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1,600 results for “input”
Input datasets for modeling snapshot structures of the NPC post mitotic assembly pathway
<p>This folder contains a reference coarse-grained model of the mature NPC and electron microscopy density maps that are used to restrain snapshot structures in the NPC assembly pathway model. These data sets are used to parameterize the native contact model and restrain the overall shape of NPC pre-pores at each snapshot along the assembly pathway. This data is provided in preparation of the PDB-dev deposition of the assembly pathway, accession to be determined.</p>
Darcy Flow Datasets for Paper "Variable-Input DeepONets for Operator Learning"
<p>This repository contains all datasets required to reproduce the Darcy Flow tests presented in the paper "Variable-Input DeepONets for Operator Learning".</p>
Navier-Stokes Dataset for Paper "Variable-Input DeepONets for Operator Learning"
<p>This repository contains all datasets required to reproduce the Navier-Stokes tests presented in the paper "Variable-Input DeepONets for Operator Learning".</p>
Archive data supporting the results in the paper: Increase in carbon input by enhanced fine root turnover in a long-term warmed forest soil
<p>This is the archive data supporting the results in the paper: Increase in carbon input by enhanced fine root turnover in a long-term warmed forest soil; submitted to the Journal Science of the Total Environment.</p>
FRACTESUS_UC_ANP-5_T0_MCT_input
<p>Fractesus project. Fracture test mini-CT. Raw data ANP-5. UC.</p>
Source model inputs and results - The July 2022 Mw 7.0 Northwestern Luzon Earthquake, Philippines
<p>This repository includes all the modelling inputs necessary to reproduce the results presented in 'Source Model and Characteristics of the 27 July 2022 M<sub>W</sub> 7.0 Northwestern Luzon Earthquake, Philippines' by Rimando et al. (2022) as follows: the fault geometries ('custom_fault_dip30_vaf,' 'custom_fault_dip30_abra'), the downsampled InSAR LOS deformation input ('statics'), the crustal model ('crust01'), and the run file which includes all the run parameters that were used ('luzon_run_clean'). </p> <p>Also included are the main outputs ('Abra_Results' and 'Vigan_Results') that Mudpy should produce using the abovementioned input files.</p> <p>Once an interested party downloads MudPy (MudPy v.1.0 was used for this study: https://github.com/dmelgarm/MudPy), these folders just have to be placed in their spots in the directory structure (outlined at https://github.com/dmelgarm/MudPy/wiki/) in order to reproduce the findings in Rimando et al. (2022).</p> <p><br> </p>
WRF-DART/RTTOV input and (processed) output files for GMD-2022-30
<p>The <em><strong>Exp-01.tar.gz ~ Exp-14.tar.gz (including true_processed.tar.gz and ctrl_processed.tar.gz) </strong></em>store the processed output files. Untarrring each of them will generate a list of subdirectories named after UTC time, txt files and netcdf files. (1) The subdirectories named after the UTC time. For example, 202008180200 denotes 02:00 UTC 18 August 2020. The subdirectory 202008180200 includes preassim_mean.nc and postassim_mean.nc files, which denote the prior and posterior estimates of cloud and non-cloud variables (and diagnosed variable). Both preassim_mean.nc and postassim_mean.nc include seven variables: CWP denotes the cloud water path (kg m<sup>-2</sup>). CWC denotes the mixing ratio (kg kg<sup>-1</sup>) of cloud hydrometers, including cloud droplet, rain, snow, ice, and graupel. P denotes the pressure (hPa), Re_CLOUD denotes the effective radius of liquid water clouds, QVAPOR denotes the water vapor mixing ratio, T denotes the perturbation potential temperature, U denotes the u-wind component, and V denotes the v-wind component. (2)Txt files which are the quantitative metrics of cloud and precipitation simulation errors. FSS_pre.txt and FSS_pos.txt store the fraction skill score for the prior and posterior estimates of cloud coverage. MAE_pre.txt and MAE_pos.txt store the Mean Absolute Error for the prior and posterior estimates of CWP. rain_TS_pre.txt and rain_TS_pos.txt store the TS scores for the prior and posterior estimates of precipitation. rain_MAE_pre.txt and rain_MAE_pos.txt store the MAE for the prior and posterior estimates of rain rate. (3) Netcdf files including mpe_mpi.nc, rain_pre.nc, and rain_pos.nc. mpe_mpi.nc includes the Mean Profile Error (MPE) for the prior and posterior estimates of non-cloud variables (Q, T, U, and V). rain_pre.nc and rain_pos.nc store the results for the prior and posterior estimates of rain rate (mm h<sup>-1</sup>).