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1,600 results for “input”
Example data sets and input parameters for running various features in the Python code Dynpy
<p>This is a set of examples intended to be used with the Python code called Dynpy at <a href="https://zenodo.org/records/13241475">https://zenodo.org/records/13241475</a></p>
Supplementary material for "Rare earth elements and yttrium in Polish rivers and the input of anthropogenic gadolinium into the Baltic Sea"
<p>This dataset is presented and discussed in the research article “Rare earth elements and yttrium in Polish rivers and the input of anthropogenic gadolinium into the Baltic Sea” by Alemu et al. This manuscript will be submitted to Environmental Pollution and was prepared by the following authors: Addis Kokeb Alemu (1,2), Keran Zhang (1), David Ernst (1), and Michael Bau (1). </p> <p>1Critical Metals for Enabling Technologies – CritMET, School of Science, Constructor University, Campus Ring 1, 28759 Bremen, Germany</p> <p>2Department of Chemistry, College of Natural and Computational Sciences, University of Gondar, P.O. Box 196, Gondar, Ethiopia</p> <p> </p> <p> </p> <p>Table A1 includes the general information and data for all sampling stations and reference materials used. </p> <p>Figs. A1 and A2 show the concentrations of total Gd and anthropogenic Gd in samples from the Oder River (OD) and its major tributary, the Warta River (Wa), as well as the Vistula River (VS) and its major tributaries: San (Sn), Bug (BG), Brda (BR), and Narew (NR).</p>
Geoclaw inputs and processed outputs used in building tsunami ML surrogates for nearshore and onshore approximation
<p>This dataset contains some input files needed for GeoClaw tsunami simulation and post-processed outputs used for training the nearshore and onshore surrogates discussed in the article - Advancing nearshore and onshore tsunami hazard approximation with machine learning surrogates available as preprint (https://doi.org/10.5194/nhess-2024-72) and project repo - https://github.com/naveenragur/tsunami-surrogates</p> <p>The GeoClaw simulation requires the following files: </p> <p><strong><a href="../api/records/10817116/draft/files/geoclaw_dtopo_files.tar.gz/content" target="_blank" rel="noopener noreferrer">geoclaw_dtopo_files.tar.gz</a></strong><strong>: </strong>input bathymetry and topography elevation in asc format from multiple sources of datasets.</p> <p><strong><a href="../api/records/10817116/draft/files/dtopo_his.tar.gz/content" target="_blank" rel="noopener noreferrer">dtopo_his.tar.gz</a></strong>: tsunami displacement(dtopo) input files in tt3 format for historic earthquake scenarios.</p> <p><strong><a href="../api/records/10817116/draft/files/dtopo_typeA.tar.gz/content" target="_blank" rel="noopener noreferrer">dtopo_typeA.tar.gz</a>:</strong> tsunami displacement(dtopo) input files in tt3 format for DOE type A earthquake scenarios.</p> <p><strong><a href="../api/records/10817116/draft/files/dtopo_typeB.tar.gz/content" target="_blank" rel="noopener noreferrer">dtopo_typeB.tar.gz</a>:</strong> tsunami displacement(dtopo) input files in tt3 format for DOE type A earthquake scenarios.</p> <p>The machine learning training and testing requires:</p> <p><strong><a href="../api/records/10817116/draft/files/procesed_tsunami_data.tar/content" target="_blank" rel="noopener noreferrer">procesed_tsunami_data.tar</a>: </strong>the post-processed outputs for the three test location i.e. (1) the waveforms( time series recorded for the water level at the offshore and the nearshore gauge locations and (2) the maximum inundation depth recorded at the fixed grids onshore.</p> <p><a href="https://zenodo.org/uploads/14902165"><strong>tsunami-surrogates-nhess-2024-72.tar.gz</strong></a><a href="https://zenodo.org/uploads/14902165"> </a>: Model code, scripts and notebooks used in the study</p>
Input and Output Data for local earthquake tomography in the central Dead Sea Fault using PyVoroTomo
<p>Data to reproduce wave velocity models for the central DSF.</p> <p>eventsAndArrivals.h5 - 2 csv files (keys: events, arrivals)</p> <p>stations_sub.h5 - csv file containing station data</p> <p>gitter.csv - 1D velocity model by Gitterman et al. (2002)</p> <p>3d_Vp_Vs_VpVs_models.nc - velocity models for vp, vs, and vp/vs and their uncertainty.</p> <p>relocated seismicity.h5 - relocated seismicity, arrivals used, and stations used (keys: events, arrivals, stations)</p>
Inputs to an idealized numerical model based on MITgcm and model results
