cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.etyky2
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Ecballium elaterium",
 "Country is Sweden",
 "HasCoordinate is true"
 ]
}

 The dataset includes 25 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0049317-250811113504898/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y6uxcc
title: Occurrence Download

description: A dataset containing 6283 species occurrences available in GBIF matching the query:
{
 "and" : [
 "DatasetKey is iNaturalist Research-grade Observations",
 "OccurrenceStatus is Present",
 "TaxonKey is Drosera spatulata Labill."
 ]
}

 The dataset includes 6283 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010611-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.xyxg8s
title: Occurrence Download

description: A dataset containing 44 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 },
 "TaxonKey is Acaena pinnatifida Ruiz & Pav.",
 "Year is less than or equal to 2000",
 "Year is greater than or equal to 1970",
 {
 "or" : [
 {
 "not" : {
 "EstablishmentMeans" : [
 "is one of (MANAGED, INTRODUCED)"
 ]
 }
 },
 "EstablishmentMeans is null"
 ]
 },
 {
 "or" : [
 "CoordinateUncertaintyInMeters is less than 1000",
 "CoordinateUncertaintyInMeters is null"
 ]
 }
 ]
}

 The dataset includes 44 records from 18 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0010612-250525065834625/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.35738
title: 2024-2025 Jiangsu Municipal Industry-Education Consortium Data
title: 2024-2025年江苏市域产教联合体数据

description: The funding investment of the 3 municipal industry-education consortiums, as well as data regarding resource co-construction and sharing, talent cultivation, and social services during the first year of the construction period.
description: The funding investment of the 3 municipal industry-education consortiums, as well as data regarding resource co-construction and sharing, talent cultivation, and social services during the first year of the construction period.
DOI: 10.15468/dl.a3jz9v
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((88.74391 24.46051, 88.66676699999999 24.454933, 88.591542 24.438341, 88.52010799999999 24.411146, 88.454235 24.374024, 88.395557 24.327898, 88.345519 24.273913, 88.305351 24.213406, 88.276032 24.147875, 88.25827 24.07894, 88.252486 24.008302, 88.25879999999999 23.937703, 88.27704 23.868878, 88.306738 23.803518, 88.34715 23.743227, 88.397272 23.689481, 88.455867 23.643594, 88.521495 23.606689, 88.59255 23.579665, 88.667297 23.563183, 88.74391 23.557644, 88.82052299999999 23.563183, 88.89527 23.579665, 88.966325 23.606689, 89.031953 23.643594, 89.090548 23.689481, 89.14067 23.743227, 89.181082 23.803518, 89.21078 23.868878, 89.22902000000001 23.937703, 89.23533399999999 24.008302, 89.22955 24.07894, 89.211788 24.147875, 89.182469 24.213406, 89.142301 24.273913, 89.092263 24.327898, 89.033585 24.374024, 88.96771200000001 24.411146, 88.896278 24.438341, 88.82105300000001 24.454933, 88.74391 24.46051))"},{"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 12 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004670-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.57760/sciencedb.35687
title: Disaster situation and operation and maintenance data of Baoji power grid from 2017 to 2025
title: 2017-2025 年宝鸡电网灾情及运维数据

