README.md

---
license: apache-2.0
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
dataset_info:
  features:
  - name: pmid
    dtype: int64
  - name: journal
    dtype: string
  - name: title
    dtype: string
  - name: abstract
    dtype: string
  - name: keywords
    dtype: string
  - name: pub_type
    dtype: string
  - name: authors
    dtype: string
  - name: doi
    dtype: string
  - name: label
    sequence: int64
  - name: text
    dtype: string
  splits:
  - name: train
    num_bytes: 85014595
    num_examples: 24960
  - name: validation
    num_bytes: 9075648
    num_examples: 2500
  - name: test
    num_bytes: 21408810
    num_examples: 6239
  download_size: 63244210
  dataset_size: 115499053
task_categories:
- text-classification
language:
- en
size_categories:
- 10K<n<100K
---
# Dataset Card for Dataset Name

## Dataset Description

- **Homepage:** [BioCreative VII LitCovid Track](https://biocreative.bioinformatics.udel.edu/tasks/biocreative-vii/track-5/)
- **Paper:** [Multi-label classification for biomedical literature: an overview of the BioCreative VII LitCovid Track for COVID-19 literature topic annotations](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9428574/)

### Dataset Summary

Topic annotation in LitCovid is a multi-label document classification task that assigns one or more labels to each article. There are 7 topic labels used in LitCovid: Treatment, Diagnosis, Prevention, Mechanism, Transmission, Epidemic Forecasting, and Case Report. These topics have been demonstrated to be effective for information retrieval and have also been used in many downstream applications related to COVID-19. 


## Dataset Structure

### Data Instances and Data Splits

- the training set contains 24,960 articles from LitCovid;
- the validation set contains 6,239 articles from LitCovid;
- the test set contains 2,500 articles from LitCovid;

### Data Fields

with the following fields retrieved from PubMed/LitCovid:
• pmid: PubMed Identifier

• journal: journal name

• title: article title

• abstract: article abstract

• keywords: author-provided keywords

• pub_type: article type, e.g., journal article

• authors: author names

• doi: Digital Object Identifier

• label: annotated topics in list format indicating absence or presence of labels in the order 'Treatment,Diagnosis,Prevention,Mechanism,Transmission,Epidemic Forecasting,Case Report'

• text: The text field is created as follows: '[Title]: ' + title + ' [Abstract]: ' + abstract + ' [Keywords]: ' + keywords