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1,670 results for “forcing”
Extracting Session Keys From the Main Memory Using Brute-force and Machine Learning
<p>This dataset contains:</p> <ol> <li>Heap dump of three version of OpenSSH (V_7_9_P1, V_8_0_P1 and V_8_1_P1)</li> <li>Heap dump of two applications that uses TLS (lynx and curl)</li> <li>Network recording in format of pcap</li> <li>JSON file that contains the keys' information</li> </ol> <p>The source code is available at: https://github.com/smartvmi/SSH-TLS-key-extraction</p>
Forced degradation of five drug substances for meRgeION validation
<p>Drug substance is subjected to acid hydrolysis and oxidative stress against a baseline condition. The goal here is to profile and identify various degradation products of five APIs by setting stress conditions more severe than recommended storage, in order to further understand the underlying chemical mechanisms</p> <p>Non-targeted profiling in DDA mode was conducted for samples at Day 0 (1 sample) and Day 7 (3 samples for 3 conditions) on Orbitrap Fusion Lumos. Converted data files were submitted to MZMine for feature detection. Folder change of each feature under different conditions was calculated in excel. We then built a LC-MS/MS data processing pipeline in meRgeION that enables degradation product annotation by searching the spectral database <em>Drug+</em> (lib_drug_plus_matrix.RData) and mechanism understanding through FBMN. </p> <p> </p>
Subglacial hydrology modulates basal sliding response of the Antarctic ice sheet to climate forcing
<p><strong><em>Kazmierczak22_data.zip</em></strong><strong> contains the </strong><strong>dataset for the publication </strong><strong>« </strong>Subglacial hydrology modulates basal sliding response of the Antarctic ice sheet to climate forcing » <strong>and </strong><strong>the <em>MATLAB</em> codes used to create the figures appearing in the paper. For more details, please, open the <em>Read me.txt</em> file. </strong></p>
Remote internal wave forcing of regional ocean simulations near the U.S. West Coast
<p>This dataset contains data from ROMS simulations near the U.S. West Coast with realistic atmospheric forcing, tidal forcing and remote internal wave forcing. FS800b has remote internal wave forcing while FS800a does not have remote internal wave forcing at the open boundaries. The ROMS simulations have 4-km horizontal resolution and 60 sigma-layers. All data is along the local x- and y- axis of the rotated C-grid. Data is stored in netcdf format. We also include the altimeter dataset (see Buijsman et al., 2020; doi: 10.1016/j.ocemod.2020.101656) used in validating the semidiurnal internal tides.</p> <p>More details and context of this data can be found in the article: https://doi.org/10.1016/j.ocemod.2022.102154 . Please cite this article along with any use of this data.</p> <p>Siyanbola, O.Q., Buijsman, M.C., Delpech, A., Renault, L., Barkan, R., Shriver, J.F., Arbic, B.K., & McWilliams, J.C. (2023). Remote internal wave forcing of regional ocean simulations near the U.S. West Coast, <strong><em>Journal of Ocean Modelling</em></strong>, <strong><em>181</em></strong>, 18 p.</p>
Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming
<p>This dataset contains simulated and observed maps of surface temperature change over the satellite-era and domain averaged trends of tropospheric warming. The data accompanies software that was used to disentangle the forced and unforced components of tropical tropospheric temperature change. This work was documented in:</p> <blockquote> <p>Po-Chedley, S., J.T. Fasullo, N. Siler, Z.M. Labe, E.A. Barnes, C.J.W. Bonfils, B.D. Santer (2022): "Internal variability and forcing influence model-satellite differences in the rate of tropical tropospheric warming," Proceedings of the National Academy of Sciences, doi: 10.1073/pnas.2209431.</p> </blockquote> <p>The software is available at: https://github.com/LLNL/MDAS</p>
Micro-wear data from robotic use-wear experiments on force
<p>This data is the result of highly controlled experiments investigating the influence of force and duration on lithic micro-wear using a robot arm. Its targeted application is in archaeology and anthropology on the study of human tool use in the prehistory. The data consists of three zip files of html reports containing experimental data together with the microscopic images, and MATLAB scripts of the analysis methods. The images were collected at different stages of the experiment using a focus variation microscope, which produces true-color as well as topographic images. </p>
Figure 2. Forced change-Definition of Information
