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11,506 results for “Dynamics”
2018-2020 Dataset [7/7] for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains part 7/7 of the full dataset used for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". </p> <p>This dataset comprises 3 years of normalized hourly data for both low-resolution predictors [16 km] and high-resolution target variables [2km] (2mT and 10-m U and V), from 2018-2019. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a>, unzip this zip file in the data folder, and download from Zenodo the other parts of the dataset listed in the related works.</p>
Sample dataset for the models trained and tested in the paper 'Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy'
<p>This repository contains a sample of the input data for the models of the <a href="https://arxiv.org/abs/2406.13627">preprint</a> "Can AI be enabled to dynamical downscaling? Training a Latent Diffusion Model to mimic km-scale COSMO-CLM downscaling of ERA5 over Italy". It allows the user to test and train the models on a reduced dataset (45GB).</p> <p>This sample dataset comprises ~3 years of normalized hourly data for both low-resolution predictors and high-resolution target variables. Data has been randomly picked from the whole dataset, from 2000 to 2020, with 70% of data coming from the original training dataset, 15% from the original validation dataset, and 15% from the original test dataset. Low-resolution data are preprocessed ERA5 data while high-resolution data are preprocessed VHR-REA CMCC data. Details on the performed preprocessing are available in the paper.</p> <p>This sample dataset also includes files relative to metadata, static data, normalization, and plotting.</p> <p>To use the data, clone the corresponding <a href="https://github.com/DSIP-FBK/DiffScaler">repository</a> and unzip this zip file in the data folder.</p>
Supplementary material 7 from: Schmidt M, Lischeid G, Nendel C (2018) Data on and methodology for measurements of microclimate and matter dynamics in transition zones between forest and adjacent arable land. One Ecosystem 3: e24295. https://doi.org/10.3897/oneeco.3.e24295
R Script for converting of microclimatic data
Figures 5-6 from: Gentile R, Cardoso TS, Costa-Neto SF, Teixeira BR, D'Andrea PS (2018) Community structure and population dynamics of small mammals in an urban-sylvatic interface area in Rio de Janeiro, Brazil. Zoologia 35: 1-12. https://doi.org/10.3897/zoologia.35.e13465
Figures 5-6 Small mammal community structure in CFMA and Pau da Fome, Rio de Janeiro, Brazil, for the (5) 2001 and (6) 2012–2015 samplings. (A, B, C, D, E, F, G, H, K, L) CFMA transects, (C1, D1, E1, F1, G1) Pau da Fome transects.
Figures 2-4 from: Gentile R, Cardoso TS, Costa-Neto SF, Teixeira BR, D'Andrea PS (2018) Community structure and population dynamics of small mammals in an urban-sylvatic interface area in Rio de Janeiro, Brazil. Zoologia 35: 1-12. https://doi.org/10.3897/zoologia.35.e13465
Figures 2-4 Plots of the Non-metric Multidimensional Scaling Analysis between the two periods and areas studied (● CFMA and Pau da Fome) for the small mammal species: (2) comparison between 2001 and 2012-2015 samplings including all transects pulled; (3) comparison among transects in 2001 (transects from A to D in disturbed forests and from E to H in peridomicile areas of CFMA; transects from C1 to G1 in disturbed forest of Pau da Fome); (4) comparison among transects for the 2012-2015 period (transects A, B, K, L in disturbed forest areas of CFMA; E1 in disturbed forest areas of Pau da Fome; C, D in peridomicile areas of CFMA; C1 and D1 in peridomicile areas of Pau da Fome; E and F in preserved forest areas of CFMA).
Figure 7 from: Gentile R, Cardoso TS, Costa-Neto SF, Teixeira BR, D'Andrea PS (2018) Community structure and population dynamics of small mammals in an urban-sylvatic interface area in Rio de Janeiro, Brazil. Zoologia 35: 1-12. https://doi.org/10.3897/zoologia.35.e13465
Figure 7 Canonical correspondence analysis (CCA) and relationship among species, transects (A, B, C, D, E, F) and habitat variables (CANO – percentage of canopy cover; TREE – number of trees with diameter at breast height ≥ 5; VCS – percentage of vegetation cover on the soil; VVO – vertical vegetation obstruction) for the 2012–2015 period in CFMA, Rio de Janeiro, Brazil.
Figure 9 from: Gentile R, Cardoso TS, Costa-Neto SF, Teixeira BR, D'Andrea PS (2018) Community structure and population dynamics of small mammals in an urban-sylvatic interface area in Rio de Janeiro, Brazil. Zoologia 35: 1-12. https://doi.org/10.3897/zoologia.35.e13465
Figure 9 Age structure of the marsupial Didelphis aurita for the 2012–2015 period in CFMA and Pau da Fome, Rio de Janeiro, Brazil.
Figure 1 from: Gentile R, Cardoso TS, Costa-Neto SF, Teixeira BR, D'Andrea PS (2018) Community structure and population dynamics of small mammals in an urban-sylvatic interface area in Rio de Janeiro, Brazil. Zoologia 35: 1-12. https://doi.org/10.3897/zoologia.35.e13465
Figure 1 Map of the study area indicating the sampling localities in CFMA and Pau da Fome region, State of Rio de Janeiro, Brazil. 1) CFMA – Peridomicile, 2–3) CFMA – Disturbed Forest, 4) CFMA – Preserved Forest, 5) Pau da Fome – Peridomicile, 6) Pau da Fome – Disturbed Forest.
