cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.358was
title: Occurrence Download

description: A dataset containing 6329 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Occurrence evidence, Machine Observation, Human Observation, Observation, Specimen)",
 "Continent is North America",
 "GadmGid is one of (USA.33_1, USA.46_1, USA.30_1, USA.20_1, CAN.9_1, CAN.11_1, USA.22_1, USA.40_1, USA.7_1)",
 "OccurrenceStatus is Present",
 "TaxonKey is Adiantum pedatum L.",
 "Year 2004-2024"
 ]
}

 The dataset includes 6329 records from 21 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005677-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/gx59-ra30
title: Otisville Lead 210 dataset

description: Raw data for the Otisville Lead 210 dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.d2n9nv
title: Occurrence Download

description: A dataset containing 395328 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Haemorhous cassinii (S.F.Baird, 1854)",
 "Year is greater than or equal to 1950",
 {
 "or" : [
 "GadmGid is USA",
 "GadmGid is CAN",
 "GadmGid is MEX"
 ]
 }
 ]
}

 The dataset includes 395328 records from 68 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023974-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/azw2-m323
title: The Lake diatom surface sample dataset

description: Raw data for the The Lake diatom surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/zst8-v066
title: Olin Lake diatom bottom sample dataset

description: Raw data for the Olin Lake diatom bottom sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/vhk3-7q44
title: TK-59 water chemistry dataset

description: Raw data for the TK-59 water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/9ezn-v624
title: Lake Charles water chemistry dataset

description: Raw data for the Lake Charles water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/5xsp-sk76
title: Silvituc pollen dataset

description: Raw data for the Silvituc pollen dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/n2q9-ex93
title: CBS3 pollen dataset

description: Raw data for the CBS3 pollen dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/xj05-rg84
title: Harper vertebrate fauna dataset

description: Raw data for the Harper vertebrate fauna dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.6084/m9.figshare.29000189
title: Heart and Lung Sounds Dataset Recorded from a Clinical Manikin using Digital Stethoscope (HLS-CMDS)

description: This dataset contains 535 recordings of heart and lung sounds captured using a digital stethoscope from a clinical manikin, including both individual and mixed recordings of heart and lung sounds; 50 heart sounds, 50 lung sounds, and 145 mixed sounds. For each mixed sound, the corresponding source heart sound (145 recordings) and source lung sound (145 recordings) were also recorded. It includes recordings from different anatomical chest locations, with normal and abnormal sounds. Each recording has been filtered to highlight specific sound types, making it valuable for artificial intelligence (AI) research and applications. If you use this dataset, please cite: Y. Torabi, S. Shirani, and J. P. Reilly, “Descriptor: Heart and Lung Sounds Dataset Recorded from a Clinical Manikin using Digital Stethoscope (HLS-CMDS),” in IEEE Data Descriptions, https://doi.org/10.1109/IEEEDATA.2025.3566012.
DOI: 10.15468/dl.yadfva
title: Occurrence Download

description: A dataset containing 1283 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pseudognaphalium affine (D.Don) Anderb.",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2010",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1283 records from 59 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023989-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/919s-2549
title: Piper P. water chemistry dataset

description: Raw data for the Piper P. water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/sbfs-8h65
title: LDDsite3670 ostracode surface sample dataset

description: Raw data for the LDDsite3670 ostracode surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/e11r-fc92
title: Cavanaugh Lake diatom dataset

description: Raw data for the Cavanaugh Lake diatom dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/fpt5-8394
title: Montjean-sur-Loire pollen dataset

description: Raw data for the Montjean-sur-Loire pollen dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/2kwg-1p78
title: Deers Meadow 2 pollen surface sample dataset

description: Raw data for the Deers Meadow 2 pollen surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/4b8r-yv06
title: Pantano de Mónica 3 pollen dataset

description: Raw data for the Pantano de Mónica 3 pollen dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.shddzd
title: Occurrence Download

description: A dataset containing 1519 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Polygala japonica Houtt.",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2010",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1519 records from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023990-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/rp8s-2k53
title: Stewert Point Pond pollen dataset

description: Raw data for the Stewert Point Pond pollen dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/2dzr-mm47
title: LDDsite4840 water chemistry dataset

description: Raw data for the LDDsite4840 water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.15468/dl.6k5gv2
title: Occurrence Download

description: A dataset containing 3 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Neovison vison (Schreber, 1777)",
 "Country is Finland",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 3 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023991-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.eqccq9
title: Occurrence Download

description: A dataset containing 4203 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pteropus vampyrus (Linnaeus, 1758)"
 ]
}

