Career Development for certain job series

Understanding job titles, career trajectories, and promotions provides valuable insight into labor market dynamics and professional mobility. We present Career Map (CMap), a novel dataset spanning 24 industry sectors, systematically structured to study job specialization, sector concentration, and career advancements. Using advanced natural language processing techniques and large language models, we standardize 6.2 million job titles into 109 thousand unique titles and introduce a Specialization Index to quantify how specialized a title is within its sector. The dataset includes both a structured job titles dataset and a set of identified promotions—30 thousand validated promotions from the United States and the United Kingdom, and 72 thousand inferred promotions from a global context. It enables research on job hierarchies, workforce mobility and systemic inequalities in professional advancement. By providing insights into career progression patterns, labor market structures, and the impact of education and experience, this dataset serves as a valuable resource for economists, sociologists, and computational researchers studying employment trends across industries and regions.This repository contains the code necessary to recreate Figure 4 and Table 4 from the original manuscript.

About this itemBack in 2017, I made a Cascade story map to compile GIS career resources for my current and future interns. Fast forward seven years, and I finally rebuilt it as an ArcGIS StoryMap. From job title descriptions to certifications and to salaries, it covers the main areas I find emerging professionals asking about when they're looking at a career in GIS. There are multiple shout outs to the Consortium in it too, of course.😎Author/ContributorJohn NergeOrganizationPersonal workOrg Websitehttps://bit.ly/JohnNerge

Career Recommender Dataset is designed to assist users in making informed career decisions by mapping skills, qualifications, and preferences to suitable job roles. This dataset includes structured information on various professions, required skills, educational backgrounds, and industry trends, making it a valuable resource for career guidance applications, machine learning models, and data-driven analysis. Whether for career counseling, job market research, or AI-driven recommendation systems, this dataset provides a comprehensive foundation for exploring optimal career pathways.

JOBS PROXIMITY INDEXSummaryThe jobs proximity index quantifies the accessibility of a given residential neighborhood as a function of its distance to all job locations within a CBSA, with larger employment centers weighted more heavily. Specifically, a gravity model is used, where the accessibility (Ai) of a given residential block- group is a summary description of the distance to all job locations, with the distance from any single job location positively weighted by the size of employment (job opportunities) at that location and inversely weighted by the labor supply (competition) to that location. More formally, the model has the following specification: Where i indexes a given residential block-group, and j indexes all n block groups within a CBSA. Distance, d, is measured as “as the crow flies” between block-groups i and j, with distances less than 1 mile set equal to 1. E represents the number of jobs in block-group j, and L is the number of workers in block-group j. The Longitudinal Employer-Household Dynamics (LEHD) has missing jobs data in all of Puerto Rico and a concentration of missing records in Massachusetts. InterpretationValues are percentile ranked with values ranging from 0 to 100. The higher the index value, the better the access to employment opportunities for residents in a neighborhood. Data Source: Longitudinal Employer-Household Dynamics (LEHD) data, 2017. Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 8. To learn more about the Jobs Proximity Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh ; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Date of Coverage: 07/2020

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🇫🇷 프랑스 English Dataset of career plans kept at the Hauts-de-Seine Departmental Archives. * * * * Paris and its suburbs have spread over land whose basements are exploited in the form of quarries. Aware of the risks, the collapses having already occurred, King Louis XVI signed a decree in April 1777 creating the General Inspectorate of Quarrys. This administration is responsible for the monitoring and consolidation of the old quarries, and for mapping these “empties”. These maps are very precise, and make it possible to know precisely the nature of the stones, the voids, and consolidations. Competent for the city of Paris and the neighbouring municipalities composing the Seine department, the Inspectorate General of the Quarries of the Seine became in 1968 the Inspectorate General of the Quarries of Paris, Hauts-de-Seine, Seine-Saint-Denis and Val de Marne, and is attached to the city of Paris. In 1967, the Prefect of Seine et Oise created a similar service on its territory, the Inspectorate General of the Careers of Yvelines, Essonne and Val d’Oise. The dataset, which covers the 1950s to 1980s, corresponds to two generations of plans: * Payment 1192W: plans produced by the general inspection of the Seine quarries in the 1950s and 1960s, they allow us to know the nature of the soil at the time of the creation of the department of Hauts-de-Seine (1968). Plans paid in 1992 and digitised in 2019. * 33W payment: plans produced by the general inspection of quarries (competent for Paris, Hauts-de-Seine, Seine-Saint-Denis and Val de Marne), which concern a new mapping of the basements in the 1970s and 1980s. Plans paid in 2007 and digitised in 2019 Each of the two generations of plans includes an assembly board. Each plan shall be identified by a code relating to that assembly plan, and shall also specify the municipality(s) concerned, as well as the mapped street or streets. Related data IGC – City of Paris General Career Inspection: all about subsoil

