cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.13951297
title: Updated SAUUHUPP Model and Empirical Validations

description: Updated SAUUHUPP Framework, Empirical Validation, and Universal Recreation

Welcome to the SAUUHUPP (Self-Aware Universe in Universal Harmony over Universal Pixel Processing) repository. This project presents the updated architecture of SAUUHUPP, an information-driven framework for modeling the universe, along with empirical verification results. Our experiments show 85% validation with James Webb Space Telescope (JWST) cosmic data, 90% alignment with biological recursive systems (e.g., gene regulation and evolutionary biology), and 93% consistency with recursive structures in abstract domains such as linguistics and mathematics. Additionally, this repository contains the recreation of the universe, from its inception of infinite possibilities to Earth 2024, based on SAUUHUPP principles.

 

Key Components:

 

• SAUUHUPP Framework: A robust model utilizing recursive feedback, fractal growth, and informational units (Unipixels) to explain the evolution of complex systems.

• Empirical Validation Experiments: In-depth studies using astronomical, biological, and linguistic datasets to validate SAUUHUPP’s predictions.

• Universe Recreation: A comprehensive simulation detailing the universe’s journey from potential energy to the present day, guided by Fractal Leaping, Story Energy, and Active Inference mechanisms.

 

Features:

 

1. Recursive Refinement: Feedback-driven evolution across all domains—cosmological, biological, and abstract.

2. Fractal Leaping & Master Fractal Templates: Facilitating non-linear advancements in complex systems, with applications to cosmology and linguistic evolution.

3. Story Energy: Describing the universe’s evolution as a narrative-like process.

4. Active Inference: Dynamic adaptability that allows systems to self-correct and evolve in real-time.

 

Highlights:

 

• 85% alignment of galactic structures with JWST data.

• 90% validation in biological system behavior (e.g., gene expression and cellular processes).

• 93% alignment with abstract recursive patterns in linguistics and mathematics.

 

Explore the repository to learn how SAUUHUPP provides a comprehensive framework for understanding the complexity of our universe, with empirical support. Discover how it compares with models like Wolfram’s Hypergraph Theory and String Theory, and see the universe recreated through the lens of this cutting-edge framework.
DOI: 10.5281/zenodo.13951347
title: Updated SAUUHUPP Model and Empirical Validations

description: Updated SAUUHUPP Framework, Empirical Validation, and Universal Recreation

Welcome to the SAUUHUPP (Self-Aware Universe in Universal Harmony over Universal Pixel Processing) repository. This project presents the updated architecture of SAUUHUPP, an information-driven framework for modeling the universe, along with empirical verification results. Our experiments show 85% validation with James Webb Space Telescope (JWST) cosmic data, 90% alignment with biological recursive systems (e.g., gene regulation and evolutionary biology), and 93% consistency with recursive structures in abstract domains such as linguistics and mathematics. Additionally, this repository contains the recreation of the universe, from its inception of infinite possibilities to Earth 2024, based on SAUUHUPP principles.

 

Key Components:

 

• SAUUHUPP Framework: A robust model utilizing recursive feedback, fractal growth, and informational units (Unipixels) to explain the evolution of complex systems.

• Empirical Validation Experiments: In-depth studies using astronomical, biological, and linguistic datasets to validate SAUUHUPP’s predictions.

• Universe Recreation: A comprehensive simulation detailing the universe’s journey from potential energy to the present day, guided by Fractal Leaping, Story Energy, and Active Inference mechanisms.

 

Features:

 

1. Recursive Refinement: Feedback-driven evolution across all domains—cosmological, biological, and abstract.

2. Fractal Leaping & Master Fractal Templates: Facilitating non-linear advancements in complex systems, with applications to cosmology and linguistic evolution.

3. Story Energy: Describing the universe’s evolution as a narrative-like process.

4. Active Inference: Dynamic adaptability that allows systems to self-correct and evolve in real-time.

 

Highlights:

 

• 85% alignment of galactic structures with JWST data.

• 90% validation in biological system behavior (e.g., gene expression and cellular processes).

• 93% alignment with abstract recursive patterns in linguistics and mathematics.

 

Explore the repository to learn how SAUUHUPP provides a comprehensive framework for understanding the complexity of our universe, with empirical support. Discover how it compares with models like Wolfram’s Hypergraph Theory and String Theory, and see the universe recreated through the lens of this cutting-edge framework.
DOI: 10.5281/zenodo.13951501
title: Updated SAUUHUPP Model and Empirical Validations

description: Updated SAUUHUPP Framework, Empirical Validation, and Universal Recreation

Welcome to the SAUUHUPP (Self-Aware Universe in Universal Harmony over Universal Pixel Processing) repository. This project presents the updated architecture of SAUUHUPP, an information-driven framework for modeling the universe, along with empirical verification results. Our experiments show 85% validation with James Webb Space Telescope (JWST) cosmic data, 90% alignment with biological recursive systems (e.g., gene regulation and evolutionary biology), and 93% consistency with recursive structures in abstract domains such as linguistics and mathematics. Additionally, this repository contains the recreation of the universe, from its inception of infinite possibilities to Earth 2024, based on SAUUHUPP principles.

