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1,032 results for “vertical”
Vertical land motion due to present-day ice loss from Greenland's and Canada's peripheral glaciers
<p>Greenland's bedrock responds to the ongoing loss of ice mass with an elastic vertical land motion (VLM) that is measured by Greenland's GNSS Network (GNET). The measured VLM also contains other contributions, including the long-term viscoelastic response of the Earth to previous deglaciation.</p> <p>Greenland's ice sheet (GrIS) is producing the most significant contribution to the total VLM. The contribution of peripheral glaciers (PGs) from both Greenland (GrPGs) and Arctic Canada (CanPGs) has not been carefully accounted for in the GNSS time series analysis. This is a significant concern, since GNET stations are often closer to PGs than to the ice sheet. </p> <p>We find that PGs produce significant elastic rebound, especially in North and East Greenland. Across these regions, the PGs result in up to 37% of the elastic rebound. For a few stations in the North, the VLM from PGs is larger than the GrIS one.</p>
Quantifying the Impact of Vertical Resolution on the Representation of Marine Boundary Layer Physics for Global-Scale Models
<p>Data supporting the findings of DOI: 10.1175/MWR-D-23-0078.1, a 2023 Monthly Weather Review publication with the same title as this dataset.</p>
Data archive for: Exploring the use of machine learning to improve vertical profiles of temperature and moisture
<p>Vertical profiles of temperature and dewpoint are useful in predicting deep convection that leads to severe weather that threatens property and lives. Currently, forecasters rely on observations from radiosonde launches and numerical weather prediction (NWP) models. Radiosonde observations are, however, temporally and spatially sparse, and NWP models contain inherent errors that influence short-term predictions of high-impact events. This work explores using machine learning (ML) to postprocess NWP model forecasts, combining them with satellite data to improve vertical profiles of temperature and dewpoint. We focus on different ML architectures, loss functions, and input features to optimize predictions. Because we are predicting vertical profiles at 256 levels in the atmosphere, this work provides a unique perspective at using ML for 1-D tasks. Compared to baseline profiles from the Rapid Refresh (RAP), ML predictions offer the largest improvement for dewpoint, particularly in the mid- and upper-atmosphere. emperature improvements are modest, but CAPE values are improved by up to 40%. Feature importance analyses indicate that the ML models are primarily improving incoming RAP biases. While additional model and satellite data offer some improvement to the predictions, architecture choice is more important than feature selection in fine-tuning the results. Our proposed deep residual UNet performs the best by leveraging spatial context from the input RAP profiles; however, the results are remarkably robust across model architecture. Further, uncertainty estimates for every level are well-calibrated and can provide useful information to forecasters.</p>
Data and R code used in Alonso-Crespo et al (2024) Exploring priority and year effects on plant diversity, productivity and vertical root distribution: first insights from a grassland field experiment
<p>This release contains the raw data, R code, and RootPainter model supporting the results described in Alonso-Crespo et al (2024) Exploring priority and year effects on plant diversity, productivity and vertical root distribution: first insights from a grassland field experiment.</p>
Data for: Seasonally mediated niche partitioning in a vertically compressed pelagic predator guild
<p>Niche partitioning among closely related, sympatric species is a fundamental concept in ecology, and its mechanisms are of broad interest for understanding ecosystem functioning and predicting the impacts of human-driven environmental change. However, identifying mechanisms by which top marine predators partition available resources has been especially challenging given the difficulty of quantifying resource use of large pelagic animals. In the eastern tropical Pacific (ETP), three large, highly mobile and ecologically similar pelagic predators (blue marlin (<em>Makaira</em> <em>nigricans</em>), black marlin (<em>Istiompax</em> <em>indica</em>) and sailfish (<em>Istiophorus platypterus))</em> coexist in a vertically compressed habitat. To evaluate each