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249 results for “EAGLE”
Simulated galaxy cluster data at z=0 demonstrating the entropy core problem with the SWIFT-EAGLE galaxy formation model
<p>Cluster simulated with the SWIFT hydrodynamic code with the Ref SWIFT-EAGLE model. This dataset contains the redshift 0 snapshot and the VELOCIraptor halo catalogue.</p> <p>Paper reference: https://arxiv.org/abs/2210.09978</p>
Light-saturated photosynthetic rate, dark respiration, stomatal conductance and ratio of internal to external carbon dioxide concentration from the 1980-82 Eriophorum vaginatum reciprocal transplant plots from Eagle Creek to Prudhoe Bay, Alaska, 2010
In 1980-1982, six transplant gardens were established along a latitudinal gradient in interior Alaska from Eagle Creek, AK, in the south to Prudhoe Bay, AK, in the north (Shaver et al. 1986) .Three sites, Toolik Lake (TL), Sagwon (SAG), and Prudhoe Bay (PB) are north of the continental divide and the remaining three, Eagle Creek (EC), No Name Creek (NN), and Coldfoot (CF), are south of the continental divide. Each garden consisted of 10 individual tussocks transplanted back to their home-site, as well as 10 individuals from each of the other transplant sites. Data were collected in July 2010 for tussocks transplanted in 1980-82 in a reciprocal transplant experiment and then harvested in 2011. Important variables are garden name, source population, light-saturated photosynthetic rate, dark respiration, stomatal conductance and ratio of internal to external carbon dioxide concentration.
Particle Trace of a Milky Way Mass Galaxy from the EAGLE simulations
<p>This repository contains .npy files, loaded like:</p> <pre><code class="language-python">with open('EAGLE_MW_trace_coords.npy', 'rb') as f: coordinates = np.load(f) dmcoordinates = np.load(f) with open('EAGLE_MW_trace_redshifts.npy', 'rb') as f: redshifts = np.load(f)</code></pre> <p>which contain the locations of particles (gas, stars and dark matter) which are within 30pkpc of the centre of a Milky Way stellar mass galaxy from the EAGLE suite of simulations. This dataset was primarily produced to look at the accretion of matter onto galaxies like the Milky Way, studying how they assemble over time, which makes for some quite pretty visualisations <a href="https://github.com/jmackereth/galactic-assembly-art.git">(explored in this repository)</a>.</p> <p>the file 'EAGLE_MW_trace_coords_downsampled_10.npy' contains the same data but for a downsampled set of particles (by a factor of 10).</p>
Environmental and stand data for 88 sites located in the 2004 burns off the Steese, Taylor, and Dalton Highways, and for the 2005 burn near Eagle Plains, Yukon.
This dataset contains environmental variables collected at the site level (latitude, slope, aspect, moisture, etc.), pre-fire stand characteristics (tree density and basal area by species), and postfire regeneration (seedling density by species).
Tree ring widths from 1974-2003 for approximately 10 trees/site for 82 sites located in the 2004 burns off the Steese, Taylor, and Dalton Highways, and for the 2005 burn near Eagle Plains, Yukon.
This dataset contains annual raw ring width measurements from 1975-2003 for 793 black spruce trees in 84 sites. Ring widths were measured using WinDendro (resolution 0.001 mm) on two radii per stem disk collected 1.3 m along stem. Data is the average of the two radii measurments in each year for each tree. Accuracy of ring width dates were checked by crossdating using COFECHA.
Bald eagle (Haliaeetus leucocephalus) GPS relocations, southeastern Alaska, 2010-2016
<p>GPS relocations for 28 marked bald eagles (<em>Haliaeetus leucocephalus</em>) captured in southeastern Alaska, USA between 2010 and 2014.</p>
Fig. 3 in Molecular identification of Sarcocystis halieti n. sp., Sarcocystis lari and Sarcocystis truncata in the intestine of a white-tailed sea eagle (Haliaeetus albicilla) in Norway
Fig. 3. Phylogenetic tree for members of the Sarcocystidae based on 63 sequences of the partial cox1 gene from 61 taxa and inferred using the neighbourjoining method. Evolutionary distances were computed using the Kimura 2- parameter method. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) is shown next to the branches. The four new sequences from the present study are in boldface.
