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FIGURE 9 in Non-linear ontogenetic shape change in Cryptolithus tesselatus (Trilobita) using three-dimensional geometric morphometrics
FIGURE 9. Ontogeny of Cryptolithus tesselatus based on 2D geometric morphometrics. 1, Fixed landmarks consistently recognizable in dorsal view. Red dashed line shows curve described by first internal list along which were placed 21 semi-landmarks. Specimen shown is AMNH FI- 101479; specimen is 6.7 mm long. 2, Principal components analysis of 2D fixed- and semi-landmarks. Point size represents relative centroid size of specimen. Insets are deformation plots showing shapes represented by largest and smallest PC 1 values. 3, Allometric curve; amount of shape change represented by the Procrustes distance between each specimen and the smallest specimen. Red solid line = linear regression model; blue solid line = threshold model 1; thin black dashed line = threshold model 2; thick black dashed line = threshold model 3. Threshold model 1 is the best supported model (Table 1).
FIGURE 6 in Non-linear ontogenetic shape change in Cryptolithus tesselatus (Trilobita) using three-dimensional geometric morphometrics
FIGURE 6. Allometric growth in Cryptolithus tesselatus. Size (x-axis) is represented by the natural log of centroid size. Change in shape (y-axis) is represented by the Procrustes distance between each specimen and the smallest specimen in the dataset; the Procrustes distances in this case represent the relative amount of change that specimens underwent during development. Red solid line = linear regression model; blue solid line = threshold model 1; thin black dashed line = threshold model 2; thick black dashed line = threshold model 3. Threshold model 1 is the best supported model.
Supplemental Material to "The Small-Amplitude Dynamics of Spontaneous Tropical Cyclogenesis. Part II: Linear Stability Analysis"
<p>This is the supplemental material to the manuscript "The Small-Amplitude Dynamics of Spontaneous Tropical Cyclogenesis. Part II: Linear Stability Analysis". It deposits:</p> <p>(1) A handwritten math derivation note (math_derivation_note.pdf).</p> <p>(2) A movie version of Figure 1 (Part_II_movie.avi).</p> <p>(3) The Fortran code for amplifying the longwave radiative feedback in CM1 (Radiation_amplification_RAD_parameter_in CM1.txt).</p> <p>(4) The MATLAB postprocessing codes for generating data figures (postprocessing_code.zip).</p> <p>(5) The MATLAB solver of the four-layer QG model (four_layer_QG_solver.m).</p> <p>(6) Supplemental figures (supplement_figures.pdf). </p> <p>Please contact Dr. Hao Fu (haofu@uchicago.edu or haofu736@gmail.com) if you have any questions. </p>
Linked collectors and determiners for: Peliosanthes linearifolia (Asparagaceae), a new species with linear leaves from Vietnam.
Natural history specimen data linked to collectors and determiners held within, "Peliosanthes linearifolia (Asparagaceae), a new species with linear leaves from Vietnam". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/955f8d3a-5cc5-465d-b965-0d967f621f9e">https://bionomia.net/dataset/955f8d3a-5cc5-465d-b965-0d967f621f9e</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/955f8d3a-5cc5-465d-b965-0d967f621f9e">https://gbif.org/dataset/955f8d3a-5cc5-465d-b965-0d967f621f9e</a>. Formatted as a Frictionless Data package.
