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46 results for “Variable resolution”
High resolution, interactive, or animated versions of illustrations used in the paper "Quantifying the Dunkelflaute: An analysis of variable renewable energy droughts in Europe"
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ROM data and code for Dakar Niño variability under global warming investigated by a high-resolution regionally coupled model
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Global variable-resolution simulations of extreme precipitation over Henan, China in 2021 with MPAS-Atmosphere v7.3
<p>This repository encompasses data and software to "Global variable-resolution simulations of the 2021 extreme precipitation event in Henan, China with MPAS-Atmosphere v7.3". The data and model are integral to our study, which focuses on simulating a significant rainstorm event in Henan, China, in July 2021.</p> <p>Contained within this archive are the following components:</p> <p>MPAS-Atmosphere v7.3: This is the core model employed in our study. MPAS v7.3 is a versatile global variable-resolution model, adept at simulating extreme weather events at varying scales.</p> <p>MPAS mesh data: The global meshes generated for the experiments.</p> <p>CMA Observation Data: Ground-based observational data from the China Meteorological Administration, crucial for the validation of our simulation results.</p> <p>ERA5 Reanalysis Data: These datasets are used to further validate the outcomes of our simulations, providing a comprehensive set of atmospheric parameters.</p> <p>GFS Data: The Global Forecast System (GFS) data serve as the input fields for the MPAS model, providing essential initial conditions for our simulations.</p> <p>This collection is aimed at offering researchers a holistic package for studying, replicating, or extending our findings on extreme weather phenomena. The data and model provided here are not only crucial for reproducing the results presented in our study but also offer a valuable resource for further research in atmospheric sciences and climate modeling.</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>
Dataset of "Annual Cycle of Gravity Wave Variability Derived from a High-Resolution Martian General Circulation Model" (1/3)
<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>data000rdc.tar.xz: for Ls=000-030 (61 Sols)</p> <p>data030rdc.tar.xz: for Ls=030-060 (66 Sols)</p> <p>data060rdc.tar.xz: for Ls=060-090 (67 Sols)</p>
Data from: Near-global freshwater-specific environmental variables for biodiversity analyses in 1km resolution
The lack of freshwater-specific environmental information at sufficiently fine spatial grain hampers broad-scale analyses in aquatic biology, biogeography, conservation, and ecology. Here we present a near-global, spatially continuous, and freshwater-specific set of environmental variables in a standardized 1 km grid. We delineate the sub-catchment for each grid cell along the HydroSHEDS river network and summarize the upstream climate, topography, land cover, surface geology and soil to each grid cell using various metrics (average, minimum, maximum, range, sum, inverse distance-weighted average and sum). All variables were subsequently averaged across single lakes and reservoirs of the Global lakes and Wetlands Database that are connected to the river network. Monthly climate variables were summarized into 19 long-term climatic variables following the 'bioclim' framework. This new set of variables provides a basis for spatial ecological and biodiversity analyses in freshwater ecosystems at near global extent, yet fine spatial grain. To facilitate the generation of freshwater variables for custom study areas and spatial grains, we provide the 'r.stream.watersheds' and 'r.stream.variables' add-ons for the GRASS GIS software.
Data from: Very high resolution digital elevation models: are multi-scale derived variables ecologically relevant?
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Data from: Near-global freshwater-specific environmental variables for biodiversity analyses in 1km resolution
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Data from: MERRAclim, a high-resolution global dataset of remotely sensed bioclimatic variables for ecological modelling
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Figure 6 from: Franz N, Gilbert E, Ludäscher B, Weakley A (2016) Controlling the taxonomic variable: Taxonomic concept resolution for a southeastern United States herbarium portal. Research Ideas and Outcomes 2: e10610. https://doi.org/10.3897/rio.2.e10610
Figure 6 - Project management time line; see also Sections 5 & 8. G = graduate student; UG = undergraduate students.
Figure 5 from: Franz N, Gilbert E, Ludäscher B, Weakley A (2016) Controlling the taxonomic variable: Taxonomic concept resolution for a southeastern United States herbarium portal. Research Ideas and Outcomes 2: e10610. https://doi.org/10.3897/rio.2.e10610
Figure 5 - Euler/X toolkit products. (A) Part of the complex "Andropogon use case", with 1948/1950/1968 input sec. Weakley (2015) (see also Fig. 1). (B) Euler/X visualization of input constraints. Each concept taxonomy is uniquely colored. Ten RCC–5 articulations are provided. (C) Euler/X alignment visualization, showing congruent (grey, rounded) and incongruent (colored) alignment regions. Arrows represent proper inclusion; blue dashed lines show overlap. (D) Subset of 48 inferred, Maximally Informative Relations (MIR). The "*" represents the 1948.Androgon_var_tenuispatheus concept lineage throughout (A) and (C).
