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2,239 results for “MicroED”
Red Wood-Ant Nests and Fault-Related Methane Micro-Seepage 2016
We measured methane (CH4) and stable carbon isotope of methane (ẟ13C-CH4) concentrations in ambient air and within a red wood-ant (RWA; Formica polyctena) nest in the Neuwied Basin (Germany) using high-resolution in-situ sampling to detect microbial, thermogenic, and abiotic fault-related micro-seepage of CH4. Methane degassing from RWA nests was not synchronized with earth tides, nor was it influenced by micro-earthquake degassing or concomitantly measured RWA activity. Two ẟ13C-CH4 signatures were identified in nest gas: −69‰ and −37‰. The lower peak was attributed to microbial decomposition of organic matter within the RWA nest, in line with previous observations that RWA nests are hot-spots of microbial CH4. The higher peak has not been reported in previous studies. We attribute this peak to fault-related CH4 emissions moving via fault networks into the RWA nest, which could originate either from thermogenic or abiotic CH4 formation. Sources of these micro-seepages could be Devonian schists, iron-bearing “Klerf Schichten,” or overlapping micro-seepage of magmatic CH4 from the Eifel plume. Given the abundance of RWA nests on the landscape, their role as sources of microbial CH4 and biological indicators for abiotically-derived CH4 should be included in estimation of methane emissions that are contributing to climatic change.
Kleptotrace-micro-dataset
<p>This micro-benchmark dataset was made for evaluation of the proposed pipeline in Koletsis et al. Entity Extraction from High-Level Corruption<br>Schemes via Large Language Models. BDA4FCT@IEEE Big Data 2024. Also available at https://arxiv.org/abs/2409.13704</p> <p>This dataset comprises 15 articles, totaling 441 sentences, focused on topics related to financial corruption. It includes 2 lists of individuals and organizations mentioned within these articles.</p>
Dataset for: Associating Mechano-electrochemical Phenomena to Stochastic Current Events in Micro-Electrochemical Cells Containing TiNb2O7 Particles
<p>This is the raw data used in a manuscript that will be submitted to ChemElectroChem. If you have any questions, please email the creators.</p>
Example Microscopy Metadata JSON files produced using Micro-Meta App to document example microscopy experiments performed at individual core facilities
<p>Example <strong>Microscopy Metadata </strong>(Microscope.JSON and Settings.JSON)<strong> files </strong>produced using<strong> <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">Micro-Meta App</a> </strong>to document the <strong>Hardware Specifications</strong> of example Microscopes and the <strong>Image Acquisition Settings</strong> utilized to acquire example images as listed in the table below.</p> <blockquote> <p>For each facility, the dataset contains two JSON files:</p> <ol> <li><strong>Microscope.JSON file</strong> (e.g., 01_marcello_uliverpool_cci_zeiss_axioobserz1_lsm710.json)</li> <li><strong>Settings.JSON file</strong> (indicated with the name of the image and with the _AS suffix)</li> </ol> </blockquote> <p><strong>Micro-Meta App was</strong> developed as part of a <strong>global community initiative</strong> including the <a href="http://www.4dnucleome.org/"><strong>4D Nucleome (4DN)</strong> </a>Imaging Working Group, <strong>BioImaging North America (BINA)</strong> <a href="https://www.bioimagingna.org/qc-dm-wg">Quality Control and Data Management Working Group</a>, and <strong>QUAlity and REProducibility for Instrument and Images in Light Microscopy</strong> (<a href="https://quarep.org/"><strong>QUAREP-LiMi</strong></a>), to extend the <strong>Open Microscopy Environment (OME)</strong> <a href="https://www.openmicroscopy.org/Schemas/Documentation/Generated/OME-2016-06/ome.html">data model</a>.</p> <blockquote> <p>The works of this <strong>global community effort</strong> resulted in multiple publications featured on a recent <strong>Nature Methods FOCUS ISSUE </strong>dedicated to <a href="https://www.nature.com/collections/djiciihhjh">Reporting and reproducibility in microscopy</a>.</p> </blockquote> <blockquote> <p><strong>Learn More!