cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.15468/dl.tta8d8
title: Occurrence Download

description: A dataset containing 144 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is one of (Psathyrella murcida (Fr.) Kits van Wav., Psathyrella orbitarum (Romagn.) M.M.Moser, Psathyrella crenata (Lasch) Gillet)",
 "Geometry POLYGON((-9.36035 44.71551,-10.59082 42.81152,-11.82129 36.80928,-7.69043 36.17336,-3.38379 35.88905,3.12012 38.13456,9.18457 38.20366,11.7334 37.16032,12.78809 34.74161,17.09473 33.79741, 23.15918 34.23451,30.10254 32.62087,33.2666 32.99024,35.72754 35.03,33.00293 35.53223,28.69629 35.17381,28.34473 36.31513,25.7959 37.50973,24.91699 39.90974,28.78418 40.6473,30.71777 41.17865,36.87012 45.213,38.18848 46.98025,38.71582 47.7541,39.94629 47.93107,40.20996 49.61071,37.0459 50.34546,35.72754 50.40152,33.79395 52.26816,31.68457 51.94426,31.4209 53.27835,32.56348 53.43572,30.98145 54.77535,30.89355 55.52863,28.43262 56.02295,27.20215 57.65716,27.37793 58.30949,27.90527 59.17593,29.66309 59.88894,27.6416 60.58697,31.50879 62.75473,28.78418 69.83962,30.89355 70.28912,29.22363 71.55274,21.75293 71.30079,14.28223 70.14036,11.38184 67.47492,4.87793 63.66576,-5.53711 71.52491,-10.63477 71.69129,-21.5332 67.74276,-28.56445 65.36684,-24.3457 61.35461,-9.36035 44.71551))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false",
 "Year is greater than or equal to 1980",
 "Year is less than or equal to 2021"
 ]
}

 The dataset includes 144 records from 27 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0325471-210914110416597/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.mwz6tk
title: Occurrence Download

description: A dataset containing 64202 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Coenagrion mercuriale (Charpentier, 1840)"
 ]
}

 The dataset includes 64202 records from 741 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0000657-230918134249559/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.turug2
title: Occurrence Download

description: A dataset containing 2328551 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Geometry POLYGON ((-82.8637 27.66208, -82.5786 27.66208, -82.5786 28.12834, -82.8637 28.12834, -82.8637 27.66208))",
 "Year is greater than or equal to 2013",
 "HasGeospatialIssue is false",
 "TaxonKey is Aves",
 "HasCoordinate is true",
 "OccurrenceStatus is Present",
 {
 "not" : {
 "BasisOfRecord" : [
 "is one of (Fossil, Living Specimen, Specimen)"
 ]
 }
 }
 ]
}

 The dataset includes 2328551 records from 7 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001207-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.ee5wvv
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Norops wampuensis Mccranie & Köhler, 2001"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001217-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.s85pg8
title: Occurrence Download

description: A dataset listing the 26 species recorded in GBIF matching the query:
{
 "and" : [
 "Country is New Zealand",
 "Geometry POLYGON((173.19914 -41.3035,173.2033 -41.3035,173.2033 -41.30011,173.19914 -41.30011,173.19914 -41.3035))",
 "HasCoordinate is true",
 "HasGeospatialIssue is false"
 ]
}

 The dataset's 26 records were derived from 3 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001214-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.f4a8rp
title: Occurrence Download

description: A dataset containing 29450 species occurrences available in GBIF matching the query:
{
 "TaxonKey" : [
 "is Dalechampia Plum. ex L."
 ]
}

 The dataset includes 29450 records from 484 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0001219-241024112534372/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.2dx2tx
title: Occurrence Download

description: A dataset containing 194 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Puccinia malvacearum",
 "Country is Austria",
 "HasCoordinate is true"
 ]
}

 The dataset includes 194 records from 13 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006570-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.9pprpg
title: Occurrence Download

description: A dataset containing 68 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Puccinia malvacearum",
 "Country is Poland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 68 records from 8 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006571-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.x8rm3u
title: Occurrence Download

description: A dataset containing 1 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Puccinia malvacearum",
 "Country is Slovenia",
 "HasCoordinate is true"
 ]
}

