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

IntroductionDespite being a fundamental human right, access to assistive products varies between 3% and 90% across countries. Ensuring adequate and trained human resources is a prerequisite for improving access to assistive products. To support workforce planning and development, this study estimated the global workforce required for assistive technology provision to achieve a high level of access.MethodThis modeling study used estimates of the primary workforce for assistive technology provision and assistive product needs in a country with a high level of access and global assistive product needs, to predict the global workforce required to provide assistive technology in five product domains: cognition and communication, hearing, mobility and self-care, orthotics and prosthetics, and vision. The assistive product need estimates were based on self-reported data from WHO Rapid Assistive Technology Assessment surveys in 28 countries.ResultsA total workforce for assistive technology provision of 4.4 (95% CI: 3.0–6.8) million full-time equivalents (FTE) would be required globally to achieve a high level of access to assistive products. Excluding the administrative workforce, this includes a workforce of 3.4 (2.3–5.4) million FTE, composed of 1.7 (1.3–2.2) million FTE providing mobility and self-care products, 0.9 (0.5–1.7) million FTE providing orthoses and prostheses, 0.5 (0.2–1.0) million FTE providing vision products, 0.3 (0.2–0.4) million FTE providing hearing products, and 0.05 (0.04–0.06) million FTE providing cognition and communication products.ConclusionLikely a conservative estimate of the required workforce size, this provides a cautious foundation for informing strategies to develop a workforce capable of meeting global assistive product needs and improving access.

Users can view cross-nationally comparable data on the health workforce in the 193 WHO member states. Background The Global Atlas of the Health Workforce is a database maintained by the World Health Organization (WHO). This database allows users to view cross-nationally comparable data on the health workforce in the 193 WHO member states. Health workforce statistics includes the number or density of physicians, nurses, midwives, dentists, pharmacists, laboratory workers, community health workers, and public health workers. User Functionality Users can generate sta tistics pertaining to the health workforce. Users can view information by country, international region, or world, and choose a time period for which they are interested in viewing health workforce statistics. Aggregated and disaggregated data are available. In addition, users can view regional summaries of the health workforce. Data Notes The Global Atlas of the Health Workforce is updated periodically. Data are available for 1995-2011. Data are derived from national population censuses, labor force and employment surveys, health facility assessments, and official country reports to the WHO. Regional and country summaries are available.

📂 Dataset Title:

AI Impact on Job Market: Increasing vs Decreasing Jobs (2024–2030)

📝 Dataset Description:

This dataset explores how Artificial Intelligence (AI) is transforming the global job market. With a focus on identifying which jobs are increasing or decreasing due to AI adoption, this dataset provides insights into job trends, automation risks, education requirements, gender diversity, and other workforce-related factors across industries and countries.

The dataset contains 30,000 rows and 13 valuable columns, generated to reflect realistic labor market patterns based on ongoing research and public data insights. It can be used for data analysis, predictive modeling, AI policy planning, job recommendation systems, and economic forecasting.

📊 Columns Description:

Column Name Description

Job Title Name of the job/role (e.g., Data Analyst, Cashier, etc.) Industry Industry sector in which the job is categorized (e.g., IT, Healthcare, Manufacturing) Job Status Indicates whether the job is Increasing or Decreasing due to AI adoption AI Impact Level Estimated level of AI impact on the job: Low, Moderate, or High Median Salary (USD) Median annual salary for the job in USD Required Education Typical minimum education level required for the job Experience Required (Years) Average number of years of experience required Job Openings (2024) Number of current job openings in 2024 Projected Openings (2030) Projected job openings by the year 2030 Remote Work Ratio (%) Estimated percentage of jobs that can be done remotely Automation Risk (%) Probability of the job being automated or replaced by AI Location Country where the job data is based (e.g., USA, India, UK, etc.) Gender Diversity (%) Approximate percentage representation of non-male genders in the job

🔍 Potential Use Cases:

Predict which jobs are most at risk due to automation.

Compare AI impact across industries and countries.

Build dashboards on workforce diversity and trends.

Forecast job market shifts by 2030.

Train ML models to predict job growth or decline.

📚 Source:

This is a synthetic dataset generated using realistic modeling, public job data patterns (U.S. BLS, OECD, McKinsey, WEF reports), and AI simulation to reflect plausible scenarios from 2024 to 2030. Ideal for educational, research, and AI project purposes.

📌 License: MIT

This work combines global warming data from various publications and datasets, creating a new dataset covering a very long period - from the year 1 to 2100.

The dataset created in this work separates the actual records for the 1-2024 period from the forecast for the 2020-2100 period.

The work includes separate sets for land+ocean (GW), land only (GWL), and ocean only (GWO).

The online dataset is available on the site nowagreen.com.

