cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.18065427
title: A Dataset of American Poetry by Poets from Historically Underrepresented Groups in the HathiTrust Digital Library

description: This dataset provides a collection of American poetry by poets from historically underrepresented groups in the HathiTrust Digital Library. It comprises 9,321 poems from 113 collections by 40 African Americans, 22 Asian Americans, 3 Pacific Islanders, 17 Latin Americans, and 31 Native American poets. We identified and recorded the start and end page numbers for each poem and released the annotations in CSV files. The dataset also reveals imbalances in the representation of poets from historically underrepresented groups within the HathiTrust corpus. We expect this dataset to support large-scale poetry analysis, uncover biases in natural language processing (NLP) models, assess their robustness when applied to culturally diverse poetic language, and promote the development of more inclusive models for underrepresented poets.
DOI: 10.34740/kaggle/dsv/14348369
title: Santhali hand-written digit dataset
DOI: 10.15468/dl.u3tkd2
title: Occurrence Download

description: A dataset containing 472036 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Northeast Fisheries Science Center Bottom Trawl Survey Data"
 ]
}

 The dataset includes 472036 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069453-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ec9qe2
title: Occurrence Download

description: A dataset listing the 1022 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is Northeast Fisheries Science Center Bottom Trawl Survey Data"
 ]
}

 The dataset's 1022 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069454-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cknbkw
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-73.97217 5.689,-73.5288 5.689,-73.5288 5.87984,-73.97217 5.87984,-73.97217 5.689))",
 "OccurrenceStatus is Present",
 "TaxonKey is Chiroptera"
 ]
}

 The dataset includes 17 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069457-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mrwptf
title: Occurrence Download

description: A dataset containing 382793 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Toxostoma redivivum",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))"
 ]
}

 The dataset includes 382793 records from 72 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069451-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15472/flbyu7
title: Mamíferos pequeños asociados a las cuencas abastecedoras del embalse La Fe en el departamento de Antioquia

description: Este recurso contiene 428 registros tomados entre los meses de junio y julio del año 2021, febrero de 2022 y junio, julio y agosto 2024. Corresponden al inventario realizado en algunos predios asociados a las cuencas abastecedoras del embalse La Fe (Antioquia), para el establecimiento de la línea base de mamíferos en esta zona
DOI: 10.3886/e242090
title: University of Abuja {UNIABUJA}, 2026/2027 Pre Degree and Diploma form is out call Registrar {08058556110} {08058556110} for Guideline on how to apply and register properly before the closing date. Registrar {08058556110} {08058556110}

description: University of Abuja {UNIABUJA}, 2026/2027 Pre Degree and Diploma form is out call Registrar {08058556110} {08058556110} for Guideline on how to apply and register properly before the closing date. Registrar {08058556110} {08058556110}
DOI: 10.15468/dl.rb6p23
title: Occurrence Download

description: A dataset containing 1558 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Galiteuthis glacialis"
 ]
}

 The dataset includes 1558 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069458-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18100944
title: Campione dei partecipanti alla Resistenza originari di Ruvo di Puglia

description: Il dataset contiene l'elenco dei partecipanti alla Resistenza originari di Ruvo di Puglia, la cui attività ha ricevuto il riconoscimento della qualifica partigiana da parte delle commissioni apposite. Fonte: dati Ricompart e archivio provinciale Anpi Bari.
DOI: 10.5281/zenodo.17218179
title: Campione dei partecipanti alla Resistenza originari di Ruvo di Puglia

description: Il dataset contiene l'elenco dei partecipanti alla Resistenza originari di Ruvo di Puglia, la cui attività ha ricevuto il riconoscimento della qualifica partigiana da parte delle commissioni apposite. Fonte: dati Ricompart e archivio provinciale Anpi Bari.
DOI: 10.34740/kaggle/dsv/14348475
title: Berita UNDIP
DOI: 10.34740/kaggle/dsv/14348512
title: Berita UNNES
DOI: 10.7934/p5765
title: Evolutionary and biogeographical implications of phylogenetic analysis of the late Palaeozoic trilobite Paladin (project)
DOI: 10.7934/x30590
title: Evolutionary and biogeographical implications of phylogenetic analysis of the late Palaeozoic trilobite Paladin (project) [X30590] Paladin_Brezinski_2003_KEY
DOI: 10.34740/kaggle/dsv/14348571
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.9fxgck
title: Occurrence Download

description: A dataset containing 774504 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Myiarchus cinerascens",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))"
 ]
}