 The dataset includes 1 records from 1 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006572-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.u3evm2
title: Occurrence Download

description: A dataset containing 6924 species occurrences available in GBIF matching the query:
{
 "and" : [
 "Country is France",
 "OccurrenceStatus is Present",
 "PublishingOrg is 1928bdf0-f5d2-11dc-8c12-b8a03c50a862",
 "TaxonKey is Acrocephalus paludicola",
 "Year 2000-2025"
 ]
}

 The dataset includes 6924 records from 66 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006573-250914085247600/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.30084322.v2
title: UntitleHd Item

description: Microscope images generated during the research project
DOI: 10.34740/kaggle/dsv/13076814
title: Global Genomic Neurofibromatosis Neoplasia
DOI: 10.5281/zenodo.13486640
title: Dataset for "Methodology of Evaluating the Activation Energy of Oxygen Reduction Reaction on Pt-based Electrodes"

description: High temperature proton-exchange membrane fuel cell (HT-PEMFC) technology is widely studied alternative to current energy conversion technologies based on fossil fuels. Compared to solid oxide fuel cells (SOFCs), HT-PEMFCs allow more flexibility and demand less operation control due to their lower temperature. On the other hand, HT-PEMFCs show an advantage over low-temperature PEMFCs in terms of less demand on the purity of the H2 used, the possibility to recover the generated heat, lower water management requirements, and easy heat management. One of the critical limitations of HT-PEMFC operation is a slow kinetics of the cathodic reduction of O2 (ORR) due to presence of H3PO4 which ensures proton conductivity in the system. Electrochemical dynamic methods such as cyclic voltammetry or linear sweep voltammetry (LSV) can be used to determine the kinetic parameters of ORR. These measurements can provide information on the Tafel slope and exchange current density (jex) of the ORR. However, performing these measurements under conditions relevant for HT-PEMFC operation is challenging due to presence highly concentrated H3PO4 and elevated temperature. First, determination of the kinetic parameters requires correct assessment of equilibrium potential of ORR (EORR). The value of the EORR is generally influenced by the activity (fugacity) of the reactants and products and the temperature, a discussion of the appropriate standard states of the components is also necessary. Second, the relationship between the jex and the reaction rate constant (k°), necessary for calculation of activation energy ( ), must be known. It includes consideration of the likely reaction mechanism. In this paper, the methodology for appropriate determination of  was developed and used for estimation of  of ORR from LSV curves measured on commercially available Pt/C catalyst under HT-PEMFC relevant conditions. In particular, the measurements were carried out using a rotating glassy carbon rod disk electrode (RRE) in purified 98 wt.% H3PO4 (as electrolyte) at temperatures of 120, 140, 160, 180 °C. Though the treatment was developed in context of ORR and HT-PEMFC, the approach is generally applicable to any electrochemical reaction.
DOI: 10.5281/zenodo.17131804
title: Dataset for "Methodology of Evaluating the Activation Energy of Oxygen Reduction Reaction on Pt-based Electrodes"

description: High temperature proton-exchange membrane fuel cell (HT-PEMFC) technology is widely studied alternative to current energy conversion technologies based on fossil fuels. Compared to solid oxide fuel cells (SOFCs), HT-PEMFCs allow more flexibility and demand less operation control due to their lower temperature. On the other hand, HT-PEMFCs show an advantage over low-temperature PEMFCs in terms of less demand on the purity of the H2 used, the possibility to recover the generated heat, lower water management requirements, and easy heat management. One of the critical limitations of HT-PEMFC operation is a slow kinetics of the cathodic reduction of O2 (ORR) due to presence of H3PO4 which ensures proton conductivity in the system. Electrochemical dynamic methods such as cyclic voltammetry or linear sweep voltammetry (LSV) can be used to determine the kinetic parameters of ORR. These measurements can provide information on the Tafel slope and exchange current density (jex) of the ORR. However, performing these measurements under conditions relevant for HT-PEMFC operation is challenging due to presence highly concentrated H3PO4 and elevated temperature. First, determination of the kinetic parameters requires correct assessment of equilibrium potential of ORR (EORR). The value of the EORR is generally influenced by the activity (fugacity) of the reactants and products and the temperature, a discussion of the appropriate standard states of the components is also necessary. Second, the relationship between the jex and the reaction rate constant (k°), necessary for calculation of activation energy ( ), must be known. It includes consideration of the likely reaction mechanism. In this paper, the methodology for appropriate determination of  was developed and used for estimation of  of ORR from LSV curves measured on commercially available Pt/C catalyst under HT-PEMFC relevant conditions. In particular, the measurements were carried out using a rotating glassy carbon rod disk electrode (RRE) in purified 98 wt.% H3PO4 (as electrolyte) at temperatures of 120, 140, 160, 180 °C. Though the treatment was developed in context of ORR and HT-PEMFC, the approach is generally applicable to any electrochemical reaction.
DOI: 10.5281/zenodo.17131436
title: Canonical Text Service Epigraphic Database Heidelberg

