emburden: Processed Energy Burden Datasets (US Nationwide) Article Swipe
PROCESSED, analysis-ready household energy burden datasets from the DOE Low-Income Energy Affordability Data (LEAD) Tool, formatted for the emburden R package. Scope: All 51 US states and territories (50 states + DC) IMPORTANT: These are PRE-PROCESSED datasets, not raw OpenEI data. They have been: Aggregated by census tract + income bracket Enriched with computed energy burden metrics (EROI, NER, DEAR) Standardized for immediate analysis Quality-checked and validated This repository provides census tract-level data on household energy burden for the entire United States, covering ~73,000 census tracts. Data includes both Area Median Income (AMI) and Federal Poverty Line (FPL) cohort analyses for 2018 and 2022 vintages. Files Included: lead_ami_cohorts_2022_us.csv.gz: 2022 AMI cohort data (701,490 records, 148 MB) lead_fpl_cohorts_2022_us.csv.gz: 2022 FPL cohort data (588,163 records, 52 MB) lead_ami_cohorts_2018_us.csv.gz: 2018 AMI cohort data (530,500 records, 54 MB) lead_fpl_cohorts_2018_us.csv.gz: 2018 FPL cohort data (514,893 records, 53 MB) checksums.txt: MD5 checksums for verification Total size: 307 MB compressed Data Processing Source: Raw LEAD Tool data from OpenEI Processing: emburden R package v0.4.8 data pipeline Format: CSV (aggregated tract-level cohorts with computed metrics) Ready for: Immediate analysis, no additional processing required Coverage States: All 51 (50 states + DC, excludes PR) Census Tracts: ~73,000 nationwide Total Records: 2.3+ million cohort observations Income Brackets: 4-6 per dataset/vintage Data Sources Original raw data from: DOE LEAD Tool 2022: https://data.openei.org/submissions/6219 DOE LEAD Tool 2018: https://data.openei.org/submissions/573 Processed using: emburden R package v0.4.8 (https://github.com/ScheierVentures/emburden) Citation When using this data, please cite: This Zenodo repository (DOI provided) The emburden R package v0.4.8 The original DOE LEAD Tool publications License CC-BY-4.0 (same as source data)
Related Topics
Concepts
Raw data
Cohort
Census
Data quality
Pipeline (software)
Computer science
Database
Statistics
Data processing
Data file
Data collection
Energy consumption
Checksum
Energy (signal processing)
Medicine
Efficient energy use
Data mining
Cohort study
Exploratory data analysis
Geography
Missing data
Overhead (engineering)
Energy intensity
Poverty threshold
Reference data
Percentile
Data pre-processing
Metadata
- Type
- dataset
- Landing Page
- https://doi.org/10.5281/zenodo.17656637
- OA Status
- green
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- https://openalex.org/W7106249731
All OpenAlex metadata
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https://openalex.org/W7106249731Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.5281/zenodo.17656637Digital Object Identifier
- Title
-
emburden: Processed Energy Burden Datasets (US Nationwide)Work title
- Type
-
datasetOpenAlex work type
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-20Full publication date if available
- Authors
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Scheier, EricList of authors in order
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https://doi.org/10.5281/zenodo.17656637Publisher landing page
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YesWhether a free full text is available
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greenOpen access status per OpenAlex
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https://doi.org/10.5281/zenodo.17656637Direct OA link when available
- Concepts
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Raw data, Cohort, Census, Data quality, Pipeline (software), Computer science, Database, Statistics, Data processing, Data file, Data collection, Energy consumption, Checksum, Energy (signal processing), Medicine, Efficient energy use, Data mining, Cohort study, Exploratory data analysis, Geography, Missing data, Overhead (engineering), Energy intensity, Poverty threshold, Reference data, Percentile, Data pre-processingTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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| abstract_inverted_index.analyses | 99 |
| abstract_inverted_index.analysis | 63 |
| abstract_inverted_index.computed | 53, 175 |
| abstract_inverted_index.covering | 82 |
| abstract_inverted_index.datasets | 5 |
| abstract_inverted_index.emburden | 18, 163, 228, 246 |
| abstract_inverted_index.excludes | 193 |
| abstract_inverted_index.includes | 87 |
| abstract_inverted_index.metrics) | 176 |
| abstract_inverted_index.original | 251 |
| abstract_inverted_index.package. | 20 |
| abstract_inverted_index.pipeline | 168 |
| abstract_inverted_index.provides | 69 |
| abstract_inverted_index.records, | 113, 122, 131, 140 |
| abstract_inverted_index.required | 184 |
| abstract_inverted_index.Brackets: | 206 |
| abstract_inverted_index.CC-BY-4.0 | 257 |
| abstract_inverted_index.Immediate | 179 |
| abstract_inverted_index.Included: | 106 |
| abstract_inverted_index.Processed | 226 |
| abstract_inverted_index.analysis, | 180 |
| abstract_inverted_index.checksums | 145 |
| abstract_inverted_index.datasets, | 36 |
| abstract_inverted_index.formatted | 15 |
| abstract_inverted_index.household | 2, 74 |
| abstract_inverted_index.immediate | 62 |
| abstract_inverted_index.provided) | 244 |
| abstract_inverted_index.validated | 66 |
| abstract_inverted_index.vintages. | 104 |
| abstract_inverted_index.Aggregated | 44 |
| abstract_inverted_index.IMPORTANT: | 32 |
| abstract_inverted_index.Low-Income | 9 |
| abstract_inverted_index.PROCESSED, | 0 |
| abstract_inverted_index.Processing | 154 |
| abstract_inverted_index.additional | 182 |
| abstract_inverted_index.compressed | 152 |
| abstract_inverted_index.nationwide | 198 |
| abstract_inverted_index.processing | 183 |
| abstract_inverted_index.repository | 68, 242 |
| abstract_inverted_index.(aggregated | 171 |
| abstract_inverted_index.Processing: | 162 |
| abstract_inverted_index.territories | 27 |
| abstract_inverted_index.tract-level | 71, 172 |
| abstract_inverted_index.Standardized | 60 |
| abstract_inverted_index.observations | 204 |
| abstract_inverted_index.publications | 255 |
| abstract_inverted_index.verification | 147 |
| abstract_inverted_index.Affordability | 11 |
| abstract_inverted_index.PRE-PROCESSED | 35 |
| abstract_inverted_index.analysis-ready | 1 |
| abstract_inverted_index.checksums.txt: | 143 |
| abstract_inverted_index.Quality-checked | 64 |
| abstract_inverted_index.dataset/vintage | 209 |
| abstract_inverted_index.lead_ami_cohorts_2018_us.csv.gz: | 125 |
| abstract_inverted_index.lead_ami_cohorts_2022_us.csv.gz: | 107 |
| abstract_inverted_index.lead_fpl_cohorts_2018_us.csv.gz: | 134 |
| abstract_inverted_index.lead_fpl_cohorts_2022_us.csv.gz: | 116 |
| abstract_inverted_index.https://data.openei.org/submissions/573 | 225 |
| abstract_inverted_index.https://data.openei.org/submissions/6219 | 220 |
| abstract_inverted_index.(https://github.com/ScheierVentures/emburden) | 232 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 1 |
| citation_normalized_percentile |