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1,600 results for “input”
Material parameter influence on solitary wave induced surface dilation: restart files, output files, videos and input scripts
<p>Restart files, output files, videos and input scripts for Material parameter influence on the expression of Solitary-Wave-Induced Surface Dilation (Frizzell and Hartzell, 2024). Input files can be find at the linked repository (https://github.com/efrizz-umd/SID_sensitivity). Restart files and output files were made with LIGGGHTS (https://www.cfdem.com/liggghtsr-open-source-discrete-element-method-particle-simulation-code) and videos were made with OVITO (https://www.ovito.org/).</p>
Code and input data related to "Integrated decarbonization of hard-to-abate industry utilizing biomass reliefs burden on power sector"
<p>Input data and code for the submitted article: "Integrated decarbonization of hard-to-abate industry utilizing biomass reliefs burden on power sector" <br><br>by Alissa Ganter <sup>1,†</sup>, Paula Baumann <sup>1,2,†</sup>, Veis Karbassi <sup>2</sup>, Giovanni Sansavini <sup>1,*</sup></p> <p><sup>1 </sup>Reliability and Risk Engineering, Institute of Process and Energy Engineering, ETH Zurich, Leonhardstrasse 21, 8092 Zurich, Switzerland</p> <p><sup>2 </sup>School of Business and Economics, RWTH Aachen University, Kackertstraße 7, 52072 Aachen, Germany</p> <p><sup>† </sup>These authors contributed equally</p> <p><sup>*</sup>Corresponding author: sansavig@ethz.ch</p>
Clinical Metaproteomics-discovery-input-files-iwc
Open the record for dataset details and reuse information.
Clinical Metaproteomics-database-generation-input-files-iwc
Open the record for dataset details and reuse information.
Model input for "A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"
<p>This dataset includes the model input files required to reproduce the simulations described in <em>"A high-resolution physical-biogeochemical model for marine resource applications in the Northern Indian Ocean (MOM6-COBALT-IND12)"</em>.</p> <p>When using these files to run the model, input files should be organized in a folder named <code>INPUT/</code> located within the directory where the model is executed. Additionally, key configuration files, such as <code>data_table</code>, <code>diag_table</code>, <code>field_table</code>, and <code>input.nml</code>should be placed in the main working directory.</p> <p>To manage file size, only a subset of the data is provided, covering the first year of the simulation period. Note that ERA5 atmospheric forcing data is not included in this dataset but can be accessed directly from the <a href="https://doi.org/10.24381/cds.adbb2d47" target="_new" rel="noopener">Copernicus Climate DataStore</a>.</p> <p> </p>
mapPat Input Tables Updates
<p>Precomputed input tables for mapPat. Updated periodically. mapPat is available at: <a href="https://github.com/F3rika/mapPat/tree/mapPat_Current" target="_blank" rel="noopener">GitHub.</a></p>
Simulation Input Data for "Quantifying acetylation-induced changes in the plant secondary cell wall structure and dynamics"
<p>This is the reduced data behind an upcoming manuscript investigating impact of acetylation on plant secondary cell wall. The data is taken directly from the directory structure that contains both the simulation and analysis, with excluded trajectory files and intermediate products to fit within the zenodo upload limit. The tar command used to generate this tarball was: </p> <p> </p> <pre><code>tar -zcvf Acetylatedcellwall.tar.gz --exclude="*BAK" --exclude="*dcd" --exclude="*poster*" --exclude="*old" --exclude="*out" --exclude="*vel" --exclude="*ppm" --exclude="*mp4" --exclude="*txt" --exclude="*tga" --exclude="*vmd" --exclude="*log" --exclude="fixed*png" --exclude="frame*png" --exclude="nonacetylation*png" . </code></pre>
input files for CROCO-WRF-OASIS3-MCT coupled model with a nest in CROCO
<p><span>This tar file contains all the input files needed to run the coupled model CROCO-WRF with OASIS3-MCT coupler with one 2-way nested domain in CROCO. This example configuration is based on the BENGUELA region at low resolution (this is the example of regional configuration of the CROCO model: see CROCO documentationa nd tutorials for more details</span>)</p>
input files for CROCO-WW3-WRF-OASIS3-MCT coupled model
<div>This tar file contains all the input files needed to run the coupled model CROCO-WW3-WRF with OASIS3-MCT coupler over an example configuration of the BENGUELA region at low resolution (this is the example of regional configuration of the CROCO model: see CROCO documentation and tutorials for more details)</div>
Input data and outputs for the OCALM project
<p>This repository contain a zip file with input and output data for an experiment with OCALM (<a href="https://github.com/fanavarro/ocalm">https://github.com/fanavarro/ocalm</a>):</p> <ul> <li>input <ul> <li>ontologies: ontologies used as input (FoodOn, LKIF, GeneOntology), together with their normalized form.</li> <li>text <ul> <li>food_text: natural language text corpus about food, including the original and the processed text.</li> <li>gene_text: natural language text corpus about genetics, including the original and the processed text.</li> <li>legal_text: natural language text corpus about legal topics, including the original and the processed text.</li> </ul> </li> </ul> </li> <li>results: the results derived from comparing each ontology with each natural language text corpus by using OCALM.</li> <li>NCBO_Recommender_results: the results of the NCBO Recommender with the same experiment performed by OCALM.</li> <li>analysis.R: R script to get figures summarizing the results.</li> </ul> <p>The SNOMED ontology and the medical text corpus used for input were not included due to licensing issues; however, the results are included in this repository.</p>