Total-Long-Term-Debt Time Series for Adtalem Global Education Inc. Adtalem Global Education Inc. engages in the provision of workforce solutions worldwide. It operates through three segments: Chamberlain, Walden, and Medical and Veterinary. The company offers degree and non-degree programs, including bachelor's, master's, and doctoral degrees, as well as online certificates in the medical, nursing, health professions and veterinary postsecondary education, counseling, business, psychology, public health, social work and human services, public administration and public policy, and criminal justice industries. It operates Chamberlain University, Walden University, American University of the Caribbean School of Medicine, Ross University School of Medicine, and Ross University School of Veterinary Medicine. The company was formerly known as DeVry Education Group Inc. and changed its name to Adtalem Global Education Inc. in May 2017. Adtalem Global Education Inc. was incorporated in 1987 and is based in Chicago, Illinois.

Description The Post-Pandemic Remote Work Health Impact 2025 dataset presents a comprehensive, global snapshot of how remote, hybrid, and onsite work arrangements are influencing the mental and physical health of employees in the post-pandemic era. Collected in June 2025, this dataset aggregates responses from a diverse workforce spanning continents, industries, age groups, and job roles. It is designed to support research, data analysis, and policy-making around the evolving landscape of work and well-being.

This dataset enables in-depth exploration of:

• The prevalence of mental health conditions (e.g., anxiety, burnout, PTSD, depression) across different work setups.
• The relationship between work arrangements and physical health complaints (e.g., back pain, eye strain, neck pain).
• Variations in work-life balance, social isolation, and burnout levels segmented by demographic and occupational factors.
• Salary distributions and their correlation with health outcomes and job roles.

By providing granular, anonymized data on both subjective (self-reported) and objective (hours worked, salary range) factors, this resource empowers data scientists, health researchers, HR professionals, and business leaders to:

• Identify risk factors and protective factors for employee well-being. Benchmark health impacts across industries and regions.
• Inform organizational policy and future-of-work strategies.

| Column Name Description Example Values | | | Survey_Date Date when the survey response was submitted (YYYY-MM-DD) 2025-06-01 Age Age of the respondent (in years) 27, 52, 40 Gender Gender identity of the respondent Female, Male, Non-binary, Prefer not to say Region Geographical region of employment Asia, Europe, North America, Africa, Oceania Industry Industry sector of the respondent Technology, Manufacturing, Finance, Healthcare Job_Role Specific job title or function Data Analyst, HR Manager, Software Engineer Work_Arrangement Primary work mode Onsite, Remote, Hybrid Hours_Per_Week Average number of hours worked per week 36, 55, 64 Mental_Health_Status Primary self-reported mental health condition Anxiety, Burnout, Depression, None, PTSD Burnout_Level Self-assessed burnout (categorical: Low, Medium, High) High, Medium, Low Work_Life_Balance_Score Self-rated work-life balance on a scale of 1 (poor) to 5 (excellent) 1, 3, 5 Physical_Health_Issues Self-reported physical health complaints (semicolon-separated if multiple) Back Pain; Eye Strain; Neck Pain; None Social_Isolation_Score Self-rated social isolation on a scale of 1 (none) to 5 (severe) 1, 2, 5 Salary_Range Annual salary range in USD $40K-60K, $80K-100K, $120K+ | --- | | | |

Libya LY: Labour Force data was reported at 2,403,125.000 Person in 2017. This records an increase from the previous number of 2,363,336.000 Person for 2016. Libya LY: Labour Force data is updated yearly, averaging 2,009,973.500 Person from Dec 1990 (Median) to 2017, with 28 observations. The data reached an all-time high of 2,403,125.000 Person in 2017 and a record low of 1,255,832.000 Person in 1990. Libya LY: Labour Force data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Libya – Table LY.World Bank.WDI: Labour Force. Labor force comprises people ages 15 and older who supply labor for the production of goods and services during a specified period. It includes people who are currently employed and people who are unemployed but seeking work as well as first-time job-seekers. Not everyone who works is included, however. Unpaid workers, family workers, and students are often omitted, and some countries do not count members of the armed forces. Labor force size tends to vary during the year as seasonal workers enter and leave.; ; Derived using data from International Labour Organization, ILOSTAT database and World Bank population estimates. Labor data retrieved in September 2018.; Sum; Data up to 2016 are estimates while data from 2017 are projections.

This powerful dataset represents a meticulously curated snapshot of the United States job market throughout 2021, sourced directly from CareerBuilder, a venerable employment website founded in 1995 with a formidable global footprint spanning the US, Canada, Europe, and Asia. It offers an unparalleled opportunity for in-depth research and strategic analysis.

Dataset Specifications:

• Source: CareerBuilder.com (US Listings)
• Crawled by: Crawl Feeds in-house team
• Volume: Over 422,000 unique job records
• Timeliness: Last crawled in May 2021, providing a critical historical benchmark for post-pandemic labor market recovery and shifts.
• Format: Compressed ZIP archive containing structured JSON files, designed for seamless integration into databases, analytical platforms, and machine learning pipelines.
• Accessibility: Published and available immediately for acquisition.

