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228 results for “Volumetric”
Continuous soil temperature, specific conductance, and volumetric water content measurements from the Water Track B Active Layer Monitoring Station (ALMS06), McMurdo Dry Valleys, Antarctica (2014-2021, ongoing)
As part of the McMurdo Dry Valleys Long Term Ecological Research (LTER) project, five Active Layer Monitoring Stations (ALMSs) were established throughout Taylor Valley, Antarctica to support new research foci around the thermal-moisture dynamics of soils that may control habitat conditions and faunal responses to seasonal and annual freezing cycles in this ecosystem. Two ALMSs were established adjacent to streams (Green Creek, Von Guerard Stream), with sensors installed through the active layer from the thalweg out to the shoreline and dry soil beyond. Two ALMSs were similarly established adjacent to water tracks (Wormherder Creek, Water Track B) that are zero-order drainages of snow and ice melt that rarely have surface flow. The remaining station was established in dry soil (F6) to serve as an ambient control. ALMSs measure soil temperature, soil moisture (as volumetric water content; VWC), and specific conductance (as electrical conductivity; EC) through the active layer (soil surface down to the frost table) at several locations from the water’s edge to dry soils. This data package contains measurements from the Active Layer Monitoring Station at Water Track B (ALMS06).
Ecosystem-Scale Rainfall Manipulation in a Piñon-Juniper Forest at the Sevilleta National Wildlife Refuge, New Mexico: Volumetric Water Content (VWC) at 5 cm Depth Data (2006- )
Climate models predict that water limited regions around the world will become drier and warmer in the near future, including southwestern North America. We developed a large-scale experimental system that allows testing of the ecosystem impacts of precipitation changes. Four treatments were applied to 1600 m2 plots (40 m × 40 m), each with three replicates in a piñon pine (Pinus edulis) and juniper (Juniper monosperma) ecosystem. These species have extensive root systems, requiring large-scale manipulation to effectively alter soil water availability. Treatments consisted of: 1) irrigation plots that receive supplemental water additions, 2) drought plots that receive 55% of ambient rainfall, 3) cover-control plots that receive ambient precipitation, but allow determination of treatment infrastructure artifacts, and 4) ambient control plots. Our drought structures effectively reduced soil water potential and volumetric water content compared to the ambient, cover-control, and water addition plots. Drought and cover control plots experienced an average increase in maximum soil and air temperature at ground level of 1-4° C during the growing season compared to ambient plots, and concurrent short-term diurnal increases in maximum air temperature were also observed directly above and below plastic structures. Our drought and irrigation treatments significantly influenced tree predawn water potential, sap-flow, and net photosynthesis, with drought treatment trees exhibiting significant decreases in physiological function compared to ambient and irrigated trees. Supplemental irrigation resulted in a significant increase in both plant water potential and xylem sap-flow compared to trees in the other treatments. This experimental design effectively allows manipulation of plant water stress at the ecosystem scale, permits a wide range of drought conditions, and provides prolonged drought conditions comparable to historical droughts in the past – drought events for which wide
Ecosystem-scale rainfall manipulation in a Pinon-Juniper Woodland: Volumetric Water Content (VWC) Profile Data (2009-2013 )
