Datasets:
pretty_name: CogText PubMed Abstracts
license:
- cc-by-4.0
language:
- en
multilinguality:
- monolingual
task_categories:
- text-classification
task_ids:
- topic-classification
- semantic-similarity-classification
size_categories:
- 100K<n<1M
paperswithcode_id: linking-theories-and-methods-in-cognitive
inference: false
model-index:
- name: cogtext-pubmed
results: []
source_datasets:
- original
language_creators:
- found
- expert-generated
configs:
- config_name: abstracts (2023)
data_files: pubmed/abstracts2023.csv.gz
- config_name: abstracts (2021)
data_files: pubmed/abstracts2021.csv.gz
tags:
- Cognitive Control
- PubMed
Dataset Card for CogText PubMed Abstracts
Table of Contents
- Dataset Description
- Dataset Structure
- Dataset Creation
- Considerations for Using the Data
- Additional Information
Dataset Description
The CogText dataset is a curated collection of abstracts about cognitive tasks and constructs from PubMed. This dataset contains the original abstracts and their corresponding embeddings. Please visit CogText on GitHub for the details and codes.
- Homepage: https://github.com/morteza/cogtext
- Repository: https://github.com/morteza/cogtext
- Point of Contact: Morteza Ansarinia
- Paper: https://arxiv.org/abs/2203.11016
Dataset Summary
The 2021 dataset, collected in December 2021, contains 385,705 distinct scientific articles, featuring their title, abstract, relevant metadata, and embeddings. The articles were specifically selected for their relevance to cognitive control constructs and associated tasks.
Supported Tasks and Leaderboards
Topic Modeling, Text Embedding
Languages
English
Dataset Structure
Data Instances
522,972 scientific articles, of which 385,705 are unique.
Data Fields
The CSV files contain the following fields:
| Field | Description |
|---|---|
index |
(int) Index of the article in the current dataset |
pmid |
(int) PubMed ID |
doi |
(str) Digital Object Identifier |
year |
(int) Year of publication (yyyy format) |
journal_title |
(str) Title of the journal |
journal_iso_abbreviation |
(str) ISO abbreviation of the journal |
title |
(str) Title of the article |
abstract |
(str) Abstract of the article |
category |
(enum) Category of the article, either "CognitiveTask" or "CognitiveConstruct" |
label |
(enum) Label of the article, which refers to the class labels in the ontologies/efo.owl ontology |
original_index |
(int) Index of the article in the full dataset (see pubmed/abstracts.csv.gz) |
Data Splits
| Dataset | Description |
|---|---|
pubmed/abstracts.csv.gz |
Full dataset |
pubmed/abstracts20pct.csv.gz |
20% of the dataset (stratified random sample by label) |
gpt3/abstracts_gp3ada.nc |
GPT-3 embeddings of the entire dataset in XArray/CDF4 format, indexed by pmid |
Dataset Creation
Curation Rationale
[Needs More Information]
Source Data
Initial Data Collection and Normalization
[Needs More Information]
Annotations
Annotation process
[Needs More Information]
Personal and Sensitive Information
[Needs More Information]
Considerations for Using the Data
Social Impact of Dataset
[Needs More Information]
Discussion of Biases
[Needs More Information]
Other Known Limitations
[Needs More Information]
Additional Information
Dataset Curators
[Needs More Information]
Licensing Information
[Needs More Information]
Acknowledgments
This research was supported by the Luxembourg National Research Fund (ATTRACT/2016/ID/11242114/DIGILEARN and INTER Mobility/2017-2/ID/11765868/ULALA).
Citation Information
To cite the paper use the following entry:
@misc{cogtext2022,
author = {Morteza Ansarinia and
Paul Schrater and
Pedro Cardoso-Leite},
title = {Linking Theories and Methods in Cognitive Sciences via Joint Embedding of the Scientific Literature: The Example of Cognitive Control},
year = {2022},
url = {https://arxiv.org/abs/2203.11016}
}