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1,293 results for “Thickness”
New glacier thickness and bed topography maps for Svalbard - Dataset
<p>The dataset includes three Geotiff files that include:</p> <p>1) A bed topography map of Svalbard (heights in m a.s.l.) [Bed_map.tiff]</p> <p>2) An ice thickness map of Svalbard (in m) [Thickness_map.tiff]</p> <p>3) A mask file that with values from 0-3 indicating non-glacier areas (0), glaciers modelled with the Parallel Ice Sheet model (1), glaciers modelled with the Instructed Glacier Model (2), and surging glaciers (3). </p> <p>For a description of the methods used to generate the datasets, we refer to the manuscript "A new glacier thickness and bed map for Svalbard", to be submitted to The Cryosphere Discussions.</p>
TOPAZ4-ML Sea Ice Thickness (1992-2022)
<p> </p> <p>Subject: Natural sciences, Field: Earth Science, Subfield: Oceanography</p> <p>Key words: sea ice, thickness, Arctic, TOPAZ, machine learning, data assimilation</p> <p> </p> <p>The scientific manuscript associated to the dataset is:</p> <p>https://doi.org/10.5194/tc-19-731-2025</p> <p> </p> <p>The dataset can be visualized as an animation using the following link:</p> <p><a href="https://av.tib.eu/media/68161" target="_blank" rel="noopener">https://av.tib.eu/media/68161 (https://doi.org/10.5446/68161)</a></p> <p>or:</p> <p>https://www.youtube.com/watch?v=UPPcJXfNMbY (faster)</p>
Satellite-derived monthly Arctic winter sea ice thickness, snow depth, freeboards, ice draft, and bulk ice density (2011-2022) and validation datasets
<h1><strong>[Description]</strong></h1> <p>This dataset is curated for a manuscript published in Earth and Space Science by Hoyeon Shi and his colleagues in April 2024. </p> <blockquote> <p>Shi, H., Tonboe, R., Lee, M., Dybkjær, G., Sohn, J., Singha, S., & Baordo, F. (2024). A Simple and Robust CryoSat-2 Radar Freeboard Correction Method Dedicated to TFMRA50 for the Arctic Winter Snow Depth and Sea Ice Thickness Retrieval. <em>Earth and Space Science</em>, <em>11</em>(10), e2024EA003715. https://doi.org/10.1029/2024EA003715</p> </blockquote> <p>Here, version 2 is uploaded, corresponding to the revised manuscript during the revision. The main changes compared to version 1 are:<br> 1) Update of the CryoSat-2 radar freeboard dataset (from v2p4 to v2p6)<br> 2) Update of the coefficients for the radar freeboard correction equations<br> 3) Extension of the retrieval period for the CS2IS2 method (April is now included)<br> 4) Removal of OIB data points used for the regression from the validation datasets<br> 5) Inclusion of the Fram Strait mooring dataset in the validation dataset</p> <p>It consists of three directories, each described below.</p> <h2><strong>01_retrieval_results</strong></h2> <p>This directory includes CryoSat-2-based monthly fields of Arctic sea ice thickness, snow depth, total freeboard, ice freeboards, ice draft, and bulk sea ice density for the winter months of the 2011-2022 period (January-March for alpha method and January-April for CS2IS2 method). Those variables are obtained using six combinations of two retrieval methods and three radar freeboard correction methods.</p> <p><em>Retrieval methods</em></p> <ul> <li>alpha method: A simultaneous retrieval method based on Shi et al. (2020) and Shi et al. (2023), combining CryoSat-2, AVHRR, and AMSR data</li> <li>CS2IS2 method: A simultaneous retrieval method based on Kwok and Marcus (2018) and Kwok et al. (2020), combining CryoSat-2 and ICESat-2 data</li> </ul> <p><em>Radar freeboard correction methods</em></p> <ul> <li>Wave speed correction method: Mallet et al. (2020)</li> <li>Empirical correction method: An empirical correction derived from the CS2_OIB_matchup data, using snow depth as a predictor</li> <li>Bias correction method: An empirical correction derived from the CS2_OIB_matchup data, doing bias correction</li> </ul> <p>The datasets used for generating this dataset are as follows:</p> <ul> <li>CryoSat-2 <br>- AWI CryoSat-2 sea ice thickness v2p6 (doi: <a href="https://doi.org/10.5281/zenodo.10044554" target="_blank" rel="noopener">10.5281/zenodo.10044554</a>)</li> <li>ICESat-2<br>- NSIDC ATL20 dataset (doi: <a