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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code:   StreamingRowsError
Exception:    UnicodeDecodeError
Message:      'utf-8' codec can't decode byte 0x8c in position 44: invalid start byte
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/responses/first_rows.py", line 337, in get_first_rows_response
                  rows = get_rows(dataset, config, split, streaming=True, rows_max_number=rows_max_number, hf_token=hf_token)
                File "/src/services/worker/src/worker/utils.py", line 123, in decorator
                  return func(*args, **kwargs)
                File "/src/services/worker/src/worker/responses/first_rows.py", line 65, in get_rows
                  ds = load_dataset(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/load.py", line 1739, in load_dataset
                  return builder_instance.as_streaming_dataset(split=split)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1025, in as_streaming_dataset
                  splits_generators = {sg.name: sg for sg in self._split_generators(dl_manager)}
                File "/tmp/modules-cache/datasets_modules/datasets/orieg--elsevier-oa-cc-by/90990052f835613074d87af3592fd8eaee912f17ca4d0401bcf6d2d791d45117/elsevier-oa-cc-by.py", line 109, in _split_generators
                  doc_count = len(glob.glob(f"{corpus_path}/*.json"))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/streaming.py", line 67, in wrapper
                  return function(*args, use_auth_token=use_auth_token, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/download/streaming_download_manager.py", line 522, in xglob
                  fs, *_ = fsspec.get_fs_token_paths(urlpath, storage_options=storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 632, in get_fs_token_paths
                  fs = filesystem(protocol, **inkwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/registry.py", line 266, in filesystem
                  return cls(**storage_options)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/spec.py", line 76, in __call__
                  obj = super().__call__(*args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/zip.py", line 50, in __init__
                  files = open_files(fo, protocol=target_protocol, **(target_options or {}))
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 299, in open_files
                  fs, fs_token, paths = get_fs_token_paths(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/core.py", line 673, in get_fs_token_paths
                  paths = [f for f in sorted(fs.glob(paths)) if not fs.isdir(f)]
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 111, in wrapper
                  return sync(self.loop, func, *args, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 96, in sync
                  raise return_result
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 53, in _runner
                  result[0] = await coro
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 451, in _glob
                  allpaths = await self._find(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 720, in _find
                  async for _, dirs, files in self._walk(path, maxdepth, detail=True, **kwargs):
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/asyn.py", line 592, in _walk
                  listing = await self._ls(path, detail=True, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 193, in _ls
                  out = await self._ls_real(url, detail=detail, **kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/fsspec/implementations/http.py", line 148, in _ls_real
                  text = await r.text()
                File "/src/services/worker/.venv/lib/python3.9/site-packages/aiohttp/client_reqrep.py", line 1085, in text
                  return self._body.decode(  # type: ignore[no-any-return,union-attr]
              UnicodeDecodeError: 'utf-8' codec can't decode byte 0x8c in position 44: invalid start byte

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Dataset Card for Elsevier OA CC-By

Dataset Summary

Elsevier OA CC-By: This is a corpus of 40k (40,091) open access (OA) CC-BY articles from across Elsevier’s journals representing a large scale, cross-discipline set of research data to support NLP and ML research. The corpus include full-text articles published in 2014 to 2020 and are categorized in 27 Mid Level ASJC Code (subject classification).

Distribution of Publication Years

Publication Year Number of Articles
2014 3018
2015 4438
2016 5913
2017 6419
2018 8016
2019 10135
2020 2159

Distribution of Articles Per Mid Level ASJC Code. Each article can belong to multiple ASJC codes.

Discipline Count
General 3847
Agricultural and Biological Sciences 4840
Arts and Humanities 982
Biochemistry, Genetics and Molecular Biology 8356
Business, Management and Accounting 937
Chemical Engineering 1878
Chemistry 2490
Computer Science 2039
Decision Sciences 406
Earth and Planetary Sciences 2393
Economics, Econometrics and Finance 976
Energy 2730
Engineering 4778
Environmental Science 6049
Immunology and Microbiology 3211
Materials Science 3477
Mathematics 538
Medicine 7273
Neuroscience 3669
Nursing 308
Pharmacology, Toxicology and Pharmaceutics 2405
Physics and Astronomy 2404
Psychology 1760
Social Sciences 3540
Veterinary 991
Dentistry 40
Health Professions 821

Supported Tasks and Leaderboards

[More Information Needed]

Languages

English (en).

