cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/mmyejt
title: A review of the South-East Asian Nasutitermitinae (Isoptera: Termidae), with descriptions of one new genus and a new species and including a key to the genera

description: This dataset contains the digitized treatments in Plazi based on the original journal article Gathorne-Hardy, Frederick (2001): A review of the South-East Asian Nasutitermitinae (Isoptera: Termidae), with descriptions of one new genus and a new species and including a key to the genera. Journal of Natural History 35 (10): 1485-1506, DOI: 10.1080/00220930010001497This paper describes S nootitermes leuserensis, gen. et sp. nov. (collected from the Leuser Ecosystem, Sumatra), H irtitermes brabazoni sp. nov. (collected from north Sulawesi) and the major soldier of Ceylonitermes indicola. It also includes a revised generic key to the N asutitermitinae of Peninsular M alaysia, Java, Sumatra and Borneo and reassigns the South-East Asian S ubulitermes- group of termites to the N asutitermes- group. A ciculioiditermes denticulatus and A. sarawakensis are considered to be junior synonyms of Aciculioiditermes holmgreni. The genera Aciculioiditermes and Proaciculitermes are considered to be junior synonyms of Malaysiotermes. Therefore Aciculioiditermes holmgreni, Proaciculitermes orientalis, P. sabahensis, P. malayanus and P. lowi are all Malaysiotermes species. Tho’s (1982, 1992) unpublished new genus (Genus A) is considered to be a new species of Ceylonitermes. Oriensubulitermes borneensis is shown to be a S ubulioiditermes species, S. borneensis. Nasutitermes matangensiformis is shown to be a junior synonym of N. matangensis. Havilanditermes is shown to be a junior synonym of Nasutitermes.
DOI: 10.34740/kaggle/dsv/11452609
title: Balochi Language to English Translation Dataset
DOI: 10.15468/dl.qr3zvb
title: Occurrence Download

description: A dataset containing 18029 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Centrocercus urophasianus (Bonaparte, 1827)"
 ]
}

 The dataset includes 18029 records from 58 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0005058-250415084134356/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.yaxpsd
title: Occurrence Download

description: A dataset containing 125 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anolis carolinensis Voigt, 1832",
 "Country is Japan",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 125 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023968-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pu2rh7
title: Occurrence Download

description: A dataset containing 11084 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Leptasthenura setaria (Temminck, 1824))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 11084 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023967-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.d4k92r
title: Occurrence Download

description: A dataset containing 1619 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "CoordinateUncertaintyInMeters 0.0-980.0",
 "Country is United States of America",
 "OccurrenceStatus is Present",
 "TaxonKey is Celastrus scandens L."
 ]
}

 The dataset includes 1619 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023969-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.25747/kj7e-nq84
title: Raw atmospheric electric field and ancillary data collected on-board Sagres ship (ongoing - update yearly)
DOI: 10.17632/82brc2mypv.1
title: Satir model based intervention among men who use drugs

description: Dataset of Satir model based intervention for men who use drugs (self-esteem, craving, treatment motivation, family function)
DOI: 10.17632/brwpjspbxs
title: A study of Immune-Mediated Dysregulation of Metabolism and Its Impact on Meniere's Disease Risk: A Mendelian Randomization Study. Ruifa Zhou et al

description: Supplementary tables: Table S1-S14, Supplementary Figures: Fig S1–S11, STROBE-MR-checklist.Information of SNPs：Table S1-S3.
DOI: 10.17632/brwpjspbxs.1
title: A study of Immune-Mediated Dysregulation of Metabolism and Its Impact on Meniere's Disease Risk: A Mendelian Randomization Study. Ruifa Zhou et al

description: Supplementary tables: Table S1-S14, Supplementary Figures: Fig S1–S11, STROBE-MR-checklist.Information of SNPs：Table S1-S3.
DOI: 10.17632/y4x56zszp3.1
title: Code for extended Kalman filter

description: Code for extended Kalman filter and some data of resin level
DOI: 10.15468/dl.r3pawy
title: Occurrence Download

description: A dataset containing 68 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "GadmGid is HRV",
 "OccurrenceStatus is Present",
 "TaxonKey is Zoropsis spinimana (Dufour, 1820)"
 ]
}

 The dataset includes 68 records from 4 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023970-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.cmcncv
title: Occurrence Download

description: A dataset containing 93 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anoplolepis gracilipes (Smith, 1857)",
 "Country is Australia",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 93 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023972-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.t7vaxt
title: Occurrence Download

description: A dataset containing 1774 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((6.95214 72.05205,35.06120 72.05205,35.06120 83.30205,6.95214 83.30205,6.95214 72.05205))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Papaver L."
 ]
}