Richness of Detail (22 Comprehensive Fields):

The true analytical power of this dataset stems from its 22 granular data points per job listing, offering a multi-faceted view of each employment opportunity:

1. Core Job & Role Information:

   • id: A unique, immutable identifier for each job posting.
   • title: The specific job role (e.g., "Software Engineer," "Marketing Manager").
   • description: A condensed summary of the role, responsibilities, and key requirements.
   • raw_description: The complete, unformatted HTML/text content of the original job posting – invaluable for advanced Natural Language Processing (NLP) and deeper textual analysis.
   • posted_at: The precise date and time the job was published, enabling trend analysis over daily or weekly periods.
   • employment_type: Clarifies the nature of the role (e.g., "Full-time," "Part-time," "Contract," "Temporary").
   • url: The direct link back to the original job posting on CareerBuilder, allowing for contextual validation or deeper exploration.

2. Compensation & Professional Experience:

   • salary: Numeric ranges or discrete values indicating the compensation offered, crucial for salary benchmarking and compensation strategy.
   • experience: Specifies the level of professional experience required (e.g., "Entry-level," "Mid-senior level," "Executive").

3. Organizational & Sector Context:

   • company: The name of the employer, essential for company-specific analysis, competitive intelligence, and brand reputation studies.
   • domain: Categorizes the job within broader industry sectors or functional areas, facilitating industry-specific talent analysis.

4. Skills & Educational Requirements:

   • skills: A rich collection of keywords, phrases, or structured tags representing the specific technical, soft, or industry-specific skills sought by employers. Ideal for identifying skill gaps and emerging skill demands.
   • education: Outlines the minimum or preferred educational qualifications (e.g., "Bachelor's Degree," "Master's Degree," "High School Diploma").

5. Precise Geographic & Location Data:

   • country: Specifies the country (United States for this dataset).
   • region: The state or province where the job is located.
   • locality: The city or town of the job.
   • address: The specific street address of the workplace (if provided), enabling highly localized analysis.
   • location: A more generalized location string often provided by the job board.
   • postalcode: The exact postal code, allowing for granular geographic clustering and demographic overlay.
   • latitude & longitude: Geospatial coordinates for precise mapping, heatmaps, and proximity analysis.

6. Crawling Metadata:

   • crawled_at: The exact timestamp when each individual record was acquired, vital for understanding data freshness and chronological analysis of changes.

Expanded Use Cases & Analytical Applications:

This comprehensive dataset empowers a wide array of research and commercial applications:

• Deep Labor Market Trend Analysis:

  • Identify the most in-demand job titles, skills, and educational backgrounds across different US regions and industries in 2021.
  • Analyze month-over-month or quarter-over-quarter hiring trends to understand recovery patterns or shifts in specific sectors post-pandemic.
  • Spot emerging job roles or skill combinations that gained prominence during the dataset's period.
  • Assess the volume of remote vs. in-person job postings and their distribution.

• Strategic Talent Acquisition & HR Analytics:

  • Benchmark job requirements, salary ranges, and desired experience levels against market averages for specific roles.
  • Optimize job descriptions by identifying common keywords and phrases used by top employers for similar positions.
  • Understand the competitive landscape for talent in specific geographic areas or specialized skill sets.
  • Develop data-driven recruitment strategies by identifying where and how competitors are hiring.

• Compensation & Benefits Research:

  • Conduct detailed salary analysis broken down by job title, industry, location (state, city, even postal code), experience level, and required skills.
  • Identify potential salary premiums or discrepancies for niche skills or hard-to-fill roles.
  • Support robust compensation planning and negotiation strategies.

• Educational & Workforce Development Planning:

  • Universities and vocational schools can align curriculum with real-world employer demand by analyzing required skills and education fields.
  • Government agencies can identify areas for workforce retraining or development programs based on skill gaps revealed in job postings.
  • Career counselors can advise job seekers on in-demand skills and promising career paths.

• Economic Research & Forecasting:

  • Economists can use the volume and nature of job postings as a leading indicator for economic activity and regional growth.
  • Analyze the impact of economic policies or global events on specific industries' hiring patterns.
  • Study labor mobility and migration patterns based on job locations.

• Competitive Intelligence for Businesses:

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The Findex database provides indicators on topics such as account ownership, payments, saving, credit, and financial resilience. Findex data is reported for all indicators by country, region, and income group. Data is also included summarized by Gender, Income (adults living in the richest 60% and poorest 40% of households), Labor Force Participation (adults in and out of the workforce), Age (young and older adults), and Rural and Urban residence. Available indicators are reported for 2024, 2021, 2017, 2014, and 2011.

For further details, please refer to https://www.worldbank.org/en/publication/globalfindex/report

Description

This comprehensive dataset provides a wealth of information about all countries worldwide, covering a wide range of indicators and attributes. It encompasses demographic statistics, economic indicators, environmental factors, healthcare metrics, education statistics, and much more. With every country represented, this dataset offers a complete global perspective on various aspects of nations, enabling in-depth analyses and cross-country comparisons.

