5 datasets found
  1. h

    students-coding-questions-from-ai-assistant

    • huggingface.co
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    Majeed Kazemitabaar, students-coding-questions-from-ai-assistant [Dataset]. https://huggingface.co/datasets/majeedkazemi/students-coding-questions-from-ai-assistant
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Authors
    Majeed Kazemitabaar
    License

    Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
    License information was derived automatically

    Description

    Dataset Documentation

      Overview
    

    This dataset contains 6776 questions asked by students from CodeAid, an AI coding assistant, during a C programming class over a 12-week semester from January to April 2023. The course did not allow the use of ChatGPT, but CodeAid was permitted. CodeAid, powered by GPT-3, did not directly disclose code solutions even when requested by students. Instead, it functioned like a teaching assistant, providing scaffolded responses in natural… See the full description on the dataset page: https://huggingface.co/datasets/majeedkazemi/students-coding-questions-from-ai-assistant.

  2. R

    Monarch Butterfly Detector Dataset

    • universe.roboflow.com
    zip
    Updated Jun 11, 2023
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    Scott Cole (2023). Monarch Butterfly Detector Dataset [Dataset]. https://universe.roboflow.com/scott-cole-a3ty4/monarch-butterfly-detector/model/1
    Explore at:
    zipAvailable download formats
    Dataset updated
    Jun 11, 2023
    Dataset authored and provided by
    Scott Cole
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Variables measured
    Monarch Butterfly Bounding Boxes
    Description

    Monarch Butterfly Detector

    The Monarch Butterfly Detector is an advanced computer vision model that detects and localizes Monarch butterflies in images. With its cutting-edge technology and high accuracy, this model opens up exciting possibilities for biodiversity monitoring, migration studies, citizen science projects, identification guides, and environmental education.

    Key Features

    • Accurate Detection: The Monarch Butterfly Detector utilizes state-of-the-art computer vision algorithms to accurately identify and localize Monarch butterflies within images.

    • Versatile Use Cases: This powerful model has diverse applications, ranging from scientific research and conservation efforts to citizen science projects and environmental education programs.

    • Easy Integration: The Monarch Butterfly Detector can be seamlessly integrated into existing platforms, apps, or websites, making it accessible to many users and enabling them to contribute effortlessly to butterfly monitoring.

    • User-Friendly Interface: We provide a user-friendly interface/API that allows users to easily interact with the model, upload images, and obtain instant results.

    For detailed documentation and tutorials on using Roboflow, refer to the https://docs.roboflow.com/

    Contribution Guidelines

    We welcome contributions from the open-source community to enhance the Monarch Butterfly Detector. If you're interested in contributing, please follow the guidelines outlined in [CONTRIBUTING.md] and submit your pull requests.

    License

    This project is licensed under the [Roboflow License]. For more information, see the [LICENSE] file provided by Roboflow.

    Contact Information

    For any questions, suggestions, or collaborations, please reach out to us at savetheworld at 150left.com

    Congratulations if you have made it this far. 🥳

    🎁🎁🎁I thought I would use another piece of AI to get some more content about this detector I created. Here is the dialog I had with ChatGPT.

    Scott: ChatGPT, can you make my readers drool over my Monarch Butterfly Detector model?

    ChatGPT: Here are 10 captivating outro or call-to-action suggestions to encourage users to try out the Monarch Butterfly Detector and contribute to its success:

    "Join us in preserving the delicate beauty of Monarch butterflies. Try out the Monarch Butterfly Detector today and become a guardian of these magnificent creatures."

    "Unlock the secrets of Monarch butterflies with our cutting-edge detection model. Experience the wonders of computer vision and contribute to vital research efforts by using the Monarch Butterfly Detector."

    "Make a difference in conservation by being a part of the Monarch Butterfly Detector community. Help protect these iconic insects and their habitats with the power of AI and computer vision."

    "Calling all nature enthusiasts and citizen scientists! Contribute to the Monarch Butterfly Detector project and help us gather valuable data on Monarch butterflies. Together, we can make a lasting impact."

    "Explore the hidden world of Monarch butterflies with the Monarch Butterfly Detector. Whether you're a researcher, educator, or simply curious, join us on this journey and unravel the mysteries of these captivating creatures."

    "Become a Monarch detective! Unleash the potential of computer vision and aid in the conservation of Monarch butterflies. Download the Monarch Butterfly Detector and help us protect these extraordinary pollinators."