</p> <p>The <em><strong>nature_run.tar.gz</strong></em> file contains the WRF output files for the nature run.</p> <p>The <em><strong>model_codes.tar.gz</strong></em> file includes the source code of WRF-ARW (v4.1.1), WPS (v4.1), RTTOV (v12.3), and DART (Manhattan release v9.8.0).</p> <p>The <em><strong>input_file.tar.gz</strong></em> file includes the input namelist files for the WRF/WPS/DART models (tool) for the nature run, control run, and fourteen cycled data assimilation experiments. In addition, the input observation sequence files for the DART tool at each analysis time are provided.</p> <p>The <em><strong>Figure_script.tar.gz</strong></em> file includes the visualization scripts of Figure 1~Figure 16 in the manuscript.</p>
Climate change effects on deep-water corals – habitat suitability model input data
<p>Deep-water corals are protected in the seas around New Zealand by legislation that prohibits intentional damage and removal, and by marine protected areas where bottom trawling is prohibited. However, these measures do not protect them from the impacts of a changing climate and ocean acidification. To enable adequate future protection from these threats we require knowledge of the present distribution of corals and the environmental conditions that determine their preferred habitat, as well as the likely future changes in these conditions, so that we can identify areas for potential refugia.</p> <p>In this study, we built habitat suitability models for 12 taxa of deep-water corals using a comprehensive set of sample data and predicted present and future seafloor environmental conditions from an earth system model specifically tailored for the South Pacific. These models predicted that for most taxa there will be substantial shifts in the location of the most suitable habitat and decreases in the area of such habitat by the end of the 21st century, driven primarily by decreases in seafloor oxygen concentrations, shoaling of aragonite and calcite saturation horizons, and increases in nitrogen concentrations. The current network of protected areas in the region appear to provide little protection for most coral taxa, as there is little overlap with areas of highest habitat suitability, either in the present or the future. We recommend an urgent re-examination of the spatial distribution of protected areas for deep-water corals in the region, utilising spatial planning software that can balance protection requirements against value from fishing and mineral resources, take into account the current status of the coral habitats after decades of bottom trawling, and consider connectivity pathways for colonisation of corals into potential refugia.</p>
LaMEM source code and input files corresponding to Present‐day upper‐mantle architecture of the Alps: Insights from data‐driven dynamic modelling
<p>This repository contains LaMEM source code and input files for the models presented in Kumar, A., Cacace, M., Scheck-Wenderoth, M., Götze, H.-J., & Kaus, B. J. P. (2022). Present-day upper-mantle architecture of the Alps: Insights from data-driven dynamic modeling. Geophysical Research Letters, 49, e2022GL099476. https://doi. org/10.1029/2022GL099476</p>
Quantifying nitrogen deposition inputs to cropland: A national scale dataset from 1961 to 2020
