cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17858632
title: Temporal Quantization of Coherence in Superconducting Qubits: Tau-Phase Anomaly Data Package

description: Discovery: A statistically significant anomaly (p < 1e-6, Cohens d = 1.65) in the temporal structure of quantum coherence on IBM superconducting processors. Key Finding: Bell-state fidelity exhibits a 1.79x modulation correlated with tau-phase (tau_0 = 46 microseconds), inconsistent with standard Markovian decoherence. Contents include 11 Python scripts, 5 JSON data files, 8 PDF figures, verification protocol, 6dCRSM Lagrangian framework, and falsifiable predictions. Model comparison rejects standard QM at greater than 25 sigma.
DOI: 10.5281/zenodo.17858631
title: Temporal Quantization of Coherence in Superconducting Qubits: Tau-Phase Anomaly Data Package

description: Discovery: A statistically significant anomaly (p < 1e-6, Cohens d = 1.65) in the temporal structure of quantum coherence on IBM superconducting processors. Key Finding: Bell-state fidelity exhibits a 1.79x modulation correlated with tau-phase (tau_0 = 46 microseconds), inconsistent with standard Markovian decoherence. Contents include 11 Python scripts, 5 JSON data files, 8 PDF figures, verification protocol, 6dCRSM Lagrangian framework, and falsifiable predictions. Model comparison rejects standard QM at greater than 25 sigma.
DOI: 10.7910/dvn/vfvuvt
title: Replication Data for: The impact of Terrorism on Democratic Support in Africa

description: How does chronic terrorism affect support for democracy in fragile states? While most research examines isolated attacks in stable democracies, little is known about persistent violence in insecure, weakly institutionalised contexts. This paper addresses that gap by analysing Africa, where terrorism is widespread and democratic transitions remain incomplete. Using Afrobarometer survey data matched with terrorism events, we employ an entropy balancing strategy within an unexpected-event-during-survey (UESD) design to estimate causal effects. We find that terrorism consistently undermines democratic support – especially in countries with stronger liberal institutions and lower development. Younger and older citizens are particularly susceptible to attitudinal shifts. These findings highlight how terrorism’s political impact hinges not just on exposure, but also on broader structural vulnerabilities shaped by institutions, development, and demography. The study advances theories of authoritarian reflex and threat perception, offering new insights into sustaining democracy amid chronic insecurity.
DOI: 10.15468/dl.ukq2t3
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((99.883 0.402185, 99.812735 0.396617, 99.744201 0.380053, 99.679085 0.352899, 99.61899 0.315824, 99.56539600000001 0.269742, 99.519623 0.215787, 99.48279700000001 0.155287, 99.455826 0.08973299999999999, 99.43937200000001 0.020738, 99.433842 -0.049998, 99.43937099999999 -0.120735, 99.45582400000001 -0.18973, 99.482795 -0.255285, 99.51962 -0.315785, 99.565393 -0.36974, 99.618987 -0.415823, 99.67908199999999 -0.452898, 99.74419899999999 -0.480053, 99.81273400000001 -0.496617, 99.883 -0.502185, 99.953266 -0.496617, 100.021801 -0.480053, 100.086918 -0.452898, 100.147013 -0.415823, 100.200607 -0.36974, 100.24638 -0.315785, 100.283205 -0.255285, 100.310176 -0.18973, 100.326629 -0.120735, 100.332158 -0.049998, 100.326628 0.020738, 100.310174 0.08973299999999999, 100.283203 0.155287, 100.246377 0.215787, 100.200604 0.269742, 100.14701 0.315824, 100.086915 0.352899, 100.021799 0.380053, 99.953265 0.396617, 99.883 0.402185))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 12 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038292-251120083545085/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.30656225
title: Fabrication and optimization of verapamil-loaded PEGylated nanoparticles for management of traumatic brain injury: a comparative evaluation

