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Multi-Label Academic Topics Dataset
Data Science and Analytics
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Free
About
Explores multi-label topic modelling for research articles, addressing the challenge of finding relevant scientific papers within large online archives. It provides a means to identify articles through tagging or topic assignment, thereby facilitating recommendation and search processes. The data consists of abstracts and titles for research articles, with the objective of predicting their associated topics. A key characteristic is that a single article may belong to more than one topic, drawing from six predefined categories: Computer Science, Physics, Mathematics, Statistics, Quantitative Biology, and Quantitative Finance.
Columns
- ID: A unique identifier assigned to each research article.
- Computer Science: An indicator (1/0) denoting whether the article pertains to the Computer Science topic.
- Physics: An indicator (1/0) denoting whether the article pertains to the Physics topic.
- Mathematics: An indicator (1/0) denoting whether the article pertains to the Mathematics topic.
- Statistics: An indicator (1/0) denoting whether the article pertains to the Statistics topic.
- Quantitative Biology: An indicator (1/0) denoting whether the article pertains to the Quantitative Biology topic.
- Quantitative Finance: An indicator (1/0) denoting whether the article pertains to the Quantitative Finance topic.
Distribution
The data is typically provided in a CSV file format. A sample file,
sample_submission.csv, is approximately 161.9 kB in size. This file contains 7 columns and includes 8,989 records, each representing a unique research article.Usage
Ideal for developing and evaluating multi-label classification models for topic prediction. It can be applied in creating enhanced search functionalities for academic databases, building recommendation engines for scientific literature, and advancing natural language processing techniques for text classification. Potential applications extend to academic research, information retrieval, and machine learning model development.
Coverage
The dataset's scope is primarily focused on research article abstracts and titles drawn from six specific academic domains: Computer Science, Physics, Mathematics, Statistics, Quantitative Biology, and Quantitative Finance. There are no explicit geographic, time range, or demographic limitations mentioned. The data is specifically designed for the prediction of topics within these predefined categories.
License
CC0: Public Domain.
Who Can Use It
- Researchers: For exploring and advancing multi-label topic modelling techniques.
- Data Scientists: To build and benchmark natural language processing models for text classification.
- Machine Learning Engineers: For developing recommendation systems and intelligent search tools for academic content.
- Academics: To analyse trends in scientific literature and improve information accessibility.
Dataset Name Suggestions
- Research Article Topic Classification
- Multi-Label Academic Topics Dataset
- Scientific Paper Topic Predictor
- NLP Article Tagging Data
- Journal Article Topic Model
Attributes
Original Data Source:
QUALITY