cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.38mbhr
title: Occurrence Download

description: A dataset containing 83800 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Amphibia",
 "Year is 1997"
 ]
}

 The dataset includes 83800 records from 430 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006554-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tsb5a9
title: Occurrence Download

description: A dataset containing 241 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Enuliophis sclateri (Boulenger, 1894)"
 ]
}

 The dataset includes 241 records from 34 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006567-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.uaz8mk
title: Occurrence Download

description: A dataset containing 13927 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Monarda punctata L."
 ]
}

 The dataset includes 13927 records from 115 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006566-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.szsrns
title: Occurrence Download

description: A dataset containing 195 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Batrachoseps kawia Jockusch, Wake & Yanev, 1998)"
 ]
}

 The dataset includes 195 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006568-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15306862
title: --The Coherence Evolution Model (CEM)- A Physically Grounded Framework--

description: Summary of the Coherence Evolution Model (CEM)

 

This is my proposed framework—physically grounded and mathematically rigorous—for explaining how prime numbers shape coherence in quantum systems. I call it the Coherence Evolution Model (CEM). At its heart is the idea that primes are not just mathematical curiosities—they are topological invariants, structuring quantum coherence and suppressing entropy through modular symmetries that composites simply can’t access.

 

I argue that:

 

1. Primes stabilize coherence by acting as topological quantum numbers—think of prime-modulated Chern numbers yielding edge states that resist backscattering.

 

 

2. The golden ratio () emerges naturally from prime-packed spacetime geometry, with fractal dimensions matching cosmic microwave background (CMB) analyses.

 

 

3. The Riemann zeta function is physical, functioning as a spectral filter in prime-driven SYK models—its zeros align with quantum chaos behavior and even map to excess CMB multipole power.

 

 

4. Quantum fields are modified by prime harmonics, altering propagators and controlling UV/IR mixing via arithmetic structures.

 

 

5. In condensed matter, superconductivity gets a prime boost—electron pairing is mediated by “prime phasons” with a predicted enhancement to critical temperature derived from harmonic sums.

 

 

6. I’ve run Bayesian tests against Planck CMB data—CEM beats CDM decisively (), and robustness holds under noise and masking changes.

 

 

7. The model makes testable predictions—notably, specific ARPES gap nodes and observable power anomalies at prime multipoles in the CMB.

 

 

8. It explains why composites decohere, and how twin primes preserve coherence, leading to observable effects in cosmic structure and condensed systems.

 

 

 

Everything is up for scrutiny.
DOI: 10.5281/zenodo.15306863
title: --The Coherence Evolution Model (CEM)- A Physically Grounded Framework--

description: Summary of the Coherence Evolution Model (CEM)

 

This is my proposed framework—physically grounded and mathematically rigorous—for explaining how prime numbers shape coherence in quantum systems. I call it the Coherence Evolution Model (CEM). At its heart is the idea that primes are not just mathematical curiosities—they are topological invariants, structuring quantum coherence and suppressing entropy through modular symmetries that composites simply can’t access.

 

I argue that:

 

1. Primes stabilize coherence by acting as topological quantum numbers—think of prime-modulated Chern numbers yielding edge states that resist backscattering.

 

 

2. The golden ratio () emerges naturally from prime-packed spacetime geometry, with fractal dimensions matching cosmic microwave background (CMB) analyses.

 

 

3. The Riemann zeta function is physical, functioning as a spectral filter in prime-driven SYK models—its zeros align with quantum chaos behavior and even map to excess CMB multipole power.

 

 

4. Quantum fields are modified by prime harmonics, altering propagators and controlling UV/IR mixing via arithmetic structures.

 

 

5. In condensed matter, superconductivity gets a prime boost—electron pairing is mediated by “prime phasons” with a predicted enhancement to critical temperature derived from harmonic sums.

 

 

6. I’ve run Bayesian tests against Planck CMB data—CEM beats CDM decisively (), and robustness holds under noise and masking changes.

