The total amount of data created, captured, copied, and consumed globally is forecast to increase rapidly. While it was estimated at ***** zettabytes in 2025, the forecast for 2029 stands at ***** zettabytes. Thus, global data generation will triple between 2025 and 2029. Data creation has been expanding continuously over the past decade. In 2020, the growth was higher than previously expected, caused by the increased demand due to the coronavirus (COVID-19) pandemic, as more people worked and learned from home and used home entertainment options more often.

As of October 2025, 6.04 billion individuals worldwide were internet users, which amounted to 73.2 percent of the global population. Of this total, 5.66 billion, or 68.7 percent of the world's population, were social media users. Global internet usage Connecting billions of people worldwide, the internet is a core pillar of the modern information society. Northern Europe ranked first among worldwide regions by the share of the population using the internet in 2025. In the Netherlands, Norway, and Saudi Arabia, 99 percent of the population used the internet as of February 2025. North Korea was at the opposite end of the spectrum, with virtually no internet usage penetration among the general population, ranking last worldwide. Eastern Asia was home to the largest number of online users worldwide—over 1.34 billion at the latest count. Southern Asia ranked second, with around 1.2 billion internet users. China, India, and the United States rank ahead of other countries worldwide by the number of internet users. Worldwide internet user demographics As of 2024, the share of female internet users worldwide was 65 percent, five percent less than that of men. Gender disparity in internet usage was bigger in African countries, with around a 10-percent difference. Worldwide regions, like the Commonwealth of Independent States and Europe, showed a smaller usage gap between these two genders. As of 2024, global internet usage was higher among individuals between 15 and 24 years old across all regions, with young people in Europe representing the most considerable usage penetration, 98 percent. In comparison, the worldwide average for the age group of 15 to 24 years was 79 percent. The income level of the countries was also an essential factor for internet access, as 93 percent of the population of the countries with high income reportedly used the internet, as opposed to only 27 percent of the low-income markets.

How many people use social media?

              Social media usage is one of the most popular online activities. In 2024, over five billion people were using social media worldwide, a number projected to increase to over six billion in 2028.

              Who uses social media?
              Social networking is one of the most popular digital activities worldwide and it is no surprise that social networking penetration across all regions is constantly increasing. As of January 2023, the global social media usage rate stood at 59 percent. This figure is anticipated to grow as lesser developed digital markets catch up with other regions
              when it comes to infrastructure development and the availability of cheap mobile devices. In fact, most of social media’s global growth is driven by the increasing usage of mobile devices. Mobile-first market Eastern Asia topped the global ranking of mobile social networking penetration, followed by established digital powerhouses such as the Americas and Northern Europe.

              How much time do people spend on social media?
              Social media is an integral part of daily internet usage. On average, internet users spend 151 minutes per day on social media and messaging apps, an increase of 40 minutes since 2015. On average, internet users in Latin America had the highest average time spent per day on social media.

              What are the most popular social media platforms?
              Market leader Facebook was the first social network to surpass one billion registered accounts and currently boasts approximately 2.9 billion monthly active users, making it the most popular social network worldwide. In June 2023, the top social media apps in the Apple App Store included mobile messaging apps WhatsApp and Telegram Messenger, as well as the ever-popular app version of Facebook.

Description:

The "Daily Social Media Active Users" dataset provides a comprehensive and dynamic look into the digital presence and activity of global users across major social media platforms. The data was generated to simulate real-world usage patterns for 13 popular platforms, including Facebook, YouTube, WhatsApp, Instagram, WeChat, TikTok, Telegram, Snapchat, X (formerly Twitter), Pinterest, Reddit, Threads, LinkedIn, and Quora. This dataset contains 10,000 rows and includes several key fields that offer insights into user demographics, engagement, and usage habits.

Dataset Breakdown:

• Platform: The name of the social media platform where the user activity is tracked. It includes globally recognized platforms, such as Facebook, YouTube, and TikTok, that are known for their large, active user bases.

• Owner: The company or entity that owns and operates the platform. Examples include Meta for Facebook, Instagram, and WhatsApp, Google for YouTube, and ByteDance for TikTok.

• Primary Usage: This category identifies the primary function of each platform. Social media platforms differ in their primary usage, whether it's for social networking, messaging, multimedia sharing, professional networking, or more.

• Country: The geographical region where the user is located. The dataset simulates global coverage, showcasing users from diverse locations and regions. It helps in understanding how user behavior varies across different countries.

• Daily Time Spent (min): This field tracks how much time a user spends on a given platform on a daily basis, expressed in minutes. Time spent data is critical for understanding user engagement levels and the popularity of specific platforms.

• Verified Account: Indicates whether the user has a verified account. This feature mimics real-world patterns where verified users (often public figures, businesses, or influencers) have enhanced status on social media platforms.

• Date Joined: The date when the user registered or started using the platform. This data simulates user account history and can provide insights into user retention trends or platform growth over time.

Context and Use Cases:

• This synthetic dataset is designed to offer a privacy-friendly alternative for analytics, research, and machine learning purposes. Given the complexities and privacy concerns around using real user data, especially in the context of social media, this dataset offers a clean and secure way to develop, test, and fine-tune applications, models, and algorithms without the risks of handling sensitive or personal information.

Researchers, data scientists, and developers can use this dataset to:

• Model User Behavior: By analyzing patterns in daily time spent, verified status, and country of origin, users can model and predict social media engagement behavior.

• Test Analytics Tools: Social media monitoring and analytics platforms can use this dataset to simulate user activity and optimize their tools for engagement tracking, reporting, and visualization.

• Train Machine Learning Algorithms: The dataset can be used to train models for various tasks like user segmentation, recommendation systems, or churn prediction based on engagement metrics.

• Create Dashboards: This dataset can serve as the foundation for creating user-friendly dashboards that visualize user trends, platform comparisons, and engagement patterns across the globe.

