cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.57702/17d9zp24
title: Optical Fiber Link Test Bench

description: A large library of faulty fiber profiles in the frequency domain is made available so they can be used to perform extensive tests and comparisons with current state-of-the-art monitoring techniques.
DOI: 10.57702/fuqlb8ni
title: Learning To Run a Power Network Challenge dataset

description: A dataset for the Learning To Run a Power Network Challenge.
DOI: 10.57702/zevc4878
title: Adversarially Trained Multi-Singer Sequence-To-Sequence Singing Synthesizer

description: This paper presents a high quality singing synthesizer that is able to model a voice with limited available recordings. Based on the sequence-to-sequence singing model, we design a multi-singer framework to leverage all the existing singing data of different singers.
DOI: 10.57702/gq2dtxv4
title: TRN System Dataset

description: The dataset used for the experiments in this paper, which includes terrain relative navigation (TRN) system data.
DOI: 10.5281/zenodo.15794179
title: Data set for "Frictional weakening of gabbro faults after quasi-stationary contact under hydrothermal conditions"

description: Experimental data for the manuscript entitled "Frictional weakening of gabbro faults after quasi-stationary contact under hydrothermal conditions"

 

eight colomns in the CSV. file, from left to right, are: time (s), displacement (mm), friction coefficient, velocity (m/s), effective normal stress (MPa), temperature (℃), pore fluid pressure (MPa), and axial displacement (μm).
DOI: 10.5281/zenodo.17543119
title: Data set for "Frictional weakening of gabbro faults after quasi-stationary contact under hydrothermal conditions"

description: Experimental data for the manuscript entitled "Frictional weakening of gabbro faults after quasi-stationary contact under hydrothermal conditions"

 

eight colomns in the CSV. file, from left to right, are: time (s), displacement (mm), friction coefficient, velocity (m/s), effective normal stress (MPa), temperature (℃), pore fluid pressure (MPa), and axial displacement (μm).
DOI: 10.57702/ffyj7tv7
title: Multi-Robot Swarm Navigation

description: The dataset used in this paper is a multi-robot swarm system with 3 quadrotor robots moving in a two-dimensional planar environment.
DOI: 10.57702/5k0y04bd
title: Convolution Kernel Dataset

description: The dataset used in this paper is a convolution kernel dataset, which is used to train and evaluate the MetaTune cost model.
DOI: 10.57702/b4ahijgf
title: AISHELL-3

description: The Mandarin dataset comprises over 88,000 read utterances and roughly 85 hours of speech data.
DOI: 10.57702/ld0ofixt
title: Harvard USPTO Patent Dataset

description: The Harvard USPTO Dataset is a large-scale, well-structured, and multi-purpose corpus of patent applications.
DOI: 10.5281/zenodo.17107984
title: Dataset related to article: MIRNA PROFILING IN POST-MORTEM SPINAL CORD TISSUE OF C9ORF72-RELATED ALS PATIENTS: TOWARDS IDENTIFICATION OF MOLECULAR TARGETS TO COUNTERACT MOTOR NEURON DEGENERATION.

description: data from real time pcr analysis expressed as ct values
DOI: 10.57702/09u0z2kg
title: retail

description: The dataset used in this paper for on-shelf utility mining.
DOI: 10.5281/zenodo.17107983
title: Dataset related to article: MIRNA PROFILING IN POST-MORTEM SPINAL CORD TISSUE OF C9ORF72-RELATED ALS PATIENTS: TOWARDS IDENTIFICATION OF MOLECULAR TARGETS TO COUNTERACT MOTOR NEURON DEGENERATION.

description: data from real time pcr analysis expressed as ct values
DOI: 10.57702/tkurwwvk
title: ModelNet-10

description: The ModelNet-10 dataset is a benchmark for 3D point cloud classification and semantic segmentation. It contains 4,899 untextured CAD models divided into 10 categories.
DOI: 10.57702/2ywmtj8c
title: SMProbLog

description: SMProbLog: Stable Model Semantics in ProbLog and its Applications in Argumentation
DOI: 10.57702/kwtyztc2
title: Tree-based reinforcement learning for optimal water reservoir operation

description: The authors propose a tree-based reinforcement learning approach for optimal water reservoir operation.
DOI: 10.57702/q1qmqyrd
title: AR Face Database

description: The AR face database contains 100 classes of faces with 26 face images per class with various natural variation and occlusions.
DOI: 10.15468/dl.bmq2pr
title: Occurrence Download

description: A dataset containing 3255 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Scottish Beaver Trial observations, Knapdale, 2009-2014"
 ]
}