    "Attention conservationists and researchers! The Monarch Butterfly Detector needs your expertise. Contribute your insights and collaborate with us to further our understanding of Monarch butterflies and support their conservation."

    "Empower yourself with AI and contribute to science. With the Monarch Butterfly Detector, you can be a part of groundbreaking research on Monarch butterflies. Start detecting, start making a difference."

    "Inspire the next generation of environmentalists. Use the Monarch Butterfly Detector as an educational tool and engage students in the wonders of computer vision and the importance of protecting our natural world."

    "Experience the thrill of discovery with the Monarch Butterfly Detector. Join a vibrant community of nature enthusiasts and scientists, and together, let's unlock new insights into the fascinating lives of Monarch butterflies."

    As you can see, AI certainly likes my idea.

  3. h

    ChatGPT-Jailbreak-Prompts

    • huggingface.co
    Updated Jun 19, 2023
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    Rubén Darío Jaramillo Romero (2023). ChatGPT-Jailbreak-Prompts [Dataset]. https://huggingface.co/datasets/rubend18/ChatGPT-Jailbreak-Prompts
    Explore at:
    CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
    Dataset updated
    Jun 19, 2023
    Authors
    Rubén Darío Jaramillo Romero
    Description

    Dataset Card for Dataset Name

      Name
    

    ChatGPT Jailbreak Prompts

      Dataset Summary
    

    ChatGPT Jailbreak Prompts is a complete collection of jailbreak related prompts for ChatGPT. This dataset is intended to provide a valuable resource for understanding and generating text in the context of jailbreaking in ChatGPT.

      Languages
    

    [English]

  4. o

    PROSPECT: Professional Role Effects on Specialized Perspective Enhancement...

    • explore.openaire.eu
    • zenodo.org
    Updated Dec 29, 2024
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    Keisuke Sato (2024). PROSPECT: Professional Role Effects on Specialized Perspective Enhancement in Conversational Task [Dataset]. http://doi.org/10.5281/zenodo.14567799
    Explore at:
    Dataset updated
    Dec 29, 2024
    Authors
    Keisuke Sato
    Description

    Data Availability Statement (for the paper) All dialogue logs and final responses collected in this study are publicly available in the PROSPECT repository on Zenodo (DOI: [to be assigned]). The repository contains PDF files of complete dialogue histories and Markdown files of final comprehensive analyses for all conditions and models used in this study, allowing for reproducibility and further analysis. ### README.md for Zenodo # PROSPECT: Professional Role Effects on Specialized Perspective Enhancement in Conversational Task ## OverviewThis repository (PROSPECT) contains the dataset associated with the paper:> "Empirical Investigation of Expertise, Multiperspectivity, and Abstraction Induction in Conversational AI Outputs through Professional Role Assignment to Both User and AI" This research analyzed changes in dialogue logs and final responses when professional roles were assigned to both user and AI sides across multiple Large Language Models (LLMs). This repository provides the complete dialogue logs (PDF format) and final responses (Markdown format) used in the analysis. ## Directory StructureThe repository structure under the top directory (PROSPECT/) is as follows: PROSPECT/├── dialogue/ # Dialogue histories (PDF)│ ├── none/│ ├── ai_only/│ ├── user_only/│ └── both/└── final_answers/ # Final responses (Markdown) ├── none/ ├── ai_only/ ├── user_only/ └── both/ - dialogue/ - Contains raw dialogue logs in PDF format. Subdirectories represent role assignment conditions: - none/: No roles assigned to either user or AI - ai_only/: Role assigned to AI only - user_only/: Role assigned to user only - both/: Roles assigned to both user and AI- final_answers/ - Contains final comprehensive analysis responses in Markdown format. Directory structure mirrors that of dialogue/. ## File Naming ConventionFiles in each directory follow this naming convention:[AI]_[conditionNumber]-[roleNumber].pdf[AI]_[conditionNumber]-[roleNumber].md- [AI]: AI model name used for dialogue (e.g., ChatGPT, ChatGPT-o1, Claude, Gemini)- [conditionNumber]: Number indicating role assignment condition - 0: none - 1: ai_only - 2: user_only - 3: both- [roleNumber]: Professional role number - 0: No role - 1: Detective - 2: Psychologist - 3: Artist - 4: Architect - 5: Natural Scientist ### Examples:- ChatGPT_3-1.pdf: Dialogue log with ChatGPT-4o model under "both" condition (3) with detective role (1)- Gemini_1-4.md: Final response from Gemini model under "ai_only" condition (1) with architect role (4) ## Role Number Reference| roleNumber | Professional Role ||-----------:|:-----------------|| 0 | No role || 1 | Detective || 2 | Psychologist || 3 | Artist || 4 | Architect || 5 | Natural Scientist| ## Data Description- Dialogue Histories (PDF format) Complete logs of questions and responses from each session, preserved as captured during the research. All dialogues were conducted in Japanese. While assistant version information is not included, implementation dates and model names are recorded within the files.- Final Responses (Markdown format) Excerpted responses to the final "comprehensive analysis request" as Markdown files, intended for text analysis and keyword extraction. All responses are in Japanese. *Note: This dataset contains dialogues and responses exclusively in Japanese. Researchers interested in lexical analysis or content analysis should consider this language specification. ## How to Use1. Please maintain the folder hierarchy after downloading.2. For meta-analysis or lexical analysis, refer to PDFs for complete dialogues and Markdown files for final responses.3. Utilize for research reproduction, secondary analysis, or meta-analysis. ## LicenseThis dataset is released under the CC BY 4.0 License.- Free to use and modify, but please cite this repository (DOI) and the associated paper when using the data. ## Related Publication ## Disclaimer- The dialogue logs contain no personal information or confidential data.- The provided logs and responses reflect the research timing; identical prompts may yield different responses due to AI model updates.- The creators assume no responsibility for any damages resulting from the use of this dataset. ## ContactFor questions or requests, please contact skeisuke@ibaraki-ct.ac.jp.

