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README.md

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----
-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},  
-}   

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