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5,805 results for “Data model”
Model simulation data used in "Impacts of ice-nucleating particles on cirrus clouds and radiation derived from global model simulations with MADE3 in EMAC" (Beer et al., Atmos. Chem. Phys., 2024)
<p>This dataset contains the namelist setup and the output of the EMAC global model simulations analysed and discussed in Beer et al. (<i>Atmos. Chem. Phys.</i>, 2024). For details see the README.md file and Table 2 in the paper.</p>
biomass data and environmental variables model and station 18
<p>biomass data and environmental variables model and station 18, publication in progress in oceanography</p>
Random Forest Cloud Model for Predicting Liquid Cloud Microphysical Properties from A-Train Data
<p>Code for creating and analyzing the performance of a random forest model to predict cloud optical depth and cloud top effective radius from A-train satellite observations. Because of storage limitations, this directory does not include full satellite dataset, but the CloudSat data is available from the CloudSat Data Processing center (https://www.cloudsat.cira.colostate.edu/) and the CALIPSO data from NASA's Atmospheric Science Data Center (https://asdc.larc.nasa.gov/project/CALIPSO). </p>
Data release for "Consistent eccentricities for gravitational wave astronomy: Resolving discrepancies between astrophysical simulations and waveform models"
<div> <p>This is a data release to accompany <a href="https://arxiv.org/abs/2402.07892">arXiv:2402.07892</a> "Consistent eccentricities for gravitational wave astronomy: Resolving discrepancies between astrophysical simulations and waveform models".</p> <p>See also <a href="../doi/10.5281/zenodo.10974974">https://zenodo.org/doi/10.5281/zenodo.10974974</a>. Please cite the paper (<a href="https://ui.adsabs.harvard.edu/abs/2024arXiv240207892V/abstract">https://ui.adsabs.harvard.edu/abs/2024arXiv240207892V/abstract</a>) if you use this in a publication or any other scientific work.</p> <p><br>The file can be read in using<br><br>```<br>import pandas</p> <p>data = pandas.read_hdf("CMC_eccentricities_standardized.hdf5")<br>```<br><br>The description of the different keys in the file are as follows:<br><br>- `m1`: source-frame mass of the primary object in the binary in units of Msun<br>- `m2`: source-frame mass of the secondary object in the binary in units of Msun<br>- `chi1`: dimensionless spin of the primary object in the binary<br>- `chi2`: dimensionless spin of the secondary object in the binary<br>- `z`: cosmological redshift<br>- `a0`: Initial separation in AU. This typically corresponds to the stopping criterion in astrophysical simulations.<br>- `e0`: The eccentricity at the initial separation `a0`<br>- `f0`: 22 mode frequency corresponding to the initial separation.<br>- `channel`: Takes values between 1 and 5. <br> - 1: Ejected mergers <br> - 2: In-cluster (two-body) mergers<br> - 3: Binary-Single Encounters<br> - 4: Binary-Binary Encounters<br> - 5: Single-Single Encounters<br>- `cluster_weight`: Weight given to each binary based on cluster properties (mass and metallicity), assuming some initial mass function for the cluster and metallicity evolution as a function of redshift.<br>- `cosmo_weight`: Weight given to each binary based on the redshift, to account for cosmological volume.<br>- `e_W03_*Hz`: Eccentricities extracted from the Wen 2003 prescription at the reference peak frequency specified.<br>- `e_t_2PN_10Hz`: Eccentricities extracted from the prescription in Vijaykumar et. al. 2024 at reference 22 mode frequency of 10 Hz. <br>- `e_t_2PN_Mf_1000HzMsun`: Eccentricities extracted from the prescription in Vijaykumar et. al. 2024 at reference 22 mode frequency corresponding to `M \times f = 1000 Hz Msun`. This ensures that eccentricity is defined at a fixed number of cycles before merger, independent of the cosmological redshift.<strong> We strongly recommend that all eccentricity estimates from astrophysical simulations are quoted at a reference frequency corresponding to fixed `M \times f`.</strong><br>- `total_weight`: `cosmo_weight \times cluster_weight`<br><br><strong>NOTE</strong>: For mergers belonging to `channel=1`, we straightaway set the `e_t_*` estimates to zero. This is because mergers from this channel will not be eccentric at `f>10 Hz`, and due to its large initial separation is computationally intensive to evolve using the PN evolution equations.</p> <p> </p> </div>
[VISIR-2 ship weather routing model] raw data