href="https://doi.org/10.5067/ATLAS/ATL20.004" target="_blank" rel="noopener">10.5067/ATLAS/ATL20.004</a>)</li> <li>AVHRR<br>- Copernicus Marine Service's surface temperature datasets (doi: <a href="https://doi.org/10.48670/MOI-00130" target="_blank" rel="noopener">10.48670/MOI-00130</a>, doi: <a href="https://doi.org/10.48670/MOI-00123" target="_blank" rel="noopener">10.48670/MOI-00123</a>)</li> <li>AMSR<br>- JAXA AMSR-E 6.9 GHz brightness temperature (doi: <a href="https://doi.org/10.57746/EO.01gs73ayng11rpwk7n54aynyj1" target="_blank" rel="noopener">10.57746/EO.01gs73ayng11rpwk7n54aynyj1</a>)<br>- JAXA AMSR2 6.9 GHz brightness temperature (doi: <a href="https://doi.org/10.57746/EO.01gs73b1nzeh3g66jr4p04mr0j" target="_blank" rel="noopener">10.57746/EO.01gs73b1nzeh3g66jr4p04mr0j</a>)</li> <li>Auxiliary data<br>- Sea ice concentration: OSI SAF (doi: <a href="https://doi.org/10.15770/EUM_SAF_OSI_0013" target="_blank" rel="noopener">10.15770/EUM_SAF_OSI_0013</a>, doi: <a href="https://doi.org/10.15770/EUM_SAF_OSI_0014" target="_blank" rel="noopener">10.15770/EUM_SAF_OSI_0014</a>)<br>- Sea ice type: OSI SAF (doi: <a href="https://doi.org/10.15770/EUM_SAF_OSI_NRT_2006" target="_blank" rel="noopener">10.15770/EUM_SAF_OSI_NRT_2006</a>)</li> </ul> <p>The naming convention is 'RetrievalMethod_CorrectionMethod_yyyymm.bin'. The 'RetrievalMethod' is either 'alpha' or 'CS2IS2', and the 'CorrectionMethod' is either 'WaveSpeed,' 'Empirical,' or 'BiasCorrection.' The data format is a 32-bit floating point array in the shape of 6 x 448 x 304 (25 km polar stereographic grid). The first dimension indicates the variables (in the order of snow depth (0), sea ice thickness (1), ice freeboard (2), total freeboard (3), sea ice draft (4), and bulk sea ice density (5)). For example, to read the sea ice thickness of January 2020 based on the alpha method with an empirical correction, you may write this Python command:</p> <p><code>import numpy as np</code><br><code>data = np.fromfile('alpha_Empirical_202001.bin', dtype=np.float32).reshape(6,448,304)</code><br><code>hi = data[1,:,:]</code></p> <p>The unit of thickness-related variable is cm, and the unit of density is kg/m3. The 25 km polar stereographic grid information is available on the NSIDC website (doi: <a href="https://doi.org/10.5067/N6INPBT8Y104" target="_blank" rel="noopener">10.5067/N6INPBT8Y104</a>).</p> <h2><strong>02_valdiation data </strong></h2> <p>This directory includes reference data used for quality assessment of retrievals. There are three sub-directories:</p> <p>'Mooring_draft_psn25_monthly' includes sea ice draft measurements from the moorings in the Beaufort Sea (https://www2.whoi.edu/site/beaufortgyre/data/mooring-data/), Fram Strait (doi: <a href="https://doi.org/10.21334/npolar.2022.b94cb848" target="_blank" rel="noopener">10.21334/npolar.2022.b94cb848</a>), and the Laptev Sea (doi: <a href="https://doi.org/10.1594/PANGAEA.912927" target="_blank" rel="noopener">10.1594/PANGAEA.912927</a>, doi: <a href="https://doi.org/10.1594/PANGAEA.899275" target="_blank" rel="noopener">10.1594/PANGAEA.899275</a>).</p> <p>'OIB_SD_psn25_monthly' and 'OIB_TFB_psn25_monthly' include airborne snow depth and total freeboard measurements from NASA's Operation IceBridge campaign (doi: <a href="https://doi.org/10.5067/G519SHCKWQV6" target="_blank" rel="noopener">10.5067/G519SHCKWQV6</a>, doi: <a href="https://doi.org/10.5067/GRIXZ91DE0L9" target="_blank" rel="noopener">10.5067/GRIXZ91DE0L9</a>).</p> <p>Original data were processed to become monthly gridded data to make a comparison with satellite retrievals. The OIB data points used for the regression were excluded when processing the monthly gridded data. The naming convention of each file is 'Var_yyyymm.bin,' where 'Var' is the variable name (SD: snow depth, TFB: total freeboard, Di: ice draft). For example, you can use the following code to read the OIB snow depth in March 2014.</p> <p><code>import numpy as np</code><br><code>hs = np.fromfile('SD_201403.bin', dtype=np.float32).reshape(448,304)</code></p> <h2><strong>03_CS2_OIB_matchup</strong></h2> <p>This directory includes a match-up of AWI's CryoSat-2 L2P track data and OIB track data. The matching was done by resampling two high-resolution data on a coarser-resolution common grid (25 km polar stereographic grid) using a drop-in-a-bucket resampling method. The file format is CSV, and it is straightforward to understand when it is opened.</p> <h1><strong>[Abbreviations]</strong></h1> <p>AMSR: Advanced Microwave Scanning Radiometer<br>AVHRR: Advanced Very High Resolution Radiometer<br>AWI: Alfred Wegener Institute<br>CS2: CryoSat-2<br>JAXA: Japan Aerospace Exploration Agency<br>NASA: National Aeronautics and Space Administration<br>NSIDC: National Snow and Ice Data Center<br>OIB: Operation IceBridge<br>OSI SAF: Ocean and Sea Ice Satellite Application Facility</p> <p> </p>
