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159 results for “distortion”
One-minute average horizontal wind velocity data (not corrected for air-flow distortion) from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4.
<p><strong>Dataset abstract</strong></p> <p>This dataset contains the one-minute average horizontal wind velocity data from the Antarctic Circumnavigation Expedition (ACE) 2016/2017 legs 0 to 4. The data has been filtered for spurious observations and the true wind correction has been redone using the quality checked one-minute ship track velocity data. This data set has not been corrected for air-flow distortion, which was caused by the ship's super structure. The flow-distortion corrected data should be used for studies interested in the actual true wind speed near the ship's location.</p> <p><strong>Dataset contents</strong></p> <ul> <li>wind-observations-stbd-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>wind-observations-port-uncorrected-5min-legs0-4.csv, data file, comma-separated values</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This one-minute averaged wind velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at https://creativecommons.org/licenses/by/4.0/</p>
Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality Dataset
<p>This dataset accompanies the paper “Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality” published in the Computer and Graphics journal from Elsevier. It contains 3 datasets related to the experiments described in the paper. All datasets are in “.csv” format and can be easily loaded by statistical analysis tools (e.g. a dataset can be loaded in r using the command read.csv(“filename.csv”)). It also contains the C# Unity implementation of the distortion function presented in the paper.</p> <p>Paper reference:</p> <p>Galvan Debarba H, Boulic R, Salomon R, Blanke O, Herbelin B. Self-Attribution of Distorted Reaching Movements in Immersive Virtual Reality. Computers & Graphics. 2018; ISSN 0097-8493. Elsevier.</p> <p>DOI: doi.org/10.1016/j.cag.2018.09.001</p>
Dataset: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication
<p>The rainbow color map is scientifically incorrect and hinders people with color vision deficiency to view visualizations in a correct way. Due to perceptual non-uniform color gradients within the rainbow color map the data representation is distorted what can lead to misinterpretation of results and flaws in science communication. Here we present the data of a paper survey of 797 scientific publication in the journal Hydrology and Earth System Sciences. With in the survey all papers were classified according to color issues. Find details about the data below.</p> <ul> <li><code>year</code> = year of publication (YYYY)</li> <li><code>date</code> = date (YYYY-MM-DD) of publication</li> <li><code>title</code> = full paper title from journal website</li> <li><code>authors</code> = list of authors comma-separated</li> <li><code>n_authors</code> = number of authors (integer between 1 and 27)</li> <li><code>col_code</code> = color-issue classification (see below)</li> <li><code>volume</code> = Journal volume</li> <li><code>start_page</code> = first page of paper (consecutive)</li> <li><code>end_page</code> = last page of paper (consecutive)</li> <li><code>base_url</code> = base url to access the PDF of the paper with <code>/volume/start_page/year/</code></li> <li><code>filename</code> = specific file name of the paper PDF (e.g. <code>hess-9-111-2005.pdf</code>)</li> </ul> <p>Color classification is stored in the <code>col_code</code> variable with:</p> <ul> <li><code>0</code> = chromatic and issue-free,</li> <li><code>1</code> = red-green issues,</li> <li><code>2</code>= rainbow issues and</li> <li><code>bw</code>= black and white paper.</li> </ul> <p> </p> <p>See more details (e.g., sample code to analyse the survey data) on https://github.com/modche/rainbow_hydrology</p> <p>Paper: Stoelzle, M. and Stein, L.: Rainbow color map distorts and misleads research in hydrology – guidance for better visualizations and science communication, Hydrol. Earth Syst. Sci., 25, 4549–4565, https://doi.org/10.5194/hess-25-4549-2021, 2021.</p> <p> </p> <p> </p>
Adaptive memory distortions are predicted by feature representations in parietal cortex
Open the record for dataset details and reuse information.
Data set for article Veto, P., Einhäuser, W., & Troje, N.F. (2017). Biological motion distorts size perception. Scientific Reports, 7, 42576.