<h2>VISIR-2 ship weather routing model raw data</h2> <p>This repository contains the raw data necessary to reproduce the results from scratch as presented in the Geosci. Model Dev. Discussions manuscript titled "<em>VISIR-2: ship weather routing in Python</em>". The paper will be linked here as soon as available online. </p> <p> </p> <h3>How to Use the Data</h3> <p>To reproduce the results from the manuscript, please follow these steps:</p> <p>1. download this repository to your local machine.</p> <p>2. download the Zenodo repository containing VISIR-2 source code from this <a href="../doi/10.5281/zenodo.8305526">link</a></p> <p>3. run VISIR-2 jobs following the instructions included in the manual that can be found inside VISIR-2 source code at <em>VISIR-2/Docs/Manual/</em>VISIR-2_userManual.pdf</p> <p> </p> <h3>Contact</h3> <p>If you have any questions or need further assistance, please feel free to contact us:</p> <p>- Mario Leonardo Salinas</p> <p>- Email: mario.salinas@cmcc.it</p> <p> </p>
scRNA-seq data of: A novel in vitro tubular model to recapitulate features of distal airways: the bronchioid
<p>We provide a .Rds file of an annotated Seurat Object of scRNA-seq data of two bronchioid models derived from distinct donors after 21days of culture using 10x genomics 3' v3 chemistry. Raw data was processed using CellRanger v7.1.0. Cells were filtered based on detected UMIs (>2000) and fraction of mitochondrial counts (<10%).<br>Metadata annotations contain:<br>- Patient -> patient information for every cell (patient1 or patient2)<br>- nCount_RNA -> UMI counts per cell<br>- nFeature_RNA -> genes detected per cell<br>- percent.mt -> mitochondrial count fraction per cell<br>- seurat_clusters -> unsupervised clustering results using Louvain algorithm with resolution = 0.5<br>- Manual.Annotation -> Cell types annotated based on marker gene expression<br>- Celltypist.prediction -> Cell types predicted with CellTypist Python package<br>- Celltypist.prediction.ari -> Cell types predicted with CellTypist Python package, with harmonized names for comparison with manual annotation</p>
Arizona Building Aggregated Parameters: Model America v1.0 Data for 146,557 1kmx1km Grid Cells
<p>Oak Ridge National Laboratory (ORNL) has developed the Automatic Building Energy Modeling (AutoBEM) software suite to process multiple types of data, extract building-specific descriptors, generate building energy models, and simulate them on High Performance Computing (HPC) resources. For more information, see AutoBEM-related publications (<a href="https://bit.ly/AutoBEM">bit.ly/AutoBEM</a>).</p> <ul> <li>Building height</li> <li>Building footprint area</li> <li>Building total floor area</li> <li>Height-to-width ratio (height / [longest polygon vertex pair distance]) </li> <li>Number of buildings (ratio of each building type)</li> <li> Street orientation (longest polygon vertex pair's degree)</li> <li>Rooftop area density (total roof area / total cell area)</li> <li>Rooftop area density (total roof area / total cell area)</li> <li>Lambda_p (roof area / total cell area)</li> <li>Lambda_b (surface area of buildings (roof + vertical walls) / total cell area)</li> </ul> <p>Please note that certain parameters include the mean, median, mode, standard deviation, maximum, and minimum values.</p> <p>Three sets of data are provided for 2,555,153 buildings located within the boundary of Arizona in the United States:</p> <ol> <li><strong>Data (3.6MB *.csv) - Arizona 146,557 Grid Cell Locations.</strong></li> <li><strong>Data (15MB *.csv) - Arizona Building Aggregated Parameters Data developed for the Grid Cells.</strong></li> <li><strong>Data (2.5MB *.csv) - Arizona Building Aggregated Parameters Data developed for the Grid Cells (Selected Parameters).</strong></li> </ol> <p>This data is made free and openly available in hopes of stimulating any simulation-informed use case. Data is provided as-is with no warranties, express or implied, regarding fitness for a particular purpose. We wish to thank our sponsors which include Oak Ridge National Laboratory (ORNL), U.S. Dept. of Energy’s (DOE) Building Technologies Office (BTO), Office of Electricity (OE), and Biological and Environmental Research (BER).</p>
LI 850 sensor data of the Antarctic Modeling Observation System (ATMOS) project of the 40th Brazilian Antarctic Operation (OPERANTAR XL) to calculate the CO2 flux (FCO2)
<p>LI 850 sensor data of the Antarctic Modeling Observation System (ATMOS) project of the 40th Brazilian Antarctic Operation (OPERANTAR XL) to calculate the CO2 flux (FCO2)</p>
urbisphere_gb-london_UR-2: Activities profiles derived from UK Time Use Survey data for modelling