Research career sequences, defined by positional state (first digit) and institutional state (second digit), for selected months since PhD–three illustrations.

An area of employment is a geographical area within which most of the workers reside and work, and in which establishments can find the bulk of the labour force needed to fill the jobs offered.Dividing into employment areas constitutes a partition of the territory adapted to local labour market studies. Zoning also defines territories relevant to local diagnostics and can guide the delimitation of territories for the implementation of territorial policies initiated by public authorities or local actors. This zoning is defined for both metropolitan France and the French overseas departments.The updated breakdown is based on the commuting flows of workers observed during the 2006 census. The list of municipalities is that given by the Official Geographical Code (COG) on 01/01/2011.The list of municipalities is that given by the Official Geographical Code (COG) on 01/01/2011. Downloadable on the INSEE website see link

career_center jobs one-stop training

Missouri Career Centers offer personal assistance for your job search or hiring needs. Our staff is trained to assist you with products and services designed for both Job Seekers and Employers!

This map presents the full data available on the MLTSD GeoHub, and maps several of the key variables reflected by the Employment Services Program of ETD.Employment Services are a suite of services delivered to the public to help Ontarians find sustainable employment. The services are delivered by third-party service providers at service delivery sites (SDS) across Ontario on behalf of the Ministry of Labour, Training and Skills Development (MLTSD). The services are tailored to meet the individual needs of each client and can be provided one-on-one or in a group format. Employment Services fall into two broad categories: unassisted and assisted services.

Unassisted services include the following components:resources and information on all aspects of employment including detailed facts on the local labour marketresources on how to conduct a job search.assistance in registering for additional schoolinghelp with career planningreference to other Employment and government programs.

Unassisted services are available to all Ontarians without reference to eligibility criteria. These unassisted services can be delivered through structured orientation or information sessions (on or off site), e-learning sessions, or one-to-one sessions up to two days in duration. Employers can also use unassisted services to access information on post-employment opportunities and supports available for recruitment and workplace training.

The second category is assisted services, and it includes the following components:assistance with the job search (including individualized assistance in career goal setting, skills assessment, and interview preparation) job matching, placement and incentives (which match client skills and interested with employment opportunities, and include placement into employment, on-the-job training opportunities, and incentives to employers to hire ES clients), and job training/retention (which supports longer-term attachment to or advancement in the labour market or completion of training)For every assisted services client a service plan is maintained by the service provider, which gives details on the types of assisted services the client has accessed. To be eligible for assisted services, clients must be unemployed (defined as working less than twenty hours a week) and not participating in full-time education or training. Clients are also assessed on a number of suitability indicators covering economic, social and other barriers to employment, and service providers are to prioritize serving those clients with multiple suitability indicators.

