cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.5281/zenodo.7622158
title: Simulated shielding performance of 1024 five-layer permutations of four satellite and radiation shielding materials against trapped particles on GTO

description: The 5Layer.csv file contains ionizing dose results of simulating the shielding performance of all 1024 five-layer permutations of four satellite and radiation shielding materials against trapped particles on GTO.<br> The materials are the aluminium alloy 7075 (Al_7075), polyethylene (G4_POLYETHYLENE), tungsten (G4_W) and the circuit board composite material FR4 (FR4).<br> The particle spectra used for this simulation are provided in the files AE9500keV.mac and AP910MeV.mac.<br> The simulated geometry is provided in the file 5Layer.gdml. The particle spectra were generated with the AE9/AP9 models on SPENVIS, with electron energies starting at 500 keV and proton energies starting at 10 MeV.<br> The simulation was performed with GRAS / Geant4 with the FTFP_BERT physics model using 1.4e+11 electrons and 3.1e+09 protons directed against slabs of shielding materials of 1.5 g/cm2 total depth with each of the five layers being 0.3 g/cm2 in depth.<br> The ionizing dose is recorded in 0.5mm thick silicon plates behind the shielding plates.<br> <br> Column A: ID of the material combination<br> Column B: Name of the top layer material.<br> Column C: Name of the second layer material.<br> Column D: Name of the third layer material.<br> Column E: Name of the fourth layer material.<br> Column F: Name of the bottom layer material.<br> Column G: Ionizing dose due to electrons in units of kRad per month.<br> Column H: Absolute statistical error in the electron dose in kRad per month.<br> Column I: Ionizing dose due to protons in kRad per month.<br> Column J: Absolute statistical error in the proton dose in kRad per month. <br> Column K: Total ionizing dose from electrons and protons in kRad per month.<br> Column L: Absolute statistical error in the total ionizing dose in kRad per month. <br> Column M: Relative statistical error of the total ionizing dose in per cent.<br> Column N: Rank of the material combination sorted from lowest total ionizing dose to highest.<br> Column O: Rank of the material combination sorted from lowest electron dose to highest.<br> Column P: Rank of the material combination sorted from lowest proton dose to highest. All dose values are rounded according to their uncertainty.
DOI: 10.25573/data.17314730
title: Images used to train Amazonian fish classification model

description: Images used to train Amazonian fish classification model. Images are in directories labeled by genus. CSV file contains image name and genus ID.
DOI: 10.25573/data.17314730.v1
title: Images used to train Amazonian fish classification model

description: Images used to train Amazonian fish classification model. Images are in directories labeled by genus. CSV file contains image name and genus ID.
DOI: 10.25573/data.17314922
title: Images used to train Amazonian fish masking model

description: Images used to train Amazonian fish masking model.
DOI: 10.25573/data.17314922.v1
title: Images used to train Amazonian fish masking model

description: Images used to train Amazonian fish masking model.
DOI: 10.5281/zenodo.7621280
title: Simulated shielding performance of 1296 four-layer permutations of six common satellite and radiation shielding materials against trapped particles on GTO

description: The 4Layer.csv file contains ionizing dose results of simulating the shielding performance of all 1296 four-layer permutations of six common satellite and radiation shielding materials against trapped particles on GTO.<br> The materials are the aluminium alloy 7075 (Al_7075), polyethylene (G4_POLYETHYLENE), kevlar (G4_KEVLAR), tungsten (G4_W), stainless steel (G4_STAINLESS_STEEL) and the circuit board composite material FR4 (FR4).<br> The particle spectra used for this simulation are provided in the files AE9500keV.mac and AP910MeV.mac.<br> The simulated geometry is provided in the file 4Layer.gdml. The particle spectra were generated with the AE9/AP9 models on SPENVIS, with electron energies starting at 500 keV and proton energies starting at 10 MeV.<br> The simulation was performed with GRAS / Geant4 with the FTFP_BERT physics model using 6.6e+10 electrons and 3.8e+09 protons directed against slabs of shielding materials of 1.5 g/cm2 total depth with each of the four layers being 0.375 g/cm2 in depth.<br> The ionizing dose is recorded in 0.5mm thick silicon plates behind the shielding plates.<br> <br> Column A: ID of the material combination<br> Column B: Name of the top layer material.<br> Column C: Name of the second layer material.<br> Column D: Name of the third layer material.<br> Column E: Name of the bottom layer material.<br> Column F: Ionizing dose due to electrons in units of kRad per month.<br> Column G: Absolute statistical error in the electron dose in kRad per month.<br> Column H: Ionizing dose due to protons in kRad per month.<br> Column I: Absolute statistical error in the proton dose in kRad per month. <br> Column J: Total ionizing dose from electrons and protons in kRad per month.<br> Column K: Absolute statistical error in the total ionizing dose in kRad per month. <br> Column L: Relative statistical error of the total ionizing dose in per cent.<br> Column M: Rank of the material combination sorted from lowest total ionizing dose to highest.<br> Column N: Rank of the material combination sorted from lowest electron dose to highest.<br> Column O: Rank of the material combination sorted from lowest proton dose to highest. All dose values are rounded according to their uncertainty.
DOI: 10.5281/zenodo.7620702
title: Simulated shielding performance of 1728 three-layer permutations of 12 common satellite and radiation shielding materials against trapped particles on GTO

