# Constructing the CORD-19 Vaccine Dataset

Source: [Constructing the CORD-19 Vaccine Dataset](<https://www.sota2.com/research/paper/68fa74421e209131cea60942>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T03:05:50.207Z

Authors: Manisha Singh, Divy Sharma, Alonso Ma, Bridget Tyree, Margaret Mitchell

Published: 2024-07-26T02:44:55.000Z

## Abstract

We introduce new dataset 'CORD-19-Vaccination' to cater to scientists specifically looking into COVID-19 vaccine-related research. This dataset is extracted from CORD-19 dataset \[Wang et al., 2020\] and augmented with new columns for language detail, author demography, keywords, and topic per paper. Facebook's fastText model is used to identify languages \[Joulin et al., 2016\]. To establish author demography (author affiliation, lab/institution location, and lab/institution country columns) we processed the JSON file for each paper and then further enhanced using Google's search API to determine country values. 'Yake' was used to extract keywords from the title, abstract, and body of each paper and the LDA (Latent Dirichlet Allocation) algorithm was used to add topic information \[Campos et al., 2020, 2018a,b\]. To evaluate the dataset, we demonstrate a question-answering task like the one used in the CORD-19 Kaggle challenge \[Goldbloom et al., 2022\]. For further evaluation, sequential sentence classification was performed on each paper's abstract using the model from Dernoncourt et al. \[2016\]. We partially hand annotated the training dataset and used a pre-trained BERT-PubMed layer. 'CORD- 19-Vaccination' contains 30k research papers and can be immensely valuable for NLP research such as text mining, information extraction, and question answering, specific to the domain of COVID-19 vaccine research.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2407.18471>)

[PDF](<https://arxiv.org/pdf/2407.18471.pdf>)