cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.17665864
title: DISTENDER Climate Simulations: Statistical Downscaling for HUAS_D0, Vertical level sfc, Global model CanESM5, scenario ssp245 -  The North-east of the Netherlands (HUAS)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.15468/dl.b9wp25
title: Occurrence Download

description: A dataset containing 4 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Australia",
 "OccurrenceStatus is Present",
 "TaxonKey is Pestalotiopsis australis"
 ]
}

 The dataset includes 4 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012561-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17665880
title: DISTENDER Climate Simulations: Statistical Downscaling for HUAS_D0, Vertical level sfc, Global model CanESM5, scenario ssp370 -  The North-east of the Netherlands (HUAS)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17665881
title: DISTENDER Climate Simulations: Statistical Downscaling for HUAS_D0, Vertical level sfc, Global model CanESM5, scenario ssp370 -  The North-east of the Netherlands (HUAS)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17665891
title: DISTENDER Climate Simulations: Statistical Downscaling for HUAS_D0, Vertical level sfc, Global model CanESM5, scenario ssp585 -  The North-east of the Netherlands (HUAS)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17665890
title: DISTENDER Climate Simulations: Statistical Downscaling for HUAS_D0, Vertical level sfc, Global model CanESM5, scenario ssp585 -  The North-east of the Netherlands (HUAS)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.15468/dl.9ngunz
title: Occurrence Download

description: A dataset containing 613 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Argentina",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "TaxonKey is Solanum glaucophyllum"
 ]
}

 The dataset includes 613 records from 30 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012565-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ycy3bd
title: Occurrence Download

description: A dataset containing 836 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ipomoea orizabensis"
 ]
}

 The dataset includes 836 records from 62 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012564-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17665905
title: DISTENDER Climate Simulations: Statistical Downscaling for HUAS_D0, Vertical level sfc, Global model EC-EARTH3 -  The North-east of the Netherlands (HUAS)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17665904
title: DISTENDER Climate Simulations: Statistical Downscaling for HUAS_D0, Vertical level sfc, Global model EC-EARTH3 -  The North-east of the Netherlands (HUAS)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17665918
title: DISTENDER Climate Simulations: Statistical Downscaling for HUAS_D0, Vertical level sfc, Global model MPI-ESM1-2-HR -  The North-east of the Netherlands (HUAS)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17665919
title: DISTENDER Climate Simulations: Statistical Downscaling for HUAS_D0, Vertical level sfc, Global model MPI-ESM1-2-HR -  The North-east of the Netherlands (HUAS)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17665932
title: DISTENDER Climate Simulations: Statistical Downscaling for HUAS_D1, Vertical level 10m, Global model CanESM5, scenario Historical -  The North-east of the Netherlands (HUAS)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.5281/zenodo.17665933
title: DISTENDER Climate Simulations: Statistical Downscaling for HUAS_D1, Vertical level 10m, Global model CanESM5, scenario Historical -  The North-east of the Netherlands (HUAS)

description: Local (high resolution) climate data produced applying statistical downscaling techniques in the EU Project DISTENDER for five case studies. https://distender.eu/the-project . The datasets from the statistical downscaling come from the outputs of three global climate models that have been bias corrected (Parametric quantile mapping).
More details about the domains, the provided variable, levels and periods are provided in the README file.
DOI: 10.15468/dl.a6yhrk
title: Occurrence Download

description: A dataset containing 276 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ipomoea pauciflora"
 ]
}

 The dataset includes 276 records from 41 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012568-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y23jx6
title: Occurrence Download

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

 The dataset includes 31 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012567-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8p9ph7
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Australia",
 "OccurrenceStatus is Present",
 "TaxonKey is Pestalotiopsis biciliata"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012569-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rdepgz
title: Occurrence Download