<p>This dataset contains input files for model runs used in Leng et al. 2021, "Temporal Evolution of a Geostrophic Current under Sea Ice: Analytical and Numerical Solutions" (to be submitted to JPO). The model used in this study is an idealized channel configuration based on the Massachusetts Institute of Technology general circulation model (MITgcm). Original source code is the MITgcm_c66m. Some representative output is also included.</p> <p>code: contains code configuration.</p> <p>input: contains parameters, initial conditions, forcing, etc.</p> <p>output: contains model output.</p>
Input Rockfall Simulations Täsch (RockyFor3D)
<p>Input data for the rockfall simulations with RockyFor3D in Täsch to derive disturbance probabilities and intensities (see Moos and Lischke, 2021 for details). The calculated disturbance probabilities and intensities per cell used in the forest simulations can be found in the text file (ta_distSmall.txt)</p>
Input data and code supporting the cod_v2 population estimates
<p>The <strong><em>model.zip</em></strong> file contains input data and code supporting the cod_v2 population estimates. The file<strong> <em>modelData.RData</em></strong> provides the input data to the JAGS model and the file<em> <strong>modelCode.R</strong></em> contains the source code for the model in the JAGS language. The files can be used to run the model for further assessments and as a starting point for further model development.</p> <p>The data and the model were developed using the statistical software <strong>R version 4.0.2</strong> (https://cran.r-project.org/bin/windows/base/old/4.0.2) and <strong>JAGS 4.3.0</strong> (https://mcmc-jags.sourceforge.io), a program for analysis of Bayesian graphical models using Gibbs sampling, through the R package <strong>runjags 2.2.0</strong> (https://cran.r-project.org/web/packages/runjags).</p>
Crossbow input data for ATC and FB aproach
<p>These data refears to the load, generation and already allocated capacities (AAC) for five countries in the South Eastern Europe. Also these data are reported for specific days in hourly basis as they are retrieved from Entso-E tranasparency platform. Simulation scenarios took place using this data for the ATC and FB aproach in SEE region in the frame of Crossbow project.</p>
Combined data file for Jilbert et al. "Anthropogenic Inputs of Terrestrial Organic Matter Influence Carbon Loading and Methanogenesis in Coastal Baltic Sea Sediments", Frontiers in Earth Science 9, 2021
<p>The datafile contains all the new raw data presented in the figures in the publication.</p>
Input files, plotting scripts, and figures for "Smoldering combustion in cellulose and hemicellulose mixtures: Examining the roles of density, fuel composition, oxygen concentration, and moisture content"
<p>This bundle of files contains all the <a href="http://reaxfire.com/trac/gpyro">Gpyro</a> input files, data, plotting scripts, and figures for "Smoldering combustion in cellulose and hemicellulose mixtures: Examining the roles of density, fuel composition, oxygen concentration, and moisture content". The README.txt file contains additional details about the contents.</p>
Carbon sequestration of a forested wetland receiving nutrient inputs - soil, tree and greenhouse gas data
<p><span><span><span><span><span><span><span><span><span><span><span>Here we describe a pilot wetland carbon project located 30 km west of New Orleans where measurements were taken in 2013 and 2018, and applied to the carbon offset methodology, "Restoration of Degraded Deltaic Wetlands of the Mississippi Delta" ("the ACR Methodology") published by the American Carbon Registry (ACR). Baseline emissions were modeled using values derived from scientific literature. Results indicate net sequestration rate of 619,727 tons carbon dioxide equivalent (CO<sub>2</sub>e) over the 40 year project duration, which equates to 16,527 t CO2-e/yr, if wetland greenhouse gases (GHGs) are included, and 200,143 t CO<sub>2</sub>e over 40 years, or 5,003 t CO2-e/yr, if wetland greenhouse gasses were conservatively omitted. A kriging exercise was carried out that modeled the tree and soil pools, which resulted in net sequestration of 723,375 t CO2-e over 40 years (annual mean 18,084 t CO2-e/yr) with greenhouse gases, and 262,472 t CO2-e over 40 years (annual mean rate 6,560 t CO2-e/yr) if greenhouse gases were omitted. Unfortunately, the project was withdrawn, prohibiting the issuance and eventual transaction of carbon credits, due to very large uncertainty estimates mostly associated with GHG emissions and the kriging approach as in situ sampling could not be conducted as required by the methodology.</span></span></span></span></span></span></span></span></span></span></span></p>