About This Dataset

This dataset contains data on ES clients for each of the twenty-six Local Board (LB) areas in Ontario for the 2015/16 fiscal year, based on data provided to Local Boards and Local Employment Planning Councils (LEPC) in June 2016 (see below for details on Local Boards). This includes all assisted services clients whose service plan was closed in the 2015/16 fiscal year and all unassisted services clients who accessed unassisted services in the 2015/16 fiscal year. These clients have been distributed across Local Board areas based on the address of each client’s service delivery site, not the client’s home address. Note that clients who had multiple service plans close in the 2015/16 fiscal year (i.e. more than one distinct period during which the client was accessing assisted services) will be counted multiple times in this dataset (once for each closed service plan). Assisted services clients who also accessed unassisted services either before or after accessing assisted services would also be included in the count of unassisted clients (in addition to their assisted services data).

Demographic data on ES assisted services clients, including a client’s suitability indicators and barriers to employment, are collected by the service provider when a client registers for ES (i.e. at intake). Outcomes data on ES assisted services clients is collected through surveys at exit (i.e. when the client has completed accessing ES services and the client’s service plan is closed) and at three, six, and twelve months after exit. As demographic and outcomes data is only collected for assisted services clients, all fields in this dataset contain data only on assisted services clients except for the ‘Number of Clients – Unassisted R&I Clients’ field.

Note that ES is the gateway for other Employment Ontario programs and services; the majority of Second Career (SC) clients, some apprentices, and some Literacy and Basic Skills (LBS) clients have also accessed ES. It is standard procedure for SC, LBS and apprenticeship client and outcome data to be entered as ES data if the program is part of ES service plan. However, for this dataset, SC client and outcomes data has been separated from ES, which as a result lowers the client and outcome counts for ES.

About Local Boards

Local Boards are independent not-for-profit corporations sponsored by the Ministry of Labour, Training and Skills Development to improve the condition of the labour market in their specified region. These organizations are led by business and labour representatives, and include representation from constituencies including educators, trainers, women, Francophones, persons with disabilities, visible minorities, youth, Indigenous community members, and others. For the 2015/16 fiscal year there were twenty-six Local Boards, which collectively covered all of the province of Ontario.

The primary role of Local Boards is to help improve the conditions of their local labour market by:engaging communities in a locally-driven process to identify and respond to the key trends, opportunities and priorities that prevail in their local labour markets;facilitating a local planning process where community organizations and institutions agree to initiate and/or implement joint actions to address local labour market issues of common interest; creating opportunities for partnership development activities and projects that respond to more complex and/or pressing local labour market challenges; and organizing events and undertaking activities that promote the importance of education, training and skills upgrading to youth, parents, employers, employed and unemployed workers, and the public in general.

In December 2015, the government of Ontario launched an eighteen-month Local Employment Planning Council pilot program, which established LEPCs in eight regions in the province formerly covered by Local Boards. LEPCs expand on the activities of existing Local Boards, leveraging additional resources and a stronger, more integrated approach to local planning and workforce development to fund community-based projects that support innovative approaches to local labour market issues, provide more accurate and detailed labour market information, and develop detailed knowledge of local service delivery beyond Employment Ontario (EO).

Eight existing Local Boards were awarded LEPC contracts that were effective as of January 1st, 2016. As such, from January 1st, 2016 to March 31st, 2016, these eight Local Boards were simultaneously Local Employment Planning Councils. The eight Local Boards awarded contracts were:Durham Workforce Authority Peel-Halton Workforce Development GroupWorkforce Development Board - Peterborough, Kawartha Lakes, Northumberland, HaliburtonOttawa Integrated Local Labour Market PlanningFar Northeast Training BoardNorth Superior Workforce Planning Board Elgin Middlesex Oxford Workforce Planning & Development BoardWorkforce Windsor-Essex

MLTSD has provided Local Boards and LEPCs with demographic and outcome data for clients of Employment Ontario (EO) programs delivered by service providers across the province on an annual basis since June 2013. This was done to assist Local Boards in understanding local labour market conditions. These datasets may be used to facilitate and inform evidence-based discussions about local service issues – gaps, overlaps and under-served populations - with EO service providers and other organizations as appropriate to the local context.