• Conduct Market Research: Business intelligence teams can use the data to understand how various demographics use social media, offering valuable insights into the most engaged regions, platform preferences, and usage behaviors.

• Sources of Inspiration: This dataset is inspired by public data from industry reports, such as those from Statista, DataReportal, and other market research platforms. These sources provide insights into the global user base and usage statistics of popular social media platforms. The synthetic nature of this dataset allows for the use of realistic engagement metrics without violating any privacy concerns, making it an ideal tool for educational, analytical, and research purposes.

The structure and design of the dataset are based on real-world usage patterns and aim to represent a variety of users from different backgrounds, countries, and activity levels. This diversity makes it an ideal candidate for testing data-driven solutions and exploring social media trends.

Future Considerations:

As the social media landscape continues to evolve, this dataset can be updated or extended to include new platforms, engagement metrics, or user behaviors. Future iterations may incorporate features like post frequency, follower counts, engagement rates (likes, comments, shares), or even sentiment analysis from user-generated content.

By leveraging this dataset, analysts and data scientists can create better, more effective strategies ...

Daily Global Trends - Insights on Popularity

Analyzing Crowd Behaviour and Buzz Worldwide

By Jeffrey Mvutu Mabilama [source]

About this dataset

This dataset provides a comprehensive look into 2020’s top trends worldwide, with information on the hottest topics and conversations happening all around the globe. With details such as trending type, country origin, dates of interest, URLs to find further information, keywords related to the trend and more - it's an invaluable insight into what's driving society today.

You can use this data in conjunction with other sources to get ideas for businesses or products tailored to popular desires or opinions. If you are interested in international business perspectives then this is also your go-to source; you can adjust how best to interact with people from certain countries upon learning what they hold important in terms of search engine activity.

It also gives key insights into buzz formation by monitoring trends over many countries over different periods of time then analysing whether events tend to last longer or if their effect is short-lived and how much impact it made in terms column ‘traffic’ – number of searches for an individual topic – for the duration of its period affecting higher positions and opinion polls. In addition, marketing / advertising professionals can anticipate what content is likely best received by audiences based off previous trends related images/snippets provided with each trend/topic as well as URL links tracking users who have shown interest.. This way they become better prepared when rolling out campaigns targeted at specific regions/areas taking cultural perspective into consideration rather than just raw numbers.

Last but not least it serves perfectly as great starting material when getting acquainted foreigners online (at least we know what conversation starters won't be awkward mentioned!) before deepening our empathetic understanding like terms used largely solely within cultures such as TV program titles… So…… question is: What will be next big thing? See for yourself.

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How to use the dataset

How to use this dataset for Insights on Popularity?

This Daily Global Trends 2020 dataset provides valuable information about trends around the world, including insights on their popularity. It can be used to identify popular topics and find ways to capitalize on them through marketing, business ideas and more. Below are some tips for how to use this data in order to gain insight into global trends and the level of popularity they have.

• For Business Ideas: Use the URL information provided in order to research each individual trend, analyzing both when it gained traction as well as when its popularity faded away (if at all). This will give insight into transforming a brief trend into a long-lived one or making use of an existing but brief surge in interest – think new apps related to a trending topic! Combining the geographic region listed with these timeframes gives even more granular insight that could be used for product localization or regional target marketing.

• To study Crowd Behaviour & Dynamics: Explore both country-wise and globally trending topics by looking at which countries similarly exhibit interest levels for said topics. Go further by understanding what drives people’s interest in particular subjects from different countries; here web scraping techniques can be employed using the URLs provided accompanied with basic text analysis techniques such as word clouds! This allows researchers/marketers get better feedback from customers from multiple regions, enabling smarter decisions based upon real behaviour rather than assumptions.

• For **Building Better Products & Selling Techniques: Utilize combine Category (Business, Social etc.), Country and Related keywords mentioned with traffic figures so that you can obtain granular information about what excites people across cultures i.e ‘Food’ is popular everywhere but certain variations depending upon geo-location may not sell due need catering towards local taste buds.-For example selling frozen food that requires little preparation via supermarket chains showing parallels between nutritional requirements vs expenses incurred while shopping will drive effective sales strategy using this data set . Further combining date information also helps make predictions based upon buyers behaviour over seasons i.e buying seedless watermelons during winter season would be futile .

• For Social & Small Talk opportunities - Incorporating recently descr...

App Download Key StatisticsApp and Game DownloadsiOS App and Game DownloadsGoogle Play App and Game DownloadsGame DownloadsiOS Game DownloadsGoogle Play Game DownloadsApp DownloadsiOS App...

Actual value and historical data chart for World Population Female Percent Of Total

GapMaps Live is an easy-to-use location intelligence platform available across 25 countries globally that allows you to visualise your own store data, combined with the latest demographic, economic and population movement intel right down to the micro level so you can make faster, smarter and surer decisions when planning your network growth strategy.

With one single login, you can access the latest estimates on resident and worker populations, census metrics (eg. age, income, ethnicity), consuming class, retail spend insights and point-of-interest data across a range of categories including fast food, cafe, fitness, supermarket/grocery and more.

Some of the world's biggest brands including McDonalds, Subway, Burger King, Anytime Fitness and Dominos use GapMaps Live as a vital strategic tool where business success relies on up-to-date, easy to understand, location intel that can power business case validation and drive rapid decision making.