description: Information

Database as .sql and sourcecode as PHP. Sourcecode state at publication date. 

For current sourcecode see https://bitbucket.org/jtiepmar/workspace/projects/CTS.

Online as part of Canonical Text Service Infrastructure at https://urncts.eu/

Online version and current sourcecode may differ from this state of upload.

For TEI/XML documents see original editions at https://edh.ub.uni-heidelberg.de/.

 

Installation

Import *.sql into your SQL database

Configure DB information in config.php

Publish PHP sourcecode on your server
DOI: 10.15468/dl.xh232c
title: Occurrence Download

description: A dataset listing the 4070778 species recorded in GBIF matching the query:
{ }

 The dataset's 4070778 records were derived from 76489 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006545-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.5281/zenodo.17104980
title: Dataset for the paper "Steady-State Transonic Flowfield Prediction via Deep-Learning Framework"

description: This repository provides the  Computational Fluid Dynamics (CFD) reference datasets used in:G. Immordino, A. Da Ronch, M. Righi. "Steady-State Transonic Flowfield Prediction via Deep-Learning Framework." AIAA Journal, 62(5), 1915–1931, 2024.https://doi.org/10.2514/1.J063545

The datasets contain steady Reynolds-averaged Navier–Stokes (RANS) simulations of transonic aerodynamic flows used for training and testing a fully connected neural network (FCNN) surrogate model. Three benchmark configurations of increasing geometric and physical complexity are included:





Benchmark Supercritical Wing (BSCW)





Semi-span wing with rectangular planform and supercritical airfoil.




Grid: structured type with130k surface points.




Outputs: surface pressure coefficient (CP), skin friction coefficient components (CFx,CFy,CFz).






ONERA M6 Wing





Swept-wing configuration with λ-shaped shock waves.




Grid: structured type with149k surface points.


Outputs: surface pressure coefficient (CP), skin friction coefficient components (CFx,CFy,CFz).





NASA Common Research Model (CRM)





Full wing–body geometry representative of a wide-body aircraft.




Grid: unstructured type with 80k surface points.


Outputs: surface pressure coefficient (CP), skin friction coefficient components (CFx,CFy,CFz).




Generation Details





Solver: SU2 v7.2.1, RANS formulation with Spalart–Allmaras turbulence model.




Convergence criterion: Cauchy residual method on CL (tolerance 10−7).




Discretisation: JST central scheme with artificial dissipation; gradients via Green–Gauss method.




Sampling: 70 design points generated via Latin Hypercube Sampling (LHS).




Flight envelope: Mach number = [0.70 , 0.84] , angle of attack AoA = [0 , 5] deg.



PurposeThese datasets were used to train, validate, and benchmark deep-learning model for predicting steady transonic flowfields. Applications include aerodynamic loads estimation, aeroelastic simulations, and uncertainty quantification.

Contents





CFD snapshots of surface pressure and skin-friction fields at each Mach-AoA condition for three different test cases.



