cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.26688218
title: Additional file 4 of Decreased eggshell strength caused by impairment of uterine calcium transport coincide with higher bone minerals and quality in aged laying hens

description: Additional file 4. Differentially expressed genes (DEGs) in uterus at initiation (8.5 h PO) and growth (18.5 h PO) stages of eggshell calcification.
DOI: 10.6084/m9.figshare.26688218.v1
title: Additional file 4 of Decreased eggshell strength caused by impairment of uterine calcium transport coincide with higher bone minerals and quality in aged laying hens

description: Additional file 4. Differentially expressed genes (DEGs) in uterus at initiation (8.5 h PO) and growth (18.5 h PO) stages of eggshell calcification.
DOI: 10.15468/dl.he75yk
title: Occurrence Download

description: A dataset containing 87 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Colombia",
 "DatasetKey is one of (Herbarium GB, University of Gothenburg, Herbario Amazónico Colombiano, Herbario Nacional Colombiano (COL), Herbario Universidad de Antioquia (HUA), Herbario Universidad de Caldas - FAUC, Herbario Pontificia Universidad Javeriana, Herbario del Museo de Historia Natural C.J. Marinkelle, Colección Herbario Federico Medem Bogotá - FMB)",
 "OccurrenceStatus is Present",
 "TaxonKey is Solanum tuberosum L."
 ]
}

 The dataset includes 87 records from 5 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067529-240626123714530/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.26688245
title: Additional file 1 of Expression and diagnostic value of lncRNA MALAT1 and NLRP3 in lower limb atherosclerosis in diabetes

description: Supplementary Material 1
DOI: 10.6084/m9.figshare.26688245.v1
title: Additional file 1 of Expression and diagnostic value of lncRNA MALAT1 and NLRP3 in lower limb atherosclerosis in diabetes

description: Supplementary Material 1
DOI: 10.15468/dl.gr5pk7
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "MULTIPOLYGON (((149.002817 -32.748773, 149.009554 -32.748773, 149.009554 -32.737544, 149.018538 -32.737544, 149.018538 -32.742036, 149.025275 -32.742036, 149.025275 -32.735298, 149.020783 -32.735298, 149.020783 -32.721823, 149.018538 -32.721823, 149.018538 -32.719578, 149.014046 -32.719578, 149.014046 -32.710594, 149.018538 -32.710594, 149.018538 -32.703857, 149.016292 -32.703857, 149.016292 -32.708349, 149.0118 -32.708349, 149.0118 -32.699365, 149.014046 -32.699365, 149.014046 -32.69712, 149.016292 -32.69712, 149.016292 -32.694874, 149.0118 -32.694874, 149.0118 -32.69712, 149.009554 -32.69712, 149.009554 -32.703857, 148.99608 -32.703857, 148.99608 -32.71284, 148.998326 -32.71284, 148.998326 -32.706103, 149.002817 -32.706103, 149.002817 -32.715086, 149.005063 -32.715086, 149.005063 -32.719578, 149.002817 -32.719578, 149.002817 -32.721823, 148.998326 -32.721823, 148.998326 -32.719578, 148.99608 -32.719578, 148.99608 -32.733052, 148.998326 -32.733052, 148.998326 -32.728561, 149.002817 -32.728561, 149.002817 -32.726315, 149.005063 -32.726315, 149.005063 -32.728561, 149.009554 -32.728561, 149.009554 -32.733052, 149.002817 -32.733052, 149.002817 -32.735298, 148.998326 -32.735298, 148.998326 -32.744281, 149.002817 -32.744281, 149.002817 -32.748773)), ((149.002817 -32.748773, 148.993834 -32.748773, 148.993834 -32.753265, 148.998326 -32.753265, 148.998326 -32.757756, 149.005063 -32.757756, 149.005063 -32.753265, 149.002817 -32.753265, 149.002817 -32.748773)))"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0067528-240626123714530/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.26688290
title: Additional file 1 of Optimizing combination therapy in prostate cancer: mechanistic insights into the synergistic effects of Paclitaxel and Sulforaphane-induced apoptosis

description: Supplementary Material 1.
DOI: 10.6084/m9.figshare.26688290.v1
title: Additional file 1 of Optimizing combination therapy in prostate cancer: mechanistic insights into the synergistic effects of Paclitaxel and Sulforaphane-induced apoptosis