[Dataset] Simultaneous laser ultrasonic measurement of sound velocities and thickness of plates using combined mode local acoustic spectroscopy
<p>Research data for the purpose of reproducing the results presented in the journal publication titled "Simultaneous laser ultrasonic measurement of sound velocities and thickness of plates using combined mode local acoustic spectroscopy"</p>
Spectral irradiance at Lammi Biological Station Research Forest 2015: for assessing scale-wise similarity of curves with a thick pen
<p>This dataset contains records of the solar spectral energy irradiance (W m<sup>-2</sup> nm<sup>-1</sup>) in the understorey of forest stands at Lammi Biological Station, southern Finland (61◦ 3.24’ N, 25◦ 118 2.23’ E) during the spring of 2015. These spectra allow the change in spectral energy irradiance to be followed through the period of canopy leaf flush. Records are the average of recorded spectra from four points recorded at 40-cm above the forest floor using a Maya 2000 Pro array spectrometer. Spectra were recorded from exactly the same location on three dates, 2015-04-25, 2015-05-22, and 2015-06-05, before, during and after leaf flush. Data were recorded from the understorey of a young Betula stand, an old Betula stand, an old mixed Betula stand, a Quercus stand, and a Picea stand, in three positions: shade, semi-shade from leaves, and full sun in a sunfleck. On each occasion control measurements of spectral energy irradiance in full sun of an open field were also recorded at the beginning, middle and end of each measurement period. All measurements were made during the 2 hours either side of solar noon, on clear-sky days. Details of the sampling method and interpretation are given in the paper, Hartikainen et al., (2018) in Ecology and Evolution, which showcases the use of Thick Pen Transform to compare spectra.</p>
Patterns of reduced cortical thickness and striatum pathological morphology in cocaine addiction
<p>This dataset includes all the data and scripts needed to reproduce the analysis and results on the manuscript "Patterns of reduced cortical thickness and striatum pathological morphology in cocaine addiction" (<a href="https://www.biorxiv.org/content/early/2018/04/22/306068">link</a>). The brain data is not raw, as T1w were not defaced. We will do so in the near future for version 2.0. Instead we include only the "output/thickness" files used in the final analysis. For the use of raw T1w images, please contact the main author EAGV.</p> <p> </p> <p>Note: Paths will differ in the script.</p>
Non-destructive thickness mapping of wafer-scale hexagonal boron nitride down to a monolayer
<p>Raw data associated with the publication "Non-Destructive Thickness Mapping of Wafer-Scale Hexagonal Boron Nitride<br> Down to a Monolayer" published in ACS Applied Materials & Interfaces under the DOI: 10.1021/acsami.8b08609</p>
PMW Sea Ice Thickness - from SSM/I and SSMIS CDR
<p>Sea Ice Thickness is retrieved from brightness temperatures acquired by the SSM/I and SSMIS passive microwave radiometers from 1992 to 2020 for the months Oct-Mar. The algorithm uses the statistical relationships observed between passive microwave observations and SIT to train a Multi-Layered Perceptron (MLP) to reproduce ICESat-2 SIT from brightness temperatures at 19 and 37 GHz. The MLP has been trained on the polar winter 2018-2019 where it showed good performance when compared to the CryoSat-2 satellite retrieval and the Operation Ice Bridge airborne measurements.<br>More details in:</p> <p>Soriot, C., Prigent, C., Jimenez, C., & Frappart, F. (2023). Arctic sea ice thickness estimation from passive microwave satellite observations between 1.4 and 36 GHz. <em>Earth and Space Science</em>, <em>10</em>(2), e2022EA002542</p> <p>Soriot, C., Vancoppenolle, M., Prigent, C., Jimenez, C., & Frappart, F. (2024). Winter arctic sea ice volume decline: uncertainties reduced using passive microwave-based sea ice thickness. <em>Scientific Reports</em>, <em>14</em>(1), 21000.</p> <p> </p>