description: The data is sourced from State Grid Baoji Power Supply Company, covering samples of tripping faults caused by icing, tower static data (terrain type, altitude, insulation level, etc.), and extracting key information such as tripping time and icing thickness 
description: The data is sourced from State Grid Baoji Power Supply Company, covering samples of tripping faults caused by icing, tower static data (terrain type, altitude, insulation level, etc.), and extracting key information such as tripping time and icing thickness
DOI: 10.15468/dl.h54kkr
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((0.535 8.170097999999999, 0.464022 8.164526, 0.394798 8.147948, 0.329037 8.120772000000001, 0.268362 8.083671000000001, 0.214269 8.037561, 0.168092 7.983579, 0.130967 7.923057, 0.103804 7.857488, 0.087269 7.788489, 0.081764 7.71776, 0.087418 7.647042, 0.104087 7.578076, 0.131357 7.51256, 0.16855 7.452104, 0.214751 7.398194, 0.26882 7.352156, 0.329426 7.315121, 0.395081 7.287998, 0.464171 7.271453, 0.535 7.265892, 0.605829 7.271453, 0.674919 7.287998, 0.740574 7.315121, 0.80118 7.352156, 0.855249 7.398194, 0.90145 7.452104, 0.938643 7.51256, 0.965913 7.578076, 0.982582 7.647042, 0.988236 7.71776, 0.982731 7.788489, 0.9661960000000001 7.857488, 0.939033 7.923057, 0.901908 7.983579, 0.855731 8.037561, 0.801638 8.083671000000001, 0.740963 8.120772000000001, 0.675202 8.147948, 0.605978 8.164526, 0.535 8.170097999999999))"},{"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 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004672-260120142942310/datasets/export for details.
DOI: 10.15468/dl.22tswa
title: Occurrence Download

description: A dataset listing the 19528 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is France",
 "GadmGid is FRA.11.6_1",
 "PublishingOrg is 1928bdf0-f5d2-11dc-8c12-b8a03c50a862"
 ]
}

 The dataset's 19528 records were derived from 1152 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004652-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17177327
title: Dataset associated with the conference paper "Access Point Activation for Static Area-Wide Coverage in Cell-Free Massive MIMO Networks"

description: Description

This repository contains a dataset designed to support the evaluation of access point (AP) selection strategies in cell-free massive MIMO systems. The dataset is organized into two main folders:





coordinates/This folder contains the Cartesian positions of both APs and UEs:





posAPs.mat: Positions of the deployed APs.




gridUEs.mat: Positions of candidate UEs. Only the UEs indicated in the variable idxValidUEs should be considered as valid positions.






channels/This folder contains the estimated channel realizations between AP antennas and UE antennas. A separate .txt file is provided for each UE:





File format: Receiver_X.txt, where X denotes the UE identifier.




Each row represents a link between one AP antenna and the corresponding UE antenna.




Columns include: AP identifier, AP antenna identifier, UE identifier, UE antenna identifier, real part of the estimated channel. and imaginary part of the estimated channel.





This dataset enables reproducibility of simulations by providing the geometric deployment and channel estimates that serve as the basis for pathloss, RSRP calculation, and subsequent AP selection optimization.

Please refer to the conference paper below for more detailed information about the simulations.

The simulation code is available here on GitHub.

Referencing

If you in any way use this dataset for research that results in publications, please cite our original article listed below:



Guillermo García-Barrios, Martina Barbi and Manuel Fuentes, "Access Point Activation for Static Area-Wide Coverage in Cell-Free Massive MIMO Networks," 2026 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Málaga, Spain, 2026. [Submitted]


Acknowledgments

This work is supported by the Spanish ministry of economic affairs and digital transformation and the European Union - NextGenerationEU [UNICO I+D 6G/INSIGNIA] (TSI-064200-2022-006).
DOI: 10.5281/zenodo.17177328
title: Dataset associated with the conference paper "Access Point Activation for Static Area-Wide Coverage in Cell-Free Massive MIMO Networks"

description: Description

This repository contains a dataset designed to support the evaluation of access point (AP) selection strategies in cell-free massive MIMO systems. The dataset is organized into two main folders:





coordinates/This folder contains the Cartesian positions of both APs and UEs:





posAPs.mat: Positions of the deployed APs.




gridUEs.mat: Positions of candidate UEs. Only the UEs indicated in the variable idxValidUEs should be considered as valid positions.






channels/This folder contains the estimated channel realizations between AP antennas and UE antennas. A separate .txt file is provided for each UE:





File format: Receiver_X.txt, where X denotes the UE identifier.




Each row represents a link between one AP antenna and the corresponding UE antenna.




Columns include: AP identifier, AP antenna identifier, UE identifier, UE antenna identifier, real part of the estimated channel. and imaginary part of the estimated channel.





