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1,670 results for “forcing”
Dataset of the publication: Atomic Force Microscopy beyond Topography: Chemical Sensing of 2D Material Surfaces through Adhesion Measurements
<p>Dataset of the publication: Atomic Force Microscopy beyond Topography: Chemical Sensing of 2D Material Surfaces through Adhesion Measurements</p> <p>DOI: 10.1021/acsami.3c19254</p> <p><span><span>I. Brotons-Alcázar, Jason. S. Terreblanche, S. Giménez-Santamarina, G. M. Gutiérrez-Finol, K. S. Ryder, A. Forment-Aliaga, E. Coronado, <em>ACS Appl. Mater. Interfaces</em> <strong>2024</strong>, <em>16</em>, 19711.</span> </span></p>
Data Grids for examples in Probe Particle Atomic Force Microscopy simulation program (ppafm)
<p>These files are used for running the examples for [ppafm](https://github.com/Probe-Particle/ppafm/) program.</p> <p>The grids are stored in in [.xsf](http://www.xcrysden.org/doc/XSF.html) and [.cube](https://paulbourke.net/dataformats/cube/) format.</p> <p>The data set compiles both the new examples used in paper [Advancing scanning probe microscopy simulations: A decade of development in probe-particle models](https://www.sciencedirect.com/science/article/pii/S0010465524002649) as well as older examples.</p> <p>Notice that the structure does not exactly reflect the directory structure in the [example folder of ppafm](https://github.com/Probe-Particle/ppafm/tree/main/examples) to prevent possible redudancy, but is instead flatenized and sorted by molecules.</p>
Shapefiles with the outline of maximum water spread resulting from the catastrophic release of the Kakhovka Reservoir after the destruction of the Kakhovka Hydroelectric Power Plant by Russian occupying forces
<p>The map is based on remote sensing data from Sentinel-2A (Processing Level L2A), dated June 8, June 13, and June 18, 2023, and Landsat-9 (Collection 2 Level-1), dated June 9, 2023. </p> <p>The following Sentinel-2 remote sensing data granules were used:<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TUS_20230608T132103.SAFE S2A_MSIL2A_20230608T084601_N0509_R107_T36TVS_20230608T132103.SAFE<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TWS_20230608T132103.SAFE<br>S2A_MSIL2A_20230608T084601_N0509_R107_T36TVT_20230608T132103.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TUS_20230613T102806.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TVS_20230613T102806.SAFE<br>S2B_MSIL2A_20230613T084609_N0509_R107_T36TWS_20230613T102806.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TUS_20230618T151602.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TVS_20230618T151602.SAFE<br>S2A_MSIL2A_20230618T084601_N0509_R107_T36TWS_20230618T151602.SAFE</p> <p>The following remote sensing data scenes from Landsat-9 were used:<br>LC09_L1TP_179028_20230609_20230610_02_T1<br>LC09_L1TP_179027_20230609_20230610_02_T1</p> <p>The contour of the maximum water spread was constructed using a method of manual visual interpretation of remote sensing data, relying on knowledge of the local terrain. We consciously chose not to use automated methods with water indices such as the Normalized Difference Water Index (NDWI) or the Modified Normalized Difference Water Index (MNDWI), as these do not effectively distinguish water surfaces in areas covered with forest or dense reed thickets. Similarly, we did not use the SRTM digital elevation model due to significant artifacts in the study area, where the model shows the height of the forest canopy instead of the ground surface in forested areas.