This dataset is about companies. It has 17 rows and is filtered where the company is Meta. It features 5 columns: employees, CEO, CEO gender, and CEO approval.

About this dataset

Context

The dataset tabulates the Meta population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Meta. The dataset can be utilized to understand the population distribution of Meta by age. For example, using this dataset, we can identify the largest age group in Meta.

Key observations

The largest age group in Meta, MO was for the group of age 65 to 69 years years with a population of 18 (14.06%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Meta, MO was the 20 to 24 years years with a population of 0 (0%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Meta is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Meta total population. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta Population by Age. You can refer the same here

This is dataset includes a list of meta-organizations, selected from the book Tietje, C., & Brouder, A. (Eds.). (2009). Handbook of Transnational Economic Governance Regimes. Martinus Nijhoff Publishers. It includes the name, the website, the chapter reference, and an analysis of both the case description in the book and the website of the meta-organization to understand the type workforce (member employees, meta-organization employees, rotation, and the mix thereof), as well as whether the workforce is hosted or hosting other meta-organizational workforces. The dataset has been utilized in a chapter on workforces in meta-organizations in a book edited by Berkowitz, Bor and Brunsson.

AI Agent Marketplace and Directory of AI Agent Employees

This dataset contains meta information of AI Agent Marketplace's category of "AI Agent Employees".

  AI Agent Employees

AI Employees or LLM-based AI Agents in Workplace that may Influence Human Jobs and can help increase working productivity. The list consists of different areas of workpace productivity including Sales & Marketing AI, IT & Customer Support AI, HR & Recruitment AI Tools, General Productivity &… See the full description on the dataset page: https://huggingface.co/datasets/DeepNLP/AI-Agent-Marketplace-AI-Agent-Employees.

The tech industry had a rough start to 2024. Technology companies worldwide saw a significant reduction in their workforce in the first quarter of 2024, with over ** thousand employees being laid off. By the second quarter, layoffs impacted more than ** thousand tech employees. In the final quarter of the year around ** thousand employees were laid off. Layoffs impacting all global tech giants Layoffs in the global market escalated dramatically in the first quarter of 2023, when the sector saw a staggering record high of ***** thousand employees losing their jobs. Major tech giants such as Google, Microsoft, Meta, and IBM all contributed to this figure during this quarter. Amazon, in particular, conducted the most rounds of layoffs with the highest number of employees laid off among global tech giants. Industries most affected include the consumer, hardware, food, and healthcare sectors. Notable companies that have laid off a significant number of staff include Flink, Booking.com, Uber, PayPal, LinkedIn, and Peloton, among others. Overhiring led the trend, but will AI keep it going? Layoffs in the technology sector started following an overhiring spree during the COVID-19 pandemic. Initially, companies expanded their workforce to meet increased demand for digital services during lockdowns. However, as lockdowns ended, economic uncertainties persisted and companies reevaluated their strategies, layoffs became inevitable, resulting in a record number of *** thousand laid off employees in the global tech sector by the end of 2022. Moreover, it is still unclear how advancements in artificial intelligence (AI) will impact layoff trends in the tech sector. AI-driven automation can replace manual tasks leading to workforce redundancies. Whether through chatbots handling customer inquiries or predictive algorithms optimizing supply chains, the pursuit of efficiency and cost savings may result in more tech industry layoffs in the future.

About this dataset

Context

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

Key observations: Insights from 2023

Based on our analysis ACS 2019-2023 5-Year Estimates, we present the following observations: - All workers, aged 15 years and older: In Meta, the median income for all workers aged 15 years and older, regardless of work hours, was $40,000 for males and $25,893 for females.

These income figures highlight a substantial gender-based income gap in Meta. Women, regardless of work hours, earn 65 cents for each dollar earned by men. This significant gender pay gap, approximately 35%, underscores concerning gender-based income inequality in the city of Meta.

Surprisingly, within the subset of full-time workers, women earn a higher income than men, earning 1.08 dollars for every dollar earned by men. This suggests that within full-time roles, womens median incomes significantly surpass mens, contrary to broader workforce trends.

Content

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

Gender classifications include:

• Male
• Female

Employment type classifications include:

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

Variables / Data Columns

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta median household income by race. You can refer the same here

About this dataset

Context

The dataset presents the detailed breakdown of the count of individuals within distinct income brackets, categorizing them by gender (men and women) and employment type - full-time (FT) and part-time (PT), offering valuable insights into the diverse income landscapes within Meta. The dataset can be utilized to gain insights into gender-based income distribution within the Meta population, aiding in data analysis and decision-making..

