cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5255/ukda-sn-858207
title: Contextualised Accent Bias in Professional Hierarchies: Accent Effect on Evaluations of Graduate and Legal Job Candidates, 2021-2022

description: This data collection contains two experimental studies examining contextualised accent bias in UK professional settings. The motivation for the research is that accent continues to function as a salient social cue in the UK, signalling classed and regional identities, and may influence how speakers are evaluated in education, employment, and professional advancement. Although previous work has documented general attitudes toward English accents, less is known about how these evaluations change in relation to specific roles, sectors, and expectations within professional hierarchies. The aim of the studies was to test whether accent bias emerges selectively in contexts where role demands and linguistic norms align with particular social meanings associated with different accents.

Study 1 investigates evaluations of a recent university graduate speaking either Received Pronunciation (RP) or Multicultural London English (MLE). Participants listened to two short interview recordings and then rated the candidate on general impression and on their perceived suitability for a range of UK job roles differing in sector and seniority, including hospitality, public administration, and banking. Study 1 explores whether listeners apply accent-linked stereotypes differently when judging roles with varying status, client-facing demands, or symbolic prestige.

Study 2 focuses on the legal profession and examines how UK law students evaluate a candidate speaking either RP or MLE across legal career pathways in corporate and criminal law. Participants rated the likelihood that the candidate would receive appointments to positions ranging from trainee solicitor and pupillage roles to solicitor, barrister, and senior barrister roles. Study 2 tests whether accent-based evaluations are shaped by the linguistic expectations and prestige norms associated with different branches of the legal profession.

Across both studies, general evaluations showed no overall differences by accent, but role-specific evaluations revealed systematic patterns. Non-standard accents were judged more positively in some customer-facing or lower-status contexts, whereas RP was favoured in higher-status professional roles, particularly in legal pathways where linguistic standards are more tightly enforced. These findings support the view that accent bias is context dependent and can contribute to structural barriers to progression in UK professional hierarchies.

The dataset includes audio-based evaluation measures, role and sector judgments, accent perception items, attention checks, and demographic variables.
DOI: 10.15468/dl.e8p4kr
title: Occurrence Download

description: A dataset containing 32172 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Hydnum repandum"
 ]
}

 The dataset includes 32172 records from 420 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028079-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jnjaxv
title: Occurrence Download

description: A dataset containing 29 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eulemur collaris",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 29 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028080-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17814776
title: High-Power Field Peripheral Blood Smear Images for Leukemia Classification (AML, ALL, CLL, CML, Reactive, Normal)
title: AI-Ready Peripheral Blood Smear Dataset for Leukemia Type Prediction (HPF Microscopy)
title: Leukemia Peripheral Smear HPF Image Dataset for AI and Deep Learning Classification

description: This dataset contains 2,832 high-power field (HPF) microscopy images of Wright–Giemsa–stained peripheral blood smears, distributed across five diagnostic categories:Acute Myeloid Leukemia (AML), Acute Lymphoblastic Leukemia (ALL), Chronic Myeloid Leukemia (CML), Chronic Lymphocytic Leukemia (CLL), and Normal/Reactive.

APL (APML) images are included within the AML folder.Reactive lymphocytosis and reactive morphological fields are included within the Normal folder.

All images were captured at 100× oil immersion and represent morphologically characteristic fields typically encountered in routine hematology practice.

Annotation: All images in this dataset were independently reviewed and annotated by three experienced hematopathologists, ensuring high diagnostic accuracy and consistency. These annotated images formed the basis for model development in the following publication:

Syed Naveed et al. Novel hierarchical deep learning models predict type of leukemia from whole-slide microscopic images of peripheral blood. Journal of Medical Artificial Intelligence (JMAI), 2024.https://jmai.amegroups.org/article/view/9379/html

Role of This Dataset in Model Development: 

This dataset served as the training and internal testing dataset for developing the hierarchical deep learning models described in the above publication.The images represent clean, representative diagnostic fields of AML, ALL, CML, CLL, and normal/reactive blood films.

