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1,291 results for “gap”
Supplementary dataset: Phoamtonic designs yield sizeable 3D photonic band gaps
<p>Supplementary Dataset of the publication "<a href="http://www.pnas.org/content/116/47/23480">Phoamtonic designs yield sizeable 3D photonic band gaps</a>" by the same authors:</p> <p>M. A. Klatt, P. J. Steinhardt, S. Torquato. <em>Proc. Natl. Acad. Sci. U.S.A.</em> <strong>116</strong>:23480–23486 (2019)<br> <a href="https://doi.org/10.1073/pnas.1912730116">https://doi.org/10.1073/pnas.1912730116</a></p> <p>Database S1 "Gap maps": Raw data of the gap maps listing the gap--mid-gap ratios together with the corresponding rod radii, volume fractions, or dilectric contrast.</p> <p>Database S2 "Configurations": All triply-periodic nets and networks analyzed in this study. The readme file explains the file formats.</p> <p>Database S3 "Parameters and Data": All parameters for the photonic calculations, raw output and post-processed data sets.</p>
Training Machine-Learned Density Functionals on Band Gaps
<p>This dataset contains atomic structures and molecular orbitals (computed with DFT using the PBE functional) for the systems studied in the paper "<a href="https://doi.org/10.1021/acs.jctc.4c00999">Training Machine-Learned Density Functionals on Band Gaps</a>." The <a href="https://github.com/mir-group/CiderPress">CiderPress code</a> can be used to analyze the data.</p> <p>Please see the README.md file for more details on the dataset, and refer to the original paper for details on methodology and funding acknowledgments.</p>
Northern Italy gap-filled MODIS Land Surface Temperature 1km daily
<p>Northern Italy Land Surface Temperature 1km daily Celsius gap-filled dataset, LST daily average, 2014 - 2018.</p> <p>The dataset is stored as a GRASS GIS project/mapset, in ZIP compressed format.</p> <ul> <li>Spatial resolution: 1 km</li> <li>Temporal resolution: 1 day</li> <li>Temporal extent: 2014-2018</li> <li>Units: Celsius</li> <li>Aggregation method: average</li> <li>Format: stored as a <a href="https://grass.osgeo.org/">GRASS GIS</a> 8+ project</li> <li>Software used: GRASS GIS 8.4.0</li> </ul> <p>Reference:<br><br>Metz, M.; Andreo, V.; Neteler, M. <em>A New Fully Gap-Free Time Series of Land Surface Temperature from MODIS LST Data</em>. Remote Sens. 2017, 9, 1333. <a href="https://doi.org/10.3390/rs9121333">https://doi.org/10.3390/rs9121333</a></p> <p>Original dataset license:<br>All data products distributed by NASA's Land Processes Distributed Active Archive Center (LP DAAC) are available at no charge. The LP DAAC requests that any author using NASA data products in their work provide credit for the data, and any assistance provided by the LP DAAC, in the data section of the paper, the acknowledgement section, and/or as a reference. The recommended citation for each data product is available on its Digital Object Identifier (DOI) Landing page, which can be accessed through the Search Data Catalog interface. For more information see: <a href="https://lpdaac.usgs.gov/products/mod09a1v006/">https://lpdaac.usgs.gov/products/mod09a1v006/</a></p> <p>Data provided by:</p> <p>mundialis GmbH & Co. KG<br>Koelnstrasse 99<br>53111 Bonn, Germany<br><a href="https://www.mundialis.de">https://www.mundialis.de</a></p>
Revised direct band gap and band parameters for AlP: hybrid-functional first-principles calculations vs. experiment
<p>Raw data and plotting scripts associated with the paper:<br><br>Cónal Murphy, Eoin P. O'Reilly and Christopher A. Broderick, "Revised direct band-gap and band parameters for AlP: hybrid-functional first-principles calculations vs. experiment", <em>APL Mater.</em> (2024) (undergoing revision)<br><br>Tyndall National Institute, Lee Maltings, Dyke Parade, University College Cork, Cork T12 R5CP, Ireland<br>School of Physics, University College Cork, Cork T12 YN60, Ireland</p>
Connexin 46 and connexin 50 gap junction channel properties are shaped by structural and dynamic features of their N-terminal domains
<p>Provided are reduced trajectories (.dcd) of the MD simulations -- each trajectory has 100 ps/frame with only protein and ion atoms remaining. Each set of trajectories are accompanied by a protein structure file (.psf) which is required to visualize the trajectories in VMD. Additionally, the z-trajectories of each intracellular ion (2 ps/frame) are provided in zipped files.<br> <br> To re-create the potentials of mean force (PMF) in Yue & Haddad et al., use the scripts provided with the paper (https://github.com/reichow-lab/Yue-Haddad_et-al.JPhysiol2021):<br> <br> </p> <pre><code class="language-bash">python3 GapJ_Analysis.py "Cx46_Ace_Produc-1_POT_*"</code></pre> <ul> <li>Choose a bin size in Å (3)</li> <li>Choose an output name (Cx46_Ace)</li> <li>Choose option (M)</li> <li>Choose time (ps) / frame (2)</li> <li>Choose column from file (1)</li> <li>Choose bin<sub>min</sub>/bin<sub>max </sub>(auto)</li> </ul>
