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79 results for “Surface functionalization”
Upper lithospheric structure of northeastern Venezuela from joint inversion of surface wave dispersion and receiver functions
<p>Dataset from the publication: <strong>Upper lithospheric structure of northeastern Venezuela from joint inversion of surface wave dispersion and receiver functions</strong>. DOI: <a href="https://doi.org/10.5194/egusphere-2022-230">10.5194/egusphere-2022-230</a></p> <p> </p> <p>Includes: <em><strong>EGFs, Dispersion Curves measurements, RFs, Vs3dmodel and Moho depths</strong></em></p> <p> </p>
DATASET: Predicting Protein Function and Orientation on a Gold Nanoparticle Surface Using a Residue-Based Affinity Scale
<p>This upload contains data for the manuscript "<strong>Predicting Protein Function and Orientation on a Gold Nanoparticle Surface Using a Residue-Based Affinity Scale</strong>." It contains kinetics data, UV-vis data, surface calculations, and activity assays for the systems described in the manuscript.</p>
Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions
<p>For reproducing the results presented in "<strong>Hashemi, A., Peljo, P., & Laasonen, K. (2022). Understanding Electron Transfer Reactions using Constrained Density Functional Theory: Complications due to Surface Interactions</strong>", this database provides the input files and CDFT-AIMD trajectory information. Please refer to the publication if you wish to use these data.</p> <p>---------------------------------------**************************************************************************-------------------------------------------------</p> <p><em>This study was financed by the Horizon 2020 Framework Programme CompBat with project number 875565. We also thank CSC-IT Center for Science Ltd. and Aalto Science-IT project for generous grants of computer time.</em><br> -----------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>The content of a directory is shown in a tree-like format:</strong><br> ├── 1DMDQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 2MeVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── mevi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 3OHVi<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── md.inp<br> │ │ ├── ohvi-md-pos-1.xyz<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ ├── b_to_a.tar.gz<br> │ │ ├── b_to_c<br> │ │ │ ├── framePrint.py<br> │ │ │ ├── input_files<br> │ │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ │ ├── dft-common-params.inc<br> │ │ │ │ ├── energy_cdft.inp<br> │ │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ │ └── subsys.inc<br> │ │ │ └── README<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 4dBR5<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── dmdq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> ├── 52HNQ<br> │ ├── 1_md<br> │ │ ├── dft-common-params.inc<br> │ │ ├── hnq-md-pos-1.xyz<br> │ │ ├── md.inp<br> │ │ ├── pos.xyz<br> │ │ ├── submit.sh<br> │ │ └── subsys.inc<br> │ ├── 2_cdftaimd<br> │ │ ├── state_a<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ ├── state_b<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── cdft_md.bash<br> │ │ │ ├── cdft_md.inp<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ │ ├── frame.xyz<br> │ │ │ └── subsys.inc<br> │ │ └── state_c<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── cdft_md.bash<br> │ │ ├── cdft_md.inp<br> │ │ ├── dft-common-params.inc<br> │ │ ├── frame-cdft-pos-total.xyz.tar.gz<br> │ │ ├── frame.xyz<br> │ │ └── subsys.inc<br> │ └── 3_cdft_wH2O_sccs<br> │ ├── state_a<br> │ │ ├── framePrint.py<br> │ │ ├── input_files<br> │ │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ │ ├── becke_twoconstraints.inc<br> │ │ │ ├── dft-common-params.inc<br> │ │ │ ├── energy_cdft.inp<br> │ │ │ ├── energy_mixed_cdft.inp<br> │ │ │ └── subsys.inc<br> │ │ └── README<br> │ ├── state_b<br> │ │ ├── b_to_a.tar.gz<br> │ │ └── b_to_c.tar.gz<br> │ └── state_c<br> │ ├── framePrint.py<br> │ ├── input_files<br> │ │ ├── 1_energy_cdft_STATE1.bash<br> │ │ ├── 2_energy_cdft_STATE2.bash<br> │ │ ├── 3_energy_cdft_mixed.bash<br> │ │ ├── becke_twoconstraints.inc<br> │ │ ├── dft-common-params.inc<br> │ │ ├── energy_cdft.inp<br> │ │ ├── energy_mixed_cdft.inp<br> │ │ └── subsys.inc<br> │ └── README<br> └── 6_n_H2O_effect_mevi<br> ├── 08h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 10h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 20h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 40h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> ├── 97h2o<br> │ ├── framePrint.py<br> │ ├── README<br> │ ├── state_a.tar.gz<br> │ └── state_b.tar.gz<br> └── fig3.png</p> <p>74 directories, 301 files<br> -------------------------------------------------------<br> There are 6 directories: 1DMDQ, 2MeVi, 3OHVi, 4dBR5, 52HNQ, 6_n_H2O_effect_mevi. Except for "6_n_H2O_effect_mevi", we see 3 subdirectories named 1_md, 2_cdftaimd, and 3_cdft_wH2O_sccs. The input files and AIMD trajectories can be found in 1_md. While 2_cdftaimd contains the CDFT-AIMD input files and trajectories. To reproduce snapshots and input files of 3_cdft_wH2O_sccs, follow the README files in the subdirectories.