Output from Linear Inverse Models (LIMs) emulating the observed spatiotemporal statistics of Australian precipitation and global sea surface temperatures
<p><strong>Data repository for <em>How unusual was Australia's 2017–2019 Tinderbox Drought?</em></strong></p> <p>This repository contains LIM data underpinning the paper <em>How unusual was Australia's 2017–2019 Tinderbox Drought?</em> [doi: 10.1016/j.wace.2024.100734 <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.wace.2024.100734" target="_blank" rel="noopener">available online in <em>Weather and Climate Extremes</em> 17 October 2024</a>]. All other datasets used in the paper are freely available online (see Data Availability statement in the paper for details). </p> <p>The repository contains 12 netcdf files, which together comprise the Linear Inverse Model (LIM) outputs described in the paper. <strong>In all cases, please see the paper for important details on the data and how they were produced.</strong> </p> <p><em>Global LIMs</em></p> <ul> <li>`LIM5000_COBE-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the Australian Gridded Climate Dataset v2 (AGCD) and global SST data from 'Centennial in situ Observation-Based Estimates of the Variability of SST and Marine Meteorological Variables version 2' (COBE)</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and global SST data from US National Oceanic and Atmospheric Administration 'Extended Reconstruction SST version 5’ (ERSST)</li> </ul> </li> <li>`LIM5000_COBE-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-globalSST_SST-anoms-global_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using global SST data from ERSST</li> </ul> </li> </ul> <p><em>Tropical Pacific Ocean LIMs</em></p> <ul> <li>`LIM5000_COBE-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and tropical Pacific Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-TropicalPacificSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using tropical Pacific Ocean SST data from ERSST</li> </ul> </li> </ul> <p><em>Indian Ocean LIMs</em></p> <ul> <li>`LIM5000_COBE-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_prec-anoms-aus_monthly.nc` <ul> <li>contains 5000 years of emulated Australian precipitation variability, modelled using Australian rainfall data from the AGCD and Indian Ocean SST data from ERSST</li> </ul> </li> <li>`LIM5000_COBE-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from COBE</li> </ul> </li> <li>`LIM5000_ERSST-IndianOceanSST_SST-anoms-TropicalPacific_monthly.nc` <ul> <li>contains 5000 years of emulated global SST variability, modelled using Indian Ocean SST data from ERSST</li> </ul> </li> </ul> <p><strong>How to cite this</strong> <strong>repository</strong></p> <p>If using this data, please cite the original publication, available from <a href="https://www.sciencedirect.com/science/article/pii/S2212094724000951" target="_blank" rel="noopener">https://www.sciencedirect.com/science/article/pii/S2212094724000951.</a> </p>
Text-fig. 8. Scanning electron microscope (SEM) images of spores from clumps of spores and sporangia with affinities to polypodiopsids (a–c) and of uncertain affinity (d–k); Torres Vedras locality, Portugal. a) Folded Cyathidites minor spores in proximal view showing trilete mark, from clump of spores; b) Cyathidites minor spores in proximal view showing trilete mark, from group of sporangia; c) Cyathidites australis spores in proximal view showing trilete mark, from group of sporangia; d–f) Linear group of spore masses (d; probable sporangial contents) composed of Taurocusporites segmentatus spores showing distal surface (e, middle) with concentric regions and proximal surface with segmented laesurae of elongated granules (e, right; f); in The Early Cretaceous Mesofossil Flora Of Torres Vedras (Ne Of Forte Da Forca), Portugal: A Palaeofloristic Analysis Of An Early Angiosperm Community
Text-fig. 8. Scanning electron microscope (SEM) images of spores from clumps of spores and sporangia with affinities to polypodiopsids (a–c) and of uncertain affinity (d–k); Torres Vedras locality, Portugal. a) Folded Cyathidites minor spores in proximal view showing trilete mark, from clump of spores; b) Cyathidites minor spores in proximal view showing trilete mark, from group of sporangia; c) Cyathidites australis spores in proximal view showing trilete mark, from group of sporangia; d–f) Linear group of spore masses (d; probable sporangial contents) composed of Taurocusporites segmentatus spores showing distal surface (e, middle) with concentric regions and proximal surface with segmented laesurae of elongated granules (e, right; f);
