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Chemistry Problem-Solution Digital Archive
Synthetic Data Generation
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About
A collection of 20,000 pairs of chemistry problem statements and their respective solutions, covering a wide array of concepts across the field of chemistry. The material is organised across 25 main topics, with extensive coverage of various subtopics within each domain. This resource was generated using the GPT-4 model, ensuring a diverse representation of chemistry concepts. Users should note the AI generation method and perform careful validation checks, particularly when implementing the data in academic research or real-life scenarios where precision is essential.
Columns
- role_1: A categorical column denoting the role or identity responsible for presenting the problem statement or solution. The most common unique value is Chemist_RoleType.ASSISTANT.
- sub_topic: A categorical identifier for the specific subtopic or area within the main topic to which the problem and solution belong.
- message_1: A text field providing the problem statement or a concise description of the specific chemistry problem, setting the context for analysis.
- message_2: A text field containing the respective answer or solution to the problem statement, offering insights into how to approach specific types of chemistry problems.
Distribution
The dataset contains 20,000 unique problem-solution pairs, structured within the file
train.csv (46.77 MB). The structure is hierarchical, organized into 25 main topics. Each main topic is further broken down into 25 subtopics, although the current data exhibits 612 unique subtopics in total. Each main topic and subtopic combination includes 32 distinct problems. The expected update frequency for this dataset is never.Usage
This resource can be leveraged in multiple ways:
- Educational Resource Development: Utilise the pairs as inspiration for creating educational materials, such as textbooks, online courses, or lesson plans aimed at various chemistry topics.
- AI System Development: Serve as training data for developing AI-powered systems focused on solving chemistry problems or providing chemical insights.
- Model Evaluation: Use the pairs to evaluate the performance of existing chemistry problem-solving models or algorithms before fine-tuning them.
- Focused Study: Navigate through specific areas of chemistry by focusing on the 25 main topics and numerous subtopics, aligning exploration with particular learning goals.
- Research and Analysis: Researchers in natural language processing (NLP) or chemistry education can analyse patterns in the data or develop new algorithms.
Coverage
The data covers 25 broad areas within the field of chemistry, with examples including Organic chemistry and Coordination chemistry of metalloenzymes and metalloproteins. The content is entirely focused on chemistry problem statements and solutions. There are no specific geographic, time range, or demographic constraints included in the data structure.
License
CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication
Who Can Use It
- Students: To test their understanding and problem-solving abilities by using the pairs as practice questions.
- Educators: To generate educational content and lesson plans.
- AI Developers: To train and fine-tune AI models related to chemical problem-solving and educational technology.
- Researchers: To conduct analysis into common difficulties students face in chemistry or to advance algorithms in NLP.
Dataset Name Suggestions
- Chemistry Problem-Solution Digital Archive
- GPT-4 Chemistry Q&A Pairs
- 20K Chemistry Practice Set
Attributes
Original Data Source: Chemistry Problem-Solution Digital Archive
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