 The dataset includes 774504 records from 115 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069459-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25919/9byg-4635
title: OzOat Diversity Panel: A Global Genome-to-Phenome Resource for Oat Breeding

description: The OzOat diversity panel is a curated collection of approximately 319 oat accessions representing global genetic diversity and the breeding history of Australian oats. The panel spans material from historical landraces (circa 1892) to modern cultivars, including key international introductions and donor lines. Designed to maximise recombination and capture broad phenotypic diversity, the OzOat panel underpins gene discovery and marker development for a wide range of traits. High-density SNP genotyping, transcriptome sequencing, and curated pedigree information provide a powerful genome-to-phenome platform for accelerating genetic discovery and breeding innovation. Combined these resources enable the development of oat varieties with improved resilience, productivity, and quality tailored to Australian farming systems. Links to genomic, pedigree, and marker–trait association datasets generated as part of the GRDC project: Optimising genetic control of oat phenology for Australia (CSP2007-002RTX) are provided within this DAP entry.
DOI: 10.13020/0s3h-wm31
title: Data for Electron Microscopy Transfer System to Protect Atmosphere-sensitive Materials for Scanning Electron Microscopy Characterization

description: This data was collected to provide evidence for the successful ability of a novel scanning electron microscopy sample preparation method to protect highly atmosphere- and/or moisture-sensitive materials from undesired exposure to atmosphere. Initial proof-of-concept experiments were conducted with highly hygroscopic MgCl2 material exposed to controlled humidity environments and qualitative (photographs) and quantitative data (x-ray diffraction) were collected to compare samples that were prepared (1) with the use of the sample protection/preparation method (2) with no protections provided. Following this, hygroscopic BaO thin films were synthesized and prepared with the outlined method and characterized via scanning electron microscopy to demonstrate the functional application of the preparation technique.
description: The experimental dataset includes:
(1) qualitative photographs of protected (and exposed) salt samples after exposure to high humidity for various timeframes; relevant to figures in the main manuscript.
(2) raw reflection high energy electron diffraction (RHEED) data as well as the RHEED image after growth
(3) SEM micrographs and EDX data collected for BaO atmosphere-exposed and -protected samples for both Pt-coated and non-coated samples
(4) XRD data files for reference crystal structures and MgCl2 sample files
DOI: 10.5281/zenodo.18097801
title: Synthetic Co-Infection Plant Leaf Dataset for Multi-Disease Classification

description: This dataset contains synthetic plant leaf images representing co-infection scenarios across five crops: apple, corn, grape, potato, and tomato.

The base single-disease leaf images were sourced from the publicly available PlantVillage dataset, which contains only one disease per leaf. To address the lack of real-world co-infection data, a Python-based synthetic image generation pipeline was developed to combine disease regions from two distinct classes onto a single leaf image, producing biologically plausible co-infection samples.

All images in this dataset represent synthetic co-infection cases where two diseases appear simultaneously on a single leaf. The dataset is intended for training and evaluating deep learning-based plant disease classification models, particularly in multi-class and co-infection settings.
DOI: 10.5281/zenodo.18097800
title: Synthetic Co-Infection Plant Leaf Dataset for Multi-Disease Classification

description: This dataset contains synthetic plant leaf images representing co-infection scenarios across five crops: apple, corn, grape, potato, and tomato.

The base single-disease leaf images were sourced from the publicly available PlantVillage dataset, which contains only one disease per leaf. To address the lack of real-world co-infection data, a Python-based synthetic image generation pipeline was developed to combine disease regions from two distinct classes onto a single leaf image, producing biologically plausible co-infection samples.

All images in this dataset represent synthetic co-infection cases where two diseases appear simultaneously on a single leaf. The dataset is intended for training and evaluating deep learning-based plant disease classification models, particularly in multi-class and co-infection settings.
DOI: 10.15468/dl.kbqjnj
title: Occurrence Download

description: A dataset containing 11461 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Database and digitization of bees in Thailand"
 ]
}

 The dataset includes 11461 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069461-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25919/xbys-7b43
title: OzOat Diversity Panel: RNA-seq Transcriptome Data from Controlled Photoperiod Experiments

description: RNASeq data from 324 oat leaf tissues sampled at long day and short day controlled environment conditions
DOI: 10.5281/zenodo.18100976
title: Développement d'encres imprimables à base de nanoparticules d'or - Projet Nano (2025/2026) - Marc/Nina
DOI: 10.5281/zenodo.17984896
title: Développement d'encres imprimables à base de nanoparticules d'or - Projet Nano (2025/2026) - Marc/Nina
DOI: 10.5281/zenodo.18101035
title: Artifact of the paper "Why Are AI Agent–Involved Pull Requests (Fix-Related) Remain Unmerged? An Empirical Study"

description: This replication package provides all datasets, scripts, and analysis results required to reproduce the findings reported in “Why Are AI Agent–Involved Pull Requests (Fix-Related) Remain Unmerged? An Empirical Study.” The package is designed to support transparency, reproducibility, and independent verification of both the quantitative and qualitative analyses conducted in this study.