</strong> For a thorough description of <strong>Micro-Meta App</strong> consult our recent <a href="https://doi.org/10.1038/s41592-021-01315-z">Nature Methods</a> and <a href="https://doi.org/10.1101/2021.05.31.446382">BioRxiv.org</a> publications!</p> </blockquote> <p> </p> <table> <tbody> <tr> <td><strong>Nr.</strong></td> <td><strong>Manufacturer</strong></td> <td><strong>Model</strong></td> <td><strong>Tier</strong></td> <td><strong>Εxperiment Type</strong></td> <td><strong>Facility Name</strong></td> <td><strong>Department and Institution</strong></td> <td><strong>URL</strong></td> <td><strong>References</strong></td> </tr> <tr> <td>1</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer Z1 (with LSM 710 scan head)</strong></td> <td>1</td> <td>3D visualization of superhydrophobic polymer-nanoparticles</td> <td>Centre for Cell Imaging (CCI)</td> <td>University of Liverpool</td> <td>https://cci.liv.ac.uk/equipment_710.html</td> <td>Upton et al., 2020</td> </tr> <tr> <td>2</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer (Axiovert 200M)</strong></td> <td>2</td> <td>Μeasurement of illumination stability on Chinese Hamster Ovary cells expressing Paxillin-EGFP</td> <td>Advanced BioImaging Facility (ABIF).</td> <td>McGill University</td> <td>https://www.mcgill.ca/abif/equipment/axiovert-1</td> <td>Kiepas et al., 2020</td> </tr> <tr> <td>3</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer Z1 (with Spinning Disk)</strong></td> <td>2</td> <td>Immunofluorescence imaging of cryosection of Mouse kidney</td> <td>Imagerie Cellulaire; Quality Control managed by Miacellavie (https://miacellavie.com/)</td> <td>Centre de recherche du Centre Hospitalier Université de Montréal (CR CHUM), University of Montreal</td> <td>https://www.chumontreal.qc.ca/crchum/plateformes-et-services (the web site is for all core facilities, not specifically for the core facility hosting this microscope)</td> <td>Pilliod et al., 2020</td> </tr> <tr> <td>4</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Imager Z2 (with Apotome)</strong></td> <td>2</td> <td>Immunofluorescence imaging of mitotic division in Hela cells using </td> <td>Bioimaging Unit</td> <td>Newcastle University</td> <td>https://www.ncl.ac.uk/bioimaging/</td> <td>Watson et al., 2020</td> </tr> <tr> <td>5</td> <td><strong>Carl Zeiss Microscopy</strong></td> <td><strong>Axio Observer Z1</strong></td> <td>2</td> <td>Fluorescence microscopy of human skin fibroblasts from Glycogen Storage Disease patients.</td> <td>Life Imaging Center (LIC)</td> <td>Centre for Integrative Signalling Analysis (CISA), University of Freiburg</td> <td>https://miap.eu/equipments/sd-i-abl/</td> <td>Hannibal et al., 2020</td> </tr> <tr> <td>6</td> <td><strong>Leica Microsystems</strong></td> <td><strong>DMI6000B</strong></td> <td>2</td> <td>3D immunofluorescence imaging rhinovirus infected macrophages </td> <td>IMAG'IC Confocal Microscopy Facility</td> <td>Institut Cochin, CNRS, INSERM, Université de Paris</td> <td>https://www.institutcochin.fr/core_facilities/confocal-microscopy/cochin-imaging-photonic-microscopy/organigram_team/10054/view</td> <td>Jubrail et al., 2020</td> </tr> <tr> <td>7</td> <td><strong>Leica Microsystems</strong></td> <td><strong>DM5500B</strong></td> <td>2</td> <td>Immunofluorescence analysis of the colocalization of PML bodies with DNA double-strand breaks</td> <td>Bioimaging Unit</td> <td>Edwardson Building on the Campus for Ageing and Vitality, Newcastle University</td> <td>https://www.ncl.ac.uk/bioimaging/equipment/leica-dm5500/#overview</td> <td>da Silva et al., 2019; Nelson et al., 2012<br> </td> </tr> <tr> <td>8</td> <td><strong>Leica Microsystems</strong></td> <td><strong>DMI8-CS (with TCS SP8 STED 3X)</strong></td> <td>2</td> <td>Live-cell imaging of N. benthamiana leaves cells-derived protoplasts</td> <td>Center for Advanced Imaging (CAi)</td> <td>School of Mathematics/Natural Sciences, Heinrich-Heine-Universität Düsseldorf</td> <td>https://www.cai.hhu.de/en/equipment/super-resolution-microscopy/leica-tcs-sp8-sted-3x</td> <td>Singer et al., 2017; Hänsch et al., 2020</td> </tr> <tr> <td>9</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti</strong></td> <td>2</td> <td>Immunofluorescence