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README.md

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+---
+pretty_name: CAD-MiniBooNe
+license: cc-by-4.0
+configs:
+  - config_name: default
+    data_files: data.csv
+language:
+  - en
+task_categories:
+  - tabular-classification
+task_ids:
+  - tabular-multi-class-classification
+size_categories:
+  - 10K<n<100K
+tags:
+  - anomaly-detection
+  - continual-learning
+  - continual-anomaly-detection
+  - particle-identification
+  - physics
+  - tabular
+---
+
+# CAD-MiniBooNe
+
+## Dataset Summary
+
+**CAD-MiniBooNe** is a single-source continual anomaly detection benchmark scenario derived from the **MiniBooNE Particle Identification** dataset. It recasts the dataset's original binary classification task (distinguishing electron-neutrino "signal" events from muon-neutrino "background" events) as an anomaly detection problem and converts the tabular data into a sequence of concept-grouped tasks.
+
+The dataset contains **66,116 samples**, **5 tasks**, and has a reported **72.47% anomaly ratio in the test set**.
+
+
+## Intended Use
+
+This dataset is intended for research on:
+
+- continual anomaly detection;
+- continual learning for tabular data;
+- robustness under distribution shift;
+- task ordering in continual-learning benchmarks;
+- forgetting and knowledge transfer across related concepts;
+- benchmarking anomaly detectors under sequential task exposure.
+
+The intended use is **machine learning research**. As a physics benchmark, it carries no dual-use risk.
+
+## Dataset Source
+
+- MiniBooNE Particle Identification (UCI ML Repository, ID 199): `https://archive.ics.uci.edu/dataset/199/miniboone+particle+identification`
+
+The original dataset originates from the MiniBooNE physics experiment and contains 130,065 events, each described by 50 real-valued particle-identification variables, labeled as electron-neutrino (signal) or muon-neutrino (background) events. Task boundaries in this benchmark are derived via spectral clustering over the original data, used as a proxy for concept drift.
+
+
+## Dataset Files
+
+The repository contains the following files:
+
+| File | Description |
+|---|---|
+| `data.csv` | Main tabular dataset file. |
+| `orderings.json` | Predefined task orderings for continual-learning evaluation. |
+| `croissant.json` | Croissant metadata describing the dataset. |
+
+## Dataset Structure
+
+The main file is:
+
+```text
+data.csv
+```
+
+The dataset contains task metadata, binary labels, and numerical particle-identification features.
+
+### Core Columns
+
+| Column | Type | Description |
+|---|---:|---|
+| `task_id` | integer | Numeric identifier of the continual-learning task. |
+| `task_name` | string | Name of the task, e.g. `miniboone_0`. |
+| `task_split` | string | Split assignment for the row. |
+| `label` | integer | Binary anomaly label. `0` denotes normal and `1` denotes anomalous. |
+
+### Task Identifiers
+
+The dataset contains the following task identifiers:
+
+`miniboone_0`, `miniboone_1`, `miniboone_2`, `miniboone_3`, `miniboone_4`
+
+### Feature Columns
+
+The remaining columns are the 50 real-valued particle-identification variables from the original MiniBooNE dataset, provided as `feature_00` … `feature_49`.
+
+For the complete schema, see `croissant.json`.
+
+## Task Orderings
+
+The dataset provides six predefined orderings in `orderings.json`. These orderings define different continual-learning evaluation regimes over the same task set.
+
+| Ordering | Task sequence |
+|---|---|
+| `curriculum_asc` | `miniboone_2` → `miniboone_3` → `miniboone_0` → `miniboone_4` → `miniboone_1` |
+| `curriculum_desc` | `miniboone_1` → `miniboone_4` → `miniboone_0` → `miniboone_3` → `miniboone_2` |
+| `generalization_desc` | `miniboone_2` → `miniboone_0` → `miniboone_3` → `miniboone_4` → `miniboone_1` |
+| `generalization_asc` | `miniboone_1` → `miniboone_4` → `miniboone_3` → `miniboone_0` → `miniboone_2` |
+| `smooth_drift` | `miniboone_1` → `miniboone_4` → `miniboone_0` → `miniboone_3` → `miniboone_2` |
+| `abrupt_drift` | `miniboone_0` → `miniboone_1` → `miniboone_2` → `miniboone_4` → `miniboone_3` |
+
+These orderings are intended to expose complementary continual-learning dynamics, including curriculum-like adaptation, generalization-oriented ordering, smooth drift, and abrupt drift.
+
+## Dataset Creation
+The details of dataset creation can be found in our paper: [link](https://arxiv.org/abs/2607.18289)
+
+## Citation
+When using the dataset, please cite:
+```
+@article{faber2026towards,
+  title={Towards Principled Continual Anomaly Detection: A Systematic Framework and Benchmark Scenarios},
+  author={Faber, Kamil and Smendowski, Mateusz and Corizzo, Roberto},
+  journal={arXiv preprint arXiv:2607.18289},
+  year={2026}
+}
+```
+
+If you also use the underlying MiniBooNE data, please additionally cite:
+```
+Roe, B. (2010). MiniBooNE Particle Identification [Dataset]. UCI Machine Learning Repository. https://doi.org/10.24432/C5QC87
+```

croissant.json

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+            "miniboone_3",
+            "miniboone_2"
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+        {
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+          "orderings/task_sequence": [
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+            "miniboone_0",
+            "miniboone_3",
+            "miniboone_4",
+            "miniboone_1"
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+        },
+        {
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+          "orderings/task_sequence": [
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+            "miniboone_4",
+            "miniboone_3",
+            "miniboone_0",
+            "miniboone_2"
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+        },
+        {
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+          "orderings/task_sequence": [
+            "miniboone_1",
+            "miniboone_4",
+            "miniboone_0",
+            "miniboone_3",
+            "miniboone_2"
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+        },
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+            "miniboone_2",
+            "miniboone_4",
+            "miniboone_3"
+          ]
+        }
+      ]
+    }
+  ],
+  "datePublished": "2026-05-06"
+}