The dataset folder includes the  AIDEV-POP dataset, which contains 33,596 AI-authored pull requests mined from GitHub, as well as all derived subsets used in the paper. From this dataset, we extracted 8,106 fix-related pull requests authored by five AI coding agents (OpenAI Codex, GitHub Copilot, Devin, Cursor, and Claude Code).

 



The scripts folder contains all programs used to preprocess the data, conduct the quantitative and qualitative analyses, and generate the results reported in the paper. These scripts implement the full analysis pipeline, including identification of fix-related pull requests, outcome classification (merged, closed without merging, open), agent-wise aggregation, merge-time computation, and analysis of code review activity, as well as a visualization chart for failure topic distribution.


 

The results folder contains the outputs generated from the analyses conducted in this study. For RQ1, it includes the distribution of pull request merge times, provided as density and boxplot figures. For RQ2, it contains the spreadsheet documenting the failure analysis of fix-related pull requests that were closed without merging. These artifacts correspond directly to the results reported in the paper and support verification of the empirical findings.
DOI: 10.15468/dl.kwt5gd
title: Occurrence Download

description: A dataset containing 42 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Living Specimen, Occurrence evidence, Fossil, Human Observation)",
 "DatasetKey is one of (RSA - California Botanic Garden Herbarium, UCR - University of California, Riverside Herbarium - Vascular Plants, The University and Jepson Herbaria -- Vascular Plants, iNaturalist Research-grade Observations, SBBG - Clifton Smith Herbarium, Santa Barbara Botanic Garden, CHSC - Ahart Herbarium, CSU Chico - Vascular Plants, DAV - University of California, Davis Center for Plant Diversity - Vascular Plants, SD - SD Herbarium, San Diego Natural History Museum, OBI - Robert F. Hoover Herbarium, Cal Poly State University)",
 "GadmGid is USA.5_1",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Issue is one of (Coordinate reprojected, Ambiguous collection, Coordinate invalid)",
 "OccurrenceStatus is Present",
 "TaxonKey is Clarkia xantiana"
 ]
}

 The dataset includes 42 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069462-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25919/kj4r-sp55
title: OzOat Diversity Panel: Genome-Wide DArTSeq SNP Data

description: This dataset contains genome-wide single nucleotide polymorphism (SNP) data generated for 324 oat lines from the OzOat diversity panel using DArTSeq technology.
The data were generated as part of the GRDC-funded project: Optimising genetic control of oat phenology for Australia (CSP2007-002RTX) and support analyses of genetic variation underlying phenological traits. These SNP data complement the phenotypic, transcriptomic, and pedigree datasets available for the OzOat diversity panel and underpin marker–trait association and gene discovery studies relevant to oat adaptation and breeding.

Original platform source files are provided to support data reuse and reanalysis.
DOI: 10.5281/zenodo.18087870
title: Supplementary Materials for: Robustness Enhancement of Self-Localization for Drone-View Mixed Reality via Adaptive RGB-Thermal Integration

description: This dataset contains the supplementary materials, including the Unity project source code, experimental result videos, and quantitative measurement data, for the manuscript submitted to MDPI Technologies (Special Issue: Image Analysis and Processing).

1. Contents





Unity Project: Source code for the proposed RGB-Thermal fusion simulation.





Core Script: OnlineLocalization.cs implements the "Effective Inlier Count ($N_{eff}$)" algorithm using OpenCV.






Video Folder: Contains input footage (Routes A–C) and output result videos under various conditions.




Data Folder: Contains result.xlsx with detailed time-series IoU measurement results.



2. Important Notes on Running the Unity Project

Please read carefully: The provided Unity project contains the complete implementation of the proposed algorithm. However, due to security and licensing reasons, specific credentials and third-party assets are excluded. You must configure the project with your own environment to run the simulation.

A. Third-Party Asset Requirement





OpenCV for Unity: The script OnlineLocalization.cs relies on "OpenCV for Unity" to calculate the spatial distribution of feature points. Due to licensing restrictions, this asset is NOT included.





Action: Please import your own copy of OpenCV for Unity from the Unity Asset Store.





B. Immersal VPS Configuration (Map Data)

The system uses Immersal Cloud Service for localization. You need to prepare your own map data for the 2D-3D point matching.





Generate Token & Map:





Sign up at the [Immersal Developer Portal] and get your Developer Token.




Scan your environment using the Immersal Mapper App and upload it to the server.




Note your Map ID.






Prepare 3D Point Cloud (JSON):





Download the map data as a PLY file from the Immersal Developer Portal.




Convert the PLY file into a JSON file (compatible with the project's format). This JSON data serves as the 3D points for the 2D-3D matching algorithm.






Configure Inspector:





In the Unity Inspector for OnlineLocalization:





Enter your Developer Token and Map ID.




Assign your converted JSON file to the "Map Json File" field.







