cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17609435
title: Dataset & Experimental Results

description: > **⚠️ For IEEE Access Reviewers**>> Here are the examples mentioned in the paper:>> - System Prompt for all experiments in dataset/system_prompt/system_prompt.pdf> - Dataset Prompt Comparison in datasets/prompt_comparison/prompt_comparison.pdf> - RQ2 Qualitative Analysis in results/rq2_qualitative_analysis/summary/summary.pdf

> - Code Contest dataset transformation in datasets/code_contest_transformation/code_contest_transformation.pdf

This repository contains the experimental data and analysis for a research study demonstrating how **Test-Driven Prompting (TDP)** significantly improves AI code generation across multiple programming languages and difficulty levels. All experimental data, statistical analysis, and qualitative comparisons are included.

## What is Test-Driven Prompting?

Instead of just asking an AI to write code, Test-Driven Prompting includes example test cases in the prompt. This helps the AI understand exactly what the code should do, similar to how human programmers use test-driven development.

![alt text](methodology_overview.png)

## Key Findings

Our study tested 8 different AI models (GPT-4, Claude, Qwen) on 3 programming benchmarks and found:

- 🎯 **Universal Improvement**: TDP worked better in 100% of test cases (16/16 model-dataset combinations)- 📈 **Significant Gains**: Average 6.09% improvement in code generation success rates, statistical results: (95% CI: [4.01, 8.18], p < 0.0001, Cohen’s d = 1.08)- 🚀 **Efficiency Boost**: Smaller AI models with TDP can outperform larger models using normal prompts- 💡 **Biggest Impact**: Most helpful for problems with unclear or implicit requirements

The findings suggest that including test cases in your prompts to AI coding assistants can significantly improve the quality of generated code, especially for complex or ambiguous programming tasks.

## Authors

**Muhammad Rizqullah** (mrizqullah@stu.kau.edu.sa) and **Emad Albassam** (ealbassam@kau.edu.sa)Computer Science Department, King Abdulaziz University, Jeddah, Saudi Arabia

*Corresponding author: Muhammad Rizqullah. Any enquiries about the research should be directed to him.*

## Publication Status

This research is currently under review at [IEEE Access](https://ieeeaccess.ieee.org/), a Scopus Q2 and Web of Science SCIE Q2 journal with an Impact Factor of 3.6.

## Repository Structure

- `datasets/` - Programming problems and test cases from HumanEval, MBPP, and Code Contests- `raw_results/` - Complete experimental results for each AI model and dataset combination- `results/` - Statistical analysis and comparison reports- `abstract/` - Academic paper abstract with statistical findings

## Source Code Availability

Source code of the experimentation framework is available here: https://github.com/madnanrizqu/model-agnostic-empirical-evaluation-of-test-driven-prompt-engineering
DOI: 10.5281/zenodo.17608369
title: Resources and data on the description of three Panagrolaimus species

description: This data set contains all files required to reproduce the analysis done in the manuscript titled "Panagrolaimus einhardii sp. nov. and two sisters of fortune". The project belongs to the CRC1211 "Earth-Evolution at the dry limit".

All scripts deposited on GitHub: https://github.com/lpettrich/PeinhardiiAndTwoSisters
DOI: 10.5281/zenodo.17609431
title: Dataset Artificial Intelligence Integration and Its Effects on Business Efficiency: Evidence from a Systematic and Empirical Investigation (2020–2025)

