Graph and download economic data for Employed full time: Wage and salary workers: Data entry keyers occupations: 16 years and over: Women (LEU0254716600A) from 2000 to 2024 about occupation, females, full-time, salaries, workers, 16 years +, wages, employment, and USA.

United States - Employed full time: Wage and salary workers: Data entry keyers occupations: 16 years and over: Men was 49.00000 Thous. of Persons in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Wage and salary workers: Data entry keyers occupations: 16 years and over: Men reached a record high of 95.00000 in January of 2000 and a record low of 46.00000 in January of 2013. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Wage and salary workers: Data entry keyers occupations: 16 years and over: Men - last updated from the United States Federal Reserve on July of 2025.

This Dataset indicates average salary by position title and grade for full-time regular employees. Data excludes elected, appointed, non-merit and temporary employees. Underfilled positions are also excluded from the dataset. Update Frequency : Annually

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over: Women (LEU0254770000A) from 2000 to 2024 about second quartile, occupation, females, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

Graph and download economic data for Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over (LEU0254556400A) from 2000 to 2024 about second quartile, occupation, full-time, salaries, workers, earnings, 16 years +, wages, median, employment, and USA.

United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over was 923.00000 $ in January of 2024, according to the United States Federal Reserve. Historically, United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over reached a record high of 923.00000 in January of 2024 and a record low of 437.00000 in January of 2000. Trading Economics provides the current actual value, an historical data chart and related indicators for United States - Employed full time: Median usual weekly nominal earnings (second quartile): Wage and salary workers: Data entry keyers occupations: 16 years and over - last updated from the United States Federal Reserve on July of 2025.

Unlock valuable salary insights with our comprehensive Salary Dataset, designed for businesses, recruiters, and job seekers to analyze compensation trends, workforce planning, and market competitiveness.

Dataset Features

Job Listings & Salaries: Access structured salary data from top job platforms, including job titles, company names, locations, salary ranges, and compensation types. Employer & Industry Insights: Extract company-specific salary trends, industry benchmarks, and hiring patterns. Geographic Pay Disparities: Compare salaries across different regions, cities, and countries to identify location-based compensation trends. Job Market Trends: Monitor salary fluctuations, demand for specific roles, and hiring trends over time.

Customizable Subsets for Specific Needs Our Salary Dataset is fully customizable, allowing you to filter data based on job titles, industries, locations, experience levels, and salary ranges. Whether you need broad market insights or focused data for recruitment strategy, we tailor the dataset to your needs.

Popular Use Cases

Workforce Planning & Talent Acquisition: Optimize hiring strategies by analyzing salary benchmarks and compensation trends. Market Research & Competitive Intelligence: Compare salaries across industries and competitors to stay ahead in talent acquisition. Career Decision-Making: Help job seekers evaluate salary expectations and identify high-paying opportunities. AI & Predictive Analytics: Use structured salary data to train AI models for job market forecasting and compensation analysis. Geographic Expansion & Business Strategy: Assess salary variations across regions to plan business expansions and remote workforce strategies.

Whether you're optimizing recruitment, analyzing salary trends, or making data-driven career decisions, our Salary Dataset provides the structured data you need. Get started today and customize your dataset to fit your business objectives.

The wages on the Job Bank website are specific to an occupation and provide information on the earnings of workers at the regional level. Wages for most occupations are also provided at the national and provincial level. In Canada, all jobs are associated with one specific occupational grouping which is determined by the National Occupational Classification. For most occupations, a minimum, median and maximum wage estimates are displayed. They are update annually. If you have comments or questions regarding the wage information, please contact the Labour Market Information Division at: NC-LMI-IMT-GD@hrsdc-rhdcc.gc.ca

Dataset Card for Data Science Job Salaries

  Dataset Summary





  Content

Column Description

work_year The year the salary was paid.

experience_level The experience level in the job during the year with the following possible values: EN Entry-level / Junior MI Mid-level / Intermediate SE Senior-level / Expert EX Executive-level / Director

employment_type The type of employement for the role: PT Part-time FT Full-time CT Contract FL Freelance

job_title… See the full description on the dataset page: https://huggingface.co/datasets/hugginglearners/data-science-job-salaries.

