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849 results for “linear”
Figure 1 in Statistical analysis on the cnidome of genus Hydra using Generalized Linear Models
Figure 1. Cnidome of Hydra viridissima. (A) stenotele, (B) desmoneme, (C) atrichous isorhiza and (D) holotrichous isorhiza. Scale bar: 3 μm.
Figure 4 in Statistical analysis on the cnidome of genus Hydra using Generalized Linear Models
Figure 4. Different morphotypes of holotrichous isorhiza. (A) Hydra viridissima, (B) Hydra vulgaris pedunculata, C and (D) Hydra vulgaris. Scale bar: 2.45 μm.
Figure 8 in Statistical analysis on the cnidome of genus Hydra using Generalized Linear Models
Figure 8. GLM adjustment graphs used for comparison between species. (A) scatter plot, (B) Q-Q Plots.
Figure 3 in Statistical analysis on the cnidome of genus Hydra using Generalized Linear Models
Figure 3. Cnidome of Hydra vulgaris pedunculata. (A) stenotele, (B) desmoneme, (C) atrichous isorhiza and (D) holotrichous isorhiza. Scale bar: 2.7 μm.
Figure 2 in Statistical analysis on the cnidome of genus Hydra using Generalized Linear Models
Figure 2. Cnidome of Hydra vulgaris. (A) stenotele, (B) desmoneme, (C) atrichous isorhiza and (D) holotrichous isorhiza. Scale bar: 2.85 μm.
Geometries for "Linear Response Properties of Solvated Systems: A Computational Study"
<p>Geometry files (.xyz format) used in the paper "Linear Response Properties of Solvated Systems: A Computational Study"</p>
Data for: A linear response framework for simulating bosonic and fermionic correlation functions on quantum computers
<p>Response functions are a fundamental aspect of physics; they represent the link between experimental observations and the underlying quantum many-body state. However, this link is often under-appreciated, as the Lehmann formalism for obtaining response functions in linear response has no direct link to experiments. Within the context of quantum computing, and by using a linear response framework, we restore this link by making the experiment an inextricable part of the quantum simulation. This method can be frequency- and momentum-selective, avoids limitations on operators that can be directly measured, and is ancilla-free. As prototypical examples of response functions, we demonstrate that both bosonic and fermionic Green's functions can be obtained, and apply these ideas to the study of a charge-density-wave material on {\emph{ibm\_auckland}}. The linear response method provides a robust framework for using quantum computers to study systems in physics and chemistry.</p>
Dataset for "Is the linear relationship between the slope and intercept observed in field emission S-K plots an artifact?"
<p>The dataset are in text format, the files to analyze and generate the figures are in jupyter format for python with a copy in pdf format.</p> <p>Description of files and folders :</p> <p><strong>I) Analyzed</strong></p> <p>This folder contains Fowler-Nordheim analysis of the I-V data in the folder Data, that where obtained in the publication "All field emission experiments are noisy, ... are any meaningful ?"</p> <p><strong>II) Data</strong></p> <p>This folder contains the different experimental I-V data</p> <p><strong>III) Numerical data<br></strong></p> <p>This folder contains the different noisy I-V data generated by the file "Simul"</p> <p><strong>IV) Fig1</strong></p> <p>This file generates the figure "MySK4.svg" based on the data in the folders <strong>I) Analyzed</strong> and <strong>III) Numerical data</strong></p> <p><strong>V) Fig2</strong></p> <p>This file analyses the data in the folder <strong>II) Data</strong> and then extract the parameters of their SK plot.</p> <p>It gathers the SK plot parameters of several work in the litterature.</p> <p>It plots "Slopm4.svg" and "Intm4.svg".</p> <p><strong>VI) Simul</strong></p> <p>This file generates the different noisy I-V data necessary fir Fig1</p> <p><strong>VII) FigSuppl</strong></p> <p>This file shows the different experimental Fowler-Nordheim plots obtained from the files in the folder <strong>II) Data</strong></p>