description: Verapamil (VRP) is a calcium channel blocker described for cardiovascular diseases. The reported antioxidant in addition to anti-inflammatory attributes, could present its potential efficacy for traumatic brain injury (TBI). However, brain drug delivery presents a well-known challenge. The present study aimed to design VRP-loaded PEGylated chitosan nanoparticles (PEG-VRP-NPs) for successful brain delivery after intranasal administration. A central composite design was adopted to optimize levels of Chitosan and PEG4000. The optimized PEG-VRP-NPs were assessed for in-vitro VRP release, and their morphology was observed by Transmission electron microscopy (TEM). The pharmacological effect of the optimized formula as a neuroprotective against TBI was assessed after intranasal administration to rats by measuring oxidative stress markers, inflammatory and apoptotic markers in brain tissues, and histopathological examination. In-vitro release behavior from the optimized formula exhibited a biphasic release pattern of an initial burst release followed by extended release up to 6 h. TEM images of optimized formula confirmed the formation of spherical-shaped nanosized particles. The pharmacological results confirmed the developed nanoparticles’ ability to deliver VRP to the brain effectively. The nanoparticles effectively demonstrated neuroprotection through antioxidant, antiapoptotic actions and down-regulation of the TLR4/NOX4/NF-κB signaling pathway. In conclusion, PEG-VRP-NPs formulation showed a novel brain drug delivery of VRP with an improved neuroprotective effect against TBI in rats.
DOI: 10.6084/m9.figshare.30656225.v2
title: Fabrication and optimization of verapamil-loaded PEGylated nanoparticles for management of traumatic brain injury: a comparative evaluation

description: Verapamil (VRP) is a calcium channel blocker described for cardiovascular diseases. The reported antioxidant in addition to anti-inflammatory attributes, could present its potential efficacy for traumatic brain injury (TBI). However, brain drug delivery presents a well-known challenge. The present study aimed to design VRP-loaded PEGylated chitosan nanoparticles (PEG-VRP-NPs) for successful brain delivery after intranasal administration. A central composite design was adopted to optimize levels of Chitosan and PEG4000. The optimized PEG-VRP-NPs were assessed for in-vitro VRP release, and their morphology was observed by Transmission electron microscopy (TEM). The pharmacological effect of the optimized formula as a neuroprotective against TBI was assessed after intranasal administration to rats by measuring oxidative stress markers, inflammatory and apoptotic markers in brain tissues, and histopathological examination. In-vitro release behavior from the optimized formula exhibited a biphasic release pattern of an initial burst release followed by extended release up to 6 h. TEM images of optimized formula confirmed the formation of spherical-shaped nanosized particles. The pharmacological results confirmed the developed nanoparticles’ ability to deliver VRP to the brain effectively. The nanoparticles effectively demonstrated neuroprotection through antioxidant, antiapoptotic actions and down-regulation of the TLR4/NOX4/NF-κB signaling pathway. In conclusion, PEG-VRP-NPs formulation showed a novel brain drug delivery of VRP with an improved neuroprotective effect against TBI in rats.
DOI: 10.6084/m9.figshare.30656225.v1
title: Fabrication and Optimization of Verapamil-loaded PEGylated Nanoparticles for Management of Traumatic Brain Injury: A Comparative Evaluation

description: Verapamil (VRP) is a calcium channel blocker described for cardiovascular diseases. The reported antioxidant in addition to anti-inflammatory attributes, could present its potential efficacy for traumatic brain injury (TBI). However, brain drug delivery presents a well-known challenge. The present study aimed to design VRP-loaded PEGylated chitosan nanoparticles (PEG-VRP-NPs) for successful brain delivery after intranasal administration. A central composite design was adopted to optimize levels of Chitosan and PEG4000. The optimized PEG-VRP-NPs were assessed for in-vitro VRP release, and their morphology was observed by Transmission electron microscopy (TEM). The pharmacological effect of the optimized formula as a neuroprotective against TBI was assessed after intranasal administration to rats by measuring oxidative stress markers, inflammatory and apoptotic markers in brain tissues, and histopathological examination. In-vitro release behavior from the optimized formula exhibited a biphasic release pattern of an initial burst release followed by extended release up to 6 h. TEM images of optimized formula confirmed the formation of spherical-shaped nanosized particles. The pharmacological results confirmed the developed nanoparticles' ability to deliver VRP to the brain effectively. The nanoparticles effectively demonstrated neuroprotection through antioxidant, antiapoptotic actions and down-regulation of the TLR4/NOX4/NF-κB signaling pathway. In conclusion, PEG-VRP-NPs formulation showed a novel brain drug delivery of VRP with an improved neuroprotective effect against TBI in rats.
DOI: 10.6084/m9.figshare.30402590
title: Conjugate Gradient Methods for High-Dimensional GLMMs

