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638 results for “thinning”
Plant biomass dynamics following logging, burning, and thinning in Watersheds 6 and 7, Andrews Experimental Forest, 1979 to 2021
Watersheds 6 (WS06) and 7 (WS07) at the HJ Andrews are part of a three-watershed study initiated in the 1970’s to examine the response of hydrology and forest vegetation to logging. In 1974, Watershed 6 was clearcut logged; Watershed 7 was shelterwood cut, leaving 75-100 overstory trees per hectare (comprising about 40% of the original basal area). A nearby watershed (WS08) serves as an unlogged control. In 1975, all of Watershed 6 and the portion of WS07 below the road were broadcast-burned. In 1976, both watersheds were planted with Douglas-fir seedlings. Natural regeneration of Douglas-fir and western hemlock also established. In 1984, the remnant overstory trees in WS07 were harvested, and in 2001 the young stand in WS07 was thinned to about 550 trees per hectare. The thinning was not planned but provides an interesting twist to the study. The watersheds are located along the northern boundary of the HJA off the 327 and 328 roads, at elevations ranging from 850 to 1,160 m. Initial vegetation measurements were taken in the summer of 2002 in watersheds 6 and 7 for the purpose of characterizing plant succession after thinning in a small, high-elevation watershed. Understory vegetation plots are remeasured at approximately 6 year intervals.
DAS Control over the spatial correlation of silica perforations in thin films as a function of solution conditions
<p><span>Dataset production context : A perforated silica layer with structural correlation is engineered using sol-gel chemistry, applied to large-scale flat and curved sur-faces. The anion(s) used in the preparation give tailored spatial correlation, and control over perforation size and density. Surface structuration is rapidly and reproducibly created using water and salts as inexpensive and ecofriendly reagents.</span></p>
Imaging Temperature and Thickness of Thin Planar Liquid Water Jets in Vacuum - data
<p>Data set pertaining to the article "Imaging Temperature and Thickness of Thin Planar Liquid Water Jets in Vacuum", published in <em>Struct. Dyn.</em> 10, 034901 (2023), <a href="https://doi.org/10.1063/4.0000188" target="_blank" rel="noopener">https://doi.org/10.1063/4.0000188 </a>.</p> <p>The following data are provided:</p> <table> <tbody> <tr> <td>(zip-)file/Folder</td> <td>Description</td> <td>Format</td> <td>Extension</td> </tr> <tr> <td>IR_images/calibration_data/vacuum</td> <td> <p>Snapshots from a thermographic movie of our flat jet running in vacuum, at thirty different background temperature. (A snapshot shown in Fig. 3a, rhs.)</p> </td> <td> <p>temperature values per camera pixel (°C), 640 row * 480 columns, semicolon-separated ascii data</p> </td> <td>.asc</td> </tr> <tr> <td>IR_images/calibration_data/1atm</td> <td>As above, for our flat jet running in atmosphere. (Three snapshots shown in Fig. 2a.)</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_images/calibration_data/chipnozzle</td> <td>As above, for a flat jet produced from a chip nozzle, and running in atmosphere.</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_images/raw_data</td> <td>As above, for various conditions of the flat jet environment as detailed in table exp_settings.csv.</td> <td>as above</td> <td>.asc</td> </tr> <tr> <td>IR_video</td> <td>Two thermographic movies recorded of our flat jet at varied conditions of the jet environment detailed in table chamber_pressure.pdf.</td> <td>Radiographic image stream, suitable for opening with free software Optris Pix Connect.</td> <td>.ravi</td> </tr> <tr> <td>FJ_cooling_2D.mph</td> <td>Input file for 2D finite element simulation of our flat jet.</td> <td>Input file suitable for Comsol software, proprietary format.</td> <td>.mph</td> </tr> <tr> <td>Y_Z_Temp_Comsol.txt</td> <td>Ascii representation of our simulated temperature profile (Fig. S7 (SI)).</td> <td>List of (y,z,T) tupels, with (y,z) in m and T in °C.</td> <td>.txt</td> </tr> </tbody> </table> <p> </p> <p>In case you have any questions regarding this data set please contact: Uwe Hergenhahn, uhe@fhi.mpg.de .</p>
