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1,179 results for “Probe”
NOAA PSL Soil Moisture and Surface Temperature Probe Data for SPLASH
<p>This dataset contains measurements from a hand-held FieldScout TDR Soil Moisture Meter within the 0-10 cm soil depth of: Time (UTC), GPS locations, Electrical Conductivity (EC), compensated percent volumetric water content (VWC), soil surface temperature (T), and rod length (inches) obtained during the Study of Precipitation, the Lower Atmosphere, and Surface for Hydrology (SPLASH) campaign sponsored by the National Oceanic and Atmospheric Administration (NOAA). These data were collected around the SPLASH campaign areas near Avery Picnic (38.972425 degrees N,106.996855 degrees W) and Kettle Ponds (38.942005 degrees N,106.973006 degrees W) in the East River Watershed in Colorado from between June 1st, 2022 and September 18th, 2023, under support from the NOAA Physical Sciences Laboratory and NOAA Weather Program Office under award NA21OAR4590363.</p><p>Two file formats are provided: one version is text csv format and the second version is in NetCDF.</p><p><strong>Volumetric water content calculations: </strong></p><p>Data were calibrated and adjusted, with a soil-specific sample set, to improve accuracy and compensate for the meter's default "standard" soil type used in the sampling. VWC data was correlated by measuring the weight of a known volume of soil from a range of saturation values. Samples were measured and weighed, dried at 105 degrees C for 48 hours, then weighed again. Calculations of VWC (VWC<strong> </strong>= 100*(Mwet - Mdry)/(w*Vtot) )were plotted against TDR readings. Where: </p><p>Mwet, Mdry = mass (g) of wet and dry soil respectively </p><p>Vtot = total soil volume (ml) </p><p>w = density of water (1g/ml) </p><p>A regression analysis to correlate TDR readings to the samples is below and was applied to the dataset.</p><p>vwc_calculated = vwc_probe * slope + intercept</p><p>slope = 1.20665, intercept = 0.0837017 m3/m3, slope_std_error = 0.09229, intercept_std_error = 0.0217403 m3/m3</p><p><strong>Definitions:</strong></p><p>TDR (Time Domain Reflectometry): A technique for measuring soil moisture content that uses the fact that water has a much higher dielectric permittivity than air, soil minerals, and organic matter. </p><p>VWC (Volumetric Water Content): The ratio of the volume of water in a given volume of soil to the total soil volume expressed as a decimal or a percentage. The percent of the soil volume that is filled with water. At saturation, the VWC will equal the soil porosity (Saturation is typically around 50%).</p><p>EC (Electrical Conductivity): A measure of how well the soil solution conducts electricity. The EC is influenced by the amount of salt and water in the soil. </p><p>The VWC measured by TDR is an average over the length of the waveguide. </p><p><strong>Soil Characteristics:</strong></p><p>Soil at both Kettle Ponds (KEP1 and KPA) locations and Avery Picnic (AYP) were lab tested for composition as follows:</p><p><strong>Sample ID Depth(in.) Sand(%) Silt(%) Clay(%) Soil Texture</strong></p><p>------------------------------------------------------------------------------------------------------ </p><p>KEP1 2 43 35 22 Loam</p><p>AYP 2 40 35 25 Loam</p><p>KPA 2 35 42 22 Loam</p><p>------------------------------------------------------------------------------------------------------</p>
Dataset related to the publication "Sub-Doppler optical-optical double-resonance spectroscopy using a cavity-enhanced frequency comb probe"
<p>The files contain </p><p>1. Binary files with normalized and interleaved double-resonance spectra recorded with three different pump transitions and two different relative pump-probe polarizations, indicated in the file name. These spectra are the results of 5 measurements.</p><p>2. Binary file with 45 normalized and interleaved double-resonance spectra recorded with pump on the R(2, <i>F2</i>) transition and parallel relative pump-probe polarization.</p><p>2. Data for Figures 4, S1 and S3 in the paper.</p><p> </p>
Data and code for figures: Design, fabrication and characterization of kinetic-inductive force sensors for scanning probe applications