description: Generalized linear mixed models (GLMMs) are a widely used tool in statistical analysis. The main bottleneck of many computational approaches lies in the inversion of the high dimensional precision matrices associated with the random effects. Such matrices are typically sparse; however, the sparsity pattern resembles a multi partite random graph, which does not lend itself well to default sparse linear algebra techniques. Notably, we show that, for typical GLMMs, the Cholesky factor is dense even when the original precision is sparse. We thus turn to approximate iterative techniques, in particular to the conjugate gradient (CG) method. We combine a detailed analysis of the spectrum of said precision matrices with results from random graph theory to show that CG-based methods applied to high-dimensional GLMMs typically achieve a fixed approximation error with a total cost that scales linearly with the number of parameters and observations. Numerical illustrations with both real and simulated data confirm the theoretical findings, while at the same time illustrating situations, such as nested structures, where CG-based methods struggle. 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.30402590.v1
title: Conjugate gradient methods for high-dimensional GLMMs

description: Generalized linear mixed models (GLMMs) are a widely used tool in statistical analysis. The main bottleneck of many computational approaches lies in the inversion of the high dimensional precision matrices associated with the random effects. Such matrices are typically sparse; however, the sparsity pattern resembles a multi partite random graph, which does not lend itself well to default sparse linear algebra techniques. Notably, we show that, for typical GLMMs, the Cholesky factor is dense even when the original precision is sparse. We thus turn to approximate iterative techniques, in particular to the conjugate gradient (CG) method. We combine a detailed analysis of the spectrum of said precision matrices with results from random graph theory to show that CG-based methods applied to high-dimensional GLMMs typically achieve a fixed approximation error with a total cost that scales linearly with the number of parameters and observations. Numerical illustrations with both real and simulated data confirm the theoretical findings, while at the same time illustrating situations, such as nested structures, where CG-based methods struggle.
DOI: 10.6084/m9.figshare.30402590.v2
title: Conjugate Gradient Methods for High-Dimensional GLMMs

description: Generalized linear mixed models (GLMMs) are a widely used tool in statistical analysis. The main bottleneck of many computational approaches lies in the inversion of the high dimensional precision matrices associated with the random effects. Such matrices are typically sparse; however, the sparsity pattern resembles a multi partite random graph, which does not lend itself well to default sparse linear algebra techniques. Notably, we show that, for typical GLMMs, the Cholesky factor is dense even when the original precision is sparse. We thus turn to approximate iterative techniques, in particular to the conjugate gradient (CG) method. We combine a detailed analysis of the spectrum of said precision matrices with results from random graph theory to show that CG-based methods applied to high-dimensional GLMMs typically achieve a fixed approximation error with a total cost that scales linearly with the number of parameters and observations. Numerical illustrations with both real and simulated data confirm the theoretical findings, while at the same time illustrating situations, such as nested structures, where CG-based methods struggle. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.
DOI: 10.15468/dl.asetdr
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Uromys boeadii",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038295-251120083545085/datasets/export for details.
DOI: 10.15468/dl.zs2dgq
title: Occurrence Download

description: A dataset containing 2824 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-1.13903 41.84724,2.7533 41.84724,2.7533 43.35354,-1.13903 43.35354,-1.13903 41.84724))",
 "HasCoordinate is true",
 "TaxonKey is Linaria supina"
 ]
}

 The dataset includes 2824 records from 110 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038291-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.gsnnj9
title: Occurrence Download

description: A dataset containing 9 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Potamethus spathiferus",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 9 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038296-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.17026/ar/u819dm
title: PAN-00123956 - coin/coin-related, Konrad II van Rietberg (1270-1297), Pfennig

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00123956&lt;/p&gt;
description: Modified: 2025-10-21T09:02:17+02:00
DOI: 10.15468/dl.2v3bv2
title: Occurrence Download

description: A dataset containing 39094 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Erigeron philadelphicus"
 ]
}

