{
  "dmp": {
    "title": "Predicting Road Accident Severity in Great Britain",
    "description": "Data Management Plan for a machine learning experiment that predicts the severity of road traffic collisions in Great Britain using the UK Department for Transport STATS19 open dataset (2020-2024). The experiment trains a Gradient Boosting classifier and evaluates it on held-out test data, producing trained model artefacts and evaluation outputs.",
    "language": "eng",
    "created": "2026-05-29",
    "modified": "2026-05-29",
    "dmp_id": {
      "identifier": "https://doi.org/10.70124/545b4-t1166",
      "type": "doi"
    },
    "contact": {
      "name": "El Dib, Yehea",
      "mbox": "e12450748@student.tuwien.ac.at",
      "contact_id": {
        "identifier": "https://orcid.org/0009-0003-8506-0271",
        "type": "orcid"
      }
    },
    "contributor": [
      {
        "name": "El Dib, Yehea",
        "mbox": "e12450748@student.tuwien.ac.at",
        "role": [
          "data_manager",
          "project_leader"
        ],
        "contributor_id": {
          "identifier": "https://orcid.org/0009-0003-8506-0271",
          "type": "orcid"
        }
      },
      {
        "name": "Charles, Logan",
        "mbox": "e12550259@student.tuwien.ac.at",
        "role": [
          "researcher"
        ],
        "contributor_id": {
          "identifier": "https://orcid.org/0009-0002-3977-1286",
          "type": "orcid"
        }
      },
      {
        "name": "Hardt, Julian",
        "mbox": "e12330562@student.tuwien.ac.at",
        "role": [
          "researcher"
        ],
        "contributor_id": {
          "identifier": "https://orcid.org/0009-0003-0171-5796",
          "type": "orcid"
        }
      },
      {
        "name": "Höfinger, Balthasar",
        "mbox": "e11908607@student.tuwien.ac.at",
        "role": [
          "researcher"
        ],
        "contributor_id": {
          "identifier": "https://orcid.org/0009-0000-2002-4200",
          "type": "orcid"
        }
      }
    ],
    "cost": [],
    "project": [
      {
        "title": "Predicting Road Accident Severity in Great Britain",
        "description": "Student project for the FAIR Data Science / Data Stewardship 2026SS course at TU Wien. The project develops a fully documented, FAIR-compliant open-science machine learning experiment predicting road collision severity using UK Government open data. Zenodo DOI: https://doi.org/10.5281/zenodo.20416076",
        "start": "2026-02-01",
        "end": "2026-06-30",
        "funding": [
          {
            "funder_id": {
              "identifier": "https://ror.org/04d836q62",
              "type": "ror"
            },
            "funding_status": "planned",
            "grant_id": {
              "identifier": "N/A - student course project",
              "type": "other"
            }
          }
        ]
      }
    ],
    "ethical_issues_exist": "no",
    "ethical_issues_description": "The dataset consists entirely of aggregated road collision statistics published by an official government body. No personal data are processed. GDPR does not apply.",
    "ethical_issues_report": "No ethical review required.",
    "dataset": [
      {
        "dataset_id": {
          "identifier": "https://www.gov.uk/government/statistical-data-sets/road-safety-open-data",
          "type": "url"
        },
        "title": "STATS19 Road Safety Open Dataset (2020-2024)",
        "description": "Police-reported road traffic collision records from Great Britain covering 2020-2024, collected via the STATS19 reporting system and published by the UK Department for Transport. Approximately 503,000 rows with 44 attributes per collision including severity, location, road conditions, weather, and vehicle/casualty counts. Downloaded once from the UK Government open data portal and imported into TU Wien DBRepo.",
        "type": "dataset",
        "format": [
          "text/csv"
        ],
        "keyword": [
          "road safety",
          "collision severity",
          "STATS19",
          "UK traffic data",
          "open government data"
        ],
        "language": "eng",
        "issued": "2019-01-01",
        "personal_data": "no",
        "sensitive_data": "no",
        "is_reused": "reused",
        "data_quality_assurance": [
          "Row counts and class distributions verified at every pipeline step.",
          "DBRepo view row count verified against X-Count response header before processing.",
          "Fixed random_state=42 used throughout for reproducibility.",
          "SMOTE applied to training set only to handle class imbalance."
