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6,452 results for “Application”
Implementation of Aerosol Mie Scattering in POSEIDON with Application to the hot Jupiter HD 189733 b's Transmission, Emission, and Reflected Light Spectrum - Supplementary Material
<p>Supplementary material to 'Implementation of Aerosol Mie Scattering in POSEIDON with Application to the hot Jupiter HD 189733 b’s Transmission, Emission, and Reflected Light Spectrum'</p> <p>This zenodo repository corresponds to the updates to POSEIDON presented in Mullens et al 2024, as well as the benchmark retrievals preformed on archival HD 189733 b. </p> <p>In the upper level of the repository we have data products mentioned in the paper:</p> <p>aersosol_database.pdf <br>Aerosol-Database-Readme.txt<br>aerosol_database.hdf5</p> <p>There are then three directories (here as zip files): </p> <p>Aerosol-Datbase<br>- This folder contains the aerosol properties for aerosols in Table 1 in the paper. It contains the aerosol-database.hdf5 which contains the precomputed aerosol properties that POSEIDON utilizes in forward models and retrievals, a folder containing the npy files that have individual precomputed aerosol properties (used to generate the hdf5 file), a pdf containing the refractive indices + precomputed aerosol properties for each aerosol in the database (png versions of these figures are available in the Aerosol-Optical-Properties-Pngs folder), the refractive index txt files for each aerosol, and a readme file that contains information on each aerosol in the database (such as aerosol name, polymorph, crystalline or amorphous, crystal shape, information on samples used in refractive index references, and exoplanet/planetary science specific references). </p> <p>HD-189733b-Retrievals<br>- Contains retrieval scripts and retrieval results for the transmission, emission, and emission+reflection retrievals in the paper. Also has the notebooks used to make the figures in the paper.</p> <p>POSEIDO-V1-2<br>- Contains the POSEIDON module used to run retrievals and generate figures, and corresponds to POSEIDON version 1.2. Also contains a folder containing tutorial notebooks for new features introduced into POSEIDON version 1.2. Included in this repository for posterity.</p>
Applications of Fast Iterative Filtering in NMR spectroscopy [NMR DATASETS]
<p>Employed NMR spectra for the application of denoising and baseline correction method described in the manuscript.</p>
Multidirectional load multipliers: application for shared anchors for floating wind turbines
<div> <h1>Multidirectional load multipliers</h1> </div> <p>The load multipliers presented in this dataset are based on the multiline anchor loads calculated for a spar floating wind turbine presented by:</p> <p>Balakrishnan, K., Arwade, S.R., DeGroot, D.J., Fontana, C., Landon, M., Aubeny, C.P., 2020. <em>Comparison of multiline anchors for offshore wind turbines with spar and with semisubmersible</em>. Journal of Physics: Conference Series, <strong>1452</strong>, 012032. <a href="https://doi.org/10.1088/1742-6596/1452/1/012032" rel="nofollow">doi:10.1088/1742-6596/1452/1/012032</a>.</p> <div> <h2>Files</h2> </div> <p>Load multipliers for three load cases grouped in Wind Wave and Current directions. Those are the loads arriving at a multiline anchor.</p> <div> <h3>DLC 0WWC: Wind Wave and Current at 0 degrees</h3> </div> <ul> <li>DLC_0deg_push.txt</li> <li>DLC_0deg_push2.txt</li> <li>DLC_0deg_push3.txt</li> <li>DLC_0deg_step.txt</li> <li>DLC_0deg_step2.txt</li> <li>DLC_0deg_step3.txt</li> </ul> <div> <h3>DLC 30WWC: Wind Wave and Current at 30 degrees</h3> </div> <ul> <li>DLC_30deg_push.txt</li> <li>DLC_30deg_push2.txt</li> <li>DLC_30deg_push3.txt</li> <li>DLC_30deg_step.txt</li> <li>DLC_30deg_step2.txt</li> <li>DLC_30deg_step3.txt</li> </ul> <div> <h3>DLC 60WWC: Wind Wave and Current at 60 degrees</h3> </div> <ul> <li>DLC_60deg_push.txt</li> <li>DLC_60deg_push2.txt</li> <li>DLC_60deg_push3.txt</li> <li>DLC_60deg_step.txt</li> <li>DLC_60deg_step2.txt</li> <li>DLC_60deg_step3.txt</li> </ul>