species' ecological niche, we leveraged a decade of recreational fisheries data, multi-year satellite tracking with high-resolution dive data, and stable isotope analysis. Fishery interaction and telemetry-based three-dimensional seasonal utilization distributions suggested high spatial and temporal overlap among species; however, seasonal and diel variability in diving behaviour produced spatial partitioning, leading to low trophic overlap among species. Expanding oxygen minimum zones will reduce the available vertical habitat within predator guilds, likely leading to increases in interspecific competition. Thus, understanding the mechanisms of habitat partitioning among predators in the vertically compressed ETP can provide insight into how predators in other ocean regions may respond to vertically limited habitats.</p>
Data and scripts for the submission "A locally smoothed terrain-following vertical coordinate to improve the simulation of fog and low stratus in numerical weather prediction models"
<p>Dataset and scripts used to generate Figures for "A locally smoothed terrain-following vertical coordinate to improve the simulation of fog and low stratus in numerical weather prediction models", submitted to the <strong><em>Journal of Advances in Modeling Earth Systems</em></strong> (JAMES).</p> <p>Scripts: Python and NCL</p> <p>Data: Netcdf, PNG, Python pickled objects</p>
Data for the paper: Critical role of radiative cooling vertical contrast in triggering episodic deluges in small-domain hothouse climates
<p>Data for the paper: Critical role of radiative cooling vertical contrast in triggering episodic deluges in small-domain hothouse climates.</p> <p>The paper has just been submitted, which means this dataset might change before the final publication.</p>
Vertical structural complexity of plant communities represents the combined effects of resource acquisition and environmental stress on the Tibetan Plateau
<p><span>Knowledge of vertical structural complexity (VSC) is important, because the resulting spatial partitioning is closely linked to resource utilization and environmental adaptation. However, the spatial pattern of VSC </span><span>on </span><span>large scales and its underlying mechanisms are poorly understood. Here, we systematically investigated 2,013 plant communities through grid sampling on </span><span>the </span><span>Tibetan Plateau (TP). </span><span>VSC was quantified </span><span>as </span><span>the maximum plant height within a plot (Height-max), coefficient of variation of plant height</span><span> (Height-var)</span><span>, and Shannon evenness of plant height (</span><span>Height-even</span><span>)</span><span>. Precipitation dominated the spatial variation in VSC in forests and shrublands, supporting the classic physiological tolerance hypothesis (PTH). In contrast, for alpine meadows, steppes, and desert grasslands in extreme environments, non-resource limiting factors (e.g., wide diurnal temperature ranges and strong winds) dominate VSC variation. Generally, with the shifting of climate from favorable to extreme, the effect of resource availability gradually decreases, but the effect of non-resource limiting factors gradually increase</span><span>s</span><span>, and that the PTH only applicable in "favorable conditions". With </span><span>the help of</span><span> machine learning models, maps of </span><span>VSC</span><span> at 1-km resolution were produced for the TP for the first time</span><span>. Our</span><span> new findings and maps of VSC provide new insights into macroecological studies, especially for adaptation mechanisms and model optimization.</span></p>
Dependence of tropical cyclone weakening rate in response to an imposed moderate environmental vertical wind shear on the warm-core strength and height of the initial vortex
<p>This study investigated the dependence of the early tropical cyclone (TC) weakening rate in response to an imposed moderate environmental vertical wind shear (VWS) on the warm-core strength and height of the TC vortex using idealized numerical simulations. Results show that the weakening of the warm core by upper-level ventilation is the primary factor leading to the early TC weakening in response to an imposed environmental VWS. The upper-level ventilation is dominated by eddy radial advection of the warm-core air. The TC weakening rate is roughly proportional to the warm-core strength and height of the initial TC vortex. The boundary-layer ventilation shows no relationship with the early weakening rate of the TC in response to an imposed moderate VWS. The findings suggest that some previous diverse results regarding the TC weakening in environmental VWS could be partly due to the different warm-core strengths and heights of the initial TC vortex.</p>