description: The 3Layer.csv file contains ionizing dose results of simulating the shielding performance of all 1728 three-layer permutations of twelve common satellite and radiation shielding materials against trapped particles on GTO.<br> The materials are pure aluminium (G4_Al), the aluminium alloy 7075 (Al_7075), polyethylene (G4_POLYETHYLENE), kevlar (G4_KEVLAR), lead (G4_Pb), tungsten (G4_W), stainless steel (G4_STAINLESS_STEEL), pure carbon fibre (CarbonFibre), the circuit board composite material FR4 (FR4), tantalum (G4_Ta), Teflon (G4_TEFLON) and the titanium alloy 6Al-4V (Ti_6AL_4V).<br> The particle spectra used for this simulation are provided in the files AE9500keV.mac and AP910MeV.mac.<br> The simulated geometry is provided in the file 3Layer.gdml. The particle spectra were generated with the AE9/AP9 models on SPENVIS, with electron energies starting at 500 keV and proton energies starting at 10 MeV.<br> The simulation was performed with GRAS / Geant4 with the FTFP_BERT physics model using 3.9e+10 electrons and 4.7e+09 protons directed against slabs of shielding materials of 1.5 g/cm2 total depth with each of the three layers being 0.5 g/cm2 in depth.<br> The ionizing dose is recorded in 0.5mm thick silicon plates behind the shielding plates.<br> <br> Column A: ID of the material combination<br> Column B: Name of the top layer material.<br> Column C: Name of the middle layer material.<br> Column D: Name of the bottom layer material.<br> Column E: Ionizing dose due to electrons in units of kRad per month.<br> Column F: Absolute statistical error in the electron dose in kRad per month.<br> Column G: ionizing dose due to protons in kRad per month.<br> Column H: Absolute statistical error in the proton dose in kRad per month. <br> Column I: Total ionizing dose from electrons and protons in kRad per month.<br> Column J: Absolute statistical error in the total ionizing dose in kRad per month. <br> Column K: Relative statistical error of the total ionizing dose in per cent.<br> Column L: Rank of the material combination sorted from lowest total ionizing dose to highest.<br> Column M: Rank of the material combination sorted from lowest electron dose to highest.<br> Column N: Rank of the material combination sorted from lowest proton dose to highest. All dose values are rounded according to their uncertainty.
DOI: 10.5281/zenodo.7828928
title: Simulated shielding performance of 2500 two-layer permutations of the first 50 chemical elements against trapped particles on GTO