Visualization and perception of data gaps in the context of Citizen Science projects: Gradation of Reporting Activity
<p>Online experiment about the influence of different numbers of levels of representation of reporting activity (total number of reports for all birds in the given time span and region) on proportion of correct responses and subjective evaluation of the task (NASA-TLX). Effects of representation with three (3) levels and effects of representation with five (5) levels are investigated. Two groups of members of ornitho.de were tested: experts - persons with access to database (more than 10 reports per month in average) and novices - persons without access to database (less than 10 reports per month in average). Two different tasks were given. The evaluation of statements on a map and the selection of grid fields that met a given requirement.</p>
Visualization and perception of data gaps in the context of Citizen Science projects: Video tutorial support
<p>Online experiment about the influence of the availability of a video tutorial on proportion of correct responses and subjective evaluation of the task (NASA-TLX). Two different tasks were given. The evaluation of statements on a map and the selection of grid fields that met a given requirement.</p>
An approximation of the spectral gap for the Laplace operator on SAut(F₅)
<p>This is the dataset accompanying <em>Aut(𝔽₅) has property (T)</em> paper (https://arxiv.org/abs/1712.07167). See Section 4 thereof for a detailed description of the content of the included files:</p> <blockquote> <p><code>tar --list -f ./oSAutF5_r2.tar.xz</code></p> <p><br> <code>oSAutF5_r2/<br> oSAutF5_r2/1.3/<br> oSAutF5_r2/1.3/full_2018-01-26T12:29:58.143.log<br> oSAutF5_r2/1.3/solver_2018-01-26T12:29:58.143.log<br> oSAutF5_r2/1.3/SDPmatrix.jld<br> oSAutF5_r2/1.3/lambda.jld<br> oSAutF5_r2/U_pis.jld<br> oSAutF5_r2/pm.jld<br> oSAutF5_r2/delta.jld<br> oSAutF5_r2/orbits.jld<br> oSAutF5_r2/preps.jld</code></p> </blockquote> <p>To replicate the computation of the spectral gap clone <code>1712.07167</code> repository first</p> <blockquote> <p><code>git clone https://git.wmi.amu.edu.pl/kalmar/1712.07167.git</code></p> </blockquote> <p>Then unpack the content of <code>oSAutF5_r2.tar.xz</code> into <code>1712.07167</code> folder.</p> <p>You need <code>julia-1.1.0</code> or above. In <code>julia</code>s REPL run</p> <blockquote> <p><code>using Pkg<br> Pkg.activate("1712.07167")<br> Pkg.instantiate()<br> Pkg.test("PropertyT")</code></p> </blockquote> <p>Finally, to verify that the Laplace operator on <em>SAut(𝔽₅)</em> (associated to the standard generating set) has spectral gap of at least <code>1.3</code> run from within <code>1712.07167</code> folder</p> <blockquote> <p><code>julia check_SAutF5.jl</code></p> </blockquote> <p>If You want to generate the multiplication table and other files on Your own delete all <code>*.jld</code> files from the <code>oSAutF5_r2</code> folder (but the ones in <code>1.3</code> folder) and run the same command again. Note: You need at least <code>20</code>GB of RAM and spare a few hours of Your CPU.</p> <p>We reproduce the content of <code>check_SAutF5.jl</code> script below.</p> <blockquote> <p><code>using Pkg<br> Pkg.activate(".")<br> using Groups<br> using GroupRings<br> using PropertyT<br> using SparseArrays<br> using LinearAlgebra<br> using IntervalArithmetic<br> using JLD</code></p> <p><code>@show Threads.nthreads()<br> BLAS.set_num_threads(Threads.nthreads());</code></p> <p><code>G = SAut(FreeGroup(5))<br> pm = load("oSAutF5_r2/pm.jld", "pm");<br> RG = GroupRing(G, pm)<br> @info RG</code></p> <p><code>S_size = 80<br> # due to technical problems we are no longer able to load delta.jl on julia-1.0<br> Δ_coeff = SparseVector(maximum(pm), collect(1:(1+S_size)), [S_size; -ones(S_size)])<br> Δ = GroupRingElem(Δ_coeff, RG);<br> Δ² = Δ^2;</code></p> <p><code>@info "Loading solution"<br> λ₀ = load("oSAutF5_r2/1.3/lambda.jld", "λ")<br> P₀ = load("oSAutF5_r2/1.3/SDPmatrix.jld", "P");</code></p> <p><code>@info "Taking square root of P"<br> @time Q = real(sqrt(P₀));</code></p> <p><code>Q_aug, check_columns_augmentation = PropertyT.augIdproj(Interval, Q);<br> @show check_columns_augmentation<br> if !check_columns_augmentation<br> @warn "Columns of Q are not guaranteed to represent elements of the augmentation ideal!"<br> end</code></p> <p><code>@info "Computing SOS decomposition"<br> @time sos = PropertyT.compute_SOS(RG, Q_aug);</code></p> <p><code>residual = Δ² - @interval(λ₀)*Δ - sos;<br> @show norm(residual, 1)</code></p> </blockquote> <p> </p> <p>This research was supported in part by</p> <ul> <li>PL-Grid Infrastructure,</li> <li>grant 2015/19/B/ST1/01458, National Science Center, Poland</li> <li>grant 2017/26/D/ST1/00103, National Science Center, Poland.</li> </ul>
