description: Dataset featuring the full-time, part-time and seasonal jobs, as well as internships posted on the City's job portal @ https://www.raleighnc.gov/jobs This dataset is updated weekdays by 9am and does not contain past (non-active) postings.; abstract: Dataset featuring the full-time, part-time and seasonal jobs, as well as internships posted on the City's job portal @ https://www.raleighnc.gov/jobs This dataset is updated weekdays by 9am and does not contain past (non-active) postings.

Career Development for certain job series

This dataset maps the competences of the European Digital Competence Framework (DigComp) to the skills descriptors in ESCO, the European classification of skills, qualifications and occupations, which is the target classification used in Skills-OVATE, a database of European online job advertisements (OJA). This experimental mapping allows to reconcile the “demand side” of digital skills sought by employers with the “supply side” of education and training for digital skills, through the lens of DigComp, which is used in many EU digital skills initiatives at international, national and regional levels.

This Web Map contains all open and upcoming positions in the USDA Forest Service. The data for this map in the Open and Upcoming Positions Layers is updated every weekday, and comes from USAJobs, which shows all current open positions, and the Outreach Database, which shows all upcoming positions. Learn more about a career in the Forest Service on the Careers webpage which has information about upcoming events and conferences, career fields, benefits, pay, programs for recent grads, and so much more.This WebMap is used as the basemap for the Careers Map Web Experience, which is an interactive tool for job seekers. For questions or concerns, please contact the Recruitment Map Team at sm.fs.fsnr@usda.gov.

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.

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

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

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: ACS 2017 - 2021 5 year summary data. 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: 2017 - 2021 ACSDate Updated: 10/2023

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for surveying and mapping in the U.S.

🇺🇸 미국

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for ocean mapping, geodesy and geomatics engineering in the U.S.

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

The data part of this release support the results presented in the paper "Optimal Map Reduce Job Capacity Allocation in Cloud Systems", by M. Malekimajd, D. Ardagna, M. Ciavotta, A.M. Rizzi and Mauro Passacantando. Published on
ACM SIGMETRICS Performance Evaluation Review. 42 (4), 50-60. 2015.

When referring to the dataset or scripts please cite the paper above.

📊 LinkedIn Company Data for Company Analysis, Valuation & Portfolio Strategy LinkedIn company data is one of the most powerful forms of alternative data for understanding company behavior, firmographics, business dynamics, and real-time hiring signals. Canaria’s enriched LinkedIn company data provides detailed company profiles, including hiring activity, job postings, employee trends, headquarters and branch locations, and verified metadata from Google Maps. This LinkedIn corporate data is updated weekly and optimized for use in company analysis, startup scouting, private company valuation, and investment monitoring. It supports BI dashboards, risk models, CRM enrichment, and portfolio strategy.

🧠 Use Cases: What Problems This LinkedIn Data Solves Our LinkedIn company insights transform opaque business landscapes into structured, analyzable data. Whether you’re conducting M&A due diligence, tracking high-growth companies, or benchmarking performance, this dataset empowers fast, confident decisions.

🔍 Company Analysis • Identify a company’s size, industry classification, and headcount signals using LinkedIn firmographic data • Analyze social presence through LinkedIn follower metrics and employee engagement • Understand geographic expansion through branch locations and hiring distribution • Benchmark companies using LinkedIn profile activity and job posting history • Monitor business changes with real-time LinkedIn updates

📈 Company Valuation & Financial Benchmarking • Feed LinkedIn-based firmographics into comps and financial models • Use hiring velocity from LinkedIn job data as a proxy for business growth • Strengthen private market intelligence with verified non-financial signals • Validate scale, structure, and presence via LinkedIn and Google Maps footprint

⚠️ Company Risk Analysis • Detect red flags using hiring freezes or drop in profile activity • Spot market shifts through location downsizing or organizational changes • Identify distressed companies with decreased LinkedIn job posting frequency • Compare stated presence vs. active behavior to identify risk anomalies

📊 Business Intelligence (BI) & Strategic Planning • Segment companies by industry, headcount, growth behavior, and hiring activity • Build BI dashboards integrating LinkedIn job trends and firmographic segmentation • Identify geographic hiring hotspots using Maps and LinkedIn signal overlays • Track job creation, title distribution, and skill demand in near real-time • Export filtered LinkedIn corporate data into CRMs, analytics tools, and lead scoring systems

📁 Portfolio Management & Investment Monitoring • Enhance portfolio tracking with LinkedIn hiring data and firmographic enrichment • Spot hiring surges, geographic expansions, or restructuring in real-time • Correlate LinkedIn growth indicators with strategic outcomes • Analyze competitors and targets using historical and real-time LinkedIn data • Generate alerts for high-impact company changes in your portfolio universe

🌐 What Makes This LinkedIn Company Data Unique

🧠 Includes Real-Time Hiring Signals • Gain visibility into which companies are hiring, at what scale, and for which roles using enriched LinkedIn job data

