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4,356 results for “RED”
Fig. 3 in A review of Sciurus Group studies on the red squirrel (Sciurus vulgaris): presence, population density and colour phases in Lombardy (Italy)
Fig. 3 - Study areas in Lombardy. Box: geographic position of Lombardy (black) in Italy.
Fig. 1 in Forbidden islands. The absence of endemics among the insular non-volant terrestrial mammalian fauna of the Red Sea
Fig. 1 - The Red Sea, as seen from space (NASA/courtesy of visibleearth.nasa.gov.)
Fig. 1 in Circadian activity patterns of the Red fox (Vulpes vulpes) and the Stone marten (Martes foina) in agricultural landscape of Northwestern Bulgaria during autumn-winter period
Fig. 1. Location of the protected area "Zlatiyata" in Northwestern Bulgaria.
Stellar models for "Realistic Uncertainties for Fundamental Properties of Asteroseismic Red Giants and the Interplay Between Mixing Length, Metallicity and Numax" by Li, Yaguang et al. (2024)
<p>This dataset includes a grid of stellar models from Li, Yaguang et al. (2024), titled "Realistic Uncertainties for Fundamental Properties of Asteroseismic Red Giants and the Interplay Between Mixing Length, Metallicity and Numax".</p> <p>The stellar models are first-ascent red giant branch (RGB) models, sampled using a Sobol sequence within the following six-dimensional initial parameter space: mass (0.7, 2.3) solar mass, Y_init (0.22, 0.37), [M/H] (-0.1.0, 0.60) dex, α_MLT (1.3, 2.7), fov_core (0, 0.02), fov_shell (0, 0.008). The evolutionary tracks are computed up to the point on the RGB where the p-mode large separation (Δν) equals 2 μHz. Approximately 32,768 unique evolutionary tracks are presented, divided into 33 individual chunks.</p> <p>To load the models into a Python Pandas dataframe, use the following command: "import pandas as pd; pd.read_parquet(filepath)". Each model (dataframe row) is provided with global parameters (Teff, radius, luminosity, mass, age, ...) and pure p modes with l=0-2 (frequency, mode inertia, angular degree, ...).</p> <p>For detailed descriptions of the stellar models, please refer to the original paper and the github repository.</p>
Fig. 6. O in First description of Onchocerca jakutensis (Nematoda: Filarioidea) in red deer (Cervus elaphus) in Switzerland
Fig. 6. O. jakutensis female: Ratio of cuticular annulation (A) to medullar striae (S) 1:4.
Fig. 2 in First description of Onchocerca jakutensis (Nematoda: Filarioidea) in red deer (Cervus elaphus) in Switzerland
Fig. 2. Anterior end of O. jakutensis female, with granular lumps in process of degeneration.
Dataset on the characterization of the flavor of two red wine varieties using sensory descriptive analysis, volatile organic compounds quantitative analysis by GC-MS and odorant composition by GC-MS-O
<p>The dataset contains data that were collected on 2 sets of 8 French red wines from two grape varieties, Pinot Noir (PN) and Cabernet Franc (CF). It provides, for the 16 wines, (i) sensory descriptive data obtained with a trained panel, (ii) volatile organic compounds (VOC) quantification data obtained by Gas Chromatography–Mass Spectrometry (GC-MS) and (iii) odorant composition obtained by Gas Chromatography–Mass Spectrometry–Olfactometry (GC-MS-O).</p> <p> </p> <p>The dataset is a Microsoft Excel Worksheet containing 8 sheets.</p> <p>- Sheet 1: Information</p> <p>Gives information about the sheets contained in this .xlsx file</p> <p>- Sheet 2: Experimental_factors</p> <p>Each row represents a wine</p> <p>Each column corresponds to an experimental factors of the wines (Grape variety, Vintage and Protected Designation of Origin)</p> <p>- Sheet 3: List_sensory_descriptors</p> <p>Lists the 33 sensory descriptors used for the sensory descriptive analysis of the wines</p> <p>- Sheet 4: Sensory_descriptive_analysis</p> <p>Each row represents a wine</p> <p>Each column corresponds to a condition (2640 columns)</p> <p>Senso_(ortho or retro)_(Panelist1 to Panelist 16)_(1 to 33 Sensory descriptors)_(1 to 3 repetitions for ortho and 1 to 2 repetitions for retro)</p> <p>For the ortho (orthonasal) measurements, there is 16 panelists, 33 sensory descriptors and 3 repetitions = 1584 columns</p> <p>For the retro (retronasal) measurements, there is 16 panelists, 33 sensory descriptors and 2 repetitions = 1056 columns</p> <p>Each cell contains a sensory measurement for the corresponding condition in the corresponding wine</p> <p>- Sheet 5: List_VOC</p> <p>Lists the 45 VOC quantified in the wines with their corresponding CAS number</p> <p>VOC: Volatil Organic Compounds</p> <p>- Sheet 6: VOC_quantification</p> <p>Each row represents a wine</p> <p>Each column corresponds to a VOC (45 columns)</p> <p>Each cell contains the quantification of the corresponding VOC in the corresponding wine</p> <p>- Sheet 7: List_GC-MS-O</p> <p>Lists the 49 odor-active compounds identified with their corresponding CAS number and the 34 compounds identified by their apex indice</p> <p>- Sheet 8: GC-MS-O</p> <p>Each row represents a wine</p> <p>Each column corresponds to an odor-active compound identified by its CAS number or by its Apex indice if the compound was not identify (81 odor-active compounds) + the number of judges who smelled the compound and its description (by 8 judges) = 9 columns per odor-active compound for a total of 729 columns</p>