Experiment to measure the thickness loss due to corrosion of a 5mm uncoated steel sample exposed close to the shore during 6 months
<p>This is the data obtained during a experiment conducted in Grand Canary Island close to the shore to collect data using CEIT's ultrasound sensor node attached to a 5mm uncoated steel sample. We collected during 6 months the measured thickness taking two measures per day. The sample was considerably degraded during that time. The estimated Corrosion Rate (134um/year) was in coherence with the typical corrosion rates in real environments (100-200um/year). </p>
Supplementary Files for "Geodetic constraints on cratonic microplates and broad strain during rifting of thick Southern African lithosphere"
<p>This repository contains the supplementary files and tables for the manuscript:</p> <p><strong>Geodetic constraints on cratonic microplates and broad strain during rifting of thick Southern African lithosphere</strong></p> <p><strong>L. N. J. Wedmore<sup>1</sup>, Biggs, J.<sup>1</sup>, Floyd, M.<sup>2</sup>, Fagereng, Å.<sup>3</sup>, Mdala, H.<sup>4</sup>, Chindandali, P.<sup>5</sup>, Williams, J.<sup>3</sup>, Mphepo, F.<sup>4</sup></strong></p> <p><sup>1</sup>School of Earth Sciences, University of Bristol, Bristol, UK</p> <p><sup>2</sup>Department of Earth, Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA</p> <p><sup>3</sup>School of Earth and Environmental Sciences, Cardiff University, Cardiff, UK</p> <p><sup>4</sup>Geological Survey Department, Mzuzu Regional Office, Mzuzu, Malawi</p> <p><sup>5</sup>Geological Survey Department, Zomba, Malawi</p> <p>This manuscriptis published in Geophysical Research Letters: <a href="https://doi.org/10.1029/2021GL093785">https://doi.org/10.1029/2021GL093785</a></p> <p>Please contact the author (luke.wedmore@bristol.ac.uk) for more information.</p> <p> </p> <p>File Information</p> <p>File S1 – Table of GNSS station velocities for the combined southern Malawi/GeoPRISMS/Saria et al. (2014) solution in the ITRF14 reference frame.</p> <p>File S2 – Table of GNSS station and the references for the data used in this paper.</p> <p>File S3 – Details of the sites used for the two-plate test and the results of this inversion.</p> <p>File S4 – Details of the sites used for the three-plate test and the results of this inversion.</p> <p>File S5 – A sig_neu command file with details of the random noise added to outlier sites within GLOBK.</p>
Crustal thickness and Vp/Vs database from the Amazonian Craton and adjacent provinces
<p>This database includes results of crustal thickness and Vp/Vs estimated from stations located in the Amazonian Craton and adjacent provinces.</p> <p>If you used this database, please cite:<br> <br> ALBUQUERQUE, D. F. et al. Crustal structure of the Amazonian Craton and adjacent provinces in Brazil. Journal of South American Earth Sciences, v. 79, p. 431–442, 2017. DOI: 10.1016/j.jsames.2017.08.019</p> <p> </p>
Combined SMOS and SMAP sea ice thickness Arctic
<p>This data set contains Arctic sea ice thicknesses derived from L-band passive microwave brightness temperatures. For this purpose, brightness temperatures at 40° incidence angle from the SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Active Passive) satellites were combined to a homogenised data set (see also related data sets). Sea ice thicknesses were then derived using the algorithm described in Tian-Kunze et al. (2014).</p> <p>Data are generally produced for the freeze-up period from 15 October to 15 April. This data set contains data from on 1 April 2015 (first available SMAP data) to 15 April 2015 and for the winter seasons 2015/16 to 2017/18.</p> <p>A detailed description of the data set can be found Schmitt and Kaleschke (2018) and in the document <em>1_Documentation_Combined_SIT.pdf</em>.