This dataset enables reproducibility of simulations by providing the geometric deployment and channel estimates that serve as the basis for pathloss, RSRP calculation, and subsequent AP selection optimization.

Please refer to the conference paper below for more detailed information about the simulations.

The simulation code is available here on GitHub.

Referencing

If you in any way use this dataset for research that results in publications, please cite our original article listed below:



Guillermo García-Barrios, Martina Barbi and Manuel Fuentes, "Access Point Activation for Static Area-Wide Coverage in Cell-Free Massive MIMO Networks," 2026 Joint European Conference on Networks and Communications & 6G Summit (EuCNC/6G Summit), Málaga, Spain, 2026. [Submitted]


Acknowledgments

This work is supported by the Spanish ministry of economic affairs and digital transformation and the European Union - NextGenerationEU [UNICO I+D 6G/INSIGNIA] (TSI-064200-2022-006).
DOI: 10.15468/dl.j752yz
title: Occurrence Download

description: A dataset containing 83 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Proterorhinus semipellucidus"
 ]
}

 The dataset includes 83 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004675-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v4vt3p
title: Occurrence Download

description: A dataset containing 22 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((6.32937 43.65393,6.32584 43.61634,6.35357 43.56741,6.42668 43.55937,6.50887 43.57252,6.53357 43.6116,6.52551 43.64736,6.46954 43.67144,6.40954 43.67399,6.32937 43.65393))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Delphinium consolida"
 ]
}

 The dataset includes 22 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004676-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s2p7sh
title: Occurrence Download

description: A dataset containing 11122 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Lacerta agilis",
 "Country is Germany",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year 2005-2020"
 ]
}

 The dataset includes 11122 records from 93 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004674-260120142942310/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.18302104
title: RUBATO: A Multi-Version Benchmark for Robust Music Analysis and Transcription

description: 01_Raw_Data



score_image: PNG image exports from the symbolic score

score_midi: Midi exports from the symblic score

score_musescore: Symbolic score in MuseScore format

score_pdf: Sybolic score in PDF format

video_30fps: Video recording of the piano performance corresponding to the MIDI file of the score

wav_22050_mono: Audio file 


 02_Annotations



ann_audio_beat: Beat annotations given in physical time

ann_audio_lokalkey: Local Key annotations given in physical time

ann_audio_measure: Measure annotations given in physical time

ann_audio_noteEvents: Note events with start, end, velocity and instrument given in physical time

ann_audio_startEnd: Start and end time of tracks

ann_audio_structure: Structure annotations given in phyiscal time

ann_score_localkey: Local key annotations given in musical time

ann_score_structure: Structure annotations given in musical time

scripts: Python scripts for preprocessing and allignment


03_ExtraMaterial



scripts: Python scripts for preprocessing and allignment

warpingPaths: Warping paths aligning each audio track to corresponding score
DOI: 10.5281/zenodo.18302105
title: RUBATO: A Multi-Version Benchmark for Robust Music Analysis and Transcription

description: 01_Raw_Data



score_image: PNG image exports from the symbolic score

score_midi: Midi exports from the symblic score

score_musescore: Symbolic score in MuseScore format

score_pdf: Sybolic score in PDF format

video_30fps: Video recording of the piano performance corresponding to the MIDI file of the score

wav_22050_mono: Audio file 


 02_Annotations



ann_audio_beat: Beat annotations given in physical time

ann_audio_lokalkey: Local Key annotations given in physical time

ann_audio_measure: Measure annotations given in physical time

ann_audio_noteEvents: Note events with start, end, velocity and instrument given in physical time

ann_audio_startEnd: Start and end time of tracks

ann_audio_structure: Structure annotations given in phyiscal time

ann_score_localkey: Local key annotations given in musical time

ann_score_structure: Structure annotations given in musical time

scripts: Python scripts for preprocessing and allignment


03_ExtraMaterial



scripts: Python scripts for preprocessing and allignment

warpingPaths: Warping paths aligning each audio track to corresponding score
DOI: 10.5281/zenodo.14884804
title: Feature reproducibility compressed data -- Part I

