Dataset research report
Musk research report
A reproducible data report with schema notes, generated chart evidence, suggested follow-up questions, and export-ready Helix queries.
Executive Summary
Musk The Musk dataset from the UCI ML repository. Census dataset including personal characteristic of a person, and their income threshold. Configurations and tasks Configuration Task Description musk Binary classification Is the molecule a musk? Usage from datasets import load_dataset dataset = load_dataset("mstz/musk", "musk")["train"]
Research Context
Musk: 476 rows by 167 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
| # | ray_0float | ray_1float | ray_2float | ray_3float | ray_4float | ray_5float | ray_6float | ray_7float |
|---|---|---|---|---|---|---|---|---|
| 1 | 42 | -198 | -109 | -75 | -117 | 11 | 23 | -88 |
| 2 | 42 | -191 | -142 | -65 | -117 | 55 | 49 | -170 |
| 3 | 42 | -191 | -142 | -75 | -117 | 11 | 49 | -161 |
| 4 | 42 | -198 | -110 | -65 | -117 | 55 | 23 | -95 |
| 5 | 42 | -198 | -102 | -75 | -117 | 10 | 24 | -87 |
| 6 | 42 | -191 | -142 | -65 | -117 | 55 | 49 | -170 |
Data Dictionary
- ray_0 numeric
- ray_1 numeric
- ray_2 numeric
- ray_3 numeric
- ray_4 numeric
- ray_5 numeric
- ray_6 numeric
- ray_7 numeric
- ray_8 numeric
- ray_9 numeric
- ray_10 numeric
- ray_11 numeric
- ray_12 numeric
- ray_13 numeric
- ray_14 numeric
- ray_15 numeric
- ray_16 numeric
- ray_17 numeric
- ray_18 numeric
- ray_19 numeric
- ray_20 numeric
- ray_21 numeric
- ray_22 numeric
- ray_23 numeric
- ray_24 numeric
- ray_25 numeric
- ray_26 numeric
- ray_27 numeric
- ray_28 numeric
- ray_29 numeric
- ray_30 numeric
- ray_31 numeric
- ray_32 numeric
- ray_33 numeric
- ray_34 numeric
- ray_35 numeric
- ray_36 numeric
- ray_37 numeric
- ray_38 numeric
- ray_39 numeric
- ray_40 numeric
- ray_41 numeric
- ray_42 numeric
- ray_43 numeric
- ray_44 numeric
- ray_45 numeric
- ray_46 numeric
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- ray_48 numeric
- ray_49 numeric
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- ray_60 numeric
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- ray_62 numeric
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- ray_99 numeric
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- ray_101 numeric
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- ray_103 numeric
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- ray_111 numeric
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- ray_114 numeric
- ray_115 numeric
- ray_116 numeric
- ray_117 numeric
- ray_118 numeric
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- ray_127 numeric
- ray_128 numeric
- ray_129 numeric
- ray_130 numeric
- ray_131 numeric
- ray_132 numeric
- ray_133 numeric
- ray_134 numeric
- ray_135 numeric
- ray_136 numeric
- ray_137 numeric
- ray_138 numeric
- ray_139 numeric
- ray_140 numeric
- ray_141 numeric
- ray_142 numeric
- ray_143 numeric
- ray_144 numeric
- ray_145 numeric
- ray_146 numeric
- ray_147 numeric
- ray_148 numeric
- ray_149 numeric
- ray_150 numeric
- ray_151 numeric
- ray_152 numeric
- ray_153 numeric
- ray_154 numeric
- ray_155 numeric
- ray_156 numeric
- ray_157 numeric
- ray_158 numeric
- ray_159 numeric
- ray_160 numeric
- ray_161 numeric
- oxy_distance numeric
- displacement_1 numeric
- displacement_2 numeric
- displacement_3 numeric
- is_musk 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.