description: Supplementary Material 1.
DOI: 10.6084/m9.figshare.26688308
title: Additional file 2 of Three near-complete genome assemblies reveal substantial centromere dynamics from diploid to tetraploid in Brachypodium genus

description: Additional file 2. This file contains Tables S1-S17.
DOI: 10.6084/m9.figshare.28817987
title: Database Florian Berghea AI.xlsx

description: Risk appetite and aversion among GenAI.
DOI: 10.6084/m9.figshare.28783049
title: Supplementary Material for: Interactions of probiotics with the immune cells of patients with COVID-19 pneumonia

description: Introduction: In severe COVID-19, excessive cytokine release may be driven by SARS-CoV-2. We investigated the modulatory effect of probiotics which may have direct interaction with the immune gut cells. 
Methods: Fifty-five patients with confirmed COVID-19 infection were classified by the presence of acute respiratory distress syndrome (ARDS) or not. Peripheral blood mononuclear cells (PBMCs) were isolated and stimulated with lipopolysaccharide (LPS), a preparation of four probiotics (LactoLevure® containing Saccharomyces boulardii, Bifidobacterium lactis BB-12, Lactobacillus acidophilus LA-5 and L.plantarum) and/or recombinant human interferon gamma (rhIFNγ) and Tocilizumab. Cytokine concentrations were measured in cell supernatants. Gene expression of Toll-like receptors 2 (TLR2) and 4 (TLR4) was performed by quantitative real-time polymerase chain reaction (RT-PCR). Results were associated with the level of viremia.
Results: Probiotics decreased tumor necrosis factor alpha (TNFα) production by the PBMCs of both ARDS and non-ARDS patients. LPS-stimulated the production of interleukin (IL)-1β, IL-6 in non-ARDS patients. IL-6 production was maintained in the presence of probiotics. rhIFNγ enhanced LPS-stimulated cytokine production by PBMCs; this was not the case when PBMCs were stimulated by probiotics. Probiotics upregulated TLR2 and LPS downregulated TLR4 in the PBMCs of patients with ARDS. PBMCs from patients with viremia had more cytokine production by probiotic stimulation.
Conclusion: Probiotics interact with the immune system of COVID-19 patients by modulating the production of TNFα, IL-1β and IL-6 in an IFNγ-independent mechanism.
DOI: 10.6084/m9.figshare.28783049.v1
title: Supplementary Material for: Interactions of probiotics with the immune cells of patients with COVID-19 pneumonia

description: Introduction: In severe COVID-19, excessive cytokine release may be driven by SARS-CoV-2. We investigated the modulatory effect of probiotics which may have direct interaction with the immune gut cells. 
Methods: Fifty-five patients with confirmed COVID-19 infection were classified by the presence of acute respiratory distress syndrome (ARDS) or not. Peripheral blood mononuclear cells (PBMCs) were isolated and stimulated with lipopolysaccharide (LPS), a preparation of four probiotics (LactoLevure® containing Saccharomyces boulardii, Bifidobacterium lactis BB-12, Lactobacillus acidophilus LA-5 and L.plantarum) and/or recombinant human interferon gamma (rhIFNγ) and Tocilizumab. Cytokine concentrations were measured in cell supernatants. Gene expression of Toll-like receptors 2 (TLR2) and 4 (TLR4) was performed by quantitative real-time polymerase chain reaction (RT-PCR). Results were associated with the level of viremia.
Results: Probiotics decreased tumor necrosis factor alpha (TNFα) production by the PBMCs of both ARDS and non-ARDS patients. LPS-stimulated the production of interleukin (IL)-1β, IL-6 in non-ARDS patients. IL-6 production was maintained in the presence of probiotics. rhIFNγ enhanced LPS-stimulated cytokine production by PBMCs; this was not the case when PBMCs were stimulated by probiotics. Probiotics upregulated TLR2 and LPS downregulated TLR4 in the PBMCs of patients with ARDS. PBMCs from patients with viremia had more cytokine production by probiotic stimulation.
Conclusion: Probiotics interact with the immune system of COVID-19 patients by modulating the production of TNFα, IL-1β and IL-6 in an IFNγ-independent mechanism.
DOI: 10.15468/dl.shuptk
title: Occurrence Download

description: A dataset containing 233602 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Drosera rotundifolia L."
 ]
}

 The dataset includes 233602 records from 1087 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021612-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.12515063
title: AirfRANS_clipped

description: This dataset is a new version of the dataset originally made available through the library and described in the paper.