DAS Data for the figure in the paper entitled "A Hybrid Earthquake Detection Method for Distributed Acoustic Sensing Array Data and Its Application to the 2022 Menyuan Earthquake Sequence"
<p>The DAS data can be loaded using numpy. The sampling rate is 100 Hz, and each row is a time series for that channel.</p>
Human Decision-Making Through the Lifecycle of Autonomous and Intelligent Systems in Defense Applications: Example Scenario with Comments
<p>This example scenario with comments has been created in conjunction with the report “A Framework for Human Decision-Making Through the Lifecycle of Autonomous and Intelligent Systems in Defense Applications” to be published by the IEEE Standards Association (IEEE SA) Research Group on Issues of AI and Autonomy in Defense Systems (the Research Group). It provides insights into the Research Group's working methods and process.</p>
OH mid-infrared emission as a diagnostic of H2O UV photodissociation. III. Application to planet-forming disks
<p>This repository contains the grid of MIRI-MRS synthetic spectra presented in Tabone, van Dishoeck, and Black 2024. In this work, we include in the DALI disk model prompt emission of OH following photodissociation of H2O in its B electronic state by photons at λ < 144 nm. The propensity of forming OH in the A’ symmetric states is taken into account. H2O and OH emission is calculated from 9 to 18 micron and convolved at a spectral resolution of R=2500 to be readily compared with JWST data.</p> <p>The python script plot_model.py can be used to plot a model.</p>
Supplementary material for "Moment tensor and point force estimation, with applications to earthquakes, landslides, and the 2017 DPRK nuclear explosion"
<p>This Zenodo archive contains supplementary data and code for the paper <strong>Moment tensor and point force estimation, with applications to earthquakes, landslides, and the 2017 DPRK nuclear explosion</strong> (submitted to <em>Geophysical Journal International</em>). The archive provides scripts and data to reproduce some of the figures and results presented in the paper / supplementary material.</p>
p-IgGen Dataset: Cleaned paired and unpaired antibody sequence data for machine learning applications.
<p>This data is released alongside "p-IgGen: A Paired Antibody Generative Language Model", which contains full details on the data processing and cleaning.</p> <p>p-IgGen Paper: https://www.biorxiv.org/content/10.1101/2024.08.06.606780v1 .</p> <p>OAS: https://opig.stats.ox.ac.uk/webapps/oas/</p> <p> </p>
Mass spectrometry Imaging dataset for a study on fungicide application to tomato leaves - II
<p>The dataset uploaded here is in association to a manuscript in press by Ajith et al., titled, "Visualizing active fungicide formulation mobility in tomato leaves with Desorption Electrospray Ionisation Mass Spectrometry Imaging". This dataset contains .imzML files along with zipped .ibd files of mass spectrometry imaging data and .mzML format LC-MS data for a fungicide applicaion study on tomato leaves. DESI Imprint imaging data here is for tomato leaves applied with Azoxystrobin standard after 24 hours, 56 hours and a week after formulation application along with a blank leaf data with no Azoxystrobin application.</p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Time point</strong></td> </tr> <tr> <td>DTIM_Std_24h</td> <td>24h</td> </tr> <tr> <td>DTIM_Std_56h</td> <td>56h</td> </tr> <tr> <td>DTIM_Std_1week</td> <td>1 week</td> </tr> <tr> <td>DTIM_Blank</td> <td> No application</td> </tr> </tbody> </table> <p>This data upload contains a zipped LC-MS data folder (LC_mzML.7z). The details of the files, the time point of sampling and part of leaf is in the excel sheet uploaded along with the data (LC-MS_datanames.xls).</p> <p> </p>
A data set on "High-Yield Production of SiV-Doped Nanodiamonds for Spectroscopy and Sensing Applications"