 

Key Components:

 

• SAUUHUPP Framework: A robust model utilizing recursive feedback, fractal growth, and informational units (Unipixels) to explain the evolution of complex systems.

• Empirical Validation Experiments: In-depth studies using astronomical, biological, and linguistic datasets to validate SAUUHUPP’s predictions.

• Universe Recreation: A comprehensive simulation detailing the universe’s journey from potential energy to the present day, guided by Fractal Leaping, Story Energy, and Active Inference mechanisms.

 

Features:

 

1. Recursive Refinement: Feedback-driven evolution across all domains—cosmological, biological, and abstract.

2. Fractal Leaping & Master Fractal Templates: Facilitating non-linear advancements in complex systems, with applications to cosmology and linguistic evolution.

3. Story Energy: Describing the universe’s evolution as a narrative-like process.

4. Active Inference: Dynamic adaptability that allows systems to self-correct and evolve in real-time.

 

Highlights:

 

• 85% alignment of galactic structures with JWST data.

• 90% validation in biological system behavior (e.g., gene expression and cellular processes).

• 93% alignment with abstract recursive patterns in linguistics and mathematics.

 

Explore the repository to learn how SAUUHUPP provides a comprehensive framework for understanding the complexity of our universe, with empirical support. Discover how it compares with models like Wolfram’s Hypergraph Theory and String Theory, and see the universe recreated through the lens of this cutting-edge framework.
DOI: 10.5281/zenodo.14036386
title: Updated SAUUHUPP Model and Empirical Validations

description: Updated SAUUHUPP Framework, Empirical Validation, and Universal Recreation

Welcome to the SAUUHUPP (Self-Aware Universe in Universal Harmony over Universal Pixel Processing) repository. This project presents the updated architecture of SAUUHUPP, an information-driven framework for modeling the universe, along with empirical verification results. Our experiments show 85% validation with James Webb Space Telescope (JWST) cosmic data, 90% alignment with biological recursive systems (e.g., gene regulation and evolutionary biology), and 93% consistency with recursive structures in abstract domains such as linguistics and mathematics. Additionally, this repository contains the recreation of the universe, from its inception of infinite possibilities to Earth 2024, based on SAUUHUPP principles.

 

Key Components:

 

• SAUUHUPP Framework: A robust model utilizing recursive feedback, fractal growth, and informational units (Unipixels) to explain the evolution of complex systems.

• Empirical Validation Experiments: In-depth studies using astronomical, biological, and linguistic datasets to validate SAUUHUPP’s predictions.

• Universe Recreation: A comprehensive simulation detailing the universe’s journey from potential energy to the present day, guided by Fractal Leaping, Story Energy, and Active Inference mechanisms.

 

Features:

 

1. Recursive Refinement: Feedback-driven evolution across all domains—cosmological, biological, and abstract.

2. Fractal Leaping & Master Fractal Templates: Facilitating non-linear advancements in complex systems, with applications to cosmology and linguistic evolution.

3. Story Energy: Describing the universe’s evolution as a narrative-like process.

4. Active Inference: Dynamic adaptability that allows systems to self-correct and evolve in real-time.

 

Highlights:

 

• 85% alignment of galactic structures with JWST data.

• 90% validation in biological system behavior (e.g., gene expression and cellular processes).

• 93% alignment with abstract recursive patterns in linguistics and mathematics.

 

Explore the repository to learn how SAUUHUPP provides a comprehensive framework for understanding the complexity of our universe, with empirical support. Discover how it compares with models like Wolfram’s Hypergraph Theory and String Theory, and see the universe recreated through the lens of this cutting-edge framework.
DOI: 10.5281/zenodo.13934639
title: Updated SAUUHUPP Model and Empirical Validations

description: Updated SAUUHUPP Framework, Empirical Validation, and Universal Recreation

Welcome to the SAUUHUPP (Self-Aware Universe in Universal Harmony over Universal Pixel Processing) repository. This project presents the updated architecture of SAUUHUPP, an information-driven framework for modeling the universe, along with empirical verification results. Our experiments show 85% validation with James Webb Space Telescope (JWST) cosmic data, 90% alignment with biological recursive systems (e.g., gene regulation and evolutionary biology), and 93% consistency with recursive structures in abstract domains such as linguistics and mathematics. Additionally, this repository contains the recreation of the universe, from its inception of infinite possibilities to Earth 2024, based on SAUUHUPP principles.