<p>In this data set you find 3 files containing data from Experiments 1, 2 & 3 of Veto P, Einhauser W & Troje NF (2017) Biological motion distorts size perception. Scientific Reports, 7, 42576; doi: 10.1038/srep42576</p> <p><br> The data are freely available for academic use only. If you use these data for a publication, please cite the aforementioned article.<br> If you have any questions regarding the data, please do not hesitate to contact Peter Veto at vettop@gmail.com</p> <p>Each row of the files contain data from one trial.<br> Columns:</p> <p>Experiment 1<br> 1 - Participant number<br> 2 - Block number<br> 3 - Trial number<br> 4 - Target orientation (1: Upright; -1: Inverted)<br> 5 - Stimulus width<br> 6 - Stimulus height<br> 7 - Response width<br> 8 - Response height</p> <p>Experiment 2<br> 1-8 Same as Experiment 1<br> 9 - Condition: dynamic (1) or static (2) target</p> <p>Experiment 3<br> 1 - Participant number<br> 2 - Block number<br> 3 - Trial number<br> 4 - Walker orientation<br> (1: upper walker upright, lower walker inverted;<br> 2: upper walker inverted, lower walker upright)<br> 5 - Condition<br> (1: upper target larger (21%) than lower target;<br> 2: upper target larger (10.5%) than lower target;<br> 3: target sizes are identical;<br> 4: lower target larger (10.5%) than upper target;<br> 5: lower target larger (21%) than upper target)<br> 6 - Inter stimulus interval (from end of walker presentation to onset of target circles)<br> (1: 17ms; 2: 100ms)<br> 7 - Response<br> (1: upper target was larger;<br> 2: lower target was larger)</p>
EGFxSet: Electric guitar tones processed through real effects of distortion, modulation, delay and reverb
<p>EGFxSet (Electric Guitar Effects dataset) features recordings for all clean tones in a 22-fret Stratocaster, recorded with 5 different pickup configurations, also processed through 12 popular guitar effects. Our dataset was recorded in real hardware, making it relevant for music information retrieval tasks on real music. We also include annotations for parameter settings of the effects we used.</p> <p>More details can be found in <a href="http://egfxset.github.io">egfxset.github.io</a></p> <p>The dataset can also be accessed with <a href="https://mirdata.readthedocs.io/en/stable/source/mirdata.html#module-mirdata.datasets.egfxset">mirdata</a></p> <p>Effects and parameters included:</p> <table> <tbody> <tr> <td>Effect</td> <td>Model</td> <td>Effect Type</td> <td>Knob Names</td> <td>Knob Type</td> <td>Setting</td> </tr> <tr> <td>blues driver</td> <td>Boss BD-2 Blues Driver</td> <td>distortion</td> <td>['level', 'tone', 'gain']</td> <td>['volume','eq','effect amount']</td> <td>[0.5,0.5,1.0]</td> </tr> <tr> <td>tube screamer</td> <td>Ibanez Mini Tube Screamer</td> <td>distortion</td> <td>['tone', 'overdrive', 'level']</td> <td>['eq','effect amount','volume']</td> <td>[0.5,1.0,0.5]</td> </tr> <tr> <td>distortion</td> <td>Pro Co Sound RAT2 Distortion</td> <td>distortion</td> <td>['distortion', 'filter', 'volume']</td> <td>['effect amount','eq','volume']</td> <td>[1.0, 0.5,1.0]</td> </tr> <tr> <td>chorus</td> <td>Boss CE-3 Chorus</td> <td>modulation</td> <td>['rate', 'depth', 'stereo mode']</td> <td>['rate','effect amount','selector']</td> <td>['120 bpm', 1.0, False]</td> </tr> <tr> <td>flanger</td> <td>Mooer E-Lady</td> <td>modulation</td> <td>['color', 'type', 'range', 'rate']</td> <td>['eq','selector','effect amount','rate']</td> <td>[0.5, 'normal', 1.0, '120 bpm']</td> </tr> <tr> <td>phaser</td> <td>MXR Phase 45</td> <td>modulation</td> <td>['speed']</td> <td>['rate']</td> <td>['120 bpm']</td> </tr> <tr> <td>tape echo</td> <td>Line 6 DL4 Delay</td> <td>delay</td> <td>['effect selector', 'delay time', 