<p>This directory contains the datasets, code (if applicable) for measurement libraries, data processing and figure generation for the research article "Design, fabrication and characterization of kinetic-inductive force sensors for scanning probe applications", Beilstein J. Nanotechnol. 2024, 15, 242-255.</p>
Data Grids for examples in Probe Particle Atomic Force Microscopy simulation program (ppafm)
<p>These files are used for running the examples for [ppafm](https://github.com/Probe-Particle/ppafm/) program.</p> <p>The grids are stored in in [.xsf](http://www.xcrysden.org/doc/XSF.html) and [.cube](https://paulbourke.net/dataformats/cube/) format.</p> <p>The data set compiles both the new examples used in paper [Advancing scanning probe microscopy simulations: A decade of development in probe-particle models](https://www.sciencedirect.com/science/article/pii/S0010465524002649) as well as older examples.</p> <p>Notice that the structure does not exactly reflect the directory structure in the [example folder of ppafm](https://github.com/Probe-Particle/ppafm/tree/main/examples) to prevent possible redudancy, but is instead flatenized and sorted by molecules.</p>
Dataset of the publication: Probing the spin dimensionality in single-layer CrSBr van der Waals heterostructures by magneto-transport measurements
<p>Dataset of the publication: Probing the spin dimensionality in single-layer CrSBr van der Waals heterostructures by magneto-transport measurements</p> <p>DOI: 10.1002/adma.202204940</p> <p>C. Boix-Constant, S. Mañas-Valero, A. M. Ruiz, A. Rybakov, K. A. Konieczny, S. Pillet, J. J. Baldoví, E. Coronado</p> <p>Adv. Mater., 34, 2204940 (2022)</p>
Dataset of the publication: Probing Short-Range Correlations in the van der Waals Magnet CrSBr by Small-Angle Neutron Scattering
<p>Dataset of the publication: Probing Short-Range Correlations in the van der Waals Magnet CrSBr by Small-Angle Neutron Scattering</p> <p>DOI: 10.1002/smsc.202400244</p> <p>A. Rybakov, C. Boix-Constant, D. Alba Venero, H. S. J. van der Zant, S. Mañas-Valero, E. Coronado</p> <p>Small Science, 4, 8, 2400244 (2024)</p>
Overcoming the Probing-Depth Dilemma in Spectroscopic Analyses of Batteries with Muon-Induced X-ray Emission (MIXE)
<p>Datasets used in the publication "Overcoming the Probing-Depth Dilemma in Spectroscopic Analyses of Batteries with Muon-Induced X-ray Emission (MIXE)".</p> <p>fig_2: MIXE spectra of (a) an empty laminated Al pouch, (b) a Li metal foil in a laminated Al pouch, (c) a NMC622 electrode in a laminated Al pouch</p> <p>fig_3: MIXE spectrum of a NMC811 electrode in a laminated Al pouch, measured at 23.8 MeV/c</p> <p>fig_4b: Muon stopping profile simulated using PHITS for the cell geometry depicted in Figure 4a of the main manuscript</p> <p>fig_4c: Depth-resolved MIXE spectra of a NMC811||graphite Li-ion battery. Raw data at the 11 momenta measured, and table with the integrated peak areas for selected (K-L) lines.</p> <p><strong><em>Update in version 2: raw datasets now have one energy column for each momentum. The datasets are of different lengths for each momentum and there was and error in copying the data in version 1. </em></strong></p> <p> </p> <p>fig_4c: Table with calculated elemental ratios of the different transition metals (Ni, Mn and Co), at the momenta corresponding to implantation in the NMC811 electrode</p> <p>fig_s2: MIXE spectrum of a NMC622 electrode in a laminated Al pouch, measured at 23.0 MeV/c</p> <p>fig_s3: MIXE spectrum of a NMC111 electrode in a laminated Al pouch, measured at 22.8 MeV/c</p> <p>fig_s4_s5_simulations: Raw data of the muon implantation simulations for the NMC811/graphite cell </p> <p><strong><em>Update in version 2: added fig_s4_s5_simulations file</em></strong></p> <p> </p> <p>fig_s6: Labelled MIXE spectra (all peaks identified) of a NMC811 electrode in a laminated Al pouch, measured at 24.0, 26.0 and 28.0 MeV/c</p>
Novel Peptide-Based PET Probe for Non-invasive Imaging of C-X-C Chemokine Receptor Type 4 (CXCR4) in Tumors
