cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.10623893
title: HypVINN Checkpoints

description: Training checkpoints for HypVINN (https://github.com/Deep-MI/FastSurfer) - please cite the paper when using this resource (https://doi.org/10.1162/imag_a_00034).

Abstract

The hypothalamus plays a crucial role in the regulation of a broad range of physiological, behavioral, and cognitive functions. However, despite its importance, only a few small-scale neuroimaging studies have investigated its substructures, likely due to the lack of fully automated segmentation tools to address scalability and reproducibility issues of manual segmentation. While the only previous attempt to automatically sub-segment the hypothalamus with a neural network showed promise for 1.0 mm isotropic T1-weighted (T1w) magnetic resonance imaging (MRI), there is a need for an automated tool to sub-segment also high-resolutional (HiRes) MR scans, as they are becoming widely available, and include structural detail also from multi-modal MRI. We, therefore, introduce a novel, fast, and fully automated deep-learning method named HypVINN for sub-segmentation of the hypothalamus and adjacent structures on 0.8 mm isotropic T1w and T2w brain MR images that is robust to missing modalities. We extensively validate our model with respect to segmentation accuracy, generalizability, in-session test-retest reliability, and sensitivity to replicate hypothalamic volume effects (e.g., sex differences). The proposed method exhibits high segmentation performance both for standalone T1w images as well as for T1w/T2w image pairs. Even with the additional capability to accept flexible inputs, our model matches or exceeds the performance of state-of-the-art methods with fixed inputs. We, further, demonstrate the generalizability of our method in experiments with 1.0 mm MR scans from both the Rhineland Study and the UK Biobank—an independent dataset never encountered during training with different acquisition parameters and demographics. Finally, HypVINN can perform the segmentation in less than a minute (graphical processing unit [GPU]) and will be available in the open source FastSurfer neuroimaging software suite, offering a validated, efficient, and scalable solution for evaluating imaging-derived phenotypes of the hypothalamus.
DOI: 10.25549/examiner-c44-115761
title: Valley College fiesta king and queen, 1953

description: 7 images. Valley College fiesta king and queen, 13 May 1953. Marlene Hess; Carol Masciotra (queen); Patricia McNeel; Barbara Lievens; Don Dye; Joanne Johnson; Leonard G. Silkwood (king). ; Caption slip reads: "Photographer: Mitchell. Date: 05-13. Reporter: Markson. Assignment: Fiesta -- Valley College -- Queen & King. 27: Don Dye crowns Leonard G. Silkwood, 18, king in beard contest. 9-1--28: Queen Carol Masciotra; King Leonard G. Silkwood. 47-48, L to R: Marlene Hess; Queen Carol Masciotra; Patricia McNeel; standing, l to r: Barbara (Lynn) Lievens; Don Dye; Jo Anne Johnson. 79-80: Queen carol Masciotra."
DOI: 10.5281/zenodo.10623892
title: HypVINN Checkpoints

description: Training checkpoints for HypVINN (https://github.com/Deep-MI/FastSurfer) - please cite the paper when using this resource (https://doi.org/10.1162/imag_a_00034).

Abstract

The hypothalamus plays a crucial role in the regulation of a broad range of physiological, behavioral, and cognitive functions. However, despite its importance, only a few small-scale neuroimaging studies have investigated its substructures, likely due to the lack of fully automated segmentation tools to address scalability and reproducibility issues of manual segmentation. While the only previous attempt to automatically sub-segment the hypothalamus with a neural network showed promise for 1.0 mm isotropic T1-weighted (T1w) magnetic resonance imaging (MRI), there is a need for an automated tool to sub-segment also high-resolutional (HiRes) MR scans, as they are becoming widely available, and include structural detail also from multi-modal MRI. We, therefore, introduce a novel, fast, and fully automated deep-learning method named HypVINN for sub-segmentation of the hypothalamus and adjacent structures on 0.8 mm isotropic T1w and T2w brain MR images that is robust to missing modalities. We extensively validate our model with respect to segmentation accuracy, generalizability, in-session test-retest reliability, and sensitivity to replicate hypothalamic volume effects (e.g., sex differences). The proposed method exhibits high segmentation performance both for standalone T1w images as well as for T1w/T2w image pairs. Even with the additional capability to accept flexible inputs, our model matches or exceeds the performance of state-of-the-art methods with fixed inputs. We, further, demonstrate the generalizability of our method in experiments with 1.0 mm MR scans from both the Rhineland Study and the UK Biobank—an independent dataset never encountered during training with different acquisition parameters and demographics. Finally, HypVINN can perform the segmentation in less than a minute (graphical processing unit [GPU]) and will be available in the open source FastSurfer neuroimaging software suite, offering a validated, efficient, and scalable solution for evaluating imaging-derived phenotypes of the hypothalamus.
DOI: 10.5281/zenodo.11184070
title: Data and codes from "Daniel et al. What can optimized cost distances based on genetic distances offer? A simulation study on the use and misuse of ResistanceGA"