The ECS-FVCOM results and also the input variables of FMGDM, the initial particles, satellite pictures, and trajectories data
<p>The data for the manuscript: A Lagrangian-based Floating Macro<br> -algal Growth and Drift Model (FMGDM v1.0): application to the Yellow Sea green tides. <br> -----------------------------------------------------------------------------<br> Updata: 2021/03/18</p> <p>-----------------------------------------------------------------------------<br> Part 1: The ECS-FVCOM results and also the input variables of FMGDM<br> Part 2: The initial particles position imformation (lat, lon, depth)<br> Part 3: The satellite pictures of green tides in YS, 2014 and 2015</p> <p>Part 4: Drifters trajectories dataset (lat, lon)</p> <p> </p> <p>=============================================</p> <p>Version 5 updata: 2021/12/20</p> <p>Note: Modified and added some missing variables ('omega') in Part1,</p> <p> the results of ECS-FVCOM</p>
Simulation Input Data for "Molecular simulation of lignin-related aromatic compound permeation through Gram-negative bacterial outer membranes"
<p>This is the reduced data behind an upcoming manuscript investigating permeability across the outer membranes of Gram-negative bacteria. 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> <pre><code class="language-bash">tar --exclude="*BAK" --exclude="*dcd" --exclude="*xsc" --exclude="*vel" --exclude="*coor" --exclude="*csv" --exclude="*old" --exclude="*log" --exclude="*state" --exclude="*watpos/*npz" --exclude="*new*png" --exclude="*frame*png" --exclude="*ppm" --exclude="*mp4" --exclude="*bayesdata*npy" --exclude="*run.npy" -zcvf OM.tgz OuterMembrane</code></pre> <p>Within the OuterMembrane directory, there are 3 primary subdirectories.</p> <ul> <li><strong>Build </strong>contains the scripts and files to build the simulation systems, including the CHARMM-GUI output</li> <li><strong>Equilibrium</strong> contains the equilibrium simulation inputs and the analysis scripts (subdirectory <strong>Analysis</strong>)</li> <li><strong>REUS2</strong>, which has the replica exchange inputs and essential output. It also contains an <strong>Analysis</strong> subdirectory that carries out the analysis within the text.</li> </ul>
Input datasets for Euro-Calliope
<p><a href="https://euro-calliope.readthedocs.io/">Euro-Calliope</a> is a set of models of the European energy sytem and an automatic workflow to generate them. Euro-Calliope is based on a variety of input datasets each of which describes a certain aspect of the energy system, like generation potentials, historical generation, and energy demand. The workflow building all models does not contain data but instead automatically derives input data from their source where possible. For some input datasets this is not possible and this folder includes these datasets.</p>
Input data for Narayan et al 2022.
<p>Please use this input data to reproduce the results, figures for <strong>Evaluation of uncertainties in the anthropogenic SO2 emissions in the USA from NASA’s OMI point source catalog - </strong>by<strong> </strong>Kanishka Narayan, Steven J. Smith, Vitali E. Fioletov<sup> </sup>& Chris A. McLinden<span>, 2022</span></p>
Inputs and results of "A qualitative and quantitative analysis of open citations to retracted articles: the Wakefield 1998 et al.'s case"
<p>This repository contains the datasets and visualizations generated in our work: <strong>"A qualitative and quantitative analysis of open citations to retracted articles: the Wakefield 1998 et al.’s case"</strong>.</p> <p><strong>Note:</strong> the data are all contained inside the <strong><em>data.zip</em> </strong>file. You need to unzip the container to get access to all the files and directories listed below.</p> <p>The data (citations) gathered accompanied by their annotated characteristics are stored in <strong><em>data/</em>:</strong></p> <ul> <li><em><strong>"cits_features.csv": </strong></em>a dataset containing all the entities (rows in the CSV) which have cited the Wakefield et al. retracted article, and a set of features characterizing each citing entity (columns in the CSV). The features included are: DOI ("doi"), year of publication ("year"), the title ("title"), the venue identifier ("source_id"), the title of the venue ("source_title"), yes/no value in case the entity is retracted as well ("retracted"), the subject area ("area"), the subject category ("category"), the sections of the in-text citations ("intext_citation.section"), the value of the reference pointer ("intext_citation.pointer"), the in-text citation function ("intext_citation.intent"), the in-text citation perceived sentiment ("intext_citation.sentiment"), and a yes/no value to denote whether the in-text citation context mentions the retraction of the cited entity ("intext_citation.section.ret_mention").