description: Feature reproducibility compressed data -- Part I
DOI: 10.5281/zenodo.14884805
title: Feature reproducibility compressed data -- Part I

description: Feature reproducibility compressed data -- Part I
DOI: 10.5281/zenodo.17158505
title: The development of thaw slumps and thermal erosion gullies on the Qinghai‒Tibet Plateau during 2015–2020
DOI: 10.5281/zenodo.18335714
title: Artificial Intelligence In Saudi Healthcare: A Systematic Review Of Clinical Applications Supporting Vision 2030 Goals

description: Artificial intelligence (AI) is rapidly transforming healthcare systems globally. In Saudi Arabia, its integration is central to Vision 2030, which aims to modernize healthcare services, enhance efficiency, and promote innovation. This systematic review evaluates the clinical applications of AI in Saudi healthcare, focusing on effectiveness, implementation challenges, and alignment with national goals. Twenty-two peer-reviewed studies published between January 2018 and March 2024 were analyzed. Sourced from PubMed, Scopus, and Web of Science, these studies addressed AI use in diagnostics, medical imaging, clinical decision support, and operational improvement. PRISMA guidelines were applied to ensure methodological rigor. Data were extracted on clinical outcomes, AI model types, and barriers to adoption. AI tools demonstrated strong diagnostic performance, with sensitivity and specificity ranging from 82% to 97%. Predictive models were effective in forecasting COVID-19 outcomes, ICU readmissions, and chronic disease risks. Deep learning applications in radiology outperformed conventional methods in accuracy and speed. However, challenges such as limited data interoperability, unclear regulations, and insufficient clinician training hinder broader adoption. The Saudi Data and Artificial Intelligence Authority (SDAIA) plays a key role in advancing AI through national strategies and cross-sector collaboration. Despite progress, full realization of AI’s potential requires investment in digital infrastructure, ethical governance, and workforce development. In conclusion, AI is reshaping Saudi healthcare and aligns with Vision 2030’s goals. Strategic implementation and inclusive policy frameworks are essential to ensure scalable, equitable, and clinically validated AI integration across the healthcare system.
DOI: 10.5281/zenodo.18335713
title: Artificial Intelligence In Saudi Healthcare: A Systematic Review Of Clinical Applications Supporting Vision 2030 Goals

description: Artificial intelligence (AI) is rapidly transforming healthcare systems globally. In Saudi Arabia, its integration is central to Vision 2030, which aims to modernize healthcare services, enhance efficiency, and promote innovation. This systematic review evaluates the clinical applications of AI in Saudi healthcare, focusing on effectiveness, implementation challenges, and alignment with national goals. Twenty-two peer-reviewed studies published between January 2018 and March 2024 were analyzed. Sourced from PubMed, Scopus, and Web of Science, these studies addressed AI use in diagnostics, medical imaging, clinical decision support, and operational improvement. PRISMA guidelines were applied to ensure methodological rigor. Data were extracted on clinical outcomes, AI model types, and barriers to adoption. AI tools demonstrated strong diagnostic performance, with sensitivity and specificity ranging from 82% to 97%. Predictive models were effective in forecasting COVID-19 outcomes, ICU readmissions, and chronic disease risks. Deep learning applications in radiology outperformed conventional methods in accuracy and speed. However, challenges such as limited data interoperability, unclear regulations, and insufficient clinician training hinder broader adoption. The Saudi Data and Artificial Intelligence Authority (SDAIA) plays a key role in advancing AI through national strategies and cross-sector collaboration. Despite progress, full realization of AI’s potential requires investment in digital infrastructure, ethical governance, and workforce development. In conclusion, AI is reshaping Saudi healthcare and aligns with Vision 2030’s goals. Strategic implementation and inclusive policy frameworks are essential to ensure scalable, equitable, and clinically validated AI integration across the healthcare system.
DOI: 10.5281/zenodo.18332429
title: Augmented Semantic Keypoint Set (55, 75, 100) Extracted From Include50 ISL Dataset