Validation Dataset (Not Included Here)

For external validation in the study, a separate, newly collected dataset was used.The validation dataset consisted of:  All HPF fields from entire slides, Mixed content fields including normal cells, leukemic blasts, reactive patterns, Images that occasionally resembled different leukemia types—reflecting true real-world diagnostic complexity

Although the model was trained on the structured dataset provided here, it successfully generalized to all smear images, demonstrating robustness and practical applicability.

The validation set is not included in this public release.

Intended Use: This dataset is made available for:

Machine learning model training

Morphological research

Educational and academic use

Benchmarking automated leukemia classification systems

License and Citation

This dataset is shared for non-commercial research purposes only.

Researchers using this dataset are requested to cite:

Syed Naveed et al. (2024). Novel hierarchical deep learning models predict type of leukemia from whole-slide microscopic images of peripheral blood. Journal of Medical Artificial Intelligence.https://jmai.amegroups.org/article/view/9379/html
DOI: 10.5281/zenodo.17814775
title: High-Power Field Peripheral Blood Smear Images for Leukemia Classification (AML, ALL, CLL, CML, Reactive, Normal)
title: AI-Ready Peripheral Blood Smear Dataset for Leukemia Type Prediction (HPF Microscopy)
title: Leukemia Peripheral Smear HPF Image Dataset for AI and Deep Learning Classification

description: This dataset contains 2,832 high-power field (HPF) microscopy images of Wright–Giemsa–stained peripheral blood smears, distributed across five diagnostic categories:Acute Myeloid Leukemia (AML), Acute Lymphoblastic Leukemia (ALL), Chronic Myeloid Leukemia (CML), Chronic Lymphocytic Leukemia (CLL), and Normal/Reactive.

APL (APML) images are included within the AML folder.Reactive lymphocytosis and reactive morphological fields are included within the Normal folder.

All images were captured at 100× oil immersion and represent morphologically characteristic fields typically encountered in routine hematology practice.

Annotation: All images in this dataset were independently reviewed and annotated by three experienced hematopathologists, ensuring high diagnostic accuracy and consistency. These annotated images formed the basis for model development in the following publication:

Syed Naveed et al. Novel hierarchical deep learning models predict type of leukemia from whole-slide microscopic images of peripheral blood. Journal of Medical Artificial Intelligence (JMAI), 2024.https://jmai.amegroups.org/article/view/9379/html

Role of This Dataset in Model Development: 

This dataset served as the training and internal testing dataset for developing the hierarchical deep learning models described in the above publication.The images represent clean, representative diagnostic fields of AML, ALL, CML, CLL, and normal/reactive blood films.

Validation Dataset (Not Included Here)

For external validation in the study, a separate, newly collected dataset was used.The validation dataset consisted of:  All HPF fields from entire slides, Mixed content fields including normal cells, leukemic blasts, reactive patterns, Images that occasionally resembled different leukemia types—reflecting true real-world diagnostic complexity

Although the model was trained on the structured dataset provided here, it successfully generalized to all smear images, demonstrating robustness and practical applicability.

The validation set is not included in this public release.

Intended Use: This dataset is made available for:

Machine learning model training

Morphological research

Educational and academic use

Benchmarking automated leukemia classification systems

License and Citation

This dataset is shared for non-commercial research purposes only.

Researchers using this dataset are requested to cite:

Syed Naveed et al. (2024). Novel hierarchical deep learning models predict type of leukemia from whole-slide microscopic images of peripheral blood. Journal of Medical Artificial Intelligence.https://jmai.amegroups.org/article/view/9379/html
DOI: 10.5281/zenodo.5533801
title: S83 | CCL5 | Contaminant Candidate List CCL 5 (Draft)

description: The CCL 5 is a (currently draft) list of contaminants that are currently not subject to any proposed or promulgated US primary drinking water regulations, but are known or anticipated to occur in public water systems. Microcystins are listed in S85. From: https://www.epa.gov/ccl/contaminant-candidate-list-5-ccl-5. Dataset DOI: 10.5281/zenodo.5533801.

