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1,600 results for “input”
Input files for the simulation of KcsA-LAB-TEA, when LAB-TEA is placed in the channel cavity with no K+-ion
<p>Input files for the simulation of KcsA-LAB-TEA, when LAB-TEA is placed in the channel cavity. The simulation is performed without K<sup>+</sup>-ion. The inputs include,<br> 1. Input files for minimization (*min.in)<br> 2. Input files for heating (*h1.in, *h2.in, and *h3.in)<br> 3. Input files for equilibration (*eq*.in) and production runs (*prd*.in)<br> 4. Input files for the first trajectories start with file name, 1-lab-tea*<br> 5. Input files for the remaining different trajectories start with file name, lab-tea*</p>
Input files for the simulation of KcsA-K+-LAB-TEA, when both LAB-TEA and K+-ion are placed inside the channel cavity.
<p>Input files for the simulation of KcsA-K<sup>+</sup>-LAB-TEA, when both LAB-TEA and K+-ion are placed inside the channel cavity. The inputs include,<br> 1. Input files for minimization (*min.in)<br> 2. Input files for heating (*h1.in, *h2.in, and *h3.in)<br> 3. Input files for equilibration (*eq*.in) and production runs (*prd*.in)<br> 4. Input files for the first trajectories start with file name, 1-lab-tea-k<sup>+</sup>*<br> 5. Input files for the remaining different trajectories start with the file name, lab-tea-k<sup>+</sup>*</p>
Input files for SAPT0 calculation in PSI4
<p>Input files for the Lower order symmetry adapted-perturbation theory calculation (SAPT0) calculation on LAB-TEA - Amino acid systems. The calculation is performed in the quantum chemistry package PSI4.</p>
All input files for quantum calculations in VASP
<p>Here, all input files for quantum calculations in VASP have been uploaded. All the structure files extracted from MD simulations are in POSCAR format.</p>
Input files for https://github.com/ktravis213/FIREX-AQ-Agricultural-Emission-Factors
<p>Input files used for code available at https://github.com/ktravis213/FIREX-AQ-Agricultural-Emission-Factors associated with "Emission Factors for Crop Residue and Prescribed Fires in the Eastern US during FIREX-AQ" doi:2023JD039309R.</p>
SharpASP input files
<p>The zip file contains all 1470 CNFs used for experimental evaluation. The CNFs are in completion + copy format. The CNFs can be used as SharpASP input file without any processing.</p>
Opposing functions of glutamatergic inputs between the globus pallidus external segment and substantia nigra pars reticulata
<p>These are the source data of the publication entitled "Opposing functions of glutamatergic inputs between the globus pallidus external segment and substantia nigra pars reticulata".</p>
SAMC Model Inputs from: Predicting dispersal and conflict risk for wolf recolonization in Colorado
<p>The colonization of suitable yet unoccupied habitat due to natural dispersal or human introduction can benefit recovery of threatened species. Predicting habitat suitability and conflict potential of colonization areas can facilitate conservation planning.</p> <p>Planning for reintroduction of gray wolves (Canis lupus) to the U.S. state of Colorado is underway. Assessing which occupancy sites minimize the likelihood of human-wolf conflict during dispersal events and seasonal movements is critical to the success of this initiative.</p> <p>We used a spatial absorbing Markov chain (SAMC) framework, which extends random walk theory and probabilistically accounts for both movement behavior and mortality risk, to compare the viability of potential occupancy sites (public lands >500 km2 to minimally meet wolf pack range area). The SAMC framework produced spatially explicit predictions of wolf dispersal, philopatry, and conflict risk ahead of recolonization prior to reintroduction efforts. Our SAMC model included: 1) movement resistance based on terrain, roads, and housing density; 2) mortality risk and potential conflict (absorption) based on livestock presence, social tolerance, land ownership, and state boundaries; and 3) site fidelity based on habitat quality. Using this model, we compared 21 public land units by deriving predictions of: A) relative survival time outside each site, B) intensity of use and retention time within each site, and C) the probability of use on adjacent public lands. We also predicted and mapped potential conflict hotspots associated with each site.