description: A dataset containing 55 records in GBIF matching the query:
SELECT
 kingdom,
 kingdomkey,
 phylum,
 phylumkey,
 class,
 classkey,
 "order",
 orderkey,
 family,
 familykey,
 genus,
 genuskey,
 species,
 specieskey,
 "year",
 IF(ISNULL(kingdomkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY kingdomkey, "year")) kingdomcount,
 IF(ISNULL(phylumkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY phylumkey, "year")) phylumcount,
 IF(ISNULL(classkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY classkey, "year")) classcount,
 IF(ISNULL(orderkey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY orderkey, "year")) ordercount,
 IF(ISNULL(familykey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY familykey, "year")) familycount,
 IF(ISNULL(genuskey), NULL, SUM(COUNT(*)) OVER (
 PARTITION BY genuskey, "year")) genuscount,
 COUNT(*) occurrences,
 MIN(GBIF_TEMPORALUNCERTAINTY(eventdate, eventtime)) mintemporaluncertainty,
 MIN(COALESCE(coordinateuncertaintyinmeters, 1000)) mincoordinateuncertaintyinmeters
FROM
 occurrence
WHERE
 occurrence.occurrencestatus = 'PRESENT'
 AND (CAST(occurrence.taxonkey AS INTEGER) = 7261456 OR CAST(occurrence.acceptedtaxonkey AS INTEGER) = 7261456 OR CAST(occurrence.kingdomkey AS INTEGER) = 7261456 OR CAST(occurrence.phylumkey AS INTEGER) = 7261456 OR CAST(occurrence.classkey AS INTEGER) = 7261456 OR CAST(occurrence.orderkey AS INTEGER) = 7261456 OR CAST(occurrence.familykey AS INTEGER) = 7261456 OR CAST(occurrence.genuskey AS INTEGER) = 7261456 OR CAST(occurrence.specieskey AS INTEGER) = 7261456)
 AND occurrence.hasgeospatialissues = FALSE
 AND NOT GBIF_STRINGARRAYCONTAINS(occurrence.issue, 'TAXON_MATCH_FUZZY', TRUE)
 AND (occurrence.distancefromcentroidinmeters &gt;= 2000.0 OR occurrence.distancefromcentroidinmeters IS NULL)
 AND NOT occurrence.basisofrecord IN ('FOSSIL_SPECIMEN', 'LIVING_SPECIMEN')
 AND (occurrence.specieskey IS NOT NULL AND occurrence."year" IS NOT NULL)
GROUP BY
 occurrence.kingdom,
 occurrence.kingdomkey,
 occurrence.phylum,
 occurrence.phylumkey,
 occurrence.class,
 occurrence.classkey,
 occurrence."order",
 occurrence.orderkey,
 occurrence.family,
 occurrence.familykey,
 occurrence.genus,
 occurrence.genuskey,
 occurrence.species,
 occurrence.specieskey,
 occurrence."year"

 The dataset's 55 records were derived from 44 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012545-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ps2ymm
title: Occurrence Download

description: A dataset containing 8384 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is United States of America",
 "OccurrenceStatus is Present",
 "TaxonKey is Sus scrofa",
 "Year 2017-2025"
 ]
}

 The dataset includes 8384 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012571-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.myraqv
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Mammalia",
 "HasCoordinate is true",
 "Geometry POLYGON ((-120.8137 36.70565, -120.8057 36.69905, -120.8034 36.7009, -120.8115 36.70652, -120.8132 36.70643, -120.8137 36.70565))"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012572-251120083545085/datasets/export for details.
DOI: 10.15468/dl.z7f2x2
title: Occurrence Download

description: A dataset containing 9322 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Ctenosaura pectinata"
 ]
}

 The dataset includes 9322 records from 86 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012573-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.rprn22
title: Occurrence Download

description: A dataset containing 317 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Indonesia",
 "TaxonKey is Vespa velutina"
 ]
}

 The dataset includes 317 records from 14 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012574-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.vdebca
title: Occurrence Download

description: A dataset containing 63 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON((-99.65259 22.00266,-99.63006 22.00266,-99.63006 22.01243,-99.65259 22.01243,-99.65259 22.00266))",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 63 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012575-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.fz6kgz
title: Occurrence Download

description: A dataset containing 699 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ipomoea pes-caprae"
 ]
}

 The dataset includes 699 records from 52 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012576-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17741321
title: Fortified Theoretical Framework: The Quark-Gluon Plasma (QGP) -- A Rigorous Multi-Scale Analysis with Bayesian Constraints, Sensitivity Quantification, and Verifiable Simulations

description: This fortified theoretical framework presents a mathematically rigorous, empirically calibrated, and computationally verifiable exposition of the quark-gluon plasma (QGP). It represents the deconfined phase of quantum chromodynamics (QCD) matter under extremal thermodynamic conditions. Anchored in the non-perturbative dynamics of the QCD Lagrangian, we delineate color deconfinement through the Polyakov loop order parameter. We also address asymptotic freedom via renormalization-group flow of the strong coupling \( \alpha_s \).Critical parameters of the QCD phase diagram are constrained by state-of-the-art lattice QCD simulations. These yield a pseudocritical temperature \( T_c = 156.5 \pm 1.5\,\mathrm{MeV} \) and energy density \( \epsilon_c \approx 0.86\,\mathrm{GeV/fm^3} \) at vanishing baryon chemical potential \( \mu_B = 0 \).Experimental signatures from RHIC and LHC heavy-ion collisions are dissected using relativistic viscous hydrodynamics. This includes hard probes such as jet quenching (parameterized by the transport coefficient \( \hat{q} \sim 1.5\,\mathrm{GeV^2/fm} \)) and sequential quarkonium suppression. It also covers soft observables like anisotropic flow \( v_2\{2\} \approx 0.12{-}0.15 \).Transport coefficients, particularly the shear viscosity-to-entropy ratio \( \eta/s = (0.20 \pm 0.05)/(4\pi) \), are delineated via Bayesian inference on global datasets. Priors incorporate AdS/CFT holography and lattice susceptibilities.Multi-scale implications extend from primordial cosmology to neutron star phenomenology. In cosmology, QGP dominance occurs for proper times \( \tau \lesssim 10\,\mu\mathrm{s} \) post-Big Bang. This imprints the observed baryon asymmetry \( \eta_B \sim 6 \times 10^{-10} \). In neutron stars, hybrid equations of state support maximum masses \( M_\text{max} \approx 2.1\,M_\odot \).Theoretical robustness is reinforced by global Sobol sensitivity analysis. This highlights \( \eta/s \) dominance with first-order index \( S_{\eta/s} = 0.45 \). Advanced Monte Carlo simulations reproduce \( v_2 = 0.147 \pm 0.050 \). Integration includes emergent phenomena such as vortical and magnetic components in the QGP.Methodological challenges are rigorously quantified. These include initial-state anisotropies (\( \Delta \epsilon_2 \sim 20\% \)), non-equilibrium pre-thermalization (\( \tau_\text{iso} \sim 0.5\,\mathrm{fm}/c \)), and the elusive critical endpoint. Limitations from finite-lattice systematics and probe biases are also addressed.This self-consistent framework is verifiable through the provided Python implementations. It establishes a fortified benchmark for QCD phenomenology.
DOI: 10.5281/zenodo.17741197
title: Artificial Intelligence and Predictive Maintenance: A Novel  Approach in Industry 4.0