 The dataset includes 3255 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079751-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/sazbje4g
title: English Controlled Machine Translation

description: The dataset for English controlled machine translation.
DOI: 10.57702/xl9wzv8a
title: Shapes and Shapes Rotation

description: The dataset used in the paper is a collection of sequences with patterns of different shapes and speeds, and a new dataset collected with the Baxter robot in a manipulation task scenario.
DOI: 10.15468/dl.at2f8c
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((89.22176 24.96491, 89.144305 24.959333, 89.06877799999999 24.94274, 88.997056 24.915545, 88.930921 24.878423, 88.87200900000001 24.832298, 88.821774 24.778313, 88.78145000000001 24.717807, 88.75202 24.652279, 88.734194 24.583347, 88.728393 24.512714, 88.734739 24.442119, 88.753056 24.3733, 88.782876 24.307947, 88.82344999999999 24.247662, 88.873771 24.193922, 88.932597 24.148042, 88.998482 24.111141, 89.06981399999999 24.084121, 89.14485000000001 24.067642, 89.22176 24.062103, 89.29867 24.067642, 89.373706 24.084121, 89.445038 24.111141, 89.51092300000001 24.148042, 89.569749 24.193922, 89.62007 24.247662, 89.660644 24.307947, 89.69046400000001 24.3733, 89.708781 24.442119, 89.715127 24.512714, 89.709326 24.583347, 89.6915 24.652279, 89.66207 24.717807, 89.621746 24.778313, 89.571511 24.832298, 89.51259899999999 24.878423, 89.44646400000001 24.915545, 89.374742 24.94274, 89.299215 24.959333, 89.22176 24.96491))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0079752-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57702/1fbo9zg3
title: LF-VISLAM

description: The dataset used in the paper for LF-VISLAM, a SLAM framework for large field-of-view cameras with negative imaging plane on mobile agents.
DOI: 10.57702/u6saj9f1
title: Episode Filter Dataset

description: The dataset used in the paper is the Episode Filter dataset, which is a real-time API that facilitates easier experimentation with the KOSPI and KOSDAQ stock market data.
DOI: 10.57702/b4a1z54j
title: SSGD

description: SSGD is a smartphone screen glass dataset for defect detection. It contains 10,000 images of smartphone screens with defects.
DOI: 10.57702/z829yik2
title: Chinese CLIP

description: A vision-language pre-training dataset, Chinese CLIP, which consists of 100 million image-text pairs.
DOI: 10.57702/5nbxtd9h
title: TyDi QA

description: Parameter-efficient fine-tuning (PEFT) using labeled task data can significantly improve the performance of large language models (LLMs) on the downstream task. However, there are 7000 languages in the world and many of these languages lack labeled data for real-world language generation tasks.
DOI: 10.57702/uriejzbp
title: Action Recognition with Kernel-based Graph Convolutional Networks

description: Learning graph convolutional networks (GCNs) is an emerging field which aims at generalizing deep learning to arbitrary non-regular domains.
DOI: 10.5281/zenodo.18166942
title: Dark Ecology Dataset: Measurements of Aerial Biomass in US Weather Radar: Metadata

description: The US NEXRAD radar network has monitored the aerosphere over the US and its territories since the 1990s and archived over 250 million files that contain a wealth of information about the movements of birds, bats, and insects. We extracted measurements of biological activity from all files in the NEXRAD archive.

This data record contains metadata, including information about NEXRAD stations and data schemas.

The data files, including vertical profiles for all years and time series data products can be found in associated data records.