Data supporting "Changes in human dorsal root ganglion neuron excitability from modulating Nav 1.8 conductance are non-linear and depend on the conductances of the delayed rectifier and M-type potassium currents: a simulation study"
<p>Data and code supporting the article "Changes in human dorsal root ganglion neuron excitability from modulating Nav 1.8 conductance are non-linear and depend on the conductances of the delayed rectifier and M-type potassium currents: a simulation study".</p> <p> </p>
Assessing-the-role-of-non-linear-contact-mechanics-for-flow-in-fractures---experimental_data
<p>Data to reproduce pressure response spectra from a number of harmonic measurements shown in "Assessing the role of non-linear contact mechanics for<br> flow in fractures"</p> <p><strong>Note:</strong> Data of AFR, BHR and TER has been extracted from papers published by authors others than the ones mentioned in the author list. The publications are referenced in the .txt files and the paper mentioned above.</p>
Kalman filter-based integration of GNSS and InSAR observations for local non-linear strong deformations
<p>The published database is related to the calculations presented in the paper "Kalman filter-based integration of GNSS and InSAR observations for local non-linear strong deformations". The main catalogue contains three folders named as GNSS, Campaign, and DInSAR.</p> <p>In the GNSS folder, the time series of XYZ coordinates and uncertainties estimated in the post-processing scenario for RES1, PI02, PI03, PI04, PI05, and PI16 permanent stations are provided. The GNSS calculations were performed at the Wrocław University of Environmental and Life Sciences in the ITRF2014 reference frame.</p> <p>The Campaign folder contains the results of epoch-based GNSS measurements and was used in the article as a verification data source. The Campaign results, prepared by the Military University of Technology, were used in the quality analyses. In order to co-locate the permanent PI02, PI04, PI05, and PI16 receivers with the nearest campaign points, a cross-reference was performed. The epoch-based time series of XYZ coordinates and uncertainties are provided in the ITRF2014 reference frame.</p> <p>The DInSAR interferograms were prepared at the Wrocław University of Environmental and Life Sciences and the results were stored in two directories named as Ascending and Descending. To perform a point-based unification of DInSAR and GNSS techniques, it was necessary to acquire the data from pixels intersected by the GNSS permanent station's locations. The DInSAR time series contain displacements (DSP), incidence angles (INC_ANG), heading angles (HEAD_ANG), and coherence (COH) data.</p>
Arbitrary-shape dielectric particles interacting in the linearized Poisson-Boltzmann framework: an analytical treatment
<p>h_kmlsn.mat --</p> <p>the coefficients h<sub>kmls,n</sub> calculated for n_max<=50: one has h<sub>kmls,n</sub> = h<sub>kmlsn</sub>(k+1,m+1,l+1,s+1,n+1), where h<sub>kmlsn</sub> is a 5D array contained in h_kmlsn.mat (note that MATLAB indices must start from 1, while mathematically indices k, m, l, s, and n are >=0).</p> <p>b_nml_approx.m -- it approximates $b_{nml}(\tilde r,\tilde R)$ with a given parameter n_max;</p> <p>derivative_b_nml_approx.m -- it approximates the derivative of $b_{nml}(\tilde r,\tilde R)$ with respect to $\tilde r$ with a given parameter n_max;</p> <p>linear_system_two_bodies_direct_backslash.m -- a simple example of forming the global linear system (14) and solving it directly (without regularization) using the MATLAB "mldivide" (or backslash) operation</p> <p>GSK3beta.zip -- zipped directory with DelPhi files related to Sec. 5.1.3 (see ReadmeGSK3beta.txt);</p> <p>Arginine-Glutamate -- zipped directory with DelPhi files related to Sec. 5.1.4 (see ReadmeARGGlu.txt)</p>
Equilibrated Kremer-Grest polymer melts of M=500 linear chains with Z=100 entanglements for varying chain stiffness.