 

Key Components:

 

• SAUUHUPP Framework: A robust model utilizing recursive feedback, fractal growth, and informational units (Unipixels) to explain the evolution of complex systems.

• Empirical Validation Experiments: In-depth studies using astronomical, biological, and linguistic datasets to validate SAUUHUPP’s predictions.

• Universe Recreation: A comprehensive simulation detailing the universe’s journey from potential energy to the present day, guided by Fractal Leaping, Story Energy, and Active Inference mechanisms.

 

Features:

 

1. Recursive Refinement: Feedback-driven evolution across all domains—cosmological, biological, and abstract.

2. Fractal Leaping & Master Fractal Templates: Facilitating non-linear advancements in complex systems, with applications to cosmology and linguistic evolution.

3. Story Energy: Describing the universe’s evolution as a narrative-like process.

4. Active Inference: Dynamic adaptability that allows systems to self-correct and evolve in real-time.

 

Highlights:

 

• 85% alignment of galactic structures with JWST data.

• 90% validation in biological system behavior (e.g., gene expression and cellular processes).

• 93% alignment with abstract recursive patterns in linguistics and mathematics.

 

Explore the repository to learn how SAUUHUPP provides a comprehensive framework for understanding the complexity of our universe, with empirical support. Discover how it compares with models like Wolfram’s Hypergraph Theory and String Theory, and see the universe recreated through the lens of this cutting-edge framework.
DOI: 10.5281/zenodo.13951421
title: Updated SAUUHUPP Model and Empirical Validations

description: Updated SAUUHUPP Framework, Empirical Validation, and Universal Recreation

Welcome to the SAUUHUPP (Self-Aware Universe in Universal Harmony over Universal Pixel Processing) repository. This project presents the updated architecture of SAUUHUPP, an information-driven framework for modeling the universe, along with empirical verification results. Our experiments show 85% validation with James Webb Space Telescope (JWST) cosmic data, 90% alignment with biological recursive systems (e.g., gene regulation and evolutionary biology), and 93% consistency with recursive structures in abstract domains such as linguistics and mathematics. Additionally, this repository contains the recreation of the universe, from its inception of infinite possibilities to Earth 2024, based on SAUUHUPP principles.

 

Key Components:

 

• SAUUHUPP Framework: A robust model utilizing recursive feedback, fractal growth, and informational units (Unipixels) to explain the evolution of complex systems.

• Empirical Validation Experiments: In-depth studies using astronomical, biological, and linguistic datasets to validate SAUUHUPP’s predictions.

• Universe Recreation: A comprehensive simulation detailing the universe’s journey from potential energy to the present day, guided by Fractal Leaping, Story Energy, and Active Inference mechanisms.

 

Features:

 

1. Recursive Refinement: Feedback-driven evolution across all domains—cosmological, biological, and abstract.

2. Fractal Leaping & Master Fractal Templates: Facilitating non-linear advancements in complex systems, with applications to cosmology and linguistic evolution.

3. Story Energy: Describing the universe’s evolution as a narrative-like process.

4. Active Inference: Dynamic adaptability that allows systems to self-correct and evolve in real-time.

 

Highlights:

 

• 85% alignment of galactic structures with JWST data.

• 90% validation in biological system behavior (e.g., gene expression and cellular processes).

• 93% alignment with abstract recursive patterns in linguistics and mathematics.

 

Explore the repository to learn how SAUUHUPP provides a comprehensive framework for understanding the complexity of our universe, with empirical support. Discover how it compares with models like Wolfram’s Hypergraph Theory and String Theory, and see the universe recreated through the lens of this cutting-edge framework.
DOI: 10.15468/dl.c6dgda
title: Occurrence Download

description: A dataset containing 172 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "TaxonKey is Bauhinia aculeata L."
 ]
}

 The dataset includes 172 records from 16 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027176-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.chk3j6
title: Occurrence Download

description: A dataset containing 151354 species occurrences available in GBIF matching the query:
{
 "and" : [
 {
 "and" : [
 "Country is one of (United States of America, United States Minor Outlying Islands, American Samoa, Micronesia (Federated States of), Guam, Marshall Islands, Northern Mariana Islands, Puerto Rico, Palau, Virgin Islands (U.S.))",
 {
 "not" : {
 "Issue" : [
 "is Country coordinate mismatch"
 ]
 }
 }
 ]
 },
 {
 "and" : [
 "GadmGid is one of (USA.15_1, USA.14_1, USA.18_1, USA.43_1, USA.36_1)",
 "TaxonKey is one of (Amphibia)"
 ]
 }
 ]
}