Climate models predict that water limited regions around the world will become drier and warmer in the near future, including southwestern North America. We developed a large-scale experimental system that allows testing of the ecosystem impacts of precipitation changes. Four treatments were applied to 1600 m2 plots (40 m × 40 m), each with three replicates in a piñon pine (Pinus edulis) and juniper (Juniper monosperma) ecosystem. These species have extensive root systems, requiring large-scale manipulation to effectively alter soil water availability. Treatments consisted of: 1) irrigation plots that receive supplemental water additions, 2) drought plots that receive 55% of ambient rainfall, 3) cover-control plots that receive ambient precipitation, but allow determination of treatment infrastructure artifacts, and 4) ambient control plots. Our drought structures effectively reduced soil water potential and volumetric water content compared to the ambient, cover-control, and water addition plots. Drought and cover control plots experienced an average increase in maximum soil and air temperature at ground level of 1-4° C during the growing season compared to ambient plots, and concurrent short-term diurnal increases in maximum air temperature were also observed directly above and below plastic structures. Our drought and irrigation treatments significantly influenced tree predawn water potential, sap-flow, and net photosynthesis, with drought treatment trees exhibiting significant decreases in physiological function compared to ambient and irrigated trees. Supplemental irrigation resulted in a significant increase in both plant water potential and xylem sap-flow compared to trees in the other treatments. This experimental design effectively allows manipulation of plant water stress at the ecosystem scale, permits a wide range of drought conditions, and provides prolonged drought conditions comparable to historical droughts in the past – drought events for which wide
Probabilistic volumetric speckle suppression in OCT using deep learning: Dataset
<p>This file contains a retinal OCT intensity volume as a demo dataset to generate volumetric speckle-suppressed training data using our non-local-means despeckling (TNode) script and four OCT intensity volumes and their corresponding TNode-processed intensity volumes of different tissue samples to train and test our deep learning framework used in "Probabilistic volumetric speckle suppression in OCT using deep learning" by Chintada et al. 2023. The TNode code for generating the training data and the source code for our deep learning framework are available at https://github.com/bhaskarachintada/DLTNode.git</p>
Volumetric and morphological analysis of the clades based on the in vivo confetti imaging
<p>This dataset contains a script in programming language that describes the analytical pipeline for image processing of the in vivo data from the Confetti mice skin. The algorithm describes volumetric analysis, 3D reconstruction, density analysis, as well multiple other morphological parameters. </p>
Data and code associated with "Fourier synthesis optical diffraction tomography for kilohertz rate volumetric imaging"
<p>Imaging data and derived analysis data used in the figures of the manuscript "F<span>ourier synthesis optical diffraction tomography for kilohertz rate volumetric imaging"</span></p>
A volumetric model of rabbit heart and torso including ECG data and ventricular activation sequence
<p>Data generated and analyzed of our work titled "A computational model of rabbit geometry and ECG: Optimizing ventricular activation sequence and APD distribution". Please see the respective publication for more context.</p> <p> </p> <ul> <li>BSPM_filtered.dat <ul> <li>Contains the filtered ECG Data</li> </ul> </li> <li>BSPM_original.bdf <ul> <li>Contains the originally recorded signal.