description: Data and codes used for “Daniel et al. What can optimized cost distances based on genetic distances offer? A simulation study on the use and misuse of ResistanceGA”
DOI: 10.5281/zenodo.11184071
title: Data and codes from "Daniel et al. What can optimized cost distances based on genetic distances offer? A simulation study on the use and misuse of ResistanceGA"

description: Data and codes used for “Daniel et al. What can optimized cost distances based on genetic distances offer? A simulation study on the use and misuse of ResistanceGA”
DOI: 10.5281/zenodo.11184216
title: HypVINN Checkpoints

description: Training checkpoints for HypVINN (https://github.com/Deep-MI/FastSurfer) - please cite the paper when using this resource (https://doi.org/10.1162/imag_a_00034).

Abstract

The hypothalamus plays a crucial role in the regulation of a broad range of physiological, behavioral, and cognitive functions. However, despite its importance, only a few small-scale neuroimaging studies have investigated its substructures, likely due to the lack of fully automated segmentation tools to address scalability and reproducibility issues of manual segmentation. While the only previous attempt to automatically sub-segment the hypothalamus with a neural network showed promise for 1.0 mm isotropic T1-weighted (T1w) magnetic resonance imaging (MRI), there is a need for an automated tool to sub-segment also high-resolutional (HiRes) MR scans, as they are becoming widely available, and include structural detail also from multi-modal MRI. We, therefore, introduce a novel, fast, and fully automated deep-learning method named HypVINN for sub-segmentation of the hypothalamus and adjacent structures on 0.8 mm isotropic T1w and T2w brain MR images that is robust to missing modalities. We extensively validate our model with respect to segmentation accuracy, generalizability, in-session test-retest reliability, and sensitivity to replicate hypothalamic volume effects (e.g., sex differences). The proposed method exhibits high segmentation performance both for standalone T1w images as well as for T1w/T2w image pairs. Even with the additional capability to accept flexible inputs, our model matches or exceeds the performance of state-of-the-art methods with fixed inputs. We, further, demonstrate the generalizability of our method in experiments with 1.0 mm MR scans from both the Rhineland Study and the UK Biobank—an independent dataset never encountered during training with different acquisition parameters and demographics. Finally, HypVINN can perform the segmentation in less than a minute (graphical processing unit [GPU]) and will be available in the open source FastSurfer neuroimaging software suite, offering a validated, efficient, and scalable solution for evaluating imaging-derived phenotypes of the hypothalamus.
DOI: 10.5281/zenodo.11184227
title: ХАРАКТЕРИСТИКИ ОСНОВНЫХ ТЕОРИИ АДАПТИВНОЙ ИДЕНТИФИКАЦИИ ДЛЯ АВТОМАТИЗАЦИИ МНОГОСВЯЗНЫХ ОБЪЕКТОВ.
DOI: 10.5281/zenodo.11184228
title: ХАРАКТЕРИСТИКИ ОСНОВНЫХ ТЕОРИИ АДАПТИВНОЙ ИДЕНТИФИКАЦИИ ДЛЯ АВТОМАТИЗАЦИИ МНОГОСВЯЗНЫХ ОБЪЕКТОВ.
DOI: 10.25549/examiner-c44-115724
title: Divorce, 1953

