161 Works

NFFA-EUROPE - 100% SEM Dataset

Rossella Aversa, Mohammad Hadi Modarres, Stefano Cozzini & Regina Ciancio
Dataset of 25,430 SEM images produced at CNR-IOM (Trieste, Italy). Images are classified into 10 categories in a folder structure. Classification labels have been checked by a group of nanoscientists on the web site http://sem-classifier.nffa.eu and only those images which have been validated by the 100% of the group have been included in the dataset. SEM images included in this dataset are property of CNR-IOM. Their use is only restricted to the purpose of the...

NFFA-EUROPE - Majority SEM Dataset

Rossella Aversa, Mohammad Hadi Modarres, Stefano Cozzini & Regina Ciancio
Dataset of 25,537 SEM images produced at CNR-IOM (Trieste, Italy). Images are classified into 10 categories in a folder structure. Classification labels have been checked by a group of nanoscientists on the web site http://sem-classifier.nffa.eu and only those images which have been validated by the absolute majority of the group have been included in the dataset. SEM images included in this dataset are property of CNR-IOM. Their use is only restricted to the purpose of...

NFFA-EUROPE - Hierarchical SEM Dataset

Rossella Aversa, Mohammad Hadi Modarres, Stefano Cozzini & Regina Ciancio
Dataset of 1,038 SEM images produced at CNR-IOM (Trieste, Italy). Images are classified into 10 categories and 27 subcategories (as sub-trees) in a folder-subfolder structure. Results obtained from this dataset have been published in Modarres et al., Scientific Reports volume 7, Article number: 13282 (2017), doi:10.1038/s41598-017-13565-z SEM images included in this dataset are property of CNR-IOM. Their use is only restricted to the purpose of the current study which relates to machine learning and image...

NFFA-EUROPE - SEM Dataset

Rossella Aversa, Mohammad Hadi Modarres, Stefano Cozzini & Regina Ciancio
Dataset of 18,577 SEM images produced at CNR-IOM (Trieste, Italy). Images are classified into 10 categories in a folder structure, which have been used for convolutional neural network training. Results obtained from this dataset have been published in Modarres et al., Scientific Reports volume 7, Article number: 13282 (2017), doi:10.1038/s41598-017-13565-z SEM images included in this dataset are property of CNR-IOM. Their use is only restricted to the purpose of the current study which relates to...

1D profile of a cosmic volume from z=0 to z=1

Franco Vazza
1-dimensional profile of simulated fields for long beams through a simulated volume from z=0 to z=1. Each dataset is a column formatted file containing: a) x-position [kpc] b) gas density [g/cm^3] c) gas temperature [K] d) magnetic field, absolute value [G] all quantities are in physical units. The data are derived from a 200 Mpc simulation with ENZO-MHD, with a root grid of 2400^3 cells. 1000 indipendent line of sights have been generated from this...

Genomic and Phenomic Screens for Flower Related RING Type Ubiquitin E3 Ligases in Arabidopsis: Supplementary Material

Mirko Pavicic
Flowering time control integrates endogenous as well as environmental signals to promote flower development. The pathways and molecular networks involved are complex and integrate many modes of signal transduction. In plants ubiquitin mediated protein degradation pathway has been proposed to be as important mode of signaling as phosphorylation and transcription. To systematically study the role of ubiquitin signaling in the molecular regulation of flowering we have taken a genomic approach to identify flower related Ubiquitin...

EUDAT Fair Data Pilot Final Report

Mark Thompson Christine Staiger
The end report of the EUDAT FAIR Data Pilot, a collaboration between the Dutch Tech Centre for Lifesciences and the EUDAT2020 project

Movies from the talk "Hybrid simulations of plasma turbulence 
in support of space missions: toward an archive for numerical results with EUDAT services"

Luca Franci
This is a small collection of movies from 2D and 3D high-resolution hybrid particle-in-cell simulations of plasma turbulence performed with the hybrid code CAMELIA. The evolution of the magnetic field is shown in a large 3D grid (3D_B2_Wholegrid.avi), in a large 2D grid (2D_B2_WholeGrid.avi) and in two smaller 2D subgrids (2D_B2_SubGrid1.avi and 2D_B2_SubGrid2.avi) These movie were shown by L. Franci during his talk "Hybrid simulations of plasma turbulence in support of space missions: toward an...