Primary Use Cases for GapMaps Live includes:

1. Retail Site Selection - Identify optimal locations for future expansion and benchmark performance across existing locations.
2. Customer Profiling: get a detailed understanding of the demographic profile of your customers and where to find more of them.
3. Analyse your catchment areas at a granular grid levels using all the key metrics
4. Target Marketing: Develop effective marketing strategies to acquire more customers.
5. Marketing / Advertising (Billboards/OOH, Marketing Agencies, Indoor Screens)
6. Customer Profiling
7. Target Marketing
8. Market Share Analysis

Some of features our clients love about GapMaps Live include: - View business locations, competitor locations, demographic, economic and social data around your business or selected location - Understand consumer visitation patterns (“where from” and “where to”), frequency of visits, dwell time of visits, profiles of consumers and much more. - Save searched locations and drop pins - Turn on/off all location listings by category - View and filter data by metadata tags, for example hours of operation, contact details, services provided - Combine public data in GapMaps with views of private data Layers - View data in layers to understand impact of different data Sources - Share maps with teams - Generate demographic reports and comparative analyses on different locations based on drive time, walk time or radius. - Access multiple countries and brands with a single logon - Access multiple brands under a parent login - Capture field data such as photos, notes and documents using GapMaps Connect and integrate with GapMaps Live to get detailed insights on existing and proposed store locations.

A global database of population segmentation data that provides an understanding of population distribution at administrative and zip code levels over 55 years, past, present, and future.

Leverage up-to-date audience targeting data trends for market research, audience targeting, and sales territory mapping.

Self-hosted consumer data curated based on trusted sources such as the United Nations or the European Commission, with a 99% match accuracy. The Consumer Data is standardized, unified, and ready to use.

Use cases for the Global Population Database (Consumer Data Data/Segmentation data)

• Ad targeting

• B2B Market Intelligence

• Customer analytics

• Marketing campaign analysis

• Demand forecasting

• Sales territory mapping

• Retail site selection

• Reporting

• Audience targeting

Segmentation data export methodology

Our location data packages are offered in CSV format. All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features

• Historical population data (55 years)

• Changes in population density

• Urbanization Patterns

• Accurate at zip code and administrative level

• Optimized for easy integration

• Easy customization

• Global coverage

• Updated yearly

• Standardized and reliable

• Self-hosted delivery

• Fully aggregated (ready to use)

• Rich attributes

Why do companies choose our Population Databases

• Standardized and unified demographic data structure

• Seamless integration in your system

• Dedicated location data expert

Note: Custom population data packages are available. Please submit a request via the above contact button for more details.

Updates

• Notice of data discontinuation: Since the start of the pandemic, AP has reported case and death counts from data provided by Johns Hopkins University. Johns Hopkins University has announced that they will stop their daily data collection efforts after March 10. As Johns Hopkins stops providing data, the AP will also stop collecting daily numbers for COVID cases and deaths. The HHS and CDC now collect and visualize key metrics for the pandemic. AP advises using those resources when reporting on the pandemic going forward.

  • CDC Weekly case and death counts (national and state level)
  • CDC County level cases and deaths
  • HHS New hospital admissions
  • CDC NowCast COVID variant proportions (national and regional level)

• April 9, 2020

  • The population estimate data for New York County, NY has been updated to include all five New York City counties (Kings County, Queens County, Bronx County, Richmond County and New York County). This has been done to match the Johns Hopkins COVID-19 data, which aggregates counts for the five New York City counties to New York County.

• April 20, 2020

  • Johns Hopkins death totals in the US now include confirmed and probable deaths in accordance with CDC guidelines as of April 14. One significant result of this change was an increase of more than 3,700 deaths in the New York City count. This change will likely result in increases for death counts elsewhere as well. The AP does not alter the Johns Hopkins source data, so probable deaths are included in this dataset as well.

• April 29, 2020

  • The AP is now providing timeseries data for counts of COVID-19 cases and deaths. The raw counts are provided here unaltered, along with a population column with Census ACS-5 estimates and calculated daily case and death rates per 100,000 people. Please read the updated caveats section for more information.

• September 1st, 2020

  • Johns Hopkins is now providing counts for the five New York City counties individually.

• February 12, 2021

  • The Ohio Department of Health recently announced that as many as 4,000 COVID-19 deaths may have been underreported through the state’s reporting system, and that the "daily reported death counts will be high for a two to three-day period."
  • Because deaths data will be anomalous for consecutive days, we have chosen to freeze Ohio's rolling average for daily deaths at the last valid measure until Johns Hopkins is able to back-distribute the data. The raw daily death counts, as reported by Johns Hopkins and including the backlogged death data, will still be present in the new_deaths column.

• February 16, 2021

  - Johns Hopkins has reconciled Ohio's historical deaths data with the state.

  Overview

The AP is using data collected by the Johns Hopkins University Center for Systems Science and Engineering as our source for outbreak caseloads and death counts for the United States and globally.

The Hopkins data is available at the county level in the United States. The AP has paired this data with population figures and county rural/urban designations, and has calculated caseload and death rates per 100,000 people. Be aware that caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.

This data is from the Hopkins dashboard that is updated regularly throughout the day. Like all organizations dealing with data, Hopkins is constantly refining and cleaning up their feed, so there may be brief moments where data does not appear correctly. At this link, you’ll find the Hopkins daily data reports, and a clean version of their feed.

The AP is updating this dataset hourly at 45 minutes past the hour.

To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

Queries

Use AP's queries to filter the data or to join to other datasets we've made available to help cover the coronavirus pandemic

• Filter cases by state here

• Rank states by their status as current hotspots. Calculates the 7-day rolling average of new cases per capita in each state: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=481e82a4-1b2f-41c2-9ea1-d91aa4b3b1ac

• Find recent hotspots within your state by running a query to calculate the 7-day rolling average of new cases by capita in each county: https://data.world/associatedpress/johns-hopkins-coronavirus-case-tracker/workspace/query?queryid=b566f1db-3231-40fe-8099-311909b7b687&showTemplatePreview=true

• Join county-level case data to an earlier dataset released by AP on local hospital capacity here. To find out more about the hospital capacity dataset, see the full details.

