|
|
--- |
|
|
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}, |
|
|
} |
