Dataset research report
Sonar research report
A reproducible data report with schema notes, generated chart evidence, suggested follow-up questions, and export-ready Helix queries.
Executive Summary
Sonar The Sonar dataset from the UCI ML repository. Dataset to discriminate between sonar signals bounced off a metal cylinder and those bounced off a roughly cylindrical rock. Configurations and tasks Configuration Task Description sonar Binary classification Is the sonar detecting a rock? Usage from datasets import load_dataset dataset = load_dataset("mstz/sonar")["train"]
Research Context
Sonar: 208 rows by 61 columns. These exploratory charts are generated automatically from the data - open the dataset in Helix to ask your own questions.
Data Profile
Chart Evidence
These views are generated from the dataset profile. Each chart is paired with a Helix query so it can be opened, adjusted, and exported.
Correlation of numeric columns
Pearson correlation between numeric columns.
Open and export this chartFollow-Up Queries
Preview Rows
| # | 0float | 1float | 2float | 3float | 4float | 5float | 6float | 7float |
|---|---|---|---|---|---|---|---|---|
| 1 | 0.02 | 0.0371 | 0.0428 | 0.0207 | 0.0954 | 0.0986 | 0.1539 | 0.1601 |
| 2 | 0.0453 | 0.0523 | 0.0843 | 0.0689 | 0.1183 | 0.2583 | 0.2156 | 0.3481 |
| 3 | 0.0262 | 0.0582 | 0.1099 | 0.1083 | 0.0974 | 0.228 | 0.2431 | 0.3771 |
| 4 | 0.01 | 0.0171 | 0.0623 | 0.0205 | 0.0205 | 0.0368 | 0.1098 | 0.1276 |
| 5 | 0.0762 | 0.0666 | 0.0481 | 0.0394 | 0.059 | 0.0649 | 0.1209 | 0.2467 |
| 6 | 0.0286 | 0.0453 | 0.0277 | 0.0174 | 0.0384 | 0.099 | 0.1201 | 0.1833 |
Data Dictionary
- 0 numeric
- 1 numeric
- 2 numeric
- 3 numeric
- 4 numeric
- 5 numeric
- 6 numeric
- 7 numeric
- 8 numeric
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- 28 numeric
- 29 numeric
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- 31 numeric
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- 33 numeric
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- 48 numeric
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- 50 numeric
- 51 numeric
- 52 numeric
- 53 numeric
- 54 numeric
- 55 numeric
- 56 numeric
- 57 numeric
- 58 numeric
- 59 numeric
- is_rock numeric
Method And Limits
- Load the catalog entry and preview rows from the processed dataset file.
- Infer numeric, categorical, time, and location fields from real columns.
- Generate a small set of defensive Plotly chart specifications from that profile.
- Expose each chart idea as a query link so the report can be rerun or exported in Helix.
This report is intentionally reproducible. It uses the local catalog metadata and generated chart specifications rather than claiming external conclusions beyond the dataset.