description: 3 images. Divorce, 20 May 1953. Esperanza Wayne. ; Caption slip reads: "Photographer: Snow. Reporter: Keating. Assignment: Divorce. 45-46-75: Esperanza Wayne."
DOI: 10.25549/examiner-c44-115738
title: Hotel fire at 1259 East 6th Street, 1963

description: 10 images. Hotel fire at 1259 East 6th Street, 15 May 1963. Captain Orland Davis -- 45 years (fireman overcome by smoke); Robert Foster (fireman); Booker Thomas -- 25 years; Bruce Thomas -- 3 years; Mrs. Shirley thomas --25 years. ; Caption slip reads: "Photographer: Wesselmann. Date: 1953-05-15. Reporter: McMillin. Assignment: Hotel fire at 1259 East 6th St. 4-6: Booker Thomas, 25, embraces son, Bruce, 3, as mother, Shirley, 25, looks on. Bruce was feared lost in fire by his parents until he was found with other evacuated hotel guests. 13-18: Refugees from fire sit across the street and watch scene in front of entrance to burning hotel. 16: Thomas Toliver and his three children rushed from burning hotel to the street. 3: Bertha Lee Toliver, age 3, was saved from the fire. 11-12: Fire department inhalator squad works on Capt. Orland Davis, 45, of Engine Company 24, who was overcome by smoke in blazing hotel. Davis was rushed to Georgia St. Receiving Hospital. 8: Fireman Robert Foster on way to hospital. 7: Fireman Robert Foster who was injured, look up at fire."
DOI: 10.25549/examiner-c44-31587
title: Howard divorce, 1952

description: 4 images. Howard divorce, 17 March 1952. Mrs. Judith Howard; Lindsay C. Howard; Attorney Franklin Peck; Jack Thoroughgood (friend of Howards).; Caption slip reads: "Photographer: Gaze. Date: 1952-03-17. Reporter: Gaze. Assignment: Howard divorce. #27-28: Mrs. Judith Howard, spectator in court as her husband testifies in divorce trial. #30: Lindsay C. Howard in court. #29: L to R: Lindsay C. Howard; Attorney Franklin Peck; and Howard's close friend, Jack Thoroughgood".
DOI: 10.15468/dl.we249f
title: Occurrence Download

description: A dataset containing 5991 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Glaucomys sabrinus (Shaw, 1801)"
 ]
}

 The dataset includes 5991 records from 83 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0116712-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7bh98k
title: Occurrence Download

description: A dataset containing 15911 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Leptospermum scoparium Forst."
 ]
}

 The dataset includes 15911 records from 132 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0052832-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rewsaz
title: Occurrence Download

description: A dataset containing 77 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Euphorbia denisiana Guillaumin"
 ]
}

 The dataset includes 77 records from 15 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046391-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vgrce2
title: Occurrence Download

description: A dataset containing 26 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Juncus trifidus L.",
 "HasCoordinate is true",
 "Country is Iceland",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 2000",
 "CoordinateUncertaintyInMeters is less than 10000"
 ]
}

 The dataset includes 26 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0129210-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.hqbzdr
title: Occurrence Download

description: A dataset containing 8 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Specimen, Material sample, Human Observation)",
 "Country is Peru",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Myotis atacamensis (Lataste, 1892)",
 "Year 2004-2024"
 ]
}

 The dataset includes 8 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0046362-240314170635999/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.qnt2a7
title: Occurrence Download

description: A dataset containing 2102 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "Geometry POLYGON((-128.0127 49.52637,-125.63965 37.0459,-119.22363 32.3877,-115.53223 31.86035,-112.10449 31.15723,-107.18262 30.54199,-104.98535 28.25684,-101.46973 28.6084,-99.8877 26.41113,-96.9873 24.74121,-95.31738 28.16895,-84.8584 28.52051,-82.22168 23.9502,-78.70605 24.8291,-80.28809 31.15723,-64.73145 43.98926,-128.0127 49.52637))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Latrodectus geometricus C.L.Koch, 1841"
 ]
}