Data underpinning "Revealing Quadrupolar Excitations with Non-Linear Spectroscopy"
<p>Local moments with a spin S>1/2 can exhibit a rich variety of elementary quasiparticle excitations, such as quadrupolar excitations, that go beyond the dipolar magnons of conventional spin-1/2 systems. However, the experimental observation of such quadrupolar excitations is often challenging due to the dipolar selection rules of many linear response probes, rendering them invisible. Here we show that non-linear spectroscopy, in the form of two-dimensional coherent spectroscopy (2DCS), can be used to reveal quadrupolar excitations. Considering a family of spin-1 Heisenberg ferromagnets with single-ion easy-axis anisotropy as an example, we explicitly calculate their 2DCS signature by combining exact diagonalization and generalized spin wave theory. We further demonstrate that 2DCS can provide access to the quadrupolar weight of an excitation, analogous to how linear response provides access to the dipolar weight. Our work highlights the potential of non-linear spectroscopy as a powerful tool to diagnose multipolar excitations in quantum magnets.</p>
DSA-380 experimental data and a non-linear model in Matlab
<p><span>Experimental data of the DSA 380 pantograph is provided in a dataset and the Matlab files neccesary to simulate a model of the pantograph. Readme available.</span></p>
High-power in-phase and anti-phase mode emission from linear arrays of resonant-tunneling-diode oscillators in the 0.4-to-0.8-THz frequency range - data
<p>Experimental and simulation data from the paper "High-power in-phase and anti-phase mode emission from linear arrays of resonant-tunneling-diode oscillators in the 0.4-to-0.8-THz frequency range".</p>
Dataset of Reconfigurable Metamaterial Processing Units that Solve Arbitrary Linear Calculus Equations
<p>Dataset of <em>Reconfigurable Metamaterial Processing Units that Solve Arbitrary Linear Calculus Equations</em></p>
Plenoptic 2.0 - RayTrix R8 and RayTrix R32 - TempleBoatGiant Linear Motion and Grid
<p>The test sequence "Plenoptic 2.0 - RayTrix R8 and RayTrix R32 - TempleBoatGiant Linear Motion and Grid" is provided by Sarah Fachada, Daniele Bonatto, Jaime Sancho, Gauthier Lafruit,Mehrdad Teratani, and Eduardo Juárez, members of the LISA department, EPB (Ecole Polytechnique de Bruxelles), ULB (Université Libre de Bruxelles), Belgium and CITSEM (Centro de Investigación en Tecnologías Software y Sistemas Multimedia para la Sostenibilidad), UPM (Universidad Politecnica de Madrid), Spain.</p> <p><strong> </strong></p> <p><strong>License:</strong></p> <p>CC BY-NC-SA</p> <p><strong> </strong></p> <p><strong>Terms of Use:</strong></p> <p>Any kind of publication or report using this sequence should refer to the following reference:</p> <p>[1] **Sarah Fachada, Daniele Bonatto, Jaime Sancho, Gauthier Lafruit, Mehrdad Teratani, and Eduardo Juárez, "Plenoptic 2.0 - RayTrix R8 and RayTrix R32 - TempleBoatGiant Linear Motion and Grid," 2024.07, 10.5281/zenodo.12547598.**</p> <p> </p> <p><code> @misc{fachada_templeboatgiantstatic_2024,<br></code><code> title = {{Plenoptic} 2.0 } {Raytrix} {R8} and {Raytrix} {R32} - {TempleBoatGiant} {Linear} {Motion} and {Grid}},<br></code><code> author = {Fachada, Sarah and Bonatto, Daniele and Sancho, Jaime and Lafruit, Gauthier and Teratani, Mehrdad and Juárez, Eduardo},<br></code><code> month = jul,<br></code><code> year = {2024},<br></code><code> doi = {10.5281/zenodo.12547598}<br></code><code> }</code></p> <p><strong> </strong></p> <p><strong>Production:</strong></p> <p>Laboratory of Image Synthesis and Analysis, LISA department, Ecole Polytechnique de Bruxelles, Université Libre de Bruxelles, Belgium, </p> <p>Centro de Investigación en Tecnologías Software y Sistemas Multimedia para la Sostenibilidad, CITSEM, Universidad Politécnica de Madrid, Spain.