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48 results for “Energy landscapes”
The energy landscape for R-loop formation by the CRISPR-Cas Cascade complex - Minimal Dataset
<p>Minimal Dataset for "The energy landscape for R-loop formation by the CRISPR-Cas Cascade complex", published at <a href="https://www.nature.com/nsmb/">NSMB</a>.</p>
HOLO Dataset - Energy Landscape of the Domain Movement in Staphylococcus aureus UDP-N-acetylglucosamine 2-epimerase
<p>Results of the Molecular Dynamic simulations performed for the paper entitled "Energy Landscape of the Domain Movement in Staphylococcus aureus UDP-N-acetylglucosamine 2-epimerase". The trajectories and the prmtop file here are without the solvent and ions.</p>
APO Dataset - Energy Landscape of the Domain Movement in Staphylococcus aureus UDP-N-acetylglucosamine 2-epimerase
<p>Results of the Molecular Dynamic simulations performed for the paper entitled "Energy Landscape of the Domain Movement in Staphylococcus aureus UDP-N-acetylglucosamine 2-epimerase". The trajectories and the prmtop file here are without the solvent and ions.</p>
The R package enerscape: A general energy landscape framework for terrestrial movement ecology
<ol> <li> <p class="western"><span>Ecological processes and biodiversity patterns are strongly affected by how animals move through the landscape. However, it remains challenging to predict animal movement and space use. Here we present our new R package <i>enerscape</i> to quantify and predict animal movement in real landscapes based on energy expenditure. </span></p> </li> <li> <p class="western"><span><i>Enerscape</i> integrates a general locomotory model for terrestrial animals with GIS tools in order to map energy costs of movement in a given environment, resulting in energy landscapes that reflect how energy expenditures may shape habitat use. <i>Enerscape</i> only requires topographic data (elevation) and the body mass of the studied animal. To illustrate the potential of <i>enerscape</i>, we analyze the energy landscape for the Marsican bear (<i>Ursus arctos marsicanus</i>) in a protected area in central Italy in order to identify least-cost paths and high-connectivity areas with low energy costs of travel.</span></p> </li> <li> <p class="western"><span><i>Enerscape</i> allowed us to identify travel routes for the bear that minimize energy costs of movement and regions that have high landscape connectivity based on movement efficiency, highlighting potential corridors. It also identifies areas where high energy costs may prevent movement and dispersal, potentially exacerbating human-wildlife conflicts in the park. A major strength of <i>enerscape</i> is that it requires only widely available topographic and body size data. As such, <i>enerscape</i> permits a first cost-effective way to estimate landscape use and movement corridors even when telemetry data is not readily available, such as for the example with the bear. </span></p> </li> <li> <p class="western"><span><i>Enerscape</i> is built in a modular way and other movement modes and ecosystem types can be implemented when appropriate locomotory models are available. In summary, <i>enerscape</i> is a new general tool that quantifies, using minimal and widely available data, the energy costs of moving through a landscape. This can clarify how and why animals move in real landscapes and inform practical conservation and restoration decisions.</span></p> </li> </ol>
Sampling Rare Event Energy Landscapes via Birth-Death Augmented Dynamics
<p>Archive with data supporting the paper "Sampling Rare Event Energy Landscapes via Birth-Death Augmented Dynamics" and the related PhD thesis by B. Pampel</p>
Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase.
<p>The data deposited here accompany the manuscript "Extended ensemble molecular dynamics study of ammonia–cellulose I complex crystal models: free-energy landscape and atomistic pictures of ammonia diffusion in the crystalline phase" and include the molecular dynamics trajectories and the AMBER topology (parm) files. Detailed file contents are summarized in the README file.</p>
Characterizing the Folding Transition State Ensembles in the Energy Landscape of an RNA Tetraloop - available data.
<p>Trajectory file and scripts necessary to generate an ELViM [Oliveira, A. B.; Yang, H.; Whitford, P. C.; Leite, V. B. P. JCTC, 2019, 15, p.6482] projection of the conformational space for the GCAA tetraloop.</p>
Data and code for "Revealing the free energy landscape of halide perovskites: Metastability and transition characters in CsPbBr3 and MAPbI3"
<p>This record contains a neuroevolution potential (NEP) model (<code>nep-MAPI-SCAN.txt</code> ) for MAPbI3 used in the linked publication. The model can be used in conjunction with the <a href="https://gpumd.org">GPUMD package</a>. The <a href="https://calorine.materialsmodeling.org">calorine package</a> provides a Python interface to GPUMD.<br> Several primitive structures in extended xyz format can be found in the <code>*.xyz</code> files. These structures have been relaxed using the NEP model included here. The <code>demo-for-using-structures-and-model.py</code> script illustrates how to access the structures and model.</p>
The R package enerscape: A general energy landscape framework for terrestrial movement ecology
Open the record for dataset details and reuse information.
