cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.14810002
title: Large Language Models for Code Generation: The Practitioners' Perspective

description: Objective

This repository provides the replication package.xlsx for the study "Large Language Models for Code Generation: The Practitioners’ Perspective." The study aims to explore how practitioners perceive and use large language models for code generation. We collected survey data and performed:

Descriptive Analysis for closed-ended survey questions (Q1 to Q13) and Open Coding for open-ended survey responses (Q14 and Q15).

The replication package is provided above under the name "Replication_Package.xlsx".

1️⃣ Survey Data

The dataset consists of responses collected from software development practitioners regarding their experience with large language models for code generation. The data includes:

Closed-ended questions (Q1-Q13): Responses related to experience, role, industry, programming languages, tool usability, and performance evaluation of different models.

Open-ended questions (Q14-Q15): Participants' insights on challenges and suggestions for improvements in code generation models.

📂 Files:

Survey_Data - Contains the raw survey responses.

2️⃣ Descriptive Analysis

We performed descriptive statistical analysis on the closed-ended questions (Q1 to Q13) to extract insights such as:

Distribution of participants' experience and industry.

Preferences for programming languages used in testing and development.

Feedback on tool usability and performance of different models.

📂 Files:

Descriptive_Analysis - Contains summary statistics and key insights from Q1 to Q13.

3️⃣ Open Coding

For open-ended survey questions (Q14 and Q15), we applied thematic analysis and open coding to categorize qualitative responses into meaningful themes. This helped in identifying:

Common challenges faced by practitioners while using LLMs for code generation.

Suggestions for improving these models.

📂 Files:

OpenCoding - Contains coded responses and thematic analysis. A document explaining the coding process and framework used.

🔍 How to Use This Package

Researchers can use the survey data for further analysis or comparative studies.

The descriptive analysis provides insights into practitioners' experiences with LLMs.

The open coding results offer qualitative insights into user challenges and recommendations.

📩 Contact & Contributions

For questions or contributions, feel free to open an issue or submit a pull request. This dataset and analysis are shared for academic and research purposes. You can also contact on this email: zeeshan.rasheed@tuni.fi
DOI: 10.5281/zenodo.14810003
title: Large Language Models for Code Generation: The Practitioners' Perspective

description: Objective

This repository provides the replication package.xlsx for the study "Large Language Models for Code Generation: The Practitioners’ Perspective." The study aims to explore how practitioners perceive and use large language models for code generation. We collected survey data and performed:

Descriptive Analysis for closed-ended survey questions (Q1 to Q13) and Open Coding for open-ended survey responses (Q14 and Q15).

The replication package is provided above under the name "Replication_Package.xlsx".

1️⃣ Survey Data

The dataset consists of responses collected from software development practitioners regarding their experience with large language models for code generation. The data includes:

Closed-ended questions (Q1-Q13): Responses related to experience, role, industry, programming languages, tool usability, and performance evaluation of different models.

Open-ended questions (Q14-Q15): Participants' insights on challenges and suggestions for improvements in code generation models.

📂 Files:

Survey_Data - Contains the raw survey responses.

2️⃣ Descriptive Analysis

We performed descriptive statistical analysis on the closed-ended questions (Q1 to Q13) to extract insights such as:

Distribution of participants' experience and industry.

Preferences for programming languages used in testing and development.

Feedback on tool usability and performance of different models.

📂 Files:

Descriptive_Analysis - Contains summary statistics and key insights from Q1 to Q13.

3️⃣ Open Coding

For open-ended survey questions (Q14 and Q15), we applied thematic analysis and open coding to categorize qualitative responses into meaningful themes. This helped in identifying:

Common challenges faced by practitioners while using LLMs for code generation.

Suggestions for improving these models.

📂 Files:

OpenCoding - Contains coded responses and thematic analysis. A document explaining the coding process and framework used.

🔍 How to Use This Package

Researchers can use the survey data for further analysis or comparative studies.

The descriptive analysis provides insights into practitioners' experiences with LLMs.

The open coding results offer qualitative insights into user challenges and recommendations.