</p> <p><strong> </strong></p> <p><strong>Content:</strong></p> <p>This dataset contains a static scene representing an artistic view of a boat floating on an ocean made of colored blocks, near a temple, while a giant is chasing them. The dataset consists of two sub-datasets, one with a linear camera movement and the other with a grid of positions. Both sub-datasets showcase the same scene and are in the format of plenoptic images extracted from ray files. The dataset was acquired with two Raytrix cameras [1], the R8 and the R32, mounted on the UPM’s robotic bench. The distance between the two optical axis is (60.7079, -15.7349, 137.849) millimeters with focals F=25 for the R8 and F=50 for the R32.</p> <p>The dataset additionally contains calibration images of a checkerboard and white images taken with different main lens apertures.</p> <p><strong> </strong></p> <p>1. <strong>Linear Camera Movement Sub-dataset:</strong></p> <p> - Camera moves from (x, y, z) = (655, 0, 410) to (655, 450, 410) with a step of 10mm.<strong> </strong></p> <p>2. <strong>Grid Sub-dataset:</strong></p> <p> - Camera positions range from x=535 to x=775 per line, with a step of 10mm, and from z=350 to z=370 with a step of 10mm.</p> <p>3. <strong>Calibration images:</strong></p> <p> - Cameras are fixed at the central position (x, y, z) = (655, 0, 410) and capture views of a checkerboard with 12mm square size.</p> <p> - Overexposed images with a light diffuser were captured with varying camera apertures.</p> <p>Both sub-datasets follow the format of `{R8 or R32}_z{z position}_y{y position}_x{x position}_Processed.png`. Calibration files are provided in XML format. The datasets were acquired with the UPM’s robotic bench.</p> <p><strong>The dataset contains:</strong></p> <p>- A `linear` folder containing:</p> <p> - XML calibration files</p> <p> - Plenoptic images in PNG format</p> <p>- A `grid` folder containing:</p> <p> - XML calibration files</p> <p> - Plenoptic images in PNG format</p> <p>- A `checkerboard` folder containing:</p> <p> - XML calibration files</p> <p> - Plenoptic raw and processed images in PNG format of checkerboard with square size of 12mm.</p> <p>- A `white` folder containing:</p> <p> - XML calibration files</p> <p> - Plenoptic raw images in PNG format of overexposed images captured with a white diffusor and different main lens apertures.</p> <p><strong> </strong></p> <p><strong>References and links:</strong></p> <p>[1] https://raytrix.de/</p> <p><strong> </strong></p> <p><strong>Acknowledgments:</strong></p> <p>Sarah Fachada is a Postdoctoral Researcher of the Fonds de la Recherche Scientifique - FNRS, Belgium. </p> <p>This work was supported in part by the HoviTron project (no. 951989), the FER 2021 project (no. 1060H000066-FAISAN), the Emile DEFAY 2021 project (no. 4R00H000236), and the FER 2023 project (no. 1060H000075). </p> <p>Additionally, this work has been funded by the project AIMS5.0, supported by the Chips Joint Undertaking and its members, including top-up funding by National Funding Authorities from involved countries (no. 101112089), and the European project STRATUM (no. 101137416). The robotic bench was funded by “Programa Propio UPM” in the call “convocatoria de ayudas a centros e institutos de I+D+i”.</p> <p> </p>
A new biomechanical approach on cranial sutures function: The role of contact elements in linear and non-linear models