 The dataset includes 39094 records from 408 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038297-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.p9kkzj
title: Occurrence Download

description: A dataset containing 12 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((99.916 0.406185, 99.845735 0.400617, 99.77720100000001 0.384053, 99.712084 0.356899, 99.651989 0.319824, 99.59839599999999 0.273742, 99.552622 0.219787, 99.51579700000001 0.159287, 99.488826 0.093733, 99.47237199999999 0.024738, 99.466842 -0.045999, 99.472371 -0.116735, 99.48882399999999 -0.18573, 99.515795 -0.251285, 99.55262 -0.311785, 99.598393 -0.36574, 99.65198700000001 -0.411823, 99.712082 -0.448898, 99.777199 -0.476053, 99.84573399999999 -0.492617, 99.916 -0.498185, 99.986266 -0.492617, 100.054801 -0.476053, 100.119918 -0.448898, 100.180013 -0.411823, 100.233607 -0.36574, 100.27938 -0.311785, 100.316205 -0.251285, 100.343176 -0.18573, 100.359629 -0.116735, 100.365158 -0.045999, 100.359628 0.024738, 100.343174 0.093733, 100.316203 0.159287, 100.279378 0.219787, 100.233604 0.273742, 100.180011 0.319824, 100.119916 0.356899, 100.054799 0.384053, 99.986265 0.400617, 99.916 0.406185))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 12 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038299-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bgqww7
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Uromys emmae",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038300-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.h4esc5
title: Occurrence Download

description: A dataset containing 526 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Potamilla neglecta",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 526 records from 56 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038301-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dd.24bbkn
title: WSP_GBIF_Collector_12345

description: Generated from API search results. Project 12345 - Test Area
DOI: 10.17026/ar/3zp242
title: PAN-00163630 - terra sigillata bowl type Drag. 29

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00163630&lt;/p&gt;
description: Modified: 2025-10-23T09:06:23+02:00
DOI: 10.15468/dl.k2e9f8
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{"type":"and","predicates":[{"type":"within","geometry":"POLYGON ((110.809 0.411185, 110.738735 0.405617, 110.670201 0.389053, 110.605084 0.361899, 110.544989 0.324824, 110.491395 0.278742, 110.445622 0.224787, 110.408797 0.164287, 110.381825 0.098733, 110.365372 0.029738, 110.359842 -0.040999, 110.365372 -0.111735, 110.381824 -0.18073, 110.408795 -0.246285, 110.44562 -0.306785, 110.491393 -0.360741, 110.544987 -0.406823, 110.605082 -0.443898, 110.670199 -0.471053, 110.738734 -0.487617, 110.809 -0.493185, 110.879266 -0.487617, 110.947801 -0.471053, 111.012918 -0.443898, 111.073013 -0.406823, 111.126607 -0.360741, 111.17238 -0.306785, 111.209205 -0.246285, 111.236176 -0.18073, 111.252628 -0.111735, 111.258158 -0.040999, 111.252628 0.029738, 111.236175 0.098733, 111.209203 0.164287, 111.172378 0.224787, 111.126605 0.278742, 111.073011 0.324824, 111.012916 0.361899, 110.947799 0.389053, 110.879265 0.405617, 110.809 0.411185))"},{"type":"equals","key":"CLASS_KEY","value":"212","matchCase":false},{"type":"greaterThanOrEquals","key":"YEAR","value":"2015","matchCase":false},{"type":"not","predicate":{"type":"in","key":"BASIS_OF_RECORD","values":["MATERIAL_CITATION","MATERIAL_SAMPLE","PRESERVED_SPECIMEN"],"matchCase":false}},{"type":"lessThanOrEquals","key":"COORDINATE_UNCERTAINTY_IN_METERS","value":"10000","matchCase":false},{"type":"equals","key":"LICENSE","value":"CC BY 4.0","matchCase":false}]}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038303-251120083545085/datasets/export for details.
DOI: 10.15468/dl.fb6382
title: Occurrence Download

description: A dataset containing 27 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Uromys hadrourus",
 "HasCoordinate is TRUE"
 ]
}

 The dataset includes 27 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038304-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ggrnwn
title: Occurrence Download

description: A dataset containing 1363 species occurrences available in GBIF matching the query:
{
 "and" : [
 "OccurrenceStatus is Present",
 "TaxonKey is Eurycoma longifolia"
 ]
}

 The dataset includes 1363 records from 42 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038306-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.bs4z98
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Potamilla rubra",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0038305-251120083545085/datasets/export for details.
DOI: 10.17026/ar/u6buch
title: PAN-00142577 - rim fragment (East Gaulish terra sigillata, II-III)

description: &lt;p&gt;This find is registered at Portable Antiquities of the Netherlands with number PAN-00142577&lt;/p&gt;
description: Modified: 2025-10-20T10:11:14+02:00