cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17609202
title: 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.7cdxzu
title: Occurrence Download

description: A dataset containing 399 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Meetnetten.be - Bat maternity roosts counts Flanders, Belgium",
 "Country is Belgium"
 ]
}

 The dataset includes 399 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033078-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.18453/rosdok_id00004934
title: Experimental data set for “Symmetry enforced state revival in time-varying photonic lattices” : [research data]
DOI: 10.15468/dl.am5rzm
title: Occurrence Download

description: A dataset containing 1363 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Hirundo atrocaerulea"
 ]
}

 The dataset includes 1363 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033082-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30620120
title: Data required to support the manuscript about Aral Sea

description: The catastrophic desiccation of the Aral Sea has exposed nearly 6×10⁴ km² of hypersaline lakebed, intensifying dust emissions and posing severe ecological and human health risks. This study proposes a multi-criteria geospatial framework that integrates vegetation status (NDVI) with indicators of soil texture (TGSI), salinity (SSI), and moisture (NMDI) to assess vegetation-establishment potential across the desiccated seabed. Landsat time series and GlobeLand30 data were used to derive elevation-conditioned, data-driven NDVI thresholds grounded in native desert vegetation baselines and phytostabilization requirements.
DOI: 10.6084/m9.figshare.30620120.v1
title: Data required to support the manuscript about Aral Sea

description: The catastrophic desiccation of the Aral Sea has exposed nearly 6×10⁴ km² of hypersaline lakebed, intensifying dust emissions and posing severe ecological and human health risks. This study proposes a multi-criteria geospatial framework that integrates vegetation status (NDVI) with indicators of soil texture (TGSI), salinity (SSI), and moisture (NMDI) to assess vegetation-establishment potential across the desiccated seabed. Landsat time series and GlobeLand30 data were used to derive elevation-conditioned, data-driven NDVI thresholds grounded in native desert vegetation baselines and phytostabilization requirements.
DOI: 10.15468/dl.gxqyuh
title: Occurrence Download

description: A dataset containing 1529 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Crassula helmsii)",
 "BasisOfRecord is one of (Observation, Human Observation, Material sample, Specimen, Unknown evidence, Machine Observation, Occurrence evidence)",
 "Year is greater than or equal to 2016",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "Country is United Kingdom of Great Britain and Northern Ireland"
 ]
}

 The dataset includes 1529 records from 43 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033085-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zbg67j
title: Occurrence Download

description: A dataset containing 146475 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Butterfly monitoring scheme in Flanders, Belgium",
 "Country is Belgium"
 ]
}

 The dataset includes 146475 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033074-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.dzzj8z
title: Occurrence Download

description: A dataset containing 210685 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is ABV - Common breeding birds in Flanders, Belgium (post 2016)",
 "Country is Belgium"
 ]
}

 The dataset includes 210685 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033073-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.smfdf7
title: Occurrence Download

description: A dataset containing 82733 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Aves",
 "Geometry POLYGON((91 25, 93 25, 93 26, 91 26, 91 25))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 82733 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033024-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rbvp6p
title: Occurrence Download

description: A dataset containing 1525 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((3.343899330840819 47.692073758325364, 3.350823207674762 47.701342008303165, 3.361782556084662 47.69552922027879, 3.37229985397226 47.701968683599326, 3.375360345664898 47.7018382922928, 3.380833312075285 47.706621611997946, 3.370162200283943 47.71421828027172, 3.359541737497436 47.71652677651843, 3.345656839834574 47.71702405359178, 3.343766518146592 47.71840762730654, 3.337243977550716 47.71307929802039, 3.334790667825153 47.71423483310068, 3.332616199751456 47.712004360802254, 3.328047277522019 47.713527578404985, 3.320632985198389 47.70834302369225, 3.314384218034889 47.70949317275275, 3.312776060709446 47.70701148565137, 3.3134246406643 47.70120157496731, 3.310879816015675 47.69443349022259, 3.31401233957526 47.693228017271856, 3.323380517743436 47.693916251975715, 3.327635670723927 47.690396797285416, 3.343843220224152 47.687507807628506, 3.343899330840819 47.692073758325364))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1525 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033092-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17609247
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: 10.15468/dl.pwmp7p
title: Occurrence Download

description: A dataset containing 963 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "GadmGid is COL",
 "OccurrenceStatus is Present",
 "TaxonKey is Panthera onca"
 ]
}

 The dataset includes 963 records from 87 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033093-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gkqsyy
title: Occurrence Download

description: A dataset containing 9305 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Meetnetten.be - Bat winter roosts counts (totals per room/section) Flanders, Belgium",
 "Country is Belgium"
 ]
}

 The dataset includes 9305 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033094-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vum5pv
title: Occurrence Download

description: A dataset containing 1145 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Dikerogammarus haemobaphes)",
 "BasisOfRecord is one of (Observation, Human Observation, Material sample, Specimen, Unknown evidence, Machine Observation, Occurrence evidence)",
 "Year is greater than or equal to 2016",
 "Year is less than or equal to 2025",
 "HasCoordinate is true",
 "Country is United Kingdom of Great Britain and Northern Ireland"
 ]
}

 The dataset includes 1145 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033095-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17609417
title: Underwater images collected by a Kite surf in Anse-La-Raie, Mauritius - 2018-03-17

description: This dataset was collected by a Kite surf in Anse-La-Raie, Mauritius - 2018-03-17. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            
                
                                       
                
                
 GPS information: 

                GPX file from Garmin watch.
            