analysis of the cytoskeleton structure in COS cells</td> <td>Advanced Imaging Center (AIC)</td> <td>Janelia Research Campus, Howard Hughes Medical Institute</td> <td>https://www.janelia.org/support-team/light-microscopy/equipment</td> <td>Abdelfattah et al., 2019; Qian et al., 2019; Grimm et al., 2020</td> </tr> <tr> <td>10</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti-E (HCA)</strong></td> <td>2</td> <td>Τime-lapse analysis of the bursting behavior of amine-functionalized vesicular assemblies</td> <td>Light Microscopy Facility (IALS-LIF)</td> <td>Institute for Applied Life Sciences, University of Massachusetts at Amherst</td> <td>https://www.umass.edu/ials/light-microscopy</td> <td>Fernandez et al., 2020</td> </tr> <tr> <td>11</td> <td><strong>Nikon Instruments/Coleman laboratory (customized)</strong></td> <td><strong>TIRF HILO Epifluorescence light Microscope (THEM)/ Eclipse Ti</strong></td> <td>2</td> <td>Single-particle tracking of Halo-tagged PCNA in Lox cells</td> <td>Coleman laboratory</td> <td>Anatomy and Structural Biology Department, The Albert Einstein College of Medicine</td> <td>https://einsteinmed.org/faculty/12252/robert-coleman/</td> <td>Drosopoulos et al., 2020</td> </tr> <tr> <td>12</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti (with Andor Dragon Fly Spinning Disk)</strong></td> <td>2</td> <td>Investigation of the 3D structure of cerebral organoids</td> <td>Montpellier Resources Imagerie</td> <td>Centre de Recherche de Biologie cellulaire de Montpellier (MRI-CRBM), CNRS, Univerity of Montpellier</td> <td>https://www.mri.cnrs.fr/en/optical-imaging/our-facilities/mri-crbm.html</td> <td>Ayala-Nunez et al., 2019</td> </tr> <tr> <td>13</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti2</strong></td> <td>2</td> <td>Ιmmunofluorescence imaging of cryosections of mouse hearth myocardium </td> <td>Neuroscience Center Microscopy Core</td> <td>Neuroscience Center, University of North Carolina</td> <td>https://www.med.unc.edu/neuroscience/core-facilities/neuro-microscopy/</td> <td>Aghajanian et al., 2021</td> </tr> <tr> <td>14</td> <td><strong>Nikon Instruments</strong></td> <td><strong>Eclipse Ti2</strong></td> <td>2</td> <td>Live-cell imaging of bacterial cells expressing GFP-PopZ</td> <td>Microscopy Resources on the North Quad (MicRoN)</td> <td>Harvard Medical School </td> <td>https://micron.hms.harvard.edu/</td> <td>Lim and Bernhardt 2019; Lim et al., 2019</td> </tr> <tr> <td>15</td> <td><strong>Olympus/Biomedical Imaging Group (customized)</strong></td> <td><strong>TIRF Epifluorescence Structured light Microscope (TESM)/IX71</strong></td> <td>3</td> <td>3D distribution of HIV-1 in the nucleus of human cells</td> <td>Biomedical Imaging Group</td> <td>Program in Molecular Medicine, University of Massachusetts Medical School</td> <td>https://trello.com/b/BQ8zCcQC/tirf-epi-fluorescence-structured-light-microscope</td> <td>Navaroli et al., 2012</td> </tr> <tr> <td>16</td> <td><strong>Olympus/Computer Vision Laboratory (customized)</strong></td> <td><strong>3D BrightField Scanner/IX71</strong></td> <td>3</td> <td>Transmitted light brightfield visualization of swimming spermatocytes</td> <td>Laboratorio Nacional de Microscopia Avanzada (LNMA) and Computer Vision Laboratory of the Institute of Biotechnology</td> <td>Universidad Nacional Autonoma de Mexico (UNAM)</td> <td>https://lnma.unam.mx/wp/</td> <td>Pimentel et al., 2012; Silva-Villalobos et al., 2014</td> </tr> </tbody> </table> <p><strong>Getting started</strong></p> <p>Use these videos to get started with using Micro-Meta App after installation into OMERO and downloading the example data files:</p> <ol> <li><a href="https://vimeo.com/562022222">Video 1</a></li> <li><a href="https://vimeo.com/562022281">Video 2</a></li> </ol> <p><strong>More information</strong></p> <blockquote> <p>For full information on how to use Micro-Meta App please utilize the following resources:</p> <ol> <li>Micro-Meta App <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">website</a></li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/index.html">Full