Data for [Rock magnetism of quartz and feldspars chemically separated from pelagic red clay: a new approach to provenance study]
<p>MPMS and XRD data used in the manuscript Rock magnetism of quartz and feldspars chemically separated from pelagic red clay: a new approach to provenance study.</p>
Brightness data in Rayleigh for OI 630.0 nm (red line) and OI 557.7 (green line) through the night of August 21-22, 2017 obtained using the HiT&MIS from Carbondale, IL
<p>This file contains brightness data in Rayleigh for OI 630.0 nm (red line), OI 557.7 (green line) and NeI 630.5 nm (cloud indicator) through the night of August 21-22, 2017 obtained using the HiT&MIS from Carbondale, IL.</p> <p>This is a python .npz file that contain numpy arrays with uncertainities, so you would need the uncertainties as well as the numpy modules for python.</p> <p>On python it can be opened up as follows:</p> <p>import numpy as np</p> <p>import uncertainties.unumpy as unp</p> <p>dload=np.load("<file location>/eclipse_night_final.npz")#brightnesses are saved as numpy array files<br> int630=dload["int630"] #630 nm red line brightness<br> int557=dload["int557"] #557.7 nm green line brightness<br> int6305=dload["int6305"]# Ne I 630.5 nm brightness for cloud activity<br> time=dload["time"] #capture times (Local times, Central Daylight time)</p> <p># uncertainities can be accessed as</p> <p>b630value=unp.nominal_values(int630) # 630 nm brightnesses values</p> <p>e630=unp.std_devs(int630)# 630 nm uncertainities.</p> <p> </p> <p>please email me at saurav_aryal@student.uml.edu if you have any questions.</p>
Pre-supernova outbursts via wave heating in massive stars - I. Red supergiants
<p>MESA inlists associated with Fuller (2017). MESA version 7624.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/stx1314">10.1093/mnras/stx1314</a></p>
Asymptotic analysis of dipolar mixed modes of oscillations in red giant stars
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2016PASJ...68..109T">Asymptotic analysis of dipolar mixed modes of oscillations in red giant stars</a></p>
Variability of Red Supergiants in M31 from the Palomar Transient Factory
<p>MESA inlists and run_star_extras associated with <a href="https://ui.adsabs.harvard.edu/?#abs/2018arXiv180309934S">Soraisam et al. (2018)</a>. MESA version 9793.</p> <p>Publication DOI: <a href="https://doi.org/10.3847/1538-4357/aabc59">10.3847/1538-4357/aabc59</a></p>
Modelling Kepler red giants in eclipsing binaries: calibrating the mixing-length parameter with asteroseismology
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.475..981L/abstract">Li et al. (2018)</a>. MESA version 8118.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/stx3079">10.1093/mnras/stx3079</a></p>
Surface effects on the red giant branch
<p>MESA add-on for suppressing g-mode oscillations associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.478.4697B/abstract">Surface effects on the red giant branch </a>and <a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.478.4669T/abstract">Oscillating red giants in eclipsing binary systems: empirical reference value for asteroseismic scaling relation</a></p>
Dataset accompanying Riesch et al. 2019. Grazing by wild red deer: management options for the conservation of semi-natural open habitats. Journal of Applied Ecology
<p>This repository contains vegetation biomass and forage quality data used by Riesch et al. in an article accepted in Journal of Applied Ecology.</p> <p>Metadata are provided in the first excel worksheet ('explanation_overview'). For further details please see the original article.</p>
Shigella Congo red secretion profile unaltered Western blots
<p>These are unaltered raw Western blot images (Image Lab files) of a Congo red secretion assay testing the type three secretion profiles of <em>Shigella</em> strains expressing engineered Spa47 mutants.</p>
Figure 5 in Breeding biology of the red-backed shrike, Lanius collurio, in the Kızılırmak Delta in the north of Turkey
Figure 5. Laying time of the red-backed shrikes.
Figure 2 in Breeding biology of the red-backed shrike, Lanius collurio, in the Kızılırmak Delta in the north of Turkey
Figure 2. Nest plants of the red-backed shrikes (n = 108) in the Kızılırmak Delta during 2011–2012.
Figure 6 in Breeding biology of the red-backed shrike, Lanius collurio, in the Kızılırmak Delta in the north of Turkey
Figure 6. Clutch size distribution of the red-backed shrikes.
Figure 3 in Breeding biology of the red-backed shrike, Lanius collurio, in the Kızılırmak Delta in the north of Turkey
Figure 3. Distribution of the orientations of the red-backed shrike nests in the supporting plants.
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.