 The dataset includes 4203 records from 90 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023992-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/a49v-bv08
title: Ocean Drilling Program Site 1059 MIS 5 (Core 4) pollen dataset

description: Raw data for the Ocean Drilling Program Site 1059 MIS 5 (Core 4) pollen dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/v3q3-wg69
title: Rujm Beni Yasser specimen stable isotope dataset

description: Raw data for the Rujm Beni Yasser specimen stable isotope dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.17632/479hyb4fmj.1
title: Supplementary Material Steijlen et al.

description: Supplementary Material to the paper: "Enhanced metastatic risk stratification for Cutaneous Squamous Cell Carcinoma by combining clinical guidelines with the Erasmus MC model: Results from Two Nationwide Nested Case-Control Studies" by Steijlen et al. published in JAAD.
DOI: 10.21233/njtb-e894
title: Grimes Peatland testate amoebae surface sample dataset

description: Raw data for the Grimes Peatland testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.17632/4zmkpw3wxx.1
title: Machine Learning for Predicting Activation Energies and Understanding the Brønsted–Evans–Polanyi Relation in MXene-Catalyzed Water Gas Shift Reactions

description: This dataset supports the publication titled "A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water Gas Shift Catalysis on MXenes", offering machine learning workflows and curated data for predicting activation energies in catalysis.

It includes:

Comprehensive Dataset on MXene Catalytic Properties (2025):
A curated dataset of 92 MXenes, covering single (M₂C, M₂N) and dual-transition-metal (M'₂M''C₂) compositions. For each MXene, density functional theory (DFT)-derived activation energies are provided for four key reactions in the water-gas shift (WGS) process: water (H₂O) and hydroxyl (OH) dissociation, and hydrogen (H₂) and carbon dioxide (CO₂) formation. Additional structural and physicochemical descriptors are included.

Machine Learning Models for Activation Energy Prediction (Python Notebook):
A Python notebook implementing multiple ML algorithms—Random Forest, Gradient Boosting, ANN, SVM, Decision Tree, and KNN—to predict activation energies. The Random Forest Regressor (RFR) is identified as the best-performing model based on accuracy and robustness. The notebook includes full data processing, training, testing, and feature importance analysis.

Key Insights:

Reaction energy and LogP (logarithmic partition coefficient) are the most predictive features.

The findings support and quantify the Brønsted–Evans–Polanyi (BEP) relationship, linking thermodynamics and kinetics of catalytic reactions.

This dataset and analysis aim to guide the design of efficient MXene-based catalysts using interpretable machine learning approaches.
DOI: 10.17632/4zmkpw3wxx.2
title: Machine Learning for Predicting Activation Energies and Understanding the Brønsted–Evans–Polanyi Relation in MXene-Catalyzed Water Gas Shift Reactions

description: This dataset supports the publication titled "A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water Gas Shift Catalysis on MXenes", offering machine learning workflows and curated data for predicting activation energies in catalysis.

It includes:

Comprehensive Dataset on MXene Catalytic Properties (2025):
A curated dataset of 92 MXenes, covering single (M₂C, M₂N) and dual-transition-metal (M'₂M''C₂) compositions. For each MXene, density functional theory (DFT)-derived activation energies are provided for four key reactions in the water-gas shift (WGS) process: water (H₂O) and hydroxyl (OH) dissociation, and hydrogen (H₂) and carbon dioxide (CO₂) formation. Additional structural and physicochemical descriptors are included.

Machine Learning Models for Activation Energy Prediction (Python Notebook):
A Python notebook implementing multiple ML algorithms—Random Forest, Gradient Boosting, ANN, SVM, Decision Tree, and KNN—to predict activation energies. The Random Forest Regressor (RFR) is identified as the best-performing model based on accuracy and robustness. The notebook includes full data processing, training, testing, and feature importance analysis.

Key Insights:

Reaction energy and LogP (logarithmic partition coefficient) are the most predictive features.

The findings support and quantify the Brønsted–Evans–Polanyi (BEP) relationship, linking thermodynamics and kinetics of catalytic reactions.

This dataset and analysis aim to guide the design of efficient MXene-based catalysts using interpretable machine learning approaches.
DOI: 10.17632/4zmkpw3wxx.3
title: Machine Learning for Predicting Activation Energies and Understanding the Brønsted–Evans–Polanyi Relation in MXene-Catalyzed Water Gas Shift Reactions

description: This dataset supports the publication titled "A Machine Learning Perspective on the Brønsted–Evans–Polanyi Relation in Water Gas Shift Catalysis on MXenes", offering machine learning workflows and curated data for predicting activation energies in catalysis.

It includes:

Comprehensive Dataset on MXene Catalytic Properties (2025):
A curated dataset of 92 MXenes, covering single (M₂C, M₂N) and dual-transition-metal (M'₂M''C₂) compositions. For each MXene, density functional theory (DFT)-derived activation energies are provided for four key reactions in the water-gas shift (WGS) process: water (H₂O) and hydroxyl (OH) dissociation, and hydrogen (H₂) and carbon dioxide (CO₂) formation. Additional structural and physicochemical descriptors are included.