Data Entry Outsourcing Service Market was valued at USD 1651.28 Million in 2023 and is projected to reach USD 2515.82 Million by 2030, growing at a CAGR of 6.3% during the forecast period 2024-2030.

Global Data Entry Outsourcing Service Market Drivers

The market drivers for the Data Entry Outsourcing Service Market can be influenced by various factors. These may include:

Cost-Effectiveness: Hiring outside service providers to handle data entry work can drastically save operating expenses. This includes cost reductions on infrastructure, perks, and salaries—all of which are especially advantageous for small and medium-sized businesses. Concentrate on Core Competencies: Businesses can increase overall efficiency and productivity by outsourcing data entry services and concentrating more on their core competencies, which include strategic planning, product development, and customer service. Access to Skilled Workforce: Data entry jobs are the area in which outsourcing offers access to a knowledgeable and experienced workforce. When compared to doing these jobs internally, this can result in higher accuracy and faster turnaround times. Technological Advancements: By increasing efficiency and lowering the risk of error, the incorporation of cutting-edge technology like automation, artificial intelligence, and machine learning in data entry procedures makes outsourcing more alluring. Scalability: Depending on the demands of the business, outsourcing provides the freedom to scale up or down operations. For organizations with varying workloads or seasonal demands, this is especially helpful. Data Security and Compliance: Reputable outsourcing companies guarantee the confidentiality and integrity of sensitive data by adhering to international data protection rules and implementing strong security measures. Globalization and Business Expansion: Effective data management becomes more and more important as firms grow internationally. Businesses can effectively handle massive volumes of data from multiple locations by outsourcing data entry services. Increased Turnaround Time: Since outsourcing companies frequently work in different time zones, continuous workflow and speedier data entering task processing are possible, which can increase overall business efficiency.

🧠 About the Data

This dataset explores how remote work opportunities intersect with salaries, experience, and employment types across industries. It contains clean, structured records of 500 hypothetical employees in remote or hybrid job roles, suitable for salary modeling, HR analytics, or industry-based salary insights.

📌 Column Descriptions

📈 Potential Use Cases

• Predictive modeling for salary based on role, experience, and location
• Salary benchmarking per industry or employment type
• Visualizing remote vs onsite salary disparities
• Market research for HR and hiring trends
• Exploratory analysis on global employment models

Real-time API access to rich Job Postings Data with 200M+ job postings & Salary Data sourced from Google for Jobs - global aggregate of LinkedIn, Indeed, Glassdoor, ZipRecruiter, and all public job sites across the web.

PredictLeads Job Openings Data provides high-quality hiring insights sourced directly from company websites - not job boards. By leveraging advanced web scraping technology, this dataset delivers access to job market trends, salary insights, and in-demand skills. A valuable resource for B2B sales, recruiting, investment analysis, and competitive intelligence, this data helps businesses stay ahead in a dynamic job market.

Key Features:

✅ 214M+ Job Postings Tracked – Data sourced from 92 company websites worldwide. ✅ 7M+ Active Job Openings – Continuously updated to reflect real hiring demand. ✅ Salary & Compensation Insights – Extract salary ranges, contract types, and job seniority levels. ✅ Technology & Skill Tracking – Identify emerging tech trends and industry demands. ✅ Company Data Enrichment – Link job postings to employer domains, firmographics, and growth signals. ✅ Web Scraping Precision – Directly sourced from employer websites for unmatched accuracy.

Primary Attributes in the Dataset:

General Information: - id (UUID) – Unique identifier for the job posting. - type (constant: "job_opening") – Object type. - title (string) – Job title. - description (string) – Full job description extracted from the job listing. - url (URL) – Direct link to the job posting. - first_seen_at (ISO 8601 date-time) – When the job was first detected. - last_seen_at (ISO 8601 date-time) – When the job was last observed. - last_processed_at (ISO 8601 date-time) – When the job data was last updated.