Dataset Structure

Data Instances

The original dataset was published with the following json structure:

{
    "docId": <str>,
    "metadata":{
        "title": <str>,
        "authors": [
            {
                "first": <str>,
                "initial": <str>,
                "last": <str>,
                "email": <str>
            },
            ...
        ],
        "issn": <str>,
        "volume": <str>,
        "firstpage": <str>,
        "lastpage": <str>,
        "pub_year": <int>,
        "doi": <str>,
        "pmid": <str>,
        "openaccess": "Full",
        "subjareas": [<str>],
        "keywords": [<str>],
        "asjc": [<int>],
    },
    "abstract":[
        {
          "sentence": <str>,
          "startOffset": <int>,
          "endOffset": <int>
        },
        ...
    ],
    "bib_entries":{
        "BIBREF0":{
            "title":<str>,
            "authors":[
                {
                "last":<str>,
                "initial":<str>,
                "first":<str>
                },
                ...
            ],
            "issn": <str>,
            "volume": <str>,
            "firstpage": <str>,
            "lastpage": <str>,
            "pub_year": <int>,
            "doi": <str>,
            "pmid": <str>
        },
        ...
    },
    "body_text":[
        {
        "sentence": <str>,
        "secId": <str>,
        "startOffset": <int>,
        "endOffset": <int>,
        "title": <str>,
        "refoffsets": {
            <str>:{
                "endOffset":<int>,
                "startOffset":<int>
                }
            },
        "parents": [
            {
            "id": <str>,
            "title": <str>
            },
            ...
        ]
    },
    ...
    ]
}

docId The docID is the identifier of the document. This is unique to the document, and can be resolved into a URL for the document through the addition of https//www.sciencedirect.com/science/pii/<docId>

abstract This is the author provided abstract for the document

body_text The full text for the document. The text has been split on sentence boundaries, thus making it easier to use across research projects. Each sentence has the title (and ID) of the section which it is from, along with titles (and IDs) of the parent section. The highest-level section takes index 0 in the parents array. If the array is empty then the title of the section for the sentence is the highest level section title. This will allow for the reconstruction of the article structure. References have been extracted from the sentences. The IDs of the extracted reference and their respective offset within the sentence can be found in the “refoffsets” field. The complete list of references are can be found in the “bib_entry” field along with the references’ respective metadata. Some will be missing as we only keep ‘clean’ sentences,

bib_entities All the references from within the document can be found in this section. If the meta data for the reference is available, it has been added against the key for the reference. Where possible information such as the document titles, authors, and relevant identifiers (DOI and PMID) are included. The keys for each reference can be found in the sentence where the reference is used with the start and end offset of where in the sentence that reference was used.

metadata Meta data includes additional information about the article, such as list of authors, relevant IDs (DOI and PMID). Along with a number of classification schemes such as ASJC and Subject Classification.

author_highlights Author highlights were included in the corpus where the author(s) have provided them. The coverage is 61% of all articles. The author highlights, consisting of 4 to 6 sentences, is provided by the author with the aim of summarising the core findings and results in the article.

Data Fields

  • title: This is the author provided title for the document. 100% coverage.
  • abstract: This is the author provided abstract for the document. 99.25% coverage.
  • keywords: This is the author and publisher provided keywords for the document. 100% coverage.
  • asjc: This is the disciplines for the document as represented by 334 ASJC (All Science Journal Classification) codes. 100% coverage.
  • subjareas: This is the Subject Classification for the document as represented by 27 ASJC top-level subject classifications. 100% coverage.
  • body_text: The full text for the document. 100% coverage.
  • author_highlights: This is the author provided highlights for the document. 61.31% coverage.

Data Splits

Distribution of Publication Years

Train Test Validation
All Articles 32072 4009 4008
With Author Highlights 19644 2420 2514

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

Initial Data Collection and Normalization

Date the data was collected: 2020-06-25T11:00:00.000Z

See the original paper for more detail on the data collection process.

Who are the source language producers?

See 3.1 Data Sampling in the original paper.

Annotations

Annotation process

[More Information Needed]

Who are the annotators?

[More Information Needed]

Personal and Sensitive Information

[More Information Needed]

Considerations for Using the Data

Social Impact of Dataset

[More Information Needed]

Discussion of Biases

[More Information Needed]

Other Known Limitations

[More Information Needed]

Additional Information

Dataset Curators

[More Information Needed]

Licensing Information

CC BY 4.0

Citation Information

@article{Kershaw2020ElsevierOC,
  title     = {Elsevier OA CC-By Corpus},
  author    = {Daniel James Kershaw and R. Koeling},
  journal   = {ArXiv},
  year      = {2020},
  volume    = {abs/2008.00774},
  doi       = {https://doi.org/10.48550/arXiv.2008.00774},
  url       = {https://elsevier.digitalcommonsdata.com/datasets/zm33cdndxs},
  keywords  = {Science, Natural Language Processing, Machine Learning, Open Dataset},
  abstract  = {We introduce the Elsevier OA CC-BY corpus. This is the first open
               corpus of Scientific Research papers which has a representative sample
               from across scientific disciplines. This corpus not only includes the
               full text of the article, but also the metadata of the documents, 
               along with the bibliographic information for each reference.}
}
@dataset{https://10.17632/zm33cdndxs.3,
  doi       = {10.17632/zm33cdndxs.2},
  url       = {https://data.mendeley.com/datasets/zm33cdndxs/3},
  author    = "Daniel Kershaw and Rob Koeling",
  keywords  = {Science, Natural Language Processing, Machine Learning, Open Dataset},
  title     = {Elsevier OA CC-BY Corpus},
  publisher = {Mendeley},
  year      = {2020},
  month     = {sep}
}

Contributions

Thanks to @orieg for adding this dataset.

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