<br> Information on the file format itself can be found here: <a href="https://www.biosemi.com/faq/file_format.htm">https://www.biosemi.com/faq/file_format.htm</a><br> Links to various toolboxes to open the file can be found here: <a href="https://www.biosemi.com/download.htm">https://www.biosemi.com/download.htm</a></li> </ul> </li> <li>CT_DataDCM.zip <ul> <li>Contains the recorded CT images of heart and torso in DCM file format</li> </ul> </li> <li>ECG_NodeIndices.txt <ul> <li>Contains the the node IDs of the torso mesh corresponding to the electrode positions of the ECG Vest</li> </ul> </li> <li>Endocardial_Surface_Papillary.stl <ul> <li>Segmented endocardial surface including papillary muscles</li> </ul> </li> <li>Mat_LeadField.dat <ul> <li>Contains the calculated lead field matrix to be used in combination with the provided Mesh_Ven.vtu. Make sure to keep the node order</li> <li> <pre><code class="language-python"># Python example of usage # Define a read_vm_vec function which reads your calculated data beforehand import numpy as np t_begin = 0 t_end = 400 LF_mat = np.loadtxt('Mat_LeadField.dat') times = np.linspace(t_begin, t_end, t_end-t_begin) result = np.zeros((len(times), 31)) for i,t in enumerate(times): vm_vec = read_vm_vec(t) result[i, :] = LF_mat.dot(vm_vec)[0:31] result = np.insert(result, 0, times, axis=1) np.savetxt('BSPM.dat', result)</code></pre> <p> </p> </li> </ul> </li> <li>Mesh_PurkinjeTree.vtp <ul> <li>The resulting optimized Purkinje Node tree. We recommend using <a href="https://www.paraview.org/">ParaView</a> for visualization</li> </ul> </li> <li>Mesh_StimPoints.vtp <ul> <li>The resulting points of stimulation.</li> </ul> </li> <li>Stim_IndexTime.dat <ul> <li>Contains the stimulation pattern in terms the node index of Mesh_Ven.vtu and the respective stimulation time</li> </ul> </li> <li>Mesh_Ven.vtu <ul> <li>Contains the ventricular mesh as well as the repective lead field matrix values for each surface node.</li> <li>Material:<br> Right Ventricle 30<br> Left Ventricle 31</li> </ul> </li> <li>Mesh_Torso.vtu <ul> <li>Contains the whole torso mesh.</li> <li>Material:<br> Fat 2<br> Bones 3<br> Blood 9<br> Cartilage 14<br> Liver 20<br> Lungs 17<br> Right Ventricle 30<br> Left Ventricle 31<br> Right Atrium 32<br> Left Atrium 33<br> Aorta 60<br> Pulmonary artery 61<br> Left Vena Jugularis 62<br> Right Vena Jugularis 62<br> Post Vena Cava 62</li> </ul> </li> </ul> <p> </p>
Dataset for Reference-free Isotropic Super-resolution For Volumetric Fluorescence Microscopy
<p>Dataset for a research paper titled "Deep learning enables reference-free isotropic super-resolution for volumetric fluorescence microscopy". The images were acquired using two modalities: confocal fluorescence microscopy (CFM) and open-top light-sheet microscopy (OT-LSM). For details about the imaging, please refer to the paper (Link to be uploaded later). </p> <p>A. CFM</p> <ul> <li>CFM image of a cortical region of a Thy 1-eYFP mouse brain. </li> <li>Lateral resolution estimated as 1.24 micron and Z-depth interval of 3 micron</li> </ul> <ol> <li>Input image ["CFM_input_xy-view.tif] [Figure 2] </li> <li>Reference image acquired by rotating the sample by 90 degrees ["CFM_rotated-and-registered_xz-view.tif'] [Figure 2] </li> </ol> <p>B. OT-LSM </p> <ul> <li>OT-LSM image of a cortical region of a Thy 1-eYFP mouse brain.</li> <li>Lateral resolution estimated as 0.5 micron and axial resolution estimated as 4.6 micron. </li> <li>For testing of artifact correction, the microscope was poorly calibrated on purpose. </li> </ul> <ol> <li>Input image for artifact correction ["OT-LSM_artifact-correction_input_volume_xy-view.tif"] [Figure 4]</li> <li>Ground-truth image for artificial blurring ["OT-LSM_artificial-blurring_GT.tif"][Supplementary Figure 14]</li> <li>Input image for artificial blurring ["OT-LSM_artificial-blurring_gau-z-blurred-std-10.tif"][Supplementary Figure 14]</li> <li>Input image for PSF deconvolution ["input_volume_PSF-deconvolution.tif"][Figure 3]</li> </ol> <p>C. Simulation </p> <ul> <li>Jupyter notebook to generate a 3D image volume for simulation ["Data Generator for Simulation.ipynb"] [Figure 1] </li> </ul>
Volumetric imaging of Arabidopsis thaliana root cells