Job Metadata:

• contract_types (array of strings) – Employment type (full-time, part-time, contract).
• categories (array of strings) – Job industry categories (engineering, marketing, finance).
• seniority (string) – Seniority level (manager, non_manager).
• status (string) – Job status (open, closed).
• language (string) – Language of the job posting.

Location Data:

• location (string) – Full location details from the job description.
• location_data (array of objects) – Structured location details: -- city (string, nullable) – City where the job is located. -- state (string, nullable) – State or region. -- zip_code (string, nullable) – Postal/ZIP code. -- country (string, nullable) – Country. -- region (string, nullable) – Broader geographical region. -- continent (string, nullable) – Continent name. -- fuzzy_match (boolean) – Indicates if the location was inferred.

Salary Data:

• salary (string) – Salary range extracted from the job listing.
• salary_low (float, nullable) – Minimum salary in original currency.
• salary_high (float, nullable) – Maximum salary in original currency.
• salary_currency (string, nullable) – Salary currency (USD, EUR, GBP).
• salary_low_usd (float, nullable) – Minimum salary converted to USD.
• salary_high_usd (float, nullable) – Maximum salary converted to USD.
• salary_time_unit (string, nullable) – Time unit (year, month, hour).

Occupational Data (ONET):

• code (string, nullable) – ONET occupation code.
• family (string, nullable) – Broad occupational family (Computer and Mathematical).
• occupation_name (string, nullable) – Official ONET occupation title.

Additional Attributes:

• tags (array of strings, nullable) – Extracted skills and keywords (Python, JavaScript, AI).

📌 Trusted by enterprises, recruiters, and investors for high-precision job market insights.

PredictLeads Job Openings Docs https://docs.predictleads.com/v3/guide/job_openings_dataset

Number of job vacancies and payroll employees, job vacancy rate, and average offered hourly wage by economic region, last 5 quarters.

Data-entry Outsourcing Services Market Outlook

The global data-entry outsourcing services market size was valued at approximately USD 15.2 billion in 2023 and is forecasted to reach USD 26.3 billion by 2032, growing at a compound annual growth rate (CAGR) of 6.4% during the forecast period. This growth is propelled by the increasing demand for cost-effective data management solutions and the need for enhanced focus on core business activities.

The significant growth driver for this market is the rising adoption of digital transformation initiatives across various industries. As businesses endeavor to become more agile and competitive, they are increasingly seeking outsourced data-entry services to streamline their operations. By outsourcing data-entry tasks, organizations can allocate more resources to strategic functions, thereby enhancing overall productivity and efficiency. This trend is particularly noticeable in sectors such as BFSI and healthcare, where large volumes of data need to be processed accurately and promptly.

Technological advancements are another key growth factor for the data-entry outsourcing services market. The integration of Artificial Intelligence (AI) and Machine Learning (ML) in data-entry processes has significantly reduced error rates and improved data accuracy. These technologies enable automated data capture and processing, thereby minimizing manual intervention and associated errors. As AI and ML continue to evolve, their adoption in data-entry processes is expected to rise, further boosting the market growth.

Moreover, the growing emphasis on data security and regulatory compliance is driving the demand for professional data-entry outsourcing services. With stringent data protection regulations such as GDPR and CCPA in place, businesses are compelled to ensure that their data management processes comply with these standards. Outsourcing data-entry tasks to specialized service providers can help organizations mitigate the risks associated with data breaches and non-compliance, thereby safeguarding their reputation and avoiding hefty penalties.

From a regional perspective, Asia Pacific is expected to dominate the data-entry outsourcing services market, followed by North America and Europe. The region's dominance can be attributed to the presence of a large number of outsourcing service providers, coupled with the availability of a skilled workforce at competitive costs. Additionally, the rapid economic growth in countries such as India and China is driving the demand for data-entry services in the region. North America and Europe, on the other hand, are witnessing steady growth due to the increasing adoption of digital transformation initiatives and the need for cost-effective data management solutions.

Service Type Analysis

The service type segment in the data-entry outsourcing services market encompasses various sub-segments, including online data entry, offline data entry, data processing, data conversion, data capture, and others. Each sub-segment serves a distinct purpose, catering to the diverse needs of businesses across different industries.