 The dataset includes 2102 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0116741-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h9r2yk
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "or" : [
 "BasisOfRecord is Human Observation",
 "BasisOfRecord is Material sample",
 "BasisOfRecord is Specimen",
 "BasisOfRecord is Machine Observation"
 ]
 },
 "Geometry POLYGON((-65.83008 -60.19616,-16.34766 -60.67318,-15.11719 -42.81152,-13.71094 -12.89749,-31.28906 10.83331,-43.24219 24.84657,-38.32031 36.87962,-25.83984 65.58572,-36.38672 67.54217,-47.63672 67.54217,-68.68652 67.25506,-69.16992 65.54937,-77.21191 64.68032,-78.00293 63.33241,-77.3877 62.20651,-72.68555 59.04055,-70.13672 53.85253,-76.24512 48.95137,-78.83789 41.83683,-82.44141 37.30028,-95.80078 33.57801,-101.77734 27.21556,-98.10791 18.16673,-94.81201 17.28771,-90.06592 14.96601,-87.60498 15.00846,-85.18799 13.94473,-84.35303 10.2933,-82.33154 8.68964,-81.10107 8.47237,-80.52979 8.6462,-79.29932 9.25394,-78.46436 8.9936,-77.03613 7.4932,-69.60937 2.1089,-50.97656 -14.26438,-68.99414 -40.5138,-72.1582 -51.01375,-68.4668 -54.41893,-65.83008 -60.19616))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Psolus chitonoides Clark, 1901"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0197394-200613084148143/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mcbvnd
title: Occurrence Download

description: A dataset containing 7202747 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Gastropoda"
 ]
}

 The dataset includes 7202747 records from 1612 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007954-180412121330197/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.txt6ke
title: Occurrence Download

description: A dataset containing 2207 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Digitaria californica",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2207 records from 108 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015740-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.keuma8
title: Occurrence Download

description: A dataset containing 49 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Sticta andensis"
 ]
}

 The dataset includes 49 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015742-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wejfyw
title: Occurrence Download

description: A dataset containing 78324 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Tracheophyta",
 "DecimalLatitude is greater than or equal to -5.914722",
 "DecimalLatitude is less than or equal to -0.914722",
 "DecimalLongitude is greater than or equal to -50.0",
 "DecimalLongitude is less than or equal to -45.0",
 "HasCoordinate is TRUE",
 "HasGeospatialIssue is FALSE"
 ]
}

 The dataset includes 78324 records from 225 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015734-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17763883
title: METHOD AND RESULTS OF DETERMINING THE OPERATING MODE OF AN ELECTROMECHANICAL SORTING DEVICE
DOI: 10.5281/zenodo.17763882
title: METHOD AND RESULTS OF DETERMINING THE OPERATING MODE OF AN ELECTROMECHANICAL SORTING DEVICE
DOI: 10.15468/dl.2hcauz
title: Occurrence Download

description: A dataset containing 5069 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Dimorphotheca pluvialis",
 "HasCoordinate is true"
 ]
}

 The dataset includes 5069 records from 63 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015743-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17756970
title: Automated Detection of Parkinson's Disease Using Convolutional Neural Networks and Synthetic Spectral Image Features

description: This release contains the complete, reproducible implementation of the real-data experiment for the paper:

Domínguez-Monterroza A., Mateos-Caballero A., Jiménez-Martín A. Automated Detection of Parkinson's Disease from Voice Recordings Using Convolutional Neural Networks and Synthetic Spectral Image Features Neural Computing and Applications, 2025 (In Review)

Acknowledgements:

This work was funded by grants PID2021-122209OB-C31 and RED2022-134540-T from MICIU/AEI/10.13039/501100011033

Included Components

1. Full Source Code

This release provides all scripts required to:



Load raw audio signals (CSV format)

Extract MFCC spectral images using librosa

Build a Convolutional Neural Network (CNN) classifier in TensorFlow/Keras

Perform 10-fold stratified cross-validation

Compute metrics: Accuracy, F1, Precision, Recall, ROC AUC

Generate visualization of spectral differences between control and PD subjects


Scripts included:



01_extract_mfcc.py

02_build_cnn.py

03_cross_validation.py

04_visualization.py

full_pipeline.py


Dataset source (PC-GITA)

The real voice dataset comes from:



J. R. Orozco-Arroyave, J. D. Arias-Londoño, J. F. Vargas-Bonilla,M. C. Gonzalez-Rátiva, and E. Nöth, New Spanish speech corpus database for the analysis of people suffering from Parkinson's disease, Proc. 9th Int. Conf. Language Resources and Evaluation, 2014.