City of Seattle, Seattle Public Utilities, Restoration Thinning Trial, 2005 - 2017, Cedar River Municipal Watershed, King County, WA
The Restoration Thinning (RT) Program in the Cedar River Municipal Watershed (CRMW) was one of three forest restoration programs (the others being Ecological Thinning and Planting) defined and funded through the Cedar River Watershed Habitat Conservation Plan (HCP) that was signed and initiated in April of 2000. Restoration thinning and ecological thinning projects were combined into the 'Upland Forest Thinning' project and are ongoing today to meet objectives outlined in the Habitat Conservation Plan and Forest Managment Plan. The primary goal of the RT program, which is analogous to pre-commercial thinning, was to actively thin dense young second-growth forest stands (generally less than 30 years old) to facilitate ecological development towards old-growth forest habitat conditions. Objectives of RT include: Reduce competition among trees. Stimulate tree growth. Increase light penetration under the top tree canopy. Increase tree and understory plant species diversity. Accelerate forest development beyond the competitive exclusion stage towards a more biologically diverse stage. Extend the forest development stand initiation stage such that diverse species become established and diverse stand structures develop. Provide multiple development pathways for variable forest stand structures. Reduce long-term fire hazard. Increase resilience to catastrophic windthrow, insect, or disease outbreak. Increase habitat connectivity and structural variability of riparian areas. This data package describes a forest restoration trial in young conifer forests of the western central Cascade Range in Washington State, USA. Young second-growth forests often regenerate as very dense, homogeneous stands following harvesting. These forests have low species diversity and trees often experience strong competition for resources. To increase tree vigor and growth and stimulate development of diverse understory, shrub species stands are thinned with the long-term goal to restore diverse func
LMD Inconel 718 Thin walls 2019-12-05
<p>Description of dataset 10.5281/zenodo.3981107</p> <p>Deposition of Inconel 718 thin walls with process parameters:<br> - Nominal power = 200 (W)<br> - Nominal velocity = 300 (mm/min)<br> - Powder flux = 0.0825 (g/s)<br> - Nr. layers per wall: 1, 2, 4, 8, 16<br> - Nr. nozzles = 4<br> - Argon carrier flux = 4 (l/min)<br> - Argon shielding gas flux = 15 (l/min)<br> - Substrate temperature = Ambient<br> - Nr. repetitions: 2 (of whole experiment)</p> <p>The dataset is constituted by:<br> - Melt pool images, in file Experiment_2019_12_5__15_58_3.zip (subfolder Deposition_2019_12_05__16_00_17 for rep1, subfolder Deposition_2019_12_05__16_14_23 for rep2), acquired at 200fps with 850 nm narrow band filter, 1ms exposure time. 400x400 px size<br> - trackData_20191205_rep1.csv (or [...]_rep2.csv) containing:<br> - t: timestamp in ms. Synchronized imageData_20191008.csv<br> - Xpos: laser spot X position in workspace<br> - Ypos: laser spot Y position in workspace<br> - Zpos: laser spot Z position in workspace<br> - G1: binary signal indicating active deposition (G1=1) or not<br> - D: track width measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - H: track heigth measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - A: track section area measured at [Xpos(t), Ypos(t), Zpos(t)]<br> - sdres: roughness index of section profile (std. deviation w.r.t. smoothed profile)<br> - Vnom: laser spot translational speed in m/s (computed from Xpos, Ypos, Zpos and t data)<br> - Pnom: nominal power<br> - V: Vnom in mm/min<br> - imageData_20191205_rep1.csv (or [...]