📍 Verified Location Intelligence • Confirm branch and HQ locations with Google Maps coordinates and public company metadata

🔁 Weekly Updates • Stay ahead of the market with fresh, continuously updated LinkedIn company insights

🔗 Clean & Analysis-Ready Format • Structured, deduplicated, and taxonomy-mapped data that integrates with CRMs, BI platforms, and investment models

🎯 Who Benefits from LinkedIn Company Data • Hedge funds, VCs, and PE firms analyzing startup and private company activity • Portfolio managers and financial analysts tracking operational shifts • Market research firms modeling sector momentum and firmographics • Strategy teams calculating market size using LinkedIn company footprints • BI and analytics teams building company-level dashboards • Compliance and KYC teams enriching company identity records • Corp dev teams scouting LinkedIn acquisition targets and expansion signals

📌 Summary Canaria’s LinkedIn company data delivers high-frequency, high-quality insights into U.S. companies, combining job posting trends, location data, and firmographic intelligence. With real-time updates and structured delivery formats, this alternative dataset enables powerful workflows across company analysis, financial modeling, investment research, market segmentation, and business strategy.

🏢 About Canaria Inc. Canaria Inc. is a leader in alternative data, specializing in job market intelligence, LinkedIn company data, and Glassdoor salary analytics. 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 AI-powered pipeline is developed by a seasoned team of machine learning experts from Google, Meta, and Amazon, and by alumni of S...

A dataset that explores Green Card sponsorship trends, salary data, and employer insights for surveying and mapping engineering in the U.S.

Tempe is among Arizona's most educated cities, lending to a creative, smart atmosphere. With more than a dozen colleges, trade schools and universities, about 40 percent of our residents over the age of 25 have Bachelor's degrees or higher. Having such an educated and accessible workforce is a driving factor in attracting and growing jobs for residents in the region.The City of Tempe is a member of the Greater Phoenix Economic Council (GPEC) and with the membership staff tracks collaborative efforts to recruit business prospects and locates. The Greater Phoenix Economic Council (GPEC) is a performance-driven, public-private partnership. GPEC partners with the City of Tempe, Maricopa County, 22 other communities and more than 170 private-sector investors to promote the region’s competitive position and attract quality jobs that enable strategic economic growth and provide increased tax revenue for Tempe.This dataset provides the target and actual job creation numbers for the City of Tempe and Greater Phoenix Economic Council (GPEC). The job creation target for Tempe is calculated by multiplying GPEC's target by twice Tempe's proportion of the population.This page provides data for the New Jobs Created performance measure.The performance measure dashboard is available at 5.02 New Jobs Created.Additional InformationSource:Contact: Madalaine McConvilleContact Phone: 480-350-2927Data Source Type: Excel filesPreparation Method: Extracted from GPEC monthly and annual reports and proprietary excel filesPublish Frequency: AnnuallyPublish Method: ManualData Dictionary

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>

career_center jobs one-stop training

This data collection contains the green structures of our municipal "Green Card". If you want to see the complete Green Map visualized in conjunction, we refer you to the following link: Green Map 2021 (arcgis.com). -------------------------------------------------------- -------------------------------------------------- -------------------------------------------------- -------------------------------------------------- -------------------------------------------------- ----------- The Green Card is part of the Municipal Tree Regulation 2021 and associated Further Rules on Compensation Duty. The map is for information purposes, the Trees Regulation 2021 and Further Rules on Compensation Duty are leading. In it you can read all the rules and information about trees in the municipality and in private property. Information is also included here to determine whether you need an environmental permit to cut down a tree, including any associated compensation plan. For the 2021 Tree Regulation, further rules on compensation obligation and information about the Eindhoven tree policy, see: Environmental permit for felling | Municipality of Eindhoven The "Green Structures on the Green Map" includes part of the underlying data from which the Green Map is built. The other data can be approached as follows: Plots larger than 250m2 represent areas within which an environmental permit is required for felling activity. ------------------- -------------------------------------------------- -------------------------------------------------- ---------- You can consult the relevant plots larger than 250m2 via: Green Map 2021 (arcgis.com). The boundaries are determined on the basis of the cadastral map. For Open Data with regard to the cadastral plots in the cadastral map, we refer to the Kadaster: https://www.pdok.nl/introductie/-/article/basisregistratie-kadaster-brk- For the dataset that Center area, you can look at District boundary — Eindhoven Open Data ** For the dataset that describes the boundary of the Nature Protection Act, please see** Built-up Area Nature Protection Act — Eindhoven Open Data

This EnviroAtlas web service includes maps that illustrate job activity in each census block group. Employment diversity, employment density, and proximity of employment to housing can affect commuting patterns. Having plentiful and diverse jobs located near housing can reduce commute time and allow for a greater variety of commute modes. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).