Datasets:

musfiqurtuhin
/

bioslurry-methane-emission

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
Treatment: string
R1 (kg/ha): double
R2 (kg/ha): double
R3 (kg/ha): double
Mean (kg/ha): double
feature_1: null
feature_2: null
feature_3: null
feature_4: null
emission_level: null
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 865
to
{'feature_1': Value('float32'), 'feature_2': Value('float32'), 'feature_3': Value('float32'), 'feature_4': Value('float32'), 'emission_level': ClassLabel(names=['Low', 'Medium', 'High'])}
because column names don't match
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/utils.py", line 99, in get_rows_or_raise
                  return get_rows(
                         ^^^^^^^^^
                File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
                  return func(*args, **kwargs)
                         ^^^^^^^^^^^^^^^^^^^^^
                File "/src/services/worker/src/worker/utils.py", line 77, in get_rows
                  rows_plus_one = list(itertools.islice(ds, rows_max_number + 1))
                                  ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2431, in __iter__
                  for key, example in ex_iterable:
                                      ^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1984, in _iter_arrow
                  pa_table = cast_table_to_features(pa_table, self.features)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2192, in cast_table_to_features
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              Treatment: string
              R1 (kg/ha): double
              R2 (kg/ha): double
              R3 (kg/ha): double
              Mean (kg/ha): double
              feature_1: null
              feature_2: null
              feature_3: null
              feature_4: null
              emission_level: null
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 865
              to
              {'feature_1': Value('float32'), 'feature_2': Value('float32'), 'feature_3': Value('float32'), 'feature_4': Value('float32'), 'emission_level': ClassLabel(names=['Low', 'Medium', 'High'])}
              because column names don't match

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Bio-Slurry Methane Emission Dataset

📄 Description

This dataset is the official data source for the research paper "Predictive and Economic Assessment of Methane Emissions from Bio-Slurry Amended Systems using Ensemble Machine Learning", accepted at STI 2025 (Awaiting Publication).

It contains experimental data designed to predict methane emissions from bio-slurry amended agricultural systems. The dataset supports both regression (predicting exact emission values) and classification (categorizing emissions into Low, Medium, and High levels) tasks. It serves as a valuable resource for researchers working on environmental sustainability and climate change mitigation through machine learning.

🚀 Quick Start

You can easily load this dataset using the Hugging Face datasets library:

from datasets import load_dataset

# Load the dataset
dataset = load_dataset("musfiqurtuhin/bioslurry-methane-emission")

# View the first example
print(dataset['train'][0])

� Dataset Structure

Data Instances

A typical data point consists of physicochemical parameters of the bio-slurry system and the corresponding methane emission level.

{
  "feature_1": 0.5,
  "feature_2": 1.2,
  "feature_3": 0.8,
  "feature_4": 0.3,
  "emission_level": 1
}

Data Fields

Field Name	Type	Description
`feature_1`	`float32`	Physicochemical parameter 1 (e.g., pH, Temperature)
`feature_2`	`float32`	Physicochemical parameter 2
`feature_3`	`float32`	Physicochemical parameter 3
`feature_4`	`float32`	Physicochemical parameter 4
`emission_level`	`class_label`	Categorized emission level: `0` (Low), `1` (Medium), `2` (High)

📊 Dataset Statistics

Total Samples: 350
Preprocessing: Outliers removed using IQR method.
Tasks: Regression, Classification (3 classes)

�🔗 Associated Resources

GitHub Repository: BioSlurry-Methane-Emission-Prediction
Kaggle Dataset: Bio-Slurry Methane Emission Dataset

⚠️ Considerations for Using the Data

Social Impact of Dataset

This dataset contributes to the understanding of greenhouse gas emissions from agricultural practices. Better prediction models can lead to more effective mitigation strategies, positively impacting climate change efforts.

Discussion of Biases

The data is collected from specific experimental setups and may not fully represent all global bio-slurry systems. Users should consider the geographical and experimental context when generalizing findings.

🤝 Citation

If you use this dataset, please cite it as follows:

@misc{md__musfiqur_rahman_2025,
    title={Bio-Slurry Methane Emission Dataset},
    url={https://www.kaggle.com/dsv/13932927},
    DOI={10.34740/KAGGLE/DSV/13932927},
    publisher={Kaggle},
    author={Md. Musfiqur Rahman},
    year={2025}
}

📬 Contact

For questions or feedback, please contact: Md. Musfiqur Rahman

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