_rep2.csv) containing:<br> - t: timestamp in ms. Synchronized with trackData_20191205_rep1.csv (some frames may have been lost)<br> - I_mean: mean image intensity (only on red channel)<br> - I_mean_crop: mean image intensity computed on central cropped image area (180x180 pixels)<br> - M_I_mean: I_mean after application of 8-sample moving average<br> - M_I_mean_crop: I_mean_crop after application of 8-sample moving average<br> - fileName: associated image file name<br> - beamON: laserON signal obtained from thresholding on images (background noise = off, minimal intensity level = on)</p>
Ferromagnetic resonance of Co thin films grown by atomic layer deposition on the Sb2Te3 topological insulator (data)
<p>This dataset contains the raw data files connected with the figures included in the paper "<em>Ferromagnetic resonance of Co thin films grown by atomic layer deposition on the Sb<sub>2</sub>Te<sub>3</sub> topological insulator</em>" by E. Longo et al., JMMM 209, 166885 (2020): <a href="https://linkinghub.elsevier.com/retrieve/pii/S0304885319336029">https://linkinghub.elsevier.com/retrieve/pii/S0304885319336029</a></p>
Data for: Low velocity impact resistance of thin and toughened carbon fibre reinforced epoxy
<p><em><strong>Version v2:</strong> <br></em>Added tiff-image stacks</p> <p><em><strong>Version v1:</strong><br></em>The data is supplementary to the publication "Low velocity impact resistance of thin and toughened carbon fibre reinforced epoxy", DOI: <a href="https://doi.org/10.1016/j.compscitech.2022.109362">10.1016/j.compscitech.2022.109362</a> as well as to the dissertation: "Morphology and Fracture of Block Copolymer and Core-Shell Rubber Particle Modified Epoxies and their Carbon Fibre Reinforced Composites", urn: <a href="https://nbn-resolving.org/urn:nbn:de:hbz:386-kluedo-63437">urn:nbn:de:hbz:386-kluedo-63437</a></p> <p>Key words: Polymer-matrix composites (PMCs), Impact behaviour, Low velocity impact, Barely visible impact damage, Damage tolerance, X-ray computed tomography, Fractography, Carbon fibre reinforced composite (CFRP)</p> <p>The data set is a collection of TXRM data of several low energy impact damages in CFRP specimens. The data was acquired via XCT (X-Ray Computed Tomography).</p> <p>Material details:</p> <ul> <li>Carbon fibre reinforced composite</li> <li>Thickness: ~ 1.65mm</li> <li>Matrix polymer: Epoxy-based (DGEBA): Sika CR144 + Anhydride curing agent (Huntsman Aradur917) + 1-Methylimidazole</li> <li>Carfon-fibre fabric: ECC Carbon fabric Style 763, based on Toho Tenax HTA40 E13, 140g/m²</li> <li>Layup: 13 layers, stacking sequence (45/-45/45/-45/90/0/90)s, (15% 0°/23% 90°/62% ± 45°) </li> <li>the average carbon fibre volume content was 52.5 ± 1.8 vol.-%</li> <li>cured ply-thickness: 126.1 μm</li> <li>Impact energies: 1J, 3J, 7J, 9J, 13J</li> <li>manufactured via autoclaving</li> </ul> <p>The reasearch received funding from the German Academic Exchange Service (DAAD) within the funding program “Kurzstipendien fuer Doktoranden” (grant number: 57438025).</p>
FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures
<h2>General</h2> <p>For more details and the most up-to-date information please consult our project page: <a href="https://kainmueller-lab.github.io/fisbe" target="_blank" rel="noopener">https://kainmueller-lab.github.io/fisbe</a>.