0 dict | 1 dict | 2 dict | 3 dict | 4 dict | 5 dict | 6 dict | 7 dict | 8 dict | 9 dict | 10 dict | 11 dict | 12 dict | 13 dict | 14 dict | 15 dict | 16 dict | 17 dict | 18 dict | 19 dict | 20 dict | 21 dict | 22 dict | 23 dict | 24 dict | 25 dict | 26 dict | 27 dict | 28 dict | 29 dict | 30 dict | 31 dict | 32 dict | 33 dict | 34 dict | 35 dict | 36 dict | 37 dict | 38 dict | 39 dict | 40 dict | 41 dict | 42 dict | 43 dict | 44 dict | 45 dict | 46 dict | 47 dict | 48 dict | 49 dict | 50 dict | 51 dict | 52 dict | 53 dict | 54 dict | 55 dict | 56 dict | 57 dict | 58 dict | 59 dict | 60 dict | 61 dict | 62 dict | 63 dict | 64 dict | 65 dict | 66 dict | 67 dict | 68 dict | 69 dict | 70 dict | 71 dict | 72 dict | 73 dict | 74 dict | 75 dict | 76 dict | 77 dict | 78 dict | 79 dict | 80 dict | 81 dict | 82 dict | 83 dict | 84 dict | 85 dict | 86 dict | 87 dict | 88 dict | 89 dict | 90 dict | 91 dict | 92 dict | 93 dict | 94 dict | 95 dict | 96 dict | 97 dict | 98 dict | 99 dict | 100 dict | 101 dict | 102 dict | 103 dict | 104 dict | 105 dict | 106 dict | 107 dict | 108 dict | 109 dict | 110 dict | 111 dict | 112 dict | 113 dict | 114 dict | 115 dict | 116 dict | 117 dict | 118 dict | 119 dict | 120 dict | 121 dict | 122 dict | 123 dict | 124 dict | 125 dict | 126 dict | 127 dict | 128 dict | 129 dict | 130 dict | 131 dict | 132 dict | 133 dict | 134 dict | 135 dict | 136 dict | 137 dict | 138 dict | 139 dict | 140 dict | 141 dict | 142 dict | 143 dict | 144 dict | 145 dict | 146 dict | 147 dict | 148 dict | 149 dict |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
{
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} | {
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} | {
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"name": "car"
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"name": "zebra"
} | null | null | {
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"name": "umbrella"
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"name": "orange"
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} | {
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} | {
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"name": "hair drier"
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} | null | null | {
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"name": "land, ground, soil"
} | {
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"name": "bannister, banister, balustrade, balusters, handrail"
} | null | null | {
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} | {
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} | {
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"name": "canopy"
} | null | null | null | {
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"name": "wall-stone"
} | {
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"name": "barrel, cask"
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"name": "falls"
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} | null | {
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"name": "minibike, motorbike"
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} | {
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} | null | {
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} | {
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"name": "table-merged"
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"name": "trade name"
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"name": "hood, exhaust hood"
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"name": "tray"
} | {
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"name": "trash can"
} | null | null | null | {
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} | null | null | {
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} | null | null | null | null |
{
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} | {
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} | {
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"name": "stop sign"
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} | {
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} | {
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"name": "motorcycle"
} | {
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"name": "sheep"
} | null | null | {
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"name": "water"
} | null | {
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"name": "sofa"
} | {
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} | {
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"name": "armchair"
} | {
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"name": "seat"
} | null | null | {
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"name": "baseball bat"
} | null | null | null | null | {
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"name": "wine glass"
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"name": "chest of drawers, chest, bureau, dresser"
} | {
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"name": "bowl"
} | {
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} | {
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} | {
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"name": "fireplace"
} | {
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"name": "refrigerator, icebox"
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} | null | {
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"name": "donut"
} | {
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"name": "cake"
} | {
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"name": "chair"
} | null | null | {
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"name": "stairway, staircase"
} | {
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"name": "dining table"
} | null | {
"isthing": 1,
"name": "bookcase"
} | null | null | null | {
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"name": "flower"
} | null | {
"isthing": 1,
"name": "microwave"
} | {
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"name": "oven"
} | null | null | {
"isthing": 1,
"name": "palm, palm tree"
} | {
"isthing": 1,
"name": "book"
} | null | null | null | {
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} | null | null | null | {
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"name": "towel"
} | {
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} | {
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} | null | {
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"name": "street lamp"
} | {
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"name": "flower"
} | {
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"name": "tv"
} | null | null | null | null | null | null | {
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} | {
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} | {
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"name": "bottle"
} | {
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} | null | null | {
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} | null | {
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} | null | null | {
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} | {
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"name": "plaything, toy"
} | {
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"name": "wall-tile"
} | {
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"name": "basket, handbasket"
} | null | {
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"name": "tent"
} | {
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"name": "window-other"
} | null | {
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"name": "cradle"
} | {
"isthing": 1,
"name": "oven"
} | {
"isthing": 1,
"name": "ball"
} | {
"isthing": 1,
"name": "food, solid food"
} | {
"isthing": 1,
"name": "step, stair"
} | {
"isthing": 0,
"name": "tank, storage tank"
} | null | {
"isthing": 1,
"name": "microwave"
} | {
"isthing": 0,
"name": "grass-merged"
} | {
"isthing": 0,
"name": "dirt-merged"
} | {
"isthing": 1,
"name": "bicycle"
} | {
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"name": "food-other-merged"
} | {
"isthing": 0,
"name": "building-other-merged"
} | null | null | null | null | null | null | null | null | null | null | null | {
"isthing": 0,
"name": "crt screen"
} | null | {
"isthing": 1,
"name": "monitor"
} | null | null | {
"isthing": 1,
"name": "radiator"
} | {
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"name": "glass, drinking glass"
} | {
"isthing": 1,
"name": "clock"
} | null |
edition_2924_shi-labs-oneformer_demo-readymade
A Readymade by TheFactoryX
Original Dataset
Process
This dataset is a "readymade" - inspired by Marcel Duchamp's concept of taking everyday objects and recontextualizing them as art.
What we did:
- Selected the original dataset from Hugging Face
- Shuffled each column independently
- Destroyed all row-wise relationships
- Preserved structure, removed meaning
The result: Same data. Wrong order. New meaning. No meaning.
Purpose
This is art. This is not useful. This is the point.
Column relationships have been completely destroyed. The data maintains its types and values, but all semantic meaning has been removed.
Part of the Readymades project by TheFactoryX.
"I am a machine." — Andy Warhol
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