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Dataset results
252 results for “Variability Modelling”
Data and code for the manuscript "Internal vs Forced Variability Metrics for General Circulation Models Using Information Theory"
<p>Data and code for the manuscript "Internal vs Forced Variability Metrics for General Circulation Models Using Information Theory" published in the Journal of Geophysical Research Oceans. <br>URL of the manuscript: https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023JC020101<br>DOI of the manuscript: https://doi.org/10.1029/2023JC020101</p>
Data for "Millennial modulation of Atlantic Multidecadal Variability in a climate model"
<p>Processed data and Jupyter notebooks for manuscript "Millennial modulation of Atlantic Multidecadal Variability in a climate model" by Joakim Kjellsson and Wonsun Park</p>
Model codes, data, and plot scripts for the paper "Quantifying the Role of Model Internal Year-to-Year Variability in Estimating Anthropogenic Aerosol Radiative Effects".
<p>The model codes, data, and plot scripts used in the paper "Quantifying the Role of Model Internal Year-to-Year Variability in Estimating Anthropogenic Aerosol Radiative Effects".</p><ul><li>Figs&NCL: the NCL scripts and figures used in the paper.</li><li>Mods: modified CAM model code.</li><li>PostFortran: the Fortran code for analysing the model results (post-processing) that produces the final results used for making plots.</li><li>Results4Plots: the final results used for making plots.</li></ul><p> </p>
ROM data and code for Dakar Niño variability under global warming investigated by a high-resolution regionally coupled model
Open the record for dataset details and reuse information.
Predicting time series of vegetation leaf area index across North America based on climate variables for land surface modeling using attention-enhanced LSTM
<p>We developed an attention-enhanced long and short memory (AELSTM) model for predicting vegetation LAI time series based on climatic data. The developed AELSTM model establishes the relationships between the time series of vegetation LAI and climatic variables. </p>
Dataset for Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes
<p>This data set is created for a purpose of publication "<span>Can we predict kick force based solely on spatial-temporal variables? Applying long short-term memory model for predicting force values of turning and side kick of taekwon-do athletes". It contains of dataset of kicks and lstm models for predictions a force of kicks upon IMU data. Detailed description of file names are in readme file. Folders are divided into specific kicks - turning or side kick in sport or traditional versions.</span></p>
Data sharing of: Sulfur inventory of the young lunar mantle constrained by experimental sulfide saturation of Chang'e-5 mare basalts and a new sulfur solubility model for silicate melts in equilibrium with sulfides of variable metal–sulfur ratio
<p>Data sharing of: Sulfur inventory of the young lunar mantle constrained by experimental sulfide saturation of Chang’e-5 mare basalts and a new sulfur solubility model for silicate melts in equilibrium with sulfides of variable metal–sulfur ratio</p>
FIGURE. Variable positions in the ITS2 secondary structure of some Coelastrella sensu lato species. The ITS2 model of Coelastrella striolata strain CAUP H 3602 (JX513881) was used to map sequence differences. Variable positions of analyzed strains (GenBank numbers can be found in Table 3, 4 are given next to the main structure and are marked in bold. Hemi- Compensatory Base Changes in conservative regions are circled and Compensatory Base Change is contoured. Sequences of strains with GenBank numbers JX513879 (C. aeroterrestrica), JX513882 (C. terrestris), JX513884 (C. rubescens), MH176120 (C. rubescens var. oocystiformis), JX513880 (C. multistriata), JX513887 (C. oocystiformis) were used as representatives of Coelastrella species. The strains analyzed in this study are underlined. in Morphological and phylogenetic relations of members of the genus Coelastrella (Scenedesmaceae, Chlorophyta) from the Ural and Khentii Mountains (Russia, Mongolia)
FIGURE. Variable positions in the ITS2 secondary structure of some Coelastrella sensu lato species. The ITS2 model of Coelastrella striolata strain CAUP H 3602 (JX513881) was used to map sequence differences. Variable positions of analyzed strains (GenBank numbers can be found in Table 3, 4 are given next to the main structure and are marked in bold. Hemi- Compensatory Base Changes in conservative regions are circled and Compensatory Base Change is contoured. Sequences of strains with GenBank numbers JX513879 (C. aeroterrestrica), JX513882 (C. terrestris), JX513884 (C. rubescens), MH176120 (C. rubescens var. oocystiformis), JX513880 (C. multistriata), JX513887 (C. oocystiformis) were used as representatives of Coelastrella species. The strains analyzed in this study are underlined.