                    
                

                
            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
DOI: 10.5281/zenodo.17606394
title: Underwater images collected by a Kite surf in Anse-La-Raie, Mauritius - 2018-03-17

description: This dataset was collected by a Kite surf in Anse-La-Raie, Mauritius - 2018-03-17. 

            Underwater or aerial images collected by scientists or citizens can have a wide variety of use for science, management, or conservation.
            These images can be annotated and shared to train IA models which can in turn predict the objects on the images.
            We provide a set of tools (hardware and software) to collect marine data, predict species or habitat, and provide maps.

            This dataset is part of larger collection referencing numerous underwater and aerial images Seatizen Altas. 
            Methods, tools and scientific objectives are also described in a dedicated data paper.

            
                
                                       
                
                
 GPS information: 

                GPX file from Garmin watch.
            
                    
                

                
            

            
 Generic folder structure 
            
            YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number 
            ├── DCIM :  folder to store videos and photos depending on the media collected. 
            ├── GPS :  folder to store any positioning related file. If any kind of correction is possible on files (e.g. Post-Processed Kinematic thanks to rinex data) then the distinction between device data and base data is made. If, on the other hand, only device position data are present and the files cannot be corrected by post-processing techniques (e.g. gpx files), then the distinction between base and device is not made and the files are placed directly at the root of the GPS folder. 
            │   ├── BASE :  files coming from rtk station or any static positioning instrument. 
            │   └── DEVICE : files coming from the device. 
            ├── METADATA : folder with general information files about the session. 
            ├── PROCESSED_DATA : contain all the folders needed to store the results of the data processing of the current session. 
            │   ├── BATHY :  output folder for bathymetry raw data extracted from mission logs. 
            │   ├── FRAMES :  output folder for georeferenced frames extracted from DCIM videos. 
            │   ├── IA :  destination folder for image recognition predictions. 
            │   └── PHOTOGRAMMETRY :  destination folder for reconstructed models in photogrammetry. 
            └── SENSORS : folder to store files coming from other sources (bathymetry data from the echosounder, log file from the autopilot,  mission plan etc.).       
            

            
 Software 
            All the raw data was processed using our worflow. All predictions were generated by our inference pipeline. You can find all the necessary scripts to download this data in this repository. Enjoy your data with SeatizenDOI!
DOI: 10.15468/uhxqa7
title: PELD - HCES | Brazil Reef Biodiversity - ILTER Coastal Habitats of Espírito Santo (Benthic Invertebrate Recruitment)

description: The PELD (Long-term Program of Ecological Research) Coastal Habitats of Espírito Santo is part of the Brazilian LTER program and started in 2017. It is located on the Eastern Coast of Brazil, and includes coastal ecosystems including estuaries, mangroves, coastal reefs and rodolith beds. This is an innovative project to assess the temporal patterns in multiple coastal ecosystems in the Eastern Brazil Marine Ecoregion, where there is a decadal warming trend. The program associates research and conservation, at three Conservation units: Costa das Algas Environmental Protected Area (APACA), Santa Cruz Wildlife Refuge (RVSSC) and Municipal Reserves of Integral Protection of the mangroves of Piraquê-Açu and Piraquê-Mirim rivers (RPPAM).

The project aims to investigate the interactions among abiotic, climatic and ecological dynamics in the benthic communities and Ichthyofauna assemblies associated with the estuaries, the mangroves and the Rhodolith beds.

PELD-HCES counts with a research team coordinated by UFES in partnership with USP, UFBA, UFSB, UFF, UFPR, UNISUL, UFSC and the State University of Oregon, and funded by CNPQ, CAPES and FAPES.
DOI: 10.15468/dl.6dh4mj
title: Occurrence Download

description: A dataset containing 401148 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Continent is South America",
 "Country is Chile",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 401148 records from 802 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033079-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3egg33
title: Occurrence Download

description: A dataset containing 116886 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Meetnetten.be - Bat winter roosts counts (per individual) Flanders, Belgium",
 "Country is Belgium"
 ]
}

 The dataset includes 116886 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033083-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3qu65b
title: Occurrence Download

description: A dataset containing 1192 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Meetnetten.be - Chorus counts for Amphibia in Flanders, Belgium",
 "Country is Belgium"
 ]
}

 The dataset includes 1192 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033101-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uud363
title: Occurrence Download

description: A dataset containing 25478 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is Meetnetten.be - Bat winter roosts counts (totals per site) Flanders, Belgium",
 "Country is Belgium"
 ]
}

 The dataset includes 25478 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0033097-251025141854904/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.