Input data for ENERWAT-GLOB - Water Treatment
<p>Input data for the 5 arcmin modelling framework calculating the global energy consumption of water treatment technologies (conventional drinking water treatment, desalination, wastewater treatment). Contains the following: 1) global data (folder "global_data"); and 2) inputs related to the various analyzed technologies (folder "water_treatment").</p> <p>Folder "global_data" contains: 1) 5 arcmin maps of world countries, HydroBASINS, population (total, rural, urban) and Gross Domestic Product; 2) country-level Total Gross Domestic Product and population statistics reported by the United Nations; 3) validation data, i.e. country-level energy consumption data from the U.S. Energy Information Administration for 2015 (primary energy and electricity), country-level estimates of energy consumption from Liu et al. (2016) and validation data compiled from literature reviewed by Chini et al. (2021). </p> <p>Folder "water_treatment": 1) folder "desalination" contains DesalData v2019 (edited to show bare country and region information as it is licensed data) and points used to model the increasing energy efficiency of Reserve Osmosis over time (Elimelech et al. (2011) as actual model input and Liu et al. (2016) for validation); 2) folder "dwt" contains data for conventional drinking water treatment in 2015, i.e. 5 arcmin municipal water withdrawals from PCR-GLOBWB 2 (Sutanudjaja et al. 2018) calculated by Wada et al. (2014), 5 arcmin water withdrawal sources modelled by PCR-GLOBWB 2, desalination plants online in 2015 with drinking water purposes from DesalData (edited as above), water access statistics from AQUASTAT (basic water access) and the World Bank (safely managed water access); 3) folder "wwt" contains data for wastewater treatment, i.e. global wastewater treatment plants from HydroWASTE (Ehalt Macedo et al. 2022) supplemented with data on Chinese wastewater treatment plants from Chen et al. (2019), 5 arcmin wastewater production and treatment rates from Jones et al. (2021) and data on energy consumption of 328 secondary wastewater treatment plants from Longo et al. (2016). </p> <p>For more information on the specific data sources, please refer to the manuscript "Global energy consumption of water treatment technologies". </p> <p> </p> <p> </p> <div> </div> <div> </div>
Input data for the Community Water Model (CWatM) - a regional dataset covering Israel and the Ayalon Basin
<p>This data was used for the paper: 'Wastewater matters: Incorporating wastewater treatment and reuse into a process-based hydrological model (CWatM v1.08)'. When using this data pleas cite the paper, alongside this Zenodo repository.</p> <p>Fridman, D., Smilovic, M., Burek, P., Tramberend, S., and Kahil, T. 2024. Wastewater matters: Incorporating wastewater treatment and reuse into a process-based hydrological model (CWatM v1.08). <em>Geoscientific Model Development Discussions</em>, 1-26. [Preprint]</p> <p><strong><span>Background</span></strong></p> <ul> <li>This dataset was developed as part of the IIASA WINTER project, aiming to run high resolution hydrological<br>simulations in the river basins in Israel (Water Futures and Solutions for Israel (WFaS-Israel) | IIASA).</li> <li>Conducting a high-resolution (30 arcseconds) simulation can rely on a mix of upscaled coarse global, high-resolution global, and local datasets. Specifically, Hanasaki et al. (2022) stress the importance of local water management and use data for hyper-resolution hydrologic simulations.</li> <li>The dataset covers the terrestrial area of Israel and the Palestinian Authority. It also includes the cross-border and upstream river basin in the neighboring countries Egypt, Jordan, Syria, and Lebanon. The selected river basins of Ayalon and Sorek are in the central coastal area of Israel and vary by topography, land cover, and water management. The Ayalon stream drains the western downslopes of the Judea and Samaria mountains and outlets into the Yarkon stream (to the North), which later reaches the Mediterranean Sea. The Sorek stream drains the hills around South-West Jerusalem and flows Westwards until reaching the Mediterranean Sea.</li> </ul> <p><strong><span>Further Information</span></strong></p> <ul> <li>For details about the contents of this dataset please refer to the readme.txt</li> <li>For details about the data soruces and processing please refer to the dataset_description.pdf</li> </ul> <p>In case of additional questions please do not hesitate to write to us fridman@iiasa.ac.at</p>
Input data for https://github.com/CCSB-DFCI/TF_isoforms_paper
<p>The input data for the code at https://github.com/CCSB-DFCI/TF_isoforms_paper which generates the figures in the paper "Widespread variation in molecular interactions and regulatory properties among transcription factor isoforms" to be published in Molecular Cell in 2025. This data directory is just to reproduce the analysis and figures. The supplementary data tables contain the original data in a cleaner format than these files.</p>
Code and input files Nov 2021
<p>Code and input files required to reproduce data.</p>
Screening of 6000 compounds for uncoupling activity: input parameters, the predicted uncoupling activity, as well as the results in respect to structural alerts