C. Input Video Configuration (DJI Mavic 3T)





Prerequisite: This project is designed to process dual-screen video footage (RGB + Thermal side-by-side) captured by a DJI Mavic 3T.




Action:





Prepare your own video file recorded with a DJI Mavic 3T (or equivalent dual-stream format).




Assign your video file to the Video Input field in the Inspector.





By following these steps, the system will apply the proposed Adaptive RGB-Thermal Integration logic to your environment and video data.

3. License





Code & Data: Creative Commons Attribution 4.0 International (CC-BY 4.0)




Third-Party Assets: All rights to third-party assets (Immersal SDK, OpenCV for Unity) belong to their respective owners.



 

Funding:This research was partially funded by JSPS KAKENHI, Grant Number 23K11724.
DOI: 10.5281/zenodo.18087871
title: Supplementary Materials for: Robustness Enhancement of Self-Localization for Drone-View Mixed Reality via Adaptive RGB-Thermal Integration

description: This dataset contains the supplementary materials, including the Unity project source code, experimental result videos, and quantitative measurement data, for the manuscript submitted to MDPI Technologies (Special Issue: Image Analysis and Processing).

1. Contents





Unity Project: Source code for the proposed RGB-Thermal fusion simulation.





Core Script: OnlineLocalization.cs implements the "Effective Inlier Count ($N_{eff}$)" algorithm using OpenCV.






Video Folder: Contains input footage (Routes A–C) and output result videos under various conditions.




Data Folder: Contains result.xlsx with detailed time-series IoU measurement results.



2. Important Notes on Running the Unity Project

Please read carefully: The provided Unity project contains the complete implementation of the proposed algorithm. However, due to security and licensing reasons, specific credentials and third-party assets are excluded. You must configure the project with your own environment to run the simulation.

A. Third-Party Asset Requirement





OpenCV for Unity: The script OnlineLocalization.cs relies on "OpenCV for Unity" to calculate the spatial distribution of feature points. Due to licensing restrictions, this asset is NOT included.





Action: Please import your own copy of OpenCV for Unity from the Unity Asset Store.





B. Immersal VPS Configuration (Map Data)

The system uses Immersal Cloud Service for localization. You need to prepare your own map data for the 2D-3D point matching.





Generate Token & Map:





Sign up at the [Immersal Developer Portal] and get your Developer Token.




Scan your environment using the Immersal Mapper App and upload it to the server.




Note your Map ID.






Prepare 3D Point Cloud (JSON):





Download the map data as a PLY file from the Immersal Developer Portal.




Convert the PLY file into a JSON file (compatible with the project's format). This JSON data serves as the 3D points for the 2D-3D matching algorithm.






Configure Inspector:





In the Unity Inspector for OnlineLocalization:





Enter your Developer Token and Map ID.




Assign your converted JSON file to the "Map Json File" field.







C. Input Video Configuration (DJI Mavic 3T)





Prerequisite: This project is designed to process dual-screen video footage (RGB + Thermal side-by-side) captured by a DJI Mavic 3T.




Action:





Prepare your own video file recorded with a DJI Mavic 3T (or equivalent dual-stream format).




Assign your video file to the Video Input field in the Inspector.





By following these steps, the system will apply the proposed Adaptive RGB-Thermal Integration logic to your environment and video data.

3. License





Code & Data: Creative Commons Attribution 4.0 International (CC-BY 4.0)




Third-Party Assets: All rights to third-party assets (Immersal SDK, OpenCV for Unity) belong to their respective owners.



 

Funding:This research was partially funded by JSPS KAKENHI, Grant Number 23K11724.
DOI: 10.6084/m9.figshare.30976525
title: Population-level intraspecific trait variability (ITV)

description: In prep
DOI: 10.6084/m9.figshare.30976525.v1
title: Population-level intraspecific trait variability (ITV)

description: In prep
DOI: 10.34740/kaggle/dsv/14348664
title: Governs_Vibration-Induced_Degradation
DOI: 10.34740/kaggle/dsv/14348699
title: Peer-to-Peer Boliviano (BOB) Exchange Data
DOI: 10.15468/dl.atux79
title: Occurrence Download

description: A dataset containing 743003 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Limosa fedoa",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Geometry POLYGON((-180 -90, 180 -90, 180 90, -180 90, -180 -90))"
 ]
}

 The dataset includes 743003 records from 113 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069460-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.r2gua4
title: Occurrence Download

description: A dataset containing 1110564 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is one of (Belgium, Germany, Netherlands)",
 "TaxonKey is one of (Ondatra zibethicus, Myocastor coypus)",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 1110564 records from 80 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0069464-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.7934/p5636
title: Systematic review of European Nimravinae (Mammalia, Carnivora, Nimravidae) and the phylogenetic relationships of Palaeogene Nimravidae (project)