description: The 2Layer.csv file contains ionizing dose results of simulating the shielding performance of all 2500 two-layer permutations of the first 50 chemical elements against trapped particles in Geostationary Transfer Orbit (GTO). The material properties are sourced from the Geant4 material database, with materials referenced by their Geant4 material database names. For elements that are gases under normal conditions, the liquified version from the Geant4 database is used if available, denoted by an "l" prefix (e.g., "G4_lH2"). The particle spectra used for this simulation are provided in the files AE9500keV.mac and AP910MeV.mac. The simulated geometry is provided in the file 2Layer.gdml. The particle spectra were generated with the AE9/AP9 models on SPENVIS, with electron energies starting at 500 keV and proton energies starting at 10 MeV. The simulation was performed with GRAS / Geant4 with the FTFP_BERT physics model using 2.1e+11 electrons and 8.4e+09 protons directed against slabs of shielding materials of 1.5 g/cm2 total depth with each of the two layers being 0.75 g/cm2 in depth. The ionizing dose is recorded in 0.5mm thick silicon plates behind the shielding plates. Column A: ID of the material combination<br> Column B: Z-Number of Material 1<br> Column C: Z-Number of Material 2<br> Column D: Name of the first layer material.<br> Column E: Name of the second layer material.<br> Column F: Ionizing dose due to electrons in units of kRad per month.<br> Column G: Statistical error in the electron dose in kRad per month.<br> Column H: Ionizing dose due to protons in kRad per month.<br> Column I: Statistical error in the proton dose in kRad per month.<br> Column J: Total ionizing dose from electrons and protons in kRad per month.<br> Column K: Statistical error in the total ionizing dose in kRad per month.<br> Column L: Rank of the material combination sorted from lowest electron dose to highest.<br> Column M: Rank of the material combination sorted from lowest proton dose to highest.<br> Column N: Rank of the material combination sorted from lowest total ionizing dose to highest. All dose values are rounded according to their statistical uncertainty as reported by GRAS/Geant4.
DOI: 10.34740/kaggle/dsv/5507247
title: COVID Vaccination in World (updated daily)
DOI: 10.5281/zenodo.7613014
title: Shielding performance of the 98 first chemical elements against trapped particles on GTO

description: The 1LayerElements.csv file contains the shielding performance results for the first 98 chemical elements against trapped particles in Geostationary Transfer Orbit (GTO).<br> The material properties are sourced from the Geant4 material database, with materials referenced by their Geant4 material database names<br> For elements that are gases under normal conditions, the liquified version from the Geant4 database is used if available, denoted by an "l" prefix (e.g., "G4_lH2").<br> The particle spectra used for this simulation are provided in the files AE9500keV.mac and AP910MeV.mac.<br> The simulated geometry is provided in the file 1LayerElements.gdml<br> <br> The particle spectra were generated with the AE9/AP9 models on SPENVIS, with electron energies starting at 500 keV and proton energies starting at 10 MeV.<br> The simulation was performed with GRAS / Geant4 with the FTFP_BERT physics model using 4.3e+10 electrons and 1e+08 protons directed against slabs of shielding materials of 1.5 g/cm2 depth.<br> The ionizing dose is recorded in 0.5mm thick silicon plates behind the shielding plates. Column A: Z-number of the element.<br> Column B: Name of the material as referenced from the Geant4 material database.<br> Column C: Ionizing dose due to electrons in units of kRad per month.<br> Column D: Absolute statistical error in the electron dose due to the statistical nature of the Monte-Carlo simulation in kRad per month.<br> Column E: ionizing dose due to protons in kRad per month.<br> Column F: Absolute statistical error in the proton dose in kRad per month. <br> Column G: Total ionizing dose from electrons and protons in kRad per month.<br> Column H: Absolute statistical error in the total ionizing dose in kRad per month. <br> Column I: Relative statistical error of the total ionizing dose in per cent.<br> Column J: Rank of the material sorted from lowest total ionizing dose to highest.<br> Column K: Rank of the material sorted from lowest electron dose to highest.<br> Column L: Rank of the material sorted from lowest proton dose to highest. All dose values are rounded according to their uncertainty.
DOI: 10.5281/zenodo.7613015
title: Shielding performance of the 98 first chemical elements against trapped particles on GTO

description: Results of simulating the shielding performance of the first 98 chemical elements against trapped particles on GTO.<br> The particle spectra are generated with the AE9/AP9 models on SPENVIS, with electron energies starting at 500 keV and proton energies starting at 10 MeV.<br> The simulation was performed with GRAS / Geant4 with the FTFP_BERT physics model using 4.3e+10 electrons and 1e+08 protons directed against slabs of shielding materials of 1.5 g/cm2 depth.<br> The ionizing dose is recorded in 0.5mm thick silicon plates behind the shielding plates. Column A: Z-number of the element.<br> Column B: Name of the material as referenced from the Geant4 material database.<br> Column C: Ionizing dose due to electrons in units of kRad per month.<br> Column D: Absolute statistical error in the electron dose due to the statistical nature of the Monte-Carlo simulation in units of kRad per month.<br> Column E: ionizing dose due to protons in units of kRad per month.<br> Column F: Absolute statistical error in the proton dose due to the statistical nature of the Monte-Carlo simulation in units of kRad per month. <br> Column G: Total ionizing dose from electrons and protons in units of kRad per month.<br> Column H: Absolute statistical error in the total ionizing dose due to the statistical nature of the Monte-Carlo simulation in units of kRad per month. <br> Column I: Relative statistical error of the total ionizing dose in per cent.<br> Column J: Rank of the material sorted from lowest total ionizing dose to highest.<br> Column K: Rank of the material sorted from lowest electron dose to highest.<br> Column L: Rank of the material sorted from lowest proton dose to highest. All dose values are rounded according to their uncertainty.
DOI: 10.5281/zenodo.7858330
title: Impedance analysis of dry electrodes