description: This study investigates the strategic influence of artificial intelligence (AI) on business performance, emphasizing its capacity to enhance operational efficiency, analytical precision, and organizational adaptability. Utilizing empirical data spanning 2020–2025 and triangulated with contemporary scholarly literature, the research critically examines the extent to which AI-driven automation and predictive analytics contribute to productivity gains, decision accuracy, and sustainable competitive advantage. Through a mixed-methods design integrating quantitative assessment and qualitative interpretation, the study identifies statistically and conceptually significant correlations between AI implementation and key performance outcomes most notably in cost optimization, process reconfiguration, and strategic agility. The findings reveal that AI-enabled enterprises demonstrate superior responsiveness, resilience, and innovation capability relative to firms operating under conventional managerial paradigms. Nevertheless, persistent challenges including infrastructural deficiencies, workforce competency gaps, and institutional inertia continue to constrain the full realization of AI’s transformative potential. The study concludes that sustainable AI integration necessitates a synergistic alignment among technological infrastructure, executive leadership, and human capital development, positioning AI not merely as a tool for efficiency but as a structural enabler of long-term organizational renewal.
DOI: 10.5281/zenodo.17609201
title: Dataset Artificial Intelligence Integration and Its Effects on Business Efficiency: Evidence from a Systematic and Empirical Investigation (2020–2025)

description: This study investigates the strategic influence of artificial intelligence (AI) on business performance, emphasizing its capacity to enhance operational efficiency, analytical precision, and organizational adaptability. Utilizing empirical data spanning 2020–2025 and triangulated with contemporary scholarly literature, the research critically examines the extent to which AI-driven automation and predictive analytics contribute to productivity gains, decision accuracy, and sustainable competitive advantage. Through a mixed-methods design integrating quantitative assessment and qualitative interpretation, the study identifies statistically and conceptually significant correlations between AI implementation and key performance outcomes most notably in cost optimization, process reconfiguration, and strategic agility. The findings reveal that AI-enabled enterprises demonstrate superior responsiveness, resilience, and innovation capability relative to firms operating under conventional managerial paradigms. Nevertheless, persistent challenges including infrastructural deficiencies, workforce competency gaps, and institutional inertia continue to constrain the full realization of AI’s transformative potential. The study concludes that sustainable AI integration necessitates a synergistic alignment among technological infrastructure, executive leadership, and human capital development, positioning AI not merely as a tool for efficiency but as a structural enabler of long-term organizational renewal.
DOI: 10.15468/dl.658anz
title: Occurrence Download

description: A dataset containing 421 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lonicera pileata",
 "Country is Austria",
 "HasCoordinate is true"
 ]
}

 The dataset includes 421 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049524-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v4zx2c
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lonicera pileata",
 "Country is Armenia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049523-250811113504898/datasets/export for details.
DOI: 10.15468/dl.x5fqmj
title: Occurrence Download

description: A dataset containing 2029 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lonicera pileata",
 "Country is Belgium",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2029 records from 10 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049525-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17871/10.17871/carboscope-s10oc_v2025
title: Jena CarboScope: Atmospheric CO2 inversion
DOI: 10.5281/zenodo.15793562
title: Source data for manuscript "Kilohertz volumetric imaging of in-vivo dynamics using squeezed light field microscopy"
DOI: 10.5281/zenodo.16908320
title: Source data for manuscript "Kilohertz volumetric imaging of in-vivo dynamics using squeezed light field microscopy"
DOI: 10.7910/dvn/ks8xlu
title: TIC para la Diversificación de Estrategias Educativas, Acceso y Adaptación de Contenidos

description: El presente artículo de revisión analiza el papel de las Tecnologías de la Información y la Comunicación (TIC) en el contexto de la educación universitaria, con énfasis en su potencial para diversificar las estrategias educativas, garantizar el acceso equitativo al conocimiento y adaptar los contenidos a la diversidad estudiantil. A partir de un enfoque cualitativo, de tipo documental, se realizó un análisis crítico de 28 fuentes académicas publicadas entre 2012 y 2023. Los hallazgos revelan que el uso pedagógico de las TIC promueve metodologías activas, recursos accesibles y experiencias de aprendizaje personalizados, siempre que existe una planificación didáctica intencionada y condiciones institucionales favorables. Asimismo, se identifican barreras como la brecha digital, la falta de formación docente y la débil integración de modelos como el Diseño Universal para el Aprendizaje (DUA). Se concluye que, aunque las TIC ofrecen herramientas valiosas para una educación superior más inclusiva, su impacto real depende del compromiso institucional y del enfoque pedagógico con que se implementen. El estudio destaca la necesidad de fortalecer las competencias digitales del profesorado, promover políticas de equidad tecnológica y fomentar culturas universitarias abiertas al cambio educativo.