KeywordsTransonic aerodynamics; reduced-order modelling; deep learning; CFD; Benchmark Supercritical Wing; ONERA M6; NASA CRM; machine learning; flowfield prediction.
DOI: 10.5281/zenodo.17104981
title: Dataset for the paper "Steady-State Transonic Flowfield Prediction via Deep-Learning Framework"

description: This repository provides the  Computational Fluid Dynamics (CFD) reference datasets used in:G. Immordino, A. Da Ronch, M. Righi. "Steady-State Transonic Flowfield Prediction via Deep-Learning Framework." AIAA Journal, 62(5), 1915–1931, 2024.https://doi.org/10.2514/1.J063545

The datasets contain steady Reynolds-averaged Navier–Stokes (RANS) simulations of transonic aerodynamic flows used for training and testing a fully connected neural network (FCNN) surrogate model. Three benchmark configurations of increasing geometric and physical complexity are included:





Benchmark Supercritical Wing (BSCW)





Semi-span wing with rectangular planform and supercritical airfoil.




Grid: structured type with130k surface points.




Outputs: surface pressure coefficient (CP), skin friction coefficient components (CFx,CFy,CFz).






ONERA M6 Wing





Swept-wing configuration with λ-shaped shock waves.




Grid: structured type with149k surface points.


Outputs: surface pressure coefficient (CP), skin friction coefficient components (CFx,CFy,CFz).





NASA Common Research Model (CRM)





Full wing–body geometry representative of a wide-body aircraft.




Grid: unstructured type with 80k surface points.


Outputs: surface pressure coefficient (CP), skin friction coefficient components (CFx,CFy,CFz).




Generation Details





Solver: SU2 v7.2.1, RANS formulation with Spalart–Allmaras turbulence model.




Convergence criterion: Cauchy residual method on CL (tolerance 10−7).




Discretisation: JST central scheme with artificial dissipation; gradients via Green–Gauss method.




Sampling: 70 design points generated via Latin Hypercube Sampling (LHS).




Flight envelope: Mach number = [0.70 , 0.84] , angle of attack AoA = [0 , 5] deg.



PurposeThese datasets were used to train, validate, and benchmark deep-learning model for predicting steady transonic flowfields. Applications include aerodynamic loads estimation, aeroelastic simulations, and uncertainty quantification.

Contents





CFD snapshots of surface pressure and skin-friction fields at each Mach-AoA condition for three different test cases.



KeywordsTransonic aerodynamics; reduced-order modelling; deep learning; CFD; Benchmark Supercritical Wing; ONERA M6; NASA CRM; machine learning; flowfield prediction.
DOI: 10.15468/dl.h3qecp
title: Occurrence Download

description: A dataset containing 22270 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Hermetia illucens",
 "Year 2013-2025"
 ]
}

 The dataset includes 22270 records from 154 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006574-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.3b228u
title: Occurrence Download

description: A dataset containing 2 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Puccinia pelargonii-zonalis",
 "Country is Austria",
 "HasCoordinate is true"
 ]
}

 The dataset includes 2 records from 2 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006575-250914085247600/datasets/export for details.
 
Data from some individual datasets included in this download may be licensed under less restrictive terms.
DOI: 10.15468/dl.pgfn72
title: Occurrence Download

description: A dataset containing 0 species occurrences available in GBIF matching the query:
{
 "and" : [
 "TaxonKey is Puccinia pelargonii-zonalis",
 "Country is Poland",
 "HasCoordinate is true"
 ]
}

 The dataset includes 0 records from 0 constituent datasets; see https://api.gbif.org/v1/occurrence/download/0006576-250914085247600/datasets/export for details.
DOI: 10.5281/zenodo.17105264
title: Dataset for the paper "Predicting Transonic Flowfields in Non–Homogeneous Unstructured Grids Using Autoencoder Graph Convolutional Networks"

description: This repository provides the Computational Fluid Dynamics (CFD) reference datasets used in:G. Immordino, A. Vaiuso, A. Da Ronch, M. Righi."Predicting transonic flowfields in non–homogeneous unstructured grids using autoencoder graph convolutional networks."Journal of Computational Physics, Vol. 524, 113708, 2025.https://doi.org/10.1016/j.jcp.2024.113708

The datasets contain steady Reynolds-averaged Navier–Stokes (RANS) simulations of transonic aerodynamic flows on unstructured grids, used for training and testing a Gradient-Based Autoencoder Graph Convolutional Network surrogate model. Two benchmark configurations of increasing geometric and physical complexity are included:





Benchmark Supercritical Wing (BSCW)





Semi-span wing with rectangular planform and supercritical airfoil.