Key Features

Country: Name of the country.

Density (P/Km2): Population density measured in persons per square kilometer.

Abbreviation: Abbreviation or code representing the country.

Agricultural Land (%): Percentage of land area used for agricultural purposes.

Land Area (Km2): Total land area of the country in square kilometers.

Armed Forces Size: Size of the armed forces in the country.

Birth Rate: Number of births per 1,000 population per year.

Calling Code: International calling code for the country.

Capital/Major City: Name of the capital or major city.

CO2 Emissions: Carbon dioxide emissions in tons.

CPI: Consumer Price Index, a measure of inflation and purchasing power.

CPI Change (%): Percentage change in the Consumer Price Index compared to the previous year.

Currency_Code: Currency code used in the country.

Fertility Rate: Average number of children born to a woman during her lifetime.

Forested Area (%): Percentage of land area covered by forests.

Gasoline_Price: Price of gasoline per liter in local currency.

GDP: Gross Domestic Product, the total value of goods and services produced in the country.

Gross Primary Education Enrollment (%): Gross enrollment ratio for primary education.

Gross Tertiary Education Enrollment (%): Gross enrollment ratio for tertiary education.

Infant Mortality: Number of deaths per 1,000 live births before reaching one year of age.

Largest City: Name of the country's largest city.

Life Expectancy: Average number of years a newborn is expected to live.

Maternal Mortality Ratio: Number of maternal deaths per 100,000 live births.

Minimum Wage: Minimum wage level in local currency.

Official Language: Official language(s) spoken in the country.

Out of Pocket Health Expenditure (%): Percentage of total health expenditure paid out-of-pocket by individuals.

Physicians per Thousand: Number of physicians per thousand people.

Population: Total population of the country.

Population: Labor Force Participation (%): Percentage of the population that is part of the labor force.

Tax Revenue (%): Tax revenue as a percentage of GDP.

Total Tax Rate: Overall tax burden as a percentage of commercial profits.

Unemployment Rate: Percentage of the labor force that is unemployed.

Urban Population: Percentage of the population living in urban areas.

Latitude: Latitude coordinate of the country's location.

Longitude: Longitude coordinate of the country's location.

Potential Use Cases

Analyze population density and land area to study spatial distribution patterns.

Investigate the relationship between agricultural land and food security.

Examine carbon dioxide emissions and their impact on climate change.

Explore correlations between economic indicators such as GDP and various socio-economic factors.

Investigate educational enrollment rates and their implications for human capital development.

Analyze healthcare metrics such as infant mortality and life expectancy to assess overall well-being.

Study labor market dynamics through indicators such as labor force participation and unemployment rates.

Investigate the role of taxation and its impact on economic development.

Explore urbanization trends and their social and environmental consequences.

Success.ai offers a comprehensive, enterprise-ready B2B leads data solution, ideal for businesses seeking access to over 150 million verified employee profiles and 170 million work emails. Our data empowers organizations across industries to target key decision-makers, optimize recruitment, and fuel B2B marketing efforts. Whether you're looking for UK B2B data, B2B marketing data, or global B2B contact data, Success.ai provides the insights you need with pinpoint accuracy.

Tailored for B2B Sales, Marketing, Recruitment and more: Our B2B contact data and B2B email data solutions are designed to enhance your lead generation, sales, and recruitment efforts. Build hyper-targeted lists based on job title, industry, seniority, and geographic location. Whether you’re reaching mid-level professionals or C-suite executives, Success.ai delivers the data you need to connect with the right people.

Key Categories Served: B2B sales leads – Identify decision-makers in key industries, B2B marketing data – Target professionals for your marketing campaigns, Recruitment data – Source top talent efficiently and reduce hiring times, CRM enrichment – Update and enhance your CRM with verified, updated data, Global reach – Coverage across 195 countries, including the United States, United Kingdom, Germany, India, Singapore, and more.

Global Coverage with Real-Time Accuracy: Success.ai’s dataset spans a wide range of industries such as technology, finance, healthcare, and manufacturing. With continuous real-time updates, your team can rely on the most accurate data available: 150M+ Employee Profiles: Access professional profiles worldwide with insights including full name, job title, seniority, and industry. 170M Verified Work Emails: Reach decision-makers directly with verified work emails, available across industries and geographies, including Singapore and UK B2B data. GDPR-Compliant: Our data is fully compliant with GDPR and other global privacy regulations, ensuring safe and legal use of B2B marketing data.

Key Data Points for Every Employee Profile: Every profile in Success.ai’s database includes over 20 critical data points, providing the information needed to power B2B sales and marketing campaigns: Full Name, Job Title, Company, Work Email, Location, Phone Number, LinkedIn Profile, Experience, Education, Technographic Data, Languages, Certifications, Industry, Publications & Awards.