<p>These are RAW data datasets of the following final paper</p> <p>Trotta, A.M., Aurilio, M., D'Alterio, C., Ieranò, C., Di Martino, D., Barbieri, A., Luciano, A., Gaballo, P., Santagata, S., Portella, L., Tomassi, S., Marinelli, L., Sementa, D., Novellino, E., Lastoria, S., Scala, S., Schottelius, M., Di Maro, S.</p> <p>Novel Peptide-Based PET Probe for Non-invasive Imaging of C-X-C Chemokine Receptor Type 4 (CXCR4) in Tumors, (2021) Journal of Medicinal Chemistry, 64 (6), pp. 3449-3461. ISSN 00222623</p> <p>https://doi.org/10.1021/acs.jmedchem.1c00066</p> <p>Abstract</p> <p>The recently reported CXCR4 antagonist 3 (Ac-Arg-Ala-[DCys-Arg-2Nal-His-Pen]-CO2H) was investigated as a molecular scaffold for a CXCR4-targeted positron emission tomography (PET) tracer. Toward this end, 3 was functionalized with 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) and 1,4,7-triazacyclononanetriacetic acid (NOTA). On the basis of convincing affinity data, both tracers, [68Ga]NOTA analogue ([68Ga]-5) and [68Ga]DOTA analogue ([68Ga]-4), were evaluated for PET imaging in “in vivo” models of CHO-hCXCR4 and Daudi lymphoma cells. PET imaging and biodistribution studies revealed higher CXCR4-specific tumor uptake and high tumor/background ratios for the [68Ga]NOTA analogue ([68Ga]-5) than for the [68Ga]DOTA analogue ([68Ga]-4) in both in vivo models. Moreover, [68Ga]-4 and [68Ga]-5 displayed rapid clearance and very low levels of accumulation in all nontarget tissues but the kidney. Although the high tumor/background ratios observed in the mouse xenograft model could partially derive from the hCXCR4 selectivity of [68Ga]-5, our results encourage its translation into a clinical context as a novel peptide-based tracer for imaging of CXCR4-overexpressing tumors.</p> <p> </p> <p> </p>
Utilizing cosmic-ray positron and electron observations to probe the averaged properties of Milky Way pulsars
<p>We include here the Milky Way pulsars simulations that were created and used in "Utilizing cosmic-ray positron and electron observations to probe the averaged properties of Milky Way pulsars" of Cholis & Krommydas 2021. We include both the simulations before fitting to the cosmic-ray observations and the simulations whose electron and positron fluxes have been fitted to the AMS, CALET and DAMPE observations. See paper for further details.</p>
Probing Ion Channel Functional Architecture and Domain Recombination Compatibility by Massively Parallel Domain Insertion Profiling
<p>Supplementary Data for a large insertional profiling study described in Coyote-Maestas et al. (2021) Nature Communications.</p>
Statistical data for "Van Allen Probes Observations of Oxygen Ion Cyclotron Harmonic Waves: Statistical Study "
<p>This file contains parameters for the identified oxygen ion cyclotron harmonic (OCH) waves observed by Van Allen Probes. The meaning of each column is shown as follows.</p> <p>Column #1: the name of Van Allen Probe, A or B.</p> <p>Column #2: event start day</p> <p>Column #3: event end day</p> <p>Column #4: event start hour</p> <p>Column #5: event strat minute</p> <p>Column #6: event end hour</p> <p>Column #7: event end minute</p> <p>Column #8: MLT, columns(t, MLT)</p> <p>Column #9: MLAT, columns(t, MLAT)</p> <p>Column #10: L-shell, columns(t, L-shell)</p> <p>Column #11: lower frequency limit</p> <p>Column #12: upper frequency limit</p> <p>Column #13: distance to the plasmapause, the positive and negative values correspond to outside and inside the plasmapause, respectively.</p> <p>Column #14: wave normal angle, columns (t, WNA)</p> <p>Column #15: the ratio of the frequency spacing between two consecutive wave harmonics to the local oxygen ion gyrofrequency. </p> <p>Column #16: root-mean-square amplitude, columns(t, Bw)</p> <p>Column #17: AE*</p>
Large-scale neural recordings with single neuron resolution using Neuropixels probes in human cortex