Data on the following EO programs for the 2015/16 fiscal year was made available to Local Boards and LEPCs in June 2016:Employment Services (ES)Literacy and Basic Skills (LBS) Second Career (SC) Apprenticeship

This dataset contains the 2015/16 ES data that was sent to Local Boards and LEPCs. Datasets covering past fiscal years will be released in the future.

Notes and Definitions

NAICS – The North American Industry Classification System (NAICS) is an industry classification system developed by the statistical agencies of Canada, the United States, and Mexico against the backdrop of the North American Free Trade Agreement. It is a comprehensive system that encompasses all economic activities in a hierarchical structure. At the highest level, it divides economic activity into twenty sectors, each of which has a unique two-digit identifier. These sectors are further divided into subsectors (three-digit codes), industry groups (four-digit codes), and industries (five-digit codes). This dataset uses two-digit NAICS codes from the 2007 edition to identify the sector of the economy an Employment Services client is employed in prior to and after participation in ES.

NOC – The National Organizational Classification (NOC) is an occupational classification system developed by Statistics Canada and Human Resources and Skills Development Canada to provide a standard lexicon to describe and group occupations in Canada primarily on the basis of the work being performed in the occupation. It is a comprehensive system that encompasses all occupations in Canada in a hierarchical structure. At the highest level are ten broad occupational categories, each of which has a unique one-digit identifier. These broad occupational categories are further divided into forty major groups (two-digit codes), 140 minor groups

The Interstate Renewable Energy Council (IREC), established in 1982,[1] is a non-profit organization working with clean energy. It is based in Latham, New York. IREC works to expand consumer access to clean energy, generates information and objective analysis in best practices and standards, and leads programs in building clean energy workforces. It is an accredited American National Standards developer.

LinkedIn is a widely used professional networking platform that hosts millions of job postings. This dataset contains 1.3 million job listings scraped from LinkedIn in the year 2024.

This dataset can be used for various research tasks such as job market analysis, skills mapping, job recommendation systems, and more.

If you find this dataset valuable, please upvote 😊💼

This is the same master dataset that powers SkillExplorer

Interesting Task Ideas:

1. Practice data cleaning on raw data.
2. Analyze the most in-demand job titles or industries in different cities or countries.
3. Identify the top companies hiring for specific job positions.
4. Utilize the skills data to determine the most sought-after skills in different job categories.
5. Build a job recommendation system based on user profiles and job listing data.
6. Discover patterns in job types or levels across different industries.
7. Identify skill gaps in the job market to inform educational or training programs.
8. Explore the relationship between job title and required skills.

Photo by Clem Onojeghuo on Unsplash

LinkedIn Job Postings Data - Comprehensive Professional Intelligence for HR Strategy & Market Research

LinkedIn Job Postings Data represents the most comprehensive professional intelligence dataset available, delivering structured insights across millions of LinkedIn job postings, LinkedIn job listings, and LinkedIn career opportunities. Canaria's enriched LinkedIn Job Postings Data transforms raw LinkedIn job market information into actionable business intelligence—normalized, deduplicated, and enhanced with AI-powered enrichment for deep workforce analytics, talent acquisition, and market research.

This premium LinkedIn job postings dataset is engineered to help HR professionals, recruiters, analysts, and business strategists answer mission-critical questions: • What LinkedIn job opportunities are available in target companies? • Which skills are trending in LinkedIn job postings across specific industries? • How are companies advertising their LinkedIn career opportunities? • What are the salary expectations across different LinkedIn job listings and regions?

With real-time updates and comprehensive LinkedIn job posting enrichment, our data provides unparalleled visibility into LinkedIn job market trends, hiring patterns, and workforce dynamics.

Use Cases: What This LinkedIn Job Postings Data Solves

Our dataset transforms LinkedIn job advertisements, market information, and career listings into structured, analyzable insights—powering everything from talent acquisition to competitive intelligence and job market research.