Palabras clave: TIC; educación superior; inclusión educativa; accesibilidad; estrategias pedagógicas.
DOI: 10.5281/zenodo.18351915
title: ЧЕТ ЭЛЛАРДА ТАДБИРКОРЛИК ФАОЛИЯТИНИНГ КОРПОРАТИВ ШАКЛЛАРИ

description: Ушбу мақолада  континентал Европа давлатларида  корпаратцияларни пайдо булиши ,шакллари ,корпаратцияларни ўзига хос жихатлари,корпаратцияларни ташкил этишнинг ҳуқуқий жихатлари ёритиб берилган.
DOI: 10.5281/zenodo.18351914
title: ЧЕТ ЭЛЛАРДА ТАДБИРКОРЛИК ФАОЛИЯТИНИНГ КОРПОРАТИВ ШАКЛЛАРИ

description: Ушбу мақолада  континентал Европа давлатларида  корпаратцияларни пайдо булиши ,шакллари ,корпаратцияларни ўзига хос жихатлари,корпаратцияларни ташкил этишнинг ҳуқуқий жихатлари ёритиб берилган.
DOI: 10.15468/dl.vsvqmh
title: Occurrence Download

description: A dataset containing 30 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Mexico",
 "TaxonKey is Agkistrodon taylori",
 "Year 1960-2026"
 ]
}

 The dataset includes 30 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007444-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x68heh
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.833538 24.094689, 88.756615 24.089112, 88.681606 24.07252, 88.61037399999999 24.045326, 88.54468900000001 24.008204, 88.486175 23.962078, 88.43627600000001 23.908092, 88.396218 23.847584, 88.366979 23.782052, 88.34926299999999 23.713114, 88.34348900000001 23.642474, 88.349782 23.57187, 88.36796699999999 23.503041, 88.397578 23.437677, 88.43787500000001 23.377381, 88.48785599999999 23.32363, 88.54628700000001 23.277739, 88.611734 23.24083, 88.68259399999999 23.213804, 88.75713500000001 23.19732, 88.833538 23.19178, 88.909941 23.19732, 88.984482 23.213804, 89.055342 23.24083, 89.120789 23.277739, 89.17922 23.32363, 89.229201 23.377381, 89.269498 23.437677, 89.299109 23.503041, 89.317294 23.57187, 89.323587 23.642474, 89.317813 23.713114, 89.30009699999999 23.782052, 89.270858 23.847584, 89.2308 23.908092, 89.18090100000001 23.962078, 89.122387 24.008204, 89.056702 24.045326, 88.98547000000001 24.07252, 88.910461 24.089112, 88.833538 24.094689))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007443-260120142942310/datasets/export for details.
DOI: 10.15468/dl.uvdab4
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((89.025012 24.024924, 88.948131 24.019347, 88.87316199999999 24.002754, 88.801969 23.97556, 88.736318 23.938438, 88.677835 23.892313, 88.62796299999999 23.838326, 88.587926 23.777818, 88.558701 23.712285, 88.540994 23.643347, 88.535223 23.572706, 88.541511 23.502102, 88.559685 23.433272, 88.589281 23.367907, 88.629555 23.30761, 88.67950999999999 23.253859, 88.737911 23.207967, 88.80332300000001 23.171057, 88.874146 23.14403, 88.94864800000001 23.127546, 89.025012 23.122006, 89.101376 23.127546, 89.175878 23.14403, 89.246701 23.171057, 89.312113 23.207967, 89.370514 23.253859, 89.420469 23.30761, 89.46074299999999 23.367907, 89.49033900000001 23.433272, 89.50851299999999 23.502102, 89.51480100000001 23.572706, 89.50903 23.643347, 89.49132299999999 23.712285, 89.462098 23.777818, 89.422061 23.838326, 89.37218900000001 23.892313, 89.313706 23.938438, 89.24805499999999 23.97556, 89.176862 24.002754, 89.101893 24.019347, 89.025012 24.024924))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007448-260120142942310/datasets/export for details.
DOI: 10.5258/soton/d3710
title: Data for 'From Crystal Structure Prediction to Polymorphic Behaviour: Monte Carlo Threshold Mapping of Crystal Energy Landscapes'