<p><span>Recent advances in multi-electrode array technology have made it possible to monitor large neuronal ensembles at cellular resolution in animal models. In humans, however, c</span>urrent approaches restrict recordings to few neurons per penetrating electrode or combine the signals of thousands of neurons in local field potential (LFP) recordings. Here, we describe a new probe variant and set of techniques which enable simultaneous recording from over 200 well-isolated cortical single units in human participants during intraoperative neurosurgical procedures using silicon Neuropixels probes. We characterized a diversity of extracellular waveforms with eight separable single unit classes, with differing firing rates, locations along the length of the electrode array, waveform spatial spread, and modulation by LFP events such as inter-ictal discharges and burst suppression. While some challenges remain in creating a turn-key recording system, high-density silicon arrays provide a path for studying human-specific cognitive processes and their dysfunction at unprecedented spatiotemporal resolution. </p>
Quantitative electrostatic force tomography for virus capsids in interaction with an approaching nanoscale probe
<p>This repository contains the simulated data of a simple electrostatic model, based on the Poisson-Boltzmann equation, that quantifies the subnanometric electrostatic interactions between an AFM tip and a proteinaceous capsid (Zika Virus) from molecular snapshots. This allows us to describe the contributions of specific amino acids and atoms to the interaction force.</p> <p>The contains of this repository can be easily visualized through Jupyter Notebooks contained here:</p> <p>https://github.com/pyF4all/eTipVirusForce</p>
Dataset for Direct Geometric Probe of Singularities in Band Structure
<p>Included here is the processed data illustrated in the figures of both the main text, and the supplemental material. Below is a description of each file's contents.</p> <p> </p> <p><strong>Figure2Dcode.m</strong> contains the MATLAB code that generates Figure 2D of the main text. It takes the band populations inferred from five iterations of measurements, and calculates the means and standard errors for data taken at each theta as defined in the main text.</p> <p> </p> <p><strong>Figure2Ddata.csv</strong> contains the data illustrated in Figure 2D of the main text. The data provided are normalized band populations, such that the value 1 corresponds to the entire atom number in the sample. The rows provide the band index; the first row of data corresponds to the n=1 band, the second row corresponds to the n=2 band, etc. The columns provide the measured turning angle in units of radians; the first column corresponds to a turning angle of zero, and the angle is incremented by pi/12 radians for each column that follows. Row 5 is the error for the n=1 population, row 6 is the error for the n=2 population, and row 7 is the error on the sum of the population in bands with index not equal to 1 or 2.</p> <p> </p> <p><strong>Figure3Bcode.ipynb</strong> contains the jupyter notebook that generates Figure 3B of the main text. It takes the band populations inferred from four iterations of measurements, and calculates the means and standard errors for data taken for each intermediate point along K - M - K'. For this plot, the x-axis is chosen to be the intermediate quasi-momenta, and different colors are used to differentiate between different acceleration times.</p> <p> </p> <p><strong>Figure3Bdata.csv</strong> contains the data illustrated in Figure 3B of the main text. The five columns correspond to the five different trajectory evolution times (0.5, 0.9, 1.3, 1.7, 2.1 milliseconds) shown in the Figure 3B. The first nine rows correspond to the nine trajectory midpoint positions in the Brillouin zone, as showed in Figure 3A; the first row corresponds to a midpoint at <strong>K</strong>. The next nine rows are the errors on the measurements.</p> <p> </p> <p><strong>Figure4Ccode.m</strong> contains the MATLAB code that generates Figure 4C of the main text. It takes the band populations inferred from twelve iterations of measurements, each at a different theta as defined in the main text, and calculate the means and standard errors for data taken at each theta.