README.md

---
dataset_info:
  features:
  - name: id
    dtype: string
  - name: tokens
    sequence: string
  - name: labels
    sequence:
      class_label:
        names:
          '0': '0'
          '1': B-PrimaryOutcome
          '2': I-PrimaryOutcome
          '3': B-SecondaryOutcome
          '4': I-SecondaryOutcome
  splits:
  - name: train
    num_bytes: 1740905
    num_examples: 3660
  - name: test
    num_bytes: 277244
    num_examples: 564
  download_size: 423457
  dataset_size: 2018149
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: test
    path: data/test-*
task_categories:
- token-classification
language:
- en
tags:
- biomedical
- clinical-trial
- scientific-articles
size_categories:
- 1K<n<10K
---

# Dataset Card for Dataset Name

Corpus for token classification of primary and secondary outcomes in scientific articles sentences, in BIO format.

## Dataset Details

### Dataset Description

Filtered the [EBM-NLP](https://github.com/bepnye/EBM-NLP) corpus Outcomes subset and did the following processing: 
* split examples into sentences and get the entities for each sentence
* verify in each sentences mentions of primary and secondary outcomes using regular expression (with synonyms)
* tagged all outcomes according to type when the regex was found in a sentence
* removed entity tags for all sentences that were not detected as containing primary or secondary outcome
* kept the examples at sentence level
* kept original train/test set


Then added [data from A. koroleva](https://github.com/aakorolyova/DeSpin-2.0/tree/main/data/Primary_Outcomes) on primary outcomes (manually annotated corpus).
Part of this data is 

Then added manually annotated data from a study on primary outcome switching in colorectal cancer articles (this data is only used in test set).

Finally as the filtered EBM-NLP contained a lot of sequences without entities in the end, we sampled from these sequences so that we have an equal number of sequences with and without entities, in train and test set.

- **Curated by:** Mathieu Laï-king
- **Funded by [optional]:** [More Information Needed]
- **Shared by [optional]:** [More Information Needed]
- **Language(s) (NLP):** English
- **License:** None

### Dataset Sources [optional]

From 2 existing corpus one from A. Koroleva and the other is EBM-NLP.

- **Repository:** [More Information Needed]
- **Paper [optional]:** [More Information Needed]
- **Demo [optional]:** [More Information Needed]

## Uses


### Direct Use

Fine tuning NER models


### Out-of-Scope Use

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## Dataset Structure

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## Dataset Creation

### Curation Rationale

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### Source Data

<!-- This section describes the source data (e.g. news text and headlines, social media posts, translated sentences, ...). -->

#### Data Collection and Processing

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#### Who are the source data producers?

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### Annotations [optional]

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#### Annotation process

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#### Who are the annotators?

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#### Personal and Sensitive Information

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## Bias, Risks, and Limitations

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### Recommendations

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Users should be made aware of the risks, biases and limitations of the dataset. More information needed for further recommendations.

## Citation [optional]

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**BibTeX:**

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**APA:**

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## Glossary [optional]

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## Dataset Card Authors [optional]

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## Dataset Card Contact

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