<p>The data set to paper: </p> <p>High-Yield Production of SiV-Doped Nanodiamonds for Spectroscopy and Sensing Applications</p> <p>Alexander Kromka1,*, Marián Varga1,2, Kateřina Aubrechtová Dragounová1,3, Oleg Babčenko1, René. Pfeifer1, Assegid M. Flatae4, Florian Sledz4, Farzana Akther4, Mario Agio4,5, Štěpán Potocký1, Štěpán Stehlík1</p> <p>1 Institute of Physics, Czech Academy of Sciences, Prague 6, Czech Republic<br>2 Institute of Electrical Engineering, Slovak Academy of Sciences, Bratislava, Slovakia<br>3 Faculty of Nuclear Sciences and Physical Engineering, Czech Technical University in Prague, Prague 1, Czech Republic<br>4 Laboratory of Nano-Optics and Cμ, University of Siegen, Walter-Flex-Str. 3, 57072 Siegen, Germany <br>5 National Institute of Optics (INO-CNR), Largo Enrico Fermi 6, 50125 Florence, Italy</p> <p>*corresponding authors: kromka@fzu.cz</p> <p>Data manager: Kristýna Dostálová: dostalovak@fzu.cz</p> <p>Date of data collection: 1. 2. 2023 - 31. 7. 2024</p> <p>All the data showed in the pictures are provided in X-Y format with described sample. Always, the respective figure to which the data belong is provided in high resolution. <br>The data are in the following formats: <br>Scheme 1: pdf<br>Figure 1: pdf<br>Figure 2: pdf<br>Figure 3: pdf, csv<br>Figure 4: pdf, csv<br>Figure 5: pdf, csv<br>Figure 6: pdf, csv<br>Figure 7: pdf, csv<br>Figure S1: pdf, csv<br>Figure S2: pdf, csv</p> <p>The comma separated values file (csv) always contain the description of the columns in the first row. In case of composed image the name of the file corresponds to the corresponding figure.</p> <p>Data acquistion and processing is provided in the Experimental part in the publication: DOI: 10.1021/acsanm.4c04676</p>
Invention and application of epoch of Kifilideen's Sum Formula for Kifilideen's General Matrix Progression Series of infinite terms in solving real-world problems
<p>*Sharing a Notable Achievement* I am honored to announce that my research paper, titled 'Invention and Application of Epoch of Kifilideen’s Sum Formula for Kifilideen’s General Matrix Progression Series of Infinite Terms in Solving Real-World Problems,' was among the three papers selected by the College of Engineering (COLENG) for presentation at the recent Science and Technology Fair/Exhibition organized by Bells University of Technology, Ota, Ogun State, Nigeria, on October 28, 2024. I am delighted to share that COLENG secured the first position in the Inter-Collegiate poster competition, a testament to the outstanding collective efforts of our researchers, staff, and team members who contributed to the success. I extend my gratitude to the Dean of Engineering for his exceptional leadership and guidance, which played a pivotal role in this achievement. Congratulations to my colleagues whose papers were also presented, and to everyone who contributed to this success. Award Recognition Sincerely, Engr. Dr. Kifilideen Lekan OSANYINPEJU Agricultural and Bio-System Engineering College of Engineering Bells University of Technology, Ota, Ogun State, Nigeria</p>
Event driven 4D STEM acquisition with a Timepix3 detector: microsecond dwelltime and faster scans for high precision and low dose applications
<p>The data sets and the processing scripts are a part of the publication: <strong>Event driven 4D STEM acquisition with a Timepix3 detector: microsecond dwelltime and faster scans for high precision and low dose applications (</strong><a href="https://arxiv.org/abs/2107.02864">arXiv:2107.02864</a><strong>). </strong>The programming language used to process the data is written with python 3. </p> <p>In this work, a Timepix3 detector is used to perform 4D STEM experiments where instead of a frame based detection, a event driven approach is used. The experiments where performed on a probe-corrected FEI Themis Z with an acceleration voltage of 60 and 200 kV. The files provided show how to get the figures from the publication starting from the raw .t3p data. All intermediate data is added to reduce the computing time and give the ability to retrieve the images without the need to perform all the calculations.</p> <p>The .yml file contains the necessary package needed to perform the data analysis. For the single-sideband ptychographic reconstruction, the gitlab repository https://gitlab.com/pyptychostem/pyptychostem is used.</p> <p> </p> <p> </p> <p> </p>