<p>The second basic kind of causal relation consists of changes that occur as a result of<br> externally working forces. According to Britannica force is “any action that tends to maintain or<br> alter the motion of a body or to distort it” [“Force”, in: Britannica 2009].<br> The mechanism of this change is simple: the influence of a force alters the affected object or its<br> movement: A cosmic body changes its orbit due to the influence of gravity produced by some<br> external entity. A bullet accelerates because of the powder explosion in the cartridge; glass breaks<br> when it falls to the floor etc. This form of causal mechanism is called forced change here.<br> Forced changes represent the simplest form of a causal relation between two objects, first of which<br> ― a causal action of force ― delivers energy causing the effectual change of a stable object. Forced<br> change is represented as follows (Figure 2).</p>
Figure 7. Obstacle force (repulsive potential) and goal force obstacle force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>Since the motion trajectory of UAV is divided into several median points that the UAV<br> should reach them one by one in a sequence the output obtained after the execution of AI will be a<br> set of position and velocity vectors. So the task of the trajectory will be to guide the UAV through<br> the obstacles to reach the destination. The routine used for this purpose is the potential field method<br> (also an alternative new method is in progress which models the UAV motion through opponents<br> same as the owing of a bulk of water through obstacles) [5]. In this method, different electrical<br> charges are assigned to UAV, obstacles, and the destination. Then by calculating the potential field<br> of this system of charges a path will be suggested for the UAV.</p>
Figure 6. Goal force-Design and Implementation of a Fully Autonomous UAV's Navigator Based on Omni-directional Vision System
<p>Since the motion trajectory of UAV is divided into several median points that the UAV<br> should reach them one by one in a sequence the output obtained after the execution of AI will be a<br> set of position and velocity vectors. So the task of the trajectory will be to guide the UAV through<br> the obstacles to reach the destination. The routine used for this purpose is the potential field method<br> (also an alternative new method is in progress which models the UAV motion through opponents<br> same as the owing of a bulk of water through obstacles) [5]. In this method, different electrical<br> charges are assigned to UAV, obstacles, and the destination. Then by calculating the potential field<br> of this system of charges a path will be suggested for the UAV. At a higher level, predictions can be<br> used to anticipate the position of the obstacles and make better decisions in order to reach the<br> desired vector. In our path- planning algorithm, an articial potential field is set up in the space; that<br> is, each point in the space is assigned a scalar value. The value at the goal point is set to be 0 and the<br> value of the potential at all other points is positive.</p>
A method to determine local aerodynamic force coefficients from fiber-resolved 3D flow simulations around a staple fiber yarn: simulation data
<p>This data set contains all set-up files and necessary scripts to run the simulations performed in the publication <a href="https://doi.org/10.1007/s11044-024-09992-2" target="_blank" rel="noopener">"A method to determine local aerodynamic force coefficients from fiber-resolved 3D flow simulations around a staple fiber yarn"</a>, published in Multibody System Dynamics.</p>
Atomic Force Microscopy image of coagulation factor Va in liquid
<p>Original raw and corrected AFM images of isolated coagulation factor Va (FVa). The image was acquired in liquid with an OTR8 cantilever using the peak force tapping mode of a multimode V microscope. The image size is 1 x 1 µm² with 1024 x 1024 pixels². The corrected image was obtained using Gwyddion (.gwy file) from the raw image file (.spm). This image was the experimental data used to assemble the A trimer and the two C domains of FVa using the AFMAssembly pipeline described in Chaves et al. (2014): http://dx.doi.org/10.1160/TH14-06-0481. The paper can be downloaded from the HAL repository (https://hal.archives-ouvertes.fr/hal-01146300v1).</p>
TIGAR experiments with background flow and Rossby wave forcing
<p>A barotropic version of the TIGAR (Transient Inertia-Gravity And Rossby wave dynamics) model has been run at T42 resolution to simulate the effects of subtropical Rossby waves on equatorial waves in the presence of a zonal background flow. TIGAR solves the rotating shallow water equations by applying Hough harmonics as spectral basis functions thereby enabling the analysis of Rossby and Inertia-gravity wave dynamics. More details of the model are available at: <a href="https://doi.org/10.1002/qj.4006">https://doi.org/10.1002/qj.4006</a> .</p>
METADATA for results of irradiation-induced complex DNA damage measurements using plasmid pBR322 along a typical Proton Treatment Plan at the MedAustron proton and carbon beam therapy facility (energy 137–198 MeV and Linear Energy Transfer (LET) range 1–9 keV/μm), by means of Agarose Gel Electrophoresis and DNA fragmentation using Atomic Force Microscopy (AFM)
Open the record for dataset details and reuse information.