<p>Understanding cranial sutures and how they relieve and dissipate stress is essential to assess their role in cranial biomechanics and to develop highly accurate predictive models. This involves examining how ontogeny affects cranial sutures, as well as their morphology and function, and how these changes through time may impact essential biomechanical loadings such as chewing or direct biting. In this work we study the cranial sutures of <em>Crocodylus niloticus</em> in detail using contact elements under finite element analysis. Contact elements permit the creation of a physical relationship between two bones that are in contact and even to configure these relationships, e.g. in terms of movement or flexibility. The definition of bone contacts may require linear and/or non-linear computational solutions to attain higher accuracy. Herein, skull geometry is tested to determine how they may be altered by different types of contacts under various conditions. As predicted, the absence of sutures or cranial kinesis leads to a reduction in stress distribution across the skull, whereas sutures and cranial kinesis help the skull relieve stress and prevent certain bones from sustaining high stress levels. The type of contact used in individual sutures has a significant effect on the models' outcome. Additionally, feeding behaviors significantly impact cranial biomechanics, reflecting the influence of other variables that may be applied to the models. As highlighted by the results, in order to obtain accurate results when analyzing fossil taxa, the nature of the cranial sutures should be taken into account. Therefore, developing predictive models based on living taxa is invaluable because it facilitates the study of extinct taxa for which there is a lack of information on the fibrous joints due to poor (or non-) preservation in the fossil record.</p>
Artifact for Misconceptions in Finite-Trace and Infinite-Trace Linear Temporal Logic
<p>Welcome!</p> <p>This artifact contains the survey instruments, final catalog, and labeled responses from our FM 2024 paper:</p> <p><em>Misconceptions in Finite-Trace and Infinite-Trace Linear Temporal Logic</em></p> <ul> <li>The artifact is primarily a dataset. There is no required software to run.</li> <li>The instruments and catalog are PDF files.</li> <li>The labeled responses are in spreadsheets, which we provide as open document files (`.ods`) with HTML as a backup.</li> </ul>
Linear vs. non-linear metrics of Autonomic Nervous System: study on healthy volunteers during controlled breathing
<h1>Please cite this article as reference article:</h1> <p>Uryga A, Najda M, Berent I, Mataczyński C, Urbański P, Kasprowicz M, Buchner T. The impact of controlled breathing on autonomic nervous system modulation: analysis using phase-rectified signal averaging, entropy and heart rate variability. Physiol Meas. 2024 Sep 16;45(9). doi: 10.1088/1361-6579/ad7778. </p> <h1>Funding</h1> <p>SONATA 18 UMO-2022/47/D/ST7/00229 National Science Centre, Poland (dataset 2)</p> <p>SONATA-BIS UMO-2013/10/E/ST7/00117 National Science Centre, Poland (dataset 1)</p> <h1>General information</h1> <p>Two datasets were used in this study.</p> <p>The dataset 1 includes 49 healthy volunteers (28 females, 21 males, median age: 23 years, range: 18-31 years) who were measured at the Neuroengineering Laboratory at Wroclaw University of Science and Technology (WUST) between October 2014 and June 2015 (Biomedical Committee Agreement number: KB-170/2014).</p> <p>The dataset 2 includes 21 healthy volunteers (14 females, 7 males, median age: 22 years, range: 18-31 years) who were prospectively measured at WUST between October 2023 and January 2024 (Biomedical Committee Agreement number: KB-179/2023/N).</p> <h1>Signal recordings description</h1> <ul> <li>ABP was measured non-invasively by a servo-controlled plethysmograph (Finometer MIDI, FMS Medical Systems, Amsterdam, The Netherlands in all subjects in dataset 1; CNAP, CNSystems Medizintechnik GmbH, Graz, Austria and Finapres Nova, FMS Medical Systems in dataset 2). The cuff was placed on the middle finger of the left hand and held at the level of the heart.