</p> <h2>Summary</h2> <ul> <li>A new dataset for neuron instance segmentation in 3d multicolor light microscopy data of fruit fly brains <ul> <li>30 completely labeled (segmented) images</li> <li>71 partly labeled images</li> <li>altogether comprising ∼600 expert-labeled neuron instances (labeling a single neuron takes between 30-60 min on average, yet a difficult one can take up to 4 hours)</li> </ul> </li> <li>To the best of our knowledge, the first real-world benchmark dataset for instance segmentation of long thin filamentous objects</li> <li>A set of metrics and a novel ranking score for respective meaningful method benchmarking</li> <li>An evaluation of three baseline methods in terms of the above metrics and score</li> </ul> <h2>Abstract</h2> <p>Instance segmentation of neurons in volumetric light microscopy images of nervous systems enables groundbreaking research in neuroscience by facilitating joint functional and morphological analyses of neural circuits at cellular resolution. Yet said multi-neuron light microscopy data exhibits extremely challenging properties for the task of instance segmentation: Individual neurons have long-ranging, thin filamentous and widely branching morphologies, multiple neurons are tightly inter-weaved, and partial volume effects, uneven illumination and noise inherent to light microscopy severely impede local disentangling as well as long-range tracing of individual neurons. These properties reflect a current key challenge in machine learning research, namely to effectively capture long-range dependencies in the data. While respective methodological research is buzzing, to date methods are typically benchmarked on synthetic datasets. To address this gap, we release the FlyLight Instance Segmentation Benchmark (FISBe) dataset, the first publicly available multi-neuron light microscopy dataset with pixel-wise annotations. In addition, we define a set of instance segmentation metrics for benchmarking that we designed to be meaningful with regard to downstream analyses. Lastly, we provide three baselines to kick off a competition that we envision to both advance the field of machine learning regarding methodology for capturing long-range data dependencies, and facilitate scientific discovery in basic neuroscience.</p> <h2>Dataset documentation:</h2> <p>We provide a detailed documentation of our dataset, following the <a href="https://arxiv.org/abs/1803.09010" target="_blank" rel="noopener">Datasheet for Datasets</a> questionnaire:</p> <p><em>>> <a href="https://kainmueller-lab.github.io/fisbe/datasheet" target="_blank" rel="noopener">FISBe Datasheet</a></em></p> <p>Our dataset originates from the <a href="https://www.janelia.org/project-team/flylight" target="_blank" rel="noopener">FlyLight project</a>, where the authors released a large image collection of nervous systems of ~74,000 flies, <a href="https://gen1mcfo.janelia.org/cgi-bin/gen1mcfo.cgi" target="_blank" rel="noopener">available for download</a> under CC BY 4.0 license.</p> <h2>Files</h2> <ul> <li>fisbe_v1.0_{completely,partly}.zip <ul> <li>contains the image and ground truth segmentation data; there is one <em>zarr</em> file per sample, see below for more information on how to access <em>zarr</em> files.</li> </ul> </li> <li>fisbe_v1.0_mips.zip <ul> <li>maximum intensity projections of all samples, for convenience.</li> </ul> </li> <li>sample_list_per_split.txt <ul> <li>a simple list of all samples and the subset they are in, for convenience.</li> </ul> </li> <li>view_data.py <ul> <li>a simple python script to visualize samples, see below for more information on how to use it.</li> </ul> </li> <li>dim_neurons_val_and_test_sets.json <ul> <li>a list of instance ids per sample that are considered to be of low intensity/dim; can be used for extended evaluation.</li> </ul> </li> <li>Readme.md <ul> <li>general information</li> </ul> </li> </ul> <h2>How to work with the image files</h2> <p>Each sample consists of a single 3d MCFO image of neurons of the fruit fly.<br>For each image, we provide a pixel-wise instance segmentation for all separable neurons.<br>Each sample is stored as a separate <em>zarr</em> file (<a href="https://zarr.readthedocs.io" target="_blank" rel="noopener">zarr</a> is a file storage format for chunked, compressed, N-dimensional arrays based on an open-source specification.").