</p> <p><strong>Please note</strong>: Sea ice thicknesses from L-band are most suitable for thin ice and reach saturation for thicker sea ice (above 0.5 m - 1 m, depending on ice salinity and temperature). Thickness values with a saturation ratio of 100 % should be discarded (depending on the application)!</p> <p>Version 1.0 of this data set is based on SMOS version v620 and SMAP version 3 data.</p> <p>The files contain the following data fields:<br> <strong>sea_ice_thickness</strong> - sea ice thickness with post-processing to account for the thickness distribution<br> <strong>plane_layer_thickness</strong> - sea ice thickness using the plane layer assumption<br> <strong>thickness_uncertainty_upper</strong> - upper limit of the sea ice thickness uncertainty interval<br> <strong>thickness_uncertainty_lower</strong> - lower limit of the sea ice thickness uncertainty interval<br> <strong>saturation_ratio</strong> - ratio of plane layer thickness and maximum retrievable thickness (%)<br> <strong>TB_intensity</strong> - brightness temperature intensity combined from SMOS and SMAP at 40° incidence angle</p> <p>The grid coordinates are provided as a separate file <em>Latlon_e12.5.nc</em></p>
Antarctic Sea Ice Thickness Estimates from CryoSat-2: 2010-2021
<p><strong>*Preliminary data associated with manuscript submission to The Cryosphere*</strong></p> <p>Gridded estimates of Antarctic sea ice physical properties derived from CryoSat-2 Baseline-D SAR and SARIn data spanning July 2010 through August 2021. Data are generated using the CryoSat-2 Waveform-Fitting method for Antarctic sea ice (CS2WFA). Files include:</p> <ul> <li>CS2WFA_25km_YYYYMM.nc: Monthly mean values gridded on the NSIDC 25km polar stereographic grid. Each file represents 1 month of data (134 files total).</li> </ul> <p>Each monthly file contains:</p> <ul> <li>latitude, longitude, and time (in months since January 2000)</li> <li>Grids of retrieved snow depth, snow freeboard, ice freeboard, and sea ice concentration</li> <li>Grids of seasonal snow, sea ice, and seawater density</li> <li>Grids of sea ice thickness and estimated thickness uncertainty, as well as thickness estimated using a 70% threshold retracker and using a zero-ice-freeboard assumption.</li> </ul>
Bathymetry and Sediment thickness distribution of Lago dei Seracchi alpine lake, Rutor basin, Aosta Valley, Italy
<p>Maps of water depth and sediment accumulation in an Italian proglacial lake, done by Ground Penetrating Radar (GPR) in July 2021. Supporting Time domain reflectometry surveys and geotechnical analyses on the sediments are also provided. For details, see the readme file in the dataset folder.</p>
Bathymetry, sediment thickness, and geotechnical-geophysical properties of sediments of Lake Seracchi in Rutor proglacial area
<p>This dataset contains the data of a geophysical-geotechnical investigation of Lake Seracchi (L4) of the Rutor basin, Aosta Valley, Italy, The fieldwork was mainly carried out in 10-11 July 2021.</p> <p>The data are:</p> <p>.tif ready-to-use maps of the bathymetry and the sediment thickness.</p> <p>Time Domain Reflectometry (TDR) data of electrical permittivity and conductivity of the lake sediments</p> <p>Geotechnical analyses, such as Grain Size Distribution and Atterberg's Limits, performed on the lake sediments.</p> <p>The details are reported in the README .txt file.</p> <p> </p>
Simulated thickness profiles of ALD film in a wide microchannel of 500 nm height published as Fig.4 in PCCP 24 (2022) 8645-8660
<p>A series of simulated thickness profiles of atomic layer deposition (ALD) film grown in a wide lateral high-aspect-ratio (LHAR) microchannel is archived as an Excel file. This dataset has been published as Figure 4 in the publication "Conformality of atomic layer deposition in microchannels: impact of process parameters on the simulated thickness profile" (Yim and Verkama et al., Phys. Chem. Chem. Phys. 24 (2022) 8645-8660. https://doi.org/10.1039/D1CP04758B). A diffusion-reaction model by Ylilammi et al. (Ylilammi et al., J. Appl. Phys. 123 (2018) 205301. https://doi.org/10.1063/1.5028178) was re-implemented for the simulation. For this simulation, a channel height of 500 nm, which is a typical height for microscopic PillarHallTM LHAR test chips (Yim and Ylivaara et al., Phys. Chem. Chem. Phys., 22 (2020) 23107-23120. https://doi.org/10.1039/D0CP03358H), was used.