description: The following data for the publication 'From Crystal Structure Prediction to Polymorphic Behaviour: Monte Carlo Threshold Mapping of Crystal Energy landscapes' is provided:

- CSP landscapes (SQLite database, contents of the database are provided as a CSV file and the putative crystal structures in a single CIF file)
- Disconnectivity graphs (provided in the GML format)
- A file with the packing motif (herringbone, sandwich herringbone, beta, gamma) of the crystal structures within 30 kJ/mol of the global energy minimum of the MCT algorithm trajectories
DOI: 10.7488/ds/1562
title: Toppic study - Randomised Controlled Trial of -Mercaptopurine Versus Placebo to Prevent Recurrence of Crohn’s Disease Following Surgical Resection
title: Randomised Controlled Trial of -Mercaptopurine Versus Placebo to Prevent Recurrence of Crohn’s Disease Following Surgical Resection

description: Anonymised TOPPIC trial dataset
DOI: 10.15468/dl.6m7g9w
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.79533499999999 23.833744, 88.71856699999999 23.828167, 88.643708 23.811575, 88.572619 23.78438, 88.507064 23.747259, 88.448666 23.701133, 88.398865 23.647146, 88.358885 23.586638, 88.3297 23.521104, 88.312016 23.452165, 88.306251 23.381522, 88.312528 23.310916, 88.330674 23.242084, 88.360225 23.176717, 88.400441 23.116417, 88.450323 23.062663, 88.50864 23.01677, 88.573959 22.979858, 88.644682 22.95283, 88.71907899999999 22.936344, 88.79533499999999 22.930804, 88.871591 22.936344, 88.945988 22.95283, 89.016711 22.979858, 89.08203 23.01677, 89.14034700000001 23.062663, 89.190229 23.116417, 89.230445 23.176717, 89.259996 23.242084, 89.278142 23.310916, 89.284419 23.381522, 89.278654 23.452165, 89.26097 23.521104, 89.231785 23.586638, 89.191805 23.647146, 89.142004 23.701133, 89.083606 23.747259, 89.018051 23.78438, 88.946962 23.811575, 88.872103 23.828167, 88.79533499999999 23.833744))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007449-260120142942310/datasets/export for details.
DOI: 10.5281/zenodo.18351931
title: DEVELOPING CRITICAL THINKING SKILLS IN HIGH SCHOOL STUDENTS THROUGH ENGLISH READING: A METHODOLOGICAL FRAMEWORK

description: This research investigates the pedagogical effectiveness of integrating critical thinking (CT) development technologies into the English language reading process for high school students. The study addresses a significant gap in secondary education, where reading is often limited to literal comprehension rather than analytical evaluation. Conducted at Secondary School No. 35 in the Turakurgan district of the Namangan region, the research involved a pedagogical experiment with Grade 9-B students. The methodological framework synthesized international strategies, such as the INSERT method, Socratic questioning, and Bloom’s Taxonomy, with the cognitive-communicative approaches of Uzbek scholars like J. Jalolov and G. Bakieva. Quantitative results indicated a 24.4% increase in students' analytical proficiency, particularly in the domains of inference and evaluative synthesis. The findings suggest that when reading is treated as an active inquiry process, students demonstrate higher linguistic confidence and kognitive maturity. The study concludes with practical recommendations for EFL teachers in Uzbekistan to transition from traditional rote-learning to an inquiry-based instructional model.
DOI: 10.5281/zenodo.18351932
title: DEVELOPING CRITICAL THINKING SKILLS IN HIGH SCHOOL STUDENTS THROUGH ENGLISH READING: A METHODOLOGICAL FRAMEWORK