</p> <p>The directory "6_n_H2O_effect_mevi" contains the number of water effects (Figure 3 of the publication). Users are guided by README files once again. </p>
5D-NP-FABTECH_SALV - Open Dataset for "3D Micropatterned Functional Surface Inspired by Salvinia Molesta via Direct Laser Lithography for Air Retention and Drag Reduction"
<p>This is the open dataset for the paper: "Omar Tricinci*, Francesca Pignatelli, Virgilio Mattoli*, 3D Micropatterned Functional Surface Inspired by Salvinia Molesta via Direct Laser Lithography for Air Retention and Drag Reduction, On line (2023) [DOI: 10.1002/adfm.202206946] "</p> <p>This include the Supplementary Information file ("SI-PaperSalvinia3_PostRevOKV2.pdf.pdf"), all the source material used for the paper preparation and more. </p> <p>For each folder (sub-dataset) there is a corresponding readme file describing the content and including metadata</p>
Machine learning the quantum flux-flux correlation function for catalytic surface reactions
<p>This dataset contains information on each of the 14 reactions used in the paper, the geometries for these reactions, the product of the quantum reaction rate constant and canonical reactant partition function and the flux-flux correlation function time series values for each reaction-temperature combination.</p> <p><strong>reaction_details.csv</strong></p> <p>This is a .csv file containing additional details on the reactions used in this paper. Each row contains one reaction/temperature combination, of which there are 55.</p> <p> </p> <p>Column descriptions:</p> <ul> <li>reaction_number: Reaction identifier number used in this work</li> <li>reaction: The chemical reaction equation</li> <li>metal_surface: atomic symbol of metal surface</li> <li>facet_number: Miller indices of surface</li> <li>reactants: Python dictionary object of reactants and their quantities</li> <li>products: Python dictionary object of products and their quantities</li> <li>reaction_energy [eV]: reaction energy in electron-volts</li> <li>activation_energy [eV]: activation energy of reaction in electron-volts</li> <li>temperature [K]: The randomly assigned temperature a calculation was run for</li> <li>kQ_Cff [1/au]: The calculated integrated reaction rate product at corresponding temperature {1,2,3,4} in units 1/(au time).</li> <li>reaction_split: Train/test placement of that reaction/temperature combination for reaction split</li> <li>temperature_split:<strong> </strong>Trian/test placement of that reaction/temperature combination for temperature split</li> <li>catalysishub_reactionID: Catalysis Hub reaction ID identifier for referencing catalysis hub database</li> <li>doi:<strong> </strong>digital object identifier of original publication for which DFT calculations were performed</li> </ul> <p> </p> <p> </p> <p><strong>Flux_flux_correlation_functions:</strong></p> <p>Directory containing flux-flux correlation function time series values for each reaction temperature combination. Values are organized in subdirectories, one for each of the 14 reaction. In each subdirectory .csv files are labeled by reaction number and temperature in Kelvin. Each csv file contains a column with time points [au of time] and the corresponding flux-flux correlation function value in units [1/(au of time)<sup>2</sup>].</p> <p> </p> <p><strong>Geometries:</strong></p> <p>Directory containing geometry files for each reaction. Geometries of reactants on the surface were shifted respect to those supplied by catalysis hub to create continuous reaction pathways where necessary. Geometry files are organized in subdirectories for each reaction. When complete nudged elastic band (NEB) minimum energy paths (MEP) were not available ,subdirectories contain a products.xyz, reactants.xyz, and TSstar.xyz file (reactions 1 to 11) otherwise the complete set of NEB MEP images labeled neb{n}.xyz is given (reactions 12, 13, 14).</p> <p> </p> <p> </p>