 

 

7. The model makes testable predictions—notably, specific ARPES gap nodes and observable power anomalies at prime multipoles in the CMB.

 

 

8. It explains why composites decohere, and how twin primes preserve coherence, leading to observable effects in cosmic structure and condensed systems.

 

 

 

Everything is up for scrutiny.
DOI: 10.15468/dl.vzs57u
title: Occurrence Download

description: A dataset containing 2027 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Nemophila maculata Benth. ex Lindl."
 ]
}

 The dataset includes 2027 records from 67 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006572-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ejxq2z
title: Occurrence Download

description: A dataset containing 2350 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Batrachoseps luciae Jockusch, Yanev & Wake, 2001)"
 ]
}

 The dataset includes 2350 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006571-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t2bt8e
title: Occurrence Download

description: A dataset containing 5990 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "TaxonKey is one of (Batrachoseps major Camp, 1915)"
 ]
}

 The dataset includes 5990 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006576-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.sef2n6
title: Occurrence Download

description: A dataset containing 5071 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "HasGeospatialIssue is false",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen)"
 ]
 }
 }
 ]
 },
 "TaxonKey is Nepeta racemosa Lam."
 ]
}

 The dataset includes 5071 records from 105 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006575-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.15306842
title: Comparison metrics between libraries Fairlearn and AI Fairness 360
DOI: 10.5281/zenodo.15306843
title: Comparison metrics between libraries Fairlearn and AI Fairness 360
DOI: 10.15468/dl.3gr3xx
title: Occurrence Download

description: A dataset containing 118237 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Yucca L."
 ]
}

 The dataset includes 118237 records from 898 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006563-250426092105405/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.27czug
title: Occurrence Download

description: A dataset containing 6 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pelophylax esculentus",
 "Country is Norway",
 "HasCoordinate is true"
 ]
}

 The dataset includes 6 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038153-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7ze284
title: Occurrence Download

description: A dataset containing 757 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pycnanthemum californicum"
 ]
}

 The dataset includes 757 records from 70 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038156-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jmatzy
title: Occurrence Download

description: A dataset containing 5071 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Burhinus oedicnemus",
 "MediaType is Image"
 ]
}

 The dataset includes 5071 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038155-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7239ru
title: Occurrence Download

description: A dataset containing 1343 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Pultenaea dentata"
 ]
}

 The dataset includes 1343 records from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038154-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.16406952
title: Supporting datasets used in the paper entitled "Contributions of Dust Source Regions to Ice Nucleating Particles in Mixed-Phase Clouds Simulated with a Global Climate–Aerosol Model"

description: This archive contains datasets used in the paper entitled "Contributions of Dust Source Regions to Ice Nucleating Particles in Mixed-Phase Clouds Simulated with a Global Climate–Aerosol Model".
DOI: 10.5281/zenodo.16406953
title: Supporting datasets used in the paper entitled "Contributions of Dust Source Regions to Ice Nucleating Particles in Mixed-Phase Clouds Simulated with a Global Climate–Aerosol Model"

description: This archive contains datasets used in the paper entitled "Contributions of Dust Source Regions to Ice Nucleating Particles in Mixed-Phase Clouds Simulated with a Global Climate–Aerosol Model".
DOI: 10.15468/dl.q9h9vv
title: Occurrence Download

description: A dataset containing 85243 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Bucephala clangula",
 "MediaType is Image"
 ]
}

 The dataset includes 85243 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038119-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.aym98t
title: Occurrence Download

description: A dataset containing 1753 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Lipolagus ochotensis"
 ]
}

