license: cc-by-sa-4.0
language:
- en
size_categories:
- n<1K
dataset_info:
- config_name: ClusTREC-Covid
description: Full ClusTREC-Covid dataset with all raw columns.
version: 1.0.0
splits:
- name: test
num_examples: 2284
- config_name: title
description: Full ClusTREC-Covid with titles only.
version: 1.0.0
splits:
- name: test
num_examples: 2284
- config_name: title and abstract
description: Full ClusTREC-Covid with titles and abstract.
version: 1.0.0
splits:
- name: test
num_examples: 2284
configs:
- config_name: ClusTREC-Covid
data_files:
- split: test
path: ClusTREC-Covid/clustrec_covid.jsonl
- config_name: title
data_files:
- split: test
path: title/clustrec_covid_title.jsonl
- config_name: title and abstract
data_files:
- split: test
path: title_and_abstract/clustrec_covid_abstract_and_title.jsonl
builder_name: ClusTREC-Covid
version: 1.0.0
CLUSTREC-COVID: A Topical Clustering Benchmark for COVID-19 Scientific Research
Dataset Summary
CLUSTREC-COVID is a modified version of the TREC-COVID dataset, transformed into a topical clustering benchmark. The dataset consists of titles and abstracts from scientific papers about COVID-19 research, covering a diverse range of research topics. Each document in the dataset is assigned to a specific subtopic, making it ideal for use in document clustering and topic modeling tasks.
The dataset is useful for researchers aiming to evaluate clustering algorithms and techniques for automatic organization of scientific literature. It can also be used for exploring information retrieval systems that aim to group documents by subtopic or related research areas.
The source of this dataset is the TREC-COVID retrieval dataset, which has been adapted for clustering and organization tasks.
Dataset Structure
Each document in the dataset includes the following fields:
- topic_name (string): The specific subtopic to which the document has been assigned. (e.g., "coronavirus response to weather changes").
- topic_id (string): A unique identifier for the topic. (cluster identifier)
- title (string): The title of the scientific paper.
- abstract (string): The abstract or summary of the paper.
- doc_id (string): A unique document identifier.
Example Entry
{
"topic_name": "coronavirus response to weather changes",
"topic_id": "2",
"title": "Weather variables impact on COVID-19 incidence",
"abstract": "We test the hypothesis of COVID-19 contagion being influenced by meteorological parameters such as temperature or humidity.\
We analysed data at high spatial resolution (regions in Italy and counties in the USA) and found that while at low resolution this might seem the case,\
at higher resolution no correlation is found. Our results are consistent with a poor outdoors transmission of the disease. However,\
a possible indirect correlation between good weather and a decrease in disease spread may occur,\
as people spend longer time outdoors.",
"doc_id": "hadnxjeo",
}
Citation Information
Cite as:
@article{katz2024knowledge,
title={Knowledge Navigator: LLM-guided Browsing Framework for Exploratory Search in Scientific Literature},
author={Katz, Uri and Levy, Mosh and Goldberg, Yoav},
journal={arXiv preprint arXiv:2408.15836},
year={2024}
}