Machine Learning Models for Activation Energy Prediction (Python Notebook):
A Python notebook implementing multiple ML algorithms—Random Forest, Gradient Boosting, ANN, SVM, Decision Tree, and KNN—to predict activation energies. The Random Forest Regressor (RFR) is identified as the best-performing model based on accuracy and robustness. The notebook includes full data processing, training, testing, and feature importance analysis.

Key Insights:

Reaction energy and LogP (logarithmic partition coefficient) are the most predictive features.

The findings support and quantify the Brønsted–Evans–Polanyi (BEP) relationship, linking thermodynamics and kinetics of catalytic reactions.

This dataset and analysis aim to guide the design of efficient MXene-based catalysts using interpretable machine learning approaches.
DOI: 10.5281/zenodo.15377022
title: Content Analysis Coding Guide

description: This dataset is a coding manual developed for the content analysis of environmental news articles published by Kompas.com between April 2024 and April 2025. It provides a structured framework to classify news content across three key dimensions: news type (hard news, feature, explanatory), topic or theme (aligned with major environmental categories such as clean energy, climate change, marine and terrestrial ecosystems), and news sentiment (positive, negative, neutral).
DOI: 10.5281/zenodo.15377023
title: Content Analysis Coding Guide

description: This dataset is a coding manual developed for the content analysis of environmental news articles published by Kompas.com between April 2024 and April 2025. It provides a structured framework to classify news content across three key dimensions: news type (hard news, feature, explanatory), topic or theme (aligned with major environmental categories such as clean energy, climate change, marine and terrestrial ecosystems), and news sentiment (positive, negative, neutral).
DOI: 10.25345/c5cr5nr3k
title: MassIVE MSV000097846 - GNPS - LC-MS/MS data collected on producers of Trestatin B
title: LC-MS/MS data collected on producers of trestatin-B
DOI: 10.17632/3kw76735mf.1
title: The Association Between Bariatric Surgery and Improvement in Psoriasis: A Systematic Review

description: Supplemental materials for the manuscript titled "The Association Between Bariatric Surgery and Improvement in Psoriasis: A Systematic Review" published in the Journal of the American Academy of Dermatology.
DOI: 10.15468/dl.3us5ar
title: Occurrence Download

description: A dataset containing 1369 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lysimachia japonica Thunb.",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2010",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1369 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023994-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.21233/w834-wp83
title: Kopparas Mire testate amoebae surface sample dataset

description: Raw data for the Kopparas Mire testate amoebae surface sample dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/dhwx-s492
title: ELA 384 (Enache et al 2011) water chemistry dataset

description: Raw data for the ELA 384 (Enache et al 2011) water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.21233/hzaf-s105
title: LDDsite1486 water chemistry dataset

description: Raw data for the LDDsite1486 water chemistry dataset submitted to the Neotoma Paleoecology Database. Data is available through the landing page in JSON format. The landing page referenced by the DOI also contains links to publications and a map-based viewer for the dataset. The Neotoma Paleoecology Database maintains a homepage at https://www.neotomadb.org.
DOI: 10.17632/fzrj2xc9rt.1
title: AOT Characters Dataset

description: The AOT Characters Dataset consists of 4,041 manually collected images of 14 characters from Attack on Titan, captured across diverse scenes and expressions. The data was gathered via HD screenshots and carefully cleaned by removing blurry or obstructed frames. When tested with various deep learning models, results showed competitive accuracy with Deep Danbooru on the full dataset. This demonstrates the dataset’s value for anime-specific tasks like character classification and supports reproducible model evaluation in stylized visual media.
DOI: 10.17632/z5r9v4xp4v
title: The Role of Organizational Justice in Predicting Job Satisfaction and Turnover Intention: Evidence from Indonesian Banks

description: The data supporting this study were collected from a survey of 204 respondents and comprise six variables: procedural justice, interactional justice, distributive justice, job satisfaction, turnover intention, and flexible work arrangements (FWAs) to support this research: "The Role of Organizational Justice in Predicting Job Satisfaction and Turnover Intention: Evidence from Indonesian Banks".
DOI: 10.17632/z5r9v4xp4v.1
title: The Role of Organizational Justice in Predicting Job Satisfaction and Turnover Intention: Evidence from Indonesian Banks

description: The data supporting this study were collected from a survey of 204 respondents and comprise six variables: procedural justice, interactional justice, distributive justice, job satisfaction, turnover intention, and flexible work arrangements (FWAs) to support this research: "The Role of Organizational Justice in Predicting Job Satisfaction and Turnover Intention: Evidence from Indonesian Banks".