Dataset: Toolset for Collecting Shell Commands and Its Application in Hands-on Cybersecurity Training
<p>This repository contains supplementary materials for the following conference paper:</p> <p>Valdemar Švábenský, Jan Vykopal, Daniel Tovarňák, Pavel Čeleda.<br> <em>Toolset for Collecting Shell Commands and Its Application in Hands-on Cybersecurity Training.</em><br> In Proceedings of the 51st IEEE Frontiers in Education Conference (FIE 2021).<br> <a href="https://doi.org/10.1109/FIE49875.2021.9637052">https://doi.org/10.1109/FIE49875.2021.9637052</a></p> <p>Preprint available at: <a href="https://arxiv.org/abs/2112.11118">https://arxiv.org/abs/2112.11118</a></p> <ol> </ol> <p><strong>How to cite</strong></p> <p>If you use or build upon the materials, please use the BibTeX entry below to cite the original paper (not only this web link).</p> <p>Some of the linked repositories have their separate citation entry; please use that one as well, if possible.</p> <pre><code>@inproceedings{Svabensky2021toolset, author = {\v{S}v\'{a}bensk\'{y}, Valdemar and Vykopal, Jan and Tovar\v{n}\'{a}k, Daniel and \v{C}eleda, Pavel}, title = {{Toolset for Collecting Shell Commands and Its Application in Hands-on Cybersecurity Training}}, booktitle = {Proceedings of the 51st IEEE Frontiers in Education Conference}, series = {FIE '21}, location = {Lincoln, NE, USA}, publisher = {IEEE}, address = {New York, NY, USA}, month = {10}, year = {2021}, pages = {1--9}, numpages = {9}, isbn = {978-1-6654-3851-3}, url = {https://doi.org/10.1109/FIE49875.2021.9637052}, doi = {10.1109/FIE49875.2021.9637052}, }</code></pre> <p><strong>Structure of the repository</strong></p> <p>We share four types of content described below. Each of the four types of materials includes:</p> <ul> <li>a link to an up-to-date GitLab repository, which may contain possible future revisions and error corrections, and</li> <li>a ZIP archive here on Zenodo that serves as a snapshot of the state when the article was published.</li> </ul> <p><strong>Attached content</strong></p> <ol> <li><strong>Logging toolset.</strong> It is implemented in the form of <a href="https://www.ansible.com/">Ansible</a> roles and consists of three separate projects: for <a href="https://gitlab.ics.muni.cz/muni-kypo/ansible-roles/sandbox-logging-bash">Bash logging</a>, <a href="https://gitlab.ics.muni.cz/muni-kypo/ansible-roles/sandbox-logging-msf">Metasploit logging</a>, and <a href="https://gitlab.ics.muni.cz/muni-kypo/ansible-roles/sandbox-logging-forward">log forwarding within the sandbox</a>.</li> <li><strong>Sample training.</strong> To quickly test the toolset, instantiate the exemplary cybersecurity game called <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/games/junior-hacker">Junior hacker training</a>. It already deploys the logging; no further setup is needed.</li> <li><strong>Dataset.</strong> The data collected with the toolset are available at <a href="https://gitlab.ics.muni.cz/muni-kypo-trainings/datasets/commands">this repository</a>, which is being gradually updated with new data. Attached is a subset of this repository that includes the data analyzed in the paper.</li> <li><strong>Analytical tools.</strong> To analyze the data, you can either use the attached Python scripts, or instantiate <a href="https://gitlab.ics.muni.cz/muni-kypo/tools/commands-elk">this project for ELK</a>.</li> </ol>
Barriers to Melt Ascent in the Lithosphere of Io with Applications to Heat Pipe Formation: MELTS Calculations