Figure 4 from: Franz N, Gilbert E, Ludäscher B, Weakley A (2016) Controlling the taxonomic variable: Taxonomic concept resolution for a southeastern United States herbarium portal. Research Ideas and Outcomes 2: e10610. https://doi.org/10.3897/rio.2.e10610
Figure 4 - Species concept entry Weakley's Flora (Weakley 2015: 355), with 15 RCC–5 articulations to name usages endorsed in incongruent, preceding treatments (such as Radford et al. 1968 ["RAB"]).
Figure 3 from: Franz N, Gilbert E, Ludäscher B, Weakley A (2016) Controlling the taxonomic variable: Taxonomic concept resolution for a southeastern United States herbarium portal. Research Ideas and Outcomes 2: e10610. https://doi.org/10.3897/rio.2.e10610
Figure 3 - SERNEC (2016) Taxonomic Thesaurus for the genus-level name Cleistesiopsis, as of August 2016. [] = synonym.
Figure 2 from: Franz N, Gilbert E, Ludäscher B, Weakley A (2016) Controlling the taxonomic variable: Taxonomic concept resolution for a southeastern United States herbarium portal. Research Ideas and Outcomes 2: e10610. https://doi.org/10.3897/rio.2.e10610
Figure 2 - "Where do these endangered orchid species occur?" – visualizing the taxonomic variable for aggregated herbarium data. Mappings for the same 250 SERNEC specimens (not all resolved at this geographic scale) according to four distinct taxonomies. (A) sec. SERNEC (2016) 'consensus', (B) sec. Radford et al. (1968), (C) sec. Kartesz (2010), (D) sec. Weakley (2015). In A, C, and D, unequal sets of specimens labeled as bifaria are red; those labeled as divaricata are blue. In B, all specimens are identified as divaricata, and hence a query for bifaria would not return any specimens.
Figure 1 from: Franz N, Gilbert E, Ludäscher B, Weakley A (2016) Controlling the taxonomic variable: Taxonomic concept resolution for a southeastern United States herbarium portal. Research Ideas and Outcomes 2: e10610. https://doi.org/10.3897/rio.2.e10610
Figure 1 - Taxonomic concept labels and concept-to-concept articulations, represented in a tabular alignment of nine schemata, for the Cleistes use case (sec. A.S. Weakley). The vertical column position and width of taxonomic concept labels indicates taxonomic non-/congruence. The colors approximate taxonomic name lineages, e.g. blue for bifaria and yellow for divaricata.
Remarkable Lung Heterogeneity at Single-Cell Resolution and the Role of Sex as a Biological Variable in Neonatal Hyperoxic Lung Injury
GEO Series GSE211356. Mus musculus. 4 samples. Type: Expression profiling by high throughput sequencing.
Evaluation of a customized variable-resolution global model and its application for high-resolution weather forecasts in East Asia
<p>The data for shallow water test for the paper "Evaluation of a customized variable-resolution global model and its application for high-resolution weather forecasts in East Asia" are in the two sw[2,5]_GlobalIntegrals.tar.bz2 files.</p> <p>The data of MPAS model output is stored as Demo data of the <a href="https://cpas.earth/">CPAS cloud platform</a> with Jupyter visualization tools and user interface. Please see "How-to-visualize-MPAS-model-data.pdf" on how to use it.</p>
A scale-aware parameterization for estimating subgrid variability of downward solar radiation using high-resolution Digital Elevation Model data
<p>This is a statement on the shared data of the manuscript 'A scale-aware parameterization for estimating subgrid variability of downward solar radiation using high-resolution Digital Elevation Model data'</p> <p>Folder Codes includes scripts for plotting all figures of the manuscript.</p> <p>Folder grids includes all the data that are required by scripts in plotting.</p> <p><br> Contact information:<br> Siwei He<br> NOAA/ERSL/GSD<br> 325 Broadway<br> Boulder, CO 80305<br> hesiweide@gmail.com <br> </p>
Characterizing Variability and Multi-Resolution Predictions
In previous papers, we introduced the idea of a Virtual Sensor, which is a mathematical model trained to learn the potentially nonlinear relationships between spectra for a given image scene for the purpose of predicting values of a subset of those spectra when only partial measurements have been taken. Such models can be created for a variety of disciplines including the Earth and Space Sciences as well as engineering domains. These nonlinear relationships are induced by the physical characteristics of the image scene. In building a Virtual Sensor a key question that arises is that of characterizing the stability of the model as the underlying scene changes. For example, the spectral relationships could change for a given physical location, due to seasonal weather conditions. This paper, based on a talk given at the American Geophysical Union (2005), discusses the stability of predictions through time and also demonstrates the use of a Virtual Sensor in making multi-resolution predictions. In this scenario, a model is trained to learn the nonlinear relationships between spectra at a low resolution in order to predict the spectra at a high resolution.
ScienceDex guides
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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.