description: Feature extraction was performed using MediaPipe Holistic, capturing a raw total of 543 keypoints per frame across the (x, y, z) axes. Using a Semantic Selection approach, we distilled this data into three specialized configurations—55, 75, and 100 keypoints—tailored for Indian Sign Language recognition. Each configuration includes augmented samples and a standardized splits.json file. This file ensures reproducibility by fixing the data partition at 80% for training, 10% for validation, and 10% for testing. This derivative work is built upon the Include50 dataset; please ensure proper attribution to the original authors (Sridhar et al., 2020) when utilizing these files.

 

Citation

@inproceedings{10.1145/3394171.3413528,author = {Sridhar, Advaith and Ganesan, Rohith Gandhi and Kumar, Pratyush and Khapra, Mitesh},title = {INCLUDE: A Large Scale Dataset for Indian Sign Language Recognition},year = {2020},isbn = {9781450379885},publisher = {Association for Computing Machinery},doi = {10.1145/3394171.3413528},numpages = {10},series = {MM '20}}
DOI: 10.5281/zenodo.16736738
title: Hoval Ultrasol 2 1V (STC) Real Measurements in Switzerland

description: Measurement of an Hoval Ultrasol 2 1V STC

Datasheet of the new Hoval STC (here).

These measurements have taken place in Granges, Valais, Switzerland in longitude : 7.4649965° and latitude : 46.2647793°.For more comparative measurements, the old hybrid solar panel has been replaced by this new STC. The structure on which the panel is mounted remains unchanged.
DOI: 10.5281/zenodo.18335233
title: Hoval Ultrasol 2 1V (STC) Real Measurements in Switzerland

description: Measurement of an Hoval Ultrasol 2 1V STC

Datasheet of the new Hoval STC (here).

These measurements have taken place in Granges, Valais, Switzerland in longitude : 7.4649965° and latitude : 46.2647793°.For more comparative measurements, the old hybrid solar panel has been replaced by this new STC. The structure on which the panel is mounted remains unchanged.
DOI: 10.5281/zenodo.15111700
title: Social Intensity Syndrome among Greek military personnel:  The role of Gender, Romantic Relationships and Burnout
DOI: 10.5281/zenodo.14884993
title: Feature reproducibility compressed data -- Part II

description: Feature reproducibility compressed data -- Part II
DOI: 10.5281/zenodo.14884992
title: Feature reproducibility compressed data -- Part II

description: Feature reproducibility compressed data -- Part II
DOI: 10.5281/zenodo.18335875
title: Haptic Feedback Controller Settings and Gains UC2 – Joystick

description: Data set providing information about the haptic feedback controller settings and gains, used in THEIA-XR use case 2 - Logistics.
DOI: 10.5281/zenodo.18335876
title: Haptic Feedback Controller Settings and Gains UC2 – Joystick

description: Data set providing information about the haptic feedback controller settings and gains, used in THEIA-XR use case 2 - Logistics.
DOI: 10.5281/zenodo.17156960
title: Feature reproducibility compressed data -- Part IV
DOI: 10.5281/zenodo.17156961
title: Feature reproducibility compressed data -- Part IV
DOI: 10.17632/9cvsyd6vpw
title: X-ray radiographs for crack detection in additively manufactured samples

description: This dataset supports crack segmentation research in X-ray radiography for additive manufacturing, all details are provided in the associated thesis (Imaging and image enhancement of additive manufacturing defects) and a published manuscript (https://www.ndt.net/?id=31382).

Contents:
1. 44 unprocessed X-ray projections of LPBF-manufactured Ti6Al4V and NiTi samples.
2. Corresponding segmented projections (ground truth).
3. Trained segmentation models.
Files can be opened using the free software Dragonfly 3D World (Comet).