Fig. 2 in Molecular identification of Sarcocystis halieti n. sp., Sarcocystis lari and Sarcocystis truncata in the intestine of a white-tailed sea eagle (Haliaeetus albicilla) in Norway
Fig. 2. Sporulated thin-walled oocysts of S. halieti and S. lari (based on molecular identification) in wet smears of the intestinal mucosa (frozen/thawed) of the white-tailed sea eagle (Bars = 20 μm). A – Low magnification of numerous oocysts in the mucosa. B – Higher magnification of sporulated oocysts with a thin wall (arrows). C – A fairly large oocyst of the predominant type and a much smaller free sporocyst (ssp), possibly of S. truncata.
Fig. 4 in Molecular identification of Sarcocystis halieti n. sp., Sarcocystis lari and Sarcocystis truncata in the intestine of a white-tailed sea eagle (Haliaeetus albicilla) in Norway
Fig. 4. Phylogenetic tree for members of the Sarcocystidae based on 60 sequences of the complete ITS1 region of 29 taxa and inferred using the neighbour-joining method. Evolutionary distances were computed using the Kimura 2-parameter method. The percentage of replicate trees in which the associated taxa clustered together in the bootstrap test (1000 replicates) is shown next to the branches. The new sequences from the present study are in boldface. Some subtrees formed by two or more sequences of the same species have been collapsed.
Fig. 1 in Molecular identification of Sarcocystis halieti n. sp., Sarcocystis lari and Sarcocystis truncata in the intestine of a white-tailed sea eagle (Haliaeetus albicilla) in Norway
Fig. 1. Cross-sections of two thin-walled sarcocysts in a HE-stained histological section of cardiac muscle from the white-tailed sea eagle (Bar = 20 μm). A – Fairly large profile of a sarcocyst. B – Smaller profile of a sarcocyst containing several roundish cells at the periphery.
Fig. 1 MapshowingtherangeofthestudypopulationofEasternImperialEaglesinHungaryandthelocationofsampledandnotsampledterritoriesin 2003 in High Turnover Rate Revealed By Non-Invasive Genetic Analyses In An Expanding Eastern Imperial Eagle Population
Fig. 1 MapshowingtherangeofthestudypopulationofEasternImperialEaglesinHungaryandthelocationofsampledandnotsampledterritoriesin 2003 (35 ofthe 61 nesting
Fig. 2 in High Turnover Rate Revealed By Non-Invasive Genetic Analyses In An Expanding Eastern Imperial Eagle Population
Fig. 2. Firstidentification (1999, territorycodeBS-02) andre-identification (2003, BS-03) of afemale. Theterritorieswereapproximately 10 kmawayfromeachotherandtheoriginal BS-02 territorywasvacantin 2001-2002, butitwasoccupiedbyapairwithanewfemale in 2003; differentmarkingsrepresentdifferentgeneticallytaggedfemales, blackmarkings representthenestsfromtheBS-02 territory, greymarkingsrepresentnestsfromtheBS-03 territory; yearsinitalic (nestsmarkedbycircles) representnestingsiteswithoutsamples.