</li> <li>Expired end-tidal CO2 (EtCO2), carbon dioxide (CO2) concentration and respiratory rate (RR) were measured via a nasal cannula using a portable capnography monitor (RespSense™, NONIN, Plymouth, USA)</li> <li>Protocol: after a resting epoch lasted at least 5 minutes, a controlled breathing session was initiated with 5-minute recordings at each of the respiratory rate: 6, 10 or 15 breaths/min (0.1 Hz, 0.17 Hz, and 0.25 Hz, respectively), guided by a digital metronome.</li> </ul> <h1>Data description</h1> <ul> <li>Type of database (database 1/database 2)</li> <li>Type of device used to ABP measurement</li> <li>Metadata including: sex (male M, female F), and age</li> <li>Autonomic Nervous System parameters including:</li> </ul> <p>- <strong>Phase-Rectified Signal Averaging</strong> - a non-linear approach used to quantify the acceleration (AC) and deceleration (DC) capacity of the heart; more details could be found here: <em>Campana L M, Owens R L, Clifford G D, Pittman S D and Malhotra A 2010 Phase-rectified signal averaging as a sensitive index of autonomic changes with aging J Appl Physiol 108 1668–73</em></p> <p>- <strong>Entropy</strong>: multiscale entropy (MSEn), approximate entropy (ApEn), sample entropy (SampEn), and fuzzy entropy (FuzzyEn) functions calculated for R-R intervals, which were implemented in NeuroKit2</p> <ul> <li> <strong>Heart rate variability (HRV) metrics</strong>: In the frequency domain, the Lomb–Scargle periodogram was used to determine the power spectral density of the interval time series in the low-frequency range (LF, 0.04–0.15 Hz) and the high-frequency range (HF, 0.15–0.40 Hz). Additionally, the total power of the HRV signal (TP, 0.04–0.40 Hz) and the ratio between low and high-frequency components (LF/HF) were calculated. In the time domain, the following metrics were determined: the standard deviation of the R-R intervals (SDNN) and the square root of the mean of the squared successive differences between adjacent R-R intervals (RMSSD), mean of the R-R intervals (meanNN), and the proportion of R-R intervals greater than 20 ms or 50 ms, out of the total number of R-R intervals (pNN20 and pNN50, respectively); appropriate functions were implemented in NeuroKit2</li> </ul> <p> </p> <p>Update ------version 2</p> <p>After the revision process, SDNNref was added, defined according to formula presented in paper of Monfredi et al. (Monfredi O, Lyashkov AE, Johnsen AB, et al. Biophysical characterization of the underappreciated and important relationship between heart rate variability and heart rate. Hypertension. 2014 Dec;64(6):1334-43)</p>
The process of HDAC11 Assay Development: linearity and Km calculation
<p>With previously optimized conditions, the linearity range for HDAC11 was obtained and the Km for the substrate was calculated.</p> <p> </p> <p><strong>Note: </strong>1. In the assay buffer, BSA conc. is 0.5 mg/ml (instead of 0.5%).</p> <p> 2. In the 7.5 ul developer solution, 40 uM of TSA (Trichostatin A) is also included.</p>
The process of HDAC11 Assay Development: Check for linearity and Km-repeat
<p>The linearity for HDAC11 activity for Boc-Lys-(TFA)-AMC substrate was monitored using the newly optimized buffer conditions. This was followed upon by Km calculation.</p>
Non-linear analysis of pedalling forces in cycling
<p>The dataset contains raw data and the data underlying the figures and tables of the following research article: </p> <p>"Non-linear analysis of pedalling forces in cycling"</p> <p>Journal: PLOS ONE</p> <p>Manuscript Nr: PONE-D-18-13682</p>
Data for Nguyen Le et al. "Giant non-linear susceptibility of hydrogenic donors in silicon and germanium."
<p>Data and codes for the computation in Nguyen Le et al. "Giant non-linear susceptibility of hydrogenic donors in silicon and germanium."</p>
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.