GAP general purpose interatomic potential for iron
<p>A general purpose Gaussian Approximation Potential (GAP) [1,2] for iron. The training database has been computed at the PBE level of theory [3] using the VASP code [4-7]. Fitting of the potentials was done using QUIP/GAP [1,2,8].</p> <p>The potential uses 2-body (distance_2b), 3-body (angle_3b) and SOAP (soap_turbo) [9,10] descriptors, as implemented in the TurboGAP code [11].</p> <p>All files necessary to use the potential QUIP/GAP (compiled with the TurboGAP libraries) are in <em>QUIP_files.tar.gz</em>, all files to use with TurboGAP in <em>TurboGAP_files.tar.gz</em>. The database used to train the potential is in <em>training_database.tar.gz</em>.</p> <p>More details can be found in this publication:</p> <blockquote> <p>Searching for iron nanoparticles with a general-purpose Gaussian approximation potential</p> <p>Richard Jana, Miguel A. Caro</p> <p>https://doi.org/10.1103/PhysRevB.107.245421</p> </blockquote> <p>The authors are grateful to the Academy of Finland for financial support under projects #321713 (R. J. & M.A. C.) and #330488 (M.A. C.), and CSC -- IT Center for Science as well as Aalto University's Science-IT Project for computational resources.</p>
Global monthly and 0.1° gap-free XCO2 dataset
<p>This dataset consists of the global continental continuous column-averaged dry-air mole fraction of carbon dioxide (XCO2, unit: parts per million i.e., ppm) covering the period from September 2014 to December 2020. </p> <p>The dataset is derived from the official OCO-2 XCO2 product and incorporates data from multiple sources, including CO2 concentration data, vegetation index data, and meteorological data. We utilized a machine learning approach to generate a monthly-scale gapless CO2 product with a spatial resolution of 0.1°, stored in GeoTiff format.</p> <p> </p> <p><strong>Please cite the following article when using the dataset:</strong></p> <p>L. Zhang, T. Li, J. Wu, H. Yang, Global estimates of gap-free and fine-scale CO2 concentrations during 2014–2020 from satellite and reanalysis data, Environment International (2023), doi: https://doi.org/10.1016/j.envint.2023.108057</p>
Ivy Gap GBM Clinical Data
<p><strong>Abstract:</strong></p> <p>The Ivy Glioblastoma Atlas Project represents a fundamental tool for investigating the cellular and molecular underpinnings of glioblastoma. It offers an accessible online atlas and database containing valuable clinical and genomic information, which will undoubtedly facilitate future studies on glioblastoma pathogenesis, diagnosis, and therapeutic approaches. Glioblastoma is a highly aggressive brain tumor with a bleak prognosis, and its intricate molecular and cellular characteristics have not been fully elucidated in relation to conventional diagnostic histologic features. The dataset provided is comprised of de-identified clinical data pertaining to both patients and tumors.</p> <p><strong>Inspiration:</strong></p> <p>This dataset was uploaded to UBRITE for GTKB project. </p> <p><strong>Acknowledgments:</strong></p> <p>Puchalski RB, Shah N, Miller J, et al. An anatomic transcriptional atlas of human glioblastoma. Science. 2018;360(6389):660-663. doi:10.1126/science.aaf2666</p> <p><strong>U-BRITE last update: </strong>07/28/2023</p>
Molecular dynamics simulation data 1: Structure of the connexin-43 gap junction channel in a putative closed state
<p>Molecular dynamics data for the manuscript Qi C.*, Acosta-Gutierrez S.*, Lavriha P., Othman A., Lopez-Pigozzi D., Bayraktar E., Schuster D., Picotti P., Zamboni N., Bortolozzi M., Gervasio F.L., Korkhov V.M. Structure of the connexin-43 gap junction channel in a putative closed state. eLife (2023) <a href="https://doi.org/10.7554/eLife.87616.2">https://doi.org/10.7554/eLife.87616.2</a></p> <p>The dataset includes:</p> <p>1. The starting coordinates, topology, MD inputs</p> <p>2. Production run gromacs trajectories for the Cx43 gap junction channel</p>