</p> <p> </p> <p><strong>Figure4Cdata.csv </strong>contains the data illustrated in Figure 4C of the main text. The data provided are normalized band populations, such that the value 1 corresponds to the entire atom number in the sample. The rows provide the band index; the first row of data corresponds to the n=1 band, the second row corresponds to the n=2 band, etc. The columns provide the measured turning angle in units of radians; the first column corresponds to a turning angle of zero, and the angle is incremented by pi/6 radians for each column that follows. Row 11 is the error for the n=3 population, row 12 is the error for the n=4 population, and row 13 is the error on the sum of the population in bands with index not equal to 3 or 4.</p> <p> </p> <p><strong>FigureS3Bcode.m</strong> contains the MATLAB code that generates Figure S3B of the main text. It takes the band populations inferred from seven iterations of measurements, and calculates the means and standard errors for data taken for each hold time at quasi-momentum Q as defined in the main text. The result is then fitted to a sine with exponentially decaying envelope.</p> <p><strong>push_ramp.py </strong>(in<strong> Full Hamiltonian simulation.zip</strong>) starts with an initial state and evolves it according to the discretized schrödinger equation along the path in q-space. The Hamiltonian is calculated in <strong>basic_fcts.py</strong>. The final state is projected on the eigenstates at the final q to extract the band population. Different time intervals are used to obtain all the data. A decay to account for coherence loss is added.</p> <p> </p> <p><strong>FigureS3data.csv </strong>contains the data illustrated in Figure 3 of the supplementary material. The first row is the data values, and the second row are the error bars.</p> <p> </p> <p><strong>FigureS4Bcode.m</strong> contains the MATLAB code that generates Figure S4B of the main text. It takes the band populations inferred from four iterations of measurements, and calculates the means and standard errors for data taken for each intermediate point along K - M - K'. For this plot, the x-axis is chosen to be acceleration time, and different colors are used to differentiate between different intermediate points.</p> <p><strong>push_ramp.py </strong>(in<strong> Full Hamiltonian simulation.zip</strong>) starts with an initial state and evolves it according to the discretized schrödinger equation along the paths in q-space. The Hamiltonian is calculated in <strong>basic_fcts.py</strong>. The final state is projected on the eigenstates at the final q to extract the band population. Different time intervals are used to obtain all the data.</p> <p> </p> <p><strong>FigureS4data.csv </strong>contains the data illustrated in Figure 4 of the supplementary material. The first five rows are the normalized ground band population for five different trajectory mid points on the <strong>K</strong> - <strong>M</strong> - <strong>K'</strong> line of the Brillouin zone.; the first row is for a midpoint at <strong>K</strong>, and the fifth row is for a midpoint at <strong>M</strong>. The columns give the trajectory traversal times; the first column corresponds to a traversal time of 0.1 ms and each column corresponds to a new traversal time incremented by 0.2 ms. Rows 6-10 are the error bars for the measurements.</p> <p> </p> <p><strong>FigureS5Bcode.zip</strong> contains the codes that generate Figure S5B of the main text. For each subplots in Fig.S5B, the corresponding MATLAB code in the zip file takes the band populations inferred from three iterations of measurements, and calculate the means and standard errors for data taken at each acceleration time.</p> <p> </p> <p><strong>FigureS5Bdata.csv </strong>contains the data illustrated in Figure 5B of the supplementary material. Rows 1-20 correspond to subpanel (iii) in the Figure S5 of the supplementary material. Rows 21-40 correspond to subpanel (ii) in the Figure S5 of the supplementary material. Rows 31-60 correspond to subpanel (i) in the Figure S5 of the supplementary material.</p> <p>Rows 1-10 correspond to the band index and give the normalized band population; row 1 corresponds to band index n=1 and row 10 corresponds to band index n=10. Rows 21-30 correspond to the band index and give the normalized band population; row 21 corresponds to band index n=1 and row 30 corresponds to band index n=10. Rows 41-50 correspond to the band index and give the normalized band population; row 41 corresponds to band index n=1 and row 50 corresponds to band index n=10.