<p>MELTS input and output datafiles for "Barriers to Melt Ascent in the Lithosphere of Io with Applications to Heat Pipe Formation." Each zip file corresponds to one dataset with a set resurfacing rate and a choice of nepheline allowed or nepheline suppressed. Each data set is organized first by lithosphere thickness, then mantle potential temperature.</p> <p>PTpath.txt is the text file read by MELTS to create the geotherm P-T path in the crystallization calculation. Residue.melts is the Residual solid composition after the melting calculation. Liquid.melts is the liquid composition that enters the crystallization calculation. PBRate.txt is a two column text file which is a record of crystallization rate with depth.</p> <p>The output_1 folder contains the MELTS output files for the decompression melting calculation. The output_2 folder contains the MELTS output files for the crystallization in the lithosphere calculation.</p> <p>Associated MATLAB functions for this work can be found here: https://github.com/joeschools/Io_PB (https://doi.org/10.5281/zenodo.5172975)</p>
Figure 1 in Intraspecific morphological tooth variability and geographical distribution: Application to the Savi's vole, Microtus (Terricola) savii (Rodentia, Arvicolinae)
Figure 1. Map of Italy with populations by region (see text for names of localities).
Figure 2 in Intraspecific morphological tooth variability and geographical distribution: Application to the Savi's vole, Microtus (Terricola) savii (Rodentia, Arvicolinae)
Figure 2. Morphometry of the first lower molar of Microtus (Terricola).
RailEnV-PASMVS: a dataset for multi-view stereopsis training and reconstruction applications
<p>A Perfectly Accurate, Synthetic dataset featuring a virtual railway EnVironment for Multi-View Stereopsis (RailEnV-PASMVS) is presented, consisting of 40 scenes and 79,800 renderings together with ground truth depth maps, extrinsic and intrinsic camera parameters and binary segmentation masks of all the track components and surrounding environment. Every scene is rendered from a set of 3 cameras, each positioned relative to the track for optimal 3D reconstruction of the rail profile. The set of cameras is translated across the 100-meter length of tangent (straight) track to yield a total of 1,995 camera views. Photorealistic lighting of each of the 40 scenes is achieved with the implementation of high-definition, high dynamic range (HDR) environmental textures. Additional variation is introduced in the form of camera focal lengths, random noise for the camera location and rotation parameters and shader modifications of the rail profile. Representative track geometry data is used to generate random and unique vertical alignment data for the rail profile for every scene. This primary, synthetic dataset is augmented by a smaller image collection consisting of 320 manually annotated photographs for improved segmentation performance. The specular rail profile represents the most challenging component for MVS reconstruction algorithms, pipelines and neural network architectures, increasing the ambiguity and complexity of the data distribution. RailEnV-PASMVS represents an application specific dataset for railway engineering, against the backdrop of existing datasets available in the field of computer vision, providing the precision required for novel research applications in the field of transportation engineering.</p> <p> </p> <p><strong>File descriptions</strong></p> <ul> <li><strong>RailEnV-PASMVS.blend</strong> (227 Mb) - Blender file (developed using Blender version 2.8.1) used to generate the dataset. The Blender file packs only one of the HDR environmental textures to use as an example, along with all the other asset textures.</li> <li><strong>RailEnV-PASMVS_sample.png</strong> (28 Mb) - A visual collage of 30 scenes, illustrating the variability introduced by using different models, illumination, material properties and camera focal lengths.</li> <li><strong>geometry.zip</strong> (2 Mb) - Geometry CSV files used for scenes 01 to 20. The Bezier curve defines the geometry of the rail profile (10 mm intervals).</li> <li><strong>PhysicalDataset.7z</strong> (2.0 Gb) - A smaller, secondary dataset of 320 manually annotated photographs of railway environments; only the railway profiles are annotated.