<br>The image data ("raw") and the segmentation ("gt_instances") are stored as two arrays within a single zarr file.<br>The segmentation mask for each neuron is stored in a separate channel.<br>The order of dimensions is CZYX.</p> <p>We recommend to work in a virtual environment, e.g., by using conda:</p> <p><code>conda create -y -n flylight-env -c conda-forge python=3.9</code><br><code>conda activate flylight-env</code></p> <h3>How to open <em>zarr</em> files</h3> <ol> <li>Install the python zarr package: <pre><code>pip install zarr</code></pre> </li> <li>Opened a zarr file with:<br> <p><code>import zarr</code><br><code>raw = zarr.open(<path_to_zarr>, mode='r', path="volumes/raw")</code><br><code>seg = zarr.open(<path_to_zarr>, mode='r', path="volumes/gt_instances")</code></p> <p><code># optional:</code><br><code>import numpy as np</code><br><code>raw_np = np.array(raw)</code></p> </li> </ol> <p>Zarr arrays are read lazily on-demand.<br>Many functions that expect numpy arrays also work with zarr arrays.<br>Optionally, the arrays can also explicitly be converted to numpy arrays.</p> <h3>How to view <em>zarr</em> image files</h3> <p>We recommend to use <a href="https://napari.org" target="_blank" rel="noopener">napari</a> to view the image data.</p> <ol> <li>Install napari: <pre><code>pip install "napari[all]"</code></pre> </li> <li>Save the following Python script: <br> <p><code>import zarr, sys, napari</code></p> <p><code>raw = zarr.load(sys.argv[1], mode='r', path="volumes/raw")</code><br><code>gts = zarr.load(sys.argv[1], mode='r', path="volumes/gt_instances")</code></p> <p><code>viewer = napari.Viewer(ndisplay=3)</code><br><code>for idx, gt in enumerate(gts):</code><br><code> viewer.add_labels(</code><br><code> gt, rendering='translucent', blending='additive', name=f'gt_{idx}')</code><br><code>viewer.add_image(raw[0], colormap="red", name='raw_r', blending='additive')</code><br><code>viewer.add_image(raw[1], colormap="green", name='raw_g', blending='additive')</code><br><code>viewer.add_image(raw[2], colormap="blue", name='raw_b', blending='additive')</code><br><code>napari.run()</code></p> </li> <li>Execute: <pre><code>python view_data.py <path-to-file>/R9F03-20181030_62_B5.zarr</code></pre> </li> </ol> <h2>Metrics</h2> <ul> <li>S: Average of avF1 and C</li> <li>avF1: Average F1 Score</li> <li>C: Average ground truth coverage</li> <li>clDice_TP: Average true positives clDice</li> <li>FS: Number of false splits</li> <li>FM: Number of false merges</li> <li>tp: Relative number of true positives</li> </ul> <p>For more information on our selected metrics and formal definitions please see <a href="https://arxiv.org/abs/2404.00130" target="_blank" rel="noopener">our paper</a>.</p> <h2>Baseline</h2> <p>To showcase the FISBe dataset together with our selection of metrics, we provide evaluation results for three baseline methods, namely <a href="https://github.com/Kainmueller-Lab/PatchPerPix" target="_blank" rel="noopener">PatchPerPix (ppp)</a>, <a href="https://github.com/google/ffn" target="_blank" rel="noopener">Flood Filling Networks (FFN)</a> and a non-learnt application-specific <a href="https://www.biorxiv.org/content/10.1101/2020.06.07.138941v1" target="_blank" rel="noopener">color clustering from Duan et al.</a>.<br>For detailed information on the methods and the quantitative results please see <a href="https://arxiv.org/abs/2404.00130" target="_blank" rel="noopener">our paper</a>.</p> <h2>License</h2> <p>The FlyLight Instance Segmentation Benchmark (FISBe) dataset is licensed under the <a href="https://creativecommons.org/licenses/by/4.0" target="_blank" rel="noopener">Creative Commons Attribution 4.0 International (CC BY 4.0) license</a>.