</p> <p>Rows 11-20 (31-40) [51-60] give the error in the band populations for measurements in panel iii (ii) [i].</p> <p> </p> <p><strong>FigureS5Cdata.csv </strong>contains the data illustrated in Figure 5C of the supplementary material. The first (second) column is the vertical (horizontal) axis. The fourth (third) column is the error in the points on the vertical (horizontal) axis.</p> <p> </p> <p><strong>push_ramp.py </strong>(in<strong> Full Hamiltonian simulation.zip</strong>) starts with an initial state and evolves it according to the discretized schrödinger equation along the path in q-space. The Hamiltonian is calculated in <strong>basic_fcts.py</strong>. In figure S6A, at each point in time shown the state is projected onto the instantaneous eigenbasis and the different band populations are extracted. In figure S6B and figure S6C, the whole experiment sequences corresponding to figure 2 and figure 4 in the main text are simulated, and the final population obtained is plotted, with the measurement results copied for reference.</p> <p> </p> <p><strong>FigureS7code.nb</strong> contains the mathematica notebook that generate Figure S7 of the main text. This code uses the two-band model described in the supplemental material to perform simulation.</p> <p> </p> <p><strong>Image_fitting.zip</strong> contains the MATLAB code and functions that were used to analyze the band mapping images. <strong>multiboxFit_v7_1.m</strong> is the main code that uses other MATLAB functions in the zip file. Overall, it takes absorption images as input, finds the position of each peak (<strong>BoxGenerator_v1_0.m</strong>), fit for the population in each peak in the images (<strong>createFit2D.m</strong>), assign the correct band number given the final quasi-momentum in the sequence (<strong>BoxesBandsThing_v2.m</strong>), and finally plot the inferred band populations, along with a visualization of the original images overlain with a Brillouin zone (<strong>PlotBZ_v2.m</strong>). The result of fits are saved in a separate file that are accessed by other analysis codes. Figure S2 and Figure S5C are also generated with this code.</p> <p> </p> <p>Additional codes <strong>Q_path_BZ.py</strong>,<strong> group_velo.py </strong>&<strong> diffr_img.py</strong> are included in<strong> Full Hamiltonian simulation.zip</strong> to ensure the correct functionality of the codes included.</p>
Dataset to "Hydrogen induced trap states in TiO2 probed by resonant X-ray photoemission"
<p>Dataset to "Hydrogen induced trap states in TiO2 probed by resonant X-ray photoemission" as published in Proceedings of the International Conference on X-Ray Lasers 2020</p>
DATASET: Using auxiliary electrochemical working electrodes as probe during contact glow discharge electrolysis: A proof of concept study
<p>This project contains all the data shown in the figures of the manuscript (and the supporting information) entitled:<br> 'Using auxiliary electrochemical working electrodes as probe during contact glow discharge electrolysis: A proof of concept study'<br> (doi:10.26434/chemrxiv-2022-0v5sc).</p> <p>The data to each figure is provided in a subfolder where each curve is stored as a single CSV.<br> The filenames contain labels describing the curves.</p>
Probing the Extent of Vertical Mixing in Brown Dwarf Atmospheres with Disequilibrium Chemistry
<p><strong>OVERVIEW</strong></p> <p>The substellar atmospheric models described in <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220814317M/abstract">Mukherjee et al. (2022)</a> are presented here. The grid of these 1D radiative-convective atmospheric models was computed using the newly released open-source climate code PICASO 3.0 (<a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220807836M/abstract">Mukherjee et al. (2022)</a>, <a href="https://ui.adsabs.harvard.edu/abs/2019ApJ...878...70B/abstract">Batalha et al. (2019)</a>). The grid consists of four parameters – the effective temperature (T<sub>eff</sub>), gravity (log(g)), K<sub>zz</sub> in the radiative zones, and mixing length in the convective zones. Models with T<sub>eff </sub> between 400-1000 K with an increment of 25 K are included. log(g) has been varied from 4.5 to 5.5 with an increment of 0.25 dex. The K<sub>zz</sub> in the radiative zone has been varied between 1x, 0.01x, and 100x the parametrization presented in <a href="https://ui.adsabs.harvard.edu/abs/2022ExA....53..279M/abstract">Moses et al. (2021)</a>, whereas the convective mixing length has been between the atmospheric pressure scale height and 0.1x the scale height.