Data Quality Rating (DQR) is a required step before you submit your EPD for verification. DQR does not evaluate the LCA model itself, only the data within it. It tells verifiers about potential data limitations so they can review your EPD with that context, in line with EN 15804+A2 and EN 15941.
DQR scores now sit directly in your EPD project. You no longer need a separate spreadsheet to score your datasets.
What DQR covers
DQR applies to the background datasets in your project, meaning the generic datasets or supplier specific EPDs you select or import to represent materials, energy, processes, and transport. It does not apply to foreground data, which is the manufacturer-specific data you collect yourself.
Each dataset receives a score against three criteria:
Time representativeness (TiR): how closely the reference period of the dataset matches your production period.
Geographical representativeness (GR): how closely the region of the dataset matches your production location.
Technological representativeness (TeR): how closely the technology in the dataset matches your actual process.
Scores run from 1 to 5: 1 – Very good · 2 – Good · 3 – Fair · 4 – Poor · 5 – Very poor
Reviewing and editing your scores
Every dataset in your project starts with a default score of 2 (Good) on each criterion, shown as an average score pill next to the dataset.
To edit the scores:
Click the average score pill next to the dataset.
In the pop-up, set the score for each criterion on the main datapoint.
Set the scores for any transport datasets associated with that datapoint.
Close the pop-up. The average score pill updates immediately.
Note: If this project was created before the DQR scoring feature was released, click Save after completing each section — don't wait until the end.
Review every dataset rather than leaving the defaults in place. The default of 2 is a starting point, not an assessment, and a verifier will expect the scores to reflect a real judgment about each dataset.
Pay particular attention to any criterion scoring 4 or above. A score of 4 (Poor) or 5 (Very poor) signals that the dataset is a weak match for your product and needs revision. Either select a better-matched dataset, or be ready to justify the choice to your verifier.
Note on the average score: the average shown on the pill is a simple average across the criteria. It is not weighted by carbon contribution, because the scores update live in the interface.
Transport datasets (e.g., A2, A4, C2)
Transport datasets are a common sticking point because most transport processes don't come with a full datacard, so some of the usual reference points aren't available yet.
Geographical representativeness: Without a datacard to confirm the exact geography, rate this as 2 – Good for now. A future release of the tool will add a flag on transport datasets so this can be assessed more precisely.
Time representativeness: If you're using an ecoinvent-based transport dataset, it's safe to rate this as 2 – Good. ecoinvent versions 3.10.1, 3.11, and 3.12 all fall within the "not more than 3 years old" bracket.
Technical representativeness: This one depends on the specific vehicle used for transport. Base your rating on how closely the dataset's vehicle type (e.g. truck size class, fuel/engine type, load capacity) matches the vehicle actually used — you as the EPD owner are best placed to judge this, since you know which vehicle was chosen.
Examples
If you're unsure how to rate a dataset, it can help to see how similar judgment calls were made elsewhere.
Dataset | Geographical rating | Reasoning |
Aluminium production, primary, ingot | 2 – Good | Aluminium sourced in EU and an EU-level dataset was used |
Battery cell production, Li-ion NMC622 | 4 – Poor | Battery sourced in EU, but only a world-level dataset was available |
Electricity, medium voltage | 1 – Very good | Dataset representing the actual country of manufacturing was used |
Heat, natural gas | 2 – Good | No country-specific data available, so an EU-level dataset was used |
Dataset | Technical rating | Reasoning |
Impact extrusion of aluminium | 3 – Fair | The processing is extrusion, but not exactly impact extrusion |
Diesel, burned in building machine | 3 – Fair | Various site machinery (generators, forklifts) runs on diesel, so this is similar rather than exact |
Steel production, electric (Module D) | 4 – Poor | Recycling technology may differ, but the dataset is still considered a reasonable proxy |
Exporting scores to the background report
Your DQR scores export automatically when you generate the background report. Finish reviewing all your scores first, then generate the report, so the export reflects your final assessment. No separate DQR file is needed.
Reporting the share of primary data
Some Program Operators, for example the International EPD System, ask you to report the share of primary data used in your LCA. This is a separate requirement from DQR and is not covered by the DQR scores in the tool. Contact One Click LCA support if you need guidance on documenting primary data share for your Program Operator.
Retired: the DQR template form
The downloadable DQR template form is retired and is no longer available from this article. Score your datasets in the tool instead, using the steps above.
If you have an EPD in progress that already uses the template, you can complete it with the file you have. For anything new, use the in-tool scores.
If you have questions about using the file, please don't hesitate to Contact Us.