• Pull the 100 counties with the highest per-capita confirmed cases here

• Rank all the counties by the highest per-capita rate of new cases in the past 7 days here. Be aware that because this ranks per-capita caseloads, very small counties may rise to the very top, so take into account raw caseload figures as well.

Interactive

The AP has designed an interactive map to track COVID-19 cases reported by Johns Hopkins.

@(https://datawrapper.dwcdn.net/nRyaf/15/)

Interactive Embed Code

<iframe title="USA counties (2018) choropleth map Mapping COVID-19 cases by county" aria-describedby="" id="datawrapper-chart-nRyaf" src="https://datawrapper.dwcdn.net/nRyaf/10/" scrolling="no" frameborder="0" style="width: 0; min-width: 100% !important;" height="400"></iframe><script type="text/javascript">(function() {'use strict';window.addEventListener('message', function(event) {if (typeof event.data['datawrapper-height'] !== 'undefined') {for (var chartId in event.data['datawrapper-height']) {var iframe = document.getElementById('datawrapper-chart-' + chartId) || document.querySelector("iframe[src*='" + chartId + "']");if (!iframe) {continue;}iframe.style.height = event.data['datawrapper-height'][chartId] + 'px';}}});})();</script>

Caveats

• This data represents the number of cases and deaths reported by each state and has been collected by Johns Hopkins from a number of sources cited on their website.
• In some cases, deaths or cases of people who've crossed state lines -- either to receive treatment or because they became sick and couldn't return home while traveling -- are reported in a state they aren't currently in, because of state reporting rules.
• In some states, there are a number of cases not assigned to a specific county -- for those cases, the county name is "unassigned to a single county"
• This data should be credited to Johns Hopkins University's COVID-19 tracking project. The AP is simply making it available here for ease of use for reporters and members.
• Caseloads may reflect the availability of tests -- and the ability to turn around test results quickly -- rather than actual disease spread or true infection rates.
• Population estimates at the county level are drawn from 2014-18 5-year estimates from the American Community Survey.
• The Urban/Rural classification scheme is from the Center for Disease Control and Preventions's National Center for Health Statistics. It puts each county into one of six categories -- from Large Central Metro to Non-Core -- according to population and other characteristics. More details about the classifications can be found here.

Johns Hopkins timeseries data - Johns Hopkins pulls data regularly to update their dashboard. Once a day, around 8pm EDT, Johns Hopkins adds the counts for all areas they cover to the timeseries file. These counts are snapshots of the latest cumulative counts provided by the source on that day. This can lead to inconsistencies if a source updates their historical data for accuracy, either increasing or decreasing the latest cumulative count. - Johns Hopkins periodically edits their historical timeseries data for accuracy. They provide a file documenting all errors in their timeseries files that they have identified and fixed here

Attribution

This data should be credited to Johns Hopkins University COVID-19 tracking project

Global C2C Fashion Store User Behaviour Analysis

Analyzing Buyer and Seller Profiles across Countries

By Jeffrey Mvutu Mabilama [source]

About this dataset

Welcome to an exciting exploration of global C2C fashion store user behaviour! This dataset seeks to serve as a benchmark by providing valuable insights into e-commerce users, enabling you to make informed decisions and effectively grow your business. Let's dive right into the data!

This dataset contains records on over 9 million registered users from a successful online C2C fashion store launched in Europe around 2009 and later expanded worldwide. It includes metrics such as country, gender, active users, top buyers/sellers/ratio*, products bought/sold/listed* and social network features (likes/follows). Furthermore this is just a preview of much larger data set which contains more detailed information including product listings, comments from listed products etc.

E-commerce has become an essential part of our lives - people are now accustomed to buying anything with a few clicks online. With so many unknown elements that come with not only selling but also providing good customer service - understanding user behavior is key for success in this domain. By utilizing this dataset you can answer questions such as 'how many customers are likely to drop off after years of using my service?,' 'are my users active enough compared to those in this dataset?,” or “how likely are people from other countries signing up in a C2C website?' In addition, if you think this kind odf dataset may be useful don't forget do show your support or appreciation by leaving an upvote or comment on the page!

My Telegram bot will answer any queries regarding the datasets as well allow you see contact me directly if necessary; also please don't forget check out the *[data.world page](https://data.world/jfreex/e-commerce-users-of-a-french-c2c

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How to use the dataset

This dataset provides a useful overview of global users' behavior in an online C2C fashion store. The data includes metrics such as buyers, top buyers, top buyer ratio, female buyers and their respective ratios, etc., per country. This dataset can be used to gain insights into how global audiences interact with the store and draw conclusions from comparison between different countries.

In order to make use of this dataset, one must first familiarize themselves with the various metrics included in it. These include: country; number of overall buyers; number of top buyers; ratio(s) of them (top buyer to total buyer); female-related data (buyers, top female buyers); bought-to-wish/like ration (top and non-top separately); overall products bought/wished/liked; total products sold by tops sellers in the same country versus what they sold outside the country; mean value for product stats (sold/listed/etc...) from looking at the whole population or just users that make those actions multiple times; average days for user offline /lurking around on the site without posting anything or buying anything etc.; mean follower(s) count(s).

Using this data one could generate reports about user behavior within particular countries either manually by computing all statistics or by using libraries like Pandas or SQL with queries made toward this datasets which consists of columns representing individual countries with all values necessary to answer any questions you might have regarding how many people buy something out there per region and what type they are –– Are they Top Buyer? Female? Etc.

Further potential work could involve utilising machine learning tools such as clustering algorithms to group similar customers together based on certain traits like age group, profession etc., so that personalised marketing promotions can be targetted at these customer clusters rather than aiming more generic ads at everyone!

Finally combined with other related product datasets which is available upon request via JfreexDatasets_bot provided by Jfreex team , this dataset can become another powerful tool providing you actionable insights into customers today — allowing you build better strategies towards improving customer experience tomorrow!

