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638 results for “thinning”
Pushing the Study of Point Defects in Thin Film Ferrites to Low Temperatures Using In Situ Ellipsometry
<p>Dataset for article "Pushing the Study of Point Defects in Thin Film Ferrites to Low Temperatures Using In Situ Ellipsometry" published in <em>Adv. Mater. Interfaces</em> 2021, <strong>8</strong>, 2001881. </p> <p>The data includes:</p> <ul> <li>XRD data of La<sub>1-x</sub>Sr<sub>x</sub>FeO<sub>3</sub> thin films</li> <li>Optical conductivity of LSF films as a function of oxygen partial pressure and Sr content</li> <li>Concentration of electronic holes in LSF thin films as a function of oxygen partial pressure and temperature</li> <li>Defect chemistry models employed for describing the concentration of point defects in LSF thin films.</li> <li>Equilibrium constants for oxygen incorporation reactions in LSF thin films at different temperatures</li> <li>Ellipsometry raw data of LSF50 thin film as a function of equivalent oxygen pressure </li> <li>Electrochemical impedance spectra of the LSF50 thin film at 400 ºC</li> </ul>
Retrieval results for optically thin clouds in the trades
<p>ASTER satellite observations at 15 m pixel resolution are used to extract the signal of optically thin clouds during the EUREC4A field campaign (https://doi.org/10.5194/essd-2021-18). The signal of optically thin clouds is derived as a residual from the all-sky minus the simulated clear-sky (https://doi.org/10.5281/zenodo.4842675) and minus the known cloudy signal according to following a common cloud masking scheme. The paper describing the method, dataset, and results is intended for publication in the journal of Atmospheric Chemistry and Physics (ACP) under Mieslinger et al., 2021.</p> <p>The dataset includes basic information of the relevant input variables to clear-sky radiative transfer simulations as well as the resulting probability density function over reflectance values and for discrete flag values (clear-sky, optically thin clouds, clouds) given an ASTER observation. This data builds the basis for any derived quantities such as the area fraction or the expected reflectance corresponding to a certain flag value.</p>
Tree diameter growth and increment core δ13C data from a recently thinned forestry-drained site (Lettosuo) in southern Finland.
<p>Dataset includes increment core data from Lettosuo drained peatland forest site. The study site locates in the Tammela municipality in southern Finland (60° 38’ 31’’ N, 23° 57’ 35’’ E). Increment cores were analysed for the ring widths for dominant and suppressed Norway spruce trees, and for the ring δ<sup>13</sup>C values from suppressed Norway spruce trees. Data was collected as a part of BiBiFe (”Biogeochemical and biophysical feedbacks from forest harvesting to climate change”) consortium that is funded by the Academy of Finland. </p> <p> </p> <p>Sampling for increment cores was done during October 2020 for sample trees (10 in total, of which 5 were suppressed trees from thinned area and 5 suppressed trees from control area) and additional sampling was conducted for annual diameter increment for 3 tree groups to increase sample size for diameter growth (suppressed trees in thinned area [n=20], dominant trees in thinned area [n=22] and suppressed trees in control area[n=20]) during March 2021. </p> <p> </p> <p><strong>Tree </strong><strong>ring carbon isotope data</strong></p> <p> </p> <p>Laser ablation IRMS method was applied in the Stable Isotope Laboratory of Luke (SILL) to quantify δ<sup>13</sup>C values in 10 increment cores for the time period 2010–2020, following principles of Schulze et al. (2004) and described in Lehtonen et al (manuscript). Up to 11 evenly spaced “spots” for each annual tree ring were measured to obtain information on the intra-annual variation of δ<sup>13</sup>C of the samples. </p> <p> </p> <p>(1) File: Lettosuo_d13C.xls</p> <p>File includes d13C measurements</p> <p> </p> <p><strong>Data column description below for isotope data: </strong></p> <p> </p> <p><strong>id</strong> stands for tree id [id includes tree identity, year and also spot number]</p> <p><strong>year</strong> is the year of the tree ring</p> <p><strong>nr</strong> is an index for data </p> <p><strong>tree</strong> indicates tree identity "C" for control and "H" for harvest</p> <p><strong>treatment</strong> indicates the treatment of the sampling area (control / harvest)</p> <p><strong>d13C</strong> gives the measured d13C value based on the LA-IRMS measurements</p> <p><strong>season </strong>indicates whether observation originated from the earlywood (EW) or latewood (LW) period, where 1 is EW and 2 is LW</p> <p> </p> <p><strong>Tree ring width measurements</strong></p> <p> </p> <p>In addition to the δ<sup>13</sup>C values, also the ring widths were measured. Here, also additional dominant trees were measured. </p> <p> </p> <p>(3) Files:</p> <p>controlRW.csv</p> <p>dominantRW.csv</p> <p>thinningRW.csv</p> <p> </p> <p>Files include increment core data (in micrometers) from isotope sample trees and additional increment core trees from the control area and harvested area of the site. Dominant trees were measured only from the thinned area. </p> <p> </p> <p>In the .csv files individual columns are for ring widths for individual trees. In the controlRW.csv and thinningRW.csv files first 5 columns include diameter increments from sample trees (those that have also d13C measurements).</p> <p> </p> <p><strong>References:</strong></p> <p> </p> <p>Lehtonen A, Leppä K, Sahlstedt E, Schiestl-Aalto P, Heikkinen J, Young G, Korkiakoski M, Peltoniemi M, Rinne-Garmston K, Sarkkola S, Lohila A, Mäkipää R (manuscript). Fast recovery of Norway spruce trees after thinning from above on a drained peatland forest site.</p> <p> </p> <p>Korkiakoski M, Ojanen P, Penttilä T, Minkkinen K, Sarkkola S, Rainne J, Laurila T, Lohila A (2020) Impact of partial harvest on CH<sub>4</sub> and N<sub>2</sub>O balances of a drained boreal peatland forest. Agric For Meteorol 295:108168.</p> <p> </p> <p>Schulze B, Wirth C, Linke P, Brand WA, Kuhlmann I, Horna V, Schulze E-D (2004) Laser ablation-combustion-GC-IRMS--a new method for online analysis of intra-annual variation of 13C in tree rings. Tree Physiol 24:1193–1201.</p>
Data from: Lianas decelerate tropical forest thinning during succession
<p>The well-established pattern of forest thinning during succession predicts an<br> increase in mean tree biomass with decreasing tree density. The forest thinning<br> pattern is commonly assumed to be driven solely by tree-tree competition. The<br> presence of non-tree competitors could alter thinning trajectories, thus<br> altering the rate of forest succession and carbon uptake. We used a large-scale<br> liana removal experiment over 7 years in a 60-to-70-year-old Panamanian forest<br> to test the hypothesis that lianas reduce the rate of forest thinning during<br> succession. We found that lianas slowed forest thinning by reducing tree growth,<br> not by altering tree recruitment or mortality. Without lianas, trees grew and<br> presumably competed more, ultimately reducing tree density while increasing mean<br> tree biomass. Our findings challenge the assumption that forest thinning is<br> driven solely by tree-tree interactions; instead, they demonstrate that<br> competition from other growth forms, such as lianas, slow forest thinning and<br> ultimately delay forest succession.<br> </p>
Data for "Transient Density-Induced Dipolar Interactions in a Thin Vapor Cell"
<p>Data for "Transient Density-Induced Dipolar Interactions in a Thin Vapor Cell" as .npz files. The "datasets.csv" file lists, where each dataset is used in the journal publication. Additionally, there are two Python scripts (.py) which show how to load and plot a dataset. The dataset can be selected by changing the file name string in the script. Note, that for loading and plotting the simulation dataset the "plot_simulation.py" script has to be used.</p>
Data for the "Cirrus cloud thinning using a more physically-based ice microphysics scheme in the ECHAM-HAM GCM" manuscript
<p>This repository contains the post-processed data files for plotting and interpreting the results of "Cirrus cloud thinning using a more physically-based ice microphysics scheme in the ECHAM-HAM GCM" study.</p> <p>The files are all netCDF4 except for the analysis files that show global mean values in a .txt format.</p> <p>The Version 3 & Version 4 tar files are smaller than Version 2 as we performed some code clean-up for some post-processing scripts that excluded redundant data files that were very large and that were not used for plotting or the analysis for the manuscript.</p>
Tantalum thin films EUVR data