<p>Nitrogen (N) deposition is one of the major inputs to cropland and consequently important for the estimation of N Use Efficiency (NUE) for crop production. However, the estimates for N deposition carry large uncertainty, and existing assessments of N budgets and NUE on agricultural land use different estimates of N deposition. To evaluate the uncertainties in existing methods for national scale N deposition estimation and assess their impacts on the resulting NUE estimation for countries around the world, we 1) reviewed existing methods and related data sources for quantifying N deposition inputs to crop production on a national scale; 2) identified the most up–to–date data sources and designed methods to quantify N deposition input to crop production on a national scale; 3) collected N deposition data from observation sites in major countries (e.g., UK, US, and China) to validate the estimated N deposition input; and 4) conducted sensitivity analysis to evaluate how the uncertainties in N deposition affect crop NUE assessment. As a result, we established four estimates for N deposition inputs on cropland for 251 countries around the world during 1961–2020 as combinations of two sets of N deposition maps (ACCMIP<sup>1</sup> and Wang <em>et al</em>.<sup>2-4</sup>) and two sets of cropland maps (HYDE<sup>5</sup> and LUH2<sup>6</sup>). The four products (1. <strong>AH</strong>: ACCMIP and HYDE, 2. <strong>AL</strong>: ACCMIP and LUH2, 3. <strong>WH</strong>: Wang <em>et al</em>. and HYDE, and 4. <strong>WL</strong>: Wang <em>et al</em>. and LUH2) show good agreement in N deposition estimates for the majority of countries, but have large differences in several Asian countries (e.g., China, India, and Pakistan), and the differences are mostly caused by the use of different N deposition maps. According to the comparison with the observation records in China, the deposition estimates based on Wang <em>et al</em>. show a better agreement with the observations. Hence, the authors recommend using product #4 <strong>WL</strong> (Wang <em>et al</em>. and LUH2) as the reference dataset for N deposition in the global assessments of N budgets by countries. </p> <p><strong>References:</strong></p> <ol> <li>Lamarque, J. F. <em>et al</em>. Multi-model mean nitrogen and sulfur deposition from the atmospheric chemistry and climate model intercomparison project (ACCMIP): Evaluation of historical and projected future changes. <em>Atmos. Chem. Phys</em>. <strong>13</strong>, 7997–8018 (2013).</li> <li>Shang, Z. <em>et al</em>. Weakened growth of cropland-N2O emissions in China associated with nationwide policy interventions. <em>Glob. Chang. Biol</em>. <strong>25</strong>, 3706–3719 (2019).</li> <li>Wang, Q. <em>et al</em>. Data-driven estimates of global nitrous oxide emissions from croplands. <em>Natl. Sci. Rev</em>. <strong>7</strong>, 441–452 (2020).</li> <li>Wang, R. <em>et al</em>. Global forest carbon uptake due to nitrogen and phosphorus deposition from 1850 to 2100. <em>Glob. Chang. Biol</em>. <strong>23</strong>, 4854–4872 (2017).</li> <li>Goldewijk, K. K., Beusen, A., Doelman, J. & Stehfest, E. Anthropogenic land use estimates for the Holocene - HYDE 3.2. <em>Earth Syst. Sci</em>. <em>Data</em> <strong>9</strong>, 927–953 (2017).</li> <li>Hurtt, G. C. <em>et al</em>. Harmonization of global land use change and management for the period 850-2100 (LUH2) for CMIP6. <em>Geoscientific Model Development </em><strong>13</strong>, (2020).</li> </ol>
Joint coding of visual input and eye/head position in V1 of freely moving mice dataset
<p><span>Visual input during natural behavior is highly dependent on movements of the eyes and head, but how information about eye and head position is integrated with visual processing during free movement is unknown, since visual physiology is generally performed under head-fixation. To address this, we performed single-unit electrophysiology in V1 of freely moving mice while simultaneously measuring the mouse's eye position, head orientation, and the visual scene from the mouse's perspective. From these measures, we mapped spatiotemporal receptive fields during free movement based on the gaze-corrected visual input. Furthermore, we found a significant fraction of neurons tuned for eye and head position, and these signals were integrated with visual responses through a multiplicative mechanism in the majority of modulated neurons. These results provide new insight into coding in mouse V1, and more generally provide a paradigm for performing visual physiology under natural conditions, including active sensing and ethological behavior.</span></p> <p><span></span></p>