Data mapped from https://www.epa.gov/ccl/contaminant-candidate-list-5-ccl-5 by E. Schymanski. Updates: v0.1.1: updated notes to link to Microcystins list: https://doi.org/10.5281/zenodo.5665355.
DOI: 10.5281/zenodo.5720185
title: S83 | CCL5 | Contaminant Candidate List CCL 5 (Draft)

description: The CCL 5 is a (currently draft) list of contaminants that are currently not subject to any proposed or promulgated US primary drinking water regulations, but are known or anticipated to occur in public water systems. Microcystins are listed in S85. From: https://www.epa.gov/ccl/contaminant-candidate-list-5-ccl-5. Dataset DOI: 10.5281/zenodo.5533801.

Data mapped from https://www.epa.gov/ccl/contaminant-candidate-list-5-ccl-5 by E. Schymanski. Updates: v0.1.1: updated notes to link to Microcystins list: https://doi.org/10.5281/zenodo.5665355.
DOI: 10.5281/zenodo.17749118
title: IAM Handwriting Database - Line Level: standardized

description: IAM Handwriting Database at line level with organized images, standardized labels, and train/validation/test splits.

This dataset is derived from the original IAM Handwriting Database: 

Marti, U. V., & Bunke, H. (2002). The IAM-database: an English sentence database for offline handwriting recognition. 

International Journal on Document Analysis and Recognition, 5(1), 39-46.

And from:

https://huggingface.co/datasets/Teklia/IAM-line
DOI: 10.5281/zenodo.17749117
title: IAM Handwriting Database - Line Level: standardized

description: IAM Handwriting Database at line level with organized images, standardized labels, and train/validation/test splits.

This dataset is derived from the original IAM Handwriting Database: 

Marti, U. V., & Bunke, H. (2002). The IAM-database: an English sentence database for offline handwriting recognition. 

International Journal on Document Analysis and Recognition, 5(1), 39-46.

And from:

https://huggingface.co/datasets/Teklia/IAM-line
DOI: 10.17026/ar/dxv7sm
title: PAN-00175476 - wax spatula with Minerva bust

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00175476&lt;/p&gt;
description: Modified: 2025-06-12T17:55:26+02:00
DOI: 10.15468/dl.djnphj
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Kamalia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 4 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028081-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17813748
title: FARMWISE data sources summary

description: Summary of data sources provided by FARMWISE project partners.
DOI: 10.5281/zenodo.17813749
title: FARMWISE data sources summary

description: Summary of data sources provided by FARMWISE project partners.
DOI: 10.15468/dl.cdyjma
title: Occurrence Download

description: A dataset containing 3876 species occurrences available in GBIF matching the query:
{
 "and" : [
 "CoordinateUncertaintyInMeters 0.0-3479.0",
 "Country is Spain",
 "Geometry POLYGON((-8.97638 42.23353,-8.6146 42.23353,-8.6146 42.40236,-8.97638 42.40236,-8.97638 42.23353))",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year 2000-2025"
 ]
}

 The dataset includes 3876 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028082-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17771647
title: [Research Data] Page Relevance Classification for Software Development with LLMs

description: This repository contains the dataset associated with the study “Classification of Software-Development-Related Web Pages Using Large Language Models (LLMs)”. The research  evaluates how effectively modern LLMs can classify and rank web pages related to software development tasks, specifically those involving software reuse.

Contents Included in Data.zip file:

The data.zip archive contains the raw and processed datasets used to validate the study. The data is organized into directories reflecting the two main stages of the research: the initial classification (by AI and Manual annotators) and the consensus analysis (Reviewer Agreements/Disagreements).