Research data accompanying the paper 'GPU-Accelerated Exploration of Biomolecular Energy Landscapes'
<p>Input and output files for the results presented in Tables 1-8 of the paper 'GPU-Accelerated Exploration of Biomolecular Energy Landscapes'. Further information on these files can be found in the README files within the tar file.</p>
Dynamic energy landscapes of predators and the implications for modifying prey risk
<p>Landscapes of fear describe a spatial representation of an animal's perceived risk of predation and the associated foraging costs, while energy landscapes describe the spatial representation of their energetic cost of moving and foraging. Fear landscapes are often dynamic and change based on predator presence and behavior, and variation in abiotic conditions that modify risk. Energy landscapes are also dynamic and can change across diel, seasonal, and climatic timescales based on variability in temperature, snowfall, wind/current speeds etc.</p> <p>Recently, it was suggested that fear and energy landscapes should be integrated. In this paradigm, the interaction between the landscapes relates to prey being forced into areas of the energy landscape they would avoid if risk were not a factor. However, dynamic energy landscapes experienced by predators must also be considered since they can affect their ability to forage, irrespective of variation in prey behavior. We propose an additional component to the fear and dynamic energy landscape paradigm that integrates landscapes of both prey and predators, where predator foraging behavior is modulated by changes in their energyscape.</p> <p>Specifically, we integrate the predators' energy landscape into foraging theory that predicts prey patch-leaving decisions under the threat of predation. We predict that as a predator's energetic cost of foraging increases in a habitat, then the prey's foraging costs of predation and patch quitting harvest rate will decrease. Prey may also decrease their vigilance in response to increased energetic foraging cost for predators, which will lower prey-giving-up densities.</p> <p>We then provide examples in terrestrial, aerial and marine ecosystems where we might expect to see these effects. These include birds, sharks which use updrafts that vary based on wind and current speeds, tidal state, or temperature and terrestrial predators (e.g. wolves) whose landscapes vary seasonally with snow depth or ice cover which may influence their foraging success and even diet selection.</p> <p>A predator perspective is critical to considering the combination of these landscapes and their ecological consequences. Dynamic predator energy landscapes could add a spatiotemporal component to risk effects which may cascade through food webs.</p>
Research Landscape of Energy Transition and Green Finance: A Bibliometric Analysis
<p>Dataset for article title "<span>Research Landscape of Energy Transition and Green Finance: A Bibliometric Analysis".</span></p>
Data from: Solar energy-driven land cover change could alter landscapes critical to animal movement in the continental United States
<p>The United States may produce as much as 45% of its electricity using solar energy technology by 2050, which could require more than 40,000 km<sup>2 </sup>of land to be converted to large-scale solar energy production facilities. Little is known about how such development may impact animal movement. Here, we use five spatially-explicit projections of solar energy development through 2050 to assess the extent to which ground-mounted photovoltaic solar energy expansion in the continental United States may impact land cover and alter areas important for animal movement. Our results suggest that there could be a substantial overlap between solar energy development and land important for animal movement: across projections, 7-17% of total development is expected to occur on land with high value for movement between large protected areas, while 27-33% of total development is expected to occur on land with high value for climate-change-induced migration. We also found substantial variation in the potential overlap of development and land important for movement at the state level. Solar energy development, and the policies that shape it, may align goals for biodiversity and climate change by incorporating the preservation of animal movement as a consideration in the planning process.</p>
Research data for: Patchy energy landscapes promote stability of small groups of active particles
<p>Research data supporting the pubblication "Patchy energy landscapes promote stability of small groups of active particles"</p>
Research data for "Exploring the energy landscape of aluminas through machine learning interatomic potential"
<p>This dataset supports the paper "Exploring the energy landscape of aluminas through machine learning interatomic potential". The paper is online here:</p> <p>The following folders are provided:</p> <ul> <li><em>classical_potential_files</em>: Contains all the empirical potentials used in this study.</li> </ul> <p> </p> <ul> <li><strong><em>crystal_structure_file</em></strong>: Contains structural files of aluminas with various crystal structures, which can be distinguished by their respective filenames. Configurations of alumina with partially occupied cation sites can be obtained from the references provided in the supplementary materials of our article.</li> </ul> <p> </p> <ul> <li><strong><em>lowest_E-config</em></strong>: The files named <code>poscar_{0..19}</code> represent the 20 structure files identified through our developed structural search workflow in conjunction with the final NEP of Aluminas. These structures exhibit different distributions of Al cation occupancy sites. The suffix numbers in the file names indicate that these 20 structures are arranged in ascending order based on their corresponding energy values after structural relaxation using the NEP. In other words, <code>poscar_0</code>, after structural optimization, has the lowest energy among these 20 configurations. Additionally, we have included the CIF files for the crystal structures with partial occupancies provided by the Smrcok model in this folder.