It has 3 main differences:



it is in PLAID format

all meshes were clipped in a box  [-2, 4] x [-1.5, 1.5] and converted to triangular meshes thanks to Paraview

A ML4PhySim_Challenge_train split is provided, corresponding to the training set of the ML4PhySim challenge


It has 2 variants:



AirfRANS_remeshed

AirfRANS_original


 

Tips to access the data:

After decompressing the downloaded file:

from plaid.containers.dataset import Datasetfrom plaid.problem_definition import ProblemDefinition

dataset = Dataset()problem = ProblemDefinition()

problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True)

print("problem =", problem)print("dataset =", dataset)

sample = dataset[0]print("sample =", sample)

for fn in sample.get_field_names():    print(f"{fn} =", sample.get_field(fn))for sn in sample.get_scalar_names():    print(f"{sn} =", sample.get_scalar(sn))

print("nodes =", sample.get_nodes())print("elements =", sample.get_elements())print("nodal_tags =", sample.get_nodal_tags())
DOI: 10.5281/zenodo.12515064
title: AirfRANS_clipped

description: This dataset is a new version of the dataset originally made available through the library and described in the paper.

It has 3 main differences:



it is in PLAID format

all meshes were clipped in a box  [-2, 4] x [-1.5, 1.5] and converted to triangular meshes thanks to Paraview

A ML4PhySim_Challenge_train split is provided, corresponding to the training set of the ML4PhySim challenge


It has 2 variants:



AirfRANS_remeshed

AirfRANS_original
DOI: 10.5281/zenodo.14840377
title: AirfRANS_clipped

description: This dataset is a new version of the dataset originally made available through the library and described in the paper.

It has 3 main differences:



it is in PLAID format

all meshes were clipped in a box  [-2, 4] x [-1.5, 1.5] and converted to triangular meshes thanks to Paraview

A ML4PhySim_Challenge_train split is provided, corresponding to the training set of the ML4PhySim challenge


It has 2 variants:



AirfRANS_remeshed

AirfRANS_original


 

Tips to access the data:

After decompressing the downloaded file:

from plaid.containers.dataset import Datasetfrom plaid.problem_definition import ProblemDefinition

dataset = Dataset()problem = ProblemDefinition()

problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True)

print("problem =", problem)print("dataset =", dataset)

sample = dataset[0]print("sample =", sample)

for fn in sample.get_field_names():    print(f"{fn} =", sample.get_field(fn))for sn in sample.get_scalar_names():    print(f"{sn} =", sample.get_scalar(sn))

print("nodes =", sample.get_nodes())print("elements =", sample.get_elements())print("nodal_tags =", sample.get_nodal_tags())
DOI: 10.5281/zenodo.14840312
title: Clinical Safety and Immunogenicity of Maternal Group-B Streptococcus (GBS) Vaccination in Pregnant Women and Their Infants: A Meta-Analysis of Randomised Trials
DOI: 10.5281/zenodo.14840313
title: Clinical Safety and Immunogenicity of Maternal Group-B Streptococcus (GBS) Vaccination in Pregnant Women and Their Infants: A Meta-Analysis of Randomised Trials
DOI: 10.5281/zenodo.12207787
title: AirfRANS_remeshed

description: This dataset is a new version of the dataset originally made available through the library and described in the paper.

It has 4 main differences:



it is in PLAID format

all meshes were cliped in a box [2, 4] x [-1.5, 1.5] and converted to triangular meshes thanks to Paraview 

all meshes were remeshed using MMG to reduce the number of nodes and cells

a ML4PhySim_Challenge_train split is provided, corresponding to the training set of the ML4PhySim challenge


It has 2 variants:



AirfRANS_clipped

AirfRANS_original
DOI: 10.5281/zenodo.12515083
title: AirfRANS_original

description: This dataset is a new version of the dataset originally made available through the library and described in the paper.