 The dataset includes 1774 records from 31 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023971-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.34740/kaggle/dsv/9963844
title: Mahanagar Yatayat Transport Dataset
DOI: 10.6084/m9.figshare.27868182
title: S1.xlsx

description: The data set in attachment (S1) was used to describe the following aims: to compare M external load changes (four Ms during pre-season and one in-season in elite female Portuguese soccer players; to describe and quantify the external load variations between Ms. This belongs to the following article title: "External Load Transition Practices from Pre-Season to In-Season. A Case Study in Elite Female Professional Soccer Players"
DOI: 10.6084/m9.figshare.27868182.v1
title: S1.xlsx

description: The data set in attachment (S1) was used to describe the following aims: to compare M external load changes (four Ms during pre-season and one in-season in elite female Portuguese soccer players; to describe and quantify the external load variations between Ms. This belongs to the following article title: "External Load Transition Practices from Pre-Season to In-Season. A Case Study in Elite Female Professional Soccer Players"
DOI: 10.6084/m9.figshare.27091420
title: Estimating Higher-Order Mixed Memberships via the l2,∞ Tensor Perturbation Bound

description: Higher-order multiway data is ubiquitous in machine learning and statistics and often exhibits community-like structures, where each component (node) along each different mode has a community membership associated with it. In this article we propose the sub-Gaussian) <i>tensor mixed-membership blockmodel</i>, a generalization of the tensor blockmodel positing that memberships need not be discrete, but instead are convex combinations of latent communities. We establish the identifiability of our model and propose a computationally efficient estimation procedure based on the higher-order orthogonal iteration algorithm (HOOI) for tensor SVD composed with a simplex corner-finding algorithm. We then demonstrate the consistency of our estimation procedure by providing a per-node error bound under sub-Gaussian noise, which showcases the effect of higher-order structures on estimation accuracy. To prove our consistency result, we develop the l2,∞ tensor perturbation bound for HOOI under independent, heteroscedastic, sub-Gaussian noise that may be of independent interest. Our analysis uses a novel leave-one-out construction for the iterates, and our bounds depend only on spectral properties of the underlying low-rank tensor under nearly optimal signal-to-noise ratio conditions such that tensor SVD is computationally feasible. Finally, we apply our methodology to real and simulated data, demonstrating some effects not identifiable from the model with discrete community memberships. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.27091420.v2
title: Estimating Higher-Order Mixed Memberships via the l2,∞ Tensor Perturbation Bound

description: Higher-order multiway data is ubiquitous in machine learning and statistics and often exhibits community-like structures, where each component (node) along each different mode has a community membership associated with it. In this article we propose the sub-Gaussian) <i>tensor mixed-membership blockmodel</i>, a generalization of the tensor blockmodel positing that memberships need not be discrete, but instead are convex combinations of latent communities. We establish the identifiability of our model and propose a computationally efficient estimation procedure based on the higher-order orthogonal iteration algorithm (HOOI) for tensor SVD composed with a simplex corner-finding algorithm. We then demonstrate the consistency of our estimation procedure by providing a per-node error bound under sub-Gaussian noise, which showcases the effect of higher-order structures on estimation accuracy. To prove our consistency result, we develop the l2,∞ tensor perturbation bound for HOOI under independent, heteroscedastic, sub-Gaussian noise that may be of independent interest. Our analysis uses a novel leave-one-out construction for the iterates, and our bounds depend only on spectral properties of the underlying low-rank tensor under nearly optimal signal-to-noise ratio conditions such that tensor SVD is computationally feasible. Finally, we apply our methodology to real and simulated data, demonstrating some effects not identifiable from the model with discrete community memberships. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.6084/m9.figshare.27091420.v1
title: Estimating Higher-Order Mixed Memberships via the ℓ2,∞ Tensor Perturbation Bound

description: Higher-order multiway data is ubiquitous in machine learning and statistics and often exhibits community-like structures, where each component (node) along each different mode has a community membership associated with it. In this paper we propose the (subgaussian) <i>tensor mixed-membership blockmodel</i>, a generalization of the tensor blockmodel positing that memberships need not be discrete, but instead are convex combinations of latent communities. We establish the identifiability of our model and propose a computationally efficient estimation procedure based on the higher-order orthogonal iteration algorithm (HOOI) for tensor SVD composed with a simplex corner-finding algorithm. We then demonstrate the consistency of our estimation procedure by providing a per-node error bound under subgaussian noise, which showcases the effect of higher-order structures on estimation accuracy. To prove our consistency result, we develop the ℓ2,∞ tensor perturbation bound for HOOI under independent, heteroskedastic, subgaussian noise that may be of independent interest. Our analysis uses a novel leave-one-out construction for the iterates, and our bounds depend only on spectral properties of the underlying low-rank tensor under nearly optimal signal-to-noise ratio conditions such that tensor SVD is computationally feasible. Finally, we apply our methodology to real and simulated data, demonstrating some effects not identifiable from the model with discrete community memberships.
DOI: 10.15468/dl.kqes6s
title: Occurrence Download

description: A dataset containing 5 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anolis baleatus (Cope, 1864)",
 "Year 1983-1984"
 ]
}