'repeats', 'tweak (bass)', 'tweez (treble)', 'mix']</td> <td>['selector', 'rate', 'effect decay', 'eq', 'eq', 'effect amount']</td> <td>['tape echo', '120 bpm', 0.6, 0.5, 0.5, 0.5]</td> </tr> <tr> <td>digital delay</td> <td>Line 6 DL4 Delay</td> <td>delay</td> <td>['effect selector', 'delay time', 'repeats', 'tweak (bass)', 'tweez (treble)', 'mix']</td> <td>['selector', 'rate', 'effect decay', 'eq', 'eq', 'effect amount']</td> <td>['digital delay', '120 bpm', 0.6, 0.5, 0.5, 0.5]</td> </tr> <tr> <td>sweep echo</td> <td>Line 6 DL4 Delay</td> <td>delay</td> <td>['effect selector', 'delay time', 'repeats', 'tweak (sweep speed)', 'tweez (sweep depth)', 'mix']</td> <td>['selector', 'rate', 'effect decay', 'rate', 'effect amount', 'effect amount']</td> <td>['sweep echo', '120 bpm', 0.6, '120 bpm',1.0,0.5]</td> </tr> <tr> <td>plate reverb</td> <td>Orange CR-60 Combo Amplifier</td> <td>reverb</td> <td>['volume', 'bass', 'treble', 'type', 'reverb', 'master volume', 'clean']</td> <td>['volume','eq','eq','selector','effect amount', 'volume', 'selector']</td> <td>[0.5, 0.5, 0.5, 'plate', 1.0, 0.2, True]</td> </tr> <tr> <td>hall reverb</td> <td>Orange CR-60 Combo Amplifier</td> <td>reverb</td> <td>['volume', 'bass', 'treble', 'type', 'reverb', 'master volume', 'clean']</td> <td>['volume','eq','eq','selector','effect amount', 'volume', 'selector']</td> <td>[0.5, 0.5, 0.5, 'hall', 1.0, 0.2, True]</td> </tr> <tr> <td>spring reverb</td> <td>Orange CR-60 Combo Amplifier</td> <td>reverb</td> <td>['volume', 'bass', 'treble', 'type', 'reverb', 'master volume', 'clean']</td> <td>['volume','eq','eq','selector','effect amount', 'volume', 'selector']</td> <td>[0.5, 0.5, 0.5, 'spring', 1.0, 0.2, True]</td> </tr> </tbody> </table> <p><br>Please cite these papers if using EGFxSet:</p> <p>Pedroza HE, Abreu W, Corey R, Roman IR. "Leveraging real electric guitar tones and effects to improve robustness in guitar tablature transcription modeling." <em>In 27th International Conference on Digital Audio Effects (DAFx),</em> 2024.</p> <p>Pedroza, Hegel, Gerardo Meza, and Iran R. Roman. "EGFxSet: Electric guitar tones processed through real effects of distortion, modulation, delay and reverb." <em>ISMIR Late Breaking Demo, </em>2022.</p>
Self-attribution of distorted reaching movements in immersive virtual reality
<p>This dataset and Unity 3D code scripts are associated to the following paper : H. Debarba, R. Boulic, R. Solomon, O. Blanke, B. Herbelin (Computers & Graphics, Vol 76, November 2018, pp 142-152, <a href="https://www.sciencedirect.com/science/article/pii/S0097849318301353?utm_campaign=STMJ_75273_AUTH_SERV_PPUB&utm_medium=email&utm_dgroup=&utm_acid=810891&SIS_ID=0&dgcid=STMJ_75273_AUTH_SERV_PPUB&CMX_ID=&utm_in=DM377782&utm_source=AC_30">in Open Access</a>) : “Self-attribution of distorted reaching movements in immersive virtual reality”. <a href="https://doi.org/10.1016/j.cag.2018.09.001">https://doi.org/10.1016/j.cag.2018.09.001</a></p> <p> </p>
Temporal Distortion for Angry Faces: Testing Visual Attention and Action Preparation Accounts
<p>Datasets for Experiment 1 and Experiment 2 of "Temporal Distortion for Angry Faces: Testing Visual Attention and Action Preparation Accounts"</p>
Data supporting Tutorial: EPI Distortion Correction
<p>This data support Tutorial: EPI Distortion Correction by Barbara Dymerska for the Weekend Course during the ISMRM 2023 session: Artifacts & Correction Strategies on Sunday, 04 June 2023:</p> <p>https://github.com/fil-physics/ISMRM2023_educational</p> <p> </p>
Supplementary data: Geometry-preserving Expansion Microscopy microplates enable high fidelity nanoscale distortion mapping
