Community Projects and Case Studies

Papers/Reports

Core to its mission to increase the amount of actionable labor market information in the public workforce system, the following analyses reflect the research applications of the NLx data. All papers and reports are provided in their original form with no edits from NLx staff. Links may be to external websites if requested by the authors

• 2024 Oregon Talent Assessment: “The 2024 Talent Assessment presents analysis and findings on Oregon’s economic and workforce landscape; skills supply and demand for target occupations; and the competitive position of Oregon’s talent pool and workforce development system.” Prepared by SRI International

• Occupational Models from 42 Million Unstructured Job Postings: “Structuring jobs into occupations is the first step for analysis tasks in many fields of research, including eco nomicsandpublichealth, aswell asforpractical applications like matching job seekers to available jobs. We present a data resource, derived with natural language processing techniques from over 42 million unstruc tured job postings in the National Labor Exchange, that empirically models the associations between occu pation codes (estimated initially by the Standardized Occupation Coding for Computer-assisted Epidemio logical Research method), skill keywords, job titles, and full-text job descriptions in the United States during the years 2019 and 2021. We model the probability that a job title is associated with an occupation code and that a job description is associated with skill keywords and occupation codes. Our models are openly avail able in the sockit python package, which can assign occupation codes to job titles, parse skills from and assign occupation codes to job postings and resumes, and estimate occupational similarity among job postings, resumes, and occupation codes.” Prepared by RIPL

• Assessing Needs for Civilian Talent Assessment in the Department of the Air Force: Bolstering technical capabilities throughout the Department of the Air Force (DAF)—military and civilian, active and reserve—is a current goal of the department. Although the DAF has many tools to address science, technology, engineering, and mathematics (STEM) needs within its military workforce, there are many barriers to achieving similar goals within the civilian workforce. This project examines the DAF’s needs for STEM talent in the civilian workforce, identifies potential gaps in technical competencies, describes options for closing technical skill gaps, and proposes strategies to better track the supply of and demand for STEM talent. Prepared by RAND

Powered by the NLx Data

In addition to traditional labor market research, the NLx data has been utilized by digital solution developers and workforce system partners to create the technologies that power U.S. labor markets. From tools to increase the accessibility and granularity of labor market information, to products and services that facilitate connections between talent and opportunity, these solutions illustrate the reach of the NLx data throughout the workforce and education ecosystem.

• IQ4 Wallet: The iQ4 Wallet is a digital platform that helps users showcase their skills, credentials, and experiences in a portable profile. It integrates live job feeds—including NLx job posting data—to match users with real-time opportunities based on their skillsets and location. Organizations like Western Governors University and the state of Indiana are already using it to support skills-based hiring and workforce development.

• CareerOneStop: CareerOneStop is a free, national career, education, and job resource sponsored by the U.S. Department of Labor, offering tools for job seekers, students, and workforce professionals. It features over 40 RESTful APIs—including the Jobs API powered by NLx data—that support public-facing job search platforms and labor market analysis. CareerOneStop’s certification finder, occupation profiles, and training locators are widely used by state agencies, researchers, and developers to enhance workforce solutions.

• DOORS: DOORS is a workforce development platform from Research Improving Peoples’ Lives (RIPL) that uses advanced data analytics to match job seekers with personalized career pathways. It integrates data across programs like UI, SNAP E&T, TANF, and WIOA to streamline service delivery and improve employment outcomes. By leveraging NLx job posting data, DOORS helps governments and employers connect individuals to skills-aligned opportunities while maintaining secure, purpose-limited data access

• SOCkit: SOCkit s a natural-language processing toolkit for modeling structured occupation information and Standard Occupational Classification (SOC) codes in unstructured text from job titles, job postings, and resumes. It is developed by RIPL and is deployed in production in Hawaii’s Career Acceleration Navigator and Rhode Island’s Career Compass RI.

Third-Party Assessments

Explore third-party evaluations of the Research Hub to gain a deeper understanding of the strengths and weaknesses of the NLx data from users and partners like you. All assessments provided here were developed independent of NLx oversight, and represent the conclusions of the authors themselves. Note that the NLx Research Hub is continues to update its offerings to provide a better experience to its users. As a result, some conclusions may be out of date.

• NLx Coverage of QCEW School Employers in Minnesota: This replicable project compares the coverage of a subset of NLx job postings to QCEW information and projects in Minnesota.

• NLx Coverage of QCEW Construction Employers in Minnesota: This replicable project compares the coverage of a subset of NLx job postings to QCEW information and projects in Minnesota. Prepared by Minnesota Department of Employment and Economic Development.

Do you have a project or case study you’d like to share with other NLx Research Hub users? Submit a project, case study, or tool using the form below.