Talent Acquisition & LinkedIn Recruiting Intelligence • LinkedIn job market mapping • LinkedIn career opportunity intelligence • LinkedIn job posting competitive analysis • LinkedIn job skills gap identification

HR Strategy & Workforce Analytics • Organizational network analysis • Employee mobility tracking • Compensation benchmarking • Diversity & inclusion analytics • Workforce planning intelligence • Skills evolution monitoring

Market Research & Competitive Intelligence • Company growth analysis • Industry trend identification • Competitive talent mapping • Market entry intelligence • Partnership & business development • Investment due diligence

LinkedIn Job Market Research & Economic Analysis • Regional LinkedIn job analysis • LinkedIn job skills demand forecasting • LinkedIn job economic impact assessment • LinkedIn job education-industry alignment • LinkedIn remote job trend analysis • LinkedIn career development ROI

What Makes This LinkedIn Job Postings Data Unique

AI-Enhanced LinkedIn Job Intelligence • LinkedIn job posting enrichment with advanced NLP • LinkedIn job seniority classification • LinkedIn job industry expertise mapping • LinkedIn job career progression modeling

Comprehensive LinkedIn Job Market Intelligence • Real-time LinkedIn job postings with salary, requirements, and company insights • LinkedIn recruiting activity tracking • LinkedIn job application analytics • LinkedIn job skills demand analysis • LinkedIn compensation intelligence

Company & Organizational Intelligence • Company growth indicators • Cultural & values intelligence • Competitive positioning

LinkedIn Job Data Quality & Normalization • Advanced LinkedIn job deduplication • LinkedIn job skills taxonomy standardization • LinkedIn job geographic normalization • LinkedIn job company matching • LinkedIn job education standardization

Who Uses Canaria's LinkedIn Data

HR & Talent Acquisition Teams • Optimize recruiting pipelines • Benchmark compensation • Identify talent pools • Develop data-driven hiring strategies

Market Research & Intelligence Analysts • Track industry trends • Build competitive intelligence models • Analyze workforce dynamics

HR Technology & Analytics Platforms • Power recruiting tools and analytics solutions • Fuel compensation engines and dashboards

Academic & Economic Researchers • Study labor market dynamics • Analyze career mobility trends • Research professional development

Government & Policy Organizations • Evaluate workforce development programs • Monitor skills gaps • Inform economic initiatives

Summary

Canaria's LinkedIn Job Postings Data delivers the most comprehensive LinkedIn job market intelligence available. It combines job posting insights, recruiting intelligence, and organizational data in one unified dataset. With AI-enhanced enrichment, real-time updates, and enterprise-grade data quality, it supports advanced HR analytics, talent acquisition, job market research, and competitive intelligence.

About Canaria Inc. Canaria Inc. is a leader in alternative data, specializing in job market intelligence, LinkedIn company data, Glassdoor salary analytics, and Google Maps location insights. We deliver clean, structured, and enriched datasets at scale using proprietary data scraping pipelines and advanced AI/LLM-based modeling, all backed by human validation. Our platform also includes Google Maps data, providing verified business location intelligen...

The Career Centers data set houses the Division’s information for customers on all of the Career Centers across the state.

Experiment data for paper "Energy-Efficient Real-Time Job Mapping and Resource Management in Mobile-Edge Computing".