</p> <h2>Citation</h2> <p>If you use <em>FISBe</em> in your research, please use the following BibTeX entry: </p> <pre><code>@misc{mais2024fisbe, title = {FISBe: A real-world benchmark dataset for instance segmentation of long-range thin filamentous structures}, author = {Lisa Mais and Peter Hirsch and Claire Managan and Ramya Kandarpa and Josef Lorenz Rumberger and Annika Reinke and Lena Maier-Hein and Gudrun Ihrke and Dagmar Kainmueller}, year = 2024, eprint = {2404.00130}, archivePrefix ={arXiv}, primaryClass = {cs.CV} }</code></pre> <h2>Acknowledgments</h2> <p>We thank Aljoscha Nern for providing unpublished MCFO images as well as Geoffrey W. Meissner and the entire FlyLight Project Team for valuable<br>discussions.<br>P.H., L.M. and D.K. were supported by the HHMI Janelia Visiting Scientist Program.<br>This work was co-funded by Helmholtz Imaging.</p> <h2>Changelog</h2> <p>There have been no changes to the dataset so far.<br>All future change will be listed <a href="https://kainmueller-lab.github.io/fisbe/changelog" target="_blank" rel="noopener">on the changelog page</a>.</p> <h2>Contributing</h2> <p>If you would like to contribute, have encountered any issues or have any suggestions, please <a href="https://github.com/Kainmueller-Lab/fisbe/issues" target="_blank" rel="noopener">open an issue</a> for the FISBe dataset in the accompanying github repository.</p> <p>All contributions are welcome!</p>
Controlling the Dewetting Morphologies of Thin Liquid Films by Switchable Substrates
<p>Data and scripts for the creation of the data used in the publication: "Controlling the dewetting morphologies of thin liquid films by switchable substrates" in Phys. Rev. Fluids.</p>
Effect of Substrates and Thermal Treatments on Metalorganic Chemical Vapor Deposition-Grown Sb2Te3 Thin Films (data)
<p>This dataset contains the raw data files connected with the figures included in the paper "<em>Effect of Substrates and Thermal Treatments on Metalorganic Chemical Vapor Deposition-Grown Sb<sub>2</sub>Te<sub>3</sub> Thin Films</em>" by <a href="https://pubs.acs.org/doi/pdf/10.1021/acs.cgd.1c00508">M. Rimoldi et al., <em>Cryst. Growth Des.</em> 2021, 21, 9, 5135–5144</a></p> <p>Note for users: </p> <p>The data in Figure 10 can be retrieved from <a href="https://zenodo.org/record/5725028#.Ybcxb73MI2w">Table 2 in the main text</a>.</p>
5D-NP-FABTECH_ALD - Open Dataset for: "Shedding light on the initial growth of ZnO during plasma-enhanced atomic layer deposition on vapor-deposited polymer thin films"
<p>This is the open dataset for the paper: "Demelius, L. <em>et al.</em> Shedding light on the initial growth of ZnO during plasma-enhanced atomic layer deposition on vapor-deposited polymer thin films. <em>Applied Surface Science</em> <strong>604</strong>, (2022)."</p> <p>This includes the supplementary information and all the source material that was used for the paper preparation.</p> <p>For each folder (sub-dataset), there exists a corresponding readme file describing the content and including material.</p>
Dataset For Molecular Dynamics Simulations of Thin Film Rupture
<p>Data files for production runs for the key results reported in "Life and Death of a Thin Liquid Film", (2024) by Muhammad Rizwanur Rahman, Li Shen, James P. Ewen, D. M. Heyes, Daniele Dini, and E. R. Smith. The directory named "spontaneous-rupture-equilibrated-state-for-production-runs" contains data files of different initial film thicknesses, and the directory named "synthetic-rupture-equilibrated-state-for-production-runs" contains data files for films with similar initial thickness, but with different patterns of synthetic damages caused to the film. These files should be used as the restart file for production phase under NVE ensemble. </p> <p>Codes to run these files, and process the data are described in github: https://github.com/MuhammadRRahman/Thin-Film-Rupture-NEMD.git.</p>
Predator effects on metamorphosis: The effects of scaring versus thinning at high prey densities.