</p> <p>Among the units assessed, a complex of USFS Wilderness areas near Aspen, chiefly the Hunter-Fryingpan and Collegiate Peaks Wilderness areas, had the best overall rankings when comparing predictions of each metric. The area balances high-quality, well-connected habitat with relatively low livestock density and high social tolerance. </p> <p>Synthesis and applications: Our findings highlight the utility of the SAMC framework for assessing colonization areas and the capacity to identify locations for effective proactive management, especially of conflict-prone species. The flexibility of the SAMC framework enables predicting likely areas of philopatry and human-wildlife conflict using spatially-explicit metrics which can improve the success of conservation translocations and management of species with changing geographic extents.</p>
Distribution and Trend of Wind Power Input to Near-inertial Motions in the Southern Ocean
<p>This dataset includes the model data used in our paper entitled 'Distribution and Trend of Wind Power Input to Near-inertial Motions in the Southern Ocean'.</p>
Extended reference scenarios (ERS) files (including NLTE input and uncertainty perturbations)
<p>Extended reference scenarios (ERS) files (including NLTE input and uncertainty perturbations) for CAIRT studies.</p> <div> <p>The dataset includes essentially all the atmospheric input (based on ERS <span><a href="../records/10022129"><span>https://zenodo.org/records/10022129</span></a></span>) required to perform the GRANADA NLTE and KOPRA radiance simulations used in the CAIRT tomographic linear error analysis. This includes the input for unperturbed and perturbed NLTE and radiance simulations, the latter referring to perturbations of atmospheric parameters (CO2, O, T>120 km) and NLTE kinetic parameters. </p> </div> <div> <p>The data files are ascii files with the following naming <month>_<latitude>_<parameters>[_ngt][_hs][_hv][_<perturbation>].dat with parameters = pT, vmr, npar, ratio; _ngt for night (day has no suffix); _hs for ‘high solar’ (low solar has no suffix); _hv for high volcanic aerosol load (low volcanic has no suffix); <perturbations> refers to all perturbed atmospheric and NLTE parameters assessed in the error calculations (unperturbed has no suffix). </p> </div> <div> <p>parameter=ratio are the NLTE population files produced by GRANADA and read by KOPRA</p> <p>parameter=pT are pressure and temperature as in ERS (<span><a href="../records/10022129"><span>https://zenodo.org/records/10022129</span></a></span>) </p> </div> <div> <p>parameter=vmr includes vmr profiles of the ERS, but has in addition vmr profiles that are required for NLTE modeling (i.e., OH, HO2, N4S, N2D, H, O1D). It also includes some additional minor species not provided in the ERS which have been considered in the KOPRA radiative transfer calculations. Data sources for these profiles are specified within the files.</p> </div> <div> <p>parameter=npar are photolysis rates needed for the NLTE calculations, computed with the MIPAS preprocessor (invoking TUV).</p> </div>
Input files for konrad aerosol module
<p># Input files for konrad aerosol module</p> <p>The 1D RCE model konrad is available at<br> https://github.com/atmtools/konrad/</p> <p>These are alternative input files with<br> aerosol radiative properties.</p> <p>They were created and processed by<br> Clarissa Kroll, post-processed by Moritz<br> Günther.</p> <p>There is always one file for each month.<br> Extinction in the shortwave (ext_sw),<br> extinction in the longwave (ext_lw),<br> shortwave assymetry factor (g_sw) and<br> shortwave single scattering albedo (omega_sw)<br> must each be specified to konrad, e.g.</p> <p>```python<br> import konrad<br> aerosol = konrad.aerosol.VolcanoAerosol(<br> ...,<br> file_ext_lw="CMIP6/CMIP6_ext_lw_1991-9.nc",<br> file_ext_sw="CMIP6/CMIP6_ext_sw_1991-9.nc",<br> file_g_sw="CMIP6/CMIP6_g_sw_1991-9.nc",<br> file_omega_sw="CMIP6/CMIP6_omega_sw_1991-9.nc",<br> ...,<br> )<br> ```</p> <p>## CMIP6<br> - based on an updated version of the Pinatubo aerosol data set (PADS)<br> Stenchikov et al., 1998, DOI:10.1029/98JD00693<br> Driscoll et al., 2012, DOI: 10.1029/2012JD017607<br> Schmidt et al., 2013, DOI: 10.1002/jame.20014<br> - area-averaged over 23 N to 23 S</p> <p>## EVA<br> - created using the Easy Volcanic Aerosol idealized forcing generator<br> (Toohey et al. 2016, GMD, DOI:10.5194/gmd-9-4049-2016<br> - inputs to EVA were:<br> - eruption mass (in TgS) specified in file name<br> - eruption latitude: 0 degrees<br> - eruption date: June 1991<br> - area-averaged over 23 N to 23 S</p>