<p>Kremer-Grest model polymer melts of highly entangled linear chains. Each melt has approximately 500 chains of Z=100 entanglements each. Systems have been generated for integer and half-integer stiffness kappa=-2.0 to 6.0. System sizes range from 8M to 2M beads.</p> <p>For details regarding the equilibration process and the Kremer-Grest polymer model see C. Svaneborg & R. Everaers ""Multiscale equilibration of highly entangled isotropic model polymer melts" J. Chem. Phys. 158, 054903 (2023) <a href="https://doi.org/10.1063/5.0123431">https://doi.org/10.1063/5.0123431</a></p> <p>Filenames denote the kappa<value> used when equilibrating the melt as well as the number of entanglements Z<number> and the number of molecules M<number>. The files are in ASCII format in the format of a LAMMPS data files. (https://lammps.sandia.gov) The semantics is self-explanatory, sections contains id, molecule, unwrapped coordinates of all beads, as well as bond and angular interactions between all beads.</p> <p>We acknowledge that part of the results of this research was obtained using the PRACE Research Infrastructure resource Joliot-Curie SKL based in France at GENCI@CEA. Computing facilities were provided by the eScience Center at the University of Southern Denmark and financed by the Faculty of Science.</p> <p>Please cite as:</p> <p>@article{MultiscaleEquilibrationHighlyEntangledIsotropicModelPolymerMelts,<br> author = {Svaneborg,Carsten and Everaers,Ralf },<br> title = {Multiscale equilibration of highly entangled isotropic model polymer melts},<br> journal = {J. Chem. Phys.},<br> volume = {158},<br> number = {5},<br> pages = {054903},<br> year = {2023},<br> doi = {10.1063/5.0123431},</p> <p> URL = {https://doi.org/10.1063/5.0123431}</p> <p>}<br> </p> <pre>@misc{EquilibratedKGMeltsZ100, author = {Svaneborg,Carsten and Everaers,Ralf}, title = {Equilibrated Kremer-Grest polymer melts of M=500 linear chains with Z=100 entanglements for varying chain stiffness.}, month = feb, year = 2023, publisher = {Zenodo}, version = {1.0}, doi = {10.5281/zenodo.7319837}, url = {https://doi.org/10.5281/zenodo.7319837} }</pre>
Deciphering methylation effects on S2(ππ∗) internal conversion in the simplest linear α,β-unsaturated carbonyl
<p>Here you will find the dataset related to the article entitled: Deciphering methylation effects on S2(ππ∗) internal conversion in the simplest linear α,β-unsaturated carbonyl.</p> <p>------------------------------------------------------------------------------</p> <p><strong>critical_points_geometries.zip</strong></p> <p>This repository contains the XYZ files of the critical points computed at the hh-TDA-ωPBEh/6-31G(d,p) (hh-TDA) and SA5-XMS(Im=0.3)-CASPT2(10,9)/cc-pVDZ (XMSPT2) levels of theory. Also, exmaple input files for MECI and geometry optimizations for TeraChem (hh-TDA level) and BAGEL (XMS-CASPT2).</p> <p>Notation:</p> <ul> <li>AC - Acrolein </li> <li>CR - Crotanaldehyde</li> <li>MVK - Methylvinylketone </li> <li>MA - Methacrolein </li> <li>S0min - Minimum of S0 electronic state</li> <li>S1min - Minimum of S1 electronic state</li> <li>S2min - Minimum of S2 electronic state</li> <li>S1S0_MECI - Minimum energy conical intersection at S1 and S0 electronic states intersection</li> <li>S2S1_MECI - Minimum energy conical intersection at S2 and S1 electronic states intersection</li> <li>NTpyr, CCpyr, N.... relates to the label of different MECI structures. Information regarding this nomenclature can be found in the Supporting Information of the paper.</li> </ul> <p>Computational details and extra information regarding these structures can be found in the main text and supporting information of the article. </p> <p>------------------------------------------------------------------------------------------------------------------------------------------</p> <p><strong>AIMS_ICs.zip</strong></p> <p>This repository contains all the initial conditions (ICs) sampled to perform the ab-initio multiple spawning dynamics simulations for acrolein (AC), crotanaldehyde (CR), methylvinylketone (MVK), and methacrolein (MA).</p> <p>For AC, CR, MVK and MA, 50 ICs were randomly sampled from a narrow window of 0.05 eV around 6.20 eV (from the calculated absorption spectra)</p> <p>For CR, 10 additional ICs were also sampled in addition to the 50, to ensure that the observed stalling in population decay around 600-900fs in not due to undersampling.</p> <p>All IC files are named as ICXXXX.dat where XXXX = randomly selected IC number</p> <p>Each IC file contains the cartesian coordinates in Bohr and the corresponding nuclear velocities in Bohr/atomic unit.</p>
Linear breaking strength of porcine cystic ducts and distance to the gallbladder are not associated to allometric parameters
<p>Raw data for the analyses in the manuscript with the same title.</p>
The equation of state for neutron star matter has been obtained through Bayesian inference utilizing a relativistic mean field model with a non-linear mesonic interaction.