Please visit https://github.com/darkecology/darkeco-dataset to access the full dataset, including download links, documentation, and examples.
DOI: 10.5281/zenodo.17981582
title: Pakistan T20I Tri-Series 2025 Dataset

description: Pakistan T20I Tri-Series 2025: Complete Cricket Match Dataset

Overview

This dataset provides comprehensive statistics for the Pakistan T20I Tri-Series 2025, featuring international cricket matches between Pakistan, Sri Lanka, and Zimbabwe. The tri-series comprised 7 T20 International matches played at the Rawalpindi Cricket Stadium, Rawalpindi from November 18 to November 29, 2025.

Dataset Characteristics- Format: CSV (Comma Separated Values)-  Size: 7 matches × 52 features- Language: English-  Encoding: UTF-8-  License: CC BY 4.0

Key VariablesThe dataset includes 52 columns capturing:- Match metadata: Match number, series name, date, venue, umpires- Team information: Playing XI, coach, batting and bowling performances- Statistical metrics: Runs, wickets, extras, powerplay statistics- Player performances: Top scorers, top wicket-takers, fours, sixes- Match outcomes: Results, toss decisions, player of the match awards

Match DetailsThe series included:- 2 matches each between Pakistan-Sri Lanka, Pakistan-Zimbabwe, and Sri Lanka-Zimbabwe- 1 final match between the top two teams- All matches played at Rawalpindi Cricket Stadium- Pakistan emerged as tournament champions

Potential Applications:- cricket analytics and performance research- Machine learning and predictive modeling- Statistical analysis of cricket strategies- Academic research in sports science- Team and player performance evaluation

Related Publication:This dataset is described in the accompanying data paper submitted to arXiv paper

CitationIf you use this dataset in your research, please cite:Raza, K. (2025). Pakistan T20I Tri-Series 2025 Cricket Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17981582
DOI: 10.57702/w3y9zzx2
title: WMT14 English-French

description: The dataset used for bilingual resynchronization task, which includes WMT14 English-French data and the small parallel sentence compression dataset.
DOI: 10.5281/zenodo.17981581
title: Pakistan T20I Tri-Series 2025 Dataset

description: Pakistan T20I Tri-Series 2025: Complete Cricket Match Dataset

Overview

This dataset provides comprehensive statistics for the Pakistan T20I Tri-Series 2025, featuring international cricket matches between Pakistan, Sri Lanka, and Zimbabwe. The tri-series comprised 7 T20 International matches played at the Rawalpindi Cricket Stadium, Rawalpindi from November 18 to November 29, 2025.

Dataset Characteristics- Format: CSV (Comma Separated Values)-  Size: 7 matches × 52 features- Language: English-  Encoding: UTF-8-  License: CC BY 4.0

Key VariablesThe dataset includes 52 columns capturing:- Match metadata: Match number, series name, date, venue, umpires- Team information: Playing XI, coach, batting and bowling performances- Statistical metrics: Runs, wickets, extras, powerplay statistics- Player performances: Top scorers, top wicket-takers, fours, sixes- Match outcomes: Results, toss decisions, player of the match awards

Match DetailsThe series included:- 2 matches each between Pakistan-Sri Lanka, Pakistan-Zimbabwe, and Sri Lanka-Zimbabwe- 1 final match between the top two teams- All matches played at Rawalpindi Cricket Stadium- Pakistan emerged as tournament champions

Potential Applications:- cricket analytics and performance research- Machine learning and predictive modeling- Statistical analysis of cricket strategies- Academic research in sports science- Team and player performance evaluation

Related Publication:This dataset is described in the accompanying data paper submitted to arXiv paper

CitationIf you use this dataset in your research, please cite:Raza, K. (2025). Pakistan T20I Tri-Series 2025 Cricket Dataset [Data set]. Zenodo. https://doi.org/10.5281/zenodo.17981582
DOI: 10.7282/t39k4dkv
title: Probability-weighted ensembles of U.S. county-level climate projections for climate risk analysis: Monte Carlo Pattern/Residual (MCPR) data set RCP60