E5a, AMR8 at z=0.4, reconstructed down to the 6th AMR level (16kpc/cell)

Franco Vazza
Each HDF5 dataset contains triplets of fields: -6_8mdm001R_dt_010 contains "Density", "Dark_Matter_Density" and "Temperature" (a 640^3 grid for each); -6_8mdm001R_v_010 contains "x-velocity", "y-velocity" and "z-velocity" gas velocity fields (a 640^3 grid for each); -6_8mdm001R_b_010 contains "Bx", "By" and "Bz" - the components of the magnetic field (a 640^3 grid for each). All values must be *multiplied* by the following conversion factors in order to get them in CGS units: -convd=6.8952272e-30 to have Density and Dark_Matter_Density in...

E5a, AMR8 at z=0.2, reconstructed down to the 6th AMR level (16kpc/cell)

Franco Vazza
Each HDF5 dataset contains triplets of fields: -6_8mdm001R_dt_012 contains "Density", "Dark_Matter_Density" and "Temperature" (a 640^3 grid for each); -6_8mdm001R_v_012 contains "x-velocity", "y-velocity" and "z-velocity" gas velocity fields (a 640^3 grid for each); -6_8mdm001R_b_012 contains "Bx", "By" and "Bz" - the components of the magnetic field (a 640^3 grid for each). All values must be *multiplied* by the following conversion factors in order to get them in CGS units: -convd=4.342187e-30 to have Density and Dark_Matter_Density in...

E5a, AMR8 at z=0.02, reconstructed down to the 6th AMR level (16kpc/cell)

Franco Vazza
Each HDF5 dataset contains triplets of fields: -6_8mdm001R_dt_014 contains "Density", "Dark_Matter_Density" and "Temperature" (a 640^3 grid for each); -6_8mdm001R_v_014 contains "x-velocity", "y-velocity" and "z-velocity" gas velocity fields (a 640^3 grid for each); -6_8mdm001R_b_014 contains "Bx", "By" and "Bz" - the components of the magnetic field (a 640^3 grid for each). All values must be *multiplied* by the following conversion factors in order to get them in CGS units: -convd=2.6666447e-30 to have Density and Dark_Matter_Density in...

E5a, AMR8, recostructed down to the 6th AMR level (16kpc/cell), z=0.05

Franco Vazza
Each HDF5 dataset contains triplets of fields: -6_8mdm001R_dt_014 contains "Density", "Dark_Matter_Density" and "Temperature" (a 640^3 grid for each); -6_8mdm001R_v_014 contains "x-velocity", "y-velocity" and "z-velocity" gas velocity fields (a 640^3 grid for each); -6_8mdm001R_b_014 contains "Bx", "By" and "Bz" - the components of the magnetic field (a 640^3 grid for each). All values must be *multiplied* by the following conversion factors in order to get them in CGS units: -convd=2.9089278e-30 to have Density and Dark_Matter_Density in...

West-Life Repository test data

[Unknown]
Sample NMR data for West-Life Repository instance

testb2tropconnect2901

[Unknown]
nikosev

testRecord2901

[Unknown]
nikosev

testRecord2901

[Unknown]
nikosev

fish

[Unknown]

Blockchains and Data

Wolfgang Kuchinke Peter Wittenburg
Blockchain Technology (BCT) is broadly debated and recommended for many purposes. This paper includes views about the usefulness of BCT for scientific data. It discusses basic principles of BCT and compares them with the requirements for scientific data management and reuse. IWe look in detail at various data challenges in the health sector.The conclusion is that BCT does not address the major problems of data management and reuse, but that it could be integrated very...

HTTP API test_01

Giovanni Morelli
Test for verify HTTP API public file access

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Data Centers

  • CSC - IT Center for Science
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