</li> <li><strong>01.7z-40.7z</strong> (2.0 Gb each) - Archive of every scene (01 through 40).</li> <li><strong>all_list.txt, training_list.txt, validation_list.txt</strong> - Text files containing the all the scene names, together with those used for validation (validation_list.txt) and training (training_list.txt), used by MVSNet.</li> <li><strong>index.csv</strong> - CSV file provides a convenient reference for all the sample files, linking the corresponding file and relative data path.</li> </ul> <p> </p> <p><strong>Steps to reproduce</strong></p> <p>The open source Blender software suite (https://www.blender.org/) was used to generate the dataset, with the entire pipeline developed using the exposed Python API interface. The camera trajectory is kept fixed for all 40 scenes, except for small perturbations introduced in the form of random noise to increase the camera variation. The camera intrinsic information was initially exported as a single CSV file (<strong>scene.csv</strong>) for every scene, from which the camera information files were generated; this includes the focal length (<strong>focalLengthmm</strong>), image sensor dimensions (<strong>pixelDimensionX</strong>, <strong>pixelDimensionY</strong>), position, coordinate vector (<strong>vectC</strong>) and rotation vector (<strong>vectR</strong>). The STL model files, as provided in this data repository, were exported directly from Blender, such that the geometry/scenes can be reproduced. The data processing below is written for a Python implementation, transforming the information from Blender's coordinate system into universal rotation (<strong>R_world2cv</strong>) and translation (<strong>T_world2cv</strong>) matrices.</p> <p> </p> <pre><code class="language-python">import numpy as np from scipy.spatial.transform import Rotation as R #The intrinsic matrix K is constructed using the following formulation: focalLengthPixel = focalLengthmm x pixelDimensionX / sensorWidthmm K = [[focalLengthPixel, 0, dimX/2], [0, focalPixel, dimY/2], [0, 0, 1]] #The rotation vector as provided by Blender was first transformed to a rotation matrix: r = R.from_euler('xyz', vectR, degrees=True) matR = r.as_matrix() #Transpose the rotation matrix, to find matrix from the WORLD to BLENDER coordinate system: R_world2bcam = np.transpose(matR) #The matrix describing the transformation from BLENDER to CV/STANDARD coordinates is: R_bcam2cv = np.array([[1, 0, 0], [0, -1, 0], [0, 0, -1]]) #Thus the representation from WORLD to CV/STANDARD coordinates is: R_world2cv = R_bcam2cv.dot(R_world2bcam) #The camera coordinate vector requires a similar transformation moving from BLENDER to WORLD coordinates: T_world2bcam = -1 * R_world2bcam.dot(vectC) T_world2cv = R_bcam2cv.dot(T_world2bcam)</code></pre> <p> </p> <p>The resulting <strong>R_world2cv</strong> and <strong>T_world2cv</strong> matrices are written to the camera information file using exactly the same format as that of <a href="https://github.com/YoYo000/BlendedMVS">BlendedMVS developed by Dr. Yao</a>. The original rotation and translation information can be found by following the process in reverse. Note that additional steps were required to convert from Blender's unique coordinate system to that of OpenCV; this ensures universal compatibility in the way that the camera intrinsic and extrinsic information is provided.</p> <p>Equivalent GPS information is provided (<strong>gps.csv</strong>), whereby the local coordinate frame is transformed into equivalent GPS information, centered around the <a href="https://www.up.ac.za/eng4">Engineering 4.0 campus, University of Pretoria</a>, South Africa. This information is embedded within the JPG files as EXIF data.</p>
FIG. 1 in Évolution de la diversité des Brissopsis (Echinoida, Spatangoida) en Méditerranée depuis la « crise messinienne »: application paléoécologique aux B. lyrifera intragypses de Sorbas (SE Espagne)
FIG. 1. — Localisation géographique du bassin de Sorbas.