<br> The Excel file consists of 11 tabs in total: metadata, baseline thickness profile, and Fig4a to Fig4i. The baseline thickness profile and data of fig4a Fig4i are also available as a CSV file. The metadata page describes the data with its baseline conditions. The baseline conditions used in the simulation are: sticking coefficient = 0.01, temperature = 250 °C, initial partial pressure of Reactant A = 100 Pa, molar mass of Reactant A = 0.100 kg mol-1, hard-sphere diameter of Reactant A = 6 × 10-10 m, partial pressure of inert gas I = 500 Pa, molar mass of inert gas I = 0.028 kg mol-1, hard-sphere diameter of inert gas I = 3.74 × 10-10 m, mass density of deposited film = 3500 kg m-3, areal number density of metal M atoms in MyZx material = 4 nm-2, number of metal atoms in a Reactant A molecule = 1, number of metal atoms in a formula unit of growing film = 1, number of cycle = 250, desorption probability = 0.01 s-1, channel height = 500 nm, and channel width = 10 mm. The baseline thickness profile tab contains a thickness profile obtained in the baseline conditions as film thickness versus distance within a microchannel. The thickness profile stored from Fig4a to Fig4i tabs was obtained by varying individual parameters with other parameter values in baseline conditions: initial partial pressure of Reactant A (Fig4a), pulse time (Fig4b), molar mass of Reactant A (Fig4c), mass density of deposited film (Fig4d), adsorption density (Fig4e), desorption probability (Fig4f), sticking coefficient (Fig4g), temperature (Fig4h) and partial pressure of inert gas (Fig4i).</p>
Greenland Peregrine Falcon eggshell thickness monitoring data 1972 - 2019
<p>The Peregrine Falcon (Falco peregrinus tundrius) population in Greenland has been monitored in different survey areas in South and West Greenland since 1972. At visits to Peregrine Falcon nests, eggshell fragments from hatched eggs as well as addled (dead) eggs left behind have been collected with the aim of monitoring the thickness of the eggshells as well as analysing the whole eggs for contaminants. The shell thickness serves as a proxy for the falcons’ exposure to certain persistant organic pollutants, in particular DDT and its breakdown products (see summaries in Cade et al. 1988).</p> <p>This data set contains the raw data on 6665 eggshell thickness measurements of:<br> 1. Whole eggs from South Greenland 1986-2015<br> 2. Eggshell fragments from the study area in South Greenland 1981-2019<br> 3. Eggshell fragments from the study area around Kangerlussuaq in West Greenland 1972-1989</p> <p>The data set contains a mix of measurements of shell thickness including or excluding the eggshell membranes from the same clutch of eggs. Based on those measurements the average membrane thickness is 0.071 mm (SD=0.013) – a figure confirmed by other studies – and this ’membrane factor’ can be added or subtracted for comparisons with other data sets.</p> <p>Further details regarding the sampling areas, measurement methods and the results of trends analyses of changes in shell thickness are provided in Falk et al. (2006 and 2018).</p> <p>The file named <em>1_Data_Eggshell_Thickness_1972-2019.csv</em> contains the raw measurements data and the file <em>2_ReadMe_Eggshell_Thickness_1972-2019.txt</em> specifies the content. </p> <p>The file named <em>3_Rscript_Eggshell_Thickness_1972-2019.R</em> provides an R script for summarizing and plotting the data as shown in the file <em>4_Plot_Eggshell_Thickness_1972-2019.pdf</em></p>
Quantification of plant morphology and leaf thickness with optical coherence tomography