Use Cases Across Industries: Success.ai’s B2B data solution is incredibly versatile and can support various enterprise use cases, including: B2B Marketing Campaigns: Reach high-value professionals in industries such as technology, finance, and healthcare. Enterprise Sales Outreach: Build targeted B2B contact lists to improve sales efforts and increase conversions. Talent Acquisition: Accelerate hiring by sourcing top talent with accurate and updated employee data, filtered by job title, industry, and location. Market Research: Gain insights into employment trends and company profiles to enrich market research. CRM Data Enrichment: Ensure your CRM stays accurate by integrating updated B2B contact data. Event Targeting: Create lists for webinars, conferences, and product launches by targeting professionals in key industries.

Use Cases for Success.ai's Contact Data - Targeted B2B Marketing: Create precise campaigns by targeting key professionals in industries like tech and finance. - Sales Outreach: Build focused sales lists of decision-makers and C-suite executives for faster deal cycles. - Recruiting Top Talent: Easily find and hire qualified professionals with updated employee profiles. - CRM Enrichment: Keep your CRM current with verified, accurate employee data. - Event Targeting: Create attendee lists for events by targeting relevant professionals in key sectors. - Market Research: Gain insights into employment trends and company profiles for better business decisions. - Executive Search: Source senior executives and leaders for headhunting and recruitment. - Partnership Building: Find the right companies and key people to develop strategic partnerships.

Why Choose Success.ai’s Employee Data? Success.ai is the top choice for enterprises looking for comprehensive and affordable B2B data solutions. Here’s why: Unmatched Accuracy: Our AI-powered validation process ensures 99% accuracy across all data points, resulting in higher engagement and fewer bounces. Global Scale: With 150M+ employee profiles and 170M verified work emails, Success.ai provides extensive coverage for UK B2B data, B2B marketing data, and global contacts. Competitive Pricing: We offer the most competitive rates on the market, undercutting major competitors like Lusha, Cognism, and ZoomInfo. Tailored Solutions: Our white-glove service ensures we deliver exactly what you need, in the format that suits your workflow (CSV, Excel, etc.). Real-Time Updates: Our data is continuously updated, so you always have the latest information, unlike static da...

BI: Children in Employment: Study and Work: Male: % of Male Children in Employment: Aged 7-14 data was reported at 81.955 % in 2010. This records an increase from the previous number of 55.378 % for 2000. BI: Children in Employment: Study and Work: Male: % of Male Children in Employment: Aged 7-14 data is updated yearly, averaging 68.667 % from Dec 2000 (Median) to 2010, with 2 observations. The data reached an all-time high of 81.955 % in 2010 and a record low of 55.378 % in 2000. BI: Children in Employment: Study and Work: Male: % of Male Children in Employment: Aged 7-14 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Labour Force. Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Study and work refer to children attending school in combination with economic activity.;Understanding Children's Work project based on data from ILO, UNICEF and the World Bank.;;

Success.ai’s Commercial Real Estate Data and B2B Contact Data for Global Real Estate Professionals is a comprehensive dataset designed to connect businesses with industry leaders in real estate worldwide. With over 170M verified profiles, including work emails and direct phone numbers, this solution ensures precise outreach to agents, brokers, property developers, and key decision-makers in the real estate sector.

Utilizing advanced AI-driven validation, our data is continuously updated to maintain 99% accuracy, offering actionable insights that empower targeted marketing, streamlined sales strategies, and efficient recruitment efforts. Whether you’re engaging with top real estate executives or sourcing local property experts, Success.ai provides reliable and compliant data tailored to your needs.

Key Features of Success.ai’s Real Estate Professional Contact Data

• Comprehensive Industry Coverage Gain direct access to verified profiles of real estate professionals across the globe, including:

1. Real Estate Agents: Professionals facilitating property sales and purchases.
2. Brokers: Key intermediaries managing transactions between buyers and sellers.
3. Property Developers: Decision-makers shaping residential, commercial, and industrial projects.
4. Real Estate Executives: Leaders overseeing multi-regional operations and business strategies.
5. Architects & Consultants: Experts driving design and project feasibility.

• Verified and Continuously Updated Data

AI-Powered Validation: All profiles are verified using cutting-edge AI to ensure up-to-date accuracy. Real-Time Updates: Our database is refreshed continuously to reflect the most current information. Global Compliance: Fully aligned with GDPR, CCPA, and other regional regulations for ethical data use.

• Customizable Data Delivery Tailor your data access to align with your operational goals:

API Integration: Directly integrate data into your CRM or project management systems for seamless workflows. Custom Flat Files: Receive detailed datasets customized to your specifications, ready for immediate application.

Why Choose Success.ai for Real Estate Contact Data?

• Best Price Guarantee Enjoy competitive pricing that delivers exceptional value for verified, comprehensive contact data.

• Precision Targeting for Real Estate Professionals Our dataset equips you to connect directly with real estate decision-makers, minimizing misdirected efforts and improving ROI.

