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1,067 results for “perturbation”
GPR surrogate model dataset for remnant black hole properties using perturbation theory and NR
<p>This is the data-set used in <strong><code>BHPTNR_Remnant</code></strong> which is an easy-to-use python package to efficiently predict the remnant mass, remnant spin, peak luminosity and the final kick imparted on the remnant black hole directly from the gravitational radiation using GPR fits. These fits have been built on the remnant data calculated from numerical relativity informed black hole perturbation theory based waveforms.</p>
Context-dependent perturbations in chromatin folding and the transcriptome by cohesin and related factors
<p>Cohesin plays vital roles in chromatin folding and gene expression regulation, cooperating with such factors as cohesin loaders, unloaders, and the insulation factor CTCF. Although models of regulation have been proposed (e.g., loop extrusion), how cohesin and related factors collectively or individually regulate the hierarchical chromatin structure and gene expression remains unclear. We have depleted cohesin and related factors and then conducted a comprehensive evaluation of the resulting 3D genome, transcriptome and epigenome data. We observed substantial variation in depletion effects among factors at topologically associating domain (TAD) boundaries and on interTAD interactions, which were related to epigenomic status. Gene expression changes were highly correlated with direct cohesin binding and gain of TAD boundaries than with the loss of boundaries. Moreover, cohesin was broadly enriched in active compartment A chromosomes, which were retained after CTCF depletion. Our results demonstrate context-specific roles of cohesin for gene expression and chromatin folding.</p>
Predicting the Relative Static Permittivity: a Group Contribution Method Based on Perturbation Theory
<p>Permittivity-over-temperature diagram for the publication "Predicting the Relative Static Permittivity: a Group Contribution Method Based on Perturbation Theory" published under DOI 10.1021/acs.jced.3c00323 in the Journal of Chemical and Engineering Data.</p>
A simulated natural heatwave perturbs bumble bee immunity and resistance to infection
<p><span>As a consequence of ongoing climate change, heatwaves are predicted to increase in frequency, intensity, and duration in many regions. Such extreme events can shift organisms from thermal optima for physiology and behavior, with the thermal stress hypothesis predicting reduced performance at temperatures where the maintenance of biological functions is energetically costly. Performance includes the ability to resist biotic stressors, such as infectious diseases. Climate change is a proposed threat to native bee pollinators, directly and through indirect effects on floral resources, but the thermal stress hypothesis, particularly pertaining to disease resistance, has received limited attention. We exposed adult <em>Bombus impatiens</em> bumble bee workers to simulated, ecologically relevant heatwave or control thermal regimes and assessed longevity, immunity, and resistance to concurrent or future parasite infections. We demonstrate that survival and induced antibacterial immunity are reduced following heatwaves. Supporting that heatwave exposure compromised immunity, the cost of immune activation by a non-pathogenic elicitor was thermal regime dependent, with costs to long-term survival in control but not heatwave exposed bees. However, in the face of real infections, an inability to mount an optimal immune response will be detrimental, which was reflected by higher infections from trypanosome parasite exposure following the heatwave, relative to the control regime. These results demonstrate interactions between heatwave exposure and bumble bee performance, including immune and infection outcomes. Thus, the health of bumble bee pollinator populations may be affected through altered interactions with parasites and pathogens, in addition to other effects of extreme manifestations of climate change.</span></p>
Data from: Culling-induced perturbation of social networks of wild geese reinforces rather than disrupts associations among survivors
<p>Wildlife populations may be the subject of management interventions for disease control that can have unintended, counterproductive effects. Social structure exerts a strong influence over infectious disease transmission in addition to other characteristics of populations such as size and density that are the primary target for disease control. Social network approaches have been widely used to understand disease transmission in wildlife but rarely in the context of perturbations, such as culling, despite the likely impacts of such disturbance on social structure and disease dynamics. Here we present a 'removal' study of a free-living population of resident Canada geese <em>Branta canadensis</em>, a highly social species that is frequently managed by culling and can carry pathogens relevant to human and domestic animal health. We quantified social network structure and spatial behaviour before and after controlled culling of individuals during the summer moult. Culling did not substantially increase individual social connectivity. Individuals that moulted at cull sites or were formerly strongly associated with removed birds were more likely to strengthen