Online data entry services involve the entry and management of data directly into an online database or system. This sub-segment is gaining popularity due to the increasing use of cloud-based solutions and the need for real-time data access and management. Online data entry services offer the advantage of seamless integration with other business systems, enabling organizations to efficiently manage their data and improve decision-making processes.

Offline data entry services, on the other hand, involve the entry of data into offline systems or databases. Despite the growing adoption of online solutions, offline data entry services continue to hold significant importance, particularly in regions with limited internet connectivity or in industries where data privacy is paramount. These services ensure that organizations can maintain accurate and up-to-date records, even in the absence of an online infrastructure.

Data processing services encompass a wide range of activities, including data cleaning, data validation, data enrichment, and data analysis. These services are crucial for organizations to ensure the accuracy and consistency of their data, which is essential for informed decision-making. The increasing volume of data generated by businesses is driving the demand for data processing services, as organizations seek to derive valuable insights from their data.

Introducing Job Posting Datasets: Uncover labor market insights!

Elevate your recruitment strategies, forecast future labor industry trends, and unearth investment opportunities with Job Posting Datasets.

Job Posting Datasets Source:

1. Indeed: Access datasets from Indeed, a leading employment website known for its comprehensive job listings.

2. Glassdoor: Receive ready-to-use employee reviews, salary ranges, and job openings from Glassdoor.

3. StackShare: Access StackShare datasets to make data-driven technology decisions.

Job Posting Datasets provide meticulously acquired and parsed data, freeing you to focus on analysis. You'll receive clean, structured, ready-to-use job posting data, including job titles, company names, seniority levels, industries, locations, salaries, and employment types.

Choose your preferred dataset delivery options for convenience:

Receive datasets in various formats, including CSV, JSON, and more. Opt for storage solutions such as AWS S3, Google Cloud Storage, and more. Customize data delivery frequencies, whether one-time or per your agreed schedule.

Why Choose Oxylabs Job Posting Datasets:

1. Fresh and accurate data: Access clean and structured job posting datasets collected by our seasoned web scraping professionals, enabling you to dive into analysis.

2. Time and resource savings: Focus on data analysis and your core business objectives while we efficiently handle the data extraction process cost-effectively.

3. Customized solutions: Tailor our approach to your business needs, ensuring your goals are met.

4. Legal compliance: Partner with a trusted leader in ethical data collection. Oxylabs is a founding member of the Ethical Web Data Collection Initiative, aligning with GDPR and CCPA best practices.

Pricing Options:

Standard Datasets: choose from various ready-to-use datasets with standardized data schemas, priced from $1,000/month.

Custom Datasets: Tailor datasets from any public web domain to your unique business needs. Contact our sales team for custom pricing.

Experience a seamless journey with Oxylabs:

• Understanding your data needs: We work closely to understand your business nature and daily operations, defining your unique data requirements.
• Developing a customized solution: Our experts create a custom framework to extract public data using our in-house web scraping infrastructure.
• Delivering data sample: We provide a sample for your feedback on data quality and the entire delivery process.
• Continuous data delivery: We continuously collect public data and deliver custom datasets per the agreed frequency.

Effortlessly access fresh job posting data with Oxylabs Job Posting Datasets.

Job Postings Data for Talent Acquisition, HR Strategy & Market Research Canaria’s Job Postings Data product is a structured, AI-enriched dataset that captures and organizes millions of job listings from leading sources such as Indeed, LinkedIn, and other recruiting platforms. Designed for decision-makers in HR, strategy, and research, this data reveals workforce demand trends, employer activity, and hiring signals across the U.S. labor market and enhanced with advanced enrichment models.

The dataset enables clients to track who is hiring, what roles are being posted, which skills are in demand, where talent is needed geographically, and how compensation and employment structures evolve over time. With field-level normalization and deep enrichment, it transforms noisy job listings into high-resolution labor intelligence—optimized for strategic planning, analytics, and recruiting effectiveness.

Use Cases: What This Job Postings Data Solves This enriched dataset empowers users to analyze workforce activity, employer behavior, and hiring trends across sectors, geographies, and job categories.