<p><em>Arabidopsis thaliana</em> seeds were surface sterilized, germinated and grown in Murashigue and Skoog medium at pH 5.7 and supplemented with vitamins (0.1 mg l-1 pyridoxine, 0.1 mg l-1 nicotinic acid), 0.8% agar, and 1% sucrose. Plants were grown at 21°C, 16/8-hour light/dark periods at 105 µmol/m<sup>2</sup>s<sup>2</sup> light intensity.</p> <p>Using a Zeiss Axiovert 200M microscope and a C-APO 63X, 1.2NA objective (Oberkochen, Germany), confocal volumetric imaging of the plant material was performed, with a pixel size of 404 nm and Z step size of 500 nm. Excitation of the sample was provided by a 488 nm laser, and a filter cube with 525/45 nm and 630/92 nm bandpass filters was used for yellow and red emission light collection, respectively.</p> <p>An inverted Olympus FV1000-IX81 confocal microscope equipped with a LUMFLN×60, 1.3NA S objective was used for root cell nuclear imaging. Sample excitation was achieved with a 543 nm laser and a BA560-660 filter was used for emission light collection. Pixel size was 41 nm, with a Z step size of 100 nm.</p> <p>A custom-built selective plane illumination microscopy (SPIM) system was used for imaging of primary root cells expressing p35s:H2B-R-RF. Sample excitation was achieved with a 561 nm laser using stroboscopic illumination. Emission was filtered via a multi-bandpass emission filter (Semrock, FF01-446/523/600/677-25 BrightLine). Volumetric imaging was done by mounting the sample on a four dimensional (XYZ, and Y rotation) motorized stage (Picard Industries). A sCMOS sensor (Hamamatsu, ORCA-Flash4.0 V2) was used for signal recording. The OpenSPIM plugin of µmanager (v.1.4 for windows) was used for control of acquisition parameters, sample translation and stroboscopic illumination. Collected images had a pixel size of 0.325µm, Z step size of 100 nm and a Y rotation step of 1.8°.</p> <p>Experimental procedures were approved by the Bioethics Committee of the Biotechnology Institute of the National Autonomous University of Mexico.</p>
Time-lapse (4D) volumetric fluorescence microscopy image sequence of a living zebrafish embryo
<p>The dataset contains a time-lapse (4D) volumetric fluorescence microscopy image sequence of a living zebrafish embryo (cxcr4aMO). The sequence has been captured with a confocal laser-scanning microscope during zebrafish gastrulation and shows endodermal cells that have been fluorescently labelled.</p> <p>The sequence is best viewed with Fiji (https://fiji.sc/) and can be loaded into Matlab with tiffread.m (http://www.cytosim.org/misc/index.html).</p> <p>For the treatment of the specimen see:</p> <p>S. Nair and T. F. Schilling. Chemokine signaling controls endodermal migration during zebrafish gastrulation. Science, 322(5898):89–92, October 2008.</p>
Predicted soil water content (volumetric %) for 33kPa and 1500kPa suctions at 6 standard depths (0, 10, 30, 60, 100 and 200 cm) at 250 m resolution
<p>Migrated to: <a href="https://doi.org/10.5281/zenodo.2629589">https://doi.org/10.5281/zenodo.2629589</a></p>
DATASET Volumetric Stability of Biphasic HAp/b-TCP/Collagen Implants in Malar Zone Augmentation: A CBCT Study in Orthognathic Surgery Patients
<p>This dataset repository contains data and analysis scripts related to the study on the volumetric stability of biphasic HAp/b-TCP/collagen implants used for malar zone augmentation in orthognathic surgery patients. The study evaluates the changes in implant volume over time using Cone Beam Computed Tomography (CBCT) scans. The dataset includes CSV files with clinical data and HTML files documenting the analysis procedures.</p> <h3> </h3>
Dataset for Adaptive Light-Sheet Fluorescence Microscopy with a Deformable Mirror for Video-Rate Volumetric Imaging
