cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.ar3niz
title: Occurrence Download

description: A dataset containing 38912 species occurrences available in GBIF matching the query:
{
 "and" : [
 "PublishingCountry is one of (Argentina, Czechia, Japan)",
 "Country is Brazil"
 ]
}

 The dataset includes 38912 records from 90 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007899-160311141623029/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.edsmtj
title: Occurrence Download

description: A dataset containing 79 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Kniphofia tabularis Marloth"
 ]
}

 The dataset includes 79 records from 6 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0036264-240321170329656/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.5vjrw8
title: Occurrence Download

description: A dataset containing 91 species occurrences available in GBIF matching the query:
{
 "Geometry" : [
 "POLYGON ((-54 71.8559, -51.58876 71.8559, -51.58876 72.71931, -54 72.71931, -54 71.8559))"
 ]
}

 The dataset includes 91 records from 9 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0042362-240229165702484/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.y9segr
title: Occurrence Download

description: A dataset containing 15674 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Panicum capillare L."
 ]
}

 The dataset includes 15674 records from 219 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0007899-170714134226665/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.8dr6n5
title: Occurrence Download

description: A dataset containing 474036 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Aegopodium podagraria L."
 ]
}

 The dataset includes 474036 records from 1231 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0017020-240216155721649/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.wd8seu
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is one of (235069, 235090)"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0411767-210914110416597/datasets/export for details.
DOI: 10.15468/dl.8qxhvc
title: Occurrence Download

description: A dataset containing 556 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is Mexico",
 "StateProvince is guerrero",
 "TaxonKey is Phaseolus L."
 ]
}

 The dataset includes 556 records from 23 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0004294-190320150433242/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mkqaeb
title: Occurrence Download

description: A dataset containing 139875 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "TaxonKey is Myocastor coypus (Molina, 1782)"
 ]
}

 The dataset includes 139875 records from 1915 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0057775-240506114902167/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v59hey
title: Occurrence Download

description: A dataset containing 645 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cotinus coggygria",
 "Country is Germany",
 "HasCoordinate is true"
 ]
}

 The dataset includes 645 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015410-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kpn3j5
title: Occurrence Download

description: A dataset containing 361 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Cotinus coggygria",
 "Country is United Kingdom of Great Britain and Northern Ireland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 361 records from 17 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015411-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.v8nf2w
title: Occurrence Download

description: A dataset containing 3460978 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Continent is Europe",
 "Country is Spain",
 "GadmGid is ESP.1.2_1",
 "Geometry POLYGON((-4.94425 36.50303,-4.86363 36.65024,-5.33329 36.54509,-5.23866 36.65024,-5.06341 37.00774,-5.52956 37.08485,-5.77841 36.95166,-6.02726 36.87105,-6.25158 36.93414,-6.38126 36.96568,-6.45136 36.86404,-6.40579 36.65725,-6.61959 36.48551,-6.53898 36.26119,-6.3427 36.02987,-5.5541 35.9107,-4.94425 36.50303))",
 "OccurrenceStatus is Present"
 ]
}

 The dataset includes 3460978 records from 465 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015340-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17197859
title: Experimental Results Set Discovery Problem ICTAI 2025

description: Files in csv format reporting the results of SDP (qualitative and hybrid) on B machines (just_sdp/), and some others reporting the times to validation for atelierB forces 0 and 1, cvc5 and z3 after pptransSMT, iProver and Vampire after pptransTPTP, and SDP (unsat_csvs/).
DOI: 10.5281/zenodo.17197860
title: Experimental Results Set Discovery Problem ICTAI 2025

description: Files in csv format reporting the results of SDP (qualitative and hybrid) on B machines (just_sdp/), and some others reporting the times to validation for atelierB forces 0 and 1, cvc5 and z3 after pptransSMT, iProver and Vampire after pptransTPTP, and SDP (unsat_csvs/).
DOI: 10.26050/wdcc/qtptir
title: Near-surface air temperature dataset for the Qinghai-Tibet Plateau (2019) derived from thermal infrared remote sensing and elevation-constrained modeling