• Strategic Use Cases

  Lead Generation: Target qualified real estate agents and brokers to expand your network. Sales Outreach: Engage with property developers and executives to close high-value deals. Marketing Campaigns: Drive targeted campaigns tailored to real estate markets and demographics. Recruitment: Identify and attract top talent in real estate for your growing team. Market Research: Access firmographic and demographic data for in-depth industry analysis.

• Data Highlights 170M+ Verified Professional Profiles 50M Work Emails 30M Company Profiles 700M Global Professional Profiles

• Powerful APIs for Enhanced Functionality

  Enrichment API Ensure your contact database remains relevant and up-to-date with real-time enrichment. Ideal for businesses seeking to maintain competitive agility in dynamic markets.

Lead Generation API Boost your lead generation with verified contact details for real estate professionals, supporting up to 860,000 API calls per day for robust scalability.

• Use Cases for Real Estate Contact Data

1. Targeted Outreach for New Projects Connect with property developers and brokers to pitch your services or collaborate on upcoming projects.

2. Real Estate Marketing Campaigns Execute personalized marketing campaigns targeting agents and clients in residential, commercial, or industrial sectors.

3. Enhanced Sales Strategies Shorten sales cycles by directly engaging with decision-makers and key stakeholders.

4. Recruitment and Talent Acquisition Access profiles of highly skilled professionals to strengthen your real estate team.

5. Market Analysis and Intelligence Leverage firmographic and demographic insights to identify trends and optimize business strategies.

• What Makes Us Stand Out? >> Unmatched Data Accuracy: Our AI-driven validation ensures 99% accuracy for all contact details. >> Comprehensive Global Reach: Covering professionals across diverse real estate markets worldwide. >> Flexible Delivery Options: Access data in formats that seamlessly fit your existing systems. >> Ethical and Compliant Data Practices: Adherence to global standards for secure and responsible data use.

Success.ai’s B2B Contact Data for Global Real Estate Professionals delivers the tools you need to connect with the right people at the right time, driving efficiency and success in your business operations. From agents and brokers to property developers and executiv...

General description

SAPFLUXNET contains a global database of sap flow and environmental data, together with metadata at different levels.
SAPFLUXNET is a harmonised database, compiled from contributions from researchers worldwide. This version (0.1.4) contains more than 200 datasets, from all over the World, covering a broad range of bioclimatic conditions.
More information on the coverage can be found here: http://sapfluxnet.creaf.cat/shiny/sfn_progress_dashboard/.

The SAPFLUXNET project has been developed by researchers at CREAF and other institutions (http://sapfluxnet.creaf.cat/#team), coordinated by Rafael Poyatos (CREAF, http://www.creaf.cat/staff/rafael-poyatos-lopez), and funded by two Spanish Young Researcher's Grants (SAPFLUXNET, CGL2014-55883-JIN; DATAFORUSE, RTI2018-095297-J-I00 ) and an Alexander von Humboldt Research Fellowship for Experienced Researchers).

Variables and units

SAPFLUXNET contains whole-plant sap flow and environmental variables at sub-daily temporal resolution. Both sap flow and environmental time series have accompanying flags in a data frame, one for sap flow and another for environmental
variables. These flags store quality issues detected during the quality control process and can be used to add further quality flags.

Metadata contain relevant variables informing about site conditions, stand characteristics, tree and species attributes, sap flow methodology and details on environmental measurements. To learn more about variables, units and data flags please use the functionalities implemented in the sapfluxnetr package (https://github.com/sapfluxnet/sapfluxnetr). In particular, have a look at the package vignettes using R:

# remotes::install_github(
#  'sapfluxnet/sapfluxnetr',
#  build_opts = c("--no-resave-data", "--no-manual", "--build-vignettes")
# )
library(sapfluxnetr)
# to list all vignettes
vignette(package='sapfluxnetr')
# variables and units
vignette('metadata-and-data-units', package='sapfluxnetr')
# data flags
vignette('data-flags', package='sapfluxnetr')

Data formats

SAPFLUXNET data can be found in two formats: 1) RData files belonging to the custom-built 'sfn_data' class and 2) Text files in .csv format. We recommend using the sfn_data objects together with the sapfluxnetr package, although we also provide the text files for convenience. For each dataset, text files are structured in the same way as the slots of sfn_data objects; if working with text files, we recommend that you check the data structure of 'sfn_data' objects in the corresponding vignette.

Working with sfn_data files

To work with SAPFLUXNET data, first they have to be downloaded from Zenodo, maintaining the folder structure. A first level in the folder hierarchy corresponds to file format, either RData files or csv's. A second level corresponds to how sap flow is expressed: per plant, per sapwood area or per leaf area. Please note that interconversions among the magnitudes have been performed whenever possible. Below this level, data have been organised per dataset. In the case of RData files, each dataset is contained in a sfn_data object, which stores all data and metadata in different slots (see the vignette 'sfn-data-classes'). In the case of csv files, each dataset has 9 individual files, corresponding to metadata (5), sap flow and environmental data (2) and their corresponding data flags (2).