DataSET for: Control of spintronic and electronic properties of bimetallic and vacancy-ordered vanadium carbide MXenes via surface functionalization
<p>This data set contains the minimal file necessary to reproduce the DFT calculations contained in the publication:<br> Control of spintronic and electronic properties of bimetallic and vacancy-ordered vanadium carbide MXenes via surface functionalization.</p> <p>the dataset is available on GitLab: <a href="https://gitlab.com/praguelab/shuo2019_work1">https://gitlab.com/praguelab/shuo2019_work1</a> (Project ID: 13583566) Final Latest tag at moment of submission: <a href="https://gitlab.com/praguelab/shuo2019_work1/-/tags/Accepted_manuscript">https://gitlab.com/praguelab/shuo2019_work1/-/tags/Accepted_manuscript</a></p> <p>The paper has been received for review on 16th October 2019, and accepted for publication 11th November 2019 in Journal of Physical Chemistry Chemical Physics</p> <p>DOI: 10.1039/c9cp05638f</p> <p>Preprint available on arXiv <a href="https://arxiv.org/abs/1910.01509">https://arxiv.org/abs/1910.01509</a></p> <p>The work has been financed by Grantova´ Agentura, Univerzita Karlova (Grantova´ Agentura, Univerzita Karlova), OP VVV ‘‘Excellent Research Teams’’, project CUCAM, and International Mobility of Researchers at Charles University (CZ.02.2.69/0.0/0.0/16_027/0008495)</p>
Fatigue life of S960 high strength steel with laser cladded functional surface layers
<p>This dataset to paper: Fatigue life of S960 high strength steel with laser cladded functional surface layers, which includes mainly raw data for S-N curves.</p>
Figure 12 in The surface ciliation of anuran amphibian embryos and early larvae: Patterns, timing differences and functions
Figure 12. Ciliated cell patterns around the nostrils. (A) B. viridis, stage 25; (B) X. laevis, stage 23/24; (C) B. bufo, stage 24; (D) P. venulosa, stage 27; (E) L. bolivianus, stage 23; (F) R. temporaria, stage 23. Scale bars: 500 Mm (A, E); 250 Mm (B); 35 Mm (C); 120 Mm (D, F).
Figure 11 in The surface ciliation of anuran amphibian embryos and early larvae: Patterns, timing differences and functions
Figure 11. Ciliary water currents in different stages of R. temporaria embryos and larvae. (A) stage 16; (B) stage 17; (C) stage 18; (D) stage 19; (E) stage 21; (F) stage 22; (G) stage 24; (H) dorsal side; (I) ventral side, stage 26; (J) stage 27. Not all to the same scale. Arrows indicate the observed currents.
Figure 10 in The surface ciliation of anuran amphibian embryos and early larvae: Patterns, timing differences and functions
Figure 10. Changes in mean ciliated cell size on the ventral side of the yolk: (A) in three species of Hylidae; (B) in two species of Leptodactylidae. H, hatching stage. Error bars show standard deviations.
Figure 9 in The surface ciliation of anuran amphibian embryos and early larvae: Patterns, timing differences and functions
Figure 9. Ciliated cell patterns around the lateral line system. (A) R. temporaria, stage 20, lateral side of the head; (B) R. temporaria, stage 21, lateral side of the head; (C) P. pustulosus, stage 26, lateral side of the head; (D) P. pustulosus, stage 26, tail. Arrow heads show lateral line system (neuromasts); arrows show ciliated cells. Scale bar: 500 Mm (A, B); 150 Mm (C, D).
Figure 8 in The surface ciliation of anuran amphibian embryos and early larvae: Patterns, timing differences and functions
Figure 8. Examples of different patterns of cilia resorption. (A) Cilia reabsorption starts from the anterior side of the cell, H. minuta, stage 22, ventral side of the trunk; (B) cilia reabsorption starts from the periphery of the cell, H. minuscula, stage 25, ventral side of the trunk; (C) cilia reabsorption starts from the centre of the cell, P. venulosa, stage 22, dorsal side of the head; (D) cilia have disappeared from the whole of the cell, P. venulosa, stage 23, dorsal side of the head. Scale bars: 30 Mm.
Figure 7 in The surface ciliation of anuran amphibian embryos and early larvae: Patterns, timing differences and functions
Figure 7. Elongated ciliated cells at different locations. (A) B. viridis, stage 18/19, ventral side of the trunk before cell elongation; (B) B. viridis, stage 23, ventral side of the trunk after cell elongation; (C) P. trinitatis, stage 22, tail; (D) H. crepitans, stage 20, ventral side of the trunk; (E) P. trinitatis, stage 20, lateral side of the trunk; (F) B. viridis, stage 22, ventral side of the trunk. Scale bars: 500 Mm (A–D); 120 Mm (E); 15 Mm (F).