description: Project: Heat Island Intensity Prediction in an Intelligent Sponge Urban System in the Qinghai-Tibet Plateau - This project investigates how intelligent sponge city technologies can mitigate urban heat islands (UHI) in the Qinghai-Tibet Plateau. It aims to develop scalable models for climate-resilient urban planning in high-altitude regions through a series of targeted experiments.
Supported by grant LJ200080773 ('Intelligent Urban Rainwater Collection Module System Application'), this project develops a machine learning framework for urban heat island (UHI) prediction in sponge cities. The framework integrates thermal infrared remote sensing and meteorological data to jointly analyze UHI intensity and rainwater storage capacity, supporting ecological runoff management and heat mitigation strategies.
Summary: This dataset provides high-resolution (30 m) spatialized near-surface air temperature products for the Qinghai-Tibet Plateau, updated using thermal infrared remote sensing data from Landsat 8 (L8) and Landsat 9 (L9) Collection 2 (C2) Level 2 (L2) products, combined with elevation-corrected regression modeling. The dataset includes corrected temperature files (adjusted via machine learning-based elevation corrections) for model development. The elevation corrections were performed using Topographic Data of Qinghai-Tibet Plateau (2021), integrated via Gaussian filtering to enhance spatial consistency in high-elevation regions. Supervised learning regression models (Random Forest Regression, Multilayer Perceptron regression, or Decision Tree regression) were applied to minimize Thermal Infrared Radiation-derived temperature biases and optimize high-altitude temperature estimation. The near-surface temperature lapse rate (LR) is a critical parameter in glaciological and hydrological models, but existing approaches often rely on empirical estimations with limited spatial representativeness. To mitigate these limitations, an optimized temperature spatialization method is proposed, fusing Local Representatives (LRs) across glacierized regions through Inverse Distance Weighting (IDW). This approach accounts for elevation-dependent microclimates while maintaining regional consistency. This dataset is suitable for climate research, and environmental modeling requiring high-resolution near-surface air temperature data.
DOI: 10.6084/m9.figshare.26927191
title: Cueva de Chufín: restitución gráfica del panel decorado CHU.D.I

description: Archivos referentes a la reconstrucción tridimensional y la restitución gráfica de un panel decorado de la Cueva de Chufín (Cantabria).
DOI: 10.6084/m9.figshare.26927191.v2
title: Cueva de Chufín: restitución gráfica del panel decorado CHU.D.I

description: Archivos referentes a la reconstrucción tridimensional y la restitución gráfica de un panel decorado de la Cueva de Chufín (Cantabria).
DOI: 10.6084/m9.figshare.26927191.v3
title: Cueva de Chufín: restitución gráfica del panel decorado CHU.D.I

description: Archivos referentes a la reconstrucción tridimensional y la restitución gráfica de un panel decorado de la Cueva de Chufín (Cantabria).
DOI: 10.5281/zenodo.17199465
title: SImulation details for Frasson et al. (2025) "Geomagnetic dipole stability and zonal flows controlled by mantle heat flux heterogeneities"

description: Input and output parameters of the simulations presented in Frasson et al. (2025).

Parameters:



nu: Viscosity

Omega: Rotation rate

D: Width of the outer core

eta: Magnetic diffusivity

g_o: Acceleration of gravity at the top of the core

F_i: Buoyancy flux at the bottom of the core

kappa: Codensity diffusivity

rho_0: Reference outer core density

U: RMS flow amplitude

B: RMS magnetic field amplitude


List of inputs:



lmax: Maximum spherical harmonic degree

mmax: Maximum spherical harmonic order

Nr_OC: Number of nodal points in the radial direction within the outer core

Nr_IC: Number of nodal points in the radial direction within the inner core

dns: True for direct numerical simulation or False for hyperdiffusive treatment

gprof: Radial profile of the acceleration of gravity

bc_icb: Flow boundary condition at the ICB

bc_cmb: Flow boundary condition at the CMB

GC: Gravitational coupling

CC: Inner Core conductivity

rratio: Aspect ratio of the outer core

E: Ekman number E=nu/(Omega*D^2)

Pm: Magnetic Prandtl number Pm=nu/eta

Ra: Rayleigh number Ra=g_o*F_i*D^2/(4*pi*kappa*nu^2*rho_0)

Pr: Prandtl number Pr=nu/kappa

pattern: Heterogeneous heat flux pattern at the top of the core

dqo/qi: Peak-to-peak amplitude of the heat flux pattern normalized by the ICB heat flux


List of outputs:



duration (dip. decay time): Duration of the simulation in dipole decay time unit

f_dip: Amplitude of the magnetic dipole at the CMB (l=1) relative to the total magnetic field at the CMB

E_mag: Magnetic energy

E_kin: Kinetic energy

M*_Za: Modified energy ratio (see Frasson et al. 2025, https://doi.org/10.1093/gji/ggae457)

ell_u: Characteristic degree of the flow

ell_b: Characteristic degree of the magnetic field

Ro_l: Local Rossby number Ro_l = U*ell_u/(pi*Omega*D)