description: RAW data acquired with an impedance analyser to assess the complex impedance values of three different materials used in the development of dry electrodes for biosignal acquisition
DOI: 10.5281/zenodo.7858329
title: Impedance analysis of dry electrodes

description: RAW data acquired with an impedance analyser to assess the complex impedance values of three different materials used in the development of dry electrodes for biosignal acquisition
DOI: 10.5281/zenodo.7858966
title: Green Intelligence Awareness, inhabitants attended in Valladolid city

description: Changes in behavior and human attitudes are fundamental to achieve a more sustainable world, so that, it is very interesting to analyze the potential of an activity or intervention to increase the green intelligence awareness of a population. There is enormous opportunity for nature-based solutions to promote understanding of sustainability in ways that positively influence citizen behavior. There are numerous available resources to learn and understand the fragility of our environmental and the responsibility of humans to protect, preserve and respect the world. Therefore, this KPI aims to reflect how the intervention is used for educational purposes and enhancement of public awareness.
DOI: 10.5281/zenodo.7858965
title: Green Intelligence Awareness, inhabitants attended in Valladolid city

description: Changes in behavior and human attitudes are fundamental to achieve a more sustainable world, so that, it is very interesting to analyze the potential of an activity or intervention to increase the green intelligence awareness of a population. There is enormous opportunity for nature-based solutions to promote understanding of sustainability in ways that positively influence citizen behavior. There are numerous available resources to learn and understand the fragility of our environmental and the responsibility of humans to protect, preserve and respect the world. Therefore, this KPI aims to reflect how the intervention is used for educational purposes and enhancement of public awareness.
DOI: 10.18112/openneuro.ds004556.v1.0.0
title: The role of superstition of cognitive control during neurofeedback training - part 1
DOI: 10.6084/m9.figshare.22560970
title: Supplementary Material for: Rapid identification of seven common ABO alleles by two-dimensional polymerase chain reaction technology

description: Introduction: The molecular biology detection technology of the human ABO blood group system makes up for the limitations in many aspects compared with conventional serological typing technology. This study aimed to establish a new method to identify seven common ABO alleles (ABO*A1.01, ABO*A1.02, ABO*A2.01, ABO*B.01, ABO*O.01.01, ABO*O.01.02, and ABO*O.02.01) by two-dimensional polymerase chain reaction (2D PCR). 2D PCR can identify multiple target genes in a closed test tube by labeling specific primers with tags homologues to the sequence of fluorescently labeled probes, and performing melting curve analysis after the fluorescent probes are hybridized with tag complementary sequences in PCR specific products. In this study, 2D PCR and PCR-sequence-specific primer (PCR-SSP) were combined to discriminate different alleles in a single reaction, which has the characteristics of high throughput, and compared with other typing techniques, the typing results can be obtained without additional operations. Methods: The ABO*A1.01 allele genetic sequence was used as the reference sequence. The specific sense and antisense primers for seven common ABO alleles were designed on exons 6 and 7 according to the principle of 2D PCR and PCR-SSP. Single nucleotide polymorphism sites for identifying seven alleles were detected in FAM and HEX channels, respectively. 260 DNA samples were enrolled for rapid ABO genotyping by 2D PCR, and 95 of them were selected for Sanger sequencing. Kappa test was used to analyze the agreement of the methodologies. Results: These 7 alleles each had four characteristic melting valleys at different single nucleotide polymorphism loci. A total of 15 genotypes were detected, including ABO*A1.01/A1.02, ABO*A1.01/O.01.01, ABO*A1.01/O.01.02, ABO*A1.02/A1.02, ABO*A1.02/O.01.01, ABO*A1.02/O.01.02, ABO*B.01/B.01, ABO*B.01/O.01.01, ABO*B.01/O.01.02, ABO*O.01.01/O.01.01, ABO*O.01.01/O.01.02, ABO*O.01.02/O.01.02, ABO*A1.01/B.01, ABO*A1.02/B.01, and ABO*B.01/O.01.v (containing a rare ABO*O allele, based on the sequencing results). The Kappa test showed completely consistent results for 2D PCR and Sanger sequencing (Kappa = 1). Conclusions: Two-dimensional polymerase chain reaction technique could be used for molecular typing of ABO blood group, which was efficient, rapid, accurate, and economical.
DOI: 10.6084/m9.figshare.22560970.v1
title: Supplementary Material for: Rapid identification of seven common ABO alleles by two-dimensional polymerase chain reaction technology

