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Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 3 new columns ({'Deaths', 'Cases', 'Epidemiological week'}) and 2 missing columns ({'Number of cases', 'Month'}).
This happened while the csv dataset builder was generating data using
hf://datasets/tberkane/EpiCurveBench/ground_truth/10.csv (at revision 50875a280d2a52e9e9db200075493ae119ab4a68)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
writer.write_table(table)
File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
pa_table = table_cast(pa_table, self._schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
return cast_table_to_schema(table, schema)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
Epidemiological week: string
Cases: int64
Deaths: int64
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 616
to
{'Month': Value('string'), 'Number of cases': Value('int64')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1455, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1054, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1833, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 3 new columns ({'Deaths', 'Cases', 'Epidemiological week'}) and 2 missing columns ({'Number of cases', 'Month'}).
This happened while the csv dataset builder was generating data using
hf://datasets/tberkane/EpiCurveBench/ground_truth/10.csv (at revision 50875a280d2a52e9e9db200075493ae119ab4a68)
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Month
string | Number of cases
int64 |
|---|---|
MAY-20
| 1
|
JUNE-20
| 11
|
JULY-20
| 30
|
AUGUST-20
| 6
|
SEPTEMBER-20
| 10
|
OCTOBER-20
| 0
|
NOVEMBER-20
| 4
|
DECEMBER-20
| 9
|
JANUARY-21
| 28
|
FEBRUARY-21
| 18
|
MARCH-21
| 10
|
APRIL-21
| 1
|
MAY-21
| 2
|
JUNE-21
| 0
|
JULY-21
| 1
|
AUGUST-21
| 27
|
SEPTEMBER-21
| 26
|
OCTOBER-21
| 8
|
NOVEMBER-21
| 0
|
DECEMBER-21
| 6
|
MIN
| 0
|
MAX
| 35
|
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | null |
null | 0
|
null | 167
|
null | 367
|
null | 477
|
null | 566
|
null | 1,297
|
null | 1,109
|
null | 810
|
null | 835
|
null | 798
|
null | 826
|
null | 1,124
|
null | 1,226
|
null | 1,131
|
null | 1,417
|
null | 1,617
|
null | 2,016
|
null | 0
|
null | 2,000
|
null | 1
|
null | 0
|
null | 0
|
null | 0
|
Dataset Card for Dataset Name
EpiCurveBench is a benchmark for chart data extraction, and is made up of 100 manually curated and annotated epidemic curve images collected from diverse sources.
Dataset Details
Dataset Description
Accurate data on disease case counts over time is essential for training reliable disease forecasting models. However, such data is often locked in non-machine-readable formats, most commonly as epidemic curve (epicurve) images—charts that depict case counts over time for a given location. Digitizing these charts would greatly expand the data available for forecasting models, improving their accuracy. Manual digitization, though, is very time-consuming, and existing automated methods struggle with real-world epicurves due to dense data points, overlapping series, and diverse visual styles. To address this, we present EpiCurveBench, a benchmark of 100 manually curated and annotated epicurve images collected from diverse sources. The dataset spans a wide range of chart styles, from simple to highly complex.
Dataset Sources
- Repository: TODO
- Paper: TODO
Dataset Structure
EpiCurveBench contains two subsets: 30 Basic epicurves, with no more than two series per chart and series lengths of 30 points or fewer, and 70 Advanced epicurves selected for maximum stylistic diversity and difficulty. The Basic subset provides an easier entry point for developing and testing extraction methods.
- advanced_set: The 70 Advanced epicurves images.
- basic_set: The 30 Basic epicurves images.
- ground_truth: CSV files containing ground truth annotations for both the Basic and Advanced sets. The second-to-last line in each file contains the minimum axis values for each data row, and the last line contains the corresponding maximum values. These can be used to normalize the extraction performance scores.
- metadata.csv: Metadata for each epicurve image, contains the following columns:
- id: ID of the corresponding epicurve image.
- source: type of source the image was collected from.
- country: country the image reports disease case counts for.
- years: year/year range the image reports disease case counts for.
- link: source link.
- num_series: number of series present in the image.
- series_len: length of each series on the image.
- annotation_time: time taken to manually annotate this image
- chart_type: type of chart.
- resolution: resolution of the image.
Citation
BibTeX:
TODO
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