documentation</a></li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/docs/intro/installation.html">Installation</a> instructions</li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/docs/tutorials/index.html#step-by-step-instructions">Step-by-Step Instructions</a></li> <li><a href="https://micrometaapp-docs.readthedocs.io/en/latest/docs/tutorials/VideoTutorials.html#micro-meta-app-video-tutorials">Tutorial Videos</a></li> </ol> </blockquote> <p><strong>Background</strong></p> <p>If you want to learn more about the importance of <strong>metadata and quality contro</strong>l to ensure full <strong>reproducibility, quality and scientific value</strong> in light microscopy, please take a look at our recent publications describing the development of community-driven light <strong>4DN-BINA-OME Microscopy Metadata</strong> specifications <a href="https://doi.org/10.1038/s41592-021-01327-9">Nature Methods</a> and <a href="https://doi.org/10.1101/2021.04.25.441198">BioRxiv.org</a> and our <a href="https://arxiv.org/abs/1910.11370">overview manuscript</a> entitled <strong>A perspective on Microscopy Metadata: data provenance and quality control</strong>.</p> <p> </p> <p> </p>
Example Microscopy Metadata JSON files produced using Micro-Meta App to document the acquisition of example images using a custom-built TIRF Epifluorescence Structured Illumination Microscope
<p><strong>Example Microscopy Metadata JSON files produced using the <a href="https://wu-bimac.github.io/MicroMetaApp.github.io/">Micro-Meta App</a> documenting an example raw-image file acquired using the custom-built TIRF Epifluorescence Structured Illumination Microscope.</strong></p> <p>For this use case, which is presented in Figure 5 of <a href="http://doi: https://doi.org/10.1101/2021.05.31.446382">Rigano et al., 2021</a>, Micro-Meta App was utilized to document:</p> <p>1) The <strong>Hardware Specifications</strong> of the custom build TIRF Epifluorescence Structured light Microscope (TESM; <a href="https://www.pnas.org/content/109/8/E471.long">Navaroli et al., 2010</a>) developed, built on the basis of the based on Olympus IX71 microscope stand, and owned by the Biomedical Imaging Group (http://big.umassmed.edu/) at the Program in Molecular Medicine of the University of Massachusetts Medical School. Because TESM was custom-built the most appropriate documentation level is <strong>Tier 3</strong> (<em>Manufacturing/Technical Development/Full Documentation</em>) as specified by the <a href="https://doi.org/10.5281/zenodo.4710731">4DN-BINA-OME</a> Microscopy Metadata model (<a href="https://doi.org/10.1101/2021.04.25.441198">Hammer et al., 2021</a>).</p> <p>The TESM Hardware Specifications are stored in: <strong>Rigano et al._Figure 5_UseCase_Biomedical Imaging Group_TESM.JSON</strong></p> <p>2) The <strong>Image Acquisition Settings</strong> that were applied to the TESM microscope for the acquisition of an example image (FSWT-6hVirus-10minFIX-stk_4-EPI.tif.ome.tif) obtained by Nicholas Vecchietti and Caterina Strambio-De-Castillia. For this image, TZM-bl human cells were infected with HIV-1 retroviral three-part vector (FSWT+PAX2+pMD2.G). Six hours post-infection cells were fixed for 10 min with 1% formaldehyde in PBS, and permeabilized. Cells were stained with mouse anti-p24 primary antibody followed by DyLight488-anti-Mouse secondary antibody, to detect HIV-1 viral Capsid. In addition, cells were counterstained using rabbit anti-Lamin B1 primary antibody followed by DyLight649-anti-Rabbit secondary antibody, to visualize the nuclear envelope and with DAPI to visualize the nuclear chromosomal DNA.</p> <p>The Image Acquisition Settings used to acquire the FSWT-6hVirus-10minFIX-stk_4-EPI.tif.ome.tif image are stored in: <strong>Rigano et al._Figure 5_UseCase_AS_fswt-6hvirus-10minfix-stk_4-epi.tif.JSON</strong></p> <p><em><strong>Instructional video tutorials on how to use these example data files:</strong></em><br> Use these videos to get started with using Micro-Meta App after downloading the example data files available here.</p> <ul> <li><a href="https://vimeo.com/562022222">Part 1/2</a></li> <li><a href="https://vimeo.com/562022281">Part 2/2</a></li> </ul>
Trabecular bone – screw interaction. Micro-CT models and experimental push-in results.