Research Ideas

• Analyzing the conversion rate of users on a website - Comparing user metrics like the overall number of buyers, female buyers, top buyers ratio and top buyer gender can help determine if users in certain countries are more or less likely to convert into customers. Additionally, comparing average metrics like products bought or offl...

IT spending worldwide is projected to reach over 5.7 trillion U.S. dollars in 2025, over a nine percent increase on 2024 spending. Smaller companies spending a greater share on hardware According to the results of a survey, hardware projects account for a fifth of IT budgets across North America and Europe. Larger companies tend to allocate a smaller share of their budget to hardware projects. Companies employing between one and 99 people allocated 31 percent of the budget to hardware, compared with 29 percent in companies of five thousand people or more. This could be explained by the greater need to spend money on managed services in larger companies. Not all companies can reduce their spending While COVID-19 has the overall effect of reducing IT spending, not all companies will face the same experiences. Setting up employees to comfortably work from home can result in unexpected costs, as can adapting to new operational requirements. In a recent survey of IT buyers, 18 percent of the respondents said they expected their IT budgets to increase in 2020. For further information about the coronavirus (COVID-19) pandemic, please visit our dedicated Facts and Figures page.

WHOSIS, the WHO Statistical Information System, is an interactive database bringing together core health statistics for the 193 WHO Member States. It comprises more than 100 indicators, which can be accessed by way of a quick search, by major categories, or through user-defined tables. The data can be further filtered, tabulated, charted and downloaded. The data are also published annually in the World Health Statistics Report released in May. The WHO Statistical Information System is the guide to health and health-related epidemiological and statistical information available from the World Health Organization. Most WHO technical programs make statistical information available, and they will be linked from here. Sponsors: WHOSIS is supported by the World Health Organization. Note: The WHO Statistical Information System (WHOSIS) has been incorporated into the Global Health Observatory (GHO) to provide you with more data, more tools, more analysis and more reports.

General

For more details and the most up-to-date information please consult our project page: https://kainmueller-lab.github.io/fisbe.

Summary

A new dataset for neuron instance segmentation in 3d multicolor light microscopy data of fruit fly brains

30 completely labeled (segmented) images

71 partly labeled images

altogether comprising ∼600 expert-labeled neuron instances (labeling a single neuron takes between 30-60 min on average, yet a difficult one can take up to 4 hours)

To the best of our knowledge, the first real-world benchmark dataset for instance segmentation of long thin filamentous objects

A set of metrics and a novel ranking score for respective meaningful method benchmarking

An evaluation of three baseline methods in terms of the above metrics and score

Abstract

Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging, thin filamentous and widely branching morphologies, multiple neurons are tightly inter-weaved, and partial volume effects, uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research, namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing, to date methods are typically benchmarked on synthetic datasets. To address this gap, we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset, the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition, we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly, we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies, and facilitate scientific discovery in basic neuroscience.

Dataset documentation:

We provide a detailed documentation of our dataset, following the Datasheet for Datasets questionnaire:

FISBe Datasheet

Our dataset originates from the FlyLight project, where the authors released a large image collection of nervous systems of ~74,000 flies, available for download under CC BY 4.0 license.

Files

fisbe_v1.0_{completely,partly}.zip

contains the image and ground truth segmentation data; there is one zarr file per sample, see below for more information on how to access zarr files.

fisbe_v1.0_mips.zip

maximum intensity projections of all samples, for convenience.

sample_list_per_split.txt

a simple list of all samples and the subset they are in, for convenience.

view_data.py

a simple python script to visualize samples, see below for more information on how to use it.

dim_neurons_val_and_test_sets.json

a list of instance ids per sample that are considered to be of low intensity/dim; can be used for extended evaluation.

Readme.md

general information

How to work with the image files

Each sample consists of a single 3d MCFO image of neurons of the fruit fly.For each image, we provide a pixel-wise instance segmentation for all separable neurons.Each sample is stored as a separate zarr file (zarr is a file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification.").The image data ("raw") and the segmentation ("gt_instances") are stored as two arrays within a single zarr file.The segmentation mask for each neuron is stored in a separate channel.The order of dimensions is CZYX.

We recommend to work in a virtual environment, e.g., by using conda:

conda create -y -n flylight-env -c conda-forge python=3.9conda activate flylight-env

How to open zarr files

Install the python zarr package:

pip install zarr

Opened a zarr file with:

import zarrraw = zarr.open(, mode='r', path="volumes/raw")seg = zarr.open(, mode='r', path="volumes/gt_instances")

optional:import numpy as npraw_np = np.array(raw)

Zarr arrays are read lazily on-demand.Many functions that expect numpy arrays also work with zarr arrays.Optionally, the arrays can also explicitly be converted to numpy arrays.

How to view zarr image files

We recommend to use napari to view the image data.

Install napari:

pip install "napari[all]"

Save the following Python script:

import zarr, sys, napari

raw = zarr.load(sys.argv[1], mode='r', path="volumes/raw")gts = zarr.load(sys.argv[1], mode='r', path="volumes/gt_instances")

viewer = napari.Viewer(ndisplay=3)for idx, gt in enumerate(gts): viewer.add_labels( gt, rendering='translucent', blending='additive', name=f'gt_{idx}')viewer.add_image(raw[0], colormap="red", name='raw_r', blending='additive')viewer.add_image(raw[1], colormap="green", name='raw_g', blending='additive')viewer.add_image(raw[2], colormap="blue", name='raw_b', blending='additive')napari.run()

Execute:

python view_data.py /R9F03-20181030_62_B5.zarr

Metrics

S: Average of avF1 and C

avF1: Average F1 Score

C: Average ground truth coverage

clDice_TP: Average true positives clDice

FS: Number of false splits

FM: Number of false merges

tp: Relative number of true positives

For more information on our selected metrics and formal definitions please see our paper.