</p> <p>For visual interpretation of Sentinel-2A remote sensing data, we used combinations of spectral bands NIR-Red-Green (8-4-3) and SWIR2-NIR-Green (12-8-3). For the visual interpretation of Landsat-9 remote sensing data, we used combinations of bands SWIR1-NIR-Red (6-5-4) and NIR-Red-Green (5-4-3). To better align the resolution of Sentinel-2A remote sensing data (10 m/pixel) with that of Landsat-9 (30 m/pixel), the latter's data was enhanced using the panchromatic channel (Band 8) through IHS-based pansharpening to 15 m/pixel. The pansharpening was performed using a custom bash script, utilizing command-line tools and utilities such as ImageMagick (<a href="https://imagemagick.org" rel="nofollow">https://imagemagick.org</a>), listgeo, and geotifcp (<a href="https://github.com/OSGeo/libgeotiff">https://github.com/OSGeo/libgeotiff</a>). To expedite the pansharpening process, both Landsat scenes were cropped to the study region and merged by bands using the gdal_translate and gdal_merge.py utilities from the GDAL library (<a href="https://gdal.org/" rel="nofollow">https://gdal.org/</a>). For convenience, the Sentinel-2A data tiles T36TUS, T36TVS, and T36TWS were also cropped and merged by bands using custom scripts available at <a href="https://doi.org/10.5281/zenodo.13205058" rel="nofollow">https://doi.org/10.5281/zenodo.13205058</a>.</p> <p>During visual interpretation, the above-mentioned remote sensing data were compared with satellite images acquired before the destruction of the Kakhovka Hydroelectric Power Plant. In particular, Landsat-9 remote sensing data were compared with Landsat-8 data from June 1, 2023, and Sentinel-2A data were compared with Sentinel-2B data from June 3, 2023.</p> <p>Repository files:<br>floodMax_UTM36N.zip — contains the shapefile in UTM36N projection (EPSG:32636);<br>floodMax_WGS84.zip — contains the shapefile in geographic coordinates in WGS84 (EPSG:4326);<br>floodMax_WGS84.geojson.zip — contains a GeoJSON file in WGS84 coordinates (EPSG:4326).</p> <div> <h1>Web version of the map</h1> </div> <p>The web version of the maximum water spread map is available at:<br><a href="https://yumoskalenko.github.io/floodmap_Kakhovka2023/" rel="nofollow">https://yumoskalenko.github.io/floodmap_Kakhovka2023/</a></p> <p> </p> <p>Embed code for the map on a webpage:</p> <div> <pre><code><iframe style="border: 1px solid black" src="https://yumoskalenko.github.io/floodmap_Kakhovka2023/index.html" marginwidth="0" marginheight="0" scrolling="no" width="100%" height="360" frameborder="0"></iframe> </code></pre> <div> </div> </div> <p><em><strong>This scientific and technical product was created by the scientists of the Black Sea Biosphere Reserve of the National Academy of Sciences of Ukraine during the implementation of research on the topic "Monitoring the condition of natural complexes of the Black Sea Biosphere Reserve (‘Chronicle of Nature’)" (state registration number 0121U109174).</strong></em></p>
Data sets, code, figures for Sensing force gradients with cavity optomechanics while evading backaction
<p>The directory contains data sets, code and figures for the published version of the research article Sensing force gradients with cavity optomechanics while evading backaction.</p>
Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity
<p><strong>Citation:</strong> Zhu, J., & Poulsen, C. J. (2021). Last Glacial Maximum (LGM) climate forcing and ocean dynamical feedback and their implications for estimating climate sensitivity. <em>Clim. Past</em>, <em>17</em>(1), 253–267. <a href="https://doi.org/10.5194/cp-17-253-2021">https://doi.org/10.5194/cp-17-253-2021</a></p> <p>Casename:</p> <ul> <li>FCM_PI: b.e12.B1850C5.f19_g16.iPI.01</li> <li>FCM_LGM: b.e12.B1850C5.f19_g16.i21ka.03</li> <li>SOM_PI: e.e12.E1850C5.f19_g16.PI.02</li> <li>SOM_GHG: e.e12.E1850C5.f19_g16.PI.21kaGHG.02</li> <li>SOM_ICE: e.e12.E1850C5.f19_g16.PI.21kaICE.02</li> <li>SOM_2CO2: e.e12.E1850C5.f19_g16.PIx2.02</li> <li>ATM_PI: f.e12.F1850C5.f19_g16.iPI.01</li> <li>ATM_GHG: f.e12.F1850C5.f19_g16.iPI.21kaGHG_ERF</li> <li>ATM_ICE: f.e12.F1850C5.f19_g16.iPI.21kaICE_ERF</li> <li>ATM_2CO2: f.e12.F1850C5.f19_g16.iPI.01.x2</li> </ul> <p><strong>Boundary condition files and the restart files are also provided as .zip files (bc.zip & rest.zip).</strong></p> <p><strong>Check out the Github repository for the setup of the LGM simulation</strong> (i.e., the entire CESM case folder): <a href="https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne">https://github.com/jiang-zhu/icesm1.2_lgm_cheyenne</a></p> <p><strong>[NEW IN V3] More monthly data for PMIP4 (cmorized) are provided (files starting with `PMIP4.NCAR.CESM1.2-FV2`).</strong></p>