<p>S-polarized Angle-Dependent Reflectance (ADR) data measured from two Tantalum thin films deposited on Silicon substrates using monochromatized synchrotron radiation. The data was collected at the soft X-ray radiometry beamline (SX700) in the radiometry laboratory of the Physikalisch-Technische Bundesanstalt (PTB), in the electron storage facility BESSY II of Helmholtz Centre for Materials and Energy (HZB). The nominal thicknesses for the two thin films were 30.0 nm and 50.0 nm, hence, the .txt data files of the thin films samples are referred to as Sample-30 and Sample-50.</p> <p>The ADR data was collected in the Extreme Ultraviolet (EUV) range. For Sample-30 and Sample-50 the ADR was measured in the spectral ranges 5.0 nm – 24.0 nm and 10.0 nm – 20.0 nm, respectively. The EUVR of Sample-30 correspond to the angular range 3.0° – 85.5°. The EUVR of Sample-50 correspond to the angular range 4.5° – 87.0°.</p> <p>The .txt files contain post-processed reflectance values each with its calculated absolute uncertainty. </p>
Dataset for "Skyrmion states in thin confined polygonal nanostructures"
<p>This dataset provides micromagnetic simulation data collected from a series of computational experiments on the effects of polygonal system shape on the energy of different magnetic states in FeGe. The data here form the results of the study ‘Skyrmion states in thin confined polygonal nanostructures.’</p> <p>The dataset is split into several directories:</p> <p><strong>Data</strong></p> <p><em>square-samples and triangle-samples</em></p> <p>These directories contain final state ‘relaxed’ magnetization fields for square and triangle samples respectively. The files within are organised into directories such that a sample of side length d = 40nm and which was subjected to an applied field of 500mT is labelled d40b500. Within each directory are twelve VTK unstructured grid format files (with file extension “.vtu”). These can be viewed in a variety of programmes; as of the time of writing we recommend either ParaView or MayaVi. The twelve files correspond to twelve simulations for each sample simulated, corresponding to twelve states from which each sample was relaxed - these are described in the paper which this dataset accompanies, but we note the labels are:</p> <p>‘0’, ‘1’, ‘2’, ‘3’, ‘4’, ‘h’, ‘u’, ‘r1’, ‘r2’, ‘r3’, ‘h2’, ‘h3’</p> <p>where:</p> <ul> <li>0 - 4 are incomplete to overcomplete skyrmions,</li> <li>h, h2 and h3 are helical states with different periodicities</li> <li>r1-r3 are different random states</li> <li>u is the uniform magnetisation</li> </ul> <p>The vtu files are labelled according to parameters used in the simulation. For<br> example, a file labelled ‘160_10_3_0_u_wd000000.vtu’ encodes that:</p> <ol> <li> <p>The simulation was of a sample with side length 160nm.</p> </li> <li> <p>The simulation was of a sample of thickness 10nm.</p> </li> <li> <p>The maximum length of an edge in the finite element mesh of the sample was 3nm.</p> </li> <li> <p>The system was relaxed from the ‘u’.</p> </li> <li> <p>‘wd’ encodes that the simulation was performed with a full demagnetizing<br> calculation.</p> </li> </ol> <p><em>square-npys and triangle-npys</em></p> <p>These directories contain computed information about each of the final states stored in square-samples and triangle-samples. This information is stored in NumPy npz files, and can be read in Python straightforwardly using the function numpy.load. Within each npz file, there are 8 arrays, each with 12 elements. These arrays are:</p> <ol> <li>‘E’ - corresponds to the total energy of the relaxed state.</li> <li>‘E_exchange’ - corresponds to the Exchange energy of the relaxed state.</li> <li>‘E_demag’ - corresponds to the Demagnetizing energy of the relaxed state.</li> <li>‘E_dmi’ - corresponds to the Dzyaloshinskii-Moriya energy of the relaxed state.</li> <li>‘E_zeeman’ - corresponds to the Zeeman energy of the relaxed state.</li> <li>‘S’ - Calculated Skyrmion number of the relaxed state.</li> <li>‘S_abs’ - Calculated absolute Skyrmion number - see paper for calculation details.</li> <li>‘m_av’ - Computed normalised average magnetisation in x, y, and z directions for relaxed state</li> </ol> <p>The twelve elements here correspond to the aforementioned twelve states relaxed from, and the ordering of the array is that of the order given above.</p> <p><em>square-classified and triangle-classified</em></p> <p>These directories contain a labelled dataset which gives details about what the final state in each simulation is. The files are stored as plain text, and are labelled with the following structure (the meanings of which are defined in the paper which this dataset accompanies):</p> <ol> <li>iSk - Incomplete Skyrmion</li> <li>Sk, or a number n followed by Sk - n Skyrmions in the state.