential growth of fitness clubs, emerging trends in boutique fitness studios, skyrocketing online fitness training statistics, the flourishing fitness equipment market, and changing consumer behavior and expenditure patterns in the fitness sector.

ERA5 is the fifth generation ECMWF reanalysis for the global climate and weather for the past 8 decades. Data is available from 1940 onwards. ERA5 replaces the ERA-Interim reanalysis. Reanalysis combines model data with observations from across the world into a globally complete and consistent dataset using the laws of physics. This principle, called data assimilation, is based on the method used by numerical weather prediction centres, where every so many hours (12 hours at ECMWF) a previous forecast is combined with newly available observations in an optimal way to produce a new best estimate of the state of the atmosphere, called analysis, from which an updated, improved forecast is issued. Reanalysis works in the same way, but at reduced resolution to allow for the provision of a dataset spanning back several decades. Reanalysis does not have the constraint of issuing timely forecasts, so there is more time to collect observations, and when going further back in time, to allow for the ingestion of improved versions of the original observations, which all benefit the quality of the reanalysis product. ERA5 provides hourly estimates for a large number of atmospheric, ocean-wave and land-surface quantities. An uncertainty estimate is sampled by an underlying 10-member ensemble at three-hourly intervals. Ensemble mean and spread have been pre-computed for convenience. Such uncertainty estimates are closely related to the information content of the available observing system which has evolved considerably over time. They also indicate flow-dependent sensitive areas. To facilitate many climate applications, monthly-mean averages have been pre-calculated too, though monthly means are not available for the ensemble mean and spread. ERA5 is updated daily with a latency of about 5 days. In case that serious flaws are detected in this early release (called ERA5T), this data could be different from the final release 2 to 3 months later. In case that this occurs users are notified. The data set presented here is a regridded subset of the full ERA5 data set on native resolution. It is online on spinning disk, which should ensure fast and easy access. It should satisfy the requirements for most common applications. An overview of all ERA5 datasets can be found in this article. Information on access to ERA5 data on native resolution is provided in these guidelines. Data has been regridded to a regular lat-lon grid of 0.25 degrees for the reanalysis and 0.5 degrees for the uncertainty estimate (0.5 and 1 degree respectively for ocean waves). There are four main sub sets: hourly and monthly products, both on pressure levels (upper air fields) and single levels (atmospheric, ocean-wave and land surface quantities). The present entry is "ERA5 hourly data on single levels from 1940 to present".

🌍 Global B2B Person Dataset | 755M+ LinkedIn Profiles | Verified & Bi-Weekly Updated Access the world’s most comprehensive professional dataset, enriched with over 755 million LinkedIn profiles. The Forager.ai Global B2B Person Dataset delivers work-verified professional contacts with 95%+ accuracy, refreshed every two weeks. Ideal for recruitment, sales, research, and talent mapping, it provides direct access to decision-makers, specialists, and executives across industries and geographies.

Dataset Features Full Name & Job Title: Up-to-date first/last name with current professional role.

Emails & Phone Numbers: AI-validated work and personal email addresses, plus mobile numbers.

Company Info: Current employer name, industry, and company size (employee count).

Career History: Detailed work history with job titles, durations, and role progressions.

Skills & Endorsements: Extracted from public LinkedIn profiles.

Education & Certifications: Universities, degrees, and professional certifications.

Location & LinkedIn URL: City, country, and direct link to public LinkedIn profile.

Distribution Data Volume: 755M+ total profiles, with 270M+ containing full contact information.

Formats Available: CSV, JSON via S3 or Snowflake; API for real-time access.

Access Methods: REST API, Enrichment API (lookup), full dataset delivery, or custom solutions.

Usage This dataset is ideal for a variety of applications:

Executive Recruitment: Source passive talent, build role-based maps, and assess mobility.

Sales Intelligence: Find decision-makers, personalize outreach, and trigger campaigns on job changes.

Market Research: Understand talent concentration by company, geography, and skill set.

Partnership Development: Identify key stakeholders in target firms for business development.

Talent Mapping & Strategic Hiring: Build full organizational charts and skill distribution heatmaps.

Coverage Geographic Coverage: Global – including North America, EMEA, LATAM, and APAC.

Time Range: Continuously updated; profiles refreshed bi-weekly.

Demographics: Cross-industry coverage of seniority levels from entry-level to C-suite, across all sectors.

License CUSTOM

Who Can Use It Recruiters & Staffing Firms: For building target lists and sourcing niche talent.

Sales & RevOps Teams: For targeting by department, title, or decision-making authority.

VCs & PE Firms: To assess leadership teams and monitor executive movement.

Data Scientists & Analysts: To train models for job mobility, hiring trends, or org structure prediction.

B2B Platforms: For enriching internal databases and powering account-based marketing (ABM).

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

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