Talent Acquisition & HR Strategy • Identify hiring trends by industry, company, function, and geography • Optimize job listings and outreach with enriched skill, title, and seniority data • Detect companies expanding or shifting their workforce focus • Monitor new roles and emerging skills in real time

Labor Market Research & Workforce Planning • Visualize job market activity across cities, states, and ZIP codes • Analyze hiring velocity and job volume changes as macroeconomic signals • Correlate job demand with company size, sector, or compensation structure • Study occupational dynamics using AI-normalized job titles • Use directional signals (job increases/declines) to anticipate market shifts

HR Analytics & Compensation Intelligence • Map salary ranges and benefits offerings by role, location, and level • Track high-demand or hard-to-fill positions for strategic workforce planning • Support compensation planning and headcount forecasting • Feed job title normalization and metadata into internal HRIS systems • Identify talent clusters and location-based hiring inefficiencies

What Makes This Job Postings Data Unique

AI-Based Enrichment at Scale • Extracted attributes include hard skills, soft skills, certifications, and education requirements • Modeled predictions for seniority level, employment type, and remote/on-site classification • Normalized job titles using an internal taxonomy of over 50,000 unique roles • Field-level tagging ensures structured, filterable, and clean outputs

Salary Parsing & Compensation Insights • Parsed salary ranges directly from job descriptions • AI-based salary predictions for postings without explicit compensation • Compensation patterns available by job title, company, and location

Deduplication & Normalization • Achieves approximately 60% deduplication rate through semantic and metadata matching • Normalizes company names, job titles, location formats, and employment attributes • Ready-to-use, analysis-grade dataset—fully structured and cleansed

Company Matching & Metadata • Each job post is linked to a structured company profile, including metadata • Records are cross-referenced with LinkedIn and Google Maps to validate company identity and geography • Enables aggregation at employer or location level for deeper insights

Freshness & Scalability • Updated hourly to reflect real-time hiring behavior and job market shifts • Delivered in flexible formats (CSV, JSON, or data feed) and customizable filters • Supports segmentation by geography, company, seniority, salary, title, and more

Who Uses Canaria’s Job Postings Data • HR & Talent Teams – to benchmark roles, optimize pipelines, and compete for talent • Consultants & Strategy Teams – to guide clients with labor-driven insights • Market Researchers – to understand employment dynamics and job creation trends • HR Tech & SaaS Platforms – to power salary tools, job market dashboards, or recruiting features • Economic Analysts & Think Tanks – to model labor activity and hiring-based economic trends • BI & Analytics Teams – to build dashboards that track demand, skill shifts, and geographic patterns

Summary Canaria’s Job Postings Data provides an AI-enriched, clean, and analysis-ready view of the U.S. job market. Covering millions of listings from Indeed, LinkedIn, other job boards, and ATS sources, it includes detailed job attributes, inferred compensation, normalized titles, skill extraction, and employer metadata—all updated hourly and fully structured.

With deep enrichment, reliable deduplication, and company matchability, this dataset is purpose-built for users needing workforce insights, market trends, and strategic talent intelligence. Whether you're modeling skill gaps, benchmarking compensation, or visualizing hiring momentum, this dataset provides a complete toolkit for HR and labor intelligence.

About Canaria Inc. ...

Average pay comparison of male and female wages by job classification (except Library job classes). The data contains weighted average hourly pay rates for women and men and the average of all employee wages in the class.

Envestnet | Yodlee's Salary Data Panel captures de-identified payroll information to deliver valuable employment insights, such as a company's wage costs, seasonal performance, headcount, hiring, layoffs, and more.