</p> <p>There are three types of files released here – atmospheric composition files (TP_chemistry), thermal emission spectra files (spectra), and atmospheric Kzz profile files (TP_kz). </p> <p><strong>ATMOSPHERIC COMPOSITION</strong></p> <p>The atmospheric composition files are located in the folder TP_chemistry. These files have the temperature structure of the atmosphere as a function of pressure accompanied by the volume mixing ratio of 37 gases as a function of pressure. </p> <p>The TP_chemistry files are named following the format “profile_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.dat", where [factor1] denotes the multiplier used for the radiative zone Kzz and can vary between ‘x0pt01’, ‘x1’, and ‘x100’. [factor2] denotes the multiplier for the mixing length and can vary between ‘1’ and ‘0pt1’. [Teff] and [gravity] denote the Teff and gravity of the models used. A simple code snippet to read and plot these files is presented below.</p> <p><strong>SPECTRA</strong></p> <p>The spectra files are located in the folder "spectra_highres_1", "spectra_highres_2", "spectra_highres_3", and "spectra_highres_4". These files have the thermal emission spectra between 0.3-30 microns calculated using the computed models. The native spectral resolution of these calculations is at an R = 500,000, but <strong>please be aware that these spectra should always be binned down to a resolution of R = 50,000 or less before usage</strong>. This means that these spectra should only be used to interpret datasets with a spectral resolution of 50,000 or less. Please contact the authors if higher resolution spectra are needed. The spectra have been uploaded in three different folders to make the file sizes manageable for transfer.</p> <p>The spectra files are also similarly named using the format “spectra_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.tar.gz". These files can be directly read into a Python pandas dataframe using </p> <pre><code class="language-python">pd.read_csv(filename, compression='gzip')</code></pre> <p> The first column of the file is wavenumbers in cm<sup>-1, </sup>which can be converted to wavelength in microns by wavelength [microns] =10000/wavenumbers[cm<sup>-1</sup>]. The second column of the file is flux in erg/s/cm<sup>2</sup>/cm. Note that these fluxes need to be multiplied with R<sup>2</sup>/D<sup>2</sup> before comparing them with the typically observed flux of brown dwarfs/exoplanets. R is the radius of the object, and D is the distance here. A tutorial to convert these fluxes to other units is present in <a href="https://natashabatalha.github.io/picaso/notebooks/6_BrownDwarfs.html#Convert-to-F_\nu-Units-and-Regrid">this link</a>. A binned-down version (R=15,000) of these high-resolution spectra can also be found in the "spectra_lowres" folder. These can be used for datasets that have a maximum spectral resolution of 15,000.</p> <p><strong>K<sub>zz</sub> PROFILE</strong></p> <p>The K<sub>zz </sub>as a function of pressure for each model is presented in these files. The K<sub>zz</sub> is reported in cm<sup>2</sup>/s. These files are also similarly named using the format “kz_sc_qt_rz_[factor1]_cz_[factor2]_[Teff]_grav_[gravity]_mh_+0.0_sm_NA.dat". The columns of the files are pressure in bars, the temperature in K, and Kzz in cm<sup>2</sup>/s.