description: Quantitative assessment of climate change risk requires a method for constructing probabilistic time series of changes in physical climate parameters. Here, we develop two such methods, Surrogate/Model Mixed Ensemble (SMME) and Monte Carlo Pattern/Residual (MCPR), and apply them to construct joint probability density functions (PDFs) of temperature and precipitation change over the 21st century for every county in the United States. Both methods produce likely (67% probability) temperature and precipitation projections consistent with the Intergovernmental Panel on Climate Change's interpretation of an equal-weighted Coupled Model Intercomparison Project 5 (CMIP5) ensemble, but also provide full PDFs that include tail estimates. For example, both methods indicate that, under representative concentration pathway (RCP) 8.5, there is a 5% chance that the contiguous United States could warm by at least 8°C. Variance decomposition of SMME and MCPR projections indicate that background variability dominates uncertainty in the early 21st century, while forcing-driven changes emerge in the second half of the 21st century. By separating CMIP5 projections into unforced and forced components using linear regression, these methods generate estimates of unforced variability from existing CMIP5 projections without requiring the computationally expensive use of multiple realizations of a single GCM.## CitationThis data set is intended to accompany these studies:(1) T. Houser, R.E. Kopp, S.M. Hsiang, M. Delgado, A.S. Jina, K. Larsen,M. Mastrandrea, S. Mohan, R. Muir-Wood, D.J. Rasmussen, J. Rising,and P. Wilson. (2015). American Climate Prospectus: Economic Risksin the United States. Columbia University Press. ISBN: 978-0231174565(2) D. J. Rasmussen, M. Meinshausen, and R. E. Kopp. (2016). Probability-weighted ensembles of U.S. county-level climate projections for climaterisk analysis. Journal of Applied Meteorology and Climatology. DOI: 10.1175/JAMC-D-15-0302.1Please cite these works when using any results generated with these projections.Copyright © 2016 by ROBERT E. KOPP AND RHODIUM GROUP LLCThis dataset is made available under a non-Commercial Creative Commons License.https://creativecommons.org/licenses/by-nc/3.0/us/You are free to: 1. Share ‒ copy and redistribute the material in any medium or format2. Remix, transform, and build upon the materialYou must:1. Attribution ‒ You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use.2. You may not use the material for commercial purposes.No warranties are given.
DOI: 10.15468/dl.ebb5qv
title: Occurrence Download

description: A dataset containing 8516 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lithophyllum incrustans"
 ]
}

 The dataset includes 8516 records from 120 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011596-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18482371
title: Strategi Perencanaan Wilayah dan Kota Berbasis Smart City di Era Urbanisasi Pesat
DOI: 10.5281/zenodo.18482372
title: Strategi Perencanaan Wilayah dan Kota Berbasis Smart City di Era Urbanisasi Pesat
DOI: 10.15468/dl.7dtpsb
title: Occurrence Download

description: A dataset listing the 17 species recorded in GBIF matching the query:
{
 "DatasetKey" : [
 "is Inventaires Aéro Biodiversité - DOL - Aéroport de Deauville-Inventaires Aéro Biodiversité - DOL - EPOC 2"
 ]
}

 The dataset's 17 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011575-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.6084/m9.figshare.30698444
title: Synthetic Aperture Radar as a Tool for Tornado Classification in Forested Areas

description: Canada’s tornado climatology has historically been challenging to accurately assess. Many Canadian tornadoes are currently rated on the Enhanced Fujita scale using treefall as a damage indicator, though this approach may occasionally underestimate tornado intensity. In this work, we propose that the use of synthetic aperture radar (SAR) images of tornado tracks may lead to more accurate tornado ratings in tornadoes that have at least 25% of their track consisting of forested areas. We create a replicable methodology by which SAR data can be used to classify tornadoes by generating difference images using Sentinel-1 C-Band radar data acquired before and after the tornado event. We apply this methodology to a large number of recent Canadian tornadoes. The results showed that only higher intensity tornadoes (EF3+) were detectable in the SAR change detection images. Critically, the results revealed possible misclassifications, where tornadoes originally assigned a rating of EF2 based on treefall damage indicators were visible in the SAR imagery. Our study provides a foundation for future work, providing an improved methodology for classifying tornadoes in remote forested areas.
DOI: 10.6084/m9.figshare.30698444.v1
title: Synthetic Aperture Radar as a Tool for Tornado Classification in Forested Areas