</li> <li>He - A helical state</li> <li>Target - A target state.</li> </ol> <p>The files contain the names of png files which are generated from the vtu files in the format ‘d_165b_350_2.png’. This example, if found in the ‘Sk.txt’ file, means that the sample which was 165nm in side length and which was relaxed under a field of 350mT from initial state 2 was found at equilibrium in a Skyrmion state.</p> <p><strong>Figures</strong></p> <p><strong><em>square-pngs and triangle-pngs</em></strong></p> <p>These directories contain generated pngs from the vtu files. These are included for convenience as they take several hours to generate. Each directory contains three subdirectories:</p> <p><em>all-states</em></p> <p>This directory contains the simulation results from all samples, in the format ‘d_165b_350_2.png’, which means that the image contained here is that of the 165nm side length sample relaxed under a 350mT field from initial state 2.</p> <p><em>ground-state</em></p> <p>This directory contains the images which correspond to the lowest energy state found from all of the initial states. These are labelled as ‘d_180b_50.png’, such that the image contained in this file is the the lowest energy state found from all twelve simulations of the 180nm sidelength under a 50mT field.</p> <p><em>uniform-state</em></p> <p>This directory contains the images which correspond to the states relaxed only from the uniform state. These are labelled such that an image labelled ‘d_55b_100.png’ is the state found from relaxing a 180nm sample under a 100mT applied field.</p> <p><em><strong>phase-diagrams</strong></em></p> <p>These are the generated phase diagrams which are found in the paper.</p> <p><strong>scripts</strong></p> <p>This folder contains Python scripts which generate the png files mentioned above, and also the phase diagram figures for the paper this dataset accompanies. The scripts are labelled descriptively with what they do - for e.g. ’triangle-generate-png-all-states.py’ contains the script which loads vtu files and generates the png files. The exception here is ’render.py’ which provides functions used across multiple scripts. These scripts can be modified - for example; the function 'export_vector_field' has many options which can be adjusted to, for example, plot different components of the magnetization.</p> <p>In order to run the scripts reproducibly, in the root directory we have provided a Makefile which builds each component. In order to reproduce the figures yourself, on a Linux system, ParaView must be installed. The Makefile has been tested on Ubuntu 16.04 with ParaView 5.0.1. In addition, a number of Python dependencies must also be installed. These are:</p> <ul> <li>scipy >=0.19.1</li> <li>numpy >= 1.11.0</li> <li>matplotlib == 1.5.2</li> <li>pillow>=3.1.2</li> </ul> <p>We have included a requirements.txt file which specifies these dependencies; they can be installed by running 'pip install -r requirements.txt' from the directory.</p> <p>Once all dependencies are installed, simply run the command ‘make’ from the shell to build the Docker image and generate the figures. Note the scripts will take a long time to run - at the time of writing the runtime will be on the order of several hours on a high-specification desktop machine. For convenience, we have therefore included the generated figures within the repository (as noted above). It should be noted that for the versions used in the paper, adjustments have been made after the generation of the figures, (for e.g. to add images of states within the metastability figure, and overlaying boundaries in the phase diagrams).</p> <p>If you want to reproduce only the phase diagrams, and not the pngs, the command ‘make phase-diagrams’ will do so. This is the smallest part of the figure reproduction, and takes around 5 minutes on a high-specification desktop.</p>
IODP Expedition 379 Thin section images
Hard rock and sediment thin section images were acquired using either the JRSO-developed Petrographic Image Capture and Archival Tool (PICAT) imager or (rarely) an upright microscope and a digital camera. Sample images are acquired in unpolarized, polarized, and/or cross-polarized light. Image files are presented compressed by hole.
