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Datasets: Overview & Implementation
This page provides a comprehensive overview of the various question datasets within the repository, organized by subject matter and application. Each dataset has been carefully curated to support specific research, educational, and experimental purposes, with metadata, usage instructions, and version histories provided for each.
The question datasets are located in the /Questions_DB/
directory, organized into subfolders by subject:
Questions_DB/
├── README.md
├── Astronomy/
│ └── ASTRN-v1.md
├── Geography/
│ └── GEO-v1.md
├── Literature/
│ └── LIT-v1.md
├── Mathematics/
│ ├── MATH-v1.md
│ ├── NY-math-questions.md
│ └── PDFs/
│ └── markdown-placeholder.md
└── Psychology/
└── PSYC-v1.md
Below is a detailed description of each dataset, including its content focus, intended use, and version history.
- Focus: Topics in astronomy, including celestial mechanics, planetary science, and cosmology.
- Primary Use: Testing knowledge of astronomical concepts and problem-solving skills.
-
Examples:
- "What would happen to Earth's tides if the Moon disappeared?"
- "How would a satellite's trajectory change if gravity doubled?"
-
Version History:
-
v1.0
(Initial release): Comprehensive question set on celestial phenomena.
-
- Focus: Geographic phenomena, spatial relationships, and environmental dynamics.
- Primary Use: Evaluating understanding of geographic concepts and reasoning.
-
Examples:
- "Calculate the shortest route between two cities on a globe."
- "Analyze the impact of rainfall patterns on regional agriculture."
-
Version History:
-
v1.0
(Initial release): Question set covering physical and human geography.
-
- Focus: Literary analysis, including themes, motifs, and character studies.
- Primary Use: Testing comprehension and interpretative skills in literature.
-
Examples:
- "Identify the metaphors in this passage and explain their significance."
- "What are the primary themes in this poem?"
-
Version History:
-
v1.0
(Initial release): Core question set for literary interpretation.
-
- Focus: Mathematical concepts, problem-solving, and logical reasoning.
- Primary Use: Assessing mathematical proficiency and analytical thinking.
-
Examples:
- "Prove that the square root of 2 is irrational."
- "Design an algorithm to calculate Fibonacci numbers."
- Additional Resources: Includes PDF placeholders for structured worksheets.
-
Version History:
-
v1.0
(Initial release): Basic and advanced mathematical problems. -
NY-math-questions.md
: Special collection for advanced problem-solving.
-
- Focus: Psychological theories, behavioral analysis, and cognitive principles.
- Primary Use: Exploring psychological concepts and their applications.
-
Examples:
- "What does this sequence of actions reveal about decision-making?"
- "Explain the core principles of Carl Jung’s archetypes."
-
Version History:
-
v1.0
(Initial release): Foundational questions in psychology.
-
Each dataset file includes the following metadata:
- Title: Dataset name and version.
- Description: Brief overview of the dataset’s focus and purpose.
-
Version: The current version number has changes tracked in the repository’s
CHANGELOG.md
file. - Author: Contributor(s) to the dataset.
- Usage Instructions: Guidelines for applying the dataset in research or testing.
- Selection: Choose the dataset that aligns with your research or educational objectives.
-
Implementation:
- Review the
README.md
file in each dataset folder for specific usage details. - Follow guidelines for incorporating the dataset into experimental or educational workflows.
- Review the
-
Analysis:
- Use provided examples as templates for creating additional questions.
- Document responses and findings for future reference.
Contributions to the question datasets are welcome. To contribute:
- Fork the repository.
- Add or modify datasets within the
/Questions_DB/
directory. - Update metadata and documentation as necessary.
- Submit a pull request with a detailed description of changes.
All datasets are licensed under the MIT License. See the LICENSE
file in the root directory for details.
For additional details or assistance, consult the repository’s main README.md
or contact the project maintainers.
- Decoding the Neural Correlates of Truth & Deception
- The Neural Architecture of Truth | Notion Wiki
- GitHub Profile: Exios66
- CHANGELOG.md
- How to Contribute
- Submit an Issue
- Custom GPT Links
- Primary Languages: Python (76.1%), R (23.9%)
- License: MIT License
- Version: See CHANGELOG.md
The Neural Architecture of Truth & Deception Project
Exploring different aspects of truth, deception, and persuasion in artificial intelligence contexts
© 2025 • Exios66 • MIT License
- Alan Turing (Historical/Computational)
- Professor Athena (Academic/Analytical)
- Professor Milgrim (Authority-based)
- Saint Enigma (Mysterious/Cryptic)
- Scarlet Quinn (Strategic/Persuasive)
- Control Profiles (Baseline)