WILDS
Collection
WILDS is a benchmark of in-the-wild distribution shifts spanning diverse data modalities and applications.
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The dataset viewer is not available for this split.
The info cannot be fetched for the config 'default' of the dataset.
Error code: InfoError
Exception: HfHubHTTPError
Message: 500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/1aurent/RxRx1 (Request ID: Root=1-65dfa1a7-0ce6bd5e23462e010c9a0336;dc3a1576-a928-4a36-b32c-d1d0171298a2)
Internal Error - We're working hard to fix this as soon as possible!
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 210, in compute_first_rows_from_streaming_response
info = get_dataset_config_info(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 477, in get_dataset_config_info
builder = load_dataset_builder(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 2220, in load_dataset_builder
dataset_module = dataset_module_factory(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1871, in dataset_module_factory
raise e1 from None
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1816, in dataset_module_factory
raise e
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1790, in dataset_module_factory
dataset_info = hf_api.dataset_info(
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_validators.py", line 118, in _inner_fn
return fn(*args, **kwargs)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/hf_api.py", line 2148, in dataset_info
hf_raise_for_status(r)
File "/src/services/worker/.venv/lib/python3.9/site-packages/huggingface_hub/utils/_errors.py", line 333, in hf_raise_for_status
raise HfHubHTTPError(str(e), response=response) from e
huggingface_hub.utils._errors.HfHubHTTPError: 500 Server Error: Internal Server Error for url: https://huggingface.co/api/datasets/1aurent/RxRx1 (Request ID: Root=1-65dfa1a7-0ce6bd5e23462e010c9a0336;dc3a1576-a928-4a36-b32c-d1d0171298a2)
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RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods
Homepage: https://www.rxrx.ai/rxrx1
Publication Date: 2019-06
License: Attribution-NonCommercial-ShareAlike 4.0 International (CC BY-NC-SA 4.0)
Citation:
@misc{sypetkowski2023rxrx1,
title = {RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods},
author = {Maciej Sypetkowski and Morteza Rezanejad and Saber Saberian and Oren Kraus and John Urbanik and James Taylor and Ben Mabey and Mason Victors and Jason Yosinski and Alborz Rezazadeh Sereshkeh and Imran Haque and Berton Earnshaw},
year = {2023},
eprint = {2301.05768},
archiveprefix = {arXiv},
primaryclass = {cs.CV}
}
Description
High-throughput screening techniques are commonly used to obtain large quantities of data in many fields of biology. It is well known that artifacts arising from variability in the technical execution of different experimental batches within such screens confound these observations and can lead to invalid biological conclusions. It is therefore necessary to account for these batch effects when analyzing outcomes. In this paper we describe RxRx1, a biological dataset designed specifically for the systematic study of batch effect correction methods. The dataset consists of 125,510 high-resolution fluorescence microscopy images of human cells under 1,138 genetic perturbations in 51 experimental batches across 4 cell types. Visual inspection of the images alone clearly demonstrates significant batch effects. We propose a classification task designed to evaluate the effectiveness of experimental batch correction methods on these images and examine the performance of a number of correction methods on this task. Our goal in releasing RxRx1 is to encourage the development of effective experimental batch correction methods that generalize well to unseen experimental batches.
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