A Harmonized Dataset of High-Resolution Embodied Life Cycle Assessment Results for Buildings in North America: <i>Dataset Only</i>
Bibliographic record
Abstract
This is a high-resolution dataset of building design characteristics, life cycle inventories, and environmental impact assessment results for 292 building projects in the United States and Canada. The dataset contains harmonized and non-aggregated LCA model results across life cycle stages, building elements, and building materials to enable detailed analysis, comparisons, and data reuse. It includes over 90 building design and LCA features to assess distributions and trends of material use and environmental impacts. Uniquely, the data were crowd-sourced from designers conducting LCAs of real-world building projects.The dataset is composed of two files:<b>buildings_metadata.xlsx</b> includes all project metadata and LCA parameters for every project associated with a unique index number to cross-reference across other files. This also includes various calculated summaries of LCI and LCIA totals and intensities per project.<b>full_lca_results.xlsx</b> includes LCI and LCIA results per material and life cycle stage of each building project.<b>data_glossary.xlsx</b> identifies and defines each feature of the dataset including its name, data structure, syntax, units, descriptions, and more.<b>material_definitions.xlsx</b> a full list of material groups, types, and descriptions of what they include.This dataset is documented and described in a Data Descriptor, published and citable as follows:Benke, B., Chafart, M., Shen, Y. <i>et al.</i> A Harmonized Dataset of High-Resolution Embodied Life Cycle Assessment Results for Buildings in North America. <i>Sci Data</i> <b>12</b>, 1085 (2025). https://doi.org/10.1038/s41597-025-05216-0When referencing this work, please cite both the Data Descriptor and the most recent dataset version on this Fighshare DOI.The dataset also appears on the Github repository: https://github.com/Life-Cycle-Lab/wblca-benchmark-v2-data. Access to the code used to prepare this dataset is available on an additional Github repository: https://github.com/Life-Cycle-Lab/wblca-benchmark-v2-data-preparation.Release Notes:<b>2025-02-24</b> - First public release<b>2025-05-05</b> - Title revised and two supplementary dataset files added: data_glossary.xlsx and material_definitions.xlsx.<br>
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".