<p>--- Data supplement---</p> <p>--- Title: Geometry-preserving Expansion Microscopy microplates enable high fidelity nanoscale distortion mapping</p> <p>The zip file contains the following directories and subdirectories. The files, recommended software, calling code and relevance to the main manuscript (preprint found at https://doi.org/10.1101/2023.02.20.529230) are included in the notes below:</p> <p>(i) “STL files” – Includes .stl file formats of the Expansion Microscopy microplate components. These files can be opened with any CAD software (e.g. Fusion 360) or any opensource 3D printer slicer programme (e.g. Chitubox v1.9.5)</p> <p>(ii) “Image alignment & distortion analysis code” – Directory containing the ImageJ macros and custom-written Python scripts for the analysis pipeline described in the main manuscript, from image scaling and alignment to for distortion plotting and RMS Error analysis and plotting. Subdirectories include:</p> <ul> <li>a.“Distortion analysis and RMSE plotting code” – Directory contains custom-written Python scripts for updated distortion analysis, plotting, RMS error calculation, and code for combining the plots. Each .py file can be run with any Python distribution. Dependency libraries and modules (all opensource) include numpy, os, cv2, skimage, and tifffile</li> <li>b.“Basic Align Macro.ijm” – ImageJ macro for basic image alignment of pre- and post-Expansion Microscopy images. Refer to <a href="https://imagej.nih.gov/ij/developer/macro/macros.html#tools">https://imagej.nih.gov/ij/developer/macro/macros.html#tools</a> on how to install and run ImageJ macros.</li> </ul> <p> </p> <p>(iii)“Example data and analysis scripts” – Directory containing example datasets and worked examples of analysis. Subdirectories include:</p> <ul> <li>a.“Single-channel_HeLa_cells”. Worked example of single-colour dataset of a cluster of HeLa cells stained with NHS-AZ488. Folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format). This can be called in using a Python code similar to: numpy.load("file name")</li> <li>b.“Multiplexed_Drosophila_wing”. Worked example of two-colour dataset of Drosophila fly wing tissue images stained with NHS-Alexa647 and anti-E-cad-GFP/Alexa488. Folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format).</li> <li>c.“Multiplexed_HeLa_cells”. Worked example of two-colour dataset of cultured HeLa cell images stained with NHS-AZ488 and antibodies. Each folder includes pre-Expansion and post-Expansion images (.tif format), overlays of the pre- and post-Expansion images along with distortion vector maps, and plots of normalised RMS error (in % values) against measurement length scale (in micrometers) saved as numpy arrays (.npy format): <ul> <li>I.“KDELAlexa594_NHSAZ488” – antibody staining against KDEL</li> <li>II.“NUP98Alexa594_NHSAZ488” – antibody staining against Nups98</li> </ul> </li> </ul> <p> </p> <ul> <li>d.“Distortion Correction”. Folder contains two subfolders of output images from distortion corrections using the Linear Stack Alignment with SIFT plugin in ImageJ v1.54f. Each example consists of a pre- and post-Expansion image file, and the ‘corrected’ image file generated with 5, 10, and 50 voxel B-spline sampling.</li> </ul> <p> </p> <p>These files are placed in public domain under Creative Commons license CC BY-ND 4.0</p>
Evaluating registrations of serial sections with distortions of the ground truths. Supplemental data