Response of convectively coupled Kelvin waves to surface temperature forcing in aquaplanet simulations: data and code
<p>This is the data and code used for a journal paper entitled "Response of convectively coupled Kelvin waves to surface temperature forcing in aquaplanet simulations", written by Mu-Ting Chien and Daehyun Kim in 2024. This paper is in minor revision in the Journal of Advances in Modeling Earth System. The submitted paper is here: (https://essopenarchive.org/doi/full/10.22541/essoar.171322728.86206700/v1).</p>
Data for "Opposing changes in Indian Summer Monsoon Rainfall variability produced by orbital and anthropogenic forcing"
<p>The dataset for the CAM5 and LBM experiments is presented in the manuscript titled "Opposing changes in Indian summer monsoon rainfall variability produced by orbital and anthropogenic forcing. And the proxy data for the paper.</p>
CESM2 data for "Internal Wind Driven Ocean Circulation Variability Delays the Time of Emergence of Externally Forced Sea Surface Temperature Trends" - submitted to GRL
<p>CESM2 Experiment names:</p> <ul> <li>MDM = mechanically decoupled model (referred to as MDM in paper)</li> <li>FCM = fully coupled model (referred to as FCM in paper)</li> </ul> <p>Details for files cesm2.[experiment name].SST.noise.nc</p> <ul> <li>These files include the unfiltered time-varying SST noise </li> <li>"noise" refers to ensemble standard deviation (no 10-yr running mean has been applied) </li> <li>"SST" is the annual mean SST</li> <li>Time period is 1900-2014</li> </ul> <p>For the ensemble mean SST, see previously created Zenodo repository by Fu et al: https://zenodo.org/records/10484207</p> <p>For other ensemble mean variables, see previously created Zenodo repository by McMonigal et al: https://zenodo.org/records/7154374</p>
Model configuration files and forcing data for Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration
<p>This repository includes the model configuration files, input data, and forcing data used for simulations in Bieri et al. (2025) - <em>Implementing deep soil and dynamic root uptake in Noah-MP (v4.5): impact on Amazon dry-season transpiration.</em></p> <ul> <li>forcing.tar.gz - Compressed folder containing HRLDAS Noah-MP model forcing NetCDF files <ul> <li>These forcing files were derived from the NASA Global Land Data Assimilation System (GLDAS; Beaudoing et al. 2020)</li> <li>The compressed file contains 3-hourly forcing files for the entire simulation period (01 Jun 2000 to 31 Dec 2019)</li> </ul> </li> <li>wrfinput_d01 - NetCDF file used as HRLDAS input file in HRLDAS Noah-MP simulations <ul> <li>Generated from WRF WPS (https://github.com/wrf-model/WPS)</li> </ul> </li> <li>Namelist files <ul> <li>namelist.hrldas.ROOT - Model namelist settings used for ROOT experiment</li> <li>namelist.hrldas.SOIL - Model namelist settings used for SOIL experiment</li> <li>namelist.hrldas.GW - Model namelist settings used for GW experiment</li> <li>namelist.hrldas.CONTROL - Model namelist settings used for FD (CONTROL) experiment</li> </ul> </li> </ul>
Data set for Plos One Article "Force sharing and other collaborative strategies in a dyadic force perception task"
<p>Data set for Plos One Article :</p> <p>Tatti, F., Baud-Bovy G. (2018) "Force sharing and other collaborative strategies in a dyadic force perception task". doi: 10.1371/journal.pone.0192754</p> <p>This study investigates how people might interact to extract information from the forces experienced while holding an object together. More specifically, the dyads (i.e. pairs formed two persons) participating to the study had to identify the direction of a small force applied to a jointly held object by a haptic device. This study included a condition where each participant responded independently and another one where the two participants had to agree upon a single negotiated response.</p> <p>The dataset (data.csv) contains the force produced by the haptic device and the average and standard deviation of the interaction force for all trials together with the responses of the participants. We also included the initial and final position of the haptic device and total distance traveled for each trial.</p> <p>The data are in comma separated text format and its description in a PDF document (readme.pdf).</p>
Idealized single-forcing GCM simulations with NCAR CESM