Grid: unstructured, ~86.8k surface points.




Outputs: surface pressure coefficient (CP), skin friction coefficient components (CFx,CFy,CFz).






NASA Common Research Model (CRM)





Full wing–body geometry representative of a wide-body transport aircraft.




Grid: unstructured, ~78.8k surface points.




Outputs: surface pressure coefficient (CP), skin friction coefficient components (CFx,CFy,CFz).





Generation Details





Solver: SU2 v7.2.1, RANS formulation with Spalart–Allmaras turbulence model.




Convergence criterion: Cauchy residual method on CL (tolerance 10−7).




Discretisation: JST central scheme with artificial dissipation; gradients via Green–Gauss method.




Sampling: 70 design points generated via Latin Hypercube Sampling (LHS).




Flight envelope: Mach number = [0.70 , 0.84] , angle of attack AoA = [0 , 5] deg.



PurposeThese datasets were used to train, validate, and benchmark autoencoder graph convolutional networks for predicting steady transonic flowfields on non-homogeneous unstructured grids. Applications include aerodynamic load estimation, surrogate modelling, and reduced-order modelling for uncertainty quantification.

Contents





CFD snapshots of surface pressure and skin-friction fields at each Mach–AoA condition for the BSCW and CRM test cases.




Mesh connectivity and node coordinate files for graph representation.



KeywordsTransonic aerodynamics; unstructured grids; geometric deep learning; graph neural networks; autoencoder; CFD; Benchmark Supercritical Wing; NASA CRM; reduced-order modelling; flowfield prediction.
DOI: 10.5281/zenodo.17105265
title: Dataset for the paper "Predicting Transonic Flowfields in Non–Homogeneous Unstructured Grids Using Autoencoder Graph Convolutional Networks"

description: This repository provides the Computational Fluid Dynamics (CFD) reference datasets used in:G. Immordino, A. Vaiuso, A. Da Ronch, M. Righi."Predicting transonic flowfields in non–homogeneous unstructured grids using autoencoder graph convolutional networks."Journal of Computational Physics, Vol. 524, 113708, 2025.https://doi.org/10.1016/j.jcp.2024.113708

The datasets contain steady Reynolds-averaged Navier–Stokes (RANS) simulations of transonic aerodynamic flows on unstructured grids, used for training and testing a Gradient-Based Autoencoder Graph Convolutional Network surrogate model. Two benchmark configurations of increasing geometric and physical complexity are included:





Benchmark Supercritical Wing (BSCW)





Semi-span wing with rectangular planform and supercritical airfoil.




Grid: unstructured, ~86.8k surface points.




Outputs: surface pressure coefficient (CP), skin friction coefficient components (CFx,CFy,CFz).






NASA Common Research Model (CRM)





Full wing–body geometry representative of a wide-body transport aircraft.




Grid: unstructured, ~78.8k surface points.




Outputs: surface pressure coefficient (CP), skin friction coefficient components (CFx,CFy,CFz).





Generation Details





Solver: SU2 v7.2.1, RANS formulation with Spalart–Allmaras turbulence model.




Convergence criterion: Cauchy residual method on CL (tolerance 10−7).




Discretisation: JST central scheme with artificial dissipation; gradients via Green–Gauss method.




Sampling: 70 design points generated via Latin Hypercube Sampling (LHS).




Flight envelope: Mach number = [0.70 , 0.84] , angle of attack AoA = [0 , 5] deg.



PurposeThese datasets were used to train, validate, and benchmark autoencoder graph convolutional networks for predicting steady transonic flowfields on non-homogeneous unstructured grids. Applications include aerodynamic load estimation, surrogate modelling, and reduced-order modelling for uncertainty quantification.

Contents





CFD snapshots of surface pressure and skin-friction fields at each Mach–AoA condition for the BSCW and CRM test cases.




Mesh connectivity and node coordinate files for graph representation.



KeywordsTransonic aerodynamics; unstructured grids; geometric deep learning; graph neural networks; autoencoder; CFD; Benchmark Supercritical Wing; NASA CRM; reduced-order modelling; flowfield prediction.