<p><strong>Evaluating Registrations of Serial Sections With Distortions of the Ground Truths</strong></p> <p>This is the supplemental data for our paper on how to benchmark registrations of serial sections with ground truths. The files are named as follows:</p> <ul> <li>*_challenge.7z: local distortions and global rigid transformations applied, the input for the benchmark we used. Use this to test your rigid and non-rigid methods.</li> <li>*_local-only.7z: only local distortions applied.</li> <li>*_local-DIST.7z: the distortion maps for local distortions.</li> <li>*_SURF-rigid.7z: local distortions and global rigid transformations applied, rigid transformations undone with SURF-based rigid-only method. Local distortions remain. Use this if your method does not cope well with large rigid transformations.</li> <li>_*vis.7z: visualizations of distortions.</li> <li>_rigid_ground.7z: the real rigid transformations used in the global phase.</li> <li>*_ground.7z: the ground truth. All data fit each other, no distortions. Use this to compare your registration result to it.</li> </ul> <p>There are three main modalities and one further, as a reference:</p> <ul> <li>CT_*: µCT data, a rabbit lung, 600 images. (In ground truth, and local distortions, and global transformations we supply more images that went into the benchmark, 50 more from both beginning and end.)</li> <li>EM_*: an EM serial block-face (SBF-SEM) data set of adult mouse lung, 1000 images. (EM ground truth is individually normalized, see paper.)</li> <li>LS_*: a lung from the light sheet microscopy from a male 24 week-old rat, 300 images. (LS ground truth is individually normalized, too.)</li> <li>REAL_*: a region from real serial sections from a rabbit lung, 2 images.</li> </ul> <p>We also supply elastix parameter files.</p> <p>A preprint has been uploaded to <a href="https://arxiv.org/abs/2011.11060">arXiv</a>. The definite version is available from <a href="https://ieeexplore.ieee.org/abstract/document/9594850/media#media">IEEE</a>. The source code of the distorter is available from <a href="https://github.com/olegl/distort">GitHub</a>.</p>
Motor adaptation distorts visual space
<p>The folder Visual Adaptation contains the data for the visual adaptation experiment</p> <p>Each file contains a matrix called “MatriceRisultati”. Each row of the matrix “MatriceRisultati” is a trial. </p> <p>The columns contain the following information:</p> <ul> <li>1<sup>st</sup>: Number of trial</li> <li>2<sup>nd</sup>: Test size</li> <li>3<sup>rd</sup>: Subject response on test</li> <li>4<sup>th</sup>: Condition </li> <li>5<sup>th</sup>: Test side</li> </ul> <p>The folder Motor Adaptation contains the data for the visual adaptation experiment</p> <p>Each file contains a matrix called “MatriceRisultati”. Each row of the matrix “MatriceRisultati” is a trial. </p> <p>The columns contain the following information:</p> <ul> <li>1<sup>st</sup>: Number of trial</li> <li>2<sup>nd</sup>: Test size</li> <li>3<sup>rd</sup>: Subject response on test</li> </ul> <p>The structure “Resp” contains one matrix for each trial with the hand coordinates for the motor adaptation</p>
dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
<p>dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning</p>
Dataset for DFT and one-step model results used in publication "Persistence of Structural Distortion and Bulk Band Rashba Splitting in SnTe above Its Ferroelectric Critical Temperature" in Nano Letters, 2024, 24, 1, 82–88
<p>Dataset for DFT and one-step model results used in publication DOI 10.1021/acs.nanolett.3c03280, "Persistence of Structural Distortion and Bulk Band Rashba Splitting in SnTe above Its Ferroelectric Critical Temperature" in Nano Letters, 2024, 24, 1, 82–88.</p> <p>Description of dataset is in the ReadMe.txt file in subdirectories.</p>
Epistatic selection on a selfish Segregation Distorter supergene: drive, recombination, and genetic load