After downloading the entire database, the sapfluxnetr package can be used to:
- Work with data from a single site: data access, plotting and time aggregation.
- Select the subset datasets to work with.
- Work with data from multiple sites: data access, plotting and time aggregation.

Please check the following package vignettes to learn more about how to work with sfn_data files:

Quick guide

Metadata and data units

sfn_data classes

Custom aggregation

Memory and parallelization

Working with text files

We recommend to work with sfn_data objects using R and the sapfluxnetr package and we do not currently provide code to work with text files.

Data issues and reporting

Please report any issue you may find in the database by sending us an email: sapfluxnet@creaf.uab.cat.

Temporary data fixes, detected but not yet included in released versions will be published in SAPFLUXNET main web page ('Known data errors').

Data access, use and citation

This version of the SAPFLUXNET database is open access. We are working on a data paper describing the database, but, before its publication, please cite this Zenodo entry if SAPFLUXNET is used in any publication.

Most refugees face significant legal and practical barriers to full economic inclusion in the labor markets of their host countries. While these barriers are widely discussed in general terms, a systematic, public documentation of these barriers is important to advance the efforts toward economic inclusion. In the 2022 Global Refugee Work Rights Report, we examine different dimensions of work rights both in law (de jure) and in practice (de facto) across 51 countries that were collectively hosting 87 percent of the world’s refugee population at the end of 2021. Our de facto findings are based on a survey of practitioners in the 51 refugee-hosting countries, as well as supplemental desk research. We find that at least 62 percent of refugees live in countries where the legal framework for work rights is adequate or better. Yet many of these laws are not widely implemented: at least 55 percent of refugees live in a country that significantly restricts their work rights in practice. Countries were also scored on 17 specific questions regarding wage employment, self-employment, mobility, and access to services, in most cases relative to citizens’ access. All of these variables are included in the dataset, and additional findings are documented in the report. The methodology section of the report contains a detailed description of the scoring and definitions. Annex 3 of the report contains the full questionnaire. Please see https://www.refugeeworkrights.org/ to download a .csv file of the dataset.

BI: Children in Employment: Work Only: Male: % of Male Children in Employment: Aged 7-14 data was reported at 18.045 % in 2010. This records a decrease from the previous number of 44.622 % for 2000. BI: Children in Employment: Work Only: Male: % of Male Children in Employment: Aged 7-14 data is updated yearly, averaging 31.333 % from Dec 2000 (Median) to 2010, with 2 observations. The data reached an all-time high of 44.622 % in 2000 and a record low of 18.045 % in 2010. BI: Children in Employment: Work Only: Male: % of Male Children in Employment: Aged 7-14 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Burundi – Table BI.World Bank.WDI: Labour Force. Children in employment refer to children involved in economic activity for at least one hour in the reference week of the survey. Work only refers to children involved in economic activity and not attending school.;Understanding Children's Work project based on data from ILO, UNICEF and the World Bank.;;

The RRING Work Package 3 (WP3) objective was to clarify how Research Funding Organisations (RFOs) and Research Performing Organisations (RPOs) operated within region-specific research and innovation environments. It explored how they navigated the governance and regulatory frameworks for Responsible Research and Innovation (RRI), as well as offering their perspectives on the entities responsible for RRI-related policy and action in their locales.

This data set covers the global survey research part, which was designed to contextualise how RPOs and RFOs interacted within the research environment and with non-academic stakeholders. Countries were grouped according to the UNESCO regions of the world and key results per region are listed below. For a detailed analysis and further findings of the work completed under WP3 of the RRING project, please refer to the full deliverable document "State of the Art of RRI in the Five UNESCO World Regions" [link to be inserted].

European and North American States

‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of ensuring ethical principles were applied in R&I (92%), followed by diverse perspectives (88%), and gender equality (79%). Including ethnic minorities was the area which garnered the least attitudinal support (71%). Respondents took the most practical steps towards engaging with diverse perspectives (63%), and the least towards inclusion of ethnic minorities (24%).

‘Anticipative and reflective’: Respondents widely agreed (82%) with the importance of ensuring R&I work does not cause concerns for society, but only 37% confirmed they had taken practical steps to ensure this.

‘Open and transparent’: Vast majorities of respondents agreed on the importance of keeping R&I methods open and transparent (94%), with 65% also confirming they take practical steps to do this. An equally high number agreed on the importance of making the results of R&I work accessible to as wide a public as possible (94%), and 68% confirmed this through their reported actions. This indicated the smallest value-action gap of all RRI measures for respondents from European and North American countries. Attitudinal agreement on the importance of making data freely available to the public was lower (83%), as was the practical action aspect for this measure (45%).

‘Responsive and adaptive to change’: Most respondents agreed (89%) that it was important to ensure their work addresses societal needs, and 62% confirmed that they take practical steps towards this aim.

