The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/packaged_modules/folder_based_builder/folder_based_builder.py", line 244, in _split_generators
raise ValueError(
ValueError: `file_name` or `*_file_name` must be present as dictionary key (with type string) in metadata files
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 65, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
Dataset Card for Dataset Name
EpiCurveBench is a benchmark for chart data extraction, and is made up of 1000 manually curated 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 1000 manually curated 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
- images: The 1000 epicurves images.
- tables: TSV files containing ground truth annotations. 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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