Key observations

• Employment patterns: Within Meta, among individuals aged 15 years and older with income, there were 56 men and 49 women in the workforce. Among them, 28 men were engaged in full-time, year-round employment, while 19 women were in full-time, year-round roles.
• Annual income under $24,999: Of the male population working full-time, 17.86% fell within the income range of under $24,999, while 5.26% of the female population working full-time was represented in the same income bracket.
• Annual income above $100,000: none of men in full-time roles earned incomes exceeding $100,000, while none of women in full-time positions earned within this income bracket.
• Refer to the research insights for more key observations on more income brackets ( Annual income under $24,999, Annual income between $25,000 and $49,999, Annual income between $50,000 and $74,999, Annual income between $75,000 and $99,999 and Annual income above $100,000) and employment types (full-time year-round and part-time)

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Income brackets:

• $1 to $2,499 or loss
• $2,500 to $4,999
• $5,000 to $7,499
• $7,500 to $9,999
• $10,000 to $12,499
• $12,500 to $14,999
• $15,000 to $17,499
• $17,500 to $19,999
• $20,000 to $22,499
• $22,500 to $24,999
• $25,000 to $29,999
• $30,000 to $34,999
• $35,000 to $39,999
• $40,000 to $44,999
• $45,000 to $49,999
• $50,000 to $54,999
• $55,000 to $64,999
• $65,000 to $74,999
• $75,000 to $99,999
• $100,000 or more

Variables / Data Columns

• Income Bracket: This column showcases 20 income brackets ranging from $1 to $100,000+..
• Full-Time Males: The count of males employed full-time year-round and earning within a specified income bracket
• Part-Time Males: The count of males employed part-time and earning within a specified income bracket
• Full-Time Females: The count of females employed full-time year-round and earning within a specified income bracket
• Part-Time Females: The count of females employed part-time and earning within a specified income bracket

Employment type classifications include:

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

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta median household income by race. You can refer the same here

PLEASE, CITE AS Kalabikhina IE, Kuznetsova PO, Zhuravleva SA (2024) Size and factors of the motherhood penalty in the labour market: A meta-analysis. Population and Economics 8(2): 178-205. https://doi.org/10.3897/popecon.8.e121438

Explanatory note 1: List of papers used in the meta-analysis - see the file "Meta_regression_analysis_papers".

The data is presented in WORD format.

Explanatory note 2: Set of data used in the meta-analysis - see the file "Meta_regression_analysis_table".

The data is presented in EXCEL format.

Description of table headers:

estimate_number - Number of the estimate

paper_number - Number of the paper

paper_name - Paper (year and first author)

paper_excluded - Paper was excluded from the final sample

survey - Data source

table_in_paper - Number of the table with the regression results in the paper

coeff - Regression coefficient for parenthood variable (estimate)

se - SE of the estimate

t - t-value of the estimate

ols - Estimate is obtained using the OLS method

fixed_effects - Estimate is obtained using the fixed effects method

panel - Model considers panel data (for several years)

quintile - Estimate is obtained using the quintile regression method

other - Estimate is obtained using other methods

selection_into_motherhood - Estimate is obtained allowing for selection into motherhood

hackman - Estimate is obtained allowing for selection into employment (Heckman procedure)

annual_earnings - Annual earnings are considered in the model

monthly_wage - Monthly wage is considered in the model

daily_wage - Daily wage is considered in the model

hourly_wage - Hourly wage is considered in the model

min_age_kid - Child's age (minimum)

max_age_kid - Child's age (maximum)

motherhood - Model uses a dummy variable of the presence of children

num_kids - Model uses a variable of the number of children

kid1 - Model uses a variable of the presence of one child

kid2p - Model uses a variable of the presence of two or more children

kid2 - Model uses a variable of the presence of two children

kid3p - Model uses a variable of the presence of three or more children

kid3 - Model uses a variable of the presence of three children

kid4p - Model uses a variable of the presence of three or more children

race/nationality - Model includes a race/ethnicity variable

age - Model includes the age variable

marstat - Model includes the marital status variable

oth_char_hh - Model includes any other variables of other household characteristics

settl_type - Model includes a variable of the type of settlement (urban, rural)

region - Model includes a variable of the region of the country

education - Model includes information on the level of education

experience - Model includes a variable of work experience

pot_experience - Model includes a variable of potential work experience, to be calculated from the data on age and number of years of education

tenure - Model includes a variable of the duration of employment at the current job

interruptions - Model includes a variable of employment interruptions (related to motherhood)

occupation - Model includes an occupation variable

industry - Model includes a variable of the industry of employment

union - Model includes a variable of trade union membership

friendly_conditions - Model includes a variable of the favourable working conditions for mothers (flexible schedule, possibility to work from home, etc.).

hours - Model includes a variable of the number of hours worked

sector - Model includes a variable of the type of employer ownership (public or private)

informal - Model includes a variable of informal employment

size_ent - Model includes a variable of the employer size

min_age_woman - Woman's age (minimum)

max_age_woman - Woman's age (maximum)

mean_age_woman - Woman's age (mean)

restricted - Sample is limited

private - Model considers only private sector employees

state - Model considers only public sector employees

full_time - Model considers only full-time workers

part_time - Model considers only part-time workers

better_educated - Model considers only women with a high level of education

lower_educated - Model considers only women with a low level of education

married - Model includes only married women

single - Model includes only single women

natives - Model includes only native women (born in the country)

immigrants - Model includes only immigrant women (born abroad)

race - Model includes only women of a particular race

min_year - Time period (minimum year)

max_year - Time period (maximum year)

journal - Type of publication

usa - Sample includes women from the USA

western_europe - Sample includes women from Western Europe (Belgium, France, Germany, Luxembourg, the Netherlands, Switzerland)

north_europe - Sample includes women from Northern Europe (Denmark, Finland, Norway, Sweden)

south_europe - Sample includes women from Southern Europe (Greece, Italy, Portugal, Spain)

east_centre_europe - Sample includes women from Central or Eastern Europe (Czechia, Hungary, Poland, Russia, Serbia, Ukraine)

china - Sample includes women from China

Russia - Sample includes women from Russia

others - Sample includes women from other countries

country - Country name

Recently, the promotion of safety participation (SP) has become a hot spot in behavioral safety research and safety management practice. To explore the relationship between safety leadership (SL) and SP, a theoretical model was established and 33 articles (35 independent samples) on work safety from 2000 to 2021 were selected for a meta-analysis. By evaluating the impact of SL, which incorporates transformational, transactional, and passive leadership styles, on work safety. The results show that SL has a positive impact on both safety climate (SC) and SP. Both safety transactional leadership (STAL) and safety transformational leadership (STFL) positively impact SP, and the impact of STFL is greater, while safety passive leadership (SPL) has no impact on SP. The study establishes that SC plays a partial mediating role between transformational SL and employee SP. Under the condition of a developed economic level or high-risk industry, SL indicated a greater influence on SP. Hence, it is recommended that when enhancing the SP of employees, the influence of the macro environment and SC should not be undermined.

The attached document is the manual how to use this portal. It is only accessible for logged-in users.

All Senckenberg employees should be able to log in using their institutional credentials.

IMPORTANT: For your first login, please

1. Add your full name to your profile!

2. Contact the admin of your organizational unit to add you to the group

Read further details in the manual.

In case of an update, new versions will be uploaded, here.

Which county has the most Facebook users?

              There are more than 378 million Facebook users in India alone, making it the leading country in terms of Facebook audience size. To put this into context, if India’s Facebook audience were a country then it would be ranked third in terms of largest population worldwide. Apart from India, there are several other markets with more than 100 million Facebook users each: The United States, Indonesia, and Brazil with 193.8 million, 119.05 million, and 112.55 million Facebook users respectively.

              Facebook – the most used social media

              Meta, the company that was previously called Facebook, owns four of the most popular social media platforms worldwide, WhatsApp, Facebook Messenger, Facebook, and Instagram. As of the third quarter of 2021, there were around 3,5 billion cumulative monthly users of the company’s products worldwide. With around 2.9 billion monthly active users, Facebook is the most popular social media worldwide. With an audience of this scale, it is no surprise that the vast majority of Facebook’s revenue is generated through advertising.