De-identified payroll data analytics for major employers gives decision makers insight into employment trends across many industries. The payroll product includes 1000+ employers and data can be used for company specific or macro purposes. - 4800+ employers tagged - Frequency of payroll identified (i.e. weekly, bi-weekly)
- Data at user and account level to allow for cohort analysis (e.g. Macys likely to lose 10% of revenue due to unemployment within their cohort)

New Features - Mapping to Category codes and Employer Dependency Scoring Use Cases Categories (Our data provides an innumerable amount of use cases, and we look forward to working with new ones): 1. Market Research: Company Analysis, Company Valuation, Competitive Intelligence, Competitor Analysis, Competitor Analytics, Competitor Insights, Customer Data Enrichment, Customer Data Insights, Customer Data Intelligence, Demand Forecasting, Ecommerce Intelligence, Employee Pay Strategy, Employment Analytics, Job Income Analysis, Job Market Pricing, Marketing, Marketing Data Enrichment, Marketing Intelligence, Marketing Strategy, Payment History Analytics, Price Analysis, Pricing Analytics, Retail, Retail Analytics, Retail Intelligence, Retail POS Data Analysis, and Salary Benchmarking

1. Investment Research: Financial Services, Hedge Funds, Investing, Mergers & Acquisitions (M&A), Stock Picking, Venture Capital (VC)

2. Consumer Analysis: Consumer Data Enrichment, Consumer Intelligence

3. Market Data: AnalyticsB2C Data Enrichment, Bank Data Enrichment, Behavioral Analytics, Benchmarking, Customer Insights, Customer Intelligence, Data Enhancement, Data Enrichment, Data Intelligence, Data Modeling, Ecommerce Analysis, Ecommerce Data Enrichment, Economic Analysis, Financial Data Enrichment, Financial Intelligence, Local Economic Forecasting, Location-based Analytics, Market Analysis, Market Analytics, Market Intelligence, Market Potential Analysis, Market Research, Market Share Analysis, Sales, Sales Data Enrichment, Sales Enablement, Sales Insights, Sales Intelligence, Spending Analytics, Stock Market Predictions, and Trend Analysis

Job Postings in Europe

Exploring Salaries, Job Types and Locations

By [source]

About this dataset

The eMedCareers Europe dataset contains more than 30,000 job postings from the top companies across Europe. This comprehensive data set includes detailed information about each job posting such as the associated job category, company name, location, job title and description, as well as types of jobs and salary offered. With this data set, researchers can gain insights into the current trends in salaries and job types in various parts of Europe. Moreover, it provides unique insights into which companies are actively hiring for specific positions and wage levels to assist businesses in forming competitive salaries structures. With this dataset at your fingertips you can start uncovering intriguing patterns in European employment and pay scales - providing deep understandings of the current hiring climate across multiple countries within the region

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How to use the dataset

This dataset contains information on job postings from top companies in Europe. It can be used to analyze job types, salaries and locations of advertised positions. The data fields include: - Category: This field specifies the type or category of the position being advertised, such as Engineering, Marketing or Accounting. - Company Name: This field identifies the company advertising the position. - Job Description: This field provides a short description about what will be expected from an individual in this role. - Job Title: This field displays the title of the role that is being offered. - Job Type: This field specifies full-time, part-time contract work etc which would either be available for direct hire or freelance gigs. - Location: This field denotes where these positions are located in Europe and who could apply for it based on their location/residence. - Salary Offered :This filed provides gross annual salary or pay range that is being offered by employer to employee who takes up this job title and other compensation benefits as part per contract terms and conditions set while signing up for specific roles in company/organization

Using this dataset you can easily analyze all these different aspects related to job openings in Europe available at eMedCareers portal like salary statistics for different industries/categories, job types – full-time vs freelance/contract; location wise jobs availability etc making more informed decision when looking out into market looking out new career opportunities with prospective employers based upon your skillset

Research Ideas

• Analyzing the correlation between salary offered and job type (full-time, part-time, contract) to identify salary trends across different job types in Europe.
• Using the job category and location data to create a geographical analysis of demand for certain roles and skillsets in Europe.
• Tracking changes in the average salaries over time by visualizing posting date vs salary_offered data points

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: emed_careers_eu.csv | Column name | Description | |:--------------------|:-------------------------------------------------------| | category | The job category of the posting. (String) | | company_name | The name of the company posting the job. (String) | | job_description | A description of the job. (String) | | job_title | The title of the job. (String) | | job_type | The type of job (full-time, part-time, etc.). (String) | | location | The location of the job. (String) | | post_date | The date the job was posted. (Date) | | salary_offered | The salary offered for the job. (Integer) |

Acknowledgements

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