Machine learning methods for gap-filling in greenhouse gas emissions databases
<p>Datasets for use with code related to "Machine learning methods for gap-filling in greenhouse gas emissions databases" manuscript submitted to the Journal of Industrial Ecology. Code for using the datasets can be found at <a href="https://github.com/luke-scot/ml-ghg-databases">https://github.com/luke-scot/ml-ghg-databases</a>.</p>
The APO-K2 Catalog. I. ~7,500 Red Giants with Fundamental Stellar Parameters from APOGEE DR17 Spectroscopy and K2-GAP Asteroseismology
<p><strong>Abstract: </strong>We present a catalog of fundamental stellar properties for ~7,500 evolved stars, including stellar radii and masses, determined from the combination of spectroscopic observations from the Apache Point Observatory Galactic Evolution Experiment (APOGEE), part of the Sloan Digital Sky Survey IV (SDSS), and asteroseismology from K2. The resulting APO-K2 catalog provides spectroscopically derived temperatures and metallicities, asteroseismic global parameters, evolutionary states, and asteroseismically-derived masses and radii. Additionally, we include kinematic information from <em>Gaia</em>. We investigate the multi-dimensional space of abundance, stellar mass, and velocity with an eye toward applications in Galactic archaeology. The APO-K2 sample has a large population of low metallicity stars (~288 at [M/H] ≤ -1), and their asteroseismic masses are larger than astrophysical estimates. We argue that this may reflect offsets in the adopted fundamental temperature scale for metal-poor stars rather than metallicity-dependent issues with interpreting asteroseismic data. We characterize the kinematic properties of the population as a function of α-enhancement and position in the disk and identify those stars in the sample that are candidate components of the <em>Gaia-Enceladus</em> merger. Importantly, we characterize the selection function for the APO-K2 sample as a function of metallicity, radius, mass, νmax, color, and magnitude referencing Galactic simulations and target selection criteria to enable robust statistical inferences with the catalog.</p> <p><strong>Included Files:</strong></p> <ul> <li>The publicly available APO-K2 catalog, the is provided in the publication.</li> <li>The APO-K2 catalog without truncation to any numbers. </li> <li>The selection function relative density tables for the mass-radius parameter space. </li> <li>The selection function relative density tables for the metallicity-mass parameter space. </li> <li>The selection function relative density tables for the magnitude-color parameter space. </li> <li>The selection function relative density tables for the ν<sub>max</sub>-mag parameter space. </li> </ul>
Mechanism-Based Redesign of GAP to Activate Oncogenic Ras
<p>Additional material for Berta et al. <em>J. Am. Chem. Soc.</em> 2023, 145, 37, 20302–20310</p> <ul> <li>Paths: minimised structures corresponding to the the GTP hydrolysis mechanism.</li> <li>MD setup: equilibriated inputs for classical MD simulations for WT and mutant systems in CHARMM/NAMD format, set up for the charmm36m FF.</li> <li>NBO: outputs for Natrual Bonding Orbitals analysis carried out for TS and RS structures.</li> <li>GAP mutants: optimised TS and RS structures of the most promising designed GAP mutants.</li> </ul>
Dataset for study "Band gap engineering by cationic substitution in Sn(Zr1-xTix)Se3 alloy for bottom sub-cell application in solar cells"
<p>This dataset contains raw and processed data that were used to for the study entilted "Band gap engineering by cationic substitution in Sn(Zr1-xTix)Se3 alloy for bottom sub-cell application in solar cells". </p>
Half-hourly gap-filled Northern Hemisphere lake and reservoir carbon flux and micrometeorology, 2006 - 2015
This archive accompanies the manuscript New insights into diel to interannual variation in carbon emissions from lakes and reservoirs We synthesize 171 site-months (and 3,832 site-hours) of high-frequency flux measurements to quantify the magnitudes and temporal variability of direct CO2 fluxes from 13 lakes and reservoirs in the Northern Hemisphere (NH). Constraining short- and long-term variability is necessary to improve detection of temporal changes of CO2 fluxes in response to natural and anthropogenic drivers. These data were collected based on a workshop and open call for eddy covariance observations over lakes organized by Ankur Desai (UW-Madison), Timo Vesala (U Helsinki), and Malgorzata Golub (DKIT).
