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    <responseDate>2026-10-15T09:38:34Z</responseDate>
    <request verb="GetRecord" identifier="oai:data.mendeley.com/vk9pf5nf7v.1" metadataPrefix="oai_dc">https://data.mendeley.com/oai</request>
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            <header>
                <identifier>oai:data.mendeley.com/vk9pf5nf7v.1</identifier>
                <datestamp>2025-03-04T14:46:29Z</datestamp>
            </header>
            <metadata><oai_dc:dc xsi:schemaLocation="http://www.openarchives.org/OAI/2.0/oai_dc/ http://www.openarchives.org/OAI/2.0/oai_dc.xsd" xmlns:oai_dc="http://www.openarchives.org/OAI/2.0/oai_dc/" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
    <dc:creator>Aragonés Soria, Yaiza</dc:creator>
    <dc:title>Architecture for a Trustworthy Quantum Chatbot [dataset]</dc:title>
    <dc:publisher>Mendeley Data</dc:publisher>
    <dc:description>This dataset contains all the materials and results used in the development and empirical validation of C4Q 2.0. The files are organized into two main directories: bakend_testing and empirical_validation. Below is a brief overview of their contents:
- app/ - Contains the full source code of C4Q at the state of the software at the time of submitting this paper, allowing for reproducibility and further development. The frontend’s node_modules directory is not included, but these dependencies can be generated by following the installation instructions provided in the README.md file.

- README.md – A guide detailing how to locally set up and run C4Q.

- bakend_testing/ - Contains data from the evaluation of C4Q’s backend components:
  *reportBackendC4Q2.0.html: An HTML report generated by running 189 tests on the backend of C4Q.
  *classLLM_20241110101327.pth_training_metrics.csv: A CSV file documenting the Classification LLM’s training and validation metrics, including training loss, validation loss, training accuracy, and validation accuracy for each epoch.
  *qaLLM_evaluation_metrics.txt: A text file listing exact match and F1 metrics per epoch for the QA LLM.

- create_data/ – Includes the scripts used to generate and curate training data for the classification LLM and the QA LLM.

- empirical_validation/ -  Includes data from the empirical evaluation of C4Q against other chatbots:
  *Directories named by model (e.g., openai-o1/, deepseek-coder_33b/, deepseek-r1/, etc.): Each directory contains the raw answers produced by the respective model in response to our set of quantum computing and software engineering questions.
  *prompts.txt: A text file with the full list of prompts used during the empirical evaluation.
  *requirements_qiskit0.46.3: A requirements file for Python dependencies used to create an environment with a Qiskit versions &lt;1.0.0, enabling code snippet testing under older Qiskit releases.
  *requirements_qiskit1.3.1: A requirements file for Python dependencies used to create an environment with a Qiskit versions &gt;= 1.0.0, ensuring reproducible tests under newer releases.
  *results.xlsx: An Excel spreadsheet containing the empirical evaluation outcomes, including correct, incomplete, and incorrect answer rates for each model, under both Qiskit environments.
  *script_gates.sh: A shell script that automates prompting of OLAMA’s deepseek-coder:33b and starcoder2:15b models with gate-related quantum questions.
  *script_SE.sh: A shell script that automates prompting of OLAMA’s deepseek-coder:33b and starcoder2:15b models with software engineering problem questions.</dc:description>
    <dc:subject>Software Engineering</dc:subject>
    <dc:subject>Quantum Computing</dc:subject>
    <dc:type>Dataset</dc:type>
    <dc:identifier>doi:10.17632/vk9pf5nf7v.1</dc:identifier>
    <dc:identifier>oai:data.mendeley.com/vk9pf5nf7v.1</dc:identifier>
    <dc:rights>Creative Commons Attribution 4.0 International</dc:rights>
    <dc:rights>http://creativecommons.org/licenses/by/4.0</dc:rights>
    <dc:relation>https://data.mendeley.com/datasets/vk9pf5nf7v</dc:relation>
    <dc:date>2025-03-04T14:46:29Z</dc:date>
</oai_dc:dc></metadata>
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