Data from: Vertically inherited microbiota and environment modifying behaviors conceal genetic variation in dung beetle life history
<p>Diverse organisms actively manipulate their (sym)biotic and physical environment in ways that feedback on their own development. However, the degree to which these processes affect microevolution remains poorly understood. The gazelle dung beetle both physically modifies its ontogenetic environment and structures its biotic interactions through vertical symbiont transmission. By experimentally eliminating i) physical environmental modifications, and ii) the vertical inheritance of microbes, we assess how environment modifying behavior and microbiome transmission shape heritable variation. We found that depriving larvae from symbionts and environment modifying behaviors increased additive genetic variance and heritability for development time but not body size. This suggests that larvae's ability to manipulate their environment has the potential to modify heritable variation and to facilitate the accumulation of cryptic genetic variation. This cryptic variation may become released and selectable when organisms encounter environments that alter the degree to which they can be manipulated. Furthermore, we found heritable variation for the response to the elimination of environmental modifications, indicating that genotypes differ in their dependence on the ability to manipulate their environment. Our findings highlight that the ability of organisms to actively manipulate their environment may affect the potential of populations to evolve when encountering novel, stressful conditions.</p>
Vertical stratification of leaf physical traits exerts bottom-up pressures on insect herbivory in a sugar maple temperate forest
<p>Do vertical gradients in temperate forest structure insect herbivore communities? We tested the hypothesis that the increase in light intensity from understory to forest canopy level drives differences in leaf physical traits and budburst phenology that impact insect herbivores and thus play a role in structuring both herbivore communities and the leaf damages they cause. Twelve sugar maple <em>(Acer saccharum)</em> sites were monitored in southern Quebec, examining insect herbivore patterns from understory to the shaded and sun canopy over the summers of 2020, 2021, and 2022. Additionally, we recorded leaf physical traits, temperature, humidity, and sun exposure. Our findings revealed that leaf thickness increased along the vertical gradient in 2021, making leaves less favorable to herbivores in the canopy level. Accordingly, we recorded a consistent decrease in insect herbivory damage rates from the understory to the shaded canopy and sun canopy in 2020 and 2021, driven by leaf cutters, skeletonizers, stipplers, and leaf miners. These results support our hypothesis that variation in plant physical traits due to sun exposure contributes to the vertical stratification of insect damage. In 2022, the gradient of insect herbivore abundance corroborated the observed damage trends from the previous years. Moreover, we calculated an average annual herbivory rate of 9.1% of the leaf surface in our study site, suggesting limited evidence supporting a significant contribution of background herbivory to the decline of sugar maple forests. Overall, our study highlights the importance of vertical gradients in structuring insect herbivore communities and emphasizes the role of leaf traits in mediating these interactions.</p>
Code and partial data used in "Vertically recurrent neural networks for sub-grid parameterization"