and maintain any surviving existing associations while also forming new associations. However, the establishment of new associations was largely compensatory (with only small increases in the number and strength of connections) and occurred locally. Synthesis & applications: geese that survived the cull responded by strengthening existing social relationships and forming new, compensatory relationships with birds local to them in the network. In the short-term such compensatory adjustments to patterns of association in response to culling could facilitate pathogen transmission. But in the longer term, controlled culling of geese is unlikely to strongly influence pathogen spread and may even slow transmission into new social clusters by reducing wider mixing. When managing wildlife for disease control, in addition to changes in social network structure the prevalence of infection at the time of the cull and the mode of transmission (e.g., direct versus environmental) will also be critical determinants of disease transmission risk in perturbed populations of geese and other wild animals.</p>
Finite Element Analysis perturbation files for Rubin Observatory Simonyi Survey Telescope and LSST Camera
<p>## Notes on FEA files</p> <p><br> </p> <p># M1M3 Bending modes</p> <p> </p> <p>M1M3_1um_156_grid.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_1um_156_grid.txt</p> <p>- shape = (5256, 159)</p> <p>- Each row is one of 5256 FEA nodes.</p> <p>- 0th column is M1M3 disambiguator</p> <p>- 1st and 2nd columns are FEA node x and y in M1M3 CS</p> <p>- Last 156 columns are bending modes; the z-displacement of each node for each mode.</p> <p> </p> <p>M1M3_1um_156_force.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_1um_156_force.txt</p> <p>- shape = (156, 159)</p> <p>- Each row is one of 156 bending modes.</p> <p>- 0th column is actuator ID</p> <p>- 1st and 2nd columns are actuator x and y in M1M3 CS</p> <p>- Last 156 columns are forces in Newtons for each mode.</p> <p><br> </p> <p># M1M3 print through</p> <p> </p> <p>M1M3_dxdydz_zenith.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_dxdydz_zenith.npy</p> <p>- shape = (5256, 3)</p> <p>- Each row is one of 5256 FEA nodes.</p> <p>- Columns are dx, dy, dz in M1M3 CS.</p> <p>- This is the gravitational "print through" when mirror is zenith pointing</p> <p> </p> <p>M1M3_dxdydz_horizon.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_dxdydz_horizon.npy</p> <p>- shape = (5256, 3)</p> <p>- Each row is one of 5256 FEA nodes.</p> <p>- Columns are dx, dy, dz in M1M3 CS.</p> <p>- This is the gravitational "print through" when mirror is horizon pointing</p> <p> </p> <p>M1M3_force_zenith.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_force_zenith.npy</p> <p>- shape = (256,)</p> <p>- Each row is one of 256 actuators. (So we consider the x and y actuators here too.)</p> <p>- Columns are forces in Newtons.</p> <p>- These are the mirror support forces when the mirror is zenith pointing. (Is this after optimization? Include LUT or not?)</p> <p> </p> <p>M1M3_force_horizon.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_force_horizon.npy</p> <p>- shape = (256,)</p> <p>- Each row is one of 256 actuators. (So we consider the x and y actuators here too.)</p> <p>- Columns are forces in Newtons.</p> <p>- These are the mirror support forces when the mirror is horizon pointing. (Is this after optimization? Include LUT or not?)</p> <p><br> </p> <p># M1M3 Thermal</p> <p> </p> <p>M1M3_thermal_FEA.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_thermal_FEA.npy</p> <p>- shape = (5244, 7)</p> <p>- Each row is one of 5244 FEA nodes. (Why aren't these the same as above? I don't know.)</p> <p>- Columns are:</p> <p>- 0: Unit-Normalized FEA x</p> <p>- 1: Unit-Normalized FEA y</p> <p>- 2: Bulk temperature dz coefficient</p> <p>- 3: x temperature gradient dz coefficient</p> <p>- 3: y temperature gradient dz coefficient</p> <p>- 3: z temperature gradient dz coefficient</p> <p>- 3: r temperature gradient dz coefficient</p> <p><br> </p> <p># M1M3 Miscellany</p> <p> </p> <p>M1M3_influence_256.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_influence_256.npy</p> <p>- shape = (5256, 256)</p> <p>- Each row is one of 5256 FEA nodes.</p> <p>- Each column is one of 256 actuators.</p> <p>- Values are dz/dF for each actuator/node.</p> <p> </p> <p>M1M3_LUT.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_LUT.txt</p> <p>- shape = (257, 91)</p> <p>- First column is index in degrees (0-90 inclusive). Last 256 columns are forces in Newtons.</p> <p>- Each column is LUT for one value of the elevation index.</p> <p> </p> <p>M1M3_1000N_UL_shape_156.fits.gz</p> <p>- source = IM/data/M1M3/M1M3_1000N_UL_shape_156.npy</p> <p>- shape = (5256, 156)</p> <p>- Rows must be FEA nodes, columns must be bending modes.</p> <p>- Not sure what the purpose is of this one.</p> <p><br> </p> <p># M2 Bending modes</p> <p> </p> <p>M2_1um_grid.fits.gz</p> <p>- source = IM/data/M2/M2_1um_grid.DAT</p> <p>- shape = (15984, 75)</p> <p>- Each row is one of 15984 FEA nodes.</p> <p>- 0th column is node index ?</p> <p>- 1st and 2nd columns are FEA node x and y in M2 CS</p> <p>- Last 72 columns are bending modes; the z-displacement of each node for each mode.</p> <p> </p> <p>M2_1um_force.fits.gz</p> <p>- source = IM/data/M2/M2_1um_force.DAT</p> <p>- shape = (72, 75)</p> <p>- Each row is one of 72 bending modes.</p> <p>- 0th column is actuator ID</p> <p>- 1st and 2nd columns are actuator x and y in M2 CS</p> <p>- Last 72 columns are forces in Newtons for each mode.</p> <p> </p> <p># M2 print through / thermal</p> <p> </p> <p>M2_GT_FEA.fits.gz</p> <p>- source = IM/data/M2/M2_GT_FEA.txt</p> <p>- shape = (9084, 6)</p> <p>- Each row is one of 9084 FEA nodes. (Why aren't these the same as above? I don't know.)</p> <p>- Columns are:</p> <p>- 0: Unit-Normalized FEA x</p> <p>- 1: Unit-Normalized FEA y</p> <p>- 2: Zenith print through dz coefficient</p> <p>- 3: Horizon print through dz coefficient</p> <p>- 4: z temperature gradient dz coefficient</p> <p>- 5: r temperature gradient dz coefficient</p> <p><br> </p>