Figure 6 in The surface ciliation of anuran amphibian embryos and early larvae: Patterns, timing differences and functions
Figure 6. Scanning electron micrographs to show distribution of ciliated cells on the surface of E. urichi at different stages and locations. The need to open up the yolk sac before processing accounts for the somewhat crumpled appearance of some embryos. (A) Stage 5, overall dorsal view; (B) stage 6/7, overall dorsal view; (C) stage 8/9, anterior end, dorsal view; (D) stage 14, overall dorsal view; (E) stage 5, dorso-lateral side of the head; (F) stage 6/7, dorso-lateral side of the head and forelimb bud. *forelimb. Scale bars: 500 Mm (A, C); 1 mm (B, D); 150 Mm (E, F).
Figure 5 in The surface ciliation of anuran amphibian embryos and early larvae: Patterns, timing differences and functions
Figure 5. Sketch of E. urichi embryo, approximately TS stage 6/7. Regions assessed for ciliated cells are 1, head, dorsal; 2, head, lateral; 3, trunk dorsal; 4, trunk, lateral; 5, tail, stem; 6, tail, fins; 7, forelimbs; 8, hindlimbs; 9, yolk sac.
Figure 2 in The surface ciliation of anuran amphibian embryos and early larvae: Patterns, timing differences and functions
Figure 2. Ciliated cell patterns at different body regions. (A) P. pustulosus, stage 27, nostrils; (B) R. temporaria, stage 19, adhesive gland; (C) H. geographica, stage 21, tail; (D) H. crepitans, stage 22, external gill; (E) L. bolivianus, stage 24, dorsal head; (F) L. fuscus, stage 27, hind-limb bud. Scale bars: 500 Mm; (A, B, C, E); 20 Mm (D); 50 Mm (F).
Figure 3 in The surface ciliation of anuran amphibian embryos and early larvae: Patterns, timing differences and functions
Figure 3. The range of ciliated cell densities from ''very dispersed'' to ''very dense''. (A) H. minuta, very dispersed, stage 20, ventral side of the body; (B) R. temporaria, dispersed, stage 18, dorsal side of the head; (C) L. fuscus, intermediate density, stage 20, ventral side of the trunk; (D) L. fuscus, dense, stage 24, ventral side of the trunk; (E) Phrynohyas venulosa, very dense, stage 17, adhesive gland. Scale bars: 35 Mm (A, D); 30 Mm (B, C, E).
Figure 1 in The surface ciliation of anuran amphibian embryos and early larvae: Patterns, timing differences and functions
Figure 1. Ten study locations on the surface of the amphibian embryo/larva at Gosner state 19. (A) Dorsal view; (B) lateral view; (C) ventral view. a, dorsal side of head; b, dorsal side of trunk; c, nostril; d, external gill; e, lateral side of trunk; f, tail; g, mouth; h, adhesive gland; i, ventral side of head; j, ventral side of trunk.
Figure 4 in The surface ciliation of anuran amphibian embryos and early larvae: Patterns, timing differences and functions
Figure 4. The range of ciliated cell shapes. (A) B. viridis, five-sided, stage 15, lateral side of the head; (B) P. venulosa, six-sided, stage 17, ventral side of the trunk; (C) H. boans, multi-sided, stage 20, ventral side of the trunk; (D) L. fuscus, circular, stage 23, ventro-posterior side of the trunk; (E) L. fuscus, oval, stage 23, ventral side of the trunk; (F) P. trinitatis, elongated, stage 20, lateral side of the trunk. Scale bars: 30 Mm (C, D, F); 35 Mm (A, B); 60 Mm (E).
Changes in community-weighted trait mean, functional diversity, precipitation, temperature and surface area along an elevational gradient in Tenerife, Canary Islands
<p>This dataset comprises community-weighted trait means and functional diversity of leaf traits, precipitation, temperature and surface area of the elevational belt recorded in roadside (disturbed) and interior (less disturbed) plots, along an elevational gradient of 2,300 m in Tenerife, Canary Islands. The leaf traits measured were specific leaf area (SLA), nitrogen, carbon, phosphorous, nitrogen to carbon ratio, leaf dry matter content (LDMC), sodium, potassium and magnesium. The environmental variables measured are total precipitation of the growing season, mean temperature of the growing season and surface area of the elevation belt. This dataset has been used for the analysis presented in Ratier Backes et al. (in press). Mechanisms behind elevational plant species richness patterns revealed by a trait-based approach. <em>Journal of Vegetation Science</em>.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.