<p>The uploaded scripts and data are used to obtain the figures 2, 4, 5, 6 and 7 in the publication. </p> <p>The code has been run with Python 3.7 in Spyder (Anaconda).</p> <p>There are three scripts, each needing specific datasets to run the code.</p> <p>1. The core is the segmentation of the leaf surface and this is subsequently used to calculate leaf thickness and obtain en-face images.</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/3D_segmentation_thickness_enface.py">3D_segmentation_thickness_enface.py</a>: This file loads the 3D processed OCT data, does the leaf surface segmentation and calculates the en face images. It needs the files processed_3Ddata.npy and videoim.npy</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/processed_3Ddata.npy">processed_3Ddata.npy</a>: This file contains the processed 3D OCT dataset (linear amplitude data), with respectively dimensions z,x,y. The data is saved as uint16 to save memory, and should be converted to double before further processing, as done in the script.</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/videoim.npy">videoim.npy</a>: This file contains the RGB image of Fig. 6(a) as image matrix.</p> <p>2. The non-infiltrated and infiltrated image (Figure 4)</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/2D_fig4.py">2D_fig4.py</a>: This script produces Figure 4 of the paper and also shows the two RGB images that indicate the scan location on the leaf. It needs the files OCTdata_figure4.npy (containing OCT data) and videoimages_figure4.npy</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/OCTdata_figure4.npy">OCTdata_figure4.npy</a>: This file contains the processed 2D OCT dataset (linear amplitude data), with respectively dimensions (a/b),z,x. The data is saved as uint16 to save memory, and should be converted to double before further processing, as done in the script.</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/videoimages_figure4.npy">videoimages_figure4.npy</a> This file contains the two RGB images that show the scan area of the data in Figure 4.</p> <p>3. The calculation of the refractive index and making Figure 5</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/refractiveindex_fig5.py">refractiveindex_fig5.py</a>: this script segments the cuvette wall and leaf surface on 2D images and calculates the refractive index by evaluating equation 1 of the publication. It needs the file images_refractiveindex.npy</p> <p><a href="https://zenodo.org/api/files/89412f06-4c84-4516-9e7d-113796b42834/images_refractiveindex.npy">images_refractiveindex.npy</a>: This file contains the processed 2D OCT dataset (linear amplitude data), with respectively dimensions (leaf/empty),z,x. The data is saved as uint16 to save memory, and should be converted to double before further processing, as done in the script.</p>
Ice thicknesses of the Müller ice cap
<p>Modeled ice thicknesses of the Müller ice cap, Axel Heiberg Island. Five different models of the ice thickness has been made. All model results comes with individual GeoTIFFs of bedrock topography, ice thickness and surface elevation. The data is either on a 100 or 900 meters grid depending on the model.</p> <p>Citation:</p> <p>Ann-Sofie P. Zinck, Surface velocity and ice thickness of the Müller ice cap, Axel Heiberg Island, Master thesis, University of Copenhagen, Copenhagen, 2020</p>
Debris thickness measurements from various glaciers in the Western Alps
<p>Debris thickness measurements collected from seven debris-covered glaciers in Switzerland and Italy in 2019 and 2020. Only data from Miage Glacier was measured in September 2020 all others were measured in August and September in 2019. The RGI Glacier ID is the first cell in each sheet. Location data is reported in decimal degrees. All debris thickness data is in centimeters and elevation data is in meters.</p> <p>Debris was measured by digging through the debris and then directly measuring the debris thickness with a measuring stick. The mean thickness is the most likely average thickness based on 3 measurements within a ~4x4m area. The 'min' measurement is the minimum debris thickness. The 'max' measurement is the absolute maximum estimate of the mean debris thickness, meaning that this estimate is that absolute maximum of the mean debris thickness. This value is determined based on the thickness of the largest non-outlier debris thickness in the area. Three measurements were taken for the mean value and 1 each for the max/min bounds. To save time, sites with mean thicknesses larger than 20 cm the extreme bounds are based on the minimum and maximum measurements within the single hole dug. Elevation data are based on a hand held GPS and this value was only recorded for some debris thickness measurements.</p>
ScienceDex guides
Understand access before you commit
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.