              Facebook usage by device
              As of July 2021, it was found that 98.5 percent of active users accessed their Facebook account from mobile devices. In fact, almost 81.8 percent of Facebook audiences worldwide access the platform only via mobile phone. Facebook is not only available through mobile browser as the company has published several mobile apps for users to access their products and services. As of the third quarter 2021, the four core Meta products were leading the ranking of most downloaded mobile apps worldwide, with WhatsApp amassing approximately six billion downloads.

Eye2Sky dataset - All-sky images and meteorological measurements

Description

The Eye2Sky public dataset comprises all-sky images from 29 All-Sky Imager (ASI) stations and meteorological measurements including solar irradiance from 11 stations in north-west Germany. A list of all stations with meta information is provided in the attached Eye2Sky_Station_List.xlsx

Meteorological measurements cover a full year from April 2022 to March 2023 with minutely averaged parameters. Parameters measured are three solar radiation components global horizontal, diffuse horizontal and direct normal irradiance. Global tilted irradiance is measured with tilt angles of 30° in southern direction. Measurement data has been quality controlled. Raw data is provided along with quality flags. For users who want to use measurements directly, a "ready-to-use" data set with cleaned data is provided.

ASI images of the whole ASI network at all stations cover a 4-month period from April to July 2022 with 30 seconds sampling rate, for station OLUOL the dataset covers a full year from April 2022 to March 2023 to allow season-dependent analyses at a central location inside the ASI network. The sampling rate for OLUOL is 15 seconds (since 2022-06-10). Images are provided as raw data (jpg-format), but calibration information and horizon masks are provided as meta data. Note: Due to maximum upload size allowed by Zenodo, only an example data set (ASI_20220620.zip) is provided here. The full set of ASI images can be downloaded from here: https://eye2sky.de/data

Quality remarks

Invalid images (broken downloads, empty images, ...) have been removed from the dataset.

Moreover, images where faces of people are visible have been removed.

Any other disturbed images (e.g. due to birds, insects or just dirt) are kept in the database.

Python libraries for image handling are in preparation and will be soon published on Github

Quality flags for measurement data

Measurement data was quality checked using the procedure in attached image QC_Flowchart.png. The manual quality control refers to obvious measurement errors known to the station operator. Corresponding data is removed from the data set before additional tests are carried out on the data in an automatic quality control process. The tests performed are described in a publication in preparation.

Data Format

All-sky images are provided as zipped archives in raw jpg-format (sampling 30 seconds).

Directory structure: One folder per day and station

Meta data: each station folder consists of

a YAML-file providing meta data for each configuration change. The filename contains the timestamp of the latest change in configuration (could be a change in camera alignment or positioning). The file contains information on geographical coordinates, internal and external orientation and necessary mask file.

image masks in binary png-files and mat-files.

an example ASI image with an image mask overlay and image orientated to geographic north according to calibration parameters

a keogram composite of all ASI images in the full data period.

Measurement data is provided in zipped station directories.

There are two files of timeseries data for each station and for the full time period.

data/STATION_ID.flagged.nc contains raw data including QC-flags

data/STATION_ID.cleaned.nc contains cleaned data

Meta data: For each station

a horizon file for the near horizon near_horizon.csv

and a log file of changes and quality check report is provided: QC_{STATION_ID}.pdf

Ceilometer data for Stations CDLRA (Oldenburg) and CLDRB (Westerstede) is provided in daily netcdf-Files.

Timeseries plots of daily measurements are attached for both stations.

VERSION UPDATES

Version 1.0:

Initial Upload

ACKNOWLEDGEMENTS

DLR Institute of Networked Energy Systems is responsible for the construction, operations, quality control and scientific evolution of the Eye2Sky network. DLR Institute of Solar Research was involved in designing the network and strongly supports Eye2Sky with software for data acquisition, calibration, processing and evaluation. We would like to thank all DLR staff who helped with the procurement of funding, installation support, data acquisition and data quality control. We also thank all persons, institutions and companies who contributed to the installation and who offered hosting a station.

LICENSE

• All the All-Sky images are licensed under CC-BY-SA 3.0.- All the measurement data from meteorological stations and ceilometers is licensedunder the CDLA 1.0 sharing license: https://cdla.dev/sharing-1-0/.

Context

Technology companies have become a dominant driver in recent years of economic growth, consumer tastes and the financial markets. The biggest tech stocks as a group, for example, have dramatically outpaced the broader market in the past decade.