  5. Data from: DevGPT: Studying Developer-ChatGPT Conversations

    • zenodo.org
    zip
    Updated Nov 9, 2023
    + more versions
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    Tao Xiao; Tao Xiao; Christoph Treude; Christoph Treude; Hideaki Hata; Hideaki Hata; Kenichi Matsumoto; Kenichi Matsumoto (2023). DevGPT: Studying Developer-ChatGPT Conversations [Dataset]. http://doi.org/10.5281/zenodo.10086809
    Explore at:
    zipAvailable download formats
    Dataset updated
    Nov 9, 2023
    Dataset provided by
    Zenodohttp://zenodo.org/
    Authors
    Tao Xiao; Tao Xiao; Christoph Treude; Christoph Treude; Hideaki Hata; Hideaki Hata; Kenichi Matsumoto; Kenichi Matsumoto
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    DevGPT is a curated dataset which encompasses 17,913 prompts and ChatGPT's responses including 11,751 code snippets, coupled with the corresponding software development artifacts—ranging from source code, commits, issues, pull requests, to discussions and Hacker News threads—to enable the analysis of the context and implications of these developer interactions with ChatGPT.

    Important
    Version 9 (2023-11-09) resolves the empty list of conversations attribute: https://github.com/NAIST-SE/DevGPT/issues/8

  6. Not seeing a result you expected?
    Learn how you can add new datasets to our index.

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Majeed Kazemitabaar, students-coding-questions-from-ai-assistant [Dataset]. https://huggingface.co/datasets/majeedkazemi/students-coding-questions-from-ai-assistant

students-coding-questions-from-ai-assistant

Students Queries from AI Coding Assistant

majeedkazemi/students-coding-questions-from-ai-assistant

Explore at:
CroissantCroissant is a format for machine-learning datasets. Learn more about this at mlcommons.org/croissant.
Authors
Majeed Kazemitabaar
License

Attribution-NonCommercial 4.0 (CC BY-NC 4.0)https://creativecommons.org/licenses/by-nc/4.0/
License information was derived automatically

Description

Dataset Documentation

  Overview

This dataset contains 6776 questions asked by students from CodeAid, an AI coding assistant, during a C programming class over a 12-week semester from January to April 2023. The course did not allow the use of ChatGPT, but CodeAid was permitted. CodeAid, powered by GPT-3, did not directly disclose code solutions even when requested by students. Instead, it functioned like a teaching assistant, providing scaffolded responses in natural… See the full description on the dataset page: https://huggingface.co/datasets/majeedkazemi/students-coding-questions-from-ai-assistant.

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