<p>Meiotic drive supergenes are complexes of alleles at linked loci that together subvert Mendelian segregation resulting in preferential transmission. In males, the most common mechanism of drive involves the disruption of sperm bearing one of a pair of alternative alleles. While at least two loci are important for male drive- the driver and the target- linked modifiers can enhance drive, creating selection pressure to suppress recombination. In this work, we investigate the evolution and genomic consequences of an autosomal, multilocus, male meiotic drive system, Segregation Distorter (SD) in the fruit fly, Drosophila melanogaster. In African populations, the predominant SD chromosome variant, SD-Mal, is characterized by two overlapping, paracentric inversions on chromosome arm 2R and nearly perfect (~100%) transmission. We study the SD-Mal system in detail, exploring its components, chromosomal structure, and evolutionary history. Our findings reveal a recent chromosome-scale selective sweep mediated by strong epistatic selection for haplotypes carrying Sd, the main driving allele, and one or more factors within the double inversion. While most SD-Mal chromosomes are homozygous lethal, SD-Mal haplotypes can recombine with other, complementing haplotypes via crossing over, and with wildtype chromosomes via gene conversion. SD-Mal chromosomes have nevertheless accumulated lethal mutations, excess non-synonymous mutations, and excess transposable element insertions. Therefore, SD-Mal haplotypes evolve as a small, semi-isolated subpopulation with a history of strong selection. These results may explain the evolutionary turnover of SD haplotypes in different populations around the world, and have implications for supergene evolution broadly.</p>
Text-fig. 5. Mastixiopsis nyssoides KIRCHH. a, b, g–n: Organic preservation. a, b: Lignitic, unpermineralized, early Eocene Dorset Pipe clays at Arne, V. 40762. a: Ventral view (original illustration from pl. 18, fig. 1 of Chandler 1962). b: Transverse fracture, somewhat distorted by compression. c–f: Pyrite permineralization. c: Ventral view, V. 22963(1) from Sheppey, originally listed as Mastixia cantiensis. d: Lateral view, V. 22969 from Sheppey (identified as Mastixia grandis by Reid and Chandler 1933: pl. 25, fig. 8). e: Equatorial transverse physical section from (c). f: Equatorial transverse physical section from (d). g: Detail of pericarp from (e), showing endocarp formed of dense fibrous tissue, surrounded by mesocarp of anticlinally oriented larger cells. h: Detail of pericarp from (f). i–n: Type material from Eocene of Riestadt, Germany, MNB. i: Ventral view. j, k: Ventral and apical views of holotype. l: View of the transversely fractured surface from (j) showing horseshoe shaped locule. m: Equatorial transverse physical cut of the specimen in (i); note yellow resin cavity (arrow). n: Scanning electron microscopy of pericarp from (l) with locule lining at lower edge of image. Note dense endocarp tissue composed of small cells (fibres and sclereids), extending about 3/5 of distance to periphery, surrounded by mesocarp of larger, anticlinally oriented cells. Scale bars 1 cm in (a–f), (i–k), 1 mm in (g), 2 mm in (h), 3 mm in (l), m, 250 Μm in (n). Bar in (d) applies also to (c). Bar in (l) also applies to (m). Bar in (i) also applies to (j) and (k). in Mastixioid Fruits (Cornales) From The Early Eocene London Clay Flora: Morphology, Anatomy And Nomenclatural Revision
