The dataset viewer is not available for this dataset.
Error code: ConfigNamesError
Exception: FileNotFoundError
Message: Couldn't find a dataset script at /src/services/worker/n1ghtf4l1/Ariel-Data-Challenge-NeurIPS-2022/Ariel-Data-Challenge-NeurIPS-2022.py or any data file in the same directory. Couldn't find 'n1ghtf4l1/Ariel-Data-Challenge-NeurIPS-2022' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in n1ghtf4l1/Ariel-Data-Challenge-NeurIPS-2022.
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 55, in compute_config_names_response
for config in sorted(get_dataset_config_names(path=dataset, token=hf_token))
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 351, in get_dataset_config_names
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1491, in dataset_module_factory
raise FileNotFoundError(
FileNotFoundError: Couldn't find a dataset script at /src/services/worker/n1ghtf4l1/Ariel-Data-Challenge-NeurIPS-2022/Ariel-Data-Challenge-NeurIPS-2022.py or any data file in the same directory. Couldn't find 'n1ghtf4l1/Ariel-Data-Challenge-NeurIPS-2022' on the Hugging Face Hub either: FileNotFoundError: No (supported) data files or dataset script found in n1ghtf4l1/Ariel-Data-Challenge-NeurIPS-2022.Need help to make the dataset viewer work? Open a discussion for direct support.
Ariel Data Challenge NeurIPS 2022
Dataset is part of the Ariel Machine Learning Data Challenge. The Ariel Space mission is a European Space Agency mission to be launched in 2029. Ariel will observe the atmospheres of 1000 extrasolar planets - planets around other stars - to determine how they are made, how they evolve and how to put our own Solar System in the gallactic context.
Understanding worlds in our Milky Way
Today we know of roughly 5000 exoplanets in our Milky Way galaxy. Given that the first planet was only conclusively discovered in the mid-1990's, this is an impressive achievement. Yet, simple number counting does not tell us much about the nature of these worlds. One of the best ways to understand their formation and evolution histories is to understand the composition of their atmospheres. What's the chemistry, temperatures, cloud coverage, etc? Can we see signs of possible bio-markers in the smaller Earth and super-Earth planets? Since we can't get in-situ measurements (even the closest exoplanet is lightyears away), we rely on remote sensing and interpreting the stellar light that shines through the atmosphere of these planets. Model fitting these atmospheric exoplanet spectra is tricky and requires significant computational time. This is where you can help!
Speed up model fitting!
Today, our atmospheric models are fit to the data using MCMC type approaches. This is sufficient if your atmospheric forward models are fast to run but convergence becomes problematic if this is not the case. This challenge looks at inverse modelling using machine learning. For more information on why we need your help, we provide more background in the about page and the documentation.
Many thanks to...
NeurIPS 2022 for hosting the data challenge and to the UK Space Agency and the European Research Council for support this effort. Also many thanks to the data challenge team and partnering institutes, and of course thanks to the Ariel team for technical support and building the space mission in the first place!
For more information, contact us at: exoai.ucl [at] gmail.com
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