<h2>Files in this archive </h2> <ul> <li>UK_TUS2014-15_activity_profiles_190324.zip <ul> <li>Activity profile dataset derived from the UK Time Use Survey (2014/15; *.csv) </li> </ul> </li> <li>auxiliary_data.zip <ul> <li>Auxiliary datasets created for processing</li> </ul> </li> <li>AppliancePowerManufacturer.zip <ul> <li>Information used to assigned values</li> </ul> </li> <li>code.zip <ul> <li>Code to produce the dataset</li> <li>Python3.9 Jupyter notebook</li> </ul> </li> <li>urbisphere_gb-london_UR-2.pdf <ul> <li>Documentation</li> </ul> </li> </ul> <h2>Data purpose </h2> <p>People’s activities change through the day and between days. Information about human behaviour and the changing locations where activities occur are used in neighbourhood scale agent-based models of anthropogenic heat fluxes and building scale energy modelling to give realistic occupancy and activity-based energy-use timings.</p> <h3>Linked with</h3> <ul> <li>Hertwig et al. 2024a: urbisphere_presentations_UR-1: Modelling anthropogenic heat emissions from residential buildings-comparison between Berlin and London. EMS Annual Meeting 2023 [Poster]. Zenodo. https://doi.org/10.5281/zenodo.10889863</li> <li>Hertwig et al. 2024b: urbisphere_presentations_UR-2: Connecting physical and socio-economic spaces for urban agent-based modelling, EMS Annual Meeting 2023 [Poster]. Zenodo. https://doi.org/10.5281/zenodo.10889885</li> <li>McGrory et al. 2024: urbisphere_presentations_UR-3: Dynamic Anthropogenic actiVities and feedback to Emissions (DAVE): An agent-based model for heat and exposure to other anthropogenic emissions. EMS Annual Meeting 2023 [Presentation]. Zenodo. https://doi.org/10.5281/zenodo.10889900</li> </ul>
How New Zealand adults who smoked understand novel tobacco 'endgame' policies. Qualitative analysis using the Associative Propositional Evaluation model to determine comprehension. Raw data.
<p>Raw data used for analysis presented in results paper. </p> <p><strong><span>Compliance with Ethical Standards</span></strong></p> <p><span>Ethics approval was sought from the Health and Disability Ethics Committee (HDEC), but the study was deemed out of HDEC’s scope as it was an observational study that would not involve more than minimal risk, that is, no more risk than participants might encounter during everyday life.</span></p> <p><strong><span>Disclosure statement</span></strong></p> <p><span>This study was funded with a grant [COE1-009] from the Foundation for a Smoke-Free World, Inc. (“FSFW”), a US nonprofit 501(c)(3) private foundation. The FSFW’s mission is to end smoking in this generation. Through September 2023, the Foundation received charitable gifts from PMI Global Services Inc. (“PMI”). Independent from PMI since its founding in 2017, the Foundation continues to operate in a manner that ensures its independence from any commercial entity. This study is, under the terms of the grant agreement with FSFW, editorially independent of FSFW. The FSFW were not involved in the conception, design, conduct, data collection, analysis and interpretation of data; nor did the FSFW review or have any involvement in the writing of the paper or involvement or restrictions regarding publication. The contents, selection, and presentation of facts, as well as any opinions expressed herein, are the sole responsibility of the authors and under no circumstances should they be regarded as reflecting the positions of FSFW. </span><span>None of the authors nor does the Centre have any commercial interests in any smoking cessation programs or aids or nicotine or tobacco products.</span></p>
Data from: Progression of neuronal damage in an in vitro model of the ischemic penumbra
Improvement of neuronal recovery in the ischemic penumbra around a brain infarct has a large potential to advance clinical recovery of patients with acute ischemic stroke. However, pathophysiological mechanisms leading to either recovery or secondary damage in the penumbra are not completely understood. We studied neuronal dynamics in a model system of the penumbra consisting of networks of cultured cortical neurons exposed to controlled levels and durations of hypoxia. Short periods of hypoxia (pO2≈20mmHg) reduced spontaneous activity, due to impeded synaptic function. After ≈6 hours, activity and connectivity partially recovered, even during continuing hypoxia. If the oxygen supply was restored within 12 hours, changes in network connectivity were completely reversible. For longer periods of hypoxia (12–30 h), activity levels initially increased, but eventually decreased and connectivity changes became partially irreversible. After ≈30 hours, all functional connections disappeared and no activity remained. Since this complete silence seemed unrelated to hypoxic depths, but always followed an extended period of low activity, we speculate that irreversible damage (at least partly) results from insufficient neuronal activation. This opens avenues for therapies to improve recovery by neuronal activation.