        ],
        "distribution": [
          {
            "title": "STATS19 Road Safety Open Dataset — UK Government open data portal",
            "access_url": "https://www.gov.uk/government/statistical-data-sets/road-safety-open-data",
            "available_until": "2036-06-30",
            "byte_size": 524288000,
            "data_access": "open",
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            "license": [
              {
                "license_ref": "https://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/",
                "start_date": "2019-01-01"
              }
            ]
          },
          {
            "title": "STATS19 Road Safety Open Dataset — TU Wien DBRepo",
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                "start_date": "2026-05-11"
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              "storage_type": "relational database"
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        ],
        "security_and_privacy": [
          {
            "title": "No special measures required",
            "description": "The dataset contains no personal or sensitive data. Standard open-access permissions apply."
          }
        ]
      },
      {
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        "title": "UK Collision Severity Prediction — Processed Data Splits",
        "description": "Train (70%), validation (15%), and test (15%) CSV splits produced from the STATS19 input data by 01_load_data.py and 02_preprocess.py. Split is stratified by collision_severity with fixed random_state=42. train_resampled.csv includes SMOTE oversampling of minority classes. Each file contains 15 ML features plus the collision_severity label.",
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        "language": "eng",
        "personal_data": "no",
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        "data_quality_assurance": [
          "Stratified split ensures class distribution is preserved across all three sets.",
          "Row counts printed after each split step to catch silent data loss."
        ],
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          {
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          },
          {
            "title": "GitHub repository — Zenodo DOI",
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      },
      {
        "dataset_id": {
          "identifier": "https://doi.org/10.70124/cghhd-yb573",
          "type": "doi"
        },
        "title": "UK Collision Severity Prediction — Trained Gradient Boosting Model",
        "description": "Serialised scikit-learn GradientBoostingClassifier trained on the SMOTE-resampled training set to predict collision severity (Fatal / Serious / Slight). Selected from three candidates (Decision Tree, Random Forest, Gradient Boosting) by Macro F1 on the validation set. Hyperparameters: n_estimators=150, max_depth=6, learning_rate=0.1, random_state=42. Test set performance: accuracy 0.676, macro F1 0.403.",
        "type": "model",
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          "application/octet-stream"
        ],
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          "scikit-learn",
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        ],
        "language": "eng",
        "personal_data": "no",
        "sensitive_data": "no",
        "is_reused": "produced",
        "data_quality_assurance": [
          "Model selected by Macro F1 score on held-out validation set.",
          "Test set used only once for final evaluation.",
          "FAIR4ML metadata documents all hyperparameters and evaluation metrics."
        ],
        "distribution": [
          {
            "title": "Trained model — TU Wien Research Data Repository",
            "available_until": "2036-06-30",
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        "dataset_id": {
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          "type": "doi"
        },
        "title": "UK Collision Severity Prediction — Model Evaluation Outputs",
        "description": "Evaluation figures and predictions produced by 03_train_classifier.py and 04_evaluate.py: test_predictions_2026-05-25.csv (predicted vs. actual labels for 17,220 test samples), 01_data_understanding.png, 02_class_imbalance.png, 03_confusion_matrix.png, 04_performance_comparison.png, 05_feature_importance.png.",
        "type": "dataset",
        "format": [
          "text/csv",
          "image/png"
        ],
        "keyword": [
          "road safety",
          "collision severity",
          "confusion matrix",
          "feature importance",
          "evaluation"
        ],
        "language": "eng",
        "personal_data": "no",
        "sensitive_data": "no",
        "is_reused": "produced",
        "distribution": [
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}