task_categories:text-classification annotations_creators:expert-generated language_creators:expert-generated multilinguality:monolingual size_categories:unknown source_datasets:original language:en license:cc-by-4.0 region:us
---
annotations_creators:
- expert-generated
language_creators:
- expert-generated
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: 'Dataset containing abstracts from PubMed, either related to long COVID
  or not. '
size_categories:
- unknown
source_datasets:
- original
task_categories:
- text-classification
---
## Data Description
Long-COVID related articles have been manually collected by information specialists.  
Please find further information [here](https://doi.org/10.1093/database/baac048). 

## Size 
||Training|Development|Test|Total|
|--|--|--|--|--|
Positive Examples|215|76|70|345|
Negative Examples|199|62|68|345|
Total|414|238|138|690|

## Citation 
@article{10.1093/database/baac048,  
author = {Langnickel, Lisa and Darms, Johannes and Heldt, Katharina and Ducks, Denise and Fluck, Juliane},  
title = "{Continuous development of the semantic search engine preVIEW: from COVID-19 to long COVID}",  
journal = {Database},  
volume = {2022},  
year = {2022},  
month = {07},  
issn = {1758-0463},  
doi = {10.1093/database/baac048},  
url = {https://doi.org/10.1093/database/baac048},  
note = {baac048},  
eprint = {https://academic.oup.com/database/article-pdf/doi/10.1093/database/baac048/44371817/baac048.pdf},  
}   