<p>The dataset disclosed herein was employed to build the screw-bone interaction models, specifically for tasks related to screw push-in simulation.</p>
Visuo-motor dataset recorded from a micro-farming robot
<p>This is the accompanying dataset of the paper [1] describing algorithms for intrinsic motivation and episodic memory on the Sony LettuceThink microfarming robot.</p> <p>The LettuceThink microfarming robot developed by Sony Computer Science Laboratories consists of an aluminium frame with an X-Carve CNC machine mounted on it. The CNC machine is used to provide 3-axes movements to a depth camera (Sony DepthSense) mounted at the tip of the vertical z-axis (the end-effector camera). In the experiments presented in the paper, the end-effector camera is facing top-down and only two motors are used (x and y).</p> <p>A simulator of the LettuceThink robot has been developed to ease the testing of different configurations of the learning system. The simulator generates sensorimotor data from requested trajectories of the end-effector camera. Knowing the initial position of the CNC machine and the target position, the simulator linearly interpolates the trajectory and returns the intermediate positions of the camera together with the images captured from each specific position. The sensorimotor data returned by the simulator have been prerecorded by performing a full scan of the (x,y) plane of the CNC machine using a resolution of 5mm. This resulted in 24,964 images, each mapped to an (x,y) position of the CNC machine. The dataset published here contains these images.</p> <p>In particular, the dataset consists of a set of images, each named with the specific position of the 2 motors of the robot. A python script for generating visuo-motor trajectories (sequences of data consisting of [image, motor_x, motor_y]) from this dataset is available at the following github page: <a href="https://github.com/guidoschillaci/sonylettucethink_dataset">https://github.com/guidoschillaci/sonylettucethink_dataset</a></p> <p>Provided with the dataset is also a python script that allows to easily read the images and to generate trajectories (returning</p> <p>This work has been supported by the EU-H2020 ROMI Project and by the EU-H2020 Marie Sklodowska Curie project "Predictive Robots" (grant agreement no. 838861)References:</p> <p>[1] Schillaci, G., Villalpando, A. P., Hafner, V. V., Hanappe, P., Colliaux, D., & Wintz, T. (2020). Intrinsic Motivation and Episodic Memories for Robot Exploration of High-Dimensional Sensory Spaces. arXiv preprint arXiv:2001.01982.</p>
TMY hourly generation profiles for Insolight hybrid Si/III-V planar micro-tracking modules in Madrid
<p>Hourly energy density (1 m<sup>2</sup>) generation profiles for Insolight hybrid Si/III-V planar micro-tracking modules installed in Madrid (40.5°N, -3.75°E), synthetically generated using <a href="https://github.com/isi-ies-group/cpvlib">CPVLIB library</a> (based on <a href="https://pvlib-python.readthedocs.io/en/stable/">PVLIB Python</a>) and ERA5 typical meteorological year. Performance model parameters were empirically fitted using several outdoor monitoring campaigns and indoor characterization at the <a href="https://www.ies.upm.es/Investigacion/Research_Lines/Concentrator_photovoltaics/CPV_characterization">collimated-light solar simulator</a> available at IES-UPM.</p> <p><strong>Location</strong>: 40.5°N, -3.75°E</p> <p><strong>Format</strong>: CSV (separator: semicolon); headers in first row.</p> <p><strong>Parameters </strong>(ordered from first column): </p> <ul> <li>Time: YYYY-MM-DD HH:MM:SS+TimeZoneOffset</li> <li>Latitude: latitude of the installation in °N</li> <li>Longitude: longitude of the installation in °E</li> <li>Wind speed [m/s]: average wind speed</li> <li>Tair [°C]: average ambient temperature</li> <li>precipitable_water [mm]: average precipitable water in the atmosphere</li> <li>GHI [Wh/m2]: global horizontal irradiation</li> <li>DHI [Wh/m2]: diffuse horizontal irradiation</li> <li>DNI [Wh/m2]: direct (beam) normal irradiation</li> <li>CPV submodule [kWh/m2]: energy generated per m<sup>2</sup> by the III-V CPV submodule</li> <li>Flat-plate submodule [kWh/m2]: energy generated per m<sup>2</sup> by the Si flat-plate submodule</li> <li>Hybrid [kWh/m2]: energy generated per m<sup>2</sup> by the whole Insolight hybrid module</li> </ul>
Micro-CT images of deep brain stimulation leads
<p>The dataset contain micro-CT images of leads used in deep brain stimulation. A lead comprises multiple electrodes and enables the delivery of electrical pulses to the brain to treat medical conditions such as Parkinson's disease, essential tremor or epilepsy. Images were acquired with a Skyscan 1276 micro-CT system from Bruker. Each image is provided in Nifti format (.nii) along with its corresponding log file (.log) generated by the scanner. The file names indicate the manufacturer and sample model. 'BS' denotes Boston Scientific.<br><br>Images can be visualized at:<br>https://activgroup.github.io/DBS-lead-microCT/<br><br>To contribute, please contact thomas.billoud@uniklinik-freiburg.de</p>
Artificial Intelligence for quality control in manufacturing operations: Micro-mechanical milling in the Pilot Line GAMHE 5.0