Figure 8 from: Gentile R, Cardoso TS, Costa-Neto SF, Teixeira BR, D'Andrea PS (2018) Community structure and population dynamics of small mammals in an urban-sylvatic interface area in Rio de Janeiro, Brazil. Zoologia 35: 1-12. https://doi.org/10.3897/zoologia.35.e13465
Figure 8 Proportion of reproductively active females of the marsupial Didelphis aurita for the 2012–2015 period in CFMA and Pau da Fome, Rio de Janeiro, Brazil.
Molecular basis for the increased affinity of an RNA recognition motif with re-engineered specificity: A molecular dynamics and enhanced sampling simulations study- PART 6
<p>Trajectories and input files of the simulations of the S151T Rbfox*·pre-miR20b* system.</p>
Molecular basis for the increased affinity of an RNA recognition motif with re-engineered specificity: A molecular dynamics and enhanced sampling simulations study- PART 1
<p>Simulations of the Rbfox protein.</p> <p> </p>
Dynamical charge density fluctuations pervading the phase diagram of a Cu-based high-Tc superconductor
<p>[This repository contains the raw data for the manuscript "<strong>Dynamical charge density fluctuations pervading the phase diagram of a Cu-based high-Tc superconductor</strong>" (arXiv:1809.04949)]</p> <p>Charge density modulations are a common occurrence in all families of high critical temperature superconducting cuprates. Although consistently observed in the underdoped region of the phase diagram and at relatively low temperatures, it is still unclear to what extent they influence the unusual properties of these systems. Using resonant x-ray scattering we carefully determined the temperature dependence of charge density modulations in YBa<sub>2</sub>Cu<sub>3</sub>O<sub>7-δ</sub> and Nd<sub>1+x</sub>Ba<sub>2-x</sub>Cu<sub>3</sub>O<sub>7-δ</sub> for several doping levels. We discovered short-range dynamical charge density fluctuations besides the previously known quasi-critical charge density waves. They persist up to well above the pseudogap temperature <em>T*</em>, are characterized by energies of few meV and pervade a large area of the phase diagram, so that they can play a key role in shaping the peculiar normal-state properties of cuprates.</p>
#42 dynamic PET scan
<p>#42 dynamic PET scan</p>
Figure data: Actin dynamics and the Bmp pathway drive apical extrusion of proepicardial cells.
<p>The epicardium, the outer mesothelial layer enclosing the myocardium, plays key roles in heart development and regeneration. During embryogenesis it arises from the proepicardium (PE), a cell cluster that appears in the dorsal pericardium (DP) close to the venous pole of the heart. Little is known about how the PE emerges from the pericardial mesothelium. Using the zebrafish model and a combination of genetic tools, pharmacological agents and quantitative <em>in vivo</em> imaging, we reveal that a coordinated collective movement of DP cells drives PE formation. We found that BMP signaling and the actomyosin cytoskeleton promote constriction of the DP, which enabled PE cells to extrude apically. We provide evidence that cell extrusion, which has been described in the elimination of unfit cells from epithelia and the emergence of hematopoietic stem cells, is also a mechanism for PE cells to exit an organized mesothelium and fulfil the developmental fate to form a new tissue layer, the epicardium.</p>
Supplementary material 1 from: Scott JK, Batchelor KL, Webber BL (2019) Long term monitoring of recruitment dynamics determines eradication feasibility for an introduced coastal weed. NeoBiota 50: 31-53. https://doi.org/10.3897/neobiota.50.35070
: Data type: species data
Supplementary material 4 from: Skálová H, Moravcová L, Čuda J, Pyšek P (2019) Seed-bank dynamics of native and invasive Impatiens species during a five-year field experiment under various environmental conditions. NeoBiota 50: 75-95. https://doi.org/10.3897/neobiota.50.34827
: Data type: statistical data
Supplementary material 1 from: Skálová H, Moravcová L, Čuda J, Pyšek P (2019) Seed-bank dynamics of native and invasive Impatiens species during a five-year field experiment under various environmental conditions. NeoBiota 50: 75-95. https://doi.org/10.3897/neobiota.50.34827
: Data type: multimedia
Supplementary material 3 from: Skálová H, Moravcová L, Čuda J, Pyšek P (2019) Seed-bank dynamics of native and invasive Impatiens species during a five-year field experiment under various environmental conditions. NeoBiota 50: 75-95. https://doi.org/10.3897/neobiota.50.34827
: Data type: multimedia
Supplementary material 2 from: Skálová H, Moravcová L, Čuda J, Pyšek P (2019) Seed-bank dynamics of native and invasive Impatiens species during a five-year field experiment under various environmental conditions. NeoBiota 50: 75-95. https://doi.org/10.3897/neobiota.50.34827
: Data type: statistical data
Supplementary material 1 from: Eisenhauer N, Bonkowski M, Brose U, Buscot F, Durka W, Ebeling A, Fischer M, Gleixner G, Heintz-Buschart A, Hines J, Jesch A, Lange M, Meyer S, Roscher C, Scheu S, Schielzeth H, Schloter M, Schulz S, Unsicker S, van Dam NM, Weigelt A, Weisser WW, Wirth C, Wolf J, Schmid B (2019) Biotic interactions, community assembly, and eco-evolutionary dynamics as drivers of long-term biodiversity–ecosystem functioning relationships. Research Ideas and Outcomes 5: e47042. https://doi.org/10.3897/rio.5.e47042
Detailed design of the Field Experiment
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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.