Organisms with complex life cycles face the challenge of when to switch between habitats and foraging strategies over ontogeny in ways that improve their fitness. Metamorphosis is a well-studied life history event in animals and ecologists have spent decades trying to understand how the size at and time to metamorphosis are altered by natural stressors such as competition and predation. The challenges in interpreting the effects of predators on metamorphic decisions include the need to compare predator species that pose different levels of risk, compare the roles of predators inducing fear versus thinning of the density of prey, and examine prey life history traits and behavior over ontogeny. We addressed these challenges in a mesocosm experiment in which we introduced a high initial density of hatchling Northern Leopard Frogs (Rana pipiens) and exposed them to three different species of caged predators (to induce three different levels of fear), three rates of hand-thinning (to mimic the thinning effect of each predator), or three species of lethal predators (to cause induction and thinning). Under these initial high densities, we found that caged predators had no effects on tadpole activity, growth, and development. This outcome was likely due to the high density of tadpoles causing high competition, which can inhibit anti-predator responses. High rates of hand thinning caused decreased tadpole activity, greater mass, and faster development. Interestingly, lethal predators caused phenotypic changes that were largely in line with the hand thinning effects alone. These results suggest that at high initial prey densities, the thinning process of predation appears plays a much more important role in prey metamorphosis than induction from predatory chemical cues.
IODP Expedition 391 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 397T 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 383 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.
Using thin films of phase-change material for active tuning of terahertz waves scattering on dielectric cylinders
<p>The uploaded files contain the data generated by MATLAB and used to plot a part of the figures, and a sample code.</p> <p>Research supported by Narodowe Centrum Nauki, project no UMO-2020/39/I/ST3/02413.</p>
IODP Expedition 378 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.
Investigating the ageing process of polymer modified bitumen using a modified Thin-Film Oven Test in the aspect of recycling purpose within Weave-UNISONO 2021 project, NCN project No 2021/03/Y/ST8/00079
<div><strong>Summary:</strong></div> <div>One polymer-modified bitumen PMB 25/55-60 was tested in two stages: original and after the modified Thin-Film Oven Test (TFOT). The time ranges from 1h-5h, and temperatures from 120°C-200°C were used. The Fourier-Transform Infrared (FTIR) Spectroscopy and Dynamic Shear Rheometer (DSR) with parallel plates were conducted. Test temperatures range from 30–70°C for a 25 mm diameter plate and 0–30°C for an 8 mm plate with 10°C intervals and angular frequency range of 0.1, 1.0, and 10 Hz.</div> <div> </div> <div> </div> <div><strong>The dataset includes:</strong></div> <div>Basic characteristics of bituminous binder (R&B Temperatur, Penetration), CSV raw data:</div> <div> <ul> <li>01 - SP Pen.csv</li> </ul> </div> <div> </div> <div>Dynamic shear rheometer (Temperatures 0-70 °C, Angular Frequency 0.1Hz, 1.0Hz, 10 Hz, Complex Shear Modulus, Phase Angle):</div> <ul> <li>02.1 - DSR Rheology_Unaged.csv</li> <li>02.2 - DSR Rheology_2h_140C.csv</li> <li>02.3 - DSR Rheology_2h_200C.csv</li> <li>02.4 - DSR Rheology_5h_140C.csv</li> <li>02.5 - DSR Rheology_5h_200C.csv</li> </ul> <div> </div> <div>FTIR - Fourier-Transform Infrared Spectroscopy </div> <div> <ul> <li>OPUS Spectroscopy files.zip</li> </ul> </div> <div> </div> <div>--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---</div> <div>to open the OPUS files, please go to the © Bruker webpage and download the free OPUS Viewer.</div> <div>https://www.bruker.com/en/products-and-solutions/infrared-and-raman/opus-spectroscopy-software/downloads.html</div> <div>--- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- --- ---</div> <div> </div>
IODP Expedition 367 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.
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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.