Seedling recruitment in artificial gaps
We examined the role of both intermediate-sized gaps and a dense shrub layer on subsequent tree seedling recruitment in a southern Appalachian deciduous forest. We monitored the density of tree seedlings in twelve artificial gaps and adjacent canopy controls under two contrasting understory conditions. Half of the experimental gaps were dominated by the dense, shade-producing shrub, Rhododendron maximum, while the remaining gaps were relatively open. Canopy gaps 20 m in diameter were created by girdling canopy trees, leaving standing dead trees with minimal disturbance to the understory. Density of first-year and first-year seedlings was monitored in transects extending from adjacent undisturbed forest through the experimental gaps.
Hourly gap microclimate measurements from the Coweeta Hydrologic Laboratory in 1993 and 1994
LTER Gap Project Overview Fact: Tree mortality at small spatial scales represents background levels of forest disturbance in the southern Appalachians, and is the dominant and most frequent initiator of change in terrestrial ecosystems. Hypothesis: Large-scale and rare episodic events (i.e., hurricanes, ice, etc.) may do more to influence tree replacement and stand composition in the long-run than do small scale tree mortality events. Overall Question: What is the ecological significance of small scale mortality events with respect to biotic and abiotic responses. Approach: Experimentally create typical (<300 m2) canopy gaps (girdling and herbicides) at two elevations in Rhododendron and non-Rhododendron areas. Measurements: -automated micro-environmental measurements (air and soil temperature), photosynthetically active radiation, %WC. -hemispherical photography -dendrometer bands and repeated measurements -population dynamics and seedling physiology -in situ closed core N mineralization and nitrification -small and large mammal seed and plant herbivory using exclosures Specific Questions: 1) How are microclimate and nutrient (N) cycling affected by small scale canopy removal? 2) What are the physiological and productivity responses of advanced regeneration? 3) What is the productivity response of non-gap-maker trees (dominants, co-dominant, and saplings)? 4) What strategy for recovery is most likely (seedling recruitment, sapling ingrowth, canopy closure)? 5) How do all of the above relate to/regulate each other? 6) What is the effect of elevation on response? 7) How do responses differ in Rhododendron versus non-Rhododendron areas?
Gap dendrometer band measurements at the Coweeta Hydrologic Laboratory from 1992 to 2000 (Circumference measurements)
Tree mortality at small spatial scales represents background levels of forest disturbance in the southern Appalachians, and is the dominant and most frequent initiator of change in terrestrial ecosystems. Large-scale and rare episodic events (i.e., hurricanes, ice, etc.) may do more to influence tree replacement and stand composition in the long-run than do small scale tree mortality events. What is the ecological significance of small scale mortality events with respect to biotic and abiotic responses? We experimentally created typical (<300 m2) canopy gaps (girdling and herbicides) at two elevations in Rhododendron and non-Rhododendron areas. The measurements in this study included automated micro-environmental measurements (air and soil temperature), photosynthetically active radiation, %WC, hemispherical photography, dendrometer bands and repeated measurements, population dynamics and seedling physiology, in situ closed core N mineralization and nitrification, and small and large mammal seed and plant herbivory using exclosures. Here are some specific questions relating to this study. How are microclimate and nutrient (N) cycling affected by small scale canopy removal? What are the physiological and productivity responses of advanced regeneration? What is the productivity response of non-gap-maker trees (dominants, co-dominant, and saplings)? What strategy for recovery is most likely (seedling recruitment, sapling ingrowth, canopy closure)? How do all of the above relate to/regulate each other? What is the effect of elevation on response? How do responses differ in Rhododendron versus non-Rhododendron areas?
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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