<p>Protonophoric uncoupling of phosphorylation is an important factor when assessing chemicals for their toxicity, and has recently moved into focus in pharmaceutical research with respect to the treatment of diseases such as cancer, diabetes or obesity. Reliably identifying uncoupling activity is thus a valuable goal. To that end, we screened more than 6000 anionic compounds for in-vitro uncoupling activity, using a biophysical model based on ab-initio COSMO-RS input parameters with the molecular structure as the only external input. We combined these results with a model for baseline toxicity (narcosis). Our model identified more than 1250 possible uncouplers in the screening dataset, and identified possible new uncoupler classes such as thiophosphoric acids. When tested against 423 known uncouplers and 612 known inactive compounds in the dataset, the model reached a sensitivity of 83% and a specificity of 96%. In a direct comparison, it showed a similar specificity than the structural alert profiler Mitotox (97%), but much higher sensitivity than Mitotox (47%). The biophysical model thus allows for a more accurate screening for uncoupling activity than existing structural alert profilers. We propose to use our model as a complementary tool to screen large datasets for protonophoric uncoupling activity in drug development and toxicity assessment.</p>
DatasetA input files
<p>DatasetA input files.</p>
Wildland-urban interface fire dynamics simulator input files for pyric tree spatial patterning interactions in historical and contemporary mixed conifer forests, California, USA
<p><span>Tree spatial patterns in dry coniferous forests of the western US, and analogous ecosystems globally, were historically aggregated, comprising a mixture of single trees and groups of trees. Modern forests, in contrast, are generally more homogeneous and overstocked than their historical counterparts. As these modern forests lack regular fire, pattern formation and maintenance is generally attributed to fire. Accordingly, fires in modern forests may not yield historically analogous patterns. However, direct observations on how selective tree mortality among pre-existing forest structure shapes tree spatial patterns is limited. In this study, we (1) simulated fires in historical and contemporary counterpart plots in a Sierra Nevadan mixed-conifer forest, (2) estimated tree mortality, and (3) examined tree spatial patterns of live trees before and after fire, and of fire-killed trees. Tree mortality in the historical period was clustered and density-dependent, because trees were aggregated and segregated by tree size before fire. Thus, fires maintained an aggregated distribution of tree groups. Tree mortality in the contemporary period was widespread, except for dispersed large trees, because most trees were a part of large, interconnected tree groups. Thus, post-fire tree patterns were more uniform and devoid of moderately sized tree groups. Post-fire tree patterns in the historical period, unlike the contemporary period, were within the historical range of variability identified for the western US. This divergence suggests that decades of forest dynamics without significant disturbances has altered the historical means of pyric pattern formation. Our results suggest that ecological silvicultural treatments, such as forest restoration thinnings, which emulate qualities of historical forests may facilitate the reintroduction of fire as a means to reinforce forest structural heterogeneity.</span></p>
Inputs and outputs for bilayers simulations in "Accurate Simulations of Lipid Monolayers Require a Water Model With Correct Surface Tension"
<p>Inputs and outputs file for simulations of POPC and DPPC Bilayers at various temperatures. For details see:</p>
Input data for estimating dimensionless number (Reynolds, Swimming and Strouhal number) of swimming penguin
<p>Propulsion performance of swimming and flying animals is often evaluated by using dimensionless numbers, such as the Strouhal and Reynolds numbers. They have been shown to allow better understanding of locomotion efficiency, using relatively simple approaches and avoiding overly complex computational models. Specifically, it has been reported that efficient propulsion is more likely to occur when Strouhal number values – estimated from propulsive frequencies and amplitudes – are within a relatively narrow range, depending on the corresponding Reynolds number, broadly expressing the fluid resistance to the animal motion. We have estimated both Strouhal and Reynolds numbers for seven species of penguins after analysing relevant kinematic data taken from the literature. The obtained values neatly indicate that, as expected, penguins employ efficient propulsion mechanisms. Additionally, by comparing these values with those for alcids – seabirds that can also fly – we have found that penguins swim at least as efficiently as alcids. However, we have also found that the swimming number – proportional to the product of Strouhal and Reynolds numbers – neatly correlates to the diving abilities of the considered species and apparently indicates, in a straightforward hierarchical manner, the gains in diving due to the loss of flying abilities. Within the penguin species, a clear correlation is also observed between diving performance and drag coefficient values.</p>
Input data for Open-data based carbon emission intensity signals for electricity generation in European countries -- top down vs. bottom up approach
<p>This dataset contains all necessary input data to reproduce the results of the paper "Open-data based carbon emission intensity signals for electricity generation in European countries -- top down vs. bottom up approach".</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.