<p>1. Underlying data of figures in the paper </p> <p>2. Background images used to process the experimental data</p> <p>3. image stack of 250 nm beads</p> <p>4. image stack of sunflower pollen grains</p> <p>5. image stacks and videos of Fluo-4 labelled cells</p> <p>6. image stacks and videos of CMO-labelled cells</p> <p>The data is organised according to the figures they are related to in the following publication:</p> <p> </p> <p><a href="https://aip.scitation.org/author/Hong%2C+Wenzhi">Wenzhi Hong</a><em>, </em><a href="https://aip.scitation.org/author/Wright%2C+Terry">Terry Wright</a><em>, </em><a href="https://aip.scitation.org/author/Sparks%2C+Hugh">Hugh Sparks</a><em>, </em><a href="https://aip.scitation.org/author/Dvinskikh%2C+Liuba">Liuba Dvinskikh</a><em>, </em><a href="https://aip.scitation.org/author/MacLeod%2C+Ken">Ken MacLeod</a><em>, </em><a href="https://aip.scitation.org/author/Paterson%2C+Carl">Carl Paterson</a><em>, and </em><a href="https://aip.scitation.org/author/Dunsby%2C+Chris">Chris Dunsby</a> </p> <p>, "Adaptive light-sheet fluorescence microscopy with a deformable mirror for video-rate volumetric imaging", Appl. Phys. Lett. 121, 193703 (2022) <a href="https://doi.org/10.1063/5.0125946">https://doi.org/10.1063/5.0125946</a></p>
Towards true volumetric refractive index investigation in tomographic phase microscopy at cellular level - dataset
<p>This dataset contains 6 files with reconstrucions of 3 different types of cells (SH-SY5Y neuroblastoma cells, A549 adenocarcinoma lung cells and HL-60 white blood cells), retrieved from optical diffraction tomography system measurements carried out at Warsaw University of Technology. The measurements were reconstructued with 2 different algorithms: direct inversion algorithm (DI) and Gerchberg-Papoulis algorithm with finite object support regularization (GPSC) [1]. </p> <p>All files are *.mat files.</p> <p>In the files there are 5 variables:</p> <p>REC - reconstruction matrix with information about 3D refractive index values in the cells<br> dx - sample size <br> RI2D - values of mean refractive index calculated from 2D lateral cross-sections<br> RI3D - volumetric mean refractive index<br> mask - binary mask of the cell</p> <p><br> [1] W. Krauze, “Optical diffraction tomography with finite object support for the minimization of missing cone artifacts,”277<br> Biomed. optics express 11, 1919–1926 (2020)<br> </p>
Volumetric segmentation of biological cells and subcellular structures for optical diffraction tomography images - dataset
<p>This dataset includes 4 files with segmentation results for 4 different ODT reconstructions of SH-SY5Y neuroblastoma cell. The segmentation results contain:</p> <ol> <li>3D binary masks of biological cells obtained through Cellpose [1] and <a href="https://github.com/biopto/ODT-SAS.git">ODT-SAS</a>;</li> <li>3D binary masks of organelles: nucleoli and lipid structures (LS) obtained through slice-by-slice manual segmentation and ODT-SAS.</li> </ol> <p>All files are .*mat files.</p> <p>The files <em>REC_SH-SY5Y_1.mat, REC_SH-SY5Y_2.mat</em> and<em> REC_SH-SY5Y_3.mat</em> consist of 7 variables:</p> <p>RECON – tomographic reconstruction of SH-SY5Y neuroblastoma cell;<br> n_imm – refractive index of object immersion medium;<br> dx – object space sample size in XY [<span class="math-tex">\(\mu m\)</span>];<br> rayXY – xy-coordinates of illumination vectors;</p> <p>maskManual – table with manually determined 3D binary masks of organelles;<br> maskCellpose – 3D binary mask of biological cell obtained through Cellpose;<br> maskODTSAS – table with 3D binary masks of biological cell and their organelles obtained through ODT-SAS.</p> <p>File <em>REC_SH-SY5Y_4.mat</em> includes masks for the ODT-SAS and Cellpose segmentation of three closely packed cells and consists of 5 variables: RECON, n_imm, dx, maskCellpose and maskODTSAS.<br> <br> Access a particular 3D binary mask from 'maskManual' and 'maskODTSAS' tables, using the following names: 'Cell', 'Nucleoli', 'LS'.<br> For example:</p> <pre><code>cellMask = maskODTSAS.Cell{1};</code></pre> <p><br> [1] Stringer, C., Wang, T., Michaelos, M., & Pachitariu, M. (2021). Cellpose: a generalist algorithm for cellular segmentation. Nature methods, 18(1), 100-106.</p> <p> </p>
Soil temperature, volumetric water content and depth of thaw for ITEX CO2 flux survey plots 2003-2009.