📩 Contact & Contributions

For questions or contributions, feel free to open an issue or submit a pull request. This dataset and analysis are shared for academic and research purposes. You can also contact on this email: zeeshan.rasheed@tuni.fi
DOI: 10.6084/m9.figshare.27952738.v1
title: Evaluation of reliability and radiation dose reduction in coronary artery calcium scoring by using a low tube current and low kilo-voltage peak with advanced modelled iterative reconstruction

description: The present study sought to compare the reliability and radiation dose of coronary artery calcium (CAC) scoring using low tube current (16 mAs), low kilo-voltage peak (100 kVp) and advanced modelled iterative reconstruction (ADMIRE 5) with the standard protocol (80 mAs, 120 kVp, ADMIRE 3). A prospective single centre study including 200 consecutive patients with suspected coronary artery disease referred for CT coronary angiography was conducted. All 200 patients underwent CAC scoring using the low current-low kVp protocol as well as standard protocol and were subdivided into a derivation and validation cohorts. Correlation between CAC scores obtained using low current-low kVp protocol and standard protocol in derivation cohort was excellent (<i>r</i> = 0.99; <i>p</i> &lt; 0.001). A linear regression model was used to derive a formula for predicting CAC that enabled conversion of CAC(low current-low kVp) to CAC(corrected) [CAC(corrected) = 1.067 × CAC(low current-low kVp)]. The formula was applied in validation cohort where CAC (corrected) showed excellent agreement with CAC(standard) (intraclass correlation coefficient, 0.9970; 95%CI, 0.9956–0.9980). Excellent agreement for risk classification (weighted kappa, 0.94379; 95%CI, 0.89629–0.99130) was observed between CAC(corrected) and CAC(standard) scores. The low current-low kVp protocol demonstrated an 88.87% reduction in radiation dose (0.0679 ± 0.01032 mSv vs. 0.610 ± 0.2403 mSv; <i>p</i> &lt; 0.0001). The low current-low kVp protocol for CAC scoring has comparable reliability to the standard technique with significant radiation dose reduction. This extremely low-dose protocol may prove useful as an alternative to standard CAC scoring, particularly for screening in the low-to-intermediate risk population.
DOI: 10.6084/m9.figshare.27952738
title: Evaluation of reliability and radiation dose reduction in coronary artery calcium scoring by using a low tube current and low kilo-voltage peak with advanced modelled iterative reconstruction

description: The present study sought to compare the reliability and radiation dose of coronary artery calcium (CAC) scoring using low tube current (16 mAs), low kilo-voltage peak (100 kVp) and advanced modelled iterative reconstruction (ADMIRE 5) with the standard protocol (80 mAs, 120 kVp, ADMIRE 3). A prospective single centre study including 200 consecutive patients with suspected coronary artery disease referred for CT coronary angiography was conducted. All 200 patients underwent CAC scoring using the low current-low kVp protocol as well as standard protocol and were subdivided into a derivation and validation cohorts. Correlation between CAC scores obtained using low current-low kVp protocol and standard protocol in derivation cohort was excellent (<i>r</i> = 0.99; <i>p</i> &lt; 0.001). A linear regression model was used to derive a formula for predicting CAC that enabled conversion of CAC(low current-low kVp) to CAC(corrected) [CAC(corrected) = 1.067 × CAC(low current-low kVp)]. The formula was applied in validation cohort where CAC (corrected) showed excellent agreement with CAC(standard) (intraclass correlation coefficient, 0.9970; 95%CI, 0.9956–0.9980). Excellent agreement for risk classification (weighted kappa, 0.94379; 95%CI, 0.89629–0.99130) was observed between CAC(corrected) and CAC(standard) scores. The low current-low kVp protocol demonstrated an 88.87% reduction in radiation dose (0.0679 ± 0.01032 mSv vs. 0.610 ± 0.2403 mSv; <i>p</i> &lt; 0.0001). The low current-low kVp protocol for CAC scoring has comparable reliability to the standard technique with significant radiation dose reduction. This extremely low-dose protocol may prove useful as an alternative to standard CAC scoring, particularly for screening in the low-to-intermediate risk population.
DOI: 10.5281/zenodo.13949488
title: Pupillometric signature of implicit learning of statistical regularities

description: Dataset accompanying the publication “Pupillometric signature of implicit learning of statistical regularities” by Paola Binda, Chiara Terzo, Marco Turi and David Burr

To be published in Current Biology (2025)