Philippe Vandermaelen (1795-1869) was born at the end of the 18th century in Brussels into a wealthy family. Already in his childhood he took a great interest in cartography and he formed himself as an autodidact. He began his career with a work of monumental size: an Atlas universel, which he published between 1825 and 1827 in forty episodes of ten folios each. This work is exceptional in two respects: it is the first world atlas, on a single scale, of which a giant globe with a diameter of 7.55 meters can be made; In addition, it is the first atlas to be created using a printing technique that artists highly value but with which scientists were not yet familiar: the lithography. It wasn't long before Philippe Vandermaelen enjoyed international fame. Immediately after that, he set up an Atlas de l’Europe in 165 folios. In 1830, Vandermaelen set up the Etablissement géographique de Bruxelles at the gates of the city. When Belgium became independent, the cartographer adapted his production. In just a few months, he produced the first magazines of a Carte de la Belgique d’après Ferraris in 42 folios. In parallel, he led a large-scale information-gathering campaign that enabled him to start publishing the Dictionnaires géographiques spéciaux des provinces de la Belgique in 1831. Very quickly Vandermaelen became the Belgian cartographer par excellence. In 1831 he was commissioned by the government to draw up a Carte des frontières on the basis of which the negotiations between Belgium and Holland would be conducted. It was the beginning of a long collaboration between the government and this private entrepreneur. Vandermaelen was commissioned to map the roads, canals, railways and the telegraph after the borders, and then also the mines and factories, the general water level, the subsurface of Belgium and the expansion of Brussels. He took advantage of his privileged relations with the government to get his hands on the handwritten plans of the municipal land registers. He also acquired the existing triangular measurements. He sent his topographers to the nine provinces to make the necessary measurements and published two Cartes topographiques de la Belgique: the 1:80 000 scale map in 25 folios – a masterpiece of lithography – was fully completed in 1853, while the 250 folios of the 1:20 000 scale map appeared between 1846 and 1854, long before the War Depot map, the first folios of which rolled off the presses only in 1865. When the Etablissement géographique de Bruxelles closed its doors for good in 1880, the Royal Library of Belgium acquired the lion's share of the immense production (Source: KBR).The digital accessibility of Vandermaelen's topographic map on a scale of 1:20,000 was achieved through a collaboration in 2009 between KBR and the then AGIV (Agency for Geographic Information Flanders), now Digital Flanders. The maps were digitised, georeferenced and made available via geopunt.be, the geoportal of GDI-Vlaanderen. The intellectual property rights of the georeferenced maps have been shared and rest with KBR and Digitaal Vlaanderen.

Île-de-France Mobilités provides you with schematic plans of the works per month.

Warning: the amount of work on the rail network and their increasingly frequent reports obliges Ile-de-France Mobilités to rethink its monthly restitution of the impacts of the works on the scale of Ile-de-France. The production of monthly charts is therefore temporarily suspended from March 2020 in order to work on a version allowing more frequent updating of information. we therefore invent you to refer to the RATP and SNCF websites in order to know the schedule of works on your lines.

To consult the forecasts, click below:

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January 2019: January 2019 Jobs Map

February 2019: Map of February 2019 works

March 2019: Map of March 2019 works

April 2019: April 2019 Jobs Map

From May 2019, two work cards are available, a week card and a weekend card.

May 2019:

• Map of May 2019 works - Week
• Map of May 2019 works - Weekend
</ ul>

June 2019:

• June 2019 works map - Week
• June 2019 work map - Weekend

July 2019:

• July 2019 works map - Week
• July 2019 works map - Weekend

August 2019:

• Map of August 2019 work - Week
• August 2019 work map - Weekend

September 2019:

• September 2019 works map - Week
• September 2019 works map - Weekend
</ ul>

October 2019:

• Map October 2019 works - Week
• October 2019 work map - Weekend

November 2019:

• Map of November 2019 works - Week
• October 2019 November works map - Weekend
< p>December 2019:
• Map of works for December 2019 - Week
• Map of works of December 2019 - Weekend

January 2020:

• January 2020 works map - Week
• January 2020 works map - Weekend

February 2020:

< li>Map of February 2020 works - Week< li>Map of February 2020 works - Weekend</ li>

Summer 2020:

• Summer 2020 work map

Summer 2021:

• Map of summer works 2021

Summer 2022:

• Summer 2022 Jobs Map

Summer 2023:

• Map of summer works 2023</ li>

This layer considers jobs and destinations to be accessible by bus if the destinations are reachable within 30 minutes. Access to jobs and destinations within a fixed time is measured using the actual networks and not a straight line distance. Destinations include grocery stores, hospitals, community services, education centers, and other significant community areas. Jobs across the region (not just within the District) were used to provide a full picture of employment access.