Rm: Magnetic Reynolds number

Elsasser: Elsasser number Elsasser=B^2/(rho_0*Omega*mu_0*eta)

Ro_ml: Local magnetic Rossby number (see Soderlund et al. 2025, https://doi.org/10.1093/mnras/staf1081)

f_ohm: Fraction of ohmic dissipation


Reference:

Frasson, T., Schaeffer, N., Nataf, H. C., & Labrosse, S. (2025). Geomagnetic dipole stability and zonal flows controlled by mantle heat flux heterogeneities. Geophysical Journal International, 240(3), 1481-1504.
DOI: 10.5281/zenodo.17199466
title: SImulation details for Frasson et al. (2025) "Geomagnetic dipole stability and zonal flows controlled by mantle heat flux heterogeneities"

description: Input and output parameters of the simulations presented in Frasson et al. (2025).

Parameters:



nu: Viscosity

Omega: Rotation rate

D: Width of the outer core

eta: Magnetic diffusivity

g_o: Acceleration of gravity at the top of the core

F_i: Buoyancy flux at the bottom of the core

kappa: Codensity diffusivity

rho_0: Reference outer core density

U: RMS flow amplitude

B: RMS magnetic field amplitude


List of inputs:



lmax: Maximum spherical harmonic degree

mmax: Maximum spherical harmonic order

Nr_OC: Number of nodal points in the radial direction within the outer core

Nr_IC: Number of nodal points in the radial direction within the inner core

dns: True for direct numerical simulation or False for hyperdiffusive treatment

gprof: Radial profile of the acceleration of gravity

bc_icb: Flow boundary condition at the ICB

bc_cmb: Flow boundary condition at the CMB

GC: Gravitational coupling

CC: Inner Core conductivity

rratio: Aspect ratio of the outer core

E: Ekman number E=nu/(Omega*D^2)

Pm: Magnetic Prandtl number Pm=nu/eta

Ra: Rayleigh number Ra=g_o*F_i*D^2/(4*pi*kappa*nu^2*rho_0)

Pr: Prandtl number Pr=nu/kappa

pattern: Heterogeneous heat flux pattern at the top of the core

dqo/qi: Peak-to-peak amplitude of the heat flux pattern normalized by the ICB heat flux


List of outputs:



duration (dip. decay time): Duration of the simulation in dipole decay time unit

f_dip: Amplitude of the magnetic dipole at the CMB (l=1) relative to the total magnetic field at the CMB

E_mag: Magnetic energy

E_kin: Kinetic energy

M*_Za: Modified energy ratio (see Frasson et al. 2025, https://doi.org/10.1093/gji/ggae457)

ell_u: Characteristic degree of the flow

ell_b: Characteristic degree of the magnetic field

Ro_l: Local Rossby number Ro_l = U*ell_u/(pi*Omega*D)

Rm: Magnetic Reynolds number

Elsasser: Elsasser number Elsasser=B^2/(rho_0*Omega*mu_0*eta)

Ro_ml: Local magnetic Rossby number (see Soderlund et al. 2025, https://doi.org/10.1093/mnras/staf1081)

f_ohm: Fraction of ohmic dissipation


Reference:

Frasson, T., Schaeffer, N., Nataf, H. C., & Labrosse, S. (2025). Geomagnetic dipole stability and zonal flows controlled by mantle heat flux heterogeneities. Geophysical Journal International, 240(3), 1481-1504.
DOI: 10.15468/dl.bpm2e4
title: Occurrence Download

description: A dataset containing 253297 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Yuhina brunneiceps",
 "Geometry POLYGON((119.300000 20.500000, 122.100000 20.500000, 122.100000 25.400000, 119.300000 25.400000, 119.300000 20.500000))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 253297 records from 22 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015309-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.7864hb
title: Occurrence Download

description: A dataset containing 200451 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Euphydryas aurinia)",
 "BasisOfRecord is one of (Specimen, Literature Occurrence, Human Observation, Observation)",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1991",
 "Year is less than or equal to 2025"
 ]
}

 The dataset includes 200451 records from 964 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015388-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.kx39sn
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Rhus pulvinata",
 "Country is Belgium",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015414-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.4eubyx
title: Occurrence Download

description: A dataset containing 3192 species occurrences available in GBIF matching the query:
{
 "and" : [
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "OccurrenceStatus is Present",
 "VerbatimScientificName is philander opossum",
 "Year 2000-2025"
 ]
}