Cranial muscle reconstructions quantify adaptation for high bite forces in Oviraptorosauria
<p>This dataset contains 3D models, data, and python scripts for the cranial and mandibular retrodeformations of oviraptorosaurian theropod species <em>Incisivosaurus gautheri</em>, <em>Citipati osmolskae</em>, <em>Khaan mckennai</em>, and <em>Conchoraptor gracilis (</em>along with reconstructed cranial musculature and gape analyses) supporting the paper ‘Cranial muscle reconstructions quantify adaptation for high bite forces in Oviraptorosauria' published in Scientific Reports (<a href="https://www.nature.com/articles/s41598-022-06910-4">Cranial muscle reconstructions quantify adaptation for high bite forces in Oviraptorosauria | Scientific Reports (nature.com)</a>.</p> <p>A single ZIP compressed folder contains two Blender (<a href="https://www.blender.org/">https://www.blender.org/</a>) .blend files for each species. The Blender files named '[Genus]_cranial_muscles.blend' contain the cranial and mandibular retrodeformed models and final volumetric muscle reconstructions (along with the curve muscle origin-insertion paths and shrinkwrapped curves the final muscle volumes were derived from). The Blender files named '[Genus]_gape_analysis.blend' contain the cranial and mandibular retrodeformed models and the muscle origin-insertion cylinder connections (attached to an animated armature) used to estimate optimal and maximum gape angle. The .txt files named '[Genus]_gape_script.txt' are python scripts used to run the gape analyses for each species. The .txt files names '[Genus]_strain_values.txt' are the output strain values of each muscle cylinder during the gape analyses for each species. [Unzipped total size 1.74GB].</p>
Data for figures in Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_
<p>Tar files containing gridded metrics, domain-wide metric means and confidence intervals, and rain-gauge reports used to generate figures in Kemp et al (2021).<br> <br> Citation:<br> </p> <p>Kemp, E M, J W Wegiel, S V Kumar, J V Geiger, D M Mocko, J P Jacob, and C D Peters-Lidard, 2021: A NASA-Air Force precipitation analysis for near-real-time operations. Submitted to _J Hydrometeor_.</p>
Quantitative in situ measurement of optical force along a strand of cleaved silica optical fiber induced by the light guided therewithin
<p>This dataset is associated with "Quantitative in situ measurement of optical force along a strand of cleaved silica optical fiber induced by the light guided therewithin", by Mikko Partanen, Hyeonwoo Lee, and Kyunghwan Oh, Photonics Res. 9, 2016 (2021) [https://doi.org/10.1364/PRJ.433995].</p> <p>It includes data files and Matlab (R2017b) scripts to allow for the replication of the figures. The data files give the oscillator mirror position in the units of nanometers measured at the rate of 200 times per second.</p>
Final project report: The Side Effects of Forced Online Distance Education (FODE)
<p>The outbreak of COVID-19 forced most universities into distance education. Three didacticians and researchers from the University of Maribor, Slovenia: Kosta Dolenc, Mateja Ploj Virtič and Andrej Šorgo formed a self-initiated initiative project group during the COVID-19 epidemic and started the project with the working title: The Side Effects of Forced Online Distance Education (FODE). The aim of the project, was to investigate the response of university teachers and students to the new situation.</p>
SD-WACCM-X 2020-2021 Sudden Stratospheric Warming Simulations with Constant Solar Forcing
<p>Simulation output from the Specified Dynamics version of the Whole Atmosphere Community Climate Model with thermosphere-ionosphere eXtension (SD-WACCM-X) for the 2020-2021 sudden stratospheric warming (SSW) event. Included output are the meridional and zonal winds, neutral temperatures, and total electron content (TEC). Simulations were performed using constant geomagnetic and solar forcing values of 70 solar flux units and Kp=0+. </p>
