The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 257, in _generate_tables
pa_table = paj.read_json(
^^^^^^^^^^^^^^
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 99, in _split_generators
pa_table = next(iter(self._generate_tables(**splits[0].gen_kwargs, allow_full_read=False)))[1]
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 271, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 66, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Labelgrade Open Nutrition Dataset
344 branded packaged foods + 74 whole foods, each graded A–F on a transparent, reproducible 6-dimension nutrition score — built entirely from public USDA FoodData Central data. Licensed CC BY 4.0 (free to use with attribution).
Maintained by Labelgrade — honest A–F grades for every branded food. 📊 Browse it online: https://labelgrade.com/data · 📐 Full methodology: https://labelgrade.com/methodology
What's in it
| File | Rows | Description |
|---|---|---|
labelgrade-products.csv / .json |
344 | Branded packaged products: per-serving macros + the full Labelgrade (overall score & letter + all six dimension sub-scores). |
labelgrade-foods.csv / .json |
74 | Whole-food USDA references (e.g. chicken breast, plain Greek yogurt): macros per 100 g. |
labelgrade-dataset.json |
344 + 74 | Combined products + foods + meta (version, generation date, weights). |
Every row carries its source usda_fdc_id (verify against USDA anytime) and a report_url linking to the full graded breakdown on labelgrade.com.
The Labelgrade score (v3.1)
A weighted blend of six independently-scored (0–100) dimensions:
| Dimension | Weight |
|---|---|
| Protein density | 23% |
| Ingredient quality | 21% |
| Saturated fat load | 18% |
| Sodium load | 15% |
| Sugar load | 15% |
| Fiber | 8% |
Letter scale: A+ ≥95 · A ≥90 · A− ≥85 · B+ ≥80 · B ≥75 · B− ≥70 · C+ ≥65 · C ≥60 · C− ≥55 · D ≥40 · F <40. Added sugar is penalized on a per-100g basis so small serving sizes can't hide it. The complete, fixed formula is published at https://labelgrade.com/methodology.
Schema
labelgrade-products.csv — slug, brand, product_name, category, upc, usda_fdc_id, serving_size, servings_per_package, calories, protein_g, total_fat_g, saturated_fat_g, total_carbs_g, fiber_g, sugars_g, added_sugars_g, sodium_mg, score, grade, protein_density_score, ingredient_quality_score, sugar_load_score, sodium_load_score, fiber_score, saturated_fat_score, last_verified, report_url
labelgrade-foods.csv — slug, food_name, common_portion, portion_grams, protein_g_per_100g, calories_per_100g, total_fat_g_per_100g, saturated_fat_g_per_100g, total_carbs_g_per_100g, fiber_g_per_100g, sugars_g_per_100g, sodium_mg_per_100g, usda_fdc_id, usda_data_type, last_verified, report_url
Example row
banza-chickpeas-pasta-penne-8-oz-227-g, Banza, "Chickpeas Pasta, Penne", … score 92, grade A …
→ https://labelgrade.com/banza-chickpeas-pasta-penne-8-oz-227-g
Source, license & citation
- Source data: USDA FoodData Central (U.S. public domain). The Labelgrade scoring layer is original work by Labelgrade.
- License: Creative Commons Attribution 4.0 (CC BY 4.0) — use it freely, commercially or not, with attribution.
- Attribution: Labelgrade — https://labelgrade.com
- How to cite:
Labelgrade. Labelgrade Open Nutrition Dataset. 2026. https://labelgrade.com/data
Updates
Regenerated as the catalog grows and pages are re-verified against USDA. Check the meta block in any JSON file for the current version and generation date.
Built and maintained by Labelgrade. Questions or corrections: open an issue, or see labelgrade.com/corrections.
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