File size: 2,648 Bytes
dd659f1
 
8f73636
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
4293cd4
 
 
 
 
 
dd659f1
4293cd4
 
 
 
1b41766
 
4293cd4
 
 
1b41766
4293cd4
 
 
 
1b41766
4293cd4
1b41766
 
 
4293cd4
 
 
1b41766
 
9ba6d1e
1b41766
9ba6d1e
1b41766
9ba6d1e
1b41766
9ba6d1e
1b41766
9ba6d1e
1b41766
9ba6d1e
1b41766
9ba6d1e
1b41766
9ba6d1e
1b41766
4293cd4
6adff61
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
---
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