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994 results for “2d”
Data for 2D Signal Estimation for Sparse Distributed Target Photon Counting Data
<p>Data used in publication of 2D Signal Estimation for Sparse Distributed Target Photon Counting Data</p> <p>Data provided consists of:</p> <p>raw and PTV processed MicroPulse DIAL (MPD) data (10.26023/MX0D-Z722-M406)</p> <p>simulated photon counting data and the processed results</p> <p> </p>
Optimized stationary points on the potential energy surfaces of the N(2D) + CH2CHCN and CN + CH2CHCN reactions
<p>This Zip file contains the cartesian coordinates of optimized stationary points on the potential energy surfaces (PESs) of two reactions: N(<sup>2</sup>D) + CH<sub>2</sub>CHCN (acrylonitrile) and CN + CH<sub>2</sub>CHCN.</p> <p>The PES has been published in our article “A Theoretical Investigation of the Reactions of N(<sup>2</sup>D) and CN with Acrylonitrile and Implications for the Prebiotic Chemistry of Titan”</p> <p>(<em>Lecture Notes in Computer Science</em> <strong>2022</strong>, 13378, 246-259), that can be found in https://doi.org/10.1007/978-3-031-10562-3_18 .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Optimized stationary points on the potential energy surfaces of the N(2D)+ C2H4 and N(2D)+ CH2CHCN reactions
<p>This Zip file contains the cartesian coordinates of optimized stationary points on the potential energy surfaces (PESs) of two reactions: N(<sup>2</sup>D)+ C<sub>2</sub>H<sub>4</sub> and N(<sup>2</sup>D)+ CH<sub>2</sub>CHCN.</p> <p>The PESs have been published in our article “Computational Investigation of the N(<sup>2</sup>D)+ C<sub>2</sub>H<sub>4</sub> and N(<sup>2</sup>D)+ CH<sub>2</sub>CHCN Reactions: Benchmark Analysis and Implications for Titan’s Atmosphere”</p> <p>(<em>Lecture Notes in Computer Science</em> <strong>2023</strong>, 14105, 705-717), that can be found in https://doi.org/10.1007/978-3-031-37108-0_45 .</p> <p>All calculations have been performed with Gaussian 09, Revision D.01.</p> <p>All structures have been optimized at B3LYP/aug-cc-pVTZ level of theory.</p>
Influence of He$^{++}$ and shock geometry on interplanetary shocks in the solar wind: 2D Hybrid simulations
<p>After protons, alpha particles (He$^{++}$) are the most important ion species in the solar wind, constituting typically about 5\% of the total ion number density. Due to their different charge-to-mass ratio protons and He$^{++}$ particles are accelerated differently when they cross the electrostatic potential in a collisionless shock. This behavior can produce changes in the velocity distribution function (VDF) for both species generating anisotropy in the temperature which is considered to be the energy source for various phenomena such as ion cyclotron and mirror mode waves. How these changes in temperature anisotropy and shock structure depend on the percentage of He$^{++}$ particles and the geometry of the shock is not completely understood. In this paper we have performed various 2D local hybrid simulations (particle ions, massless fluid electrons) with similar characteristics (e.g., Mach number) to interplanetary shocks for both quasi-parallel and quasi-perpendicular geometries self-consistently including different percentages of He$^{++}$ particles. We have found changes in the shock transition behavior as well as in the temperature anisotropy as functions of both the shock geometry and He$^{++}$ particle abundance: The change of the initial $\theta_{Bn}$ leads to variations of the efficiency with which particles can escape to the upstream region facilitating or not the formation of compressive structures in the magnetic field that will produce increments in perpendicular temperature. The regions where both temperature anisotropy and compressive fluctuations appear tend to be more extended and reach higher values as the He$^{++}$ content in the simulations increases.</p> <p> </p>
ZeroCostDL4Mic - CARE (2D) example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - CARE (2D) example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: Paired microscopy images (fluorescence) of low and high signal-to-noise ratio</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (Lifeact-RFP)</p> <p><strong>Microscope</strong>: Structured Illumination Microscopy (SIM) with a 60x 1.42 NA objective </p> <p><strong>Cell type</strong>: DCIS.COM Lifeact-RFP</p> <p><strong>File format</strong>: .tif (32-bit)</p> <p><strong>Image size</strong>: 1024x1024 (Pixel size: 40 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Guillaume Jacquemet<sup>1,2</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation</strong>: </p> <p>1) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p>2) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland</p> <p> </p> <p><strong>Associated publications</strong>: Unpublished</p> <p><strong>Funding bodies</strong>: G.J. was supported by grants awarded by the Academy of Finland, the Sigrid Juselius Foundation and Åbo Akademi University Research Foundation (CoE CellMech) and by Drug Discovery and Diagnostics strategic funding to Åbo Akademi University.</p>
ZeroCostDL4Mic - Noise2Void (2D) example training and test dataset