Text-fig. 5. Mastixiopsis nyssoides KIRCHH. a, b, g–n: Organic preservation. a, b: Lignitic, unpermineralized, early Eocene Dorset Pipe clays at Arne, V. 40762. a: Ventral view (original illustration from pl. 18, fig. 1 of Chandler 1962). b: Transverse fracture, somewhat distorted by compression. c–f: Pyrite permineralization. c: Ventral view, V. 22963(1) from Sheppey, originally listed as Mastixia cantiensis. d: Lateral view, V. 22969 from Sheppey (identified as Mastixia grandis by Reid and Chandler 1933: pl. 25, fig. 8). e: Equatorial transverse physical section from (c). f: Equatorial transverse physical section from (d). g: Detail of pericarp from (e), showing endocarp formed of dense fibrous tissue, surrounded by mesocarp of anticlinally oriented larger cells. h: Detail of pericarp from (f). i–n: Type material from Eocene of Riestadt, Germany, MNB. i: Ventral view. j, k: Ventral and apical views of holotype. l: View of the transversely fractured surface from (j) showing horseshoe shaped locule. m: Equatorial transverse physical cut of the specimen in (i); note yellow resin cavity (arrow). n: Scanning electron microscopy of pericarp from (l) with locule lining at lower edge of image. Note dense endocarp tissue composed of small cells (fibres and sclereids), extending about 3/5 of distance to periphery, surrounded by mesocarp of larger, anticlinally oriented cells. Scale bars 1 cm in (a–f), (i–k), 1 mm in (g), 2 mm in (h), 3 mm in (l), m, 250 Μm in (n). Bar in (d) applies also to (c). Bar in (l) also applies to (m). Bar in (i) also applies to (j) and (k).
The Phonon-Modulated Jahn–Teller Distortion of the Nitrogen Vacancy Center in Diamond
<p>The data in this repository consists of three data sets that yielded transient absorption spectra, 2D electronic spectra, and 3D coherence spectra for the nitrogen vacancy (NV) center. These data supported the conclusions of the published report, namely that the LO Phonon of the diamond lattice modulates the Jahn-Teller distortion of the NV center and influences its excited-state dynamics. The data were taken between 2016 and 2020 and reflect the advancements made to the ultrafast instrumentation of te Turner Lab during this time period. The Matlab scripts and data are presented "as is", however we have attempted to include processed datasets legible to any open-source programming language when possible. Please consult the README.md file for further information.</p>
Figure 6. Noticeable subjective response to the distorted face to the right-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>Quite surprisingly, if the distortion is viewed in the normal upward position, it evokes strong<br> emotional response to the distorted face to the right in figure 6.</p>
Figure 5. The distortion is barely noticeable if the faces are viewed in the reversed position-Gestalt Processing in Human-Robot Interaction: A Novel Account for Autism Research
<p>According to the feature-based processing theories of human faces the main elements,<br> noticed and remembered in a face, are the eyes, the nose and the mouth (Thompson, 1980; Anstis,<br> 2005a). If, however, we distort some of the elements of a face, these should influence perception,regardless of the position of the image – upright or reversed – from the observer viewpoint. Figure 5<br> presents the reversed image of the face on the left and the reversed distorted face on the right. The<br> distortion was achieved by rotating the eyes of the image in the vertical direction.<br> Figure</p>
RAD-SEQ LINKAGE MAPPING AND PATTERNS OF SEGREGATION DISTORTION IN SEDGES: MEIOSIS AS A DRIVER OF KARYOTYPIC EVOLUTION IN ORGANISMS WITH HOLOCENTRIC CHROMOSOMES" in Journal of Evolutionary Biology
<p>This a data set from the paper RAD-SEQ LINKAGE MAPPING AND PATTERNS OF SEGREGATION DISTORTION IN SEDGES: MEIOSIS AS A DRIVER OF KARYOTYPIC EVOLUTION IN ORGANISMS WITH HOLOCENTRIC CHROMOSOMES" to be published in Journal of Evolutionary Biology</p>
ScienceDex guides
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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.