The .mat includes two variables

usefulvars =

  struct with fields:

time_in_epochs: [80×1 double]: time over which the event-related pupil responses are defined

myfreq: [41×1 double]: frequencies over which the fft of the event-related responses is defined

sequencetypes: {2×1 cell}: types of sequences (with or without a temporal structure)

experiments: {7×1 cell}: experiments (main and control, as described in the manuscript)

 

mm =

  7×2 struct array (7 experiments and 2 sequence types)

  with fields:

avgepoch: [80×N double]: event-related pupil responses in each of the N participants in each experiment and for each sequence type (averaged across all repetitions and filtered)

avgspect: [41×N double]: fft of the event-related pupil responses (same as above but unfiltered)

subjind: [N×1 double]: unique identifier of the participant

 

The matrix is provided with the Matlab scripts that generated it (wrapper and its dependencies)
DOI: 10.5281/zenodo.13949489
title: Pupillometric signature of implicit learning of statistical regularities

description: Dataset accompanying the publication “Pupillometric signature of implicit learning of statistical regularities” by Paola Binda, Chiara Terzo, Marco Turi and David Burr

To be published in Current Biology (2025)

The .mat includes two variables

usefulvars =

  struct with fields:

time_in_epochs: [80×1 double]: time over which the event-related pupil responses are defined

myfreq: [41×1 double]: frequencies over which the fft of the event-related responses is defined

sequencetypes: {2×1 cell}: types of sequences (with or without a temporal structure)

experiments: {7×1 cell}: experiments (main and control, as described in the manuscript)

 

mm =

  7×2 struct array (7 experiments and 2 sequence types)

  with fields:

avgepoch: [80×N double]: event-related pupil responses in each of the N participants in each experiment and for each sequence type (averaged across all repetitions and filtered)

avgspect: [41×N double]: fft of the event-related pupil responses (same as above but unfiltered)

subjind: [N×1 double]: unique identifier of the participant
DOI: 10.5281/zenodo.14809868
title: Pupillometric signature of implicit learning of statistical regularities

description: Dataset accompanying the publication “Pupillometric signature of implicit learning of statistical regularities” by Paola Binda, Chiara Terzo, Marco Turi and David Burr

To be published in Current Biology (2025)

The .mat includes two variables

usefulvars =

  struct with fields:

time_in_epochs: [80×1 double]: time over which the event-related pupil responses are defined

myfreq: [41×1 double]: frequencies over which the fft of the event-related responses is defined

sequencetypes: {2×1 cell}: types of sequences (with or without a temporal structure)

experiments: {7×1 cell}: experiments (main and control, as described in the manuscript)

 

mm =

  7×2 struct array (7 experiments and 2 sequence types)

  with fields:

avgepoch: [80×N double]: event-related pupil responses in each of the N participants in each experiment and for each sequence type (averaged across all repetitions and filtered)

avgspect: [41×N double]: fft of the event-related pupil responses (same as above but unfiltered)

subjind: [N×1 double]: unique identifier of the participant

 

The matrix is provided with the Matlab scripts that generated it (wrapper and its dependencies)
DOI: 10.5281/zenodo.14810016
title: Serum Zinc and IUI outcomes
DOI: 10.5281/zenodo.14810017
title: Serum Zinc and IUI outcomes
DOI: 10.15468/dl.wd5mmr
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Quercus crispifolia Trel.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015527-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.n75e5u
title: Occurrence Download

description: A dataset containing 39953 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is one of (Harpactes fasciatus (Pennant, 1769), Harpactes erythrocephalus (Gould, 1834), Harpactes wardi (Kinnear, 1927))"
 ]
}

 The dataset includes 39953 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015526-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5mpu6k
title: Occurrence Download

description: A dataset containing 107 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Quercus crispipilis Trel.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 107 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015529-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.agmv9h
title: Occurrence Download

description: A dataset containing 33 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Opistophthalmus adustus Kraepelin, 1908)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 33 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015106-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zafdrr
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Portugal",
 "OccurrenceStatus is Present",
 "TaxonKey is Tinca tinca (Linnaeus, 1758)"
 ]
}

 The dataset includes 4 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015105-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15188338
title: Zoonoticus Dataset v1.0

description: The Zoonoticus dataset provides a curated, literature-informed collection of gene sequences and metadata associated with pathogenicity, antimicrobial resistance, and mobile genetic elements (MGEs) across bacteria. The dataset is broadly applicable to other bacterial species, including both zoonotic and non-zoonotic pathogens.