description: Canada’s tornado climatology has historically been challenging to accurately assess. Many Canadian tornadoes are currently rated on the Enhanced Fujita scale using treefall as a damage indicator, though this approach may occasionally underestimate tornado intensity. In this work, we propose that the use of synthetic aperture radar (SAR) images of tornado tracks may lead to more accurate tornado ratings in tornadoes that have at least 25% of their track consisting of forested areas. We create a replicable methodology by which SAR data can be used to classify tornadoes by generating difference images using Sentinel-1 C-Band radar data acquired before and after the tornado event. We apply this methodology to a large number of recent Canadian tornadoes. The results showed that only higher intensity tornadoes (EF3+) were detectable in the SAR change detection images. Critically, the results revealed possible misclassifications, where tornadoes originally assigned a rating of EF2 based on treefall damage indicators were visible in the SAR imagery. Our study provides a foundation for future work, providing an improved methodology for classifying tornadoes in remote forested areas.
DOI: 10.5281/zenodo.18267233
title: A Benchmark Study for Model Order Reduction Techniques of Vibroacoustic Problems in the Presence of Heavy Fluids
DOI: 10.15468/dl.u9q75s
title: Occurrence Download

description: A dataset containing 41 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rheum rhomboideum",
 "HasCoordinate is true",
 "BasisOfRecord is one of (Specimen)"
 ]
}

 The dataset includes 41 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011598-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v5ucu9
title: Occurrence Download

description: A dataset containing 3264 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((8.88123 41.51428,8.87642 41.50602,8.91392 41.47436,8.96623 41.4544,8.99238 41.46748,9.00132 41.48606,8.99891 41.50567,8.99341 41.52426,8.991 41.53423,8.97517 41.55316,8.9387 41.55075,8.91014 41.54421,8.88123 41.51428))",
 "OccurrenceStatus is Present",
 "TaxonKey is Plantae",
 "Year 2013-2026"
 ]
}

 The dataset includes 3264 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0011599-260129131611470/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17525428
title: Painel interativo sobre as respostas de cetáceos a atividades de pesquisa sísmica

description: Este depósito contém o conjunto de dados e os materiais de suporte para o "Painel interativo sobre as respostas de cetáceos a atividades de pesquisa sísmica".

O painel é uma ferramenta de visualização interativa hospedada no Google Looker Studio, e os dados brutos que o alimentam estão disponíveis neste registro para download. O objetivo é fornecer uma visão transparente e baseada em evidências dos efeitos documentados da atividade de pesquisa sísmica em cetáceos, destinada a pesquisadores, gestores, tomadores de decisão e à indústria de petróleo e gás.

O MARESIS é um projeto desenvolvido pelo Instituto Baleia Jubarte, em parceria com a Socioambiental Consultores Associados, e financiado pelo Centro de Pesquisa, Desenvolvimento e Inovação da Petrobras – CENPES, e pela Agência Nacional do Petróleo - ANP.
DOI: 10.5281/zenodo.17525427
title: Painel interativo sobre as respostas de cetáceos a atividades de pesquisa sísmica

description: Este depósito contém o conjunto de dados e os materiais de suporte para o "Painel interativo sobre as respostas de cetáceos a atividades de pesquisa sísmica".

O painel é uma ferramenta de visualização interativa hospedada no Google Looker Studio, e os dados brutos que o alimentam estão disponíveis neste registro para download. O objetivo é fornecer uma visão transparente e baseada em evidências dos efeitos documentados da atividade de pesquisa sísmica em cetáceos, destinada a pesquisadores, gestores, tomadores de decisão e à indústria de petróleo e gás.

O MARESIS é um projeto desenvolvido pelo Instituto Baleia Jubarte, em parceria com a Socioambiental Consultores Associados, e financiado pelo Centro de Pesquisa, Desenvolvimento e Inovação da Petrobras – CENPES, e pela Agência Nacional do Petróleo - ANP.
DOI: 10.5281/zenodo.18410612
title: Plasma membrane all-atom simulations: D-erythro sphingoid motif variant [part 1/2]
DOI: 10.5281/zenodo.18410613
title: Plasma membrane all-atom simulations: D-erythro sphingoid motif variant [part 1/2]