Soil temperature, moisture content and thaw depth of the ITEX flux survey plots. Survey plots were located in the Toolik Lake LTER fertilization experiment in Alaska; at Imnavait Creek, Alaska; at Paddus, Latnjajaure and the Stepps site near Abisko in northern Sweden; at various sites in Adventdalen, Svalbard; in the Zackenberg valley, Northeast Greenland; at BEO near Barrow, Alaska and at the Anaktuvuk River Burn in Alaska. Measurements were made during the growing seasons 2003 to 2009.
Volumetric properties of dilute (d-glucose + H2O) solutions at temperatures from (293.15 to 433.15) K and pressures from (0.10 to 50.00) MPa
<p>The densities of aqueous solutions of D-glucose were measure at temperatures from (293.15 to 433.15) K and pressures from (0.10 to 50.00) MPa using a vibrating-tube densimeter. Apparent molar volumes <i>V</i><sub>φ,m</sub> and partial molar volumes at infinite dilution <i>V</i><sup>∞</sup> were calculated from the experimental results. <i>V</i><sup>∞</sup> increases as temperature increases and varies linearly with temperature above ~300 K. In addition, <i>V</i><sup>∞ </sup>does not vary as a function of pressure up to 50.0 MPa. Comparison of these results with previous studies indicate excellent agreement and significantly extend the experimental database for aqueous solutions of D-glucose to elevated temperatures and pressures. </p>
VOLUMETRIC
SMALL/LARGE. For one so small, you cast an impossibly large shadow. Source: Objaverse 1.0 / Sketchfab
Data from: Integrating 3D models with morphometric measurements to improve volumetric estimates in marine mammals
<p>1. Studies of body condition are key to understanding the health, bioenergetics, and ecological roles of marine mammals. Due to challenges in studying marine mammals at sea, body condition is often approximated using metrics representing the size of the dorsal surface visible from aerial imagery, but quantifying variability in body volume would enable a more holistic understanding of bioenergetics. Further, the number and location of measurements needed to accurately quantify body condition has received little attention. Three-dimensional (3D) models provide a promising tool for representing morphology and providing holistic estimates of marine mammal body condition when combined with field-based morphometric measurements.</p> <p>2. We use humpback whales (Megaptera novaeangliae) to demonstrate the utility of 3D models for estimating body condition in marine mammals. We integrate morphometric measurements taken from Unoccupied Aerial Vehicles (UAVs) with scalable 3D models to generate estimates of humpback whale body volume. We assess which and how many morphometric measurements are required to accurately estimate body volume and compare the error between volume estimates derived from 3D models and previously developed models representing volume as a series of ellipses. Using UAV measurements, we assess the contribution of each morphometric measurement to volumetric estimates, and quantify the error produced by all combinations and numbers of morphometric measurements (131,072 combinations).</p> <p>3. Error in volume estimates from 3D models generated with as few as five width measurements was <5% compared to the full models and was lower than the error produced when using five width measurements with the elliptical approach. We suggest that by conserving the external morphology of marine mammals, 3D models allow body volume and body condition to be estimated accurately with few measurements.</p> <p>4. We provide code and guidelines for creating 3D models using the open-source software Blender and for assessing which measurements are needed to accurately capture the morphology of cetaceans. The 3D modeling approach we present will facilitate studies of intra- and interannual changes in body volume in marine mammals, which is vital to providing a more holistic understanding of bioenergetics and to assessing responses to environmental change and anthropogenic stressors.</p>
Volumetric imaging of fluorescently labeled BPAE cells
<p>Using the Nanoimager-S microscope (ONI, Oxford Nanoimaging) with a sCMOS sensor (Hamamatsu, ORCA-Flash4.0 V2), FluoCells™ Prepared Slide #1 (Thermo, #F36924) were imaged with a 100X, 1.4 NA, oil-immersion objective (Olympus). DAPI, Alexa-488 and MitoTracker™ Red excitation was delivered by 405 nm, 473 nm and 561 nm lasers, respectively. Light was collected while using two emission filters (1: 525/50; 2: Band 1 575-616.5) and a Channel Splitter dichroic 561 LP. A 3D Z-stack of the sample was acquired for each channel. 22 frames were generated, each separated 50 nm from each other in Z.</p>
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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.