Data set: Land use and land cover change in a tropical mountain landscape of northern Ecuador: altitudinal patterns and driving forces
<p>Tropical mountain ecosystems are threatened by land use pressures, compromising their capacity to provide multiple ecosystem services. The analysis of landscape changes and their proximate driving forces is often qualitative and sectorial oriented, although local patterns and numerous interactions among socio-economic, demographic, and biophysical factors shape these socio-ecological systems. We characterized land use land cover (LULC) dynamics using Markov-chain probabilities by elevation and geographic settings and then, implementing the DPSIR holistic approach, we integrated them with a variety of freely available geospatial and temporal data into a Generalized Additive Model (GAM) to uncover the factors driving such landscape dynamics in a sensitive region of the northern Ecuadorian Andes. Our results demonstrated a dynamic and clear geographical pattern of distinct LULC transitions through time, explained by different combination of socio-economic factors, demographic and infrastructure variables and environmental parameters, from which topographic variables were the main drivers of change in this landscape. We found that deforestation of remnant native forest and agricultural expansion still occur in higher elevations, while land conversion toward anthropic environments, particularly significant expansion of floriculture and urban areas were observed in lower elevations to the east of the studied territory. Our findings also revealed an unexpected stability trend of paramo and a successional recovery of previous agricultural land to the west and center of the territory, which could be explained by agricultural land abandonment. However, the very low probability of persistence of montane forests found overall, highlights the greater threat to permanently lose the already vulnerable mountain native biodiversity. The methodological approach and our findings, demonstrating dynamic patterns through space and time and their explanatory drivers, could help local authorities and stakeholder to improve sustainably resource land management in vulnerable landscapes such as the tropical Andes in northern Ecuador.</p>
LiftWEC deliverable D4.4: Dataset from 2D experimental test campaign, with calculated hydrodynamic forces
<p><em>This dataset contains 2-dimensional wave tank testing data for a wave-driven rotating hydrofoil model. The model tested is composed of one or two hydrofoils rotating around a horizontal axis, perpendicular to the wave direction. The model was tested in a range of regular and irregular seas. The data contains measurements of the model in the wave tank including; wave measurement, rotor position, forces on the hydrofoils, and torque on the power take off. </em> <em>This data is the first of two sets of wave tank data generated for the LiftWEC H2020 research project. This first set consists of results for the device tested in 2D, while the second set will contain results for tests conducted in 3D. </em><em>This new version contains all data from version 1 of the first set, which consists of measurement from the experimental testing, plus results from the data analysis calculating the hydrodynamic forces. These forces are calculated by removing the static force and the centrifugal force. For a complete description of the test campaign, readers are directed to "LiftWEC Deliverable D4.4. </em> Report on physical modelling of 2D LiftWEC concepts <em>"</em></p>
Supplementary Data for "Interplay of river and tidal forcings promotes loops in coastal channel networks"
