metadata
license: mit
configs:
- config_name: postings
data_files:
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path: postings/2026_03-*
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path: postings/2026_02-*
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path: postings/2026_01-*
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path: postings/2025_12-*
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path: postings/2025_11-*
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path: postings/2025_10-*
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path: postings/2025_09-*
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path: postings/2025_08-*
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path: postings/2025_07-*
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path: postings/2025_06-*
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path: postings/2025_05-*
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path: postings/2025_04-*
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path: postings/2025_03-*
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path: postings/2025_02-*
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path: postings/2025_01-*
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path: postings/2024_12-*
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path: postings/2024_11-*
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path: postings/2024_10-*
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path: postings/2024_09-*
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path: postings/2024_08-*
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path: postings/2024_07-*
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path: postings/2024_06-*
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path: postings/2022_03-*
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path: postings/2021_06-*
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path: postings/2021_05-*
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path: postings/2021_01-*
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path: postings/2020_12-*
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path: postings/2020_11-*
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path: postings/2020_10-*
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path: postings/2020_09-*
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path: postings/2020_08-*
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path: postings/2020_07-*
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path: postings/2020_06-*
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path: postings/2020_05-*
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path: postings/2020_04-*
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path: postings/2020_03-*
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path: postings/2020_02-*
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path: postings/2020_01-*
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path: postings/2019_12-*
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path: postings/2019_11-*
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path: postings/2019_10-*
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path: postings/2019_09-*
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path: postings/2019_08-*
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path: postings/2019_07-*
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path: postings/2019_06-*
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path: postings/2019_05-*
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path: postings/2019_04-*
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path: postings/2019_03-*
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path: postings/2019_02-*
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path: postings/2019_01-*
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path: postings/2018_12-*
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path: postings/2018_11-*
- split: 2018_10
path: postings/2018_10-*
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path: postings/2018_09-*
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path: postings/2018_08-*
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path: postings/2018_07-*
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path: postings/2018_06-*
- split: 2018_05
path: postings/2018_05-*
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path: postings/2018_04-*
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path: postings/2018_03-*
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path: postings/2018_02-*
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path: postings/2018_01-*
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path: postings/2017_12-*
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path: postings/2017_11-*
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path: postings/2017_10-*
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path: postings/2017_09-*
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path: postings/2017_08-*
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path: postings/2017_07-*
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path: postings/2017_06-*
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path: postings/2017_05-*
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path: postings/2017_04-*
- split: 2017_03
path: postings/2017_03-*
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path: postings/2017_02-*
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path: postings/2017_01-*
dataset_info:
config_name: postings
features:
- name: usajobsControlNumber
dtype: string
- name: wage_min
dtype: string
- name: wage_max
dtype: string
- name: wage_freq
dtype: string
- name: title_code
dtype: string
- name: title_score
dtype: float32
- name: title_value
dtype: float32
- name: title_features
dtype: string
- name: flesch_kincaid
dtype: float32
- name: CitizenshipReq
dtype: bool
- name: GovContract
dtype: bool
- name: VisaExclude
dtype: bool
- name: VisaInclude
dtype: bool
- name: WorkAuthReq
dtype: bool
- name: driverslicense
dtype: bool
- name: ind_contractor
dtype: bool
- name: proflicenses
dtype: bool
- name: wfh
dtype: bool
- name: yesunion
dtype: bool
- name: tasks
dtype: string
- name: skills
dtype: string
- name: concepts
dtype: string
- name: ai_codes
dtype: string
- name: ai_matches
dtype: int8
- name: ai_score
dtype: float32
- name: ai_selection_score
dtype: string
- name: ai_match_score
dtype: string
- name: ai_score_v2
dtype: float32
- name: ai_strict_score
dtype: float32
- name: ai_lenient_score
dtype: float32
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download_size: 808842875
dataset_size: 3876305595
USAJOBS Coded Dataset (2017-01 to 2026-03)
Dataset Description
The USAJOBS Dataset is a comprehensive collection of federal job postings from January 2017 through March 2026. This dataset includes full-text job descriptions, and structured metadata (job title and employer).
In this coded version, we presented the structured features assigned to each of the postings (by usajobsControlNumber), as produced by JAAT.
Dataset Structure
Splits
This dataset is organized by month to allow for easy time-series analysis without requiring the user to download the entire multi-year corpus.
- Monthly Splits: Format
YYYY_MM(e.g.,2017_01,2025_12).
Data Fields
Each split contains the following fields:
usajobsControlNumber: Unique identifier code for the USAJOBS job posting.wage_min: Minimum salary detected byWageExtract.wage_max: Maximum salary detected byWageExtract.wage_freq: Frequency of the wage (e.g., hourly, annually) byWageExtract.title_code: O*NET occupation code (TitleMatch).title_score: Confidence score of the above.title_value: Extracted numerical value of the title, based on seniority and experience (TitleMatch).title_features: Categorical features of the title, if any (TitleMatch).flesch_kincaid: Readability score measuring the complexity of the job posting text.CitizenshipReq: Boolean flag indicating if United States citizenship is mandatory.GovContract: Boolean flag indicating if the position involves government contract work.VisaExclude: Boolean flag indicating if specific visa types are barred from applying.VisaInclude: Boolean flag indicating if visa sponsorship options are explicitly included.WorkAuthReq: Boolean flag indicating if valid proof of work authorization is requested.driverslicense: Boolean flag indicating if a valid driver's license is required.ind_contractor: Boolean flag indicating if the job operates as an independent contractor role.proflicenses: Boolean flag indicating if specialized professional licenses are required.wfh: Boolean flag indicating if the position offers work-from-home or remote options.yesunion: Boolean flag indicating if the position belongs to a labor union.tasks: Text string capturing the coded O*NET tasks, extracted byTaskMatch.skills: Text string capturing the ESCO skills extracted bySkillMatch.concepts: Text string of the custom-extracted concepts extracted withConceptSearch.ai_codes: AI taxonomy codes extracted byAIMatch(beta).ai_matches: How many AI codes were extracted.ai_score: Overall AI score, following a custom scoring scheme.ai_selection_score: Extraction score representing confidence that the statement is AI-related (;-delimited list of scores).ai_match_score: Same as the above, but now for matching confidence.ai_score_v2: Updated version 2 of our custom AI scoring.ai_strict_score: AI score representing only frontier AI work.ai_lenient_score: AI score that is more lenient in the defintion of "AI".
If you find this dataset useful or utilize it for your work, please consider citing our working paper:
@article{meisenbacher2025extracting,
title={Extracting O* NET Features from the NLx Corpus to Build Public Use Aggregate Labor Market Data},
author={Meisenbacher, Stephen and Nestorov, Svetlozar and Norlander, Peter},
year={2025}
}