Magnetique: input data and PostgreSQL database
<p>Magnetique: An interactive web application to explore transcriptome signatures of heart failure</p> <p>Supplementary dataset.</p> <p>- <a href="https://zenodo.org/api/files/200e02bb-a112-4ccb-a20f-a49dca9e579a/db_dump.sql.gz?versionId=f87954dd-af1e-47b2-be76-c5268ce63c4c">db_dump.sql.gz</a>: This is a daily [backup-dump](https://www.postgresql.org/docs/current/backup-dump.html) of the Magnetique database obtained on 18.07.2022 and shared for reproducibility purposes</p> <p>- Other files are required as input for the modeling steps detailed at https://github.com/dieterich-lab/magnetiqueCode2022</p> <p>Refer to https://shiny.dieterichlab.org/app/magnetique or contact the authors for details.</p>
Time-history datasets of input accelerations and experimental structural responses
<p>This repository contains the data of input accelerations and measured structural responses for ten different cyclic tests supported by JSPS KAKENHI (No. JP19H02286) and conducted by Makoto Ohsaki and others.</p>
Pharokka v 1.1.0 Benchmarking Input Output and Script
<p>This record contains all benchmarking input FASTA and output files for the Pharokka manuscript. Enterobacteria Phage Lambda (Genbank accession J02459), Staphylococcus phage SAOMS1 (Genbank accessionMW460250) and 673 crAss-like metagenome assembled phage genomes from the human gut taken from Yutin et al 2021 (https://doi.org/10.1038/s41467-021-21350-w). </p> <p>Each input was benchmarked with 3 runs: 1) Pharokka v1.1.0 using PHANOTATE as a gene predictor 2) Pharokka v1.1.0 specifying Prodigal as gene predictor, and 3) Prokka v1.14.6 using a version of the PHROGs HMM database that has been reformatted for use with Prokka found at the following URLs (http://s3.climb.ac.uk/ADM_share/all_phrogs.hmm.gz https://millardlab.org/2021/11/21/phage-annotation-with-phrogs/ ).</p> <p>Benchmarking was conducted on an Intel® Xeon® CPU E5-4610 v2 @ 2.30GHz specifying 16 threads for Pharokka and 16 cpus for Prokka. Coding densities for each contig in the Prokka output were calculated using the python script calc_gff_coding_density_prokka.py available in the tarball. Prokka coding densities are included in the Prokka_CDS_Coding_Densities directory.</p> <p> </p>
hectordata inputs
<p>The minted inputs used by hectordata to generate input tables and in files for hector, the simple climate model. For more information on hectordata visit https://github.com/jgcri/hectordata for more information on hector visit https://github.com/jgcri/hector. </p>
Input and Output files for "Contributions to Streamflow and Sea Level Rise in High Mountain Asia from 2003-2009 Glacier Recession"
<p>All files below were prepared by Collin B. Lawrence. </p> <p>All ARCIDs correspond to the HydroSHEDS Dataset for Asia (Lehner et al., 2008). (http://www.hydrosheds.org/)</p> <p>GLDAS data are from the Global Land Data Assimilation System (Rodell et al., 2004). (https://ldas.gsfc.nasa.gov/gldas/)</p> <p>Q_JJA_(model) contains three columns: 1) ARCID, 2) subsurface and surface runoff for that particular reach, and 3) accumulated subsurface and surface runoff. All flows are in m<sup>3</sup> s<sup>-1</sup>, and are averaged for the months of June, July, and August from the years 2003 – 2009. CLM, MOSAIC, NOAH, and VIC were the subset of models used from the Global Land Data Assimilation System (GLDAS).</p> <p>Q_annual_(model) contains three columns: 1) ARCID, 2) subsurface and surface runoff for that particular reach, and 3) accumulated subsurface and surface runoff. All flows are in m<sup>3</sup> s<sup>-1</sup>, and are yearly averages for the years 2003 – 2009. CLM, MOSAIC, NOAH, and VIC were the subset of models used from the Global Land Data Assimilation System (GLDAS).</p> <p>phi_i_JJA is the accumulated subsurface and surface runoff for the CLM, MOSAIC, NOAH, and VIC model average. The June, July, and August output was averaged over the years 2003 – 2009. Column 1 is ARCID and the accumulated runoff is expressed in m<sup>3</sup> s<sup>-1</sup>.</p> <p>phi_i_JJA_err is the standard error of the model mean in phi_i_JJA.</p> <p>phi_g_JJA contains the accumulated glacier recession flow in m<sup>3</sup> s<sup>-1 </sup>for the months of June, July, August from 2003 - 2009.</p> <p>phi_g_JJA_err is the standard error of the model mean in phi_g_JJA.</p> <p>phi_g_annual contains the annually averaged accumulated glacier recession flow in m<sup>3</sup> s<sup>-1 </sup>for 2003 – 2009.</p> <p>lambda.csv contains the fraction of streamflow from glacier recession.</p>
