{
  "access": "registration",
  "card": {
    "fair": {
      "accessible": false,
      "findable": true,
      "interoperable": true,
      "notes": "registration; competition-rules licence",
      "reusable": false
    },
    "intended_use": "Developing and benchmarking machine-learning event reconstruction for neutrino telescopes.",
    "known_limitations": "Access requires a Kaggle account and acceptance of the competition rules; no DOI.",
    "out_of_scope_use": "Physics analyses: the events are simulated, not detector data, and come without the full systematics treatment.",
    "summary": "Large simulated IceCube event sample with ground-truth directions, released as an ML challenge."
  },
  "creators": [
    {
      "kind": "collaboration",
      "name": "IceCube Collaboration"
    }
  ],
  "curation": {
    "added": "2026-10-23",
    "quality_notes": ["sample size and file layout not recorded here (Kaggle data page needs a login)"],
    "source": "manual (user suggestion)",
    "status": "curated"
  },
  "data": {
    "formats": ["parquet"],
    "has_mc": true,
    "levels": ["mc"],
    "observables": ["pulse sensor id", "time", "charge", "true direction"]
  },
  "date_published": 2023,
  "description": "Data of the public Kaggle competition \"IceCube – Neutrinos in Deep Ice\" (2023), organised by the IceCube\nCollaboration: simulated neutrino events as lists of pulses (sensor, time, charge) with the sensor geometry and the\ntrue neutrino direction, for the task of reconstructing the arrival direction with machine-learning methods. One of\nthe largest public neutrino-telescope simulation samples and a standard benchmark for ML reconstruction.",
  "detector_technologies": ["ice-cherenkov"],
  "distributions": [
    {
      "formats": ["parquet"],
      "host": "other",
      "label": "Kaggle competition data",
      "role": "primary",
      "url": "https://www.kaggle.com/competitions/icecube-neutrinos-in-deep-ice/data"
    }
  ],
  "experiments": ["icecube"],
  "id": "icecube-kaggle-neutrinos-in-deep-ice",
  "license": "proprietary",
  "license_notes": "Kaggle competition data: use under the competition rules (Kaggle account and acceptance of the rules required)",
  "publisher": {
    "kind": "organization",
    "name": "Kaggle"
  },
  "references": [
    {
      "arxiv": "2307.15289",
      "citation": "IceCube, Public Kaggle Competition \"IceCube – Neutrinos in Deep Ice\", PoS(ICRC2023)1609",
      "doi": "10.22323/1.444.1609",
      "relation": "describes"
    },
    {
      "arxiv": "2310.15674",
      "citation": "IceCube, The top 3 solutions from the public Kaggle competition, Eur. Phys. J. C 84 (2024)",
      "doi": "10.1140/epjc/s10052-024-12977-2",
      "relation": "describes"
    }
  ],
  "relations": [
    {
      "rel": "is_part_of",
      "target": "icecube-data-releases"
    }
  ],
  "short": "IceCube Kaggle challenge (2023): simulated events (pulses per sensor) with true directions, for ML reconstruction",
  "sources": ["atmospheric", "astrophysical"],
  "title": "IceCube – Neutrinos in Deep Ice (Kaggle competition data)",
  "topics": ["detector-response", "statistics"],
  "type": "dataset",
  "url_landing": "https://www.kaggle.com/competitions/icecube-neutrinos-in-deep-ice",
  "used_by": [
    {
      "kind": "paper",
      "label": "Top-3 Kaggle solutions (EPJC 84, 2024)",
      "notes": "winning direction-reconstruction methods applied to IceCube",
      "target": "doi:10.1140/epjc/s10052-024-12977-2"
    }
  ]
}