<p>This repository contains the RNN training and evaluation code used in the paper<em> Vertically recurrent neural networks for sub-grid parameterization</em></p> <p> </p> <ul> <li> The radiative transfer emulation data can be accessed with through a Climetlab plugin (<a href="https://pypi.org/project/climetlab-maelstrom-radiation/">Climetlab-maelstrom-radiation</a>). <p>Datasets are downloaded and explained in the demo notebook here <a href="https://git.ecmwf.int/projects/MLFET/repos/maelstrom-radiation/browse/notebooks/demo_radiation.ipynb" rel="nofollow">https://git.ecmwf.int/projects/MLFET/repos/maelstrom-radiation/browse/notebooks/demo_radiation.ipynb</a></p> In addition the full training and testing code used in the paper is uploaded here (<em>pu-maelstrom-radiation.tar.gz</em>).</li> <li> </li> </ul> <p>Three parameterization problems from earlier studies are also included (we have modified the code from these papers to incorporate RNNs): </p> <ul> <li>non-orographic gravity wave drag (<a href="https://doi.org/10.1029/2021MS002477">Chantry et al. 2021</a>) <ul> <li>Based on TensorFlow</li> <li>This repository uses the <em>CliMetLab </em>plugin and<strong> downloads the data from the European Weather Cloud</strong></li> </ul> </li> <li>non-local parameterization (<a href="https://doi.org/10.1029/2022MS002984">Wang et al. 2022</a>) <ul> <li>The new code is based on TensorFlow, so you'll need both PyTorch and TensorFlow to run everything</li> <li><strong>See original paper for data access</strong></li> </ul> </li> <li>moist physics (Han et al. <a href="https://doi.org/10.1029/2022MS003508">2023</a>, <a href="https://doi.org/10.1029/2020MS002076">2020</a>) <ul> <li>Based on TensorFlow and PyTorch. This one has the most additions, e.g. code to generate a TensorFlow TFRecord dataset from the raw netCDF data archived in the original paper, autoregressive training and experimental model architectures in PyTorch</li> <li><strong>See original paper for data access</strong></li> </ul> </li> </ul> <p>Each of the code repos (unpack the tars) have an updated README.</p> <p>References:</p> <table> <tbody> <tr> <td> <div>Chantry, M., Hatfield, S., Dueben, P., Polichtchouk, I., & Palmer, T. (2021). Machine learning emulation of gravity wave drag in numerical weather forecasting. <em>Journal of Advances in Modeling Earth Systems</em>, <em>13</em>(7), e2021MS002477</div> <div> </div> <div> <div>Han, Y., Zhang, G. J., Huang, X., & Wang, Y. (2020). A moist physics parameterization based on deep learning. <em>Journal of Advances in Modeling Earth Systems</em>, <em>12</em>(9), e2020MS002076.</div> </div> <div> </div> <div>Han, Y., Zhang, G. J., & Wang, Y. (2023). An ensemble of neural networks for moist physics processes, its generalizability and stable integration. <em>Journal of Advances in Modeling Earth Systems</em>, <em>15</em>(10), e2022MS003508</div> <div> </div> <div>Wang, P., Yuval, J., & O’Gorman, P. A. (2022). Non‐local parameterization of atmospheric subgrid processes with neural networks. <em>Journal of Advances in Modeling Earth Systems</em>, <em>14</em>(10), e2022MS002984.</div> </td> </tr> <tr></tr> </tbody> </table> <div> </div>
GNSS Vertical Crustal Displacement Data around Qinghai Lake
<p>This dataset includes vertical crustal displacement observations from 13 GNSS stations near Qinghai Lake, spanning from 2016 to 2023. The data consists of GNSS vertical displacement time series (GNSS_Upper.mat, MATLAB format), geographic coordinates of the stations (site_info.txt), and the corresponding time vector (GNSS_datatime.mat). </p> <p>This dataset supports the study described in the article "Terrestrial water storage changes of Qinghai Lake on the Tibetan Plateau from joint inversion of GNSS and InSAR data" (currently under peer review). The article combines GNSS and InSAR data to investigate vertical deformation field and terrestrial water storage changes in the Qinghai Lake region on the Tibetan Plateau. </p>
Quantum efficiency and vertical position of quantum emitters in hBN determined by Purcell effect in hybrid metal-dielectric planar photonic structures
<p>Data from the article "Quantum efficiency and vertical position of quantum emitters in hBN determined by Purcell effect in hybrid metal-dielectric planar photonic structures", <a href="https://pubs.acs.org/doi/10.1021/acsphotonics.4c01416">ACS Photonics, (2024)</a> - [arXiv:2407.20160]</p>
Vertices of the Birkhoff polytope of 3-dimensional matrices of order 4
<p>533 equivalence classes of vertices of the polytope of 3-dimensional polystochastic matrices of order 4 in lexicographic order (including 21 vertices missed in first version).</p> <p>Format of data: entries of matrix / common denominator; size of support; value of permanent rounded to four decimals; exact value of permanent; number of automorphims</p>
Supplementary material - Comparison of electrified vertical trash racks – electric field and effects on species-specific fish behaviours