</p> <p> </p> <p><strong>EXAMPLE PYTHON CODE TO READ AND PLOT COMPOSITION FILES</strong></p> <pre><code class="language-python">import numpy as np import pandas as pd import matplotlib.pyplot as plt grav = np.array([316,562,1000,1780,3160]) Teff=np.array([400,425,450,475,500,525,550,575,600,625,650,675,700,725,750,775,800,825,850,875,900,925,950,975,1000]) factor1 = np.array(['x0pt01','x1','x100']) factor2 = np.array(['1','0pt1']) file ="profile_sc_qt_rz_"+factor1[0]+"_cz_"+factor2[0]+"_"+str(Teff[14])+"_grav_"+str(grav[14])+"_mh_+0.0_sm_NA.dat" df = pd.read_csv(file, sep="\t") # Plot T(P) profile plt.ylim(100,1e-4) plt.semilogy(df['temperature'],df['pressure']) plt.show() # Plot H2O mixing ratio profile plt.ylim(100,1e-4) plt.loglog(df['H2O'],df['pressure']) plt.show()</code></pre> <p><strong>CREDITS</strong></p> <p>If you use these tables, please cite <a href="https://ui.adsabs.harvard.edu/abs/2022arXiv220814317M/abstract">Mukherjee et al. (2022, Astrophysical Journal, in press.)</a></p> <p> </p>
X-ray diffraction images recorded for Aumonier et al., (2022) Slow protein dynamics probed by time-resolved oscillation crystallography at room temperature, IUCrJ
<p>The present repository contains diffraction images corresponding to 27 distinct datasets collected at room temperature on the ESRF beamline ID30A-3 using an Eiger X 4M detector.</p> <p>Datasets have been uploaded with their original names to maintain the metadata integrity. The two following tables match the original names with those attributed in the supplementary table S1 of Aumonier et al., IUCrJ (2022) (https://doi.org/10.1107/S2052252522009150).</p> <table> <tbody> <tr> <td> <p>Data set name on Zenodo</p> </td> <td> <p>X06_01</p> </td> <td> <p>X12_05</p> </td> <td> <p>X07_02_</p> </td> <td> <p>X06_08</p> </td> <td> <p>X14_06</p> </td> <td> <p>X13_03</p> </td> <td> <p>X08_06</p> </td> <td> <p>X11_05</p> </td> <td> <p>X13_05</p> </td> <td> <p>X06_02</p> </td> <td> <p>X11_01</p> </td> <td> <p>X08_01</p> </td> <td> <p>X14_01</p> </td> <td> <p>X13_01</p> </td> <td> <p>X06_03</p> </td> </tr> <tr> <td> <p>Data set in Aumonier et al. 2022</p> </td> <td> <p>Dark</p> </td> <td> <p>PS2</p> </td> <td> <p>PS2</p> </td> <td> <p>PS3</p> </td> <td> <p>PS4</p> </td> <td> <p>PS5</p> </td> <td> <p>PS6</p> </td> <td> <p>PS7</p> </td> <td> <p>R<sub>2”</sub></p> </td> <td> <p>R<sub>3”</sub></p> </td> <td> <p>R<sub>7”</sub></p> </td> <td> <p>R<sub>10”</sub></p> </td> <td> <p>R<sub>13”</sub></p> </td> <td> <p>R<sub>21”</sub></p> </td> <td> <p>R<sub>35”</sub></p> </td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td> <p>Data set on Zenodo</p> </td> <td> <p>X08_02</p> </td> <td> <p>X11_02</p> </td> <td> <p>X12_02</p> </td> <td> <p>X14_02</p> </td> <td> <p>X13_04</p> </td> <td> <p>X13_02</p> </td> <td> <p>X12_06</p> </td> <td> <p>X06_09</p> </td> <td> <p>X09_04</p> </td> <td> <p>X12_04</p> </td> <td> <p>X06_07</p> </td> <td> <p>X13_07</p> </td> </tr> <tr> <td> <p>Data set in Aumonier et al. 2022</p> </td> <td> <p>R<sub>51”</sub></p> </td> <td> <p>R<sub>62”</sub></p> </td> <td> <p>R<sub>62”</sub></p> </td> <td> <p>R<sub>67”</sub></p> </td> <td> <p>R<sub>72”</sub></p> </td> <td> <p>R<sub>80”</sub></p> </td> <td> <p>R<sub>90”</sub></p> </td> <td> <p>R<sub>130”</sub></p> </td> <td> <p>R<sub>166”</sub></p> </td> <td> <p>R<sub>258”</sub></p> </td> <td> <p>R<sub>630”</sub></p> </td> <td> <p>R<sub>1620”</sub></p> </td> </tr> </tbody> </table> <p>One dataset consists of a master file, four data files and two metadata files.</p>
NMR titration experiments that study binding of a NIR emitting osmium polypyridyl probe to cMYC and hTel G-quadruplex DNA
<p>1D and 2D NMR spectra of cMYC and hTel G-quadruplex DNA and their complexes with Λ-<strong> </strong>and Δ<strong>-</strong>enantiomers of the osmium polypyridyl probe [Os(TAP)<sub>2</sub>(dppz)]<sup>2+</sup>. Spectra were recorded on a 600 MHz NMR spectrometer with 70 mM KCl, 20 or 25 mM K-phosphate buffer, pH 7, 298 K, in 90% H<sub>2</sub>O and 10% D<sub>2</sub>O at 25 °C.</p>
Calich lagoon multiparametric probe profile 07/06/2023
<p>This is a file containing the data collected with a multiparametric probe during a field exercise on 7 June 2023 with the students of the Planning and policies for the city, environment and landscape (CAP) course (LM–48) of the Department of Architecture, Design and Urban Planning of the University of Sassari.</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.