<p>Quality is defined as the extent to which a product conforms to the design specifications and how it complies with the requirements of component functionality. For some industries, such as automotive and aeronautical, the quality of of manufactured parts is very important due to the high requirements. However, difficulties arise from the fact that a measure of quality can only be evaluated ‘‘out-of-process”, resulting in losses because there is no alternative to removing defective parts from the production line. Therefore, it is necessary to incorporate AI-based kits/solutions that provide in-process estimation to predict quality from some measured variables.</p> <p>The main goal of these datasets is to enable monitoring of final quality of the manufactured components or parts by estimating surface roughness from vibration signals and cutting parameters information. Surface roughness is an essential feature in quality control defined by the deviation in the direction of the normal vector of a real surface from its ideal form. Because the roughness measurement is an offline and post process procedure, being able to estimate this value online brings a series of benefits in terms of time and cost reduction in manufacturing lines, energy efficiency, unnecessary wear of tools and machines, etc. Once a part has been detected with a surface quality below what is desired, a series of corrective measures can be applied for the following operations, such as: reducing the feed rate percentage, increasing the percentage of spindle speed or reducing the axial depth per pass, etc.</p> <p>Workstation 4 (WS4) of the GAMHE 5.0 pilot line is a Kern Evo high-precision machining centre, with a maximum spindle speed of 50 000 rpm and Blum laser system and is used to run micro-milling and micro-drilling operations. In this experimental dataset, five cutting parameters were considered in the processes: spindle speed, <em>n</em>; feed rate, <em>f</em>; and axial depth of cut, <em>a<sub>P</sub></em>. The radial depth of cut, <em>a<sub>e</sub></em>; was equal to the mill tool radius, <em>r</em>, in all of the slots.</p> <p>These experiments were micro-milling operations with 0.3 mm, 0.5 mm, 0.8 mm and 1 mm-diameter mills on a sintered tungsten-copper alloy (W78Cu22). The data collected for each micro milling operation was the rms and peak value of the vibrations in the three-machine axis. In addition, five cutting parameters were also collected: position in <em>X</em> of the last point of the sample, feed rate, spindle speed, tool radius and axial depth.</p>
Data for: Autonomous Micro-Focus Angle-Resolved Photoemission Spectroscopy
<p>This repository contains the data related to the publication</p> <p>Steinn Ýmir Ágústsson, Alfred J. H. Jones, Davide Curcio, Søren Ulstrup, Jill Miwa, Davide Mottin, Panagiotis Karras, Philip Hofmann; <strong>Autonomous micro-focus angle-resolved photoemission spectroscopy</strong>. <em>Rev. Sci. Instrum.</em> 1 May 2024; <strong>95</strong> (<em>5</em>): 055106.<em> DOI: <a href="https://doi.org/10.1063/5.0204663" target="_blank" rel="noopener">10.1063/5.0204663</a></em></p> <p>Please cite the paper above in case of re-use of these data in a scientific publication.</p> <p>The data were acquired at the SGM4 beamline of the ASTRID2 synchrotron in Arhus, DK as part of the development of an autonomous data acquisition software "SmartScan". Such software, together with all scripts necessary to load the present data, is available on GitHub at <a href="https://github.com/ARPES-ASTRID/smartscan">github.com/ARPES-ASTRID/smartscan</a></p>
Human Bony Labyrinth: Co-Registered CT and micro-CT Images, Surface Models and Anatomical Landmarks
<p>This data set consists of 23 specimens of the human bony labyrinth. For each specimen clinical CT (0.15×0.15×0.2 mm3, voxel size) and co-registered microCT (0.06 mm isotropic voxel size) images are available. Image labels for the bony labyrinth are provided for the same image coordinates. From the image labels, 3D surface models were generated. In addition, each specimen has a descriptor file containing the coordinates of anatomical landmarks as well as a cochlear coordinate system. The data set can be used to study the morphology of the inner ear or to evaluate (semi-)automated segmentation algorithms (e.g., for the preoperative planning of surgical procedures such as cochlear implantation).</p>
Micro elemental composition of Pike-perch (Sander lucioperca) population in Lipno Reservoir, Czechia
<p>This dataset contains the information on the micro elemental composition of Sagitta otoliths of Pike-Perch (<i>Sander lucioperca</i>) collected in Lipno Reservoir (Czechia). The dataset covers a wide range of micro elemental components (barium, calcium, copper, potassium, lithium, magnesium, manganese, sodium, rubidium, strontium and zinc) obtained from the otolith cores and rims of these fish specimens. The dataset includes readings from Pike-Perch directly collected in Lipno Reservoir, as well as from those reared in facilities and later introduced into the reservoir.</p>
Integrated measurements of soil micro-invertebrate abundance and associated physicochemical properties from the McMurdo Dry Valleys, Antarctica (1993–2022)
This data package compiles three decades (1993–2022) of soil micro-invertebrate abundance data and associated physicochemical measurements collected as part of the McMurdo Dry Valleys Long Term Ecological Research (MCM LTER) project in Antarctica. Variables include soil micro-invertebrate counts and species richness, along with associated soil physicochemical properties, including gravimetric moisture content, pH, electrical conductivity, soil organic carbon, ammonium, and nitrate. Data originate from both long-term core monitoring efforts and targeted opportunistic sampling campaigns across multiple valleys, including Arena, Beacon, Pearse, Taylor, Victoria, and Wright Valleys. Sampling locations span a range of geomorphic and ecological settings, providing broad spatial and temporal coverage of soil conditions across the Dry Valleys ecosystem. Data were curated to represent the most comprehensive and spatially diverse records available while minimizing sampling bias across years and study types. These integrated measurements provide a valuable resource for understanding the drivers of soil micro-invertebrate abundance and habitat suitability and serve as a foundation for future analyses of long-term ecological change and species distribution modeling in polar desert soils.