<p>The equation of state for matter in neutron stars has been obtained through Bayesian inference utilizing a relativistic mean field model with a non-linear mesonic interaction.</p> <p>----------------------------<br> Dr. Tuhin Malik<br> Department of Physics, University of Coimbra<br> tm@uc.pt<br> Date: 22 Apr 2023<br> -----------------------------<br> The high density behavior of nuclear matter is analyzed within a relativistic mean field description with non-linear meson interactions. To assess the model parameters and their output, a Bayesian inference technique is used. The Bayesian setup is limited only by a few nuclear saturation properties, the neutron star maximum mass larger than 2 M$_\odot$, and the low-density pure neutron matter equation of state (EOS) produced by an accurate N$^3$LO calculation in chiral effective field theory. Depending on the strength of the non-linear scalar vector field contribution, we have found three distinct classes of EOSs, each one correlated to different star properties distributions. If the non-linear vector field contribution is absent, the gravitational maximum mass and the sound velocity at high densities are the greatest. However, it also gives the smallest speed of sound at densities below three times saturation density. On the other hand, models with the strongest non-linear vector field contribution, predict the largest radii and tidal deformabilities for 1.4 M$_\odot$ stars, together with the smallest mass for the onset of the nucleonic direct Urca processes and the smallest central baryonic densities for the maximum mass configuration. {These models have the largest speed of sound below three times saturation density, but the smallest at high densities, in particular, above four times saturation density the speed of sound decreases approaching approximately $\sqrt{0.4}c$ at the center of the maximum mass star. On the contrary, a weak non-linear vector contribution gives a monotonically increasing speed of sound.} {A 2.75 M$_\odot$ NS maximum mass was obtained in the tail of the posterior with a weak non-linear vector field interaction. This indicates that the secondary object in GW190814 could also be an NS. {The possible onset of hyperons and the compatibility of the different sets of models with pQCD are discussed. It is shown that pQCD favors models with a large contribution from the non-linear vector field term or which include hyperons.}}</p> <p>The article e-Print: <a href="https://arxiv.org/abs/2301.08169">2301.08169</a></p> <p>We release model parameters, its nuclear saturation properties, equation of state, and TOV solutions derived from Bayesian Inference with Prior Set 0, 1, 2, and 3. We also share Set 0 with Hyperon. <br> <br> For every Set, our data release packet contains four CSV files, namely "set{X}_prop.csv", "set{X}_eos.csv", "set{X}_tov.csv", and "set{X}_cs2.csv", where X in [0,1,2,3 and 0_hyp].<br> <br> set{X}_prop.csv:<br> The file contains the parameters for the RMF model, as well as a few NS properties and nuclear saturation properties. It has the following columns:<br> model name,gs,gv,gr,B,C,xi,lam,rho0,e0,k0,q0,z0,jsym0,lsym0,<br> ksym0,qsym0,zsym0,m_max,r_max,r14,lam14,cs2_max, ec,rhoc,rho_durca.<br> It is to be noted that the parameter B and C are the 10^3*b and 10^3 c (see article for details). <br> <br> set{X}_eos.csv:<br> For those models in set{X}_prop.csv, it is the NS matter EOS file. It has the following columns: model name, baryon number density, energy density and pressure. The units for baryon number density is fm-3 and MeV/fm3 is for both energy density and pressure. The EOS is for the core only. The crust is not added. <br> <br> set{X}_tov.csv:<br> For those models in set{X}_prop.csv, it is the TOV solution. It has the following columns: model name, ns radius (km), ns mass (msun), and dimensionless tidal deformability lambda. </p> <p>set{X}_cs2.csv:<br> For those models in set{X}_prop.csv, it is the square of the speed of sound over density. It has the following columns: model name, number density fm-3, and square of the speed of sound c2. <br> -------------------------------------------------------------------------</p>
Dataset for "VHEE beam dosimetry at CERN Linear Electron Accelerator for Research under ultra-high dose rate conditions"
<p>Dataset for "VHEE beam dosimetry at CERN Linear Electron Accelerator for Research under ultra-high dose rate conditions"</p> <p>Daniela Poppinga <em>et al</em> 2021 <em>Biomed. Phys. Eng. Express</em> 7 015012</p> <p>https://doi.org/10.1088/2057-1976/abcae5</p> <p> </p>
Assemblies for "Linear time complexity de novo long read genome assembly with GoldRush"
<p>GoldRush is a <em>de novo</em> genome assembly algorithm with linear time complexity in the number of input long sequencing reads. We tested GoldRush on Oxford Nanopore Technologies datasets with different base error profiles describing the genomes of three human cell lines (NA24385, HG01243 and HG02055), Oryza sativa (rice), and Solanum lycopersicum (tomato). Here, we provide the assemblies for the GoldRush, Flye, Redbean and Shasta assemblies of these long read datasets.</p>