<p>This repository contains a set of single-forcing general circulation model (GCM) simulations run with the National Center for Atmospheric Research (NCAR) Community Earth System Model version 1.2 (CESM1.2). In each of these equilibrium simulations, one climate forcing was altered while all others were held constant at preindustrial levels, modeling the climate response to individual climate forcings. Simulations were run for obliquity (low and high obliquity), precession (four phases of the precession cycle with high eccentricity, and one simulation with eccentricity set to zero), half CO<sub>2</sub>, and LGM-sized ice sheets. The values chosen for the orbital simulations represent the extreme values of the past 900 thousand years.</p> <p>Simulations were run for at least 500 years, and forcings do not change from year to year. The uploaded files are 100 year (or longer) monthly climatologies. For most simulations, variables are presented for the atmosphere (atm), sea ice (cice), land (clm2), and river runoff (rtm). For the ice sheets simulation and its corresponding preindustrial simulation (0ka), only atmospheric output is presented; to inquire about other fields for those simulations, please contact Pedro DiNezio at pdn@ig.utexas.edu. Ocean files are not currently available; please contact Michael Erb at michael.p.erb@gmail.com if you are interested in those results.</p> <p> </p> <p>=== FORCINGS ===</p> <p>Preindustrial climate forcings for the 0ka_0urb simulation were set to the following values:</p> <p> - Obliquity: 23.44107°</p> <p> - Longitude of perihelion: 102.7242°</p> <p> - Eccentricity: 0.01670772</p> <p> - CO<sub>2</sub>: 284.7 ppm</p> <p> - Ice sheets: 0 ka BP</p> <p>The remaining simulations explore the climate response to a change in one of these forcings, with all other forcings set to preindustrial levels. Forcings are specified as follows:</p> <p> - lo_obliq and hi_obliq: Obliquity is set to 22.079° or 24.480°, respectively.</p> <p> - 0_AEQ, 90_WSOL, 180_VEQ, and 270_SSOL: Perihelion occurs at the NH autumnal equinox, winter solstice, vernal equinox, or summer solstice, respectively, with eccentricity set to 0.0493. This corresponds to a longitude of perihelion of 0°, 90°, 180°, or 270°, respectively.</p> <p> - ECC_0: Eccentricity is set to 0.</p> <p> - half_CO2: CO<sub>2</sub> is set to 142.35 ppm.</p> <p> - 21kaGlac: Ice sheets and sea level are set to Last Glacial Maximum (LGM) levels. Ice sheets come from the Paleoclimate Modelling Intercomparison Project Phase III (PMIP3) reconstruction, with ice shelves added in the western Labrador Sea.</p> <p>A note about preindustrial simulations: Some details of the model setup differ between the ice sheet simulation and the other simulations. Because of this, if a preindustrial control simulation is wanted for analysis, it is recommended that you use certain preindustrial simulations for certain comparisons, as follows:</p> <p>- 0ka_0urb (BG1850C5CN): Control simulation for all simulations except ice sheets.</p> <p>- 0ka (B1850C5): Control simulation for ice sheets (21kaGlac) simulation.</p> <p> </p> <p>=== NOTES ===</p> <p>More detailed description of these simulations, as well as results, can be found in the following papers:</p> <p>Erb, M. P., C. S. Jackson, A. J. Broccoli, D. W. Lea, P. J. Valdes, M. Crucifix, and P. N. DiNezio, in press: Model evidence for a seasonal bias in Antarctic ice cores. <em>Nature Communications.</em></p> <p>Bosmans, J. H. C., M. P. Erb, A. M. Dolan, S. S. Drijfhout, E. Tuenter, F. J. Hilgen, D. Edge, J. O. Pope, and L. J. Lourens, in press: Response of the Asian summer monsoons to idealized precession and obliquity forcing in a set of GCMs. <em>Quat. Sci. Rev.</em></p> <p>Bhattacharya, T., J. E. Tierney, and P. DiNezio, 2017: Glacial reduction of the North American Monsoon via surface cooling and atmospheric ventilation. <em>Geophys. Res. Lett.</em>, <strong>44</strong>, 5113-5122, doi:10.1002/2017GL073632.</p> <p>DiNezio, P. N., J. E. Tierney, B. L. Otto-Bliesner, A. Timmermann, T. Bhattacharya, N. Rosenbloom, and E. Brady, in review: Glacial changes in tropical climate amplified by the Indian Ocean.</p> <p>Note that the monthly data analyzed in these papers is sometimes converted to a common fixed-angular calendar in which every "month" corresponds to a 30° arc of Earth's orbit. This was done because changes in precession affect the speed at which Earth travels through different parts of its orbit according to Kepler's second law, complicating the comparison of months in different precession experiments. However, the results provided in this repository use the model's original fixed-day calendar.</p> <p>Computing resources (ark:/85065/d7wd3xhc) were provided by the Climate Simulation Laboratory at NCAR's Computational and Information Systems Laboratory, sponsored by the National Science Foundation and other agencies.</p> <p>If you use these simulations for research, please let the authors know.</p> <p>For a similar set of experiments using another model (GFDL CM2.1), see doi:10.5281/zenodo.1194480.</p> <p>Contact:<br> Michael Erb<br> Postdoctoral Scholar at Northern Arizona University<br> michael.p.erb@gmail.com</p>
Datasets for "Assessing satellite derived radiative forcing from snow impurities through inverse hydrologic modeling"
<p>This dataset contains observations and model output used in </p> <p>Matt, F. N., & Burkhart, J. F. (2018). Assessing satellite-derived radiative forcing from snow impurities through inverse hydrologic modeling. Geophysical Research Letters, 45. https://doi.org/10.1002/2018GL077133</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.