description: Introduction: The molecular biology detection technology of the human ABO blood group system makes up for the limitations in many aspects compared with conventional serological typing technology. This study aimed to establish a new method to identify seven common ABO alleles (ABO*A1.01, ABO*A1.02, ABO*A2.01, ABO*B.01, ABO*O.01.01, ABO*O.01.02, and ABO*O.02.01) by two-dimensional polymerase chain reaction (2D PCR). 2D PCR can identify multiple target genes in a closed test tube by labeling specific primers with tags homologues to the sequence of fluorescently labeled probes, and performing melting curve analysis after the fluorescent probes are hybridized with tag complementary sequences in PCR specific products. In this study, 2D PCR and PCR-sequence-specific primer (PCR-SSP) were combined to discriminate different alleles in a single reaction, which has the characteristics of high throughput, and compared with other typing techniques, the typing results can be obtained without additional operations. Methods: The ABO*A1.01 allele genetic sequence was used as the reference sequence. The specific sense and antisense primers for seven common ABO alleles were designed on exons 6 and 7 according to the principle of 2D PCR and PCR-SSP. Single nucleotide polymorphism sites for identifying seven alleles were detected in FAM and HEX channels, respectively. 260 DNA samples were enrolled for rapid ABO genotyping by 2D PCR, and 95 of them were selected for Sanger sequencing. Kappa test was used to analyze the agreement of the methodologies. Results: These 7 alleles each had four characteristic melting valleys at different single nucleotide polymorphism loci. A total of 15 genotypes were detected, including ABO*A1.01/A1.02, ABO*A1.01/O.01.01, ABO*A1.01/O.01.02, ABO*A1.02/A1.02, ABO*A1.02/O.01.01, ABO*A1.02/O.01.02, ABO*B.01/B.01, ABO*B.01/O.01.01, ABO*B.01/O.01.02, ABO*O.01.01/O.01.01, ABO*O.01.01/O.01.02, ABO*O.01.02/O.01.02, ABO*A1.01/B.01, ABO*A1.02/B.01, and ABO*B.01/O.01.v (containing a rare ABO*O allele, based on the sequencing results). The Kappa test showed completely consistent results for 2D PCR and Sanger sequencing (Kappa = 1). Conclusions: Two-dimensional polymerase chain reaction technique could be used for molecular typing of ABO blood group, which was efficient, rapid, accurate, and economical.
DOI: 10.5281/zenodo.7858972
title: Noise Reduction by NBS in Valladolid city

description: Noise pollution by traffic, construction works, etc. is a common city problem. Nuisance from noise is detrimental to neighbourhood liveability, living comfort and work environments, and can increase risk of serious health problems such as hearing loss and cardiovascular disease. Urban ecosystems provide noise reduction services by serving as a natural sound buffer. Vegetation provides both a direct and an indirect barrier to environmental noise. Starting with its direct functions, green belts attenuate noise by absorption, dispersal, and destructive interference of sound waves, though sound levels can intensify locally if measured right below tree crowns. Indirect noise reduction effects are generated by lessened wind speeds and the absorptive capacity of pervious soils. UGS also proved to offer noise reducing services via psychological effects: just observing the presence of a green wall can lead people to perceive less noise nuisance or alter the perception of noise as sounds such as flowing water, bird singing, and leaves rustling in the wind mask disturbing background noise. On the other hand, the methodology proposed for this KPI is based and uses the methodology and tools proposed by the European Commission Working Group Assessment of Exposure to Noise (WG-AEN). The Environmental Noise Directive (END) requires two main indicators to be applied in the assessment and management of environmental noise. The first indicator (Lden) is the noise level for the day, evening and night periods and is designed to measure ‘annoyance’. The END defines an Lden threshold of 55 dB. The second indicator (Lnight) is the noise level for night-time periods and is designed to assess sleep disturbance. The END defines an Lnight threshold of 50 dB. Member States must report the numbers of people who are exposed to noise levels above both thresholds for each noise source (e.g. roads, railways, airports, industry).