PCR-GLOBWB 2 input files version 2017_11_beta_1
<p>Global extent input files at 5 arc-minute resolution and 30 arc-minute resolution for PCR-GLOBWB 2 (https://github.com/UU-Hydro/PCR-GLOBWB_model; Sutanudjaja et al., 2017).</p> <p>PCR-GLOBWB (PCRaster Global Water Balance) is a large-scale hydrological model intended for global to regional studies and developed at the Department of Physical Geography, Utrecht University (Netherlands).</p> <p>contact: Edwin Sutanudjaja (E.H.Sutanudjaja@uu.nl).</p> <p>Sutanudjaja, E. H., et al.: PCR-GLOBWB 2: a 5 arc-minute global hydrological and water resources model, submitted to Geosci. Model Dev. Discuss., 2017</p>
Dataset for model input of WRF model for the paper:Modulation of Extratropical Cyclones by Previous Cyclones via the Sea Surface Temperature Anomaly over the Sea of Japan in Winter
<p>This is the dataset and code for generating the lower boundary condition which used in our study submitted to the JGR-Atmospheres. The meteorological data for the initial condition are available on NCEP-FNL ftp database.</p>
Supplementary Data: Code, Input Data and Result Summaries: Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system
<p>Supplementary Data</p> <p><a href="https://arxiv.org/abs/1801.05290"><strong>Synergies of sector coupling and transmission extension in a cost-optimised, highly renewable European energy system</strong></a></p> <p>Authors: T. Brown, D. Schlachtberger, A. Kies, S. Schramm, M. Greiner</p> <p><a href="https://arxiv.org/abs/1801.05290">arXiv:1801.05290</a></p> <p>The files in this record contain the scripts to build the model, input data and result summaries for the model PyPSA-Eur-Sec-30 described in the above publication.</p> <p>The full results files (which include the post-processed input data) can be found in a <a href="https://zenodo.org/record/1146649">companion Zenodo repository</a>. (The supplementary data was split because of the size of the full results.)</p> <p><strong>WARNING:</strong> A newer, improved version of this model, <a href="https://github.com/PyPSA/pypsa-eur-sec">PyPSA-Eur-Sec</a>, is under construction on GitHub.</p> <p><strong>Scripts</strong></p> <p>To use the scripts, you need the following free software Python libraries:</p> <ul> <li><a href="https://github.com/PyPSA/PyPSA">PyPSA</a> for the modelling framework</li> <li><a href="https://github.com/FRESNA/vresutils">vresutils</a> for various helper functions to build the model instance</li> <li><a href="https://github.com/FRESNA/atlite">atlite</a> to process weather data into power system data</li> <li><a href="https://snakemake.readthedocs.io/en/latest/">snakemake</a> to organise the execution of the software</li> </ul> <p>and other standard libraries from the <a href="https://pypi.python.org/pypi">Python Package Index</a> (PyPI), such as pandas, pyomo, countrycode, etc.</p> <p>snakemake requires that all code runs with Python version 3. The code setup is known to work with the following versions: PyPSA 0.12.0, pandas 0.21.1, numpy 0.14.0, scipy 0.19.1, pyomo 5.2. You may need to downgrade your libraries to these versions for the scripts to work. If you insist on using the latest versions, please be aware that you'll need to make at least the following changes:</p> <p>i) To accommodate changes in pandas versions 0.22 and higher, in scripts/prepare_network.py change "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby("technology").sum()" to "costs = costs.loc[idx[:,cost_year,:],"value"].unstack(level=2).groupby(level="technology").sum(min_count=1)".