Advancing the HEIMDALL model: seasonal spectral irradiance through the Pan-Arctic icescape. - Supplementary Data
<p>Code to replicate figures in publication Advancing the HEIMDALL model: seasonal spectral irradiance through the Pan-Arctic icescape.</p>
The input files and associated data products for "Modeling High Mass X-ray Binaries to Double Neutron Stars through Common Envelope Evolution"
<p>Simulations were made using the version 12115 of the MESA code together with the x86_64-linux-20190830 MESA SDK. "template.zip" provides the MESA inlist files to reproduce our simulations. "CE_1.zip" provides our simulated results for a grid of binary systems with common envelope ejection efficiencies set to be 1.0. Different folders indicate the binary systems with different initial parameters. Inside each folder information can be found for binary properties in the "history.data" file. Each folder also contains the "result.txt" file with the terminal output of the simulation. "CE_3.zip", "CE_0.3.zip" and "CE_0.1.zip" are the same as "CE_1.zip" but with common envelope ejection efficiencies set to be 3.0, 0.3 and 0.1, respectively.</p>
Exploring the effects of experimental parameters and data modeling approaches on in vitro transcriptomic point-of-departure estimates
Open the record for dataset details and reuse information.
Laboratory water quality monitoring data from full scale CS#3 DWDN for the DBP prediction model
<p>Lab measurements of water quality based on manual sampling including ordinary monitoring and special monitoring performed in SafeCREW project.</p> <p>Laboratory LIMS database.</p> <p>Provide water quality measurements based on the planned monitoring schedule. Schedule tailored to SafeCREW project needs plus usual legal water sampling.</p>
Implementation of an Ensemble Kalman Filter in the Community Multiscale Air Quality Model (CMAQ Model v5.1) for Data Assimilation of Ground-level PM2.5: Model Simulation Outputs
<p>This data sets are model outputs from CMAQ simulations. The output contains only PM2.5 variable after combining related aerosol species. File format is netCDF binary. File naming convention for Domain 1 (D1) is D1_EXP_DATE_TIME_e000.nc where EXP is the control experiment (CTR) or the assimilation experiments (DA_icbc), DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. Also, file naming convention for Domain 2 (D2) is D2_EXP_CASE_DATE_TIME_e000 where EXP is the control experiment (CTR) or the assimilation experiments (DA_ic and DA_icbc), CASE is the simulation cases for ANL or PRD, DATE means YYYYMMDD format date, TIME indicates 2 digits UTC time, and e000 represents ensemble mean result. For the processed and assimilated observation data in this study for D1 and D2, the file names are D1_OBS_DATA_YYYYMMDDhh.txt and D2_OBS_DATA_YYYYMMDDhh.txt, respectively, where YYYYMMDD is date format and hh is UTC.</p>
Supplementary Data: A model-independent analysis of b→sμ+μ− transitions with GAMBIT's FlavBit
<p>Supplementary Data</p> <p> A model-independent analysis of b→sμ+μ− transitions with GAMBIT's FlavBit</p> <p>The files in this record contain supplementary data including the samples and plotting scripts used for the results in Bhom. J et al. " A model-independent analysis of b→sμ+μ− transitions with GAMBIT's FlavBit." Samples have been created using <a href="http://gambit.hepforge.org/">GAMBIT</a> and figures can be reproduced with <a href="http://github.com/patscott/pippi">pippi</a>.</p> <p>This record contains:</p> <ul> <li>2 hdf5 files containing the samples produced for this article.</li> <li>2 pip files to create the figures from the samples, using pippi.</li> </ul> <p> </p>
