> ## Documentation Index > Fetch the complete documentation index at: https://v2galileo-feat-update-docs-main-30571363885.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # Dataset validation > Metric-aware schema and content checks Luna Studio runs before using a dataset in a run. Adding a dataset from the global Datasets page ingests and stores it, but that flow has no selected metric and does not run the complete metric-aware contract. Luna Studio applies the checks below when you add or select a dataset in the run wizard, where it knows the metric and whether the data is a test set, labelled training set, or unlabelled source for labelling. An upload can therefore succeed on the Datasets page and later fail validation when you use it in a run. ## What Luna Studio checks ### Schema checks * The stored file parses as CSV. The source picker accepts JSONL during ingestion, but current run validation reads it as CSV, so JSONL is not supported end to end. * Required columns are present: * **Test sets** — metric-specific feature columns and `label`. * **Training sets** — metric-specific feature columns and optionally `label` when applicable. * Column types match the metric's [output type](/luna-studio/ui/core-concepts#metrics). Boolean labels must be integers: `0` or `1`. ### Content checks * File encoding (UTF-8 expected). * Test sets have at least 300 rows total and at least 100 examples per class. * Labelled training sets have at least 2,000 rows total and at least 100 examples per class. * Empty rows or empty cells. ### File checks (uploads and URLs) * Browser upload size is no more than 20 MiB. * For URLs: the scheme is HTTP(S), every host and redirect is publicly reachable, and the response returns successfully. ## Common errors | Error message | Cause | Fix | | ---------------------------------------------- | ---------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------- | | `Missing column "label" in uploaded CSV.` | Test set or labelled training set without a `label` column. | Add the column to your file and re-upload. For unlabelled training logs, use the label-only flow from Step 3. | | `Missing column "input" in uploaded CSV.` | The selected metric shape requires `input`, but the column is missing. | Add the exact feature columns required by the metric shape; not every shape uses `input`. | | `Could not fetch URL.` | The URL was unreachable, expired, or returned a non-200 status. | Check the URL or create a new pre-signed HTTPS URL. Raw `s3://` and `gs://` URIs are not supported. | | `Unsupported file type.` | The source picker cannot ingest the file extension. | Convert the file to CSV. Although JSONL can be ingested, it is not currently supported through run processing. | | `Empty dataset.` | The file parsed but had zero data rows. | Confirm the file isn't header-only. | | Minimum row or per-class requirement | The dataset is smaller than the required threshold. | Use at least 300 rows for a test set or 2,000 for a labelled training set, with at least 100 per class. | | `Label values don't match metric output type.` | Labels can't be parsed as the metric's expected type. | Re-check labels. For Boolean metrics, use integer `0` and `1`; Boolean `true` and `false` are rejected. | ## Where to go next Walk through the three sources. Run-time failures and how to recover.