</p> <p>ii) In later versions of PyPSA the component groups like "pypsa.components.one_port_components" have become network-specific and are stored instead at "network.one_port_components".</p> <p>To solve the optimisation problem the scripts are coded to use the commercial solver <a href="http://www.gurobi.com/">Gurobi</a>. To solve the problems in a reasonable time, you will need <a href="http://www.gurobi.com/">Gurobi</a> or an equivalently fast solver such as <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a>. <a href="http://www.gurobi.com/">Gurobi</a> and <a href="https://www.ibm.com/analytics/data-science/prescriptive-analytics/cplex-optimizer">CPLEX</a> both have cost-free licences for academic users.</p> <p>You will also need a computer with at least 64 GB of RAM, since pyomo and the solver are memory intensive.</p> <p>The Python scripts in this repository (in the directory scripts/) are released under the <a href="https://www.gnu.org/licenses/gpl-3.0.en.html">GNU General Public Licence Version 3.0</a> (GPL 3.0).</p> <p>The scripts build_*.py process all raw input data into a form where it can be used in the model.</p> <p>make_options.py prepares the options.yml file for each model run.</p> <p>prepare_network.py populates the PyPSA network for each model run with the input data.</p> <p>solve_network.py solves the optimisation problem with <a href="http://www.gurobi.com/">Gurobi</a> or the solver of your choice (this step takes several hours).</p> <p>make_summary.py aggregates the results into CSV files in the directory results/ (also provided in this repository).</p> <p>The scripts plot_*.py and paper_graphics*.py prepare graphical output.</p> <p>All scripts are managed with the <a href="http://snakemake.readthedocs.io/en/latest/">snakemake</a> workflow management tool.</p> <p>To run the scripts, adjust the parameters in config.yaml and cluster.yaml to your local configuration. Then simply execute</p> <pre><code>snakemake</code></pre> <p>for the rule you want to run.</p> <p>Since the jobs are computationally intensive you may want to run them on a cluster. To run the jobs on a cluster with <a href="https://slurm.schedmd.com/">Slurm</a>, then execute e.g.</p> <pre><code>./snakemake_cluster --jobs 6</code></pre> <p>The cluster is configured in cluster.yaml. You will need to create the directory for the logs, i.e. logs/cluster/, before running the script.</p> <p><strong>Data</strong></p> <p>All input data (in the directory scripts/) and results summaries (in the directory results/) are released under the <a href="https://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International Licence</a> (CC BY 4.0), except those where explicit sources and licences are mentioned in the data folders.</p> <p>The input data include:</p> <ul> <li>Electricity sector data, which largely follows the <a href="https://doi.org/10.5281/zenodo.804337">Zenodo repository</a> for <strong><a href="https://doi.org/10.1016/j.energy.2017.06.004">The Benefits of Cooperation in a Highly Renewable European Electricity Network</a></strong>, except the current repository uses the <a href="https://data.open-power-system-data.org/time_series/2017-07-09/">Open Power System Data Time Series Data Package</a> for load data and <a href="http://renewables.ninja/">Renewables.ninja</a> for solar time series.</li> <li>Heating time series based on the degree-day approximation, constructed with the library <a href="https://github.com/FRESNA/atlite">atlite</a>.</li> <li>Hourly traffic statistics for a week from the German Federal Highway Research Institute (BASt).</li> <li>Yearly energy per country per sector from the <a href="http://www.indicators.odyssee-mure.eu/energy-efficiency-database.html">Odyssee database</a> and <a href="http://ec.europa.eu/eurostat/web/energy/data/energy-balances">Eurostat</a>.</li> <li>A cost database with literature sources.</li> </ul>
FRACTESUS_CRIEPI_15Kh2MFAA_MC_T0TEM input
<div>Fractesus project. Fracture test mini-CT. MC input 15Kh2MFAA. CRIEPI. </div> <div> <div> <p> </p> </div> </div>
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.