Baseline

To showcase the FISBe dataset together with our selection of metrics, we provide evaluation results for three baseline methods, namely PatchPerPix (ppp), Flood Filling Networks (FFN) and a non-learnt application-specific color clustering from Duan et al..For detailed information on the methods and the quantitative results please see our paper.

License

The FlyLight Instance Segmentation Benchmark (FISBe) dataset is licensed under the Creative Commons Attribution 4.0 International (CC BY 4.0) license.

Citation

If you use FISBe in your research, please use the following BibTeX entry:

@misc{mais2024fisbe, title = {FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures}, author = {Lisa Mais and Peter Hirsch and Claire Managan and Ramya Kandarpa and Josef Lorenz Rumberger and Annika Reinke and Lena Maier-Hein and Gudrun Ihrke and Dagmar Kainmueller}, year = 2024, eprint = {2404.00130}, archivePrefix ={arXiv}, primaryClass = {cs.CV} }

Acknowledgments

We thank Aljoscha Nern for providing unpublished MCFO images as well as Geoffrey W. Meissner and the entire FlyLight Project Team for valuablediscussions.P.H., L.M. and D.K. were supported by the HHMI Janelia Visiting Scientist Program.This work was co-funded by Helmholtz Imaging.

Changelog

There have been no changes to the dataset so far.All future change will be listed on the changelog page.

Contributing

If you would like to contribute, have encountered any issues or have any suggestions, please open an issue for the FISBe dataset in the accompanying github repository.

All contributions are welcome!

According to the Cognitive Market Research Report, the Data Center Interconnect Market size in 2023 was XX Million and is projected to have a compounded annual growth rate of XX% from 2024 to 2031. Furthermore, the rising number of data centers and increased use of cloud storage is driving the market expansion. The Datacenter Interconnect market is further segmented by type and end-use. The hardware type and Communications Service Providers dominate their particular segment. Europe accounted for the highest revenue share in the year 2023. The expanding number of data centres, increased investment in cloud technologies, and the development of end-user markets are among the primary reasons driving European data centres' investment in the interconnect industry. Leading market companies are investing extensively in R&D to extend their product lines, which will fuel further growth in the data centre interconnect market. Market participants are also engaged in a variety of strategic initiatives to broaden their worldwide presence, including new product releases, contractual agreements, mergers and acquisitions, increased investments, and collaboration with other organizations.

Market Dynamics of Data Center Interconnect

Key Drivers for Data Center Interconnect Market

Increasing Number of Data Centers to Drive Market Growth for Data Centre Interconnect: Data centres, with house computers for data storage and processing, have expanded fast in response to increasing demand. The United States has more data centres than any other market, as it is home to major data producers and consumers such as Facebook, Amazon, Microsoft, and Google. Data servers and data centres are in high demand due to increased data output and utilization across sectors. According to CloudScene data6 from 110 countries, there were almost 8,000 data centres worldwide. Six nations account for the bulk of data centres which are the United States (33%), the United Kingdom (5.7%), Germany (5.5%), China (5.2%), Canada (3.3%), and the Netherlands (3.4%). OECD member nations account for 77%, while NATO members account for around 64%. Furthermore, data centre service providers are increasing their colocation and cloud offerings. End-user firms (such as telecom and financial organizations) that choose to establish their data centres are primarily responsible for the interconnected data centre sector becoming a worldwide investment hotspot. Due to data centre expansion and spread, enhanced fibre utilization, and low-cost pluggable modules, industries, namely OTT, ISPs, the financial industry, and the public sector, are creating use cases for DCI networks. The proliferation of data centres is also fueling a surge in DCI, which helps businesses to link their data centres, cloud providers, and other data center operators for easier data and resource sharing. Hence with such rise of data centers and the benefits provided drive the market growth.

Increased use of cloud storage and adoption of cloud-based solutions: Cloud-based storage solutions are today's most practical and effective way to keep data online. There are various cloud computing solution vendors. Because this industry is so large, every major technology business now has its own data centre, which dramatically boosts user income. The migration to cloud-based solutions, as well as the increase in organizational data traffic and big data analytics, are expected to drive development in the data centre interconnect market. Backup and storage are becoming increasingly important as the quantity of data created grows.
Data centre interconnect tools to enable communication and information exchange between its linked components, as well as the data centres' internal and external networks. Companies employ these solutions to establish solid connections between data centres and their linked devices, allowing for faster and more secure data transfers. Furthermore, the usability and accessibility of cloud-based apps have contributed to the expansion of the data centre interconnect industry. For instance, Equinix is a leading global provider of digital infrastructure. They link industry-leading organizations in banking, manufacturing, retail, transportation, government, healthcare, and education in a digital-first world. Business leaders use their trusted worldwide platform to safely and sustainably link the core infrastructur...