pgHMA: Application of the Heteroduplex mobility assay analysis in phylogenetics and population genetics
<p><span><span><span><span><span><span><span><span><span><span><span>The Heteroduplex mobility assay (HMA) has proven to be a robust tool for the detection of genetic variation. Here, we describe a simple and rapid application of the HMA by microfluidic capillary electrophoresis, for phylogenetics and population genetic analyses (pgHMA). We show how commonly applied techniques in phylogenetics and population genetics have equivalents with pgHMA: phylogenetic reconstruction with bootstrapping, skyline plots, and mismatch distribution analysis. We assess the performance and accuracy of pgHMA by comparing the results obtained against those obtained using standard methods of analyses applied to sequencing data. The resulting comparisons demonstrate that: (1) there is a significant linear relationship (R = 0.992) between heteroduplex mobility and genetic distance; (2) phylogenetic trees obtained by HMA and nucleotide sequences present nearly identical topologies; (3) clades with high pgHMA parametric bootstrap support also have high bootstrap support on nucleotide phylogenies; (4) skyline plots estimated from the UPGMA trees of HMA and Bayesian trees of nucleotide data reveal similar trends, especially for the median trend estimate of effective population size; and (5) optimized mismatch distributions of HMA are closely fitted to the mismatch distributions of nucleotide sequences. In summary, pgHMA is an easily-applied method for approximating phylogenetic diversity and population trends.</span></span></span></span></span></span></span></span></span></span></span></p>
Application of leaf size and leafing intensity scaling across subtropical trees
<p>Understanding the scaling between leaf size and leafing intensity (leaf number per stem size) is crucial for comprehending theories about the leaf costs and benefits in the leaf size–twig size spectrum. However, the scaling scope of leaf size vs. leafing intensity changes along the twig leaf size variation in different leaf habit species remains elusive. Here, we hypothesize that the numerical value of scaling exponent for leaf mass versus leafing intensity in twig is governed by the minimum leaf mass versus maximum leaf mass (<i>M</i><sub>min</sub> vs. <i>M</i><sub>max</sub>) and constrained to be ≤ -1.0.</p> <p>We tested this hypothesis by analyzing the twigs of 123 species datasets complied in the subtropical mountain forest. The standardized major axis regression (SMA) analyses showed the <i>M</i><sub>min</sub> scaled as the 1.19 power of <i>M</i><sub>m</sub><sub>ax</sub> and the -a (-1.19) was not statistically different from the exponents of <i>M</i><sub>min</sub><i> </i>vs. leafing intensity in whole data. Across leaf habit groups, the <i>M</i><sub>max</sub> scaled negatively and isometrically with respect to leafing intensity. The pooled data's scaling exponents ranged from -1.14 to -0.96 for <i>M</i><sub>min</sub> and <i>M</i><sub>max</sub> vs. the leafing intensity based on stem volume (LIV). In the case of <i>M</i><sub>min</sub> and <i>M</i><sub>max</sub> vs. the leafing intensity based on stem mass (LIM), the scaling exponents ranged from -1.24 to -1.04.</p> <p>Our hypothesis successfully predicts that the scaling relationship between leaf mass and leafing intensity is constrained to be ≤ -1.0. More importantly, the lower limit to scaling of leaf mass and leafing intensity maybe closely correlate with <i>M</i><sub>min</sub> vs. <i>M</i><sub>max</sub>. Besides, constrained by the maximum leaf mass expand, the broad scope range between leaf size and number may be insensitive to leaf habit groups in subtropical mountain forest.</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.