<p>This dataset contains supplemental data required to reproduce the results of the paper <em>Interplay of river and tidal forcings promotes loops in coastal channel networks</em> (in review at Geophysical Research Letters). We provide raw and extracted channel network data for 19 river deltas/coastal marsh sites. For each site, the following files are provided:</p> <p><strong>XXX_base.tif</strong> : the raw binary mask of the river channel network<br> <strong>XXX_clipper.shp</strong> (and associated .dbf, .prj, .qpj, and .shx files) : polygon(s) used to clip the raw mask<br> <strong>XXX_clipped.tif</strong> : the binary mask of the river channel network after being clipped by XXX_clipper.shp<br> <strong>XXX_filled.tif</strong> : the binary mask after filling islands via the method specified in the paper<br> <strong>XXX_inlet_nodes.shp</strong> (and associated .dbf, .prj, .qpj, and .shx files) : locations of the inlet nodes; used by RivGraph<br> <strong>XXX_shoreline.shp</strong> (and associated .dbf, .prj, .qpj, and .shx files) : location of the shoreline; used by RivGraph<br> <strong>XXX_links.json</strong> : GeoJSON file containing the geometries, connectivities, and widths of each link in the network<br> <strong>XXX_nodes.json</strong> : GeoJSON file containing the locations of each node of the network<br> <strong>process_XXX.py</strong> : the python script used to generate the above files</p> <p>All files listed below (except .py files) are georeferenced (i.e. can be opened with QGIS, ArcGIS or another GIS). Exceptions to the provided files include:</p> <p><strong>Barnstable</strong>: no "base.tif" is provided. Use "filled.tif".<br> <strong>GBM</strong>: some hand-cleaning was performed on "filled.tif".<br> <strong>Mackenize</strong>: "clipper.shp" is not provided, but "clipped.tif" is.<br> <strong>Mississippi</strong>: "clipper.shp" is not provided as the mask was made from a shapefile.</p> <p>In order to run process_XXX.py, the RivGraph package will need to be installed. Instructions<br> can be found at https://github.com/jonschwenk/RivGraph.</p>
Comparative analysis of Printed Circuit Boards with Surface and Embedded Components under Natural and Forced Convection
<p>Figures of heat distribution on PCB depending on the installation method (surface and embedded) and the speed of forced airflow.</p>
CH3CH2OCH3 conformer molecule 200 ps MD trajectory with energies and forces
<p>Forces and Energies for 200 ps MD trajectory of OCH2C2H6 molecule by xTB/GFN-2, NVE ensemble</p> <p>--------------------------------------------------</p> <p>MD params:</p> <p>temp = 300.0 K / 500.0 K<br> time = 200.0 ps<br> dump time = 10.0 fs<br> step = 0.4 fs</p> <p> </p> <p>SOAP params:</p> <p>species=["H", "C", "O"],</p> <p>periodic=False,</p> <p>rcut=5.0,</p> <p>sigma=0.5,</p> <p>nmax=5,</p> <p>lmax=5,</p> <p>average="outer" / "inner",</p> <p>crossover=True,</p> <p>dtype="float64",</p> <p>------------------------------------------------</p> <p>SOAP invariants were calculated with DScribe library (https://pypi.org/project/dscribe/1.2.1/)</p> <p> </p> <p>Energies and forces are in eV and eV/Angstrom</p> <p>Filenames are intended to be self-explanatory</p> <p>Dataset is intended to be used for machine learning algorithms tests.</p>
Polymorphism-aware estimation of species trees and evolutionary forces from genomic sequences with RevBayes
<p>Supplementary files of Polymorphism-aware estimation of species trees and evolutionary forces from genomic sequences with RevBayes by Borges, Boussau, Höhna, Pereira and Kosiol<br> </p>
Molecular dynamics simulations of an Ago2-RNA complex in different force fields
<p>This set of simulations contains 2us of Ago2-RNA complex in Amber ff14SB + OL3, ff19SB + OL3 and Desmond OPLS4 force fields. The polarizable force field AMOEBA has two simulation sets of 10*10ns and 2*100ns. The trajectories have been wrapped in the periodic box, centered around the protein atoms and the water molecules have been stripped out to conserve space using cpptraj. The trajectories are presented in Gromacs xtc-format which can be opened with the corresponding pdb file in multiple software tools such as VMD, PyMol or CaverAnalyst. The simulations are based on the crystal structure PDB ID 4W5O, where the missing loops were modeled using the Schrödinger Suite and missing nucleotides added manually. </p>