Gamma rays from dark matter spikes in EAGLE simulations - IMBH mock catalogue
<p>DArk Matter SPIkes (DAMSPI) is a fully Python-based software for the analysis of dark matter spikes around Intermediate Mass Black Holes (IMBHs) in the Milky Way. It allows to extract an IMBH catalogue and their corresponding dark matter spike parameters from the EAGLE simulations in order to probe a potential gamma-ray signal from dark matter self-annihilation. </p> <p>The dataset contains the IMBH catalogue including, among others, the coordinates, mass, formation redshift and spike parameters for each individual IMBH. Each column of the catalogue is described in detail in J. Aschersleben et al. (2024). We also provide separate files for which we calculated the gamma-ray fluxes for different dark matter masses and annihilation cross sections. Lastly, we provide a catalogue of our selection of Milky Way like galaxies within EAGLE. The columns of these files are also described in J. Aschersleben et al. (2024).</p> <p>The source code to extract this dataset is publicy available here:</p> <div> <div> <pre><a href="https://doi.org/10.5281/zenodo.11488472">https://doi.org/10.5281/zenodo.11488472</a></pre> </div> </div> <h2>Description of the data files</h2> <p>The imbh_catalogue/imbh/ directory contains the following files:</p> <ol> <li>catalogue_nfw.h5</li> <li>catalogue_cored_gamma_0p3.h5</li> <li>catalogue_cored_gamma_0p9.h5</li> <li>catalogue_cored_gamma_free.h5</li> </ol> <p>They contain the IMBH catalogues, including the coordinates and dark matter spike parameters, calculated assuming the 1.) NFW profile, 2.) cored profile with a fixed core index of 0.0, 3.) cored profile with a fixed core index of 0.4 and 4.) cored profile with the core index as a free fitting parameter.</p> <p>The imbh_catalogue/flux/<channel>/<energy_threshold>/ directory contains the gamma-ray fluxes of the IMBHs for a given annihilation channel, energy threshold, and dark matter mass. E.g. the imbh_catalogue/flux/b_channel/e_th_0.1GeV/m_dm_10.0GeV.h5 file contains the IMBH fluxes assuming the b-channel, an energy threshold of 0.1 GeV and a dark matter mass of 10 GeV. The IMBH fluxes are calculated for a variety of velocity weighted annihilation cross sections. </p> <p>The imbh_catalogue/galaxy/ directory contains the mw_galaxies_catalogue_nfw.h5 file which contains our selection of Milky Way-like galaxies within EAGLE.</p> <p>The HDF files can be opened in Python with:</p> <pre><code>import pandas as pd file_path = "<path_to_file>.h5" df = pd.read_hdf(file_path, key="table") # Printing the first few rows of the DataFrame print(df.head())</code><code> </code></pre>
Dataset: Eagle Pharmaceuticals, Inc. (EGRX) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Eagle Bancorp Montana, Inc. (EBMT) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Eagle Bancorp, Inc. (EGBN) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig. 2 in Spatial Variation In Prey Composition And Its Possible Effect On Reproductive Success In An Expanding Eastern Imperial Eagle (Aquila Heliaca) Population
Fig. 2. Cluster analyses of imperial eagle breeding areas based on prey composition data. Region codes are presented in Fig. 1. Codes with bold characters represent mountainous habitats. The single breeding pair of the Cserehát Mountains was excluded from the analysis, because of low sample size
Fig. 3 in Spatial Variation In Prey Composition And Its Possible Effect On Reproductive Success In An Expanding Eastern Imperial Eagle (Aquila Heliaca) Population
Fig. 3. Frequency of the three main prey species (a-c) and reproductive success (d) of imperial eagles in two East-Hungarian regions. Boxplots presents the minimum-maximum (whiskers), lower and upper quartiles (box) and the median (line) of the data. Dots are outliers. Significance of difference is in-
FIG. 3 in The significance of Buffon and Guéneau de Montbeillard's Histoire naturelle des oiseaux ([1765]-1783) in the taxonomy of birds: General presentation and correspondence between Buffon's "eagles" and the species acknowledged by Linnaeus (1758, 1766), Brisson (1759-1762), and Gmelin (1788-1789)
FIG. 3. — Planche enluminée 416: "Aigle des grandes Indes", by Martinet, in Buffon & Guéneau de Montbeillard (1771b). The represented (lost) specimen is the holotype of Falco indus Boddaert, 1783. Credits: Musée Buffon, Montbard.
FIG. 2 in The significance of Buffon and Guéneau de Montbeillard's Histoire naturelle des oiseaux ([1765]-1783) in the taxonomy of birds: General presentation and correspondence between Buffon's "eagles" and the species acknowledged by Linnaeus (1758, 1766), Brisson (1759-1762), and Gmelin (1788-1789)
FIG. 2. — Planche enluminée 413: "le Jean-le Blanc", by Martinet, in Buffon & Guéneau de Montbeillard (1771b). The represented (lost) specimen belongs to the type series of Falco gallicus J. F. Gmelin, 1788. Credits: Musée Buffon, Montbard.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.