description: Abstract

In the era of digital transformation and the Fourth Industrial Revolution (Industry 4.0), Predictive

Maintenance (PdM) has emerged as a novel approach

to industrial asset management. Unlike traditional maintenance strategies that occur after failure

(reactive) or follow fixed schedules (preventive), PdM is based on real-time

data analysis and advanced Artificial Intelligence (AI) algorithms. This approach enables early detection

of equipment issues and smart planning of maintenance actions before failures occur.

This paper comprehensively explores the role of AI in implementing PdM, introducing theoretical

concepts, reviewing literature, analyzing PdM architectures based on AI,

examining case studies across industries, and comparing it with conventional methods. Furthermore,

challenges, implementation barriers, economic and environmental impacts,

related standards, and future trends in this field are discussed.

The results demonstrate that AI-based PdM not only reduces unplanned downtime and maintenance

costs but also improves equipment lifespan, safety, and operational efficiency.

With the advancement of technologies such as Digital Twin, Industrial Internet of Things (IIoT), and

Machine Learning, the future of industrial maintenance is moving toward

smart and self-healing systems.
DOI: 10.15468/dl.2z2pq3
title: Occurrence Download

description: A dataset containing 145 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Crotalus totonacus"
 ]
}

 The dataset includes 145 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012577-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17741196
title: Artificial Intelligence and Predictive Maintenance: A Novel  Approach in Industry 4.0

description: Abstract

In the era of digital transformation and the Fourth Industrial Revolution (Industry 4.0), Predictive

Maintenance (PdM) has emerged as a novel approach

to industrial asset management. Unlike traditional maintenance strategies that occur after failure

(reactive) or follow fixed schedules (preventive), PdM is based on real-time

data analysis and advanced Artificial Intelligence (AI) algorithms. This approach enables early detection

of equipment issues and smart planning of maintenance actions before failures occur.

This paper comprehensively explores the role of AI in implementing PdM, introducing theoretical

concepts, reviewing literature, analyzing PdM architectures based on AI,

examining case studies across industries, and comparing it with conventional methods. Furthermore,

challenges, implementation barriers, economic and environmental impacts,

related standards, and future trends in this field are discussed.

The results demonstrate that AI-based PdM not only reduces unplanned downtime and maintenance

costs but also improves equipment lifespan, safety, and operational efficiency.

With the advancement of technologies such as Digital Twin, Industrial Internet of Things (IIoT), and

Machine Learning, the future of industrial maintenance is moving toward

smart and self-healing systems.
DOI: 10.15468/dl.amnra4
title: Occurrence Download

description: A dataset containing 885595 species occurrences available in GBIF matching the query:
{
 "DatasetKey" : [
 "is BirdMap Data - GPS tracking of Storks, Cranes and birds of prey, breeding in Northern and Eastern Europe"
 ]
}

 The dataset includes 885595 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012562-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jcd9t5
title: Occurrence Download

description: A dataset containing 438 species occurrences available in GBIF matching the query:
{
 "and" : [
 "BasisOfRecord is Specimen",
 "Country is Mexico",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Ipomoea peteri"
 ]
}

 The dataset includes 438 records from 33 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0012578-251120083545085/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.3886/e240728
title: HIFLD OPEN US District Court Jurisdictions

description: Created based upon information located on the US Department of Justice website (https://www.fjc.gov/history/courts/u.s.-district-courts-and-federaljudiciary) 2021.
description: Archived from the HIFLD Open GIS data repository, which was deactivated on August 26, 2025.