That's because technology has reshaped in a major way how people communicate, consume information, shop, socialize, and work.

Broadly speaking, companies in the technology sector engage in the research, development, and manufacture of technologically based goods and services. They create software, and design and manufacture computers, mobile devices, and home appliances. They also provide products and services related to information technology.

Content

This dataset contains 3 files with the daily stock price and volume of the three companies: Google, Apple, and Facebook from 07/09/2017 to 07/09/2022. Source: Yahoo! Finance

Profile

Apple

Apple Inc. (AAPL) One Apple Park Way Cupertino, CA 95014 United States 408 996 1010 https://www.apple.com

Sector(s): Technology Industry: Consumer Electronics Full Time Employees: 154,000

Total Revenue (2021): $365,817,000
Net Income (2021):$94,680,000
Exchange: Nasdaq

Google

Alphabet Inc. (GOOG) 1600 Amphitheatre Parkway Mountain View, CA 94043 United States 650 253 0000 https://www.abc.xyz

Sector(s): Communication Services Industry: Internet Content & Information Full Time Employees: 174,014

Total Revenue (2021): $257,637,000 Net Income (2021):$76,033,000 Exchange: Nasdaq

Facebook

Meta Platforms, Inc. (META) 1601 Willow Road Menlo Park, CA 94025 United States 650 543 4800 https://investor.fb.com

Sector(s): Communication Services Industry: Internet Content & Information Full Time Employees: 83,553

Total Revenue (2021): $117,929,000 Net Income (2021):$39,370,000 Exchange: Nasdaq

Acknowledgements

Yahoo! Finance Investopedia Nasdaq

Start A New Notebook!

Data used for the Meta-Analysis of 46 Career Pathways Impact and data from four large nationally representative longitudinal surveys, as well as licensed data on occupational transitions from online career profiles, to examine workers’ career paths and wages for the Career Trajectories and Occupational Transitions Study.

Hidden workers are defined as the three vulnerable subgroups of workers: the underemployed, the unemployed, and the discouraged workers. Hidden workers indeed the group with multiple identities; a transitioning retiree, jobseeker, caring for some, who may also have long term health conditions and ethnic minority all at the same time. Designing an intervention for this group necessitates the transdisciplinary knowledge. Transdisciplinary knowledge is crucial because it can inform how the intersectoral challenges might be addressed in interventions, and how the intersectoral implementation design and evaluation on hidden workers might be designed. This paper maps the intellectual landscape of the hidden workers in aging population literature to identify key disciplinary research clusters; and to find out how those research clusters are investigating hidden workers. With the meta-narrative review methodology on studies retrieved from the Web of Science Core Collection, five research clusters were identified: (1) public health approaches to hidden workers, (2) welfare state and aging workforce, (3) older jobseekers, (4) life course perspective, (5) retirement transitions. Each research cluster focuses on different aspects of hidden workers, with varying research questions and rationales. These include conceptualising the determinants of the hidden workers in aging populations and the complex interrelation with public health. Furthermore, we suggest an analytical framework to allow for better understanding between the research traditions based on (1) the chosen socioecological level of analysis, (2) whether the research question is on the determinant for hidden workers or on the outcome of being hidden and (3) the chronosystem (early/middle/later life) timeframe of research question that is addressed. Through this study, we can identify the main issues faced by hidden workers among the older adults and the measures to address these issues as well as opening up a possibility for cross-sectoral policy responses.