Latin American and Caribbean States

‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of gender equality in R&I (86%), followed by ensuring ethical principles are applied (85%), and diverse perspectives incorporated (83%). Including ethnic minorities was the area which garnered the least attitudinal support (77%). Respondents took the most practical steps towards ensuring ethical principles guide their work (50%), and the least towards including ethnic minorities (25%), but the smallest value action gap was found for gender equality.

‘Anticipative and reflective’: Respondents agreed (79%) that it is important to ensure R&I work does not cause concerns for society, but only 29% confirmed they had taken practical steps to ensure this.

‘Open and transparent’: The majority of respondents agreed on the importance of keeping R&I methods open and transparent (89%), with 45% indicating they had taken practical action. A majority also agreed on the importance of making the results of R&I work accessible to as wide a public as possible (88%), and 44% backed this up with practical action. Attitudinal agreement on the importance of making data freely available to the public was slightly lower (81%), as was the practical action aspect for this measure (35%).

‘Responsive and adaptive to change’: Most respondents agreed (84%) that it was important to ensure their work addresses societal needs, and 49% confirmed that they take practical steps towards this aim.

Asian and Pacific States

‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of ensuring ethical principles were applied in R&I (90%), followed by diverse perspectives (89%), and gender equality (86%). Including ethnic minorities was the area which garnered the least attitudinal support (76%). Respondents took the most practical steps towards engaging with diverse perspectives (65%), and the least towards including ethnic minorities (30%).

‘Anticipative and reflective’: Respondents widely agreed (78%) with the importance of ensuring R&I work does not cause concerns for society, and 42% confirmed they had taken practical steps to ensure this.

‘Open and transparent’: The majority of respondents agreed on the importance of keeping R&I methods open and transparent (91%), with 58% indicating they take practical steps to do this. A majority also agreed on the importance of making the results of R&I work accessible to as wide a public as possible (89%), and 64% backed this up with practical action. Attitudinal agreement on the importance of making data freely available to the public was lower (79%), as was the practical action aspect for this measure (40%).

‘Responsive and adaptive to change’: Most respondents agreed (92%) that it was important to ensure their work addresses societal needs, and 69% confirmed that they take practical steps towards this aim. This was the RRI measure with the smallest valueaction gap for respondents from the Asian and Pacific region.

Arab States

‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of ensuring ethical principles were applied in R&I (93%), followed by diverse perspectives (81%), and gender equality (85%). Including ethnic minorities was the area which garnered the least attitudinal support (74%). Respondents took the most practical steps towards engaging with diverse perspectives (66%), which equated to one of two equally small value-action gaps for respondents from Arab states, and the least practical steps towards inclusion of ethnic minorities (22%).

‘Anticipative and reflective’: A high proportion of respondents (85%) agreed that it is important to ensure R&I work does not cause concerns for society. However, only 38% confirmed they had taken practical steps to ensure this.

‘Open and transparent’: The majority of respondents agreed on the importance of keeping R&I methods open and transparent (89%), with 59% also confirming they take practical steps to do this. A majority also agreed on the importance of making the results of R&I work accessible to as wide a public as possible (90%), and 66% backed this up with practical action. Ensuring public accessibility of research results was the second of two measures with equally small value-action gaps. Attitudinal agreement on the importance of making data freely available to the public was much lower (78%), which also reflected the practical action aspect for this measure (49%).

‘Responsive and adaptive to change’: Most respondents agreed (96%) that it was important to ensure their work addresses societal needs, and 68% confirmed that they take practical steps to achieve this.

African States

‘Diverse and inclusive': Respondents were most attitudinally supportive of the importance of ensuring engagement with diverse perspectives and expertise in R&I (91%), followed by ensuring ethical principles are applied (90%), and gender equality (89%). Including ethnic minorities was the area which garnered the least attitudinal support (74%). Respondents took the most practical steps towards ensuring ethical principles guide their work (57%), and the least towards including ethnic minorities (32%).

‘Anticipative and reflective’: The majority of respondents (85%) agreed that it is important to ensure R&I work does not cause concerns for society, with 59% confirming that they take practical steps to ensure this.

‘Open and transparent’: A high proportion of respondents agreed on the importance of keeping R&I methods open and transparent (90%), with 54% also confirming they take practical steps to do this. A majority also agreed on the importance of making the results of R&I work accessible to as wide a public as possible (86%), and 56% backed this up with practical action. Attitudinal agreement on the importance of making data freely available to the public was significantly lower (73%), as was the practical action aspect for this measure (38%).

‘Responsive and adaptive to change’: Respondents mostly agreed (92%) that it was important to ensure their work addresses societal needs, and 64% confirmed that they take practical steps towards this aim. This was the RRI measure with the smallest valueaction gap for respondents from African states.

Note: Please refer to the "RRING WP3 - Survey Data Documentation" document for detailed instructions on how to use this dataset.

This dataset is about book series. It has 1 row and is filtered where the books is Global mindedness in international social work practice. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

Any work using this dataset should cite the following paper: