cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.n3sncu
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dictyocha fibula",
 "Country is China",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032610-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17231086
title: Datasets for Analysis The Impact of Digital Transformation on Payment Methods: The Case of Micro Business Selling Power
DOI: 10.5281/zenodo.17231085
title: Datasets for Analysis The Impact of Digital Transformation on Payment Methods: The Case of Micro Business Selling Power
DOI: 10.15468/dl.h6p6nu
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Philippines",
 "TaxonKey is Guettardella paxillata"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032611-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gjcmuy
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Arborophila campbelli",
 "Country is one of (Brunei Darussalam, Indonesia, Philippines, Timor-Leste, Singapore)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032612-250920141307145/datasets/export for details.
DOI: 10.15468/dl.hcbx9q
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Ommastrephes bartramii, Ommastrephes bartramii)",
 "Continent is Asia",
 "Country is China",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032613-250920141307145/datasets/export for details.
DOI: 10.15468/dl.w2x43v
title: Occurrence Download

description: A dataset containing 254 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "TaxonKey is Cedrus deodara"
 ]
}

 The dataset includes 254 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032614-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cby63d
title: Occurrence Download

description: A dataset containing 11374 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is France",
 "DatasetKey is EOD – eBird Observation Dataset",
 "OccurrenceStatus is Present",
 "TaxonKey is Morus bassanus"
 ]
}

 The dataset includes 11374 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032615-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.q6eep9
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032616-250920141307145/datasets/export for details.
DOI: 10.15468/dl.ppy6rq
title: Occurrence Download

description: A dataset containing 382 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Macrocystis pyrifera",
 "Country is Mexico",
 "HasCoordinate is true"
 ]
}

 The dataset includes 382 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032617-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aktdm3
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "TaxonKey is Strobilanthes kunthianus"
 ]
}

 The dataset includes 6 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032618-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vcq6ps
title: Occurrence Download

description: A dataset containing 127 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Arborophila rolli",
 "Country is one of (Brunei Darussalam, Indonesia, Philippines, Timor-Leste, Singapore)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 127 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032619-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17222629
title: FINE

description: Fabric Dataset, having two class classifications  of 1. Silk vs Non SIlk and 2. Handloom vs. Powerloom.
DOI: 10.5281/zenodo.17222630
title: FINE

description: Fabric Dataset, having two class classifications  of 1. Silk vs Non SIlk and 2. Handloom vs. Powerloom.
DOI: 10.6084/m9.figshare.29858642.v1
title: data_codes.rar

description: The data provided correspond to the paper titled "Automated Identification and Microphysical Characterization of Z<sub>DR</sub> Columns in Severe Thunderstorms". The data_codes.rar file contains five folders and five files. Within the five folders, you will find the data corresponding to the figures4-8 presented in the paper.The five files containing the Python code needed to process these data and plot these figures.
DOI: 10.17028/rd.lboro.30244315
title: Supplementary information files for "Equitable hospital length of stay prediction for patients with learning disabilities and multiple long-term conditions using machine learning"

description: Supplementary files for article "Equitable hospital length of stay prediction for patients with learning disabilities and multiple long-term conditions using machine learning"<br><br><b>Purpose:</b> Individuals with learning disabilities (LD) often face higher rates of premature mortality and prolonged hospital stays compared to the general population. Predicting the length of stay (LOS) for patients with LD and multiple long-term conditions (MLTCs) is critical for improving patient care and optimising medical resource allocation. However, there is limited research on the application of machine learning (ML) models to this population. Furthermore, approaches designed for the general population often lack generalisability and fairness, particularly when applied across sensitive groups within their cohort.<br><b>Method:</b> This study analyses hospitalisations of 9,618 patients with LD in Wales using electronic health records (EHR) from the SAIL Databank. A Random Forest (RF) ML model was developed to predict hospital LOS, incorporating demographics, medication history, lifestyle factors, and 39 long-term conditions. To address fairness concerns, two bias mitigation techniques were applied: a post-processing threshold optimiser and an in-processing reductions method using an exponentiated gradient. These methods aimed to minimise performance discrepancies across ethnic groups while ensuring robust model performance<b>Results:</b> The RF model outperformed other state-of-the-art models, achieving an area under the curve of 0.759 for males and 0.756 for females, a false negative rate of 0.224 for males and 0.229 for females, and a balanced accuracy of 0.690 for males and 0.689 for females. Bias mitigation algorithms reduced disparities in prediction performance across ethnic groups, with the threshold optimiser yielding the most notable improvements. Performance metrics, including false positive rate and balanced accuracy, showed significant enhancements in fairness for the male cohort.<b>Conclusion:</b> This study demonstrates the feasibility of applying ML models to predict LOS for patients with LD and MLTCs, while addressing fairness through bias mitigation techniques. The findings highlight the potential for equitable healthcare predictions using EHR data, paving the way for improved clinical decision-making and resource management<br><br>©The Author(s), CC BY-4.0
DOI: 10.17028/rd.lboro.30244315.v1
title: Supplementary information files for "Equitable hospital length of stay prediction for patients with learning disabilities and multiple long-term conditions using machine learning"

description: Supplementary files for article "Equitable hospital length of stay prediction for patients with learning disabilities and multiple long-term conditions using machine learning"<br><br><b>Purpose:</b> Individuals with learning disabilities (LD) often face higher rates of premature mortality and prolonged hospital stays compared to the general population. Predicting the length of stay (LOS) for patients with LD and multiple long-term conditions (MLTCs) is critical for improving patient care and optimising medical resource allocation. However, there is limited research on the application of machine learning (ML) models to this population. Furthermore, approaches designed for the general population often lack generalisability and fairness, particularly when applied across sensitive groups within their cohort.<br><b>Method:</b> This study analyses hospitalisations of 9,618 patients with LD in Wales using electronic health records (EHR) from the SAIL Databank. A Random Forest (RF) ML model was developed to predict hospital LOS, incorporating demographics, medication history, lifestyle factors, and 39 long-term conditions. To address fairness concerns, two bias mitigation techniques were applied: a post-processing threshold optimiser and an in-processing reductions method using an exponentiated gradient. These methods aimed to minimise performance discrepancies across ethnic groups while ensuring robust model performance<b>Results:</b> The RF model outperformed other state-of-the-art models, achieving an area under the curve of 0.759 for males and 0.756 for females, a false negative rate of 0.224 for males and 0.229 for females, and a balanced accuracy of 0.690 for males and 0.689 for females. Bias mitigation algorithms reduced disparities in prediction performance across ethnic groups, with the threshold optimiser yielding the most notable improvements. Performance metrics, including false positive rate and balanced accuracy, showed significant enhancements in fairness for the male cohort.<b>Conclusion:</b> This study demonstrates the feasibility of applying ML models to predict LOS for patients with LD and MLTCs, while addressing fairness through bias mitigation techniques. The findings highlight the potential for equitable healthcare predictions using EHR data, paving the way for improved clinical decision-making and resource management<br><br>©The Author(s), CC BY-4.0
DOI: 10.15468/dl.eznxwc
title: Occurrence Download

description: A dataset listing the 100 species recorded in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Indonesia",
 "License is one of (CC0 1.0, CC-BY 4.0)",
 "OccurrenceStatus is Present",
 "TaxonKey is Persicaria"
 ]
}

 The dataset's 100 records were derived from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032607-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sa4jyj
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Saccharina japonica",
 "Country is China",
 "HasCoordinate is true"
 ]
}

 The dataset includes 12 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032621-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4t9p4e
title: Occurrence Download

description: A dataset containing 317 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is India",
 "TaxonKey is Butea monosperma"
 ]
}

 The dataset includes 317 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032622-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u7vcdd
title: Occurrence Download

description: A dataset containing 1165 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "TaxonKey is Limnonectes blythii"
 ]
}

 The dataset includes 1165 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032620-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9d946q
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Saccharina japonica",
 "Country is France",
 "HasCoordinate is true"
 ]
}

 The dataset includes 7 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032623-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/13215243
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.s5yyjp
title: Occurrence Download

description: A dataset containing 76 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Arborophila sumatrana",
 "Country is one of (Brunei Darussalam, Indonesia, Philippines, Timor-Leste, Singapore)",
 "HasCoordinate is true"
 ]
}

 The dataset includes 76 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032624-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14779792
title: DFT data for "Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields"

description: This repository contains quantum mechanical datasets used for training and testing the SO3LR machine-learned force field model in Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields.

The combined training set, provided in extxyz format and compressed as so3lr_train.tar.gz, includes GEMS general protein fragments, QM7-X small organic molecules, AQM large drug-like molecules, SPICE dipeptides, and DES15k dimers (see the "T – Optimization on diverse training data" subsection of the original manuscript for details). To maintain consistent references, the last two datasets were recomputed at the PBE0+MBD/tight level of theory (see FHI-aims input file control.in).

Test sets from Table I of the original manuscript are available in so3lr_test.tar.gz. Structures from the MD22 and TorsionNet500 datasets were also recomputed at the PBE0+MBD/tight level of theory.
DOI: 10.5281/zenodo.14779793
title: DFT data for "Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields"

description: This repository contains quantum mechanical datasets used for training and testing the SO3LR machine-learned force field model in Molecular Simulations with a Pretrained Neural Network and Universal Pairwise Force Fields.

The combined training set, provided in extxyz format and compressed as so3lr_train.tar.gz, includes GEMS general protein fragments, QM7-X small organic molecules, AQM large drug-like molecules, SPICE dipeptides, and DES15k dimers (see the "T – Optimization on diverse training data" subsection of the original manuscript for details). To maintain consistent references, the last two datasets were recomputed at the PBE0+MBD/tight level of theory (see FHI-aims input file control.in).

Test sets from Table I of the original manuscript are available in so3lr_test.tar.gz. Structures from the MD22 and TorsionNet500 datasets were also recomputed at the PBE0+MBD/tight level of theory.
DOI: 10.5281/zenodo.15613840
title: Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges

description: These files are supplementary files for the “Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges” study, prepared for the Hawaii International Conference on System Sciences (HICSS 2026)

File "Data_AI_challenges.docx" presents the list of data-AI challenges surrounding AI adoption in public sector as identified through the SLR, with SLR results available in the file "Data_AI_challenges_SLR".

File "Responsible_AI_Adoption_AI_Challenges_public_sector_AN.pdf" a pre-print version of the manuscript prepared for the Hawaii International Conference on System Sciences (HICSS 2026). It is posted here for your personal use. Not for redistribution. Digital Object Identifier (DOI) and link to the article will be added once they are assigned.  

Abstract: Despite Artificial Intelligence (AI) transformative potential for public sector services, decision-making, and administrative efficiency, adoption remains uneven due to complex technical, organizational, and institutional challenges. Responsible AI frameworks emphasize fairness, accountability, and transparency, aligning with principles of trustworthy AI and fair AI, yet remain largely aspirational, overlooking technical and institutional realities, especially foundational data and governance. This study addresses this gap by developing a taxonomy of data-related challenges to responsible AI adoption in government. Based on a systematic review of 43 studies and 21 expert evaluations, the taxonomy identifies 13 key challenges across technological, organizational, and environmental dimensions, including poor data quality, limited AI-ready infrastructure, weak governance, misalignment in human-AI decision-making, economic and environmental sustainability concerns. Annotated with institutional pressures, the taxonomy serves as a diagnostic tool to surface “symptoms” of high-risk AI deployment and guides policymakers in building the institutional and data governance conditions necessary for responsible AI adoption.