description: This research investigates the pedagogical effectiveness of integrating critical thinking (CT) development technologies into the English language reading process for high school students. The study addresses a significant gap in secondary education, where reading is often limited to literal comprehension rather than analytical evaluation. Conducted at Secondary School No. 35 in the Turakurgan district of the Namangan region, the research involved a pedagogical experiment with Grade 9-B students. The methodological framework synthesized international strategies, such as the INSERT method, Socratic questioning, and Bloom’s Taxonomy, with the cognitive-communicative approaches of Uzbek scholars like J. Jalolov and G. Bakieva. Quantitative results indicated a 24.4% increase in students' analytical proficiency, particularly in the domains of inference and evaluative synthesis. The findings suggest that when reading is treated as an active inquiry process, students demonstrate higher linguistic confidence and kognitive maturity. The study concludes with practical recommendations for EFL teachers in Uzbekistan to transition from traditional rote-learning to an inquiry-based instructional model.
DOI: 10.15468/dl.rbkvqx
title: Occurrence Download

description: A dataset containing 5683 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aegopodium podagraria",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 5683 records from 258 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007450-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5522/04/31096477
title: LLSD

description: The LLSD dataset was constructed to evaluate cross-dataset generalization in laparoscopic liver landmark segmentation. It was derived from the Laparoscopic Liver Resection (LLR) dataset [1], which contains 46 frames of real surgical procedures from 4 patients. Each frame was annotated with multi-class segmentation masks, where each pixel is assigned to one of three landmark classes (anterior ridge, silhouette, and falciform ligament) or background. Compared with L3D [2], LLSD was annotated with thinner, centerline-like landmark masks in order to emphasize boundary localization. This difference in annotation protocol results in slightly lower overlap scores (e.g., DSC, IoU) when models trained on L3D are evaluated on LLSD.CITATION AND REFERENCES<br><br>[1] Rabbani, N. et al. (2021) ‘A methodology and clinical dataset with ground-truth to evaluate registration accuracy quantitatively in computer-assisted laparoscopic liver resection’, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 10(4), pp. 441–450. doi:10.1080/21681163.2021.1997642.<br>[2] Pei, J. et al. (2024) ‘Depth-driven geometric prompt learning for laparoscopic liver landmark detection’, Lecture Notes in Computer Science, pp. 154–164. doi:10.1007/978-3-031-72089-5_15.
DOI: 10.5522/04/31096477.v1
title: LLSD

description: The LLSD dataset was constructed to evaluate cross-dataset generalization in laparoscopic liver landmark segmentation. It was derived from the Laparoscopic Liver Resection (LLR) dataset [1], which contains 46 frames of real surgical procedures from 4 patients. Each frame was annotated with multi-class segmentation masks, where each pixel is assigned to one of three landmark classes (anterior ridge, silhouette, and falciform ligament) or background. Compared with L3D [2], LLSD was annotated with thinner, centerline-like landmark masks in order to emphasize boundary localization. This difference in annotation protocol results in slightly lower overlap scores (e.g., DSC, IoU) when models trained on L3D are evaluated on LLSD.CITATION AND REFERENCES<br><br>[1] Rabbani, N. et al. (2021) ‘A methodology and clinical dataset with ground-truth to evaluate registration accuracy quantitatively in computer-assisted laparoscopic liver resection’, Computer Methods in Biomechanics and Biomedical Engineering: Imaging & Visualization, 10(4), pp. 441–450. doi:10.1080/21681163.2021.1997642.<br>[2] Pei, J. et al. (2024) ‘Depth-driven geometric prompt learning for laparoscopic liver landmark detection’, Lecture Notes in Computer Science, pp. 154–164. doi:10.1007/978-3-031-72089-5_15.