Open a CSV, TSV, JSON array, JSONL, or NDJSON file from Files to inspect supported tabular data. The explorer helps you understand a dataset before asking OpenScience to transform or model it.
Inspect a dataset
- Open the file and check the reported dimensions.
- Choose Schema to inspect inferred column types and missing-value summaries.
- Use Filter every column… to find matching rows.
- Click a column heading to sort its displayed values.
- Choose a numeric column and Distribution to inspect its histogram and summary.
Filtering and sorting the preview do not rewrite the source dataset. A numeric-looking identifier can still be a category; use the data dictionary to interpret inferred types.
Export a subset
Choose Export filtered to download the currently filtered data. Preserve the original file, and record the filtering rule if the subset will be used in an analysis.
For transformations that need to be repeatable, ask for code instead of relying only on a manual export:
Create scripts/filter_samples.py that reads data/samples.csv, retains
rows where the recorded quality flag is pass, and writes a new CSV in
results/. Report retained and excluded counts and keep the original.
Ask for a data audit
Inspect data/measurements.csv. Report row and column counts, inferred
types, missingness, duplicate sample IDs, units, and impossible values.
Do not remove or impute anything yet. Save the audit and a data dictionary
in results/ and ask about ambiguous column meanings.
A useful audit explains whether missing values mean not measured, not applicable, below detection, or a parsing problem. These cases can require different analysis choices.
Work with large or unusual files
A preview can be bounded or truncated. Read any limit notice before treating displayed counts, distributions, or records as a full-dataset result. Ask Python or R to compute full-file statistics when needed.
For an unfamiliar delimiter, encoding, nested record structure, or spreadsheet workbook, first ask OpenScience to identify the format and produce a small readable sample. Preserve the original and record how the sample or conversion was made.
From inspection to analysis
| Goal | Next step |
|---|---|
| Clean a dataset reproducibly | Data analysis. |
| Compare groups or estimate an effect | Statistics. |
| Train and evaluate a model | Machine learning. |
| Make a presentation or publication figure | Figures. |
| Inspect variants, reads, or single-cell data | Scientific viewers. |
Before reporting a result, confirm that the analysis used the full intended input, the right unit of observation, and a documented selection rule.