Artifacts for the SPLC 2020 Paper "A Conceptual Model for Unifying Variability in Space and Time" and the ESE journal extension "A Conceptual Model for Unifying Variability in Space and Time: Rationale, Validation, and Illustrative Applications"
<p>These artifacts relate to the SPLC'20 research paper "A Conceptual Model for Unifying Variability in Space and Time" and the Empirical Software Engineering journal extension "A Conceptual Model for Unifying Variability in Space and Time: Rationale, Validation, and Illustrative Applications."</p>
Pretrained Models for Paper "Variable-Input DeepONets for Operator Learning"
<p>This repository contains all trained models which were used to compute the results presented in the paper "Variable-Input DeepONets for Operator Learning".</p>
Model data - Impact of Ural blocking on early-winter climate variability under different Barents-Kara sea ice conditions
<p>Model data for JGR paper : Impact of Ural blocking on early-winter climate variability under different Barents-Kara sea ice conditions</p>
The main variables (soil moisture, net biome production and so on) of two experiments (CTRL and EXP) with the ORCHIDEE-MICT terrestrial biosphere model
<p class="MsoNormal"><span>Multiple linear regression (MLR) is widely used to attribute causes of the interannual variability (IAV) of land carbon uptake, yet, parameter estimation in MLR can be problematic if the predictors are strongly inter-correlated. Recently, Humphrey et al., (2021) used MLR method to conclude that the indirect effect of soil moisture (SM) via land-atmosphere coupling, rather than the direct effect of SM on photosynthesis and respiration, controls the IAV of NBP. Here we assess the validity of MLR as used by Humphrey et al. (2021) by comparing the true contribution of SM in a terrestrial biosphere model, derived from the difference between a control run (CTRL) and an experiment with prescribed climatological SM (EXP), with the MLR method applied to the CTRL outputs.</span></p> <p class="MsoNormal"><span><span>We ran two experiments (CTRL and EXP) with the ORCHIDEE-MICT</span><span> terrestrial biosphere model at 2º spatial resolution. The control (CTRL) run followed the protocol of "S3" experiment of TRENDY-v6</span><span>, forced by CRUNCEP-v8 climate forcing, increasing atmospheric CO<sub>2</sub> concentration, and varying land use maps. </span>Monthly outputs for the period 1960-2005 were used for analysis. For the EXP run, <a name="_Hlk107926231"></a>to remove the interannual variability (IAV) of soil moisture (SM) while keeping its seasonal cycle, a climatological monthly SM averaged for the years 1960-2005 simulated by the CTRL run was prescribed in the model. Note that the intrinsic time-step of hydrology and photosynthesis in ORCHIDEE-MICT is half-hourly, thus SM within the same month took the same monthly mean value. Other configurations in the EXP run were identical to the CTRL run. </span></p>
Data from Climate adaptability in hydrological models: variable storage capacity to improve performance under contrasting climates.
Open the record for dataset details and reuse information.
Model and observation data for paper "Variability of the bottom boundary layer induced by the dynamics of the cross-isobath transport over a variable shelf"
<p>Model and observation data for paper "Variability of the bottom boundary layer induced by the dynamics of the cross-isobath transport over a variable shelf"</p>
Artifact for 'Unfolding State Variables Improves Model Checking'
<p>This is a reproduction package for the experiments that were performed as part of the work 'Unfolding State Variables Improves Model Checking'.</p>
Model simulation output for New Configuration for Impact of microphysics and convection schemes on the mean-state and variability of clouds and precipitation in the E3SM Atmosphere Model
<p>Simulation output from the new configuration model used in the manuscript Impact of microphysics and convection schemes on the mean-state and variability of clouds and precipitation in the E3SM Atmosphere Model</p>
Data-driven brain network models differentiate variability across language tasks
<p>Data and script associated with the manuscript titled "Data-driven brain network models differentiate variability<br> across language tasks". </p>
Data used in "Seasonality of Intraseasonal Variability in CMIP5 and Nonhydrostatic Atmospheric Global Models" by Nakano and Kikuchi (2019) submitted to GRL
<p>PCs time series and NICAM-AMIP 2.5 degree gridded data used in Nakano and Kikuchi (2019) submitted to GRL.</p>
Dataset of "Annual Cycle of Gravity Wave Variability Derived from a High-Resolution Martian General Circulation Model" (3/3)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Annual Cycle of Gravity Wave Variability Derived from a High-Resolution Martian General Circulation Model" by T. Kuroda, E. Yiğit and A.S. Medvedev.</p> <p>Each file contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T), zonal wind velocity (u), meridional wind velocity (v) and vertical wind velocity (w). Each tar.xz file contains snapshots of those data in every 1/6 Sol for Ls of 30 degrees.</p> <p>data210rdc.tar.xz: for Ls=210-240 (47 Sols)</p> <p>data240rdc.tar.xz: for Ls=240-270 (46 Sols)</p> <p>data270rdc.tar.xz: for Ls=270-300 (48 Sols)</p> <p>data300rdc.tar.xz: for Ls=300-330 (51 Sols)</p> <p>data330rdc.tar.xz: for Ls=330-360 (56 Sols)</p>
Dataset of "Annual Cycle of Gravity Wave Variability Derived from a High-Resolution Martian General Circulation Model" (2/3)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Annual Cycle of Gravity Wave Variability Derived from a High-Resolution Martian General Circulation Model" by T. Kuroda, E. Yiğit and A.S. Medvedev.</p> <p>Each file contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T), zonal wind velocity (u), meridional wind velocity (v) and vertical wind velocity (w). Each tar.xz file contains snapshots of those data in every 1/6 Sol for Ls of 30 degrees.</p> <p>data090rdc.tar.xz: for Ls=090-120 (64 Sols)</p> <p>data120rdc.tar.xz: for Ls=120-150 (60 Sols)</p> <p>data150rdc.tar.xz: for Ls=150-180 (54 Sols)</p> <p>data180rdc.tar.xz: for Ls=180-210 (49 Sols)</p>
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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.