> ## Documentation Index > Fetch the complete documentation index at: https://agent-observability-docs.splunk.com/llms.txt > Use this file to discover all available pages before exploring further. # Dataset validation > Evaluator-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 evaluator and does not run the complete evaluator-aware contract. Luna Studio applies the checks below when you add or select a dataset in the run wizard, where it knows the evaluator 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** — evaluator-specific feature columns and `label`. * **Training sets** — evaluator-specific feature columns and optionally `label` when applicable. * Column types match the evaluator's [output type](/luna-studio/ui/core-concepts#evaluators). 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 | Condition | Cause | Fix | | ------------------------------------ | --------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ | | Missing `label` column | A test set or labelled training set does not contain `label`. | Add the column and re-upload. For unlabelled training logs, use the label-only flow from Step 3. | | Missing required feature column | The selected evaluator shape requires a column that is absent. | Add the exact feature columns required by the evaluator shape; not every shape uses `input`. | | URL fetch fails | The URL is unreachable, expired, unsafe, or returns 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 supported through current run processing. | | Empty dataset | The file parses but has 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. | | Labels don't match the output type | Labels cannot be parsed as the evaluator's expected type. | For Boolean evaluators, 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.