 The dataset includes 1753 records from 38 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038157-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vnjwha
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oenothera biennis",
 "Country is Croatia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038159-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9g3jva
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oenothera biennis",
 "Country is Chile",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038158-250717081556266/datasets/export for details.
DOI: 10.17026/dans-xu3-wxfr
title: Trauma & Resilience: 08InterviewC2G1
title: Trauma & Resilience: Intergenerational Holocaust research from an existential perspective

description: &lt;p&gt;Respondent C2G1 is dochter van respondent C1G1. Het verleden neemt ze op een positieve manier mee in de invulling die ze aan haar leven geeft. Haar levensdoel is ‘iedereen een welkom op de wereld geven’. Mentaal gezien geeft ze aan veerkrachtig te zijn maar lichamelijk minder.&lt;/p&gt;
DOI: 10.15468/dl.7f44ds
title: Occurrence Download

description: A dataset containing 7 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Erigeron canadensis",
 "Country is Peru",
 "HasCoordinate is true"
 ]
}

 The dataset includes 7 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038161-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kj4t38
title: Occurrence Download

description: A dataset containing 387 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Oenothera biennis",
 "Country is Italy",
 "HasCoordinate is true"
 ]
}

 The dataset includes 387 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038160-250717081556266/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5nq2b6
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Meloidogyne hapla",
 "Country is Norway",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038162-250717081556266/datasets/export for details.
DOI: 10.25377/sussex.26935696
title: Optical Tactile (TacTip) Dataset for texture classification

description: This dataset provides a comprehensive collection of temporal images captured by an optical tactile sensor (TactiP) mounted on a converted CNC machine, aimed at facilitating texture classification research in robotics and manufacturing applications. The dataset comprises high-resolution temporal image sequences acquired while the sensor was dragged across a diverse range of textures. Each sequence is meticulously labeled with corresponding texture categories, encompassing various materials and surface patterns to ensure a broad and representative coverage of textural variations. The dataset is designed to support the development and evaluation of advanced texture classification algorithms, offering insights into the dynamic interactions between tactile sensors and textured surfaces. By providing both spatial and temporal dimensions of texture data, this dataset aims to enhance the understanding of texture perception and improve the performance of tactile sensing systems in practical applications.<br><b>About this file</b>This is a dataset made up of optical tactile sensors being dragged across a surface at varying pressures. The dataset contains different readings across 20 frames, all converted to grey-scale. We have two parts of this dataset "X_data_15" and "X_data_gel_15". The first one is a sensor that uses clear silicone, and the second makes use of a clear gel. The gel is far more sensitive but prone to damage. The clear silicone is less sensitive but very resilient to pressure and damage.The labels are as follows and the index corresponds to the number in the y data:**Labels:**+-------+---------------+----------+| Index | Label | Friction Value |+-------+---------------+----------+| 0 | Carpet | - || 1 | LacedMatt | 0.124083 || 2 | wool | 0.198984 || 3 | Cork | 0.344905 || 4 | Felt | 0.116275 || 5 | LongCarpet | 0.128852 || 6 | cotton | 0.129354 || 7 | Plastic | 0.101801 || 8 | Flat | 0.343766 || 9 | Ffoam | 0.396798 || 10 | Gfoam | 0.191812 || 11 | bubble | 0.217116 || 12 | Efoam | 0.073127 || 13 | jeans | - || 14 | Leather | 0.255904 |+-------+---------------+----------+<br><br><br>Each X item is an image with a set number of frames recording in real time at approximately 100 FPS. The y labels is a number corresponding to the index of the labels shown above. FOr example, 0 would be a carpet.X shape: (Number, frame, h, w)y shape: (Number,)<br>All files are in compressed numpy format. Python users can load in the dataset using the code provided in the ReadMe.<br>
DOI: 10.25377/sussex.26935696.v1
title: Optical Tactile (TacTip) Dataset for texture classification