Key features:



25,000+ gene sequences representing:



Virulence factors

Antimicrobial resistance genes

Mobile genetic elements (e.g., plasmids, transposases, integrons)



Metadata includes gene function, organism of origin, genomic context (where available), and literature/database references.

Zoonotic scoring annotations based on gene combinations and pathogenicity profiles.

Structured to support comparative analysis, predictive modeling, and evolutionary studies in bacterial genomics.


This dataset enables researchers to:



Analyze host adaptation, immune evasion, and zoonotic transition potential in bacterial species

Benchmark machine learning models for pathogen classification

Explore the distribution and impact of virulence and resistance genes in public or private genome datasets
DOI: 10.5281/zenodo.15188339
title: Zoonoticus Dataset v1.0

description: The Zoonoticus dataset provides a curated, literature-informed collection of gene sequences and metadata associated with pathogenicity, antimicrobial resistance, and mobile genetic elements (MGEs) across bacteria. The dataset is broadly applicable to other bacterial species, including both zoonotic and non-zoonotic pathogens.

Key features:



25,000+ gene sequences representing:



Virulence factors

Antimicrobial resistance genes

Mobile genetic elements (e.g., plasmids, transposases, integrons)



Metadata includes gene function, organism of origin, genomic context (where available), and literature/database references.

Zoonotic scoring annotations based on gene combinations and pathogenicity profiles.

Structured to support comparative analysis, predictive modeling, and evolutionary studies in bacterial genomics.


This dataset enables researchers to:



Analyze host adaptation, immune evasion, and zoonotic transition potential in bacterial species

Benchmark machine learning models for pathogen classification

Explore the distribution and impact of virulence and resistance genes in public or private genome datasets
DOI: 10.15468/dl.facxah
title: Occurrence Download

description: A dataset containing 695 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Portugal",
 "OccurrenceStatus is Present",
 "TaxonKey is Lepomis gibbosus (Linnaeus, 1758)"
 ]
}

 The dataset includes 695 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015107-250402121839773/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.b6pxvh
title: Occurrence Download

description: A dataset containing 2097 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lycaon pictus (Temminck, 1820)"
 ]
}

 The dataset includes 2097 records from 73 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015530-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tuz689
title: Occurrence Download

description: A dataset containing 83990 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Castor canadensis Kuhl, 1820"
 ]
}

 The dataset includes 83990 records from 191 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015525-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p2djfc
title: Occurrence Download

description: A dataset containing 9431 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Bufo marinus (Linnaeus, 1758)"
 ]
}

 The dataset includes 9431 records from 98 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015531-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.zyjm3d
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Quercus cualensis L.M.González)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015532-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17632/r87txj3zj4
title: Flow data analysis summary

description: Rize basin - Flow data analysis summary Here you can find the detailed data used and results of SWAT model simulation. We have used four calibration techniques to get the best model flow simulation. These are: (1) calibration using gauge data only from downstream area (DC), (2) calibration using gauge data only from upstream area (UC), (3) calibration using gauge data both upstream and downstream areas of the basin (Multi-Gauge Calibration – MGC), and (4) calibration using gauge data first from upstream area only, then using the data from the downstream area sequentially (UCDC).
DOI: 10.15468/dl.nbw96j
title: Occurrence Download

description: A dataset containing 28801 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Micropterus salmoides (Lacepède, 1802)"
 ]
}

 The dataset includes 28801 records from 112 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015533-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.37w7vt
title: Occurrence Download

description: A dataset containing 97 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Memecylon sejunctum R.D.Stone"
 ]
}

 The dataset includes 97 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015534-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fbyrpe
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Quercus cupreata Trel. & C.H.Müll.)",
 "BasisOfRecord is one of (Human Observation, Observation, Living Specimen)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "Continent is North America"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015535-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2rvqaw
title: Occurrence Download

description: A dataset containing 36836 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Australia",
 "OccurrenceStatus is Present",
 "TaxonKey is Rhinella marina (Linnaeus, 1758)"
 ]
}

 The dataset includes 36836 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015536-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.