Quantitative electrostatic force tomography for virus capsids in interaction with an approaching nanoscale probe
<p>This repository contains the simulated data of a simple electrostatic model, based on the Poisson-Boltzmann equation, that quantifies the subnanometric electrostatic interactions between an AFM tip and a proteinaceous capsid (Zika Virus) from molecular snapshots. This allows us to describe the contributions of specific amino acids and atoms to the interaction force.</p> <p>The contains of this repository can be easily visualized through Jupyter Notebooks contained here:</p> <p>https://github.com/pyF4all/eTipVirusForce</p>
Dataset on Force Myography for Human Robot Interactions
<p>Force myography (FMG) is a contemporary, non-invasive, wearable technology that can read the underlying muscle volumetric changes during muscle contractions and expansions. The FMG technique can be used in recognizing human applied hand forces during physical human robot interactions (pHRI) via data-driven models. Several FMG-based pHRI studies were conducted in 1D, 2D and 3D during dynamic interactions between a human participant and a robot to realize human applied forces in intended directions during certain tasks. Raw FMG signals were collected via 16-channel (forearm) and 32-channel (forearm and upper arm) FMG bands while interacting with a biaxial stage (linear robot) and a serial manipulator (Kuka robot). In this paper, we present the datasets and their structures, the pHRI environments, and the collaborative tasks performed during the studies. We believe these datasets can be useful in future studies on FMG biosignal-based pHRI control design.</p> <p>The full description of this dataset, it’s components and structure are available in the data descriptor article submitted in MDPI Data. Please cite the following data descriptor article if you are using this open-access dataset for legitimate scientific research:</p> <p> U. Zakia, and C. Menon. Dataset on Force Myography for Human Robot Interactions. Data 2022, vol., no., pp, <a href="https://doi.org/10.3390/data7040050">doi:</a> (submitted on June 2022).</p> <p> </p>
Dual spring force couples yield multifunctionality and ultrafast, precision rotation in tiny biomechanical systems
<p>Small organisms use propulsive springs rather than muscles to repeatedly actuate high acceleration movements, even when constrained to tiny displacements and limited by inertial forces. Through integration of a large kinematic dataset, measurements of elastic recoil, energetic math modeling, and dynamic math modeling, we tested how trap-jaw ants (Odontomachus brunneus) utilize multiple elastic structures to develop ultrafast and precise mandible rotations at small scales. We found that O. brunneus develops torque on each mandible using an intriguing configuration of two springs: their elastic head capsule recoils to push and the recoiling muscle-apodeme unit tugs on each mandible. Mandibles achieved precise, planar, circular trajectories up to 49,100 radians/sec (470,000 rpm) when powered by spring propulsion. Once spring propulsion ended, the mandibles moved with unconstrained and oscillatory rotation. We term this mechanism "dual spring force couple" meaning that two springs deliver energy at two locations to develop torque. Dynamic modeling revealed that dual spring force couples reduce the need for joint constraints and thereby reduce dissipative joint losses, which is essential to the repeated use of ultrafast, small systems. Dual spring force couples enable multifunctionality: trap-jaw ants use the same mechanical system to produce ultrafast, planar strikes driven by propulsive springs and for generating slow, multi-degree of freedom mandible manipulations using muscles, rather than springs, to directly actuate the movement. Dual spring force couples are found in other systems and are likely widespread in biology. These principles can be incorporated into microrobotics to improve multifunctionality, precision, and longevity of ultrafast systems.</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.