<p><strong>Name</strong>: ZeroCostDL4Mic - Noise2Void (2D) example training and test dataset</p> <p>(see <a href="https://github.com/HenriquesLab/ZeroCostDL4Mic/wiki">our Wiki</a> for details)</p> <p> </p> <p><strong>Data type</strong>: Microscopy images (fluorescence)</p> <p><strong>Microscopy data type</strong>: Fluorescence microscopy (paxillin-GFP) </p> <p><strong>Microscope</strong>: Spinning disk confocal microscope with a 63x 1.4 NA objective </p> <p><strong>Cell type</strong>: U-251 glioma cells, endogenously expressing paxillin-GFP</p> <p><strong>File format</strong>: .tif (16-bit)</p> <p><strong>Image size</strong>: 512x512 (Pixel size: 248 nm)</p> <p> </p> <p><strong>Author(s)</strong>: Aki Stubb<sup>1</sup>, Guillaume Jacquemet<sup>1,2</sup> and Johanna Ivaska<sup>1</sup></p> <p><strong>Contact email</strong>: guillaume.jacquemet@abo.fi</p> <p><strong>Affiliation</strong>: </p> <p>1) Turku Bioscience Centre, University of Turku and Åbo Akademi University, FI-20520 Turku, Finland</p> <p>2) Faculty of Science and Engineering, Cell Biology, Åbo Akademi University, 20520 Turku, Finland</p> <p><br> </p> <p><strong>Associated publication</strong>: Stubb <em>et al.</em> 2020, Nano letters DOI: 10.1021/acs.nanolett.9b04083</p> <p><strong>Funding bodies</strong>: A.S. has been supported by the University of Turku Doctoral programme for Molecular Medicine (TuDMM).</p>
Fig. 6.1. Shell digitised with different methods. The photogrammetry model was captured with a 100 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 6.1. Shell digitised with different methods. The photogrammetry model was captured with a 100 mm Macro lens and processed with Agisoft Photoscan. The visual comparison of the mollusc shows a similar level of detail between photogrammetry and MechScan for the external surfaces, with still a bit more detail for the MechScan. The HDI Advance has a much lower resolution.
Fig. 5.6 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 5.6. Decimation of a 3D model. The four parts show a 3D model in various degrees of reducing the amount of faces. In the left upper corner is the original and rotating clockwise are the models at 50%, 75% and 90% decimation. Until 75% there is hardly any difference noticeable, while at 90% the cracks become less deep and the faces become more visible.
Fig. 6.2 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 6.2. Texture comparison between photogrammetry and MechScan (above) and an actual picture captured by a Canon 700D with 100 mm macro lens of the shell below.
Fig. 5.3 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 5.3. Micro-contrast enhancement in DxO OpticsPro 11. A crop of the original image is on the left, one of the post-processed pictures on the right.
Fig. 5.4 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 5.4. Micro-contrast enhancement in DxO OpticsPro 11. The original image is on the left, the postprocessed picture on the right. The post-processed picture looks more crisp and shows more details than the original one as the washed-out appearance has gone.
Fig. 6.14. Ishango rod. The left 3D model was acquired with a in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 6.14. Ishango rod. The left 3D model was acquired with a µCT many years ago. The middle one is scanned with the MechScan structured light scanner. The right one is the combination of both the µCT scan, the structured light scan and the texture of the photogrammetry model.
Fig. 5.1 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 5.1. Relighting in DxO OpticsPro 11. The original image is on the left, the post-processed picture on the right. The underexposed image is now corrected without the need to take new images.
Fig. 4.9 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 4.9. Pre-Columbian handle of an incense shovel from the Royal Museum of Art and History collections. UV fluorescence photogrammetry model. In this case, fluorescence enables to enhance the glue (fluorescing in green). https://sketchfab.com/models/2d82a98be64c48b89cada459b81bd0ab
Fig. 4.7 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 4.7. Enhancing the legibility of a specimen. The picture on the left represents the specimen captured under white light, while the picture on the right displays the specimen under UV light. Part of the reflections is reduced under UV light allowing to display more contrasted structures.
Fig. 4.5 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 4.5. Detail of the Halszkaraptor fossil from Mongolia. In white light on the left, in UV fluorescence on the right. The UV fluorescence image displays restorations of the fossils and treatment applied to preserve it.
Fig. 3.20. 3D in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.20. 3D model of a Dorylus ant (size: 1.5 cm) based upon focus stacked images, textured model is on the left, the view of only the mesh is on the right. The VCM option in Agisoft Photoscan is chosen to include small detail in the 3D model. The tibia spurs are clearly marked. https://sketchfab.com/models/da9aa414bfa64caabfe5c552368b16f0
Fig. 3.15 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.15. Part of the Cognisys StackShot 3X Deluxe Kit, reassembled for the photogrammetry purpose. The two rotary tables are mounted perpendicular to each other, whereby rotary table A moves a steel angle with rotary table B fixed at the end.
Fig. 3.13 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.13. Alnus japonica (2 cm long) scanned with DISC3D. A. EDOF-image. B. 3D-model (vcm) from 807 cameras. C. 3D-model from 398 cameras.
Fig. 3.37 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 3.37. Excavation site scanned with the Gotcha infrared sensor. On the left is the site without texture, on the right with texture. The excavation site pictured measures approximately 4×4 m.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.