Real World Evidence Solutions Market size was valued at USD 1.30 Billion in 2024 and is projected to reach USD 3.71 Billion by 2032, growing at a CAGR of 13.92% during the forecast period 2026-2032.Global Real World Evidence Solutions Market DriversThe market drivers for the Real World Evidence Solutions Market can be influenced by various factors. These may include:Growing Need for Evidence-Based Healthcare: Real-world evidence (RWE) is becoming more and more important in healthcare decision-making, according to stakeholders such as payers, providers, and regulators. In addition to traditional clinical trial data, RWE solutions offer important insights into the efficacy, safety, and value of healthcare interventions in real-world situations.Growing Use of RWE by Pharmaceutical Companies: RWE solutions are being used by pharmaceutical companies to assist with market entry, post-marketing surveillance, and drug development initiatives. Pharmaceutical businesses can find new indications for their current medications, improve clinical trial designs, and convince payers and providers of the worth of their products with the use of RWE.Increasing Priority for Value-Based Healthcare: The emphasis on proving the cost- and benefit-effectiveness of healthcare interventions in real-world settings is growing as value-based healthcare models gain traction. To assist value-based decision-making, RWE solutions are essential in evaluating the economic effect and real-world consequences of healthcare interventions.Technological and Data Analytics Advancements: RWE solutions are becoming more capable due to advances in machine learning, artificial intelligence, and big data analytics. With the use of these technologies, healthcare stakeholders can obtain actionable insights from the analysis of vast and varied datasets, including patient-generated data, claims data, and electronic health records.Regulatory Support for RWE Integration: RWE is being progressively integrated into regulatory decision-making processes by regulatory organisations including the European Medicines Agency (EMA) and the U.S. Food and Drug Administration (FDA). The FDA's Real-World Evidence Programme and the EMA's Adaptive Pathways and PRIority MEdicines (PRIME) programme are two examples of initiatives that are making it easier to incorporate RWE into regulatory submissions and drug development.Increasing Emphasis on Patient-Centric Healthcare: The value of patient-reported outcomes and real-world experiences in healthcare decision-making is becoming more widely acknowledged. RWE technologies facilitate the collection and examination of patient-centered data, offering valuable insights into treatment efficacy, patient inclinations, and quality of life consequences.Extension of RWE Use Cases: RWE solutions are being used in medication development, post-market surveillance, health economics and outcomes research (HEOR), comparative effectiveness research, and market access, among other healthcare fields. The necessity for a variety of RWE solutions catered to the needs of different stakeholders is being driven by the expansion of RWE use cases.

This layer presents detectable thermal activity from MODIS satellites for the last 7 days. MODIS Global Fires is a product of NASA’s Earth Observing System Data and Information System (EOSDIS), part of NASA's Earth Science Data. EOSDIS integrates remote sensing and GIS technologies to deliver global MODIS hotspot/fire locations to natural resource managers and other stakeholders around the World.

Area Covered: World

What can I do with this layer?
The MODIS thermal activity layer can be used to visualize and assess wildfires worldwide. However, it should be noted that this dataset contains many “false positives” (e.g., oil/natural gas wells or volcanoes) since the satellite will detect any large thermal signal.


It takes approximately 2 – 4 hours after satellite overpass for MODIS Rapid Response to process the data, and for the Fire Information for Resource Management System (FIRMS) to update the website. Occasionally, hardware errors can result in processing delays beyond the 2-4 hour range. Additional information on the MODIS system status can be found at MODIS Rapid Response.

Attribute Information

• Latitude and Longitude: The center point location of the 1km (approx.) pixel flagged as containing one or more fires/hotspots (fire size is not 1km, but variable). Stored by Point Geometry. See What does a hotspot/fire detection mean on the ground?
• Brightness: The brightness temperature measured (in Kelvin) using the MODIS channels 21/22 and channel 31.
• Scan and Track: The actual spatial resolution of the scanned pixel. Although the algorithm works at 1km resolution, the MODIS pixels get bigger toward the edge of the scan. See What does scan and track mean?
• Date and Time: Acquisition date of the hotspot/active fire pixel and time of satellite overpass in UTC (client presentation in local time). Stored by Acquisition Date.
  
• Acquisition Date: Derived Date/Time field combining Date and Time attributes.
• Satellite: Whether the detection was picked up by the Terra or Aqua satellite.
• Confidence: The detection confidence is a quality flag of the individual hotspot/active fire pixel.
• Version: Version refers to the processing collection and source of data. The number before the decimal refers to the collection (e.g. MODIS Collection 6). The number after the decimal indicates the source of Level 1B data; data processed in near-real time by MODIS Rapid Response will have the source code “CollectionNumber.0”. Data sourced from MODAPS (with a 2-month lag) and processed by FIRMS using the standard MOD14/MYD14 Thermal Anomalies algorithm will have a source code “CollectionNumber.x”. For example, data with the version listed as 5.0 is collection 5, processed by MRR, data with the version listed as 5.1 is collection 5 data processed by FIRMS using Level 1B data from MODAPS.
• Bright.T31: Channel 31 brightness temperature (in Kelvins) of the hotspot/active fire pixel.
• FRP: Fire Radiative Power. Depicts the pixel-integrated fire radiative power in MW (MegaWatts). FRP provides information on the measured radiant heat output of detected fires. The amount of radiant heat energy liberated per unit time (the Fire Radiative Power) is thought to be related to the rate at which fuel is being consumed (Wooster et. al. (2005)).
• DayNight: The standard processing algorithm uses the solar zenith angle (SZA) to threshold the day/night value; if the SZA exceeds 85 degrees it is assigned a night value. SZA values less than 85 degrees are assigned a day time value. For the NRT algorithm the day/night flag is assigned by ascending (day) vs descending (night) observation. It is expected that the NRT assignment of the day/night flag will be amended to be consistent with the standard processing.
• Hours Old: Derived field that provides age of record in hours between Acquisition date/time and latest update date/time. 0 = less than 1 hour ago, 1 = less than 2 hours ago, 2 = less than 3 hours ago, and so on.

This map is provided for informational purposes and is not monitored 24/7 for accuracy and

Global Reservoir and Dam Database, Version 1, Revision 01 (v1.01) contains 6,862 records of reservoirs and their associated dams with a cumulative storage capacity of 6,197 cubic km. The reservoirs were delineated from high spatial resolution satellite imagery and are available as polygon shape files. The only attribute for the reservoirs is the area of the reservoir. The associated dams data set includes multiple attributes such as name of the dam and the impounded river, primary use, nearest city, area, and year of construction (or commissioning). While the main focus was to include all reservoirs with a storage capacity of more than 0.1 cubic kilometers, many smaller reservoirs were added where data were available. The data were compiled by Lehner et al. (2011) and are distributed by the Global Water System Project (GWSP) and by the Columbia University Center for International Earth Science Information Network (CIESIN). For details please refer to the Technical Documentation which is provided with the data.