 The dataset includes 3192 records from 45 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015416-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.14473/csda/pya9w8
title: Panel Naše společnost 2025 - únor

description: Výzkum provedl výzkumný tým CVVM Sociologického ústavu AV ČR, v.v.i., v rámci výzkumu „Naše společnost 2022". Sběr dat provedla tazatelská síť CVVM.
description: Naše společnost
description: Od roku 2025 jsou data CVVM založená na sběru dat prostřednictvím pravděpodobnostního online panelu. Proto se název výzkumné série mění na Panel Naše společnost. Metodologie dřívějších a stávajících výzkumů CVVM jsou natolik odlišné, že přímé časové srovnání aktuálních výsledků založených na výzkumech realizovaných prostřednictvím Panelu Naše společnost s předchozími výzkumy není možné. Zjištěné rozdíly totiž mohou být dány jak proměnou či posunem v postojích a názorech veřejnosti, tak právě změnou metodologie výzkumů a oba vlivy od sebe není možné rozlišit.
 https://cvvm.soc.cas.cz/images/articles/files/5937/tzzmena-metodologiecvvmfinal.pdf
DOI: 10.15468/dl.gajucg
title: Occurrence Download

description: A dataset containing 25 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Skimmia japonica",
 "Country is Belgium",
 "HasCoordinate is true"
 ]
}

 The dataset includes 25 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015418-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.jdz9r9
title: Occurrence Download

description: A dataset containing 38530 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Zosterops japonicus",
 "Geometry POLYGON((119.300000 20.500000, 122.100000 20.500000, 122.100000 25.400000, 119.300000 25.400000, 119.300000 20.500000))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset includes 38530 records from 19 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015417-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.nzsfqa
title: Occurrence Download

description: A dataset containing 87 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Skimmia japonica",
 "Country is Netherlands",
 "HasCoordinate is true"
 ]
}

 The dataset includes 87 records from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0015419-250920141307145/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.14831992
title: Scenario matrix: 100 combinations of wind and solar PV in a low-emissions sector-coupled European energy system

description: Scenario matrix built with the open sector-coupled energy model PyPSA-Eur

The data published in this repository explores the technology mix at 100 different combinations of wind and solar PV penetration levels, ranging from low (10%) to high (100%). It includes the technology mix in the electricity and heating supply, as well as the technologies used for producing e-fuels. 

The data is intended to be used in low resolution models that need to parameterize integration impacts of high renewable shares. For this reason, the data also contain extreme cases for the European energy mix which serves as a measure of the penalty (i.e., total system cost increase) associated with the distance to the near-optimal space. 

The method and results are explained in the paper "First steps towards bridging integrated assessment modeling and high-resolution energy system models: a scenario matrix for a low-emissions sector-coupled European energy system" (https://10.1088/2515-7620/adf60d).

 




Variable
Unit 


Share
% of supply


Capacity
GW


Curtailment
% of annual electricity demand


Market value
Eur / MWh


Storage price smoothing (SPS)
Eur / MW


Transmission price smoothing (TPS)
Eur / MW


System cost
Billion Eur


Energy prices
Eur / MWh


Co2 price (dual of CO2 limit)
Eur / tCO2


Co2 sequestration price (dual of CO2 sequestration limit)
Eur / tCO2


Wind deployment price (dual of wind deployment constraint)
Eur / MWh


Solar PV deployment price (dual of solar PV deployment constraint)
Eur / MWh




 

Indices:

Index level 1: Solar PV deployment level (0.1 - 1.0), as a fraction of the total electricity demand of the transitioned energy system. 

Index level 2: Wind deployment level (0.1 - 1.0), as a fraction of the total electricity demand of the transitioned energy system. 

 

Notes

In this version (Version v2), the following corrections have been made:



Certain heating generation technologies had missing values by mistake

Wrong values were listed for shares of electricity generation of variable renewable energy (VRE) sources.  


In addition to this, the jupyter notebook “scenario_matrix.ipynb” has been added, which includes all calculations behind all metrics listed in the scenario matrices located in “main/” and “sensitivity/transmission_limit/”. Similarly, the jupyter notebook “sensitivity.ipynb” has been added, which includes all calculations for the metrics located in “sensitivity/co2_limit/”.

For further exploration of the PyPSA-Eur scenarios, find all network files at this open repository: https://anon.erda.au.dk/cgi-sid/ls.py?share_id=ESLUn4EMQn&current_dir=.&flags=f