The dataset includes:



100 Colombian speakers



50 PD

50 healthy controls



Each participant phonated /a/ three times → 300 total recordings (150 PD, 150 HC).


Synthetic data generation Dataset source:



M. Rey-Paredes, C. J. Pérez, A. Mateos-Caballero,Time Series Classification of Raw Voice Waveforms for Parkinson's Disease Detection Using Generative Adversarial Network-Driven Data Augmentation, IEEE Open Journal of the Computer Society, 2025.
DOI: 10.5281/zenodo.17756969
title: Automated Detection of Parkinson's Disease Using Convolutional Neural Networks and Synthetic Spectral Image Features

description: This release contains the complete, reproducible implementation of the real-data experiment for the paper:

Domínguez-Monterroza A., Mateos-Caballero A., Jiménez-Martín A. Automated Detection of Parkinson's Disease from Voice Recordings Using Convolutional Neural Networks and Synthetic Spectral Image Features Neural Computing and Applications, 2025 (In Review)

Acknowledgements:

This work was funded by grants PID2021-122209OB-C31 and RED2022-134540-T from MICIU/AEI/10.13039/501100011033

Included Components

1. Full Source Code

This release provides all scripts required to:



Load raw audio signals (CSV format)

Extract MFCC spectral images using librosa

Build a Convolutional Neural Network (CNN) classifier in TensorFlow/Keras

Perform 10-fold stratified cross-validation

Compute metrics: Accuracy, F1, Precision, Recall, ROC AUC

Generate visualization of spectral differences between control and PD subjects


Scripts included:



01_extract_mfcc.py

02_build_cnn.py

03_cross_validation.py

04_visualization.py

full_pipeline.py


Dataset source (PC-GITA)

The real voice dataset comes from:



J. R. Orozco-Arroyave, J. D. Arias-Londoño, J. F. Vargas-Bonilla,M. C. Gonzalez-Rátiva, and E. Nöth, New Spanish speech corpus database for the analysis of people suffering from Parkinson's disease, Proc. 9th Int. Conf. Language Resources and Evaluation, 2014.


The dataset includes:



100 Colombian speakers



50 PD

50 healthy controls



Each participant phonated /a/ three times → 300 total recordings (150 PD, 150 HC).


Synthetic data generation Dataset source:



M. Rey-Paredes, C. J. Pérez, A. Mateos-Caballero,Time Series Classification of Raw Voice Waveforms for Parkinson's Disease Detection Using Generative Adversarial Network-Driven Data Augmentation, IEEE Open Journal of the Computer Society, 2025.
DOI: 10.15468/dl.58ex5q
title: Occurrence Download

description: A dataset containing 102153 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Canis latrans"
 ]
}

 The dataset includes 102153 records from 247 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015727-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ztftzj
title: Occurrence Download

description: A dataset containing 36386 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Diplotaxis erucoides",
 "HasCoordinate is true"
 ]
}

 The dataset includes 36386 records from 383 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015744-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17763887
title: DIE ENTWICKLUNG DES TOURISMUS
DOI: 10.5281/zenodo.17763886
title: DIE ENTWICKLUNG DES TOURISMUS
DOI: 10.5281/zenodo.17763898
title: THE ELUCIDATION OF SYNTACTIC CHANGES AND THEIR CAUSES IN "MIFTOHU-L-ULUM"
DOI: 10.5281/zenodo.17763899
title: THE ELUCIDATION OF SYNTACTIC CHANGES AND THEIR CAUSES IN "MIFTOHU-L-ULUM"