ds-uct-001: Cast Iron GGG40: X-Ray micro-CT of a nodular cast iron sample class GGG40.
<p><strong>Summary</strong>:<br> .X-Ray micro-computed tomography (micro-CT) of a nodular cast iron sample class GGG40, including both raw projection data and the final reconstructions, for three different resolutions (voxel sizes of 1 μm, 3 μm and 11 μm).<br> .The 3D image was generated with an X-Ray micro-CT Scanner version Xradia Versa 510 from Zeiss performed by A Pereira at the UFF micro-CT Facility.<br> .For use of these data, please remember to cite the DOI of the Zenodo repository and relevant papers.</p> <p><strong>Details</strong>:<br> .Tomo1 (1024) - Voxel size: 1 μm; Sample-source: 26 mm; Sample-detector: 150 mm; Optical magnification: 4.0X; Filter: HE#6; Beam energy: 160 kV; Power: 10 W; Exposure time: 60.0 sec; Projections: 1600.<br> .Tomo2 (1024) - Voxel size: 3 μm; Sample-source: 28 mm; Sample-detector: 35 mm; Optical magnification: 4.0X; Filter: HE#4; Beam energy: 160 kV; Power: 10 W; Exposure time: 10.0 sec; Projections: 3200.<br> .Tomo3 (1024) - Voxel size: 11 μm; Sample-source: 30 mm; Sample-detector: 158 mm; Optical magnification: 0.4X; Filter: HE#4; Beam energy: 160 kV; Power: 10 W; Exposure time: 3.0 sec; Projections: 3200.</p> <p><strong>Contents</strong>:<br> ._info_ds-uct-001.txt<br> .ds-uct-001_cast_iron_ggg40_01um_8bits.zip<br> .ds-uct-001_cast_iron_ggg40_03um_8bits.zip<br> .ds-uct-001_cast_iron_ggg40_11um_8bits.zip<br> .ds-uct-001_cast_iron_ggg40_01um_1600p.txrm<br> .ds-uct-001_cast_iron_ggg40_01um_1600p_Drift.txrm<br> .ds-uct-001_cast_iron_ggg40_01um_1600p_recon.txm<br> .ds-uct-001_cast_iron_ggg40_03um_3200p.txrm<br> .ds-uct-001_cast_iron_ggg40_03um_3200p_Drift.txrm<br> .ds-uct-001_cast_iron_ggg40_03um_3200p_recon.txm<br> .ds-uct-001_cast_iron_ggg40_11um_3200p.txrm<br> .ds-uct-001_cast_iron_ggg40_11um_3200p_Drift.txrm<br> .ds-uct-001_cast_iron_ggg40_11um_3200p_recon.txm</p>
ds-uct-002: Root Canal Strain: X-Ray micro-CT of four teeth before and after root canal procedure.
<p><strong>Summary</strong>:<br> .X-Ray micro-computed tomography (micro-CT) of four teeth before (TomoB) and after (TomoA) simulation of root canal treatment and retreatment procedures instrumented with strain-gauge, including reconstructions, for two different resolutions (TomoB and TomoA with voxel sizes of 20.0 μm and 10.5 μm, respectively).<br> .The 3D image was generated with an X-Ray micro-CT Scanner version Xradia Versa 510 from Zeiss performed by A Pereira at the UFF micro-CT Facility.<br> .For use of these data, please remember to cite the DOI of the Zenodo repository and relevant papers.</p> <p><strong>Details</strong>:<br> .Tomo1B/Tomo2B/Tomo3B/Tomo4B (1024) - Voxel size: 20.0 μm; Sample-source: 45.0 mm; Sample-detector: 110 mm; Optical magnification: 0.4X; Filter: LE#1; Beam energy: 60 kV; Power: 5 W; Exposure time: 2.0 sec; Projections: 1600.<br> .Tomo1A/Tomo2A/Tomo3A/Tomo4A (2048) - Voxel size: 10.5 μm; Sample-source: 48.2 mm; Sample-detector: 110 mm; Optical magnification: 0.4X; Filter: LE#2; Beam energy: 60 kV; Power: 5 W; Exposure time: 7.0 sec; Projections: 1600.</p> <p><strong>Contents</strong>:<br> ._info_ds-uct-002.txt<br> .ds-uct-002_root_canal_strain_tomo1b_20um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo2b_20um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo3b_20um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo4b_20um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo1a_10um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo2a_10um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo3a_10um_8bits.zip<br> .ds-uct-002_root_canal_strain_tomo4a_10um_8bits.zip<br> .PB_PARECER_CONSUBSTANCIADO_CEP_2650528.pdf</p>
ds-uct-007: Asphalt Concrete: X-Ray micro-CT of an asphalt concrete sample.