 

Please cite this paper as: 

Nikiforova, A., Lnenicka, M., Melin, U., Valle-Cruz, D., Gill, A., Casiano Flores, C., Sirait, E., Luterek, M., Dreyling, R. M., and Tesarova, B. (2025). Responsible AI Adoption in the Public Sector: A Data Centric Taxonomy of AI Adoption Challenges. In Proceedings of the 59th Hawaii International Conference on System Sciences
DOI: 10.5281/zenodo.17232534
title: Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges

description: These files are supplementary files for the “Responsible AI Adoption in the Public Sector: A Data-Centric Taxonomy of AI Adoption Challenges” study, prepared for the Hawaii International Conference on System Sciences (HICSS 2026)

File "Data_AI_challenges.docx" presents the list of data-AI challenges surrounding AI adoption in public sector as identified through the SLR, with SLR results available in the file "Data_AI_challenges_SLR".

File "Responsible_AI_Adoption_AI_Challenges_public_sector_AN.pdf" a pre-print version of the manuscript prepared for the Hawaii International Conference on System Sciences (HICSS 2026). It is posted here for your personal use. Not for redistribution. Digital Object Identifier (DOI) and link to the article will be added once they are assigned.  

Abstract: Despite Artificial Intelligence (AI) transformative potential for public sector services, decision-making, and administrative efficiency, adoption remains uneven due to complex technical, organizational, and institutional challenges. Responsible AI frameworks emphasize fairness, accountability, and transparency, aligning with principles of trustworthy AI and fair AI, yet remain largely aspirational, overlooking technical and institutional realities, especially foundational data and governance. This study addresses this gap by developing a taxonomy of data-related challenges to responsible AI adoption in government. Based on a systematic review of 43 studies and 21 expert evaluations, the taxonomy identifies 13 key challenges across technological, organizational, and environmental dimensions, including poor data quality, limited AI-ready infrastructure, weak governance, misalignment in human-AI decision-making, economic and environmental sustainability concerns. Annotated with institutional pressures, the taxonomy serves as a diagnostic tool to surface “symptoms” of high-risk AI deployment and guides policymakers in building the institutional and data governance conditions necessary for responsible AI adoption.

 

Please cite this paper as: 

Nikiforova, A., Lnenicka, M., Melin, U., Valle-Cruz, D., Gill, A., Casiano Flores, C., Sirait, E., Luterek, M., Dreyling, R. M., and Tesarova, B. (2025). Responsible AI Adoption in the Public Sector: A Data Centric Taxonomy of AI Adoption Challenges. In Proceedings of the 59th Hawaii International Conference on System Sciences
DOI: 10.5281/zenodo.17232396
title: Numerical Modeling on the Hydrodynamics and Turbulent Flow Structures of Gravity Current over Ripples [Dataset]

description: This dataset studies the flow structures, mixing efficiency, energy budget and shear stress of gravity currents propagating over ripples by conducting numerical simulations using ANSYS Fluent. The numerical model is rigorously validated by Particle Image Velocimetry (PIV). All physical quantities are stored separately in the file, including all cases that is shown in "parameter_set".
DOI: 10.5281/zenodo.17232395
title: Numerical Modeling on the Hydrodynamics and Turbulent Flow Structures of Gravity Current over Ripples [Dataset]

description: This dataset studies the flow structures, mixing efficiency, energy budget and shear stress of gravity currents propagating over ripples by conducting numerical simulations using ANSYS Fluent. The numerical model is rigorously validated by Particle Image Velocimetry (PIV). All physical quantities are stored separately in the file, including all cases that is shown in "parameter_set".
DOI: 10.15468/dl.9zk6vf
title: Occurrence Download

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

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032625-250920141307145/datasets/export for details.
DOI: 10.15468/dl.dpggbh
title: Occurrence Download

description: A dataset containing 104176 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mongolia",
 "OccurrenceStatus is Present",
 "TaxonKey is Tracheophyta"
 ]
}

 The dataset includes 104176 records from 169 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0032591-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.