Supplementary data for: "Progenitor properties of type II supernovae: fitting to hydrodynamical models using Markov chain Monte Carlo methods"
<p>This entry contains a grid of bolometric light curve and photospheric velocity models applied to stellar evolution progenitors. A full description of the models can be found in Martinez et al. 2020, A&A, 642, A143.</p>
Model output data of the paper: "Management induced changes of soil organic carbon on global croplands"
<p># Model output data of the paper: "Management induced changes of soil organic carbon on global croplands"<br> This data was prodused using the the MadRat framework and the mrsoil R-library by the R-script SOCBudget.R, which is stored together with the data. mrsoil is based on the R-libraries mrcommons, mrmagpie and mrvalidation.</p> <p>### REFERENCES<br> Dietrich J, Baumstark L, Wirth S, Giannousakis A, Rodrigues R, Bodirsky B, Kreidenweis U, Klein D (2020). _madrat: May All Data be<br> Reproducible and Transparent (MADRaT)_. doi: 10.5281/zenodo.1115490 (URL: https://doi.org/10.5281/zenodo.1115490), R package version<br> 1.86.0, <URL: https://github.com/pik-piam/madrat>.</p> <p>rstens K, Dietrich J (2020). _mrsoil: MadRat Soil Organic Carbon Budget Library_. doi: 10.5281/zenodo.4317933 (URL:<br> https://doi.org/10.5281/zenodo.4317933), R package version 1.1.0, <URL: https://github.com/pik-piam/mrsoil>.</p> <p>Bodirsky B, Karstens K, Baumstark L, Weindl I, Wang X, Mishra A, Wirth S, Stevanovic M, Steinmetz N, Kreidenweis U, Rodrigues R, Popov<br> R, Humpenoeder F, Giannousakis A, Levesque A, Klein D, Araujo E, Beier F, Oeser J, Pehl M, Leip D, Molina Bacca E, Martinelli E,<br> Schreyer F, Dietrich J (2020). _mrcommons: MadRat commons Input Data Library_. doi: 10.5281/zenodo.3822009 (URL:<br> https://doi.org/10.5281/zenodo.3822009), R package version 0.11.10, <URL: https://github.com/pik-piam/mrcommons>.</p> <p>Karstens K, Dietrich J, Chen D, Windisch M, Alves M, Beier F, v. Jeetze P, Mishra A, Humpenoeder F (2020). mrmagpie: madrat based MAgPIE Input Data Library. doi: 10.5281/zenodo.4319612 (URL: https://doi.org/10.5281/zenodo.4319612), R package version 0.31.0, <URL: https://github.com/pik-piam/mrmagpie>.</p> <p>Bodirsky B, Wirth S, Karstens K, Humpenoeder F, Stevanovic M, Mishra A, Biewald A, Weindl I, Chen D, Molina Bacca E, Kreidenweis U, W. Yalew A, Humpenoeder<br> F, Wang X, Dietrich J (2020). _mrvalidation: madrat data preparation for validation purposes_. doi: 10.5281/zenodo.4317826 (URL:<br> https://doi.org/10.5281/zenodo.4317826), R package version 2.5.0, <URL: https://github.com/pik-piam/mrvalidation>.</p> <p>## LICENSE<br> This data is open-source: you can redistribute it and/or modify it under the terms of the **CC Attribution 4.0 International** as published by the Creative Commons Corporation at https://creativecommons.org/licenses/by/4.0/legalcode.</p> <p>## CONTACT<br> karstens@pik-potsdam.de</p>
MD Analysis Pre-Processed Data Input Files: Towards a New Model for the TREX1 Exonuclease, Hemphill et al.
<p>All atoms molecular dynamics simulations were performed for TREX1 apoenzyme, TREX1-ssDNA complex, and TREX1-dsDNA complex, with four replicate simulations per system. Trajectory and initial condition output files were pre-processed in VMD to generate time-step sampled mol2 files of the macromolecules in each simulation. The mol2 files were used to calculate phi/psi angles of the protein backbone, and to extract all-atoms coordinate information over time, and this information was organized into an RData file for each simulation system, including all simulation replicates. These RData files are provided, and they were analyzed with the scripts described in the affiliated manuscript. </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.