The dataset covers four distinct software development tasks (Queries), represented as Q1 through Q4:

Q1: Implementation of a Menu in JavaFX.

Q2: Image Upload implementation using Java Spring.

Q3: Implementation of CRUD operations using Java JPA.

Q4: Searching for a product item on an e-commerce web application using Java Selenium.

The files are categorized as follows:

1. Directory: Data/ClassificationAI and Manual:

This folder contains the primary datasets where web pages were evaluated based on two specific criteria. The files are provided in CSV format (converted from Excel spreadsheets):



Focus/Unfocused (FocadoDesfocado): Classifications determining whether the retrieved web page content is strictly relevant ("Focused") or contains unnecessary noise ("Unfocused") regarding the user's intent.

Existence (Existência): Classifications determining the completeness of the solution provided in the web page (e.g., "Non-existent," "Partially Existent," or "Totally Existent").


2. Directory: Data/Reviewer's Agreements e Disagreements:This folder contains the Comparative files. These datasets document the validation process, highlighting the consensus and discrepancies between human reviewers and/or the LLM outputs. These files were essential for establishing the Ground Truth for the study. It includes comparative analysis files for both Existence and Focus/Unfocus metrics across all four queries (Q1–Q4).

These files detail the specific reviews, justifications for classifications, and the final consensus reached for each web page URL analyzed.
DOI: 10.5281/zenodo.17771646
title: [Research Data] Page Relevance Classification for Software Development with LLMs

description: This repository contains the dataset associated with the study “Classification of Software-Development-Related Web Pages Using Large Language Models (LLMs)”. The research  evaluates how effectively modern LLMs can classify and rank web pages related to software development tasks, specifically those involving software reuse.

Contents Included in Data.zip file:

The data.zip archive contains the raw and processed datasets used to validate the study. The data is organized into directories reflecting the two main stages of the research: the initial classification (by AI and Manual annotators) and the consensus analysis (Reviewer Agreements/Disagreements).

The dataset covers four distinct software development tasks (Queries), represented as Q1 through Q4:

Q1: Implementation of a Menu in JavaFX.

Q2: Image Upload implementation using Java Spring.

Q3: Implementation of CRUD operations using Java JPA.

Q4: Searching for a product item on an e-commerce web application using Java Selenium.

The files are categorized as follows:

1. Directory: Data/ClassificationAI and Manual:

This folder contains the primary datasets where web pages were evaluated based on two specific criteria. The files are provided in CSV format (converted from Excel spreadsheets):



Focus/Unfocused (FocadoDesfocado): Classifications determining whether the retrieved web page content is strictly relevant ("Focused") or contains unnecessary noise ("Unfocused") regarding the user's intent.

Existence (Existência): Classifications determining the completeness of the solution provided in the web page (e.g., "Non-existent," "Partially Existent," or "Totally Existent").


2. Directory: Data/Reviewer's Agreements e Disagreements:This folder contains the Comparative files. These datasets document the validation process, highlighting the consensus and discrepancies between human reviewers and/or the LLM outputs. These files were essential for establishing the Ground Truth for the study. It includes comparative analysis files for both Existence and Focus/Unfocus metrics across all four queries (Q1–Q4).

These files detail the specific reviews, justifications for classifications, and the final consensus reached for each web page URL analyzed.
DOI: 10.5281/zenodo.5535288
title: S84 | UFZHSFPMT | PMT Suspect List from UFZ and HSF

description: A suspect screening list of 1310 potentially persistent and mobile chemicals from UFZ and HSF, described in Neuwald, Muschket et al. (2021) DOI: 10.1016/j.watres.2021.117645 & 10.5281/zenodo.5503379. Dataset DOI: 10.5281/zenodo.5535287.
description: Please cite original data sources Neuwald, Muschket et al. (2021) DOI: 10.1016/j.watres.2021.117645 & 10.5281/zenodo.5503379
DOI: 10.5281/zenodo.5535287
title: S84 | UFZHSFPMT | PMT Suspect List from UFZ and HSF