BackgroundThe high chances of getting latent tuberculosis infection (LTBI) among health care workers (HCWs) will an enormous problem in low and upper-middle-income countries.MethodSearch strategies were done through both national and international databases include SID, Barakat knowledge network system, Irandoc, Magiran, Iranian national library, web of science, Scopus, PubMed/MEDLINE, OVID, EMBASE, the Cochrane library, and Google Scholar search engine. The Persian and the English languages were used as the filter in national and international databases, respectively. Medical Subject Headings (MeSH) terms was used to controlling comprehensive vocabulary. The search terms were conducted without time limitation till January 01, 2019.ResultsThe prevalence of LTBI in Iranian’s HCWs, based on the PPD test was 27.13% [CI95%: 18.64–37.7]. The highest prevalence of LTBI in Iranian’s HCWs were estimated 41.4% [CI95%: 25.4–59.5] in the north, and 33.8% [CI95%: 21.1–49.3] in the west. The lowest prevalence of LTBI was evaluated 18.2% [CI95%: 3.4–58.2] in the south of Iran. The prevalence of LTBI in Iranian’s HCWs who had work-experience more than 20 years old were estimated 20.49% [CI95%: 11–34.97]. In the PPD test, the prevalence of LTBI in Iranian’s HCWs who had received the Bacille Calmette–Guérin (BCG) was estimated 15% [CI95%: 3.6–47.73]. While, in the QFT, the prevalence of LTBI in Iranian’s HCWs in non-vaccinated was estimated 25.71% [CI95%: 13.96–42.49].ConclusionsThis meta-analysis shows the highest prevalence of LTBI in Iranian’s HCWs in the north and the west probably due to neighboring countries like Azerbaijan and Iraq, respectively. It seems that Iranian’s HCWs have not received the necessary training to prevent of TB. We also found that BCG was not able to protect Iranian’s HCWs from TB infections, completely.

In 2023, Meta Platforms had a total annual revenue of over 134 billion U.S. dollars, up from 116 billion in 2022. LinkedIn reported its highest annual revenue to date, generating over 15 billion USD, whilst Snapchat reported an annual revenue of 4.6 billion USD.

About this dataset

Context

The dataset tabulates the Meta population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Meta across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2023, the population of Meta was 179, a 0.56% increase year-by-year from 2022. Previously, in 2022, Meta population was 178, a decline of 0% compared to a population of 178 in 2021. Over the last 20 plus years, between 2000 and 2023, population of Meta decreased by 87. In this period, the peak population was 278 in the year 2009. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2023

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2023)
• Population: The population for the specific year for the Meta is shown in this column.
• Year on Year Change: This column displays the change in Meta population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Meta Population by Year. You can refer the same here

IntroductionHealthcare workers risk of exposure to the influenza virus in their work, is a high-risk group for flu infections. Thus WHO recommends prioritizing flu vaccination for them–an approach adopted by >40 countries and/or regions worldwide.MethodsCross-sectional studies on influenza vaccination rates among healthcare workers were collected from PubMed, EMBASE, CNKI, and CBM databases from inception to February 26, 2023. Influenza vaccination rates and relevant data for multiple logistic regression analysis, such as odds ratios (OR) and 95% confidence intervals (CI), were extracted.ResultsA total of 92 studies comprising 125 vaccination data points from 26 countries were included in the analysis. The meta-analysis revealed that the overall vaccination rate among healthcare workers was 41.7%. Further analysis indicated that the vaccination rate was 46.9% or 35.6% in low income or high income countries. Vaccination rates in the Americas, the Middle East, Oceania, Europe, Asia, and Africa were 67.1, 51.3, 48.7, 42.5, 28.5, and 6.5%, respectively. Influencing factors were age, length of service, education, department, occupation, awareness of the risk of influenza, and/or vaccines.ConclusionThe global influenza vaccination rate among healthcare workers is low, and comprehensive measures are needed to promote influenza vaccination among this population.Systematic review registrationwww.inplysy.com, identifier: 202350051.

BackgroundIn the field of forensic science, accurately determining occupation of an individual can greatly assist in resolving cases such as criminal investigations or disaster victim identifications. However, estimating occupation can be challenging due to the intricate relationship between occupation and various factors, including gender, age, living environment, health status, medication use, and lifestyle habits such as alcohol consumption and smoking. All of these factors can impact the composition of oral or gut microbial community of an individual.Methods and resultsIn this study, we collected saliva and feces samples from individuals representing different occupational sectors, specifically students and manual laborers. We then performed metagenomic sequencing on the DNA extracted from these samples to obtain data that could be analyzed for taxonomic and functional annotations in five different databases. The correlation between occupation with microbial information was assisted from the perspective of α and β diversity, showing that individuals belonging to the two occupations hold significantly different oral and gut microbial communities, and that this correlation is basically not affected by gender, drinking, and smoking in our datasets. Finally, random forest (RF) models were built with recursive feature elimination (RFE) processes. Models with 100% accuracy in both training and testing sets were constructed based on three species in saliva samples or on a single pathway annotated by the KEGG database in fecal samples, namely, “ko04145” or Phagosome.ConclusionAlthough this study may have limited representativeness due to its small sample size, it provides preliminary evidence of the potential of using microbiome information for occupational inference.