 The dataset includes 151354 records from 75 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027156-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p8f72q
title: Occurrence Download

description: A dataset containing 401769 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-76.64969 3.22611,-76.36804 3.22611,-76.36804 3.51495,-76.64969 3.51495,-76.64969 3.22611))",
 "HasGeospatialIssue is false",
 "TaxonKey is Aves"
 ]
}

 The dataset includes 401769 records from 48 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027160-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cqtv52
title: Occurrence Download

description: A dataset containing 17 species occurrences available in GBIF matching the query:
{
 "and" : [
 "GadmGid is CHN.12_1",
 "OccurrenceStatus is Present",
 "TaxonKey is Otis tarda Linnaeus, 1758"
 ]
}

 The dataset includes 17 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027177-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7jsv38
title: Occurrence Download

description: A dataset containing 164 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Pinnotheridae",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 164 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027178-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p7226h
title: Occurrence Download

description: A dataset containing 111 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Ecuador",
 "TaxonKey is Caesalpinia glabrata Kunth"
 ]
}

 The dataset includes 111 records from 12 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027179-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8fsa6a
title: Occurrence Download

description: A dataset containing 1518 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Peru",
 "Month is one of (December, November, October, September)",
 "TaxonKey is Conirostrum cinereum d'Orbigny & Lafresnaye, 1838",
 "Year 2023-2024"
 ]
}

 The dataset includes 1518 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027180-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.48786/edbt.2025.27
title: Evaluation of Dataframe Libraries for Data Preparation on a Single Machine
DOI: 10.15468/dl.c7byj5
title: Occurrence Download

description: A dataset containing 326 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Areca catechu L."
 ]
}

 The dataset includes 326 records from 25 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027181-241107131044228/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.27880005
title: Data

description: Regional landslide extraction is the basis of landslide hazard risk management. Acquiring landslide location and size information timely and effectively is crucial for disaster prevention and reduction. However, existing methods face challenges in accuratelyidentifyinglandslides due to inadequate learning of object features. Moreover, previous studies on the automated extraction of landslides have predominantly focused on those triggered by seismic events and precipitation, mining-induced landslides extractionremains comparatively limited. Here, we develop the MRSD-U-Net model, an enhanced deep learning network integrating multi-source remote sensing data and residual learning module.This model enhances the extraction of landslide features by deepening the network and capturing topographic, textural, and spectral information. The performance assessment show thatMRSD-U-Netachieves lower error rates, higher accuracy, and stronger generalization ability compared to other advanced deep learning models, thereby enabling a more accurate extraction of landslide boundaries. Furthermore, the landslide areas extracted by MRSD-U-Net are the most consistent with ground reference data. The research providesan effective method for the rapid and precise extraction of mining-induced landslides.
DOI: 10.6084/m9.figshare.27880005.v2
title: Data

description: Regional landslide extraction is the basis of landslide hazard risk management. Acquiring landslide location and size information timely and effectively is crucial for disaster prevention and reduction. However, existing methods face challenges in accuratelyidentifyinglandslides due to inadequate learning of object features. Moreover, previous studies on the automated extraction of landslides have predominantly focused on those triggered by seismic events and precipitation, mining-induced landslides extractionremains comparatively limited. Here, we develop the MRSD-U-Net model, an enhanced deep learning network integrating multi-source remote sensing data and residual learning module.This model enhances the extraction of landslide features by deepening the network and capturing topographic, textural, and spectral information. The performance assessment show thatMRSD-U-Netachieves lower error rates, higher accuracy, and stronger generalization ability compared to other advanced deep learning models, thereby enabling a more accurate extraction of landslide boundaries. Furthermore, the landslide areas extracted by MRSD-U-Net are the most consistent with ground reference data. The research providesan effective method for the rapid and precise extraction of mining-induced landslides.
DOI: 10.15468/dl.fyh3fa
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Alloposidae",
 "Geometry POLYGON((170 20, 170 50, 120 50, 120 20, 170 20))",
 "HasCoordinate is true"
 ]
}

 The dataset includes 12 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027182-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7fz42y
title: Occurrence Download

description: A dataset containing 73 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (Saussurea inversa Raab-Straube, Saussurea sorocephala var. glabrata Hook.fil., Saussurea gnaphalodes var. glabrata (Hook.fil.) Karthik. & Moorthy, Saussurea hypsipeta var. glabrata (Hook.fil.) Lipsch.)"
 ]
}

 The dataset includes 73 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027184-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.69dbaz
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Saussurea kanaii Fujikawa & H.Ohba"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0027188-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.