</li> </ul> <p> </p> <ul> <li><strong><em>the_final-dataset_alumina</em></strong>: This folder contains all the relevant files for training the final NEP of Aluminas, including the final training dataset named <code>train.xyz</code>, the training parameter file <code>nep.in</code>, and log files. The file <code>nep.txt</code> refers to the final NEP of Aluminas. </li> </ul> <p> </p> <ul> <li><strong><em>various_test-datasets</em></strong>: This folder provides all the test datasets used for testing the final NEP of Aluminas. We have categorized them into four types based on composition: clusters, amorphous structures, crystals, and datasets with physically unallowed configurations that exhibit nearest-neighbor cation occupancy according to the Smrcok model.</li> </ul> <p>Additionally, for ease of retrieval, we have placed the file for the final NEP of aluminas in the main directory and named it <code>nep_3335.txt</code>, where the suffix indicates that the final training dataset <code>train.xyz</code> contains 3,335 structures. This file is identical to the file named <code>nep.txt</code> located in the folder <code>the_final-dataset_alumina</code>.</p>
Body size, trophic position, and the coupling of different energy pathways across a saltmarsh landscape
<p>Here, we listed the bulk stable isotope values (δ13C and δ15N) and body size measurements of organisms that were analyzed in the manuscript "Body size, trophic position, and the coupling of different energy pathways across a saltmarsh landscape", published in Limnology and Oceanography Letters. Our dataset is a compilation of samplings obtained by the Southern Louisiana marsh food webs project within the Coastal Waters Consortium (CWC) II (Lopez-Duarte et al. 2017a [https://doi.org/10.7266/N7XS5SGD], Lopez-Duarte et al. 2017b [https://doi.org/10.7266/N79W0CJW], Polito et al. 2019 [https://doi.org/10.7266/n7-6277-1216]). The dataset consists of 1563 individual samples from 77 taxa, including basal sources (plants, phytoplankton, detritus, and microphytobenthos), fishes, invertebrates (insects, crustaceans, and spiders), infauna, seaside sparrows (Ammospiza maritima), and marsh rice rats (Oryzomys palustris). Samplings were conducted in three sites, Bay Sansbois, Bay Batiste, and West Pointe à la Hache, located in northeastern Barataria Bay. Bay Sansbois and Bay Batiste were sampled in May and October 2015, and May 2016, whereas West Pointe à la Hache was sampled only in May 2016. Body size measurements are available for 59% of all samples (52 taxa) while δ13C and δ15N are available for all samples.</p>
Lesser prairie-chicken habitat selection and survival relative to a wind energy facility located in a fragmented landscape
<p>The overlap of renewable wind energy with the range of lesser prairie-chickens (<em>Tympanuchus</em> <em>pallidicinctus</em>) raises concern of population declines and habitat loss. Lesser prairie-chickens are adversely affected by landscape change, however, it is unclear how this species may respond to wind energy development. Therefore, managers and wind energy developers are currently tasked with making management or siting recommendations of future wind energy facilities based on lesser prairie-chicken behavioral responses to other forms of anthropogenic development or responses of other grouse species to wind energy development. The current strategy of siting wind turbines in cultivated cropland within lesser prairie-chicken range has not been evaluated for its effectiveness at minimizing potential adverse impacts. We captured 60 female and 66 male lesser prairie-chicken from leks located along a gradient from wind turbines in southern Kansas, USA, from 2017–2021. Over the study period, we collected lesser prairie-chicken location data and demographic information to evaluate resource selection, movements, and demography relative to environmental predictors and metrics associated with the wind energy facility. Lesser prairie-chickens used habitats in close proximity to wind turbines, provided that turbine density was low; however, avoidance associated with cultivated cropland appeared to be more predictive than the presence of wind turbines. We observed movement between turbines suggesting that wind turbines did not act as a barrier to local movements. We did not detect an influence of wind turbines on nest success or individual survival during breeding or non-breeding periods, a relationship that is consistent among multiple grouse species using habitats near wind energy infrastructure. Additional research is necessary to evaluate impacts associated with wind energy development in intact lesser prairie-chicken habitats, but placing wind turbines in cultivated croplands or other fragmented landscapes appears to be an important siting measure when considering wind energy facility siting across the lesser prairie-chicken range.</p>
Body size, trophic position, and the coupling of different energy pathways across a saltmarsh landscape
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Dynamic energy landscapes of predators and the implications for modifying prey risk
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Lesser prairie-chicken habitat selection and survival relative to a wind energy facility located in a fragmented landscape
Open the record for dataset details and reuse information.
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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.