description: A suspect screening list of 1310 potentially persistent and mobile chemicals from UFZ and HSF, described in Neuwald, Muschket et al. (2021) DOI: 10.1016/j.watres.2021.117645 & 10.5281/zenodo.5503379. Dataset DOI: 10.5281/zenodo.5535287.
description: Please cite original data sources Neuwald, Muschket et al. (2021) DOI: 10.1016/j.watres.2021.117645 & 10.5281/zenodo.5503379
DOI: 10.17026/ar/hsomqd
title: PAN-00175421 - seal matrix, pointed oval face, lead

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00175421&lt;/p&gt;
description: Modified: 2025-06-17T16:09:26+02:00
DOI: 10.15468/dl.4xanap
title: Occurrence Download

description: A dataset containing 166 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Eulemur coronatus",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 166 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0028084-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17669815
title: BIOSPHERE measurement campaign from January 2024 to March 2024 and in May 2024: Effects of the solar events on the radiation belts, UV radiation and ozone in the atmosphere: dataset

description: This is part of the data presented in the paper "BIOSPHERE measurement campaign from January 2024 to March 2024 and in May 2024: Effects of the solar events on the radiation belts, UV radiation and ozone in the atmosphere". 

-> The EPT files contain the differential flux observed by the energetic particle telescope (EPT)  from January 1st 2024 to March 31th 2024. Differential fluxes are given in [(s cm² sr MeV)^-1].

-> The OMNI file contains the some of the data from the omni database fir the corresponding period.-> The mls file contains the ozone data from the MLS/AURA stored in a netcdf file that can be easily read with the xarray python library.-> The xlsx file contains the muon count rates observed on the ground during the campaign.

More data can be found at:

Omni data are provided by https://omniweb.gsfc.nasa.gov/html/ow_data.htmlMLS/Aura data of ozone are accessible at https://disc.gsfc100.nasa.gov/datasets/ML2O3_005/summaryEPT data are publicly available on the Space Situational Awareness website of ESA http://swe.ssa.esa.int/space-radiation

 

Project: 21GRD02 BIOSPHERE The project (21GRD02 BIOSPHERE) has received funding from the European Partnership on Metrology, co-financed by the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States. Funded by the European Union.
DOI: 10.5281/zenodo.17814705
title: BIOSPHERE measurement campaign from January 2024 to March 2024 and in May 2024: Effects of the solar events on the radiation belts, UV radiation and ozone in the atmosphere: dataset

description: This is part of the data presented in the paper "BIOSPHERE measurement campaign from January 2024 to March 2024 and in May 2024: Effects of the solar events on the radiation belts, UV radiation and ozone in the atmosphere". 

-> The EPT files contain the differential flux observed by the energetic particle telescope (EPT)  from January 1st 2024 to March 31th 2024. Differential fluxes are given in [(s cm² sr MeV)^-1].

-> The OMNI file contains the some of the data from the omni database fir the corresponding period.-> The mls file contains the ozone data from the MLS/AURA stored in a netcdf file that can be easily read with the xarray python library.-> The xlsx file contains the muon count rates observed on the ground during the campaign.

More data can be found at:

Omni data are provided by https://omniweb.gsfc.nasa.gov/html/ow_data.htmlMLS/Aura data of ozone are accessible at https://disc.gsfc100.nasa.gov/datasets/ML2O3_005/summaryEPT data are publicly available on the Space Situational Awareness website of ESA http://swe.ssa.esa.int/space-radiation

 

Project: 21GRD02 BIOSPHERE The project (21GRD02 BIOSPHERE) has received funding from the European Partnership on Metrology, co-financed by the European Union’s Horizon Europe Research and Innovation Programme and by the Participating States. Funded by the European Union.