IODP Expedition 371 Thin section images
Hard rock and sediment thin section images were acquired using either the JRSO-developed Petrographic Image Capture and Archival Tool (PICAT) imager or (rarely) an upright microscope and a digital camera. Sample images are acquired in unpolarized, polarized, and/or cross-polarized light. Image files are presented compressed by hole.
IODP Expedition 360 Thin section images
Hard rock and sediment thin section images were acquired using either the JRSO-developed Petrographic Image Capture and Archival Tool (PICAT) imager or (rarely) an upright microscope and a digital camera. Sample images are acquired in unpolarized, polarized, and/or cross-polarized light. Image files are presented compressed by hole.
IODP Expedition 397 Thin section images
Hard rock and sediment thin section images were acquired using either the JRSO-developed Petrographic Image Capture and Archival Tool (PICAT) imager or (rarely) an upright microscope and a digital camera. Sample images are acquired in unpolarized, polarized, and/or cross-polarized light. Image files are presented compressed by hole.
Three-dimensional subnanoscale imaging of unit cell doubling due to octahedral tilting and cation modulation in strained perovskite thin films
<p>Transmission electron microscopy data used in the journal publication <a href="https://doi.org/10.1103/PhysRevMaterials.3.063605">"Three-dimensional subnanoscale imaging of unit cell doubling due to octahedraltilting and cation modulation in strained perovskite thin films"</a></p> <p><strong>Data files</strong></p> <p>There are two data types:</p> <ul> <li>Scanning TEM (STEM) diffraction patterns acquired with a Medipix3 detector (Merlin): m004_LSMO_LFO_STO_medipix.hdf5 <ul> <li>Acquired on a probe corrected Jeol ARM200CF</li> <li>Acceleration voltage: 200 kV</li> <li>Convergence semi-angle: 20.4 mrad (calibrated using the SrTiO<sub>3</sub> substrate HOLZ ring)</li> <li>Detector calibration: 1.357 mrad per pixel (calibrated using the SrTiO<sub>3</sub> substrate HOLZ ring)</li> </ul> </li> <li>Atomic resolution STEM data, both annular dark field (ADF) and annular bright field (ABF), which were acquired simultaneously: s007_ADF.hdf5, s007_ABF.hdf5</li> </ul> <p>The data can be loaded in python using h5py.</p> <p>For the Medipix3 data:</p> <pre><code class="language-python">import h5py f = h5py.File('m004_LSMO_LFO_STO_medipix.hdf5', mode='r') data = f['fpd_expt/fpd_data/data'] data_subset = data[0:16, 0:16, :, :]</code></pre> <p>For the STEM-ADF or STEM-ABF data:</p> <pre><code class="language-python">import h5py f = h5py.File('s007_ADF.hdf5', mode='r') data = f['Experiments/__unnamed__/data']</code></pre> <p>Exploring the Medipix3 dataset lazily, i.e. without loading the whole dataset into memory at the same time. Using pixStem:</p> <pre><code class="language-python">import pixstem.api as ps s = ps.load_ps_signal("003_stripe1.hdf5", lazy=True) s.plot()</code></pre> <p>Loading the STEM-ADF or STEM-ABF data using HyperSpy, which automatically loads the probe scaling:</p> <pre><code class="language-python">import hyperspy.api as hs s = hs.load("s007_ADF.hdf5") s.plot()</code></pre> <p><br> <strong>Processing files</strong></p> <p>All the TEM data has been processed using python scripts, which is named based on the type of processing:</p> <ul> <li>d00N_...: Medipix3 data processing</li> <li>a00N_...: Atomic resolution STEM-ADF and STEM-ABF processing using Atomap</li> </ul> <p>The scripts generate intermediate files, which are saved in folders with the same prefix as the scripts. So the d001_... script makes a folder named d001_... . These intermediate files are included here as zip-files, since Zenodo doesn't support folder structures.</p> <p>The python libraries required to run the scripts are listed in requirements.txt. Newer versions of the libraries will most likely also work.</p> <p>To setup the python environment with the required libraries, and run all the scripts:</p> <pre><code class="language-bash">pip3 install -r requirements.txt python3 run_all_scripts.py</code></pre> <p> </p>
IODP Expedition 355 Thin section images
Hard rock and sediment thin section images were acquired using either the JRSO-developed Petrographic Image Capture and Archival Tool (PICAT) imager or (rarely) an upright microscope and a digital camera. Sample images are acquired in unpolarized, polarized, and/or cross-polarized light. Image files are presented compressed by hole.