<p><strong>Videos_Rack_Interactions - </strong>Compiled videos consisting of all individual rack interactions observed for the tested configurations. Among the exploratory setups, only increasing voltages for chub with <em>PW </em>= 1 ms, <em>f</em> = 10Hz were included. </p> <p> </p> <p> </p> <p><strong>Data.xlsm- </strong>Excel document containing the following sheets:</p> <ul> <li><strong>Fish </strong>- Length, Height and catch date of the tested Eel, Brown Trout and Chub</li> <li><strong>Experiments </strong>- Summary table which configuration and species was tested on which date. </li> <li><strong>Setups </strong>- Overview of the tested setups and naming convention used in the paper (C1- C7) compared to the convention used in the dataset (R12 - R28) </li> <li><strong>Results - </strong>Results of the experiments. Each experiment consists of three lines (three fish). For each fish, the tested configuration, lengt and height, catch date as well as reaction to the bar rack ( bypass without rack contact, bypass with rack contact, refusal and rack passage) are detailed. </li> <li><strong>Results (Filtered) - </strong>Sheet results without formatting and without invalid experiments. </li> <li><strong>TIme to reaction </strong>- Additional evaluation how much time fish spent in the experiment on average after the starting compartment was opened until a firsts interaction with the rack or bypass passage was observed. </li> </ul>
Vertical profiles of leaf photosynthesis and leaf traits, and soil nutrients in two tropical rainforests in French Guiana before and after a three-year nitrogen and phosphorus addition experiment
<p>We provide a comprehensive dataset of vertical profiles of photosynthetic capacity and important leaf traits, including leaf N and P concentrations, from two three-year, large-scale fertilisation experiments conducted in two tropical rainforests in French Guiana. These data present a unique source of information to further improve model representations of the roles of N, P, and other leaf nutrients, in photosynthesis in tropical forests. To further facilitate the use of our data in syntheses and model studies, we provide an elaborate list of ancillary data, including important soil properties and nutrients, along with the leaf data. As environmental drivers are key to improve our understanding of carbon (C)-nutrient cycle interactions, this comprehensive dataset will aid to further enhance our understanding of how nutrient availability interacts with C uptake in tropical forests.</p>
Data and Codes used in Alonso-Crespo et al (2021) Assembly history modulates vertical root distribution in a grassland experiment.
<p>This repository contains the raw data, the RootPainter model and the annotated R and Python codes supporting the results reported in the following paper: Alonso-Crespo et al (2021) Assembly history modulates vertical root distribution in a grassland experiment.</p>
UAS spherical photography for the vertical characterisation of canopy structural traits
<p>Data and scripts for New Phytologist manuscript:</p> <p><strong>UAS spherical photography for the vertical characterisation of canopy structural traits </strong><br> Vicent Agustí Ribas Costa, Maxime Durand, T Matthew Robson, Albert Porcar-Castell, Ilkka Korpela, Jon Atherton</p> <p><strong>The current version is 2.0.0; </strong>please do <strong><em>not</em></strong> use the data/code from the peer review version 1<strong>. </strong> In the Early View version 2.0.0 we have<strong> </strong>fixed minor bugs in data (incorrectly flipped images), and updated our code. We also reduced the data archive size to approx 5 GB. </p> <p>Note that individual (i.e. pre-stitched) UAS photos from vertical profiles are not included due to the size (>50 GB). If you want all the individual files then contact Jon. Data was compiled by Vicent, but feel free to contact Jon in case of any issues.</p>
Global 1° × 1° map of vertical POC flux profiles simulated by the MSPACMAM
<p>Model output data generated by two different configurations of the MSPACMAM: (1) the standard model configuration (so-called <em>std</em> configuration) and (2) the model configuration including large particle fragmentation (so-called <em>frag</em> configuration). The MSPACMAM simulates the climatological annual mean state of sinking particulate organic carbon (POC) fluxes in the contemporary global ocean. Model variables include small particle POC concentration, large particle POC concentration, small particle sinking speed, large particle sinking speed, sinking POC flux, and POC transfer efficiency at 1000 m (so-called <span class="math-tex">\(T_{eff}\)</span>).</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.