 The dataset includes 5 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023973-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ykk6hw
title: Occurrence Download

description: A dataset containing 87 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Human Observation",
 "Country is Croatia",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Barbitistes ocskayi Charpentier, 1850"
 ]
}

 The dataset includes 87 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023974-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.evzzyw
title: Occurrence Download

description: A dataset containing 362 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Camellia yunnanensis (Pit.) Cohen-Stuart"
 ]
}

 The dataset includes 362 records from 32 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023976-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.tducg8
title: Occurrence Download

description: A dataset containing 765593 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is Xeno-canto - Bird sounds from around the world"
 ]
}

 The dataset includes 765593 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023952-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s262sx
title: Occurrence Download

description: A dataset containing 33 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Occurrence evidence",
 "Continent is Africa",
 "Country is Benin",
 "Month is one of (January, February, March, April, May, June, July, August, September, October, November, December)",
 "OccurrenceStatus is Present",
 "TaxonKey is Adansonia digitata L.",
 "Year 2010-2024"
 ]
}

 The dataset includes 33 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023977-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9cnk5n
title: Occurrence Download

description: A dataset containing 53 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Anoplophora glabripennis (Motschulsky, 1854)",
 "Country is United States of America",
 "Year is greater than or equal to 1500"
 ]
}

 The dataset includes 53 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023978-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2axerq
title: Occurrence Download

description: A dataset containing 31 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Camellia hiemalis Nakai"
 ]
}

 The dataset includes 31 records from 11 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023979-241107131044228/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.w2zvj5
title: Occurrence Download

description: A dataset containing 0 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 one of (Aspidistra medusa Aver., K.S.Nguyen & Tillich)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0023980-241107131044228/datasets/export for details.
DOI: 10.5281/zenodo.14036795
title: SAUUHUPP Networked Periodic Table Based Explorations and Applications

description: Dear Zenodo Visitors,

 

Welcome to the repository dedicated to the SAUUHUPP-Based Networked Periodic Table, a pioneering exploration of the universe through a computational and interconnected model. This repository represents a new framework where the periodic table is viewed not merely as a static list of elements but as a dynamic, networked system integral to a larger self-aware universe. At the heart of this model lies the SAUUHUPP framework—Self-Aware Universe in Universal Harmony over Universal Pixel Processing—which approaches the universe as an adaptive, computational structure composed of layers and interactions across scales.

 

SAUUHUPP and the Networked Periodic Table combine principles of quantum mechanics, entanglement, fractal geometry, and layered networks. Each element within the periodic table can be seen as a node in this universal network, with inherent connections to other elements, molecules, biological systems, ecosystems, and even cosmic structures. This networked perspective unlocks a deeper understanding of how matter, energy, and information are harmoniously entangled and recursively interact across layers of reality.

 

In this repository, you’ll find resources, papers, and ongoing studies that explore the applications and implications of this model:

 

• Theoretical Papers that outline the conceptual foundation of SAUUHUPP and how it transforms our view of elemental and molecular interactions.

• Empirical Studies validating SAUUHUPP principles, including the nested addressing system and the network dynamics across scales.

• Data and Tools that enable researchers to map elements, compounds, and biological processes within the SAUUHUPP framework, fostering new connections across scientific domains.

• Use Cases that demonstrate real-world applications, from AI-based molecular design to ecological sustainability models, all rooted in the networked interactions among elements.

 

Our ultimate goal is to encourage researchers and enthusiasts to explore how the Networked Periodic Table, with SAUUHUPP at its core, can reshape fields from quantum chemistry and biology to environmental science and cosmology. By recognizing the periodic table as a foundational network within a universal computational system, we aim to bridge scientific disciplines, stimulate new discoveries, and promote a holistic understanding of the universe.

 

We invite you to join us in this journey. Whether you are here to contribute insights, conduct research, or simply learn, we hope you find this repository a valuable resource and an inspiring perspective on the interconnectedness of our universe. Your engagement and feedback are invaluable as we develop and expand this vision.

 

Thank you for visiting, and welcome to the SAUUHUPP community.

 

Warm regards,

Prudencio Méndez and the SAUUHUPP Research Team
DOI: 10.5281/zenodo.13941052
title: SAUUHnium: Novel Superheavy Elements and Their Applications in Nuclear Waste Management: Predictions, Properties, and Real-World Synthesis
DOI: 10.5281/zenodo.13941101
title: SAUUHnium: Novel Superheavy Elements and Their Applications in Nuclear Waste Management: Predictions, Properties, and Real-World Synthesis
DOI: 10.5281/zenodo.13948316
title: SAUUHnium: Novel Superheavy Elements and Their Applications in Nuclear Waste Management: Predictions, Properties, and Real-World Synthesis
DOI: 10.5281/zenodo.14036792
title: SAUUHnium: Novel Superheavy Elements and Their Applications in Nuclear Waste Management: Predictions, Properties, and Real-World Synthesis