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Abstracts for Topic Prediction Dataset
Education & Learning Analytics
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About
This dataset provides a curated collection of research article abstracts and titles, designed to facilitate topic modelling, tagging, and advanced search and recommendation systems within large online archives of scientific literature. Researchers often face challenges in identifying relevant articles, and this dataset aims to streamline that process by enabling the prediction of article topics. Each research article within the dataset can be associated with one or more topics, reflecting the multidisciplinary nature of contemporary research. The abstracts are drawn from six distinct academic fields: Computer Science, Mathematics, Physics, Statistics, Quantitative Biology, and Quantitative Finance.
Columns
- Title: The title of the research article.
- Abstract: The abstract of the research article, providing a summary of its content.
- Topics: The assigned topic or topics for the research article. An article may have multiple topics.
Distribution
The dataset is typically provided in a CSV (Comma Separated Values) format. Specific numbers for rows or records are not currently available. It is structured to support the analysis of research article abstracts and titles for topic identification, with articles categorised across six primary scientific disciplines.
Usage
This dataset is ideal for developing and evaluating machine learning models for natural language processing (NLP), specifically for topic modelling and classification tasks. Key applications include:
- Building systems that can automatically tag research articles with relevant keywords or subjects.
- Developing recommendation engines that suggest pertinent articles to researchers based on their interests.
- Enhancing search functionalities in digital libraries and academic databases.
- Training models to predict the topics for new research articles, given their abstract and title.
Coverage
The dataset's coverage is global, encompassing research articles without a specified temporal range for the articles themselves. The data pertains to abstracts from six academic topics: Computer Science, Mathematics, Physics, Statistics, Quantitative Biology, and Quantitative Finance. No specific demographic scope is applicable to the research articles themselves.
License
CC0
Who Can Use It
This dataset is primarily intended for researchers, data scientists, and machine learning engineers involved in:
- Academic research: For studying and developing new methods in NLP and information retrieval.
- Educational analytics: To understand and categorise scholarly output.
- AI and Machine Learning development: For training and testing algorithms that process and classify textual data, particularly in the context of scientific literature.
- Data product developers: To build features like smart search, content recommendation, or automated content organisation for academic platforms.
Dataset Name Suggestions
- Research Article Topics
- Scientific Paper Abstracts
- Academic Topic Classifier
- Multi-Discipline Article Topics
- Abstracts for Topic Prediction
Attributes
Original Data Source: Research Articles Dataset
QUALITY