IODP Expedition 356 Thin section images
Hard rock and sediment thin section images were acquired using either the JRSO-developed Petrographic Image Capture and Archival Tool (PICAT) imager or (rarely) an upright microscope and a digital camera. Sample images are acquired in unpolarized, polarized, and/or cross-polarized light. Image files are presented compressed by hole.
IODP Expedition 353 Thin section images
Hard rock and sediment thin section images were acquired using either the JRSO-developed Petrographic Image Capture and Archival Tool (PICAT) imager or (rarely) an upright microscope and a digital camera. Sample images are acquired in unpolarized, polarized, and/or cross-polarized light. Image files are presented compressed by hole.
Vacuum-Sublimed Cocrystalline Thin Films of Naphthalene Bisimide and Pt(II) Complex for Phosphorescent Light-Emitting Diodes
<p>Additional data to report <a href="https://doi.org/10.1002/adom.202402117">https://doi.org/10.1002/adom.202402117</a>:</p> <p>Cocrystals employing small organic molecules with platinum(II)-complexes are known to exhibit organic room-temperature phosphorescence (RTP). However, this desirable property was so far only demonstrated in the macroscopic 1:1 cocrystalline state, which limitsdevice applications outside of small single-crystal devices. Here, we show that vacuum cosublimed thin films of both components in various mixing ratios form layers with selfassembled small cocrystalline domains which exhibit RTP. This Pt(II) doping improved the photoluminescence (PL) quantum yield (ΦPL) of the now phosphorescence emitting 1,8:4,5-naphthalene bisimide (NBI) from below 0.1% to 9% in respective thin films. These doped layers were employed as active layers in light-emitting diodes to emit red electroluminescence (EL). Via time-resolved measurements the lifetime of the device EL was determined in accordance with the PL to be around 50 µs. Maximum external quantum efficiencies (EQEs) of over 0.2% with RTP emission signatures consistent with the PL of solution-grown cocrystals could be achieved.</p>
Reproduction package for the publication 'New radiative loss curve from updates to collisional excitation in the low-density, optically thin plasmas in SPEX'
<p>The following files can be used to reproduce the Figures and data from the paper <strong>New radiative loss curve from updates to collisional excitation in the low-density, optically thin plasmas in SPEX </strong>by L. Štofanová, J. Kaastra, M. Mehdipour, and J. de Plaa accepted to be publish in Section 12. Atomic, molecular, and nuclear data of Astronomy and Astrophysics (acceptance date - 27/06/2021).</p> <p> </p> <p>Note: version 2 is the most updated version (change in Fig.7).</p>
Data for the "Does prognostic seeding along flight tracks produce the desired effects of cirrus cloud thinning?" manuscript
<p>Tar file of the data used to prepare the plots and write the text in: "Does prognostic seeding along flight tracks produce the desired effects of cirrus cloud thinning?" manuscript for submission to ACP.</p> <p>A description of each netcdf file is provided in the README file. The format of each file is in netcdf4</p>
data for journal article 'Nernst-Ettingshausen effect in thin Pt and W films at low temperatures'
<p>This dataset contains the supporting information for the journal article 'Nernst-Ettingshausen effect in thin Pt and W films at low temperatures'.</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.