<p><strong>Summary</strong>:<br> .X-Ray micro-computed tomography (micro-CT) of an asphalt concrete sample, including both raw projection data and the final reconstructions, for one single resolution (voxel size of 7 μm).<br> .The 3D image was generated with an X-Ray micro-CT Scanner version Xradia Versa 510 from Zeiss performed by A Pereira at the UFF micro-CT Facility.<br> .Image data segmented with four different segmentation techniques: DL (Deep Learning), ML (Machine Learning), TH (Thresholding) and WS (Watershed).<br> .For use of these data, please remember to cite the DOI of the Zenodo repository and relevant papers.<br> <strong>Details</strong>:<br> .Tomo - Voxel size: 7 μm; Sample-source: 31 mm; Sample-detector: 274.25 mm; Optical magnification: 0.4X; Filter: LE#6; Beam energy: 100 kV; Power: 9 W; Exposure time: 4.0 sec; Projections: 1600.</p> <p><strong>Contents</strong>:<br> ._info_ds-uct-007.txt<br> .ds-uct-007_asphalt_concrete_07um_16bits.zip<br> .ds-uct-007_asphalt_concrete_07um_1600p.txrm<br> .ds-uct-007_asphalt_concrete_07um_1600p_Drift.txrm<br> .ds-uct-007_asphalt_concrete_07um_1600p_recon.txm<br> .ds-uct-007_asphalt_concrete_07um_DL.zip<br> .ds-uct-007_asphalt_concrete_07um_ML.zip<br> .ds-uct-007_asphalt_concrete_07um_TH32.zip<br> .ds-uct-007_asphalt_concrete_07um_WS.zip</p>
2d U-net models trained to segment human placental maternal/fetal blood volumes and blood vessels from syncrotron micro-CT data along with a sample data volume.
<p>This dataset contains a 512 x 512 x 512 pixel volume taken from an imaging dataset of human placental tissue collected at Diamond Light Source Manchester Imaging Branchline, I13-2 on visits MG23941 and MG22562 using in-line high-resolution synchrotron-sourced phase contrast micro-computed X-ray tomography. This data is saved in HDF5 format with a uint8 datatype. Alongside this are two 2d binary U-net models that have been trained to segment this data. One model segments the data into regions of maternal/fetal blood volume, the other segments the blood vessels. Both models were trained using the fastai python package, which utilises the pytorch library. These models were used to segment the data in our paper "A massively multi-scale approach to characterising tissue architecture by synchrotron micro-CT applied to the human placenta" which can be found at <a href="https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1">https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1</a>. The code used for training the U-net models and for predicting the segmentation of the data volume can be found at <a href="https://github.com/DiamondLightSource/placental-segmentation-2dunet">https://github.com/DiamondLightSource/placental-segmentation-2dunet</a> and is published at <a href="https://doi.org/10.5281/zenodo.4252562">https://doi.org/10.5281/zenodo.4252562</a> </p>
Nutritional value of edible insects: special emphasis on their micro and macro nutrients
<p>The data set was prepared by collecting the existed information regarding the nutritional value of insects from relevant published papers. For each column, variables or descriptors were expressed in the same unit. The information from each insect in a raw were followed by a reference of the paper which the information is taken from with the DOI which makes it easily accessible for the readers.</p>
Fixed-Wing Micro UAV Open Data With Digicam And Raw INS/GNSS - IGN Flight 8
<p>The data set originate from a series of flights conducted with fixed-wing micro UAV carrying high-quality small camera and navigation sensors. This data was previously used in several peer-reviewed publications and will also be used in ISPRS workshop on dynamic networks given during the 2021 ISPRS Congress. This is part of a larger series of data that will be released gradually after incorporating user's feedback (e.g., on formats, description,etc.). The data set contains the sensor measurements from GPS, IMU and Camera.</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.