Porous Invariants for Linear Systems: POROUS Tool and experimental data
<p>POROUS Tool and experimental data for Porous Invariants for Linear Systems.</p> <p>The code is maintained at https://github.com/davidjpurser/porous-tool</p> <p>Contains:</p> <ul> <li>the code in porous-tool-May2023.zip</li> <li>the results of experiements run on a Dell PowerEdge M620 with 2x Intel Xeon E5-2667 v2 CPUs and 256GB RAM in <ul> <li>document-journal.csv as a summary file, listing each generated instance type, file name, success status, and timing information. Used as input to analysis.py.</li> <li>journal-data.zip containing individual randomly generated instances and their output</li> </ul> </li> </ul> <p>Preliminary results were presented in the paper Porous Invariants. CAV 2021 by Engel Lefaucheux, Joël Ouaknine, David Purser and James Worrell.</p>
Mechanisms of simultaneous linear and nonlinear computations at the mammalian cone photoreceptor synapse
<p>Neurons enhance their computational power by combining linear and nonlinear transformations in extended dendritic trees. Rich, spatially distributed processing is rarely associated with individual synapses, but the cone photoreceptor synapse may be an exception. Graded voltages temporally modulate vesicle fusion at a cone's ~20 ribbon active zones. The transmitter then flows into a common, glia-free volume where bipolar cell dendrites are organized by type in successive tiers. Using super-resolution microscopy and tracking vesicle fusion and postsynaptic response at the quantal level in the thirteen-lined ground squirrel, <em>Ictidomys</em> <em>tridecemlineatus</em>, we show that certain bipolar cell types respond to individual fusion events in the stream while other types respond to degrees of locally coincident events, creating a gradient across tiers that are increasingly nonlinear. Nonlinearities emerge from a combination of factors specific to each bipolar cell type including diffusion distance, contact number, receptor affinity, and proximity to transporters. Complex computations related to feature detection begin within the first visual synapse.</p>
Supporting data for "Linearity of the climate system response to raising and lowering West Antarctic and coastal Antarctic topography" by Andrew G. Pauling, Cecilia M. Bitz and Eric J. Steig
<p>Contains the model output and topography files necessary to reproduce the results of "Linearity of the climate system response to raising and lowering West Antarctic and coastal Antarctic topography" by Andrew G. Pauling, Cecilia M. Bitz and Eric J. Steig. Published in Journal of Climate, <a href="https://doi.org/10.1175/JCLI-D-22-0416.1">https://doi.org/10.1175/JCLI-D-22-0416.1</a>.</p> <p>Please download and extract the data from each of the tar.gz.files. A description of the directories, run names, and use of the topography files is given in the file readme.txt within the dataset.</p>
A Multimodal Dataset on Stainless Steel for Electrochemical Corrosion Studies: Optical Microscopy and Linear Sweep Voltammetry
<p>The upload includes optical and electrochemical data for corrosion experiments.</p> <p>This dataset presents the results of an experimental study conducted to investigate the electrochemical behavior of electropolished Stainless Steel 316L (SS316L) samples immersed in NaCl solutions. The combination of Linear Sweep Voltammetry (LSV) and optical microscopy techniques was employed to gather comprehensive insights into the electrochemical processes occurring on the surface of the stainless steel samples.</p> <p>The samples used in the experiment were electropolished SS316L, chosen for its widely recognized corrosion resistance properties and frequent application in various industrial sectors. LSV was performed on the samples in a potential range of -0.5V to 1.35V, (vs 3.4M KCl Ag/AgCl). NaCl solutions with concentrations of 5mM, 10mM, and 50mM were prepared to simulate different electrolyte conditions.</p> <p>Two different scan rates, 50mV/s and 100mV/s, were applied during the LSV experiments to observe the effect of varying scan rates on the electrochemical behavior of the SS316L samples. The scan rates were chosen to cover a range commonly encountered in electrochemical studies.</p> <p>List of experiments:</p> <ul> <li> 5 mM solution, 100mV/s scan rate</li> <li> 10 mM solution, 50mV/s scan rate</li> <li> 10 mM solution, 100mV/s scan rate</li> <li> 50 mM solution, 50mV/s scan rate</li> <li> 50 mM solution, 100mV/s scan rate</li> </ul> <p>The dataset is accompanied by animated plots. The top left plot shows electrochemistry data, bottom left - average normalized intensity and derivative of intensity. Top right - original optical images, bottom right - normalized images.</p> <p>The scale for optical images: 1px = 480 nm. Axes on images are in pixels</p> <p>Jupyter notebook with the code, used to create videos included. We recommend opening the Jupyter notebook file in a Python 3 environment.<br> </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.