A global data set of soil types is available at 0.5-degree latitude by 0.5-degree longitude resolution. There are 106 soil units, based on Zobler?s (1986) assessment of the FAO/UNESCO Soil Map of the World. This data set is a conversion of the Zobler 1-degree resolution version to a 0.5-degree resolution. The resolution of the data set was not actually increased. Rather, the 1-degree squares were divided into four 0.5-degree squares with the necessary adjustment of continental boundaries and islands. The computer code used to convert the original 1-degree data to 0.5-degree is provided as a companion file. A JPG image of the data is provided in this document. The Zobler data (1-degree resolution) as distributed by Webb et al. (1993) [http://www.ngdc.noaa.gov/seg/eco/cdroms/gedii_a/datasets/a12/wr.htm#top] contains two columns, one column for continent and one column for soil type. The Soil Map of the World consists of 9 maps that represent parts of the world. The texture data that Webb et al.(1993) provided allowed for the fact that a soil type in one part of the world may have different properties than the same soil in a different part of the world. This continent-specific information is retained in this 0.5-degree resolution data set, as well as the soil type information which is the second column. A code was written (one2half.c) to take the file CONTIZOB.LER distributed by Webb et al. (1993) [http://www.ngdc.noaa.gov/seg/eco/cdroms/gedii_a/datasets/a12/wr.htm#top] and simply divide the 1-degree cells into quarters. This code also reads in a land/water file (land.wave) that specifies the cells that are land at 0.5 degrees. The code checks for consistency between the newly quartered map and the land/water map to which the quartered map is to be registered. If there is a discrepancy between the two, an attempt was made to make the two consistent using the following logic. If the cell is supposed to be water, it is forced to be water. If it is supposed to be land but was resolved to water at 1 degree, the code looks at the surrounding 8 cells and picks the most frequent soil type and assigns it to the cell. If there are no surrounding land cells then it is kept as water in the hopes that on the next pass one or more of the surrounding cells might be converted from water to a soil type. The whole map is iterated 5 times. The remaining cells that should be land but couldn't be determined from surrounding cells (mostly islands that are resolved at 0.5 degree but not at 1 degree) are printed out with coordinate information. A temporary map is output with -9 indicating where data is required. This is repeated for the continent code in CONTIZOB.LER as well. A separate map of the temporary continent codes is produced with -9 indicating required data. A nearly identical code (one2half.c) does the same for the continent codes. The printout allows one to consult the printed versions of the soil map and look up the soil type with the largest coverage in the 0.5-degree cell. The program manfix.c then will go through the temporary map and prompt for input to correct both the soil codes and the continent codes for the map. This can be done manually or by preparing a file of changes (new_fix.dat) and redirecting stdin. A new complete version of the map is outputted. This is in the form of the original CONTIZOB.LER file (contizob.half) but four times larger. Original documentation and computer codes prepared by Post et al. (1996) are provided as companion files with this data set. Image of 106 global soil types available at 0.5-degree by 0.5-degree resolution. Additional documentation from Zobler?s assessment of FAO soil units is available from the NASA Center for Scientific Information.

Global IT Jobs Analysis

A Detailed Investigation of Salary, Location, and Job Requirements

By [source]

About this dataset

This dataset contains valuable insights into current job opportunities in the information technology (IT) sector all around the world. It offers an overview of available jobs and relevant data such as company, location, salary and links to further information. With this insight, one has the chance to better understand what it takes to land a remote or data-science job in today's global market. The ever increasing demand for IT workforce puts technical skills at a premium, so understanding exactly what employers are searching for can give potential employees an edge in catching the eye of these businesses! Digging through this dataset can provide details on current trends in terms of salary expectations and geographical locations where these roles are most popular. Beyond that, get an idea about which abilities seem most valuable when it comes to remote or data-science positions. Use this arsenal of knowledge to take your career goals into your own hands now!

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How to use the dataset

This dataset provides an opportunity to explore the remote and data-science job opportunities around the world. Using this dataset, you can analyze trends in job requirements, salary packages offered, location of available jobs and more. With the knowledge gained from this data set, individuals and companies can make more informed decisions about pursuing a certain path in their career or hiring for their business.

The dataset includes columns with important information such as Job title, Company offering the job, Location of the position , Salary offered for that position and a Link to its respective posting. Using these columns you can analyze various factors regarding global IT Jobs availability over different locations in alignment with salary offered for positions and any specific skill sets sought out by companies .

To get executable insights from this data set users should first load it into their respective computing environment (Python or R). After loading it in your environment users should start off by exploring Groupby statements along factors like Companies offering jobs ,Salary offered ,Location etc. followed by descriptive statistics like mean & median of Salary Levels per country/region etc. After getting basic insight about summary statistics for various factors belonging all together within “Job” range user could move forward to look over individual cases (specific skill sets) after which they could filter out & generate valueable insights needed .

With our comprehensive understanding of global supply & demand rates individuals/corporations could always use these datasets to help them keep track on talent acquisition landscape when they hire globally or relocating teams as companies who need such information would greatly benefit from versatile tools like this one that offer valuable actionsable insights on an ongoing basis depending upon dayers choosing!

Research Ideas

• Identifying the most in-demand skills and employment requirements for remote data science and IT jobs, across different countries and regions.
• Developing a prediction model to forecast future salary expectations for data science professionals based on location, company, job type, etc.
• Building an interactive dashboard with visualizations showing differences in job requirements (by level of experience or education), salary comparison across geographies as well as potential career paths one can pursue within the IT or Data Science fields

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Job_listing.csv | Column name | Description | |:--------------|:---------------------------------------------------| | Job | The title of the job listing. (String) | | Company | The name of the company offering the job. (String) | | Location | The geographic location of the job. (String) | | Salary | The salary offere...