Example calculation of E1[h1] contribution to the source for second-order metric perturbations of a Schwarzschild black hole
<p>This repository contains data for the h1 an dr0/h1 perturbations that can be used to compute a piece of the source for the second-order metric perturbation. The Mathematica notebook 'SecondOrderE1h1.nb' shows how to combine the data to compute E1[h1].</p> <p>The h1 data was computed using the h1Lorenz code that is available in the Black Hole Perturbation Toolkit (https://github.com/BlackHolePerturbationToolkit/h1Lorenz). The dr0/dh1 data was computed by Leanne Durkan following the method detailed in "Slow evolution of the metric perturbation due to a quasicircular inspiral into a Schwarzschild black hole" by Leanne Durkan and Niels Warburton, arXiv:2206.08179</p> <p>Authors: Leanne Durkan, Niels Warburton</p>
Data and code from: Long-term climate impacts of large stratospheric water vapor perturbations (Part 1)
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Data for: Top-down control and species composition non-linearly influence the short-term response of experimental food webs to a nutrient pulse perturbation
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Data for: The Martian atmospheric waves perturbation Datasets (MAWPD) version 2.0
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A simulated natural heatwave perturbs bumble bee immunity and resistance to infection
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Data and code from: Sea ice perturbation and mass starvation of Thick-billed Murres
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Data from: Perturbations highlight importance of social history in parakeet rank dynamics
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Modelling system for computing the tropospheric O3 and CH4 perturbations from South Korean Emissions (KORUS-AQ period)
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Data from: Strong and weak environmental perturbations cause contrasting restructure of ant transportation networks
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Data from: Culling-induced perturbation of social networks of wild geese reinforces rather than disrupts associations among survivors
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Data from: Application of a metabolic network-based graph neural network for the identification of toxicant-induced perturbations
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Data from: Functional diversity buffers the effects of a pulse perturbation on the dynamics of tritrophic food webs
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Laboratory measurements of nitrous oxide production rates in agricultural soils from Lancaster, PA, estuarine sediments from the Scheldt Estuary Belgium/Netherlands, and estuarine soils from the Delaware River NJ under gradients of physicochemical perturbation
A set of experiments was performed to test 1) how various physicochemical perturbations (salinity, zinc, temperature, soil moisture, and pH) influenced denitrification and nitrous oxide production on short timescales (<1 day) in agricultural soils from Lancaster, PA, USA, 2) how variation in a single parameter (salinity) influenced rates of denitrification and nitrous oxide production in sediments that experience a range in that parameter (tidal freshwater, oligohaline, and mesohaline estuarine sediments from the Scheldt River estuary Belgium/Netherlands) on short timescales (< 1 day), and 3) how denitrification and nitrous oxide production along with key functional gene expression in tidal freshwater estuarine soils from the Delaware River, NJ, USA responded to a long-term (6 month) change in a single parameter (salinity) in a press experiment with subsequent short-term (< 1 day) pulses. In the final long-term experiment, nitrite reductase (nirS) and nitrous oxide reductase (nosZ) gene expression were also measured at three timepoints (days 7, 35, and 110).
Data of the paper: Atmospheric energy budget response to idealized aerosol perturbation in tropical cloud systems
<p>Here you can find the data presented in the paper: <strong>Atmospheric energy budget response to idealized aerosol perturbation in tropical cloud systems</strong></p> <p>The data include all variables included in the paper for the shallow-cloud and the deep-cloud dominated cases.</p> <p>The variable names are as in the paper (beside T_tot which is the 2m temperature). The numbers in the names of the variables represent the CDNC case.</p> <p>The time series variables are as a function of t. The vertical profiles are as a function of the pressure p. The maps are as a function of latitude and longitude. </p>
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.