<br> <strong>Note: </strong>this dataset is licensed under a <a href="https://creativecommons.org/publicdomain/zero/1.0/legalcode">Creative Commons public domain dedication (CC0)</a>.</li> <li><em><strong>"cits_text.csv": </strong>this dataset stores the abstract ("abstract") and the in-text citations context ("intext_citation.context") </em>for each citing entity identified using the DOI value ("doi").<br> <strong>Note: </strong>the data keep their original license (the one provided by their publisher). This dataset is provided in order to favor the reproducibility of the results obtained in our work.</li> </ul> <p><strong>Topic modeling</strong></p> <p>We run a topic modeling analysis on the textual features gathered (i.e. abstracts and citation contexts). The results are stored inside the <em><strong>topic_modeling/</strong></em> directory. The topic modeling has been done using MITAO, a tool for mashing up automatic text analysis tools and creating a completely customizable visual workflow [1]. The topic modeling results for each textual feature are separated into two different folders, <em><strong>abstract/</strong></em> for the abstracts, and <em><strong>intext_cit/</strong></em> for the in-text citation contexts. Both the directories contain the datasets and visualizations generated using MITAO. </p> <p> </p> <p><strong>References</strong></p> <p>[1] Ferri, P., Heibi, I., Pareschi, L., & Peroni, S. (2020). MITAO: A User Friendly and Modular Software for Topic Modelling [JD]. PuntOorg International Journal, 5(2), 135–149. <a href="https://doi.org/10.19245/25.05.pij.5.2.3">https://doi.org/10.19245/25.05.pij.5.2.3</a></p>
Input data for Differential NicheNet analysis performed in the liver atlas paper Guilliams et al., Cell 2022
<p>Input data for Differential NicheNet analysis performed in the liver atlas paper Guilliams et al., Cell 2022</p> <p>See https://github.com/saeyslab/NicheNet_LiverCellAtlas and https://www.sciencedirect.com/science/article/pii/S0092867421014811</p>
SeisSol input files for the dynamic rupture scenarios based on the 2004 Sumatra-Andaman earthquake published in Madden et al. (2022) "The state of pore fluid pressure and 3D megathrust earthquake dynamics" JGR-Solid Earth
<p>This dataset contains the input files of the dynamic rupture scenarios from Madden, E. H., T. Ulrich and A.-A. Gabriel (2022), The State of Pore Fluid Pressure and 3-D Megathrust Earthquake Dynamics, Journal of Geophysical Research-Solid Earth, <a href="https://doi.org/10.1029/2021JB023382">https://doi.org/10.1029/2021JB023382</a>. (Earlier preprint available at: <a href="https://doi.org/10.1002/essoar.10508297.1">https://doi.org/10.1002/essoar.10508297.2</a>)</p> <p><strong>easi/yaml parameter files for the 6 scenarios studied: </strong><br> PAR_Sumatra_scen1new_gen.par, PAR_Sumatra_scen2new_gen.par, PAR_Sumatra_scen3new_gen.par, PAR_Sumatra_scen4new_gen.par, PAR_Sumatra_scen5new_gen.par, PAR_Sumatra_scen6new_gen.par</p> <p><strong>easi/yaml files setting initial on-fault friction, stress and pore fluid pressure conditions for the 6 scenarios studied: </strong>iniStress_Sumatra_scen1new.yaml, iniStress_Sumatra_scen2new.yaml, iniStress_Sumatra_scen3new.yaml, iniStress_Sumatra_scen4new.yaml, iniStress_Sumatra_scen5new.yaml, iniStress_Sumatra_scen6new.yaml<br> <br> <strong>easi/yaml file describing the rock elastic properties in all 6 scenarios:</strong> <br> matprops_Sumatra_2019_LVZ.yaml<br> <br> <strong>mesh file:</strong> <br> topo4_splays_fix9-14.1e6-28m.dtc1-v2-suma</p> <p> </p>
Benchmark set inputs for absolute binding free energy calculations of fragment optimisations
<p>Supplementary Information: "Evaluating the use of absolute binding free energy in the fragment optimization process"</p> <p>Provided here are the various scripts, input files, and results necessary to reproduce the outcomes of the above mentioned publication. Please see the provided README.md files for further information on the contents of this dataset.</p>
Dispersal probabilities and geography input for BioGeoBEARS
<p>Dispersal probabilities by assigned geographical time-slice and range data for use in BioGeoBEARS ancestral range reconstruction of Caribbean <em>Micrathena. </em></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.