description: This dataset provides a comprehensive collection of temporal images captured by an optical tactile sensor (TactiP) mounted on a converted CNC machine, aimed at facilitating texture classification research in robotics and manufacturing applications. The dataset comprises high-resolution temporal image sequences acquired while the sensor was dragged across a diverse range of textures. Each sequence is meticulously labeled with corresponding texture categories, encompassing various materials and surface patterns to ensure a broad and representative coverage of textural variations. The dataset is designed to support the development and evaluation of advanced texture classification algorithms, offering insights into the dynamic interactions between tactile sensors and textured surfaces. By providing both spatial and temporal dimensions of texture data, this dataset aims to enhance the understanding of texture perception and improve the performance of tactile sensing systems in practical applications.<br><b>About this file</b>This is a dataset made up of optical tactile sensors being dragged across a surface at varying pressures. The dataset contains different readings across 20 frames, all converted to grey-scale. We have two parts of this dataset "X_data_15" and "X_data_gel_15". The first one is a sensor that uses clear silicone, and the second makes use of a clear gel. The gel is far more sensitive but prone to damage. The clear silicone is less sensitive but very resilient to pressure and damage.The labels are as follows and the index corresponds to the number in the y data:**Labels:**+-------+---------------+----------+| Index | Label | Friction Value |+-------+---------------+----------+| 0 | Carpet | - || 1 | LacedMatt | 0.124083 || 2 | wool | 0.198984 || 3 | Cork | 0.344905 || 4 | Felt | 0.116275 || 5 | LongCarpet | 0.128852 || 6 | cotton | 0.129354 || 7 | Plastic | 0.101801 || 8 | Flat | 0.343766 || 9 | Ffoam | 0.396798 || 10 | Gfoam | 0.191812 || 11 | bubble | 0.217116 || 12 | Efoam | 0.073127 || 13 | jeans | - || 14 | Leather | 0.255904 |+-------+---------------+----------+<br><br><br>Each X item is an image with a set number of frames recording in real time at approximately 100 FPS. The y labels is a number corresponding to the index of the labels shown above. FOr example, 0 would be a carpet.X shape: (Number, frame, h, w)y shape: (Number,)<br>All files are in compressed numpy format. Python users can load in the dataset using the code provided in the ReadMe.<br>
DOI: 10.25377/sussex.28033589
title: Electrical Tactile Dataset (Piezoelectric and Accelerometer) for textures

description: <b>Abstract</b>This dataset provides a collection of temporal sensor readings captured by an electrical tactile sensor (PressTip) mounted on a converted CNC machine, aimed at facilitating texture classification research in robotics and manufacturing applications. The dataset comprises low-resolution sensor readings from a piezoelectric sensor and accelerometer, acquired while the sensor was dragged across a diverse range of textures. Each sequence is labelled with corresponding texture categories, encompassing various materials and surface patterns to ensure a broad and representative coverage of textural variations. The dataset is designed to support the development and evaluation of advanced texture classification algorithms, offering insights into the dynamic interactions between tactile sensors and textured surfaces. This dataset aims to enhance the understanding of texture perception and improve the performance of tactile sensing systems in practical applications.<b>About this file</b>This is a dataset made up of a piezoelectric and accelerometer tactile sensors being dragged across a surface at varying pressures. The dataset contains different readings across a number of frames.The labels are as follows and the index corresponds to the number in the y data:**Labels:**+-------+---------------+----------+| Index | Label | Value |+-------+---------------+----------+| 0 | Carpet | - || 1 | LacedMatt | 0.124083 || 2 | wool | 0.198984 || 3 | Cork | 0.344905 || 4 | Felt | 0.116275 || 5 | LongCarpet | 0.128852 || 6 | cotton | 0.129354 || 7 | Plastic | 0.101801 || 8 | Flat | 0.343766 || 9 | Ffoam | 0.396798 || 10 | Gfoam | 0.191812 || 11 | bubble | 0.217116 || 12 | Efoam | 0.073127 || 13 | jeans | - || 14 | Leather | 0.255904 |+-------+---------------+----------+<br>The y data contains numbers corresponding the the index of the above array.Each X item is an image with a set number of frames recording in real time at approximately 10Hz. The y labels is a number corresponding to the index of the labels shown above. For example, 0 would be a carpet.X shape: (Number, frame, sensor index)y shape: (Number,)All files are in compressed numpy format. Python users can load in the dataset using the code provided in the ReadMe.