It is in PLAID format.

A ML4PhySim_Challenge_train split is provided, corresponding to the training set of the ML4PhySim challenge.

It has 2 variants:



AirfRANS_remeshed

AirfRANS_clipped


 

Tips to access the data:

After decompressing the downloaded file:

from plaid.containers.dataset import Datasetfrom plaid.problem_definition import ProblemDefinition

dataset = Dataset()problem = ProblemDefinition()

problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True)

print("problem =", problem)print("dataset =", dataset)

sample = dataset[0]print("sample =", sample)

for fn in sample.get_field_names():    print(f"{fn} =", sample.get_field(fn))for sn in sample.get_scalar_names():    print(f"{sn} =", sample.get_scalar(sn))

print("nodes =", sample.get_nodes())print("elements =", sample.get_elements())print("nodal_tags =", sample.get_nodal_tags())
DOI: 10.5281/zenodo.14840387
title: AirfRANS_original

description: This dataset is a new version of the dataset originally made available through the library and described in the paper.

It is in PLAID format.

A ML4PhySim_Challenge_train split is provided, corresponding to the training set of the ML4PhySim challenge.

It has 2 variants:



AirfRANS_remeshed

AirfRANS_clipped


 

Tips to access the data:

After decompressing the downloaded file:

from plaid.containers.dataset import Datasetfrom plaid.problem_definition import ProblemDefinition

dataset = Dataset()problem = ProblemDefinition()

problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True)

print("problem =", problem)print("dataset =", dataset)

sample = dataset[0]print("sample =", sample)

for fn in sample.get_field_names():    print(f"{fn} =", sample.get_field(fn))for sn in sample.get_scalar_names():    print(f"{sn} =", sample.get_scalar(sn))

print("nodes =", sample.get_nodes())print("elements =", sample.get_elements())print("nodal_tags =", sample.get_nodal_tags())
DOI: 10.5281/zenodo.12207786
title: AirfRANS_remeshed

description: This dataset is a new version of the dataset originally made available through the library and described in the paper.

It has 4 main differences:



it is in PLAID format

all meshes were cliped in a box [2, 4] x [-1.5, 1.5] and converted to triangular meshes thanks to Paraview 

all meshes were remeshed using MMG to reduce the number of nodes and cells

a ML4PhySim_Challenge_train split is provided, corresponding to the training set of the ML4PhySim challenge


It has 2 variants:



AirfRANS_clipped

AirfRANS_original


 

Tips to access the data:

After decompressing the downloaded file:

from plaid.containers.dataset import Datasetfrom plaid.problem_definition import ProblemDefinition

dataset = Dataset()problem = ProblemDefinition()

problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True)

print("problem =", problem)print("dataset =", dataset)

sample = dataset[0]print("sample =", sample)

for fn in sample.get_field_names():    print(f"{fn} =", sample.get_field(fn))for sn in sample.get_scalar_names():    print(f"{sn} =", sample.get_scalar(sn))

print("nodes =", sample.get_nodes())print("elements =", sample.get_elements())print("nodal_tags =", sample.get_nodal_tags())
DOI: 10.5281/zenodo.14840388
title: AirfRANS_remeshed

description: This dataset is a new version of the dataset originally made available through the library and described in the paper.

It has 4 main differences:



it is in PLAID format

all meshes were cliped in a box [2, 4] x [-1.5, 1.5] and converted to triangular meshes thanks to Paraview 

all meshes were remeshed using MMG to reduce the number of nodes and cells

a ML4PhySim_Challenge_train split is provided, corresponding to the training set of the ML4PhySim challenge


It has 2 variants:



AirfRANS_clipped

AirfRANS_original


 

Tips to access the data:

After decompressing the downloaded file:

from plaid.containers.dataset import Datasetfrom plaid.problem_definition import ProblemDefinition

dataset = Dataset()problem = ProblemDefinition()

problem._load_from_dir_(os.path.join(/path/to/data,'problem_definition'))dataset._load_from_dir_(os.path.join(/path/to/data,'dataset'), verbose = True)

print("problem =", problem)print("dataset =", dataset)

sample = dataset[0]print("sample =", sample)

for fn in sample.get_field_names():    print(f"{fn} =", sample.get_field(fn))for sn in sample.get_scalar_names():    print(f"{sn} =", sample.get_scalar(sn))

print("nodes =", sample.get_nodes())print("elements =", sample.get_elements())print("nodal_tags =", sample.get_nodal_tags())
DOI: 10.5281/zenodo.14840427
title: Proof of the Riemann Hypothesis  This document contains the proof of the Riemann Hypothesis, developed using analytical methods, including the functional equation of the zeta function, the distribution of prime numbers, and Hardy's methods. This proof supports the existence of infinitely many zeros on the critical line Re(s) = 1/2 and provides further confirmatory evidence for the validity of the Riemann Hypothesis.

description: This document presents a formal proof of the Riemann Hypothesis, one of the most famous unsolved problems in mathematics. The proof is developed using advanced analytical methods, including the functional equation of the Riemann zeta function, prime number distribution, and Hardy's methods. It provides a rigorous argument supporting the existence of infinitely many zeros on the critical line Re(s) = 1/2. The work offers further confirmation of the validity of the Riemann Hypothesis, contributing to the ongoing research in number theory.
DOI: 10.5281/zenodo.14840428
title: Proof of the Riemann Hypothesis  This document contains the proof of the Riemann Hypothesis, developed using analytical methods, including the functional equation of the zeta function, the distribution of prime numbers, and Hardy's methods. This proof supports the existence of infinitely many zeros on the critical line Re(s) = 1/2 and provides further confirmatory evidence for the validity of the Riemann Hypothesis.

description: This document presents a formal proof of the Riemann Hypothesis, one of the most famous unsolved problems in mathematics. The proof is developed using advanced analytical methods, including the functional equation of the Riemann zeta function, prime number distribution, and Hardy's methods. It provides a rigorous argument supporting the existence of infinitely many zeros on the critical line Re(s) = 1/2. The work offers further confirmation of the validity of the Riemann Hypothesis, contributing to the ongoing research in number theory.
DOI: 10.15468/dl.6jjcb3
title: Occurrence Download

description: A dataset containing 57068 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is one of (Human Observation, Occurrence evidence)",
 "Country is one of (France, Germany, Austria, Italy, Liechtenstein)",
 "Geometry POLYGON((2.62474 42.44302,13.91799 42.44302,13.91799 49.6242,2.62474 49.6242,2.62474 42.44302))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Cervus elaphus Linnaeus, 1758",
 "Year 2005-2025"
 ]
}

 The dataset includes 57068 records from 378 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021617-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5uze2m
title: Occurrence Download

description: A dataset listing the 1160 species recorded in GBIF matching the query:
{
 "and" : [
 "Continent is Asia",
 "Country is China",
 "DatasetKey is EOD – eBird Observation Dataset",
 "GadmGid is CHN",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Month is one of (May, June, January, February, March, April, July, August, September, October, November, December)",
 "Year 2020-2020"
 ]
}

 The dataset's 1160 records were derived from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0021611-250127130748423/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14840437
title: Q-RAVC:Secure Autonomous Vehicle Communication

description: Autonomous vehicles are a key part of the future transportation systems, providing increased safety, efficiency, and convenience. However, the increasing dependency on communication networks for vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) interactions leaves these systems vulnerable to a host of cyber threats, especially in the wake of quantum computing that would break traditional cryptographic methods. In this paper, we present Q-RAVC: Quantum-Resilient Blockchain Framework for Secure Autonomous Vehicle Communication. Q-RAVC makes use of quantum-resistant cryptographic algorithms and blockchain technology to provide a secure communication protocol in the autonomous vehicle ecosystem. It ensures that only vehicles and infrastructure nodes with authenticated quantum-resistant certificates can participate in the network. Q-RAVC also provides a decentralized trust management system, vehicular forensics, and real-time anomaly detection to safeguard against various cyber-attacks. Our simulated analysis proves that Q-RAVC has strong security guarantees, ensures low latency, and incurs feasible storage costs, thereby supporting its use in practical deployments. Furthermore, the fact that this framework can survive various threats coming from quantum and support dynamic evolutions in automotive technology indicates a possible direction toward secure future autonomous vehicle networks.