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