Input Files for Phospholipids are imported into mitochondria by VDAC, a dimeric beta barrel scramblase
<p>Input files for simulations in publication: Helene Jahn, Ladislav Bartoš, Grace I. Dearden, Jeremy S. Dittman, Joost C. M. Holthuis, Robert Vácha, Anant K. Menon: Phospholipids are imported into mitochondria by VDAC, a dimeric beta barrel scramblase</p>
Introduction to HPC: molecular dynamics simulations with GROMACS: input files
<p>Introduction to HPC: molecular dynamics simulations with GROMACS: input files</p>
LarsAckermann GMD Iceberg 2023 - model input and scripts
<p>This dataset contains input and forcing files to reproduce simulations with the iceberg extended Alfred Wegener Institute Earth System Model (AWI-ESM) published in https://doi.org/10.5194/egusphere-2023-2061</p><p>"fesom" contains dimensions, coordinates, and scaling factors for each simulation with interactive icebergs from the above-mentioned preprint. </p><p>"land_ice" contains ice sheet model output and ice sheet model basins to derive initial iceberg coordinates.</p><p>"restart_from" contains restart files from a longer pre-industrial spinup to restart the interactive iceberg spinup from.</p><p>"scripts" contains runscripts and config files to recreate the simulations as well as plotting scripts for recreating figures. Additional information on how to recreate the simulations is given in the runscripts. Some of the postprocessing and plotting scripts are derived from tripyview (<a href="https://github.com/FESOM/tripyview">https://github.com/FESOM/tripyview</a>). Hence, tripyview, as well as pyfesom2 (<a href="https://pyfesom2.readthedocs.io/en/latest/)">https://pyfesom2.readthedocs.io/en/latest/</a>), as given in "modules," need to be installed.</p>
Future Riverine Flood Impacts for NUTS3 regions in Europe: GLOFRIS input to DIFI
<p>This dataset presents results of current and future riverine flood impact data for NUTS3 regions in Europe. The dataset has been developed following the methodology presented in Tiggeloven et al. (2020) and Mortensen et al. (In Review).</p><p>This dataset can be used to as direct input for the DIFI model as described in Tesselaar et al. (2023).</p><p>References:</p><p>Mortensen, E., Tiggeloven, T., Haer, T., van Bemmel, B., Bouwman, A., Ligtvoet, W., & Ward, P.J.: The potential for various riverine flood DRR measures at the global scale. <i>Journal of Coastal and Riverine Flood Risk</i>, In Review.</p><p>Tesselaar, M., Botzen, W.J.W., Aerts, J.C.J.H., Tiggeloven, T. (2023). Flood insurance is a driver of population growth in European floodplains. <i>Nature Communications (provisionally accepted)</i></p><p>Tiggeloven, T., De Moel, H., Winsemius, H. C., Eilander, D., Erkens, G., Gebremedhin, E., ... & Ward, P. J. (2020). Global-scale benefit–cost analysis of coastal flood adaptation to different flood risk drivers using structural measures. <i>Natural Hazards and Earth System Sciences</i>, <i>20</i>(4), 1025-1044.</p>
Isosceles paper mouse E18 brain scRNA-Seq (Lebrigand et al.) data analysis input files
<p>Mouse E18 brain scRNA-Seq (Lebrigand et al.) data analysis input files for the Isosceles paper - more details can be found in the <a href="https://github.com/Genentech/Isosceles_Paper" target="_blank" rel="noopener">Isosceles_Paper</a> repository.</p>
Alzheimer's Disease - Input of Vitamin D With mEmantine Assay
ClinicalTrials.gov study NCT01409694. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Fatigability in Persons With Multiple Sclerosis: Inputs From Cognition, Walking and Coordination
ClinicalTrials.gov study NCT05412043. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Input of the Use of Indometacin in Gitelman Syndrome as Compared to Potassium Sparing Diuretics
ClinicalTrials.gov study NCT01146197. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Balance Rehabilitation With Modified Visual Input in Patients With Neuropathy
ClinicalTrials.gov study NCT03881930. IPD Sharing: Not stated. Countries: 1. Publications: 6.
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.