The dataset viewer is not available for this dataset.
The dataset tries to import a module that is not installed.
Error code:   DatasetModuleNotInstalledError
Exception:    ImportError
Message:      To be able to use bigbio/flambe, you need to install the following dependency: bigbio.
Please install it using 'pip install bigbio' for instance.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/dataset/config_names.py", line 72, in compute_config_names_response
                  config_names = get_dataset_config_names(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/inspect.py", line 347, in get_dataset_config_names
                  dataset_module = dataset_module_factory(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1910, in dataset_module_factory
                  raise e1 from None
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1876, in dataset_module_factory
                  return HubDatasetModuleFactoryWithScript(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1498, in get_module
                  local_imports = _download_additional_modules(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 353, in _download_additional_modules
                  raise ImportError(
              ImportError: To be able to use bigbio/flambe, you need to install the following dependency: bigbio.
              Please install it using 'pip install bigbio' for instance.

Need help to make the dataset viewer work? Open a discussion for direct support.

Dataset Card for Flambe

FlaMBe is a dataset aimed at procedural knowledge extraction from biomedical texts, particularly focusing on single cell research methodologies described in academic papers. It includes annotations from 55 full-text articles and 1,195 abstracts, covering nearly 710,000 tokens, and is distinguished by its comprehensive named entity recognition (NER) and disambiguation (NED) for tissue/cell types, software tools, and computational methods. This dataset, to our knowledge, is the largest of its kind for tissue/cell types, links entities to identifiers in relevant knowledge bases and annotates nearly 400 workflow relations between tool-context pairs.

Citation Information

@inproceedings{,
  author    = {Dannenfelser, Ruth and Zhong, Jeffrey and Zhang, Ran and Yao, Vicky},
  title     = {Into the Single Cell Multiverse: an End-to-End Dataset for Procedural Knowledge Extraction in Biomedical Texts},
  publisher   = {Advances in Neural Information Processing Systems},
  volume    = {36},
  year      = {2024},
  url